Will commercial fishing be a safe occupation in future? A framework to quantify future fishing risks due to climate change scenarios
Bibliographic record
Abstract
Weather factors are an intrinsic part of the fishing environment. Changes in weather patterns due to climate change may affect the fishing environment and fishing safety. This article proposes a general framework to quantify fishing incident risks in the future due to changes in weather conditions. This framework first builds relationships between fishing safety and weather conditions based on historical data and then predicts future risks according to these relationships with respect to potential changes in weather patterns. This paper applies the suggested framework using fishing incident data, fishing activity levels, and extreme weather conditions in Atlantic Canada to estimate the spatial distribution of fishing incident rates in the future. To do so, a classification tree is applied to historical storm tracks based on several climate models and then generated rules are applied to future storm tracks projected by selected climate change models towards the end of this century to predict fishing risk rates associated with changes in weather factors. We conclude that the environmental conditions that drive fishing incidents are projected to remain very similar by the end of this century.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".