Impact and performance of Mediterranean fishing gear by side-scan sonar technology
Bibliographic record
Abstract
An innovative approach with side-scan sonar was implemented to observe directly during towing operations the behaviour and the performance of hydraulic dredge, Rapido trawl, and two types of Mediterranean bottom trawls. The innovative aspect of the current paper is that a side-scan sonar was used alongside fishing operations when towing was in progress. Based on available literature, the type and level of the physical impact assessed were used to infer biological and ecological consequences caused by the fishing gear. The sonar used in real time can be applied to a wide range of research and management issues, such as the fishing gear impact on benthic ecosystems and the in situ fishing gear performance. We demonstrated that gear type and rigging, together with environmental conditions, are the main factors affecting the disturbance caused by fishing. Hydraulic dredges scrape the surface of the substratum and dig into it by resuspending large amounts of sediment. Rapido trawls used in muddy areas for common sole (Solea solea) dig deeper into the sediment, making furrows up to 10–13 cm deep. The most evident physical effects of trawling were the furrows produced by the otterboards, while other parts of the trawl created only faint marks.
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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 | 0.000 |
| 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".