A path forward in the investigation of seabird strandings attributed to light attraction
Bibliographic record
Abstract
Abstract A variety of anthropogenic threats cause mortality and population declines of procellariiform seabirds. Globally, fledglings of many colonial procellariiforms become stranded in towns and cities during their first flights from the nest, which occur at night. Since the 1960s, when the phenomenon became widely known, these strandings have been largely attributed to attraction toward artificial lights at night (ALAN). Artificial light attraction has been blamed due to the predictable, annual nature of strandings; the large numbers of birds found in lighted areas during stranding events; and the inexperience of fledglings in interpreting sensory stimuli. However, up‐to‐date, few alternative hypotheses to that of light attraction have been suggested, and few if any have been explored experimentally. In this paper, we do not seek to refute the light attraction hypothesis. Instead, our objectives are threefold. We wish to (1) highlight the current evidence for light attraction in procellariiforms; (2) identify where evidence may be lacking or subject to confirmation bias; and (3) suggest alternative hypotheses and possible experimental approaches to study them. Given the imperiled nature of many of the affected species and the need to explore and address this source of mortality, our goal in this review is to accelerate and diversify research efforts on this topic.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".