The Influence of Weather Conditions on the Relative Incident Rate of Fishing Vessels
Why this work is in the frame
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Bibliographic record
Abstract
There is a long history of studying the relationship between weather and maritime activities. This article analyzes the link between relative incident rate (RIR) and general weather factors within certain gridded areas and time periods. The study area, which encompasses a broad extent of Atlantic Canadian waters, includes fishing incidents recorded by the Canadian Coast Guard from 1997 to 1999. Methodologies used for traffic track generation in a geographical information system and aggregation of all relevant weather data needed for the statistical analyses are presented. Ultimately, a regression tree was built to illustrate the relationship between incident rate and the following six weather factors: wave height; sea surface temperature; air temperature; ice concentration; fog presence; and precipitation. Results from the regression tree reveal that the RIR defined as (incident number per area-day)/(traffic amount per area-day) across grid cells with incidents, increases as the weather conditions deteriorate in a general way, and the concentration of ice has the biggest influence on the magnitude of incident rates for a given level of traffic exposure. The results from this analysis may assist administrators of maritime traffic, especially those associated with fishing activities, through a better understanding of the influence on RIR of certain weather conditions within given areas in specific time periods.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 it