Discovering Interactions Present During the Growth and Development of a Craniofacial Bone
Bibliographic record
Abstract
The study of skeletal (bone and cartilage) development includes the investigation of intramembranous bones of the head and facial skeleton as well as endochondral bones (e.g. long bones) of the body. The development of skeletal tissues often relies on an interaction between the mesenchyme and the epithelium. Bone is formed by signalling interactions between these two tissues. Often these signals experience cross‐talk, where one signal is inducing or inhibiting another signal. When it comes to scleral ossicles (an example of intramembranous bone), it has recently been shown by our lab that the Hedgehog gene family is involved in its development. Using whole mount in situ hybridization further investigation was performed to discover if multiple members of the Hedgehog family are involved, and also to determine the location and distribution of the Patched (Ptc) receptor. Bead implantations using a common Bone morphogenetic protein inhibitor, noggin, were performed to determine the involvement of the BMPs during ossicle development. The relationship and interactions between these two genes families ( BMP , and Hedgehog ) during scleral ossicle development will be discussed. These investigations have allowed for further understanding of the mechanism of development for the ocular skeleton and intramembranous bones, in general. Grant Funding Source: NSERC
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".