Killer whale (<i>Orcinus orca</i>) interactions with the tuna and swordfish longline fishery off southern and south-eastern Brazil: a comparison with shark interactions
Why this work is in the frame
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Bibliographic record
Abstract
Depredation by cetaceans and sharks on longline fisheries is a global issue that can have negative impacts on both animals and fisheries and has concerned researchers, managers and the fishing industry. Nevertheless, detailed information on depredation is only available for a few regions where the problem exists. With the purpose of evaluating killer whale depredation on longline-caught tuna ( Thunnus spp.) and swordfish ( Xiphias gladius ) in the waters off southern and south-eastern Brazil and comparing it to shark depredation, data sheets were distributed to the captains of tuna vessels in Santos, south-eastern Brazil, between 1993 and 1995. Data on the catch per unit effort (CPUE) of tuna and swordfish and some records of interactions were also obtained from fishing vessel logbooks. Dockside interviews with fishermen and with researchers on board tuna vessels provided additional information. Killer whale and shark interactions were analysed per longline set and per trip. Killer whale interactions occurred from June to February, mainly between June and October, while shark interactions occurred year round. The number of sets and trips involving shark interactions was significantly higher than the number of sets and trips involving killer whale interactions. However, when depredation occurred, the proportion of fish damaged by killer whales was significantly higher than by sharks. Furthermore, killer whales removed or damaged significantly more hooked swordfish than hooked tuna, whereas sharks damaged significantly more hooked tuna than swordfish. This study also shows that cetacean by-catch is experienced by the tuna and swordfish longline fishery in Brazilian waters.
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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 | 0.001 | 0.000 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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