Sea urchin neural development and the metazoan paradigm of neurogenesis
Bibliographic record
Abstract
Summary:Urchin embryos continue to prove useful as a means of studying embryonic signaling and gene regulatory networks, which together control early development. Recent progress in understanding the molecular mechanisms underlying the patterning of ectoderm has renewed interest in urchin neurogenesis. We have employed an emerging model of neurogenesis that appears to be broadly shared by metazoans as a framework for this review. We use the model to provide context and summarize what is known about neurogenesis in urchin embryos. We review morphological features of the differentiation phase of neurogenesis and summarize current understanding of neural specification and regulation of proneural networks. Delta-Notch signaling is a common feature of metazoan neurogenesis that produces committed progenitors and it appears to be a critical phase of neurogenesis in urchin embryos. Descriptions of the differentiation phase of neurogenesis indicate a stereotypic sequence of neural differentiation and patterns of axonal growth. Features of neural differentiation are consistent with localized signals guiding growth cones with trophic, adhesive, and tropic cues. Urchins are a facile, postgenomic model with the potential of revealing many shared and derived features of deuterostome neurogenesis.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".