A Physiologically Based Oiling Model (PBOM) to Predict Thermoregulatory Response in Birds
Bibliographic record
Abstract
Abstract When birds are exposed to oil, their ability to thermoregulate is impaired as a result of damage to the insulative properties of their feathers. If an oiled bird is unable to maintain thermal homeostasis, hypothermia and death can follow rapidly. Physiological responses to oiling depend on several variables including environmental conditions (e.g., ambient air and water temperatures), life history of bird species (e.g., body size and habitat selection), and foraging strategies (e.g., divers, surface feeders, shorebirds). The most widely used approach to assess the effect of spilled oil on birds is the US Comprehensive Environmental Response, Compensation, and Liability Act (CERCLA) Type A Natural Resource Damage Assessment (NRDA) Model for Coastal and Marine Environments. This approach addresses body size and time spent in a hypothetical slick but lacks the flexibility to assess other variables related to physiological response. We developed a dynamic physiologically based oiling model (PBOM) for birds to address this need. The PBOM has been validated against empirical data from a dose–response study in which common eiders were exposed to Statfjord A crude oil. The PBOM can be used to predict time to hypothermia for birds of differing body size and habitat preferences, in environments ranging from Arctic to subtropical. The model presently includes 5 representative bird species: common eider, spotted sandpiper, great blue heron, dovekie, and American white pelican, but could be adapted to represent almost any bird. Environ Toxicol Chem 2021;40:251–260. © 2020 SETAC Abstract A bird's thermoregulatory response to oiling depends on variables related to environmental conditions, life history of the bird species, and foraging strategies. The physiologically based oiling model is a predictive model that can be used to quantify thermoregulatory responses in oiled birds across a range of exposure levels and environmental conditions.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".