Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Evaluating the impact of genomic variations (GV) on protein-coding transcripts is an important step in identifying variants of functional significance. Currently available programs for variant annotation depend on external databases or annotate multiple variants affecting the same transcript independently, which limits program use to organisms available in these databases or results in potentially incorrect or incomplete annotations. FINDINGS: We have developed CooVar (Co-occurring Variant Analyzer), a database-independent program for assessing the impact of GVs on protein-coding transcripts. CooVar takes GVs, reference genome sequence, and protein-coding exons as input and provides annotated GVs and transcripts as output. Other than similar programs, CooVar considers the combined impact of all GVs affecting the same transcript, generating biologically more accurate annotations. CooVar is operated from the command-line and supports standard file formats VCF, GFF/GTF, and GVF, which makes it easy to integrate into existing computational pipelines. We have extensively tested CooVar on worm and human data sets and demonstrate that it generates correct annotations in only a short amount of time. CONCLUSIONS: CooVar is an easy-to-use and lightweight variant annotation tool that considers the combined impact of GVs on protein-coding transcripts. CooVar is freely available at http://genome.sfu.ca/projects/coovar/.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 it