Overview of Aquatic Toxicity Testing under the U.S. EPA Oil Research Program
Bibliographic record
Abstract
2017-063 ABSTRACT The U.S. EPA Office of Research and Development is developing baseline data on the ecotoxicity of selected petroleum products, chemical dispersants, and other spill mitigating substances as part of its Oil Research Program. Two diluted bitumens (dilbits) from the Alberta Tar Sands region are being tested for acute and chronic toxicity to standard freshwater and marine organisms given their spill potential during shipment within the United States. Separately, crude oils representing a range of characteristics and representative dispersants are being tested to evaluate acute and chronic toxicity to marine organisms in support of proposed regulatory amendments to Subpart J of the U.S. National Contingency Plan. Water accommodated fractions (WAF) of oil are prepared using traditional slow-stir methods and toxicity tests follow U.S. EPA standard effluent testing guidelines, modified for testing petroleum products. WAFs are characterized for petroleum hydrocarbons including alkyl PAH homologs. Future research plans include evaluating oil spill mitigating substances such as surface washing and bioremediation agents. The results of the research program will assist the U.S. EPA in assessing toxicity of unconventional oils (dilbits), and establish baseline toxicity data for selected crude oils and spill mitigating substances in support of planning and response activities.
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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 | 0.007 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.006 |
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".