Bibliographic record
Abstract
Abstract Dispersant application is becoming considerably more utilized as a tool inthe response toolbox at oil spills. The primary application tool in the pasthas been through aircraft. While aircraft application is extremely effective, boat application can be efficiently utilized as an additional tool for deliveryof dispersant. This paper discusses the potential use and awareness for boatapplication of dispersant. Introduction Dispersant use on offshore oil spills has become increasingly more common asa tool to combat the spread of oil spills. Traditionally, here in the United States, oil companies have looked toward aerial application of dispersants. Theuse of aircraft is a valuable tool in the ongoing fight of oil spills andshould never be overlooked. Boat application of dispersants will not replaceaircraft spraying. Boat application should be considered as another toolavailable to qualified individuals (QI), the incident commander (IC), theresponsible party (RP), and the spill management team (SMT) of the oil ortransportation company, or the federal and state government that gets involvedin the task of mitigating the affects of an oil spill. Boat Spray Application of Dispersant Dispersant use in approved locations is not the topic in this paper. Thegovernment has well defined avenues for approving the use of dispersant. Itshould be noted that any use of any chemical in the response to an oil spillmust have complete authorized approval prior to any application. The Oil Pollution Act of 1990 (OPA 90) has had a tremendous impact on oil, transportation, storage and response companies and in the short decade since1989, the United States has seen an unsurpassed effort of the buildup ofresponse equipment, primarily containment, and mechanical recovery equipment. Interestingly enough, the primary dispersant delivery system in the Gulf of Mexico (GOM) was available to QI's, IC's, SMT's, and RP's and was in operationprior to OPA 90.
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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 | 0.000 | 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) | 0.007 | 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".