Evaluating the use of side scan sonar for improved detection and targeted retrieval of abandoned, lost, or otherwise discarded fishing gear
Bibliographic record
Abstract
Abandoned, lost, or otherwise discarded fishing gear (ALDFG) has surfaced as a significant conservation issue that continues to compromise the economic, social, and ecological aspects of the marine environment. To alleviate these concerns, methods of lost gear detection at the seabed can be applied to increase the precision of derelict gear retrieval and potentially improve the likelihood of success. Targeted in Canada's most productive American lobster (Homarus americanus) fishing area, a “hotspot” analysis was performed by mapping the density of reported lost gear at a 3 km × 3 km grid resolution. The hotspot analysis was used to target the collection of 27 side scan sonar (SSS) transects in Lobster Fishing Area (LFA) 34 over a 12-day survey period in Clark's Harbour, Nova Scotia, to evaluate the benefits of gear detection in large-scale retrieval missions. Following a comprehensive review of the SSS data post-processing, 114 potential ALDFG targets were visually identified, and only one item was confirmed retrieved. Despite this, a large volume of ALDFG was retrieved in areas where there was no SSS coverage based on fisher's local knowledge. The findings from this study demonstrate that retrieval of ALDFG based on SSS data can be difficult due to limitations of retrieval methods used, and that costs of SSS data acquisition may prohibit widescale use of this technology on an annual basis in spatially extensive fishing areas. Nonetheless, the use of SSS to identify ALDFG is recommended at smaller geographic scales in areas where the benthic environment may be sensitive. Although the use of fisher's local knowledge was not evaluated in this study, where the impacts of grappling for ALDFG are considered minimal, local knowledge can be extremely effective in the removal of lost fishing gear and other debris from the benthic environment.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".