Setting the stage to advance oil toxicity testing: Overview of knowledge gaps, and recommendations
Bibliographic record
Abstract
The Chemical Response to Oil Spills: Ecological Effects Research Forum created a standardized protocol for comparing the in vivo toxicity of physically dispersed oil to chemically dispersed oil to support science-based decision making on the use of dispersants in the early 2000s. Since then, the protocol has been frequently modified to incorporate advances in technology; enable the study of unconventional and heavier oils; and provide data for use in a more diverse manner to cover the growing needs of the oil spill science community. Unfortunately, for many of these lab-based oil toxicity studies consideration was not given to the influence of modifications to the protocol on media chemistry, resulting toxicity and limitations for the use of resulting data in other contexts (e.g., risk assessments, models). To address these issues, a working group of international oil spill experts from academia, industry, government, and private organizations was convened under the Multi-Partner Research Initiative of Canada's Oceans Protection Plan to review publications using the CROSERF protocol since its inception to support their goal of coming to consensus on the key elements required within a "modernized CROSERF protocol".
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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".