Development and testing of a 2D offshore oil spill modeling tool (OSMT) supported by an effective calibration method
Bibliographic record
Abstract
Abstract Spill trajectory prediction (STP) is essential to decision-making support for oil pollution preparedness and response. An Oil Spill Modeling Tool (OSMT) has been developed in this study to predict the transport and fate of oil spills from surface releases. Particularly, the Kullback-Leibler (KL) divergence method is adopted as a performance metric for the first time to formulate a calibration framework for STP from the Lagrangian transport model (LTM). By finding the candidate with minimal KL divergences from modeling scenarios using designed parameter combinations, the prediction discrepancy between simulated trajectories of the LTM and oil slicks detected from satellite images are reduced. The developed approach has first been evaluated through a numerical comparison analysis between OSMT and Operational Oil Modeling Environment (GNOME) model to examine model validity in a hypothetical case. Subsequently, a real case study is conducted to examine the applicability and effectiveness of the KL divergence-based oil spill trajectory modeling method. The study results indicate that OSMT can provide reliable spill trajectory simulations, and the KL divergence-based calibration method is effective in calibrating the oil spill LTM.
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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 | 0.003 | 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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 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".