Influence of artificial food provisioning from fisheries on killer whale reproductive output
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 Prey availability is a critical factor influencing demographic trajectories of long‐lived, top predators, which may therefore be strongly affected by artificial food provisioning. In the C rozet archipelago, killer whales feed on a wide range of species including birds, marine mammals and fish. Following the development of the P atagonian toothfish fisheries in 1996, killer whales began to also depredate longlines. Social groups, hereafter referred to as matrilines, exhibited different levels of interaction; some were involved in most of the depredation events, while others were never observed interacting with fisheries. These differences in interaction levels influenced reproduction. An extensive photo‐identification effort from 2003 to 2012 allowed us to estimate the probability of calving for 21 reproductive females. Using multi‐model inference, we found a positive effect of depredation on female calving rate. These results suggest an effect of artificial food provisioning on female reproductive output with potentially far‐reaching consequences on the demography of the C rozet killer whale population. Our findings evidence the need to account for both intra‐population heterogeneity and level of interaction with fisheries when assessing conservation strategies of long‐lived marine predators involved in similar depredation worldwide.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 it