Methods of estimating marine mammal diets: A review of validation experiments and sources of bias and uncertainty
Bibliographic record
Abstract
Abstract Diet estimation in marine mammals relies on indirect methods including recovery of prey hard parts from stomachs and feces, quantitative fatty acid signature analysis (QFASA), stable isotope mixing models, and identification of prey DNA in stomach contents and feces. Experimental evidence (9 species/13 studies) shows that digestion strongly influences the proportion and size of otoliths that can be recovered in feces. Number correction factors (NCF) and digestion coefficients have been experimentally determined to reduce the biases in fecal analysis. Correction factors and coefficients have not been determined for diet estimated from stomach contents. QFASA estimates which prey species and amounts must have been eaten to account for the fatty acid composition of the predator. Experimental studies on mammals and seabirds (9 species/10 studies) indicate that accurate estimates of diet can be determined using QFASA. Stable isotope mixing models provide rather coarse taxonomic resolution of diet composition. Prey DNA analysis shows promise as a method to estimate the species composition of diet, but further development and testing is needed to validate its use. Obtaining a representative sample from marine mammal populations is a significant challenge. Therefore, the use of complementary methods is recommended to obtain the most informative results.
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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 | 0.018 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".