Estimating the numbers of aquatic birds affected by oil spills: pre-planning, response, and post-incident considerations
Bibliographic record
Abstract
Oil spills most visibly affect waterbirds and often the number of birds affected, a key measure of environmental damage from an incident, is required for public communication, population management, and legal reasons. We review and outline steps that can be taken to improve accuracy in the estimation of the number of birds affected in each of three phases: (1) pre-planning; (2) during a response; and (3) post-response. The more pre-planning undertaken, the more robust the estimates will be. Personnel involved in damage assessment efforts must have training in quantitative biology and need support during all three phases. The main approaches currently used to estimate the number of birds affected include probability exposure models and carcass sampling — both onshore and on the water. Probability exposure models can be used in the post-incident phase, particularly in offshore scenarios where beached bird surveys are not possible, and requires three datasets: (1) at-sea bird densities; (2) bird mortality; and (3) the spill trajectory. Carcass sampling using beached bird surveys is appropriate if trajectories indicate affected birds will reach shore. Carcass sampling can also occur via on-water transects and may overlap with risk assessment efforts. Damage assessment efforts should include a measure of sublethal effects following the post-acute phase of spills, yet this area has significant knowledge gaps. We urge jurisdictions worldwide to improve pre-incident planning. We provide guidance on how, in the absence of pre-incident data, quality data can be obtained during or after an incident. These recommendations are relevant for areas with aquatic-based industrial activities which can result in a spill of substances that could injure or kill waterbirds.
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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 | 0.010 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".