Impact of diet-index selection and the digestion of prey hard remains on determining the diet of the Steller sea lion (Eumetopias jubatus)
Bibliographic record
Abstract
Nine prey species (n = 7431) were fed to four captive female Steller sea lions ( Eumetopias jubatus (Schreber, 1776)) in 11 feeding trials over 75 days to investigate the effectiveness of different methods used to determine diet from prey hard remains. Trials aimed to replicate short (1–2 days) and long feeding bouts, and consisted of single species and mixed daily diets. Overall, 25.2% ± 22.2% (mean ± SD, range 0%–83%) otoliths were recovered, but recovery rates varied by species (ANOVA, P = 0.01) and were linearly related to otolith robustness (R2 = 0.88). Squid beaks were recovered at higher frequencies (mean 96%) than the otoliths of all species. Enumerating both non-otolith skeletal structures and otoliths (together termed bones) increased species recovery rates by twofold, on average (P < 0.001), with increases up to 2.5 times for Pacific herring ( Clupea pallasii Valenciennes in Cuvier and Valenciennes, 1847) and 3–4 times for salmonids. Using bones reduced interspecific differences (P = 0.08), but recovery varied among sea lions. Bones were distributed over more scats per meal (mean 2.9 scats, range 0–5) than otoliths (mean 1.9 scats, range 0–4). In three different 15-day mixed diet trials, biomass reconstruction (BR) indices performed better than frequency of occurrence indices in predicting diet fed. Applying our experimentally derived numerical correction factors (to account for species differences in complete prey digestion) further improved BR estimates, resulting in all 12 unweighted comparisons within 5% (for otoliths) and 12% (for bones) of the actual diet fed.
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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 | 0.001 | 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) | 0.001 | 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".