Miniaturized data loggers and computer programming improve seabird risk and damage assessments for marine oil spills in Atlantic Canada.
Bibliographic record
Abstract
Obtaining useful information on marine birds that can aid in oil spill (and other hydrocarbon release) risk and damage assessments in offshore environments is challenging. Technological innovations in miniaturization have allowed archival data loggers to be deployed successfully on marine birds vulnerable to hydrocarbons on water. A number of species, including murres (both Common, Uria aalge, and Thick-billed, U. lomvia) have been tracked using geolocation devices in eastern Canada, increasing our knowledge of the seasonality and colony-specific nature of their susceptibility to oil on water in offshore hydrocarbon production areas and major shipping lanes. Archival data tags are starting to resolve questions around behaviour of vulnerable seabirds at small spatial scales relevant to oil spill impact modelling, specifically to determine the duration and frequency at which birds fly at sea. Advances in data capture methods using voice activated software have eased the burden on seabird observers who are collecting increasingly more detailed information on seabirds during ship-board and aerial transects. Computer programs that integrate seabird density and bird behaviour have been constructed, all with a goal of creating more credible seabird oil spill risk and damage assessments. In this paper, we discuss how each of these technological and computing innovations can help define critical inputs into seabird risk and damage assessments, and when combined, can provide a more realistic understanding of the impacts to seabirds from any hydrocarbon release.
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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 | 0.001 |
| 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".