Non-linear source term and scenario for an operational oil spill model
Bibliographic record
Abstract
Abstract This study presents time-varying oil spill discharge functions and scenarios for operational oil spill models. This study prescribes non-linear models based on experimental measurements (Tavakoli et al. in Ocean Eng 38(17–18):1894–1907, 2011) and then upscaled to the spill duration and discharge quantity for actual oil spill incidents. Scenarios consist in collision and grounding incidents for the instantaneous spill mode; light , medium , and severe incidents for the continuous spill mode; spilt , containment , and retention practices for the spill management mode. A performance analysis of deterministic simulations indicates that the non-linear source terms and scenarios present realistic and reasonable results, showing the detailed spill patterns on the surface ocean, tail-off oil sheens along the areas swept by the dispersion and significantly different results when oil spill management and mitigation practices are activated. For oil spill modelling in support of field operations, responders and decision makers should be made aware of the variability of oil sheen spatial patterns induced by the oil spill source term to better interpret simulation results and assess the impact of source uncertainty on the clean-up, mitigation, ecological and socio-economic risk.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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 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".