Reduction of coastal lighting decreases seabird strandings
Bibliographic record
Abstract
Abstract Leach’s Storm-Petrels ( Hydrobates leucorhous ) are small, threatened seabirds with an extensive breeding range in the North Atlantic and North Pacific Oceans. The Atlantic population, which represents approximately 40 - 48% of the global population, is declining sharply. Positive phototaxis, the movement towards artificial light at night (ALAN), is considered to be a key contributing factor. Many seabirds exhibit positive phototaxis, which can result in stranding on land. The Leach’s Storm-Petrel is the seabird most often found stranded around ALAN in the North Atlantic, though there is little experimental evidence showing that reducing ALAN will decrease the occurrence of stranded storm-petrels. During a two-year study at a large, brightly illuminated seafood processing plant adjacent to the Leach’s Storm-Petrel’s largest colony, we compared the number of birds that stranded when the lights at the plant were turned on versus off. We recorded survival, performed carcass counts of both adults and juveniles, and released any rescued individuals. Turning the lights off reduced strandings by 39.15% (CI: 11.45% - 58.19%). The peak stranding period occurred from 25 September to 28 October, and most of the stranded birds were fledglings. These results provide evidence to support the widespread reduction and modification of coastal artificial light, especially during avian fledging and migration periods.
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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 | 0.000 |
| 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) | 0.003 | 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".