SUBCOMMITTEE ON ENERGY AND THE ENVIRONMENT U.S. HOUSE OF REPRESENTATIVES
Bibliographic record
Abstract
pleasure to be here today to discuss EPA’s oil spill research program, its past accomplishments, and future research plans. For the past 20 years, I have led EPA’s oil spill research and development program to conduct basic and applied research in both the laboratory and the field in the area of spill response technology development. I was an EPA team leader in the Exxon Valdez bioremediation project in 1989 and 1990. I also conceived and led an important controlled oil spill project on the shoreline of Delaware Bay in 19941, which demonstrated statistically that bioremediation with simple inorganic nutrients enhances the biodegradation rate of crude oil on a marine shoreline compared to natural attenuation without amendments. I repeated a similar experiment in 19992 on a Quebec freshwater wetland and again in 20013 on a Nova Scotia salt marsh in collaboration with our Canadian government partners. In addition to those field studies, I led a research team in developing laboratory protocols to test the effectiveness of commercial bioremediation agents and chemical dispersant products for use in treating oil spills4-6. I have conceived and led numerous other studies to understand how best to respond to and mitigate oil spills on land. The Environmental Threat of Oil Spills
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.010 | 0.006 |
| Insufficient payload (model declined to judge) | 0.109 | 0.059 |
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".