Investigation of Deepwater horizon oil spill movement in the Gulf of Mexico
Bibliographic record
Abstract
In this paper, the performance of the MIKE21 numerical model in modeling the dispersion and transport of spilt oil related to the Deep-Water Horizon oil platform disaster in the northern part of the Gulf of Mexico is studied. Our model predicts the distribution and movement of spilt oil based on the wind-waves, current flows and vorticities taking into account evaporation, emulsion, and absorption. In this research, two types of large scale and local scale models are considered. The radiation stress of the waves, the water level and the flow speed in the Gulf are modeled using a large-scale model. After calibration and verification, the large-scale model is used to extract the boundary conditions for the local scale model and the dispersion and transport of the spilt oil is done in the local model. The accuracy of the numerical simulation using MIKE21 are confirmed by comparisons to observed satellite images. Results showed that the length of the oil spill reached 55 kilometers and covered an area of 2,800 Km2 by April 25. After two weeks, the oil spill had apparently divided into two slicks, each with an area of about 2420 and 960 Km2, respectively. Eventually, by May 28, the slick area appeared to reach over 48,400 Km2 which much of the oil evaporated because it was lightweight oil. Meanwhile, the Deep-Water Horizon oil spill occurred in spring and summer seasons; we also consider possible results assuming that the spill occurred at other times, such as autumn or winter.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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 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".