CLASSIFYING PREY HARD PART STRUCTURES RECOVERED FROM FECAL REMAINS OF CAPTIVE STELLER SEA LIONS (<i>EUMETOPIAS JUBATUS</i>)
Bibliographic record
Abstract
Abstract Feces were collected from six Steller sea lions (Eumetopias jubatus) that consumed known amounts of Atka mackerel (Pleurogrammus monopterygius), Pacific herring (Clupea barengus), pink salmon (Oncorhynchus gorbuscha), walleye pollock (Theragra chalcogramma), and squid (Loligo opalacens). The goal was to determine the numbers and types of taxon‐specific hard parts that pass through the digestive tract and to develop correction factors for certain abundantly occurring structures. Over 20,000 fish and squid were consumed during 267 d of fecal collection. During this period, over 119,000 taxon‐specific hard parts, representing 56 different structures, were recovered. Skeletal structures and non‐skeletal structures accounted for 72% and 28% of all hard parts, respectively. The branchiocranium, axial skeleton, and dermocranium regions of the skeletal system accounted for the greatest number of hard parts recovered. Over 70% of all recovered hard parts were represented by one to six taxa specific structures for each prey type. The average number of hard parts (3.1–31.2) and structure types (2.0–17.7) recovered per individual prey varied across taxa and were used to derive correction factors (to reconstruct original prey numbers). A measure of the variability of hard part recovery among sea lions showed no difference for certain herring, pollock, and squid structures, however, there was a significant difference for salmon and Atka mackerel structures. Identifying all taxon‐ specific prey hard parts increases the likelihood of identifying and estimating the number of prey consumed.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.001 | 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 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".