phenomenon · claim-type · cross-domain-transfer · mechanism · claim-relation · replication-status · limitation — grounded in the full text| phenomenon | a preprocessing choice that decides what counts as biological signal - the same data yielding heterogeneity or not depending on how the error model is constrained— “To our surprise, we observed significant heterogeneity in the estimates of all three parameters, even for genes with similar average abundance (Fig. 2). These differences could reflect true biological variation in the distribution of single-cell gene expression, but could also represent irreproducible variation driven by overfitting in the… | |
| claim-type | method— “We utilize the Pearson residuals (response residuals divided by the expected standard deviation), effectively representing a variance-stabilizing transformation (VST), where both lowly and highly expressed genes are transformed onto a common scale.” | |
| method — evidence | — “We therefore considered the possibility of constraining the model parameters through regularization, by combining information across similar genes to increase robustness and reduce sampling variation. This approach is commonly applied in learning error models for bulk RNA-seq in the context of differential expression analysis, but to our… | |
| object of study — evidence | — “The primary goal of single-cell normalization is to remove the influence of technical effects in the underlying molecular counts, while preserving true biological variation.” | |
| research question — evidence | — “These results demonstrate inherent challenges for “size factor”-based normalization strategies. Notably, while recent normalization strategies leverage more advanced strategies to learn cell “size factors”, the use of a single factor will introduce distinct effects on different gene sets, given their average abundance. This suggests that… | |
| contribution — evidence | — “We utilize the Pearson residuals (response residuals divided by the expected standard deviation), effectively representing a variance-stabilizing transformation (VST), where both lowly and highly expressed genes are transformed onto a common scale.” | |
| transfer | from bulk RNA-seq analysis: parameter regularisation by pooling across similar features, carried from bulk RNA-seq differential-expression error models into single-cell normalisation— “We therefore considered the possibility of constraining the model parameters through regularization, by combining information across similar genes to increase robustness and reduce sampling variation. This approach is commonly applied in learning error models for bulk RNA-seq in the context of differential expression analysis, but to our… | transfers[0] |
| transfer | from statistics: generalized linear models and Pearson residuals, carried from regression theory to serve as the normalised data values themselves rather than as a diagnostic— “We utilize the Pearson residuals (response residuals divided by the expected standard deviation), effectively representing a variance-stabilizing transformation (VST), where both lowly and highly expressed genes are transformed onto a common scale.” | transfers[1] |
| mechanism | abundance-dependent depth effect defeating a single scaling factor counts of low- and high-abundance genes respond differently to sequencing depth, so one factor per cell cannot correct both; dividing by depth leaves the high-abundance tiers still confounded and the variance contribution of the other tiers dampened— “While this approach mitigated the relationship between sequencing depth and gene expression, we found that genes with different overall abundances exhibited distinct patterns after log-normalization, and only low/medium-abundance genes in the bottom three tiers were effectively normalized (Fig. 1d).” | mechanisms[0] |
| mechanism | overfitting absorbing biological signal an unconstrained per-gene model has enough freedom to explain real cell-type differences as technical effects of depth, so the residuals it produces have had the biology subtracted along with the noise - even cell-type markers come out with low residual variance— “When applying standard negative binomial regression, we found that the procedure strikingly removed both technical and biological sources of variation from the data, driven by overfitting of the unconstrained distribution.” | mechanisms[1] |
| mechanism | pooling across genes as a constraint estimating each gene's parameters independently makes them a function of that gene's sampling noise; smoothing them against mean abundance replaces the noise with the population trend, and the estimates become reproducible across bootstraps— “We therefore considered the possibility of constraining the model parameters through regularization, by combining information across similar genes to increase robustness and reduce sampling variation. This approach is commonly applied in learning error models for bulk RNA-seq in the context of differential expression analysis, but to our… | mechanisms[2] |
| mechanism | low RNA content masquerading as a depth relation megakaryocytes carry little RNA, so they appear at low sequencing depth for reasons of biology rather than sampling; an unconstrained fit reads that as a negative slope between depth and the marker's count, which cannot be true of any single cell type— “We observe similar results for the Megakaryocyte (Mk) marker PPBP, but note that both non-regularized models actually fit a negative slope relating total sequencing depth to gene molecule counts. This is because Mk cells have very little RNA content and therefore exhibit lower UMI counts compared to other cell types, even independent of… | mechanisms[3] |
| critiques | size-factor normalisation for single-cell data - including recent methods that compute the factor more cleverly, since the problem is the single factor and not its estimation— “These results demonstrate inherent challenges for “size factor”-based normalization strategies. Notably, while recent normalization strategies leverage more advanced strategies to learn cell “size factors”, the use of a single factor will introduce distinct effects on different gene sets, given their average abundance. This suggests that… | claim_relations[0] |
| critiques | unconstrained negative binomial and zero-inflated negative binomial error models as used in current statistical and deep-learning methods for scRNA-seq— “Importantly, statistical and deep-learning methods designed for single-cell RNA-seq data often utilize a negative binomial (or zero-inflated negative binomial) error model. Our results suggest that these and future methods could benefit by substituting a regularized model and that including an additional parameter for zero-inflation could… | claim_relations[1] |
| builds-on | regularisation of error-model parameters across genes of similar abundance, standard in bulk RNA-seq differential expression and not previously used for single-cell normalisation— “We therefore considered the possibility of constraining the model parameters through regularization, by combining information across similar genes to increase robustness and reduce sampling variation. This approach is commonly applied in learning error models for bulk RNA-seq in the context of differential expression analysis, but to our… | claim_relations[2] |
| qualifies | SCnorm's conclusion that gene groups need separate treatment - reached for non-UMI data by a method that ignores the zero values which dominate droplet-based assays— “These results demonstrate inherent challenges for “size factor”-based normalization strategies. Notably, while recent normalization strategies leverage more advanced strategies to learn cell “size factors”, the use of a single factor will introduce distinct effects on different gene sets, given their average abundance. This suggests that… | claim_relations[3] |
| sense | parameter heterogeneity across genes - READING 1: true biological variation in the distribution of single-cell gene expression— “To our surprise, we observed significant heterogeneity in the estimates of all three parameters, even for genes with similar average abundance (Fig. 2). These differences could reflect true biological variation in the… | senses[0] |
| sense | parameter heterogeneity across genes - READING 2: irreproducible variation driven by overfitting. Resolved in favour of this one by a control dataset with no biological variation in it, where the heterogeneity persisted Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | senses[1] |
| sense | "effectively normalized" - UNDER LOG-NORMALIZATION: true only of the bottom three abundance tiers; high-abundance genes remain confounded with sequencing depth Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | senses[2] |
| sense | "effectively normalized" - UNDER PEARSON RESIDUALS: level and variance independent of sequencing depth across the full range of expression Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | senses[3] |
| control | a dataset with NO biological variation by construction - droplets that are technical replicates of one bulk RNA sample - run through the unregularised model to see whether the parameter heterogeneity persists when biology cannot explain it — failed Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. — rules out true biological variation as the explanation of the parameter heterogeneity across genes - in a sample where every droplet is a technical replicate of one bulk RNA extract there is no biology left that could produce it Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | controls[0] |
| control | the same control dataset run through the FINISHED method, where a correct model must find no high-variance genes at all — held Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. — rules out residual technical variation as the source of the high-variance genes the finished method selects - run on a sample containing no biology, a correct model must find none, and does Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | controls[1] |
| control | bootstrapping the fit over randomised subsets of cells, to separate real between-gene differences from sampling variation in the estimates — failed Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. — rules out real between-gene differences as the explanation of the parameter spread - estimates that move when the cells are resampled are measuring the sample and not the gene Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | controls[2] |
| control | substituting a Poisson and an unconstrained NB error model into the same pipeline, so the regularisation step is tested rather than assumed — held Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. — rules out the generalised linear model framework itself as the source of the result - swapping only the error model shows the work is done by the regularisation, not by using a GLM Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | controls[3] |
| control | checking what the high-residual-variance genes actually are, rather than reporting that some exist — held Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. — rules out residual technical noise as the explanation of the surviving high-variance genes - noise does not produce a list that is exclusively markers of known immune cell subsets Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | controls[4] |
| control | repeating the whole analysis on five UMI datasets from plate- and droplet-based protocols rather than on the one dataset the method was developed against — held Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. — rules out the single development dataset, and the droplet protocol it came from, as the source of the result Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | controls[5] |
| weight | size-factor normalisation for single-cell data - including recent methods that compute the factor more cleverly, since the problem is the single factor and not its estimation — 0.95 Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | claim_relations[0] |
| weight | unconstrained negative binomial and zero-inflated negative binomial error models as used in current statistical and deep-learning methods for scRNA-seq — 0.9 Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | claim_relations[1] |
| weight | regularisation of error-model parameters across genes of similar abundance, standard in bulk RNA-seq differential expression and not previously used for single-cell normalisation — 0.9 Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | claim_relations[2] |
| weight | SCnorm's conclusion that gene groups need separate treatment - reached for non-UMI data by a method that ignores the zero values which dominate droplet-based assays — 0.8 Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | claim_relations[3] |
| audit | size factor —distinct-from→ normalization — Audit 2026-09-28: DOUBTFUL - read against its span and kept, but the span says a single size factor has distinct effects; distinct-from is loose. Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | concept_edges[0] |
| differs in (stated by the tagger, no quotation) | size factor —distinct-from→ normalization — whether one constant per cell can serve genes across the whole range of abundance | concept_edges[0] |
| relation | size factor —distinct-from→ normalization — a single per-cell scaling factor introduces different effects on genes of different average abundance, so it is not normalization for all of them Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | concept_edges[0] |
| weight | size factor —distinct-from→ normalization — 0.95 Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | concept_edges[0] |
| differs in (stated by the tagger, no quotation) | Poisson —distinct-from→ regularized NB — whether the error model can represent overdispersion without absorbing the biology | concept_edges[3] |
| relation | Poisson —distinct-from→ regularized NB — the single-parameter Poisson cannot represent overdispersion and leaves residual variance above one for highly expressed genes; the unconstrained NB overfits in the other direction and flattens the markers Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | concept_edges[3] |
| weight | Poisson —distinct-from→ regularized NB — 0.95 Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | concept_edges[3] |
| outcome | control — failed Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | controls[0] |
| outcome | control — held Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | controls[1] |
| outcome | control — failed Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | controls[2] |
| outcome | control — held Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | controls[3] |
| outcome | control — held Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | controls[4] |
| outcome | control — held Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | controls[5] |
| limitation | the regularisation assumes the majority of genes do not vary biologically, which the authors say may be overly simplistic on a highly heterogeneous sample Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | limitations[0] |
| limitation | the kernel bandwidth is a user-adjustable parameter; robust over an order of magnitude and producing over- or under-smoothing at the extremes Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | limitations[1] |
| limitation | the batch extension requires all cell types to share a similar batch effect, which is an assumption about the experiment rather than a property of the model Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | limitations[2] |
| limitation | Pearson residuals are an intermediate rather than a model - an alternative would fit the count distribution directly, as GLM-PCA does, instead of passing residuals to methods that assume normal errors Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | limitations[3] |
finding · quantity · variable · definition · key-term — grounded in the full text| finding | Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | content_tags.findings[0] |
| finding | Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | content_tags.findings[1] |
| finding | Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | content_tags.findings[2] |
| finding | Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | content_tags.findings[3] |
| finding | Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | content_tags.findings[4] |
| finding | Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | content_tags.findings[5] |
| quantity | the unit residual variance expected for a gene with no biological heterogeneity Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | content_tags.quantities[0] |
| variable | sequencing depth Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | content_tags.variables[0] |
| definition | normalization Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | content_tags.definitions[0] |
| definition | Pearson residuals Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | content_tags.definitions[1] |
| definition | Chromium control dataset Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | content_tags.definitions[2] |
| concept | normalization — not defined in this paper's own words; removing the influence of technical effects from molecular counts while preserving true biological variation - two goals that pull against each other, which is the whole difficulty Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | concepts[0] |
| concept | size factor — not defined in this paper's own words; one scaling constant per cell - the incumbent approach, and one this paper shows cannot work for genes of different abundance regardless of how the factor is computed Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | concepts[1] |
| concept | Pearson residuals — not defined in this paper's own words; response residuals divided by the expected standard deviation - a variance-stabilising transformation that puts lowly and highly expressed genes on one scale Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | concepts[2] |
| concept | overfitting — not defined in this paper's own words; the unconstrained model absorbing biological signal into its own parameters, so that normalisation removes the thing it was meant to preserve Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | concepts[3] |
| concept | regularization — not defined in this paper's own words; pooling parameter estimates across genes of similar mean abundance - carried from bulk RNA-seq, and the step that makes the estimates reproducible Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | concepts[4] |
| concept | Chromium control dataset — not defined in this paper's own words; droplets that are technical replicates of one bulk RNA sample - a dataset with NO biological variation by construction, and the thing that settles the two readings Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | concepts[5] |
| concept | kernel bandwidth — not defined in this paper's own words; the smoothing parameter of the regularisation, robust over an order of magnitude and harmful at the extremes Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | concepts[6] |
| concept | zero-inflated negative binomial — not defined in this paper's own words; the three-parameter alternative widely used in this field; the extra parameter is argued here to make overfitting worse rather than better Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | concepts[7] |
| concept | Poisson — not defined in this paper's own words; the single-parameter alternative, which cannot represent overdispersion and so reads technical noise as biological heterogeneity Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | concepts[8] |
| concept | sequencing depth — not defined in this paper's own words; molecules detected per cell - the technical covariate the whole model exists to remove, and the one that can vary by an order of magnitude within a cell type Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | concepts[9] |
| concept | PPBP — not defined in this paper's own words; a megakaryocyte marker on which both non-regularised models fit a negative slope - a physically impossible relation, diagnosed rather than reported Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | concepts[10] |
| concept | regularized NB — not defined in this paper's own words; the paper's own model, named in its figure captions - a negative binomial whose intercept, slope and dispersion are fixed to values pooled across genes of similar abundance, so it sits between a Poisson that cannot represent overdispersion and an unconstrained NB that absorbs the biology Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | concepts[11] |
| negative binomial | Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | content_tags.statistics[0] |
| kernel bandwidth | Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | content_tags.statistics[1] |
| generalized linear model | Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | content_tags.statistics[2] |
When we have changed our mind. Some entries in the table begin with a word in capitals and the reason for it. EVIDENCE CORRECTED — the finding still stands, but we had quoted the wrong sentence from the paper, and the quotation has been replaced with the right one. RETRACTED — the entry itself was wrong; it stays on the page, marked, with the reason, rather than being deleted. WITHDRAWN AND REPLACED — the entry was wrong, and a corrected version of it appears in the entries below. Each mark carries its own reason, in brackets, because the reasons differ from one entry to the next. An entry we have WITHDRAWN is removed from this page and kept in the record, with its reason. A line reading Audit and a date is a later re-reading of the entry against its whole sentence: DOUBTFUL means it was kept, and the reason says what is loose about it. Quotation marks inside the tagger's sentence. Where a tag is the tagger's own sentence, a phrase in quotation marks inside it is borrowed from the paper — the tagger's wording around it is not. The paper's own sentence is the evidence quote for that row; on this page it is withheld with the article, and the article is at its publisher. Nothing inside quotation marks anywhere on this page is ours: if it is quoted, it is the paper's. Whose words a concept carries. A concept entry records a term the paper itself uses, so the term, the sense given for it, and its evidence are the paper's own words, quoted where the paper fixes them. This is the opposite of a sense entry in the tag table, which is the tagger's sentence about how the paper uses a term. Where a concept entry could not be grounded in the paper's words, the entry says so on its own row.
Tag layer © the Meta-Tagging Project, CC BY 4.0. The paper text is © its authors/publisher
and is linked, not reproduced. Excerpts shown are short quotations for identification and verification.
· back to index
· concepts · authors
· עברית
© 2026 Shir Sivroni · ORCID 0009-0000-2597-2824