Quantifying errors associated with using fecal samples to determine the diet of the California sea lion (<i>Zalophus californianus</i>)
Bibliographic record
Abstract
The purpose of this study was to quantify the errors associated with using fecal samples to determine the diet of the California sea lion (Zalophus californianus). Fishes and squids of known size and number were fed to five sea lions held in enclosures with seawater-filled pools. Enclosures were washed and pools were drained periodically so that sea lion feces could be collected using a 0.5 mm mesh bag. Fish otoliths and squid beaks were collected from feces and used to estimate number and size of prey eaten. An average of 50.7% (SE = 6.4%) of 430 fishes and 73.5% (SE = 12.0%) of 49 cephalopods fed to sea lions were represented by otoliths and beaks in feces, respectively. Estimated lengths of fish from feces were less than lengths of fish fed to sea lions by an average of 30.1% (SE = 2.8%). Beaks were not digested significantly; estimated lengths of squid were underestimated by an average of only 3.3% (SE = 1.5%) relative to actual lengths. Passage rates of otoliths varied, but more than 70% were recovered within 48 h after the fish was consumed. Passage rates of beaks were generally less than those of otoliths; six beaks (11%) were collected in feces 4 days after the squid were eaten. Correction factors were created to more reliably estimate the number and size of fishes and cephalopods eaten by California sea lions.
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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 | 0.008 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".