Bibliographic record
Abstract
Over the past three decades, extensive genetic, physical, transcript, and sequence maps have assisted in the mapping of over 30 genetic diseases and in the identification of over 550 genes on human chromosome 14. Additional genetic disorders were assigned to chromosome 14 by studying either constitutional or acquired chromosome aberrations of affected subjects. Studies of benign and malignant tumours by karyotype analyses and by allelotyping with a panel of polymorphic genetic markers have further suggested the presence of several tumour suppressor loci on chromosome 14. The search for disease genes on human chromosome 14 has also been achieved by exploiting the human-mouse comparative maps. Research on uniparental disomy and on the search for imprinted genes has supported evidence of epigenetic inheritance as a result of imprinting on human chromosome 14. This review focuses on the current developments on human chromosome 14 with respect to genetic maps, physical maps, transcript maps, sequence maps, genes, diseases, mouse-human comparative maps, and imprinting.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".