Avian tissue sampling following oil pollution events: quantifying impacts and monitoring recovery
Bibliographic record
Abstract
Oil pollution is a persistent threat to coastal environments and can cause significant harm to wildlife, particularly aquatic birds. Additionally, shipping rates are increasing across the globe, and while regulations are improving on certain aspects of fuel choice and chemical composition, there is still a growing potential for spills and increased chronic discharge. Existing and planned offshore oil production adds further complexity to oil pollution risks worldwide. Despite this, there is little guidance for wildlife responders on what avian tissues to sample, how to preserve them, and how many samples are needed in the immediate aftermath of a spill. We provide a practical guide for responders and researchers to assist with sample collection, preservation, logistics, and relevant laboratory analyses to address project goals. We also highlight the value of timely sample collection, especially in time-sensitive situations. We emphasize laboratory analyses that provide individual-level exposure metrics that cannot be obtained from population-level information alone. These metrics are helpful for understanding the spill’s immediate impact, supporting damage assessments, and informing legal actions, if applicable. We also provide guidance on long-term monitoring plans to evaluate the potential inter-annual impacts of spills on marine bird populations. Sampling techniques are reviewed for assessment of polycyclic aromatic compounds (PACs), trace metals, epigenetics, toxicogenomics, foraging ecology, endocrine disruption, and hemolytic anemia. Flowcharts are included to assist with quick decision-making while considering key factors such as Indigenous concerns, species selection, and seasonality.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".