Bibliographic record
Abstract
In recent years, researchers have generated a variety of mouse models in an attempt to dissect the contribution of individual genes to the complex phenotype associated with Williams syndrome (WS). The mouse genome is easily manipulated to produce animals that are copies of humans with genetic conditions, be it with null mutations, hypomorphic mutations, point mutations, or even large deletions encompassing many genes. The existing mouse models certainly seem to implicate hemizygosity for ELN, BAZ1B, CLIP2, and GTF2IRD1 in WS, and new mice with large deletions of the WS region are helping us to understand both the additive and potential combinatorial effects of hemizygosity for specific genes. However, not all genes that are haploinsufficient in humans prove to be so in mice and the effect of genetic background can also have a significant effect on the penetrance of many phenotypes. Thus although mouse models are powerful tools, the information garnered from their study must be carefully interpreted. Nevertheless, mouse models look set to provide a wealth of information about the neuroanatomy, neurophysiology and molecular pathways that underlie WS and in the future will act as essential tools for the development and testing of therapeutics.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.005 |
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".