Characterizing seabird bycatch in the eastern Australian tuna and billfish pelagic longline fishery in relation to temporal, spatial and biological influences
Bibliographic record
Abstract
Abstract Seabirds killed incidentally in Australia's eastern tuna and billfish (ETBF) longline fishery between September 2001 and June 2006 were examined to evaluate species composition and to relate, where possible, capture events to operational and environmental factors. During this period 2.129 million hooks on 2202 shots were observed, and 369 birds were reported killed. The majority (78%) of these were flesh‐footed shearwaters ( Puffinus carniepes ), 53% of which were male and 44% female. Smaller numbers of medium to large sized albatrosses ( Diomedeidae , predominantly female) and other shearwaters ( Puffinus spp.) and petrels ( Pterodroma spp.) dominated the remainder of the bycatch. Of the 369 birds reported taken as bycatch, 280 were available for necropsy, and species identifications performed in situ by observers were assessed. While observer identifications were generally correct for common species, performance was poor for less common ones. The geographical location (latitude) of shots, season, time of day at which shots were set, and bait type and life status (dead or alive) influenced the seabird bycatch rate. The majority of captures (87% overall) occurred between 30 and 35°S, with bycatch being lowest in winter, and remaining at similar levels across the other seasons. The use of live fish bait was generally associated with increased captures of both seabirds overall, and flesh‐footed shearwaters in particular. Copyright © 2010 John Wiley & Sons, Ltd.
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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 | 0.001 | 0.001 |
| 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".