A novel method to estimate prey contributions to predator diets
Bibliographic record
Abstract
Stomach content data are frequently used to characterize predator feeding habits, often by describing the proportional contribution by mass or number of each prey type (diet fractions). These data pose several statistical challenges for analysis and estimation that have hindered our ability to create quantitative diet fraction estimates from stomach content data. To address these challenges, we developed a novel, likelihood-based mixture model to quantitatively estimate diet fractions. Simulation testing indicated that estimated diet fractions from the mixture model were more precise than those estimated either from a (stomach mass) weighted mean or the sample mean and were more accurate than a sample mean. Additionally, we applied the mixture model, a weighted mean, and sample mean to stomach content data for multiple types of predators. For three of four of these data sets, the mixture model demonstrated higher precision than and similar accuracy to a weighted mean and similar precision and better accuracy than a sample mean. The mixture model represents an important step in advancing statistical methods to address the challenges of stomach content data.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".