Inferring prey perception of relative danger in large-scale marine systems
Bibliographic record
Abstract
Problem: Infer relative danger from spatially segregated predators in large-scale marine systems, using individual differences in prey foraging behaviour. Mathematical models: Optimization of trade-offs between predation risk and energy gain. Key assumption: Foraging individuals choosing to incur higher risk of predation should experience higher energetic gain than individuals choosing safer foraging options. Organisms: Alaskan harbour seals foraging under predation risk from killer whales and Pacific sleeper sharks in shallow and deep strata, respectively. Sleeper sharks are elusive predators and their interactions with prey are difficult to measure directly. Method: Our method infers the relative danger from different predator types that is theoretically most plausible according to intermediate consumer behaviour. We predicted net energy gain and predation risk per foraging dive, parameterizing an analytical model with data on seal behaviour and the spatial distributions of predators and resources. We used assumptions about relative danger from different predator types as treatments in a computer experiment. We sought the treatment combination best supporting the assumption that individuals that risk more should gain more by plotting the individual seals’ predicted cumulative energy gain against cumulative predation risk. Improved statistical fit for a positive and monotonic relationship implied greater support. Conclusions: Our key assumption was best supported when assuming that relative danger from sleeper sharks is much greater than that from killer whales, suggesting that sub-lethal effects of sharks on Alaskan harbour seals might be much stronger than previously thought.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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 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".