Great shearwater Ardenna gravis attendance at commercial fisheries in the Argentine economic fishing zone
Bibliographic record
Abstract
The great shearwater Ardenna gravis is a pelagic seabird that forages in waters of the southwestern Atlantic Ocean mainly during the pre-laying and chick-rearing periods. There, the species has been reported in the bycatch of longline and trawl fisheries. The aim of this study was to evaluate the effect of fishing effort on the foraging behavior of shearwaters, analyzing the distribution and behavior of birds and fishing effort and using evidence from isotope analysis to assess their use of fishery discards and facilitated prey. Tracking data of immature and adult shearwaters and fishing effort of different Argentine commercial fishing fleets were used to determine the effect of fishing effort on the foraging behavior of the species through generalized additive mixed models. Adult and immature shearwaters are more likely to forage when the fishing effort of demersal high-seas ice-trawlers increases and that of coastal demersal ice-trawlers decreases (and mid-water ice-trawlers for immatures). The isotope analysis showed higher contribution of zooplanktonic species and mid-water fish, followed by demersal species (which can be only available through the consumption of discards and offal). These results are related to the common use of highly productive waters and the attraction of shearwaters generated by prey captured in nets and by discards as a predictable source of food. Understanding the impact of fisheries on seabird behavior is essential for implementing measures aimed at reducing the incidental capture of seabirds by fishing fleets.
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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 | 0.000 | 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) | 0.002 | 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".