Advances to the CROSERF protocol to improve oil spill response decision making
Bibliographic record
Abstract
The Chemical Response to Oil Spills: Ecological Effects Research Forum (CROSERF) created a standardized protocol for comparing the toxicity of physically dispersed oil versus chemically dispersed oil to address environmental concerns related to the proposed use of dispersants in the early 2000s. Since then, many revisions have been made to the original protocol to diversify the intended use of the data generated, incorporate emerging technologies, and to examine a wider range of oil types including non-conventional oils and fuels. Under the Multi-Partner Research Initiative (MPRI) for oil spill research under Canada's Oceans Protection Plan (OPP), a network of 45 participants from seven countries representing government, industry, non-profit, private, and academic sectors was established to identify the current state of the science and formulate a series of recommendations to modernize the oil toxicity testing framework. The participants formed a series of working groups, targeting specific aspects of oil toxicity testing, including: experimental conduct; media preparation; phototoxicity; analytical chemistry; reporting and communicating results; interpreting toxicity data; and appropriate integration of toxicity data to improve oil spill effects models. The network participants reached a consensus that a modernized protocol to assess the aquatic toxicity of oil should be sufficiently flexible to address a broad range of research questions in a 'fit-for-purpose' manner, where methods and approaches are driven by the need to generate scientifically-defensible data to address specific study objectives. Considering the many needs and varied objectives of aquatic toxicity tests currently being conducted to support and inform oil spill response decision making, it was also concluded that the development of a one size fits all approach would not be feasible.
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 | 0.173 | 0.291 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.169 | 0.071 |
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".