HD-CNV: hotspot detector for copy number variants
Bibliographic record
Abstract
SUMMARY: Copy number variants (CNVs) are a major source of genetic variation. Comparing CNVs between samples is important in elucidating their potential effects in a wide variety of biological contexts. HD-CNV (hotspot detector for copy number variants) is a tool for downstream analysis of previously identified CNV regions from multiple samples, and it detects recurrent regions by finding cliques in an interval graph generated from the input. It creates a unique graphical representation of the data, as well as summary spreadsheets and UCSC (University of California, Santa Cruz) Genome Browser track files. The interval graph, when viewed with other software or by automated graph analysis, is useful in identifying genomic regions of interest for further study. AVAILABILITY AND IMPLEMENTATION: HD-CNV is an open source Java code and is freely available, with tutorials and sample data from http://daleylab.org. CONTACT: jcamer7@uwo.ca
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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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".