A Comprehensive System for Simulating Oil Spill Trajectory and Behaviour in Subsurface and Surface Water Environments
Bibliographic record
Abstract
Abstract 2017-184: Accidental release of pollutants such as offshore oil spills can cause significant negative impacts on the environment and socio-economy, and constitutes a direct hazard to marine life and human health. Particularly, deepwater blowout released spills are more challenging to study because the trajectory and behaviour of oil are difficult to be comprehensively simulated. Although there are many integrated or coupled models available, there still lacks open source deepwater oil spill models to predict not only the trajectory but also the mass balance of oil. It is the objective of this study to fill this gap by coupling the Texas A&M Oilspill Calculator (TAMOC) for nearfield simulation and the advanced oil spill module in the Modelo Hidrodinâmico (MOHID) 3D Water modeling system. In addition, the Weber number scaling approach is also integrated in both the near- and far-field simulation for oil droplet size prediction. The applicability of the proposed comprehensive system is tested by a case study of simulation of oil spills released from a depth of 3,500 m in the Scotian Shelf, Canada. The results demonstrate a high feasibility of the proposed system. By providing comprehensive simulation for oil spills, the developed system should provide significant support to the response to offshore oil spill, especially from deepwater blowout.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".