Robust digestion and passage rate estimates for hard parts of grey seal (<i>Halichoerus grypus</i>) prey
Bibliographic record
Abstract
Application of digestion correction factors to measurements and counts of fish otoliths and cephalopod beaks recovered from seal scats is required before the size or quantity of prey consumed can be accurately estimated. We carried out 86 feeding trials with seven grey seals (Halichoerus grypus) and 18 prey species to derive estimates of digestion coefficients (to account for partial digestion), recovery rates (to account for complete digestion), and passage rates (to estimate the time between consumption and excretion of an item). Mean digestion coefficients were greatest for sandeel (Ammodytes marinus) and then less for large gadoid, flatfish, and Trisopterus spp. otoliths; and finally squid (Loligo forbesii) beaks. Recovery rates were greatest for squid beaks and then less for large gadoid, Trisopterus spp., flatfish, and sandeel otoliths. Greater than 95% of otoliths and beaks recovered were passed within 4 days (~88 h) of consumption. The large differences in partial and complete digestion rates found among prey species reinforce the importance of obtaining robust estimates of these quantities. Results from this study are the most comprehensive and systematically obtained for any species of pinniped and will allow accurate and precise estimation of the number and size of fish represented by otoliths recovered from grey seal scat samples collected in the wild.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 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".