The tip of the iceberg: Three case studies of spill risk assessments used in environmental impact statements
Bibliographic record
Abstract
Environmental impact statements (EISs) are based on science produced about specific project proposals, which results in a large body of grey literature. Spill risk estimates are part of that body of work. This is a critique the spill risk models cited in EISs for proposed drilling on the Alaskan Coastal Plain, the Pebble Mine transportation corridor, and Arctic offshore drilling, which are scored against published standards of best practices for ecological risk assessments. After a detailed peer review of Arctic offshore drilling risks, the findings and results from internal and external review processes of those reports are described. The amount of grey literature cited in recent EISs and how the alphanumeric ratings of draft EISs changed in 2017 are shown. Suggestions of how agencies, scientists, and peer reviewed journals can contribute to meaningful review of grey literature in regulatory science are offered.
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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 | 0.033 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.013 | 0.007 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".