Acoustic and stiffness properties of gillnets as they relate to small cetacean bycatch
Bibliographic record
Abstract
Abstract Mooney, T. A., Au, W. W. L., Nachtigall, P. E., and Trippel, E. A. 2007. Acoustic and stiffness properties of gillnets as they relate to small cetacean bycatch. – ICES Journal of Marine Science, 64: 1324–1332. Small cetaceans are incidental bycatch in gillnet fisheries. Dolphin and porpoise bycatch has been reduced by the use of barium sulphate-enhanced gillnets. This decreased entanglement is likely the result of either higher acoustic reflectivity or greater stiffness for barium nets. To address these variables, our study quantified the acoustic reflectivity and stiffness of six net types including barium sulphate, iron oxide-enhanced and control demersal gillnets of sizes which typically target cod (Gadus morhua) and monkfish (Lophius americanus). Acoustic reflectivity, or target strength (TS), was assessed using dolphin and porpoise-like sonar signals from 0° to 40°. TS values were used to calculate likely detection ranges. Barium sulphate- and iron oxide-enhanced nets showed increased reflectivity compared with control nets, with the barium sulphate nets generating the highest TS values. Dolphins should detect these nets in time to avoid contact, but porpoises, with typically lower source levels, may not detect nets at a range great enough to avoid entanglement. Barium sulphate line was significantly stiffer than comparable nylon line. All lines lost stiffness when soaked in seawater for 24 h. Barium sulphate nets proved stiffer and more acoustically reflective, and both factors are likely important in reducing harbour porpoise bycatch.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".