A Bayesian‐loss function‐based method in assessing loss caused by ship‐source oil spills in the arctic area
Bibliographic record
Abstract
It is difficult to assess the risk of ship-source oil spills in Arctic waters for insurance purposes due to many unknowns and the lack of reliable data. However, maritime activities in the Arctic area continue to grow, indicating the urgent needs for the development of innovative methods to estimate loss from potential ship-source oil spills in the Arctic area. To fill this gap, we develop a hybrid Bayesian-loss function-based method to assess ship-source oil spill-related loss and implement an illustrative test on Baffin Island, Nunavut in Canada. The results confirm that our method can accurately assess loss and, subsequently, develop reliable insurance premiums for shipping activities in the Arctic area. This enables governmental and nongovernmental organizations alike to use the method as a reliable loss estimation mechanism for ship-source oil spills. Also, it is a valuable tool in designing measures for safer and more resilient Arctic shipping.
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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 | 0.020 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.026 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".