Using bycatch data to understand habitat use of small cetaceans: lessons from an experimental driftnet fishery
Bibliographic record
Abstract
Abstract Stenson, G. B., Benjamins, S., and Reddin, D. G. 2011. Using bycatch data to understand habitat use of small cetaceans: lessons from an experimental driftnet fishery. – ICES Journal of Marine Science, 68: 937–946. Many marine mammals inhabit offshore areas where it is difficult to determine distribution and abundance. Historical bycatch data of marine mammals in the Northwest Atlantic obtained from the Canadian experimental Atlantic salmon (Salmo salar) driftnet fishery were examined to obtain information on seasonal distribution and relative abundance. From 1965 to 2001, 47 cruises were undertaken totalling 12 566.5 km-h of fishing effort; four species of small cetacean and two species of pinniped were caught. Harbour porpoises (Phocoena phocoena) were the most frequently caught species in all areas except the Labrador Sea, where Atlantic white-sided dolphins (Lagenorhynchus acutus) were more common. Long-finned pilot whales (Globicephala melas), common dolphins (Delphinus delphis), harp seals (Pagophilus groenlandicus), and harbour seals (Phoca vitulina) were also taken occasionally. Although typically considered an inshore species, harbour porpoises were regularly reported in deep water (>2000 m), in the Newfoundland Basin and Labrador Sea. Atlantic white-sided dolphins were often caught along the edge of the continental shelf and appeared to prefer relatively warm water. Finally, catch records indicate that waters of the Newfoundland Basin and Southern Grand Banks may contain important winter habitat for several small species of cetacean.
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 | 0.011 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".