Methods of estimating marine mammal diets: A review of validation experiments and sources of bias and uncertainty
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.004 |
| 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 it