FishFace: interactive atlas of zebrafish craniofacial development at cellular resolution
Bibliographic record
Abstract
BACKGROUND: The vertebrate craniofacial skeleton may exhibit anatomical complexity and diversity, but its genesis and evolution can be understood through careful dissection of developmental programs at cellular resolution. Resources are lacking that include introductory overviews of skeletal anatomy coupled with descriptions of craniofacial development at cellular resolution. In addition to providing analytical guidelines for other studies, such an atlas would suggest cellular mechanisms underlying development. DESCRIPTION: We present the Fish Face Atlas, an online, 3D-interactive atlas of craniofacial development in the zebrafish Danio rerio. Alizarin red-stained skulls scanned by fluorescent optical projection tomography and segmented into individual elements provide a resource for understanding the 3D structure of the zebrafish craniofacial skeleton. These data provide the user an anatomical entry point to confocal images of Alizarin red-stained zebrafish with transgenically-labelled pharyngeal arch ectomesenchyme, chondrocytes, and osteoblasts, which illustrate the appearance, morphogenesis, and growth of the mandibular and hyoid cartilages and bones, as viewed in live, anesthetized zebrafish during embryonic and larval development. Confocal image stacks at high magnification during the same stages provide cellular detail and suggest developmental and evolutionary hypotheses. CONCLUSION: The FishFace Atlas is a novel learning tool for understanding craniofacial skeletal development, and can serve as a reference for a variety of studies, including comparative and mutational analyses.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.040 | 0.006 |
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".