Why (and where) are ghosts fishing? Mapping areas of commercial gear loss risk in British Columbia
Bibliographic record
Abstract
Lost fishing gear is a globally under studied problem, damaging marine ecosystems via habitat degradation, killing marine life, and negatively affecting commercial fishery operations. Since 2018, Canada has been mitigating this damage by funding derelict gear retrieval and responsible disposal programs. In British Columbia (BC), gear retrieval work has been spearheaded by NGOs and environmental consultants, but operations are expensive and funds are often limited. Additionally, a lack of peer-reviewed research has produced a large knowledge gap regarding why and where commercial fishing gear is lost in the province. This research investigates gear loss factors and locations in BC, building on previous predictive transboundary mapping and NGO-led workshops. This work is a collaborative effort with the T Buck Suzuki Foundation (TBSF), an NGO who has received federal funding to charter commercial fishers and their vessels to conduct gear retrievals. Together we surveyed fishers in ports around BC and online during the fall of 2021. We asked fishers to identify and rank reasons for gear loss for their most economically important industries and to mark areas on a map where they have either lost their own gear or come across existing lost gear. The survey data will be modelled to create maps of BC's marine environment indicating high-risk areas for commercial gear loss for different gear types, such as nets, traps and lines. Prior observations of lost gear and their associated environmental variables (e.g. depth, rugosity, vessel traffic etc.) will also be modelled to create a second set of maps to be compared to those generated by the survey data. Ultimately, this work will support gear retrieval operations in the BC, reducing ecological harm and negative industry impacts while providing employment opportunities to fishers through the TBSF.
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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 | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".