Pinniped diets inferred from scats: analysis of biases in prey occurrence
Bibliographic record
Abstract
The diets of pinnipeds have often been used to study their ecology and resource dynamics and in ecosystem monitoring. Scat analysis is now the most widely used method of inferring the diets of pinnipeds. Using a mathematical model the present study explores the expected biases in prey occurrence that are related to prey mass, proportion of loss of remains, predator assimilation efficiency, size of the scats collected, and meal size. With the exception of meal size, variation in parameters implied variation in the probability of biases in prey occurrence. Biases were asymmetric among prey sizes, but in contrast with previous ideas, the results indicate that small prey have smaller biases than large ones do. Furthermore, the number of scats required to detect a large prey was notably larger than that needed to detect a small prey with the same occurrence. The present study is not limited to a particular pinniped or prey species, so it has the potential to represent a general framework for interpreting the results of scat analysis in pinnipeds. The model complements empirical studies, advancing our comprehension of biases associated with prey occurrence in dietary studies of pinnipeds.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".