Bibliographic record
Abstract
Next article FreeFront CoverPDFPDF PLUSFull Text Add to favoritesDownload CitationTrack CitationsPermissionsReprints Share onFacebookTwitterLinked InRedditEmailQR Code SectionsMoreOn the Cover: Left, thermal image of an American water shrew (Sorex palustris) as it approaches water before a dive. Research by Gusztak et al. (this issue, pp. 438–463) shows that this species consistently elevates its core body temperature by ~1°C immediately before entering the water and that it employs behavioral thermoregulation to maintain it at or above resting levels during aquatic foraging bouts. This behavior likely heightens the impressive sensory abilities, reaction times, and hence foraging efficiency of this diminutive predator (photo taken at Willard Lake, Ontario, by Glenn Tattersall). Top right, a shrew at the water’s edge (photo taken at Willard Lake, Ontario, by Joe Pontecorvo). Bottom right, up to 50%–80% of American shrew food intake comes from minnows, tadpoles, insect larvae, nymphs, crayfish, and other aquatic invertebrates (photo taken in Winnipeg, Manitoba, by Robert A. MacArthur). Next article DetailsFiguresReferencesCited by Physiological and Biochemical Zoology Volume 95, Number 5September/October 2022 Sponsored by Division of Comparative Physiology and Biochemistry, Society for Integrative and Comparative Biology Article DOIhttps://doi.org/10.1086/722501 Views: 34Total views on this site © 2022 The University of Chicago. All rights reserved.PDF download Crossref reports no articles citing this article.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.966 | 0.947 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".