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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".