Entrapment of harbour porpoises (Phocoena phocoena) in herring weirs in the Bay of Fundy, Canada
Bibliographic record
Abstract
Harbour porpoises (Phocoena phocoena) are small coastal cetaceans vulnerable to mortality in fishing operations. Not all interactions are fatal, however, and each year many porpoises swim into and are subsequently released from herring weirs in the Bay of Fundy, Canada through a targeted release programme. This study examines catch composition, body condition, characteristics associated with mortality and factors affecting entrapment of porpoises in weirs between 1992-2001. A total of 886 porpoises were recorded in weirs during this period. A total of 657 animals were involved in attempted releases: 588 were released alive and 69 were incidentally killed during release. The remainder of the animals swam out on their own or their fates were unknown. Estimated annual mortality represents less than 0.01% of the Bay of Fundy/Gulf of Maine population and only 1.03% of its annual potential biological removal level. The number of porpoises caught in weirs varied from eight in 1996 to 312 in 2001. Of the 390 animals released with a numbered identification tag, 25 were recaptured in weirs and 4 of those porpoises entered a weir a third time. Males comprised 63.5% of entrapments. Weirs and demersal gillnets captured animals from the same population, but the weir bycatch was biased towards younger, smaller animals. Porpoises that became trapped in weirs exhibited measures of body condition similar to those killed in gillnets and by gunshot wounds in the same waters. None were considered emaciated. Mortality in weirs appeared to be random; porpoises that died during release attempts were of the same age and sex composition and body condition as the individuals that survived. The use of a specialised large-mesh seine significantly increased the probability of successful release. Observations of the stomach contents data of porpoises killed in weirs indicate that porpoises feed while trapped in weirs, but perhaps not at the same rate as animals killed in gillnets. Entrapments peaked in August, concurrent with the highest landings of Atlantic herring, the target species of the weir fishery. Based on a logistical regression model, porpoises are 3.3 times more likely to swim into a weir on a night in which high tide falls during darkness. Weir entrapments do not have a significant effect on this population, largely because of on-going efforts to release porpoises from weirs.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".