Spiny Dogfish Mortality Induced by Gill-Net and Trawl Capture and Tag and Release
Bibliographic record
Abstract
Abstract The spiny dogfish Squalus acanthias was once classified as an underutilized species along the U.S. East Coast, but it constituted a lucrative fishery in the 1990s until recruitment overfishing caused stock collapse. Coastwide restrictions currently apply; federal stock assessment models use bycatch mortality estimates of 50% for trawling, 75% for gill netting, and 100% for hook-and-line fishing. This study examined mortality at the southern end of commercial fishing operations caused by trawling for 30- and 90-min periods and by gill nets of various mesh sizes set for 19- to 24-h periods. Both experiments used tagged and untagged fish placed in rectangular cages anchored to the seafloor for 48 h. Tags were the Floy SS-94 single-barb nylon dart with a stainless steel wire insert. A total of 635 spiny dogfish were captured by trawl and all were alive, for a 0% initial mortality rate. A total of 2,284 spiny dogfish were collected by gill net for an initial mortality rate of 17.5%. There was no additional mortality in the 480 trawl-caught fish held for 48 h, but there was 33.3% mortality among the 480 gill-net-caught fish held under the same conditions, for an overall gill-net mortality rate of 55.0%. Examination of subsampled catches indicated that 88.6% of gill-net-caught fish had gill-net marks on the head and 41.2% had gill-net marks on the girth but, interestingly, 26.1% of trawl-caught fish had the same markings, indicating prior gill-net capture and release. Female spiny dogfish caught by gill net had a 3.6% abortion rate, compared with zero incidences of those caught by trawl. There was no significant difference in mortality between tagged and untagged fish caught by trawl or by gill net. Tag loss after 48 h was less than 1%.
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 | 0.001 | 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) | 0.001 | 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".