The California current predator diet database: synthesis of common forage species
Bibliographic record
Abstract
Characterization of the diets of upper-trophic predators is a key ingredient in management including the development of ecosystem-based fishery management plans, conservation efforts for top predators, and ecological and economic modeling of predator prey interactions. The California Current Predator Diet Database (CCPDD) synthesizes data from published records of predator food habits over the past century. The database includes diet information for 100+ upper-trophic level predator species, based on over 200 published citations from the California Current region of the Pacific Ocean, ranging from Baja, Mexico to Vancouver Island, Canada. We include diet data for all predators that consume forage species: seabirds, cetaceans, pinnipeds, bony and cartilaginous fishes, and a predatory invertebrate; data represent seven discrete geographic regions within the CCS (Canada, WA, OR, CA-n, CA-c, CA-s, Mexico). The database is organized around predator-prey links that represent an occurrence of a predator eating a prey or group of prey items. Here we present synthesized data for the occurrence of 32 forage species (see Table 2 in the affiliated paper) in the diet of pelagic predators (currently submitted to Ecological Informatics). Future versions of the shared-data will include diet information for all prey items consumed, not just the forage species of interest.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.012 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.013 |
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".