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Record W4236651345 · doi:10.3410/f.727865749.793549223

Faculty Opinions recommendation of Sorting at embryonic boundaries requires high heterotypic interfacial tension.

2018· dataset· en· W4236651345 on OpenAlexfundno aff
David G. Wilkinson

Bibliographic record

VenueFaculty Opinions – Post-Publication Peer Review of the Biomedical Literature · 2018
Typedataset
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCellular Mechanics and Interactions
Canadian institutionsnot available
FundersCanadian Cancer Society Research InstituteCanadian Institutes of Health ResearchLaboratoire d'Excellence EpiGenMedAgence Nationale de la Recherche
KeywordsEctodermMesodermCell sortingXenopusTension (geology)SortingAdhesionEmbryonic stem cellContractilityCell biologySurface tensionBiologyComputer scienceCellMaterials scienceEmbryogenesisEmbryoPhysicsGeneticsAlgorithmComposite materialEndocrinology

Abstract

fetched live from OpenAlex

The establishment of sharp boundaries is essential for segregation of embryonic tissues during development, but the underlying mechanism of cell sorting has remained unclear.Opposing hypotheses have been proposed, either based on global tissue adhesive or contractile properties or on local signalling through cell contact cues.Here we use ectoderm-mesoderm separation in Xenopus to directly evaluate the role of these various parameters.We find that ephrin-Eph-based repulsion is very effective at inducing and maintaining separation, whereas differences in adhesion or contractility have surprisingly little impact.Computer simulations support and generalise our experimental results, showing that a high heterotypic interfacial tension between tissues is key to their segregation.We propose a unifying model, in which conditions of sorting previously considered as driven by differential adhesion/tension should be viewed as suboptimal cases of heterotypic interfacial tension.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.050
Threshold uncertainty score0.169

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.008
Meta-epidemiology (narrow)0.0020.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0030.004
Science and technology studies0.0010.000
Scholarly communication0.0030.001
Open science0.0030.002
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0500.076

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.024
GPT teacher head0.334
Teacher spread0.310 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreDataset

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2018
Admission routes1
Has abstractyes

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