Abstracts of papers presented at the sixteenth Genetics Society's Mammalian Genetics and Development Workshop held at the Institute of Child Health, University College London on 21 and 22 November 2005
Bibliographic record
Abstract
Prior to cranial neural tube closure, the neural folds adopt a biconvex morphology which is thought to be due to expansion of the underlying mesenchyme. Dorso-lateral hinge points (DLHPs) then form, which allow the dorsal tips of the neural folds to ‘flip around’ resulting in apposition of the tips and facilitating subsequent fusion. Cranial closure is particularly prone to perturbation, leading to exencephaly in many mouse mutants and as a result of a variety of teratogenic influences. This may reflect mechanical tensions affecting the closing cranial neural folds. For example, the presence of ventral flexures of the body axis at the mid- and forebrain levels mechanically opposes the formation of DLHPs. Several processes have been implicated as important in overcoming these mechanical tensions, thereby assisting in cranial neural tube closure. These include contraction of actin microfilaments at the luminal surface of the neuroepithelium and apoptosis in the dorsal and dorsolateral neuroepithelium. The latter may act to increase flexibility in the dorsal neural folds, enhancing DLHP formation. Neural crest cells (NCC) originate in the dorsal tips of the neuroepithelium and undergo an epithelial-to-mesenchymal transition, allowing them to delaminate, exit the neuroepithelium and migrate extensively throughout the embryo to form numerous derivatives. We hypothesized that delamination of the NCC from the neuroepithelium may enhance the mechanical flexibility of the dorsal tips of the neural folds, allowing the ‘flip around’ event to occur.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.407 | 0.128 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".