Hotspots for porbeagle shark (<i>Lamna nasus</i>) bycatch in the southwestern Atlantic (51°S–57°S)
Bibliographic record
Abstract
Fisheries bycatch can severely affect the population status of species with low resilience such as sharks. Bycatch monitoring is an important issue for the development of conservation and management plans for these species. The main objectives of this study were to quantify and model the spatiotemporal trend of bycatch for porbeagle shark (Lamna nasus) in the Argentinean surimi trawl fleet to identify hotspots in the southwestern Atlantic Ocean. Using onboard observer data, we have demonstrated that L. nasus was usually caught as bycatch by the surimi trawl fleet operating in the southern limits of the southwestern Atlantic (51°S–57°S), representing an important part of the reported catch for the Atlantic Ocean. Delta and Tweedie models indicated that bycatch had a relatively stable trend, was concentrated in a limited region of the study area, and was associated with spatiotemporal, operational, environmental, and prey availability variables. The model with the best predictive capability used for the spatial delineation of hotspots for L. nasus bycatch showed that the areas located south of 54°12′S and over the continental shelf-break were critical for the porbeagle conservation and management strategies in the southwestern Atlantic Ocean.
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 | 0.000 |
| 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 | 0.000 |
| 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".