Will commercial fishing be a safe occupation in future? A framework to quantify future fishing risks due to climate change scenarios
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 it