Oiling of American white pelicans, common loons, and northern gannets in the winter following the Deepwater Horizon (MC252) oil spill
Bibliographic record
Abstract
The Natural Resource Damage Assessment and Restoration Trustees for the Deepwater Horizon oil spill assessed the external oiling of migratory bird species dependent on open water in the Gulf of Mexico following the aforementioned spill. The assessment was designed to evaluate birds that use open water during the winter within 40 km of the Gulf shoreline. We focused on the American white pelican (Pelecanus erythrorhynchos), common loon (Gavia immer), and northern gannet (Morus bassanus). Point counts (pelican, loon) or strip transects (gannet) were used and each target species was assessed for oiling (unoiled, trace, light, moderate, or heavy amounts) and photographed. Due to distance at sighting and/or poor visibility, not all visible birds were assessed. The percentage of birds oiled varied by species, with the common loon being the highest (23.6%), followed by American white pelican (16.9%), and northern gannet (6.9%). Most of the American white pelicans and common loons had trace (83% and 72%, respectively) or light levels (11% and 24%, respectively) of oiling. The northern gannet had just trace levels of oiling. Some pelicans (6%) and loons (4%) had moderate amounts of oiling. Based on expert derived-mortality estimates and our estimates of oil exposure, we used Monte Carlo simulations to predict expected decreases of 2.5%, 4%, and 11% in the observed population for the northern gannet, American white pelican, and common loon, respectively. While these values are underestimates of the true values given the long time lag (10-12 months) between the oil spill and the assessment, these data represent some of the few estimates of exposure for these species and describe minimum risk estimates to these species.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".