Following-Up on Uncertain Environmental Assessment Predictions: The Case of Offshore Oil Projects and Seabirds Off Newfoundland and Labrador
Bibliographic record
Abstract
Environmental assessments (EAs) predict project environmental effects with varying degrees of certainty. Articulating prediction uncertainty and linking it to EA follow-up is a best practice for reducing uncertainty. This study examines predictions from Canadian oil projects off Newfoundland and Labrador between 1985 and 2012 concerning seabirds, the valued ecosystem component identified as the most vulnerable to oil exploitation in an area frequented by millions of migratory birds. We asked if these EA predictions: (a) reported uncertainty ratings; (b) for those reporting medium and high uncertainty ratings whether the predictions were addressed by EA follow-up; and (c) if prediction uncertainty was reduced by EA follow-up and reflected in subsequent EAs. Prediction uncertainty reporting was rare and uncertainties were not resolved through EA follow-up. Assumptions of negligible or low environmental effects on seabirds off Newfoundland and Labrador from offshore oil and gas extraction have been supported through decades by sustaining uncertainty.
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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 | 0.008 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".