Bibliographic record
Abstract
Evolution is the unifying framework in biology and scales to all dwelling systems. It is the central organizing questioning to grant clarification for reputedly disparate herbal phenomena; from the very small (individual molecules) to the very large (ecosystems), from the upward push and unfold of molecular editions to the habits and physique shapes of elephants. In modern times, our maintain shut for evolution in medicinal pills has acquired momentum. Individuals have championed the cause, dedicated journals have emerged, and new books on the trouble are often posted (“The Evolution and Medicine Review” is an terrific web-based advisable useful resource presenting up to date information on the subject, http://evmedreview.com). This union between evolution and cure has already most fulfilled our hold close of pathological techniques (Maccallum, 2007, Nesse & Stearns, 2008). Drug enchancment and therapeutic strategies are areas in which evolutionary standards would perchance moreover be specially helpful. The avalanche of bioinformatic methods, genomic data, and the subsequent emergence of evolutionary genomics in the ultimate few many years propose that integrating these fields into drug graphs is now a possibility. Incorporating evolutionary documents is now not completely really useful a posteriori when we might also moreover in addition hope to apprehend why resistance to a special compound emerged. It is in addition treasured a priori, to structure increased efficacious drugs, suggest possible resistance profiles and conceptualize novel therapeutic strategies
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".