Predicting factors of fishing gear loss and distribution across Canada’s Pacific Ocean
Bibliographic record
Abstract
Abandoned, lost, and otherwise discarded fishing gear (ALDFG) comprises a large portion of the world’s marine plastic pollution, damaging marine habitats, wildlife, and fishing industries globally. Lost gear retrieval can be an effective short-term mitigation strategy, and spatial modelling has been helpful tool determine where to target efforts. Using Canada’s Pacific Ocean as a case study, we examined how environmental, and fishing attributes contribute to gear loss. We predicted areas of potentially high ALDFG occurrence based on key variables using a Species Distribution Modeling approach. We determined that important variables for predicting gear loss included bathymetry, fishing effort, and wind speed. Our projections of ALDFG occurrence indicated that the coastal areas of Canada’s Pacific Ocean had the highest probability of gear loss. Our research has the potential to increase the efficiency of future gear retrieval and provide insight to fisheries management to effectively mitigate the negative effects of lost fishing gear in Canada’s Pacific Ocean.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".