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Record W1983144947 · doi:10.4161/19336918.2014.970028

Ephrin-Eph signaling in embryonic tissue separation

2014· review· en· W1983144947 on OpenAlexaff
François Fagotto, Rudolf Winklbauer, Nazanin Rohani

Bibliographic record

VenueCell Adhesion & Migration · 2014
Typereview
Languageen
FieldNeuroscience
TopicAxon Guidance and Neuronal Signaling
Canadian institutionsUniversity of TorontoMcGill University
Fundersnot available
KeywordsEphrinErythropoietin-producing hepatocellular (Eph) receptorEmbryonic stem cellMulticellular organismMorphogenesisCell biologyBiologyCell adhesionCellCell typeAnatomySignal transductionGenetics

Abstract

fetched live from OpenAlex

The physical separation of the embryonic regions that give rise to the tissues and organs of multicellular organisms is a fundamental aspect of morphogenesis. Pioneer experiments by Holtfreter had shown that embryonic cells can sort based on "tissue affinities," which have long been considered to rely on differences in cell-cell adhesion. However, vertebrate embryonic tissues also express a variety of cell surface cues, in particular ephrins and Eph receptors, and there is now firm evidence that these molecules are systematically used to induce local repulsion at contacts between different cell types, efficiently preventing mixing of adjacent cell populations.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.003

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 head0.080
GPT teacher head0.362
Teacher spread0.282 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations69
Published2014
Admission routes1
Has abstractyes

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