Casting a light on the shoreline: The influence of light pollution on intertidal settings
Bibliographic record
Abstract
Light pollution is becoming prevalent among other coastal stressors, particularly along intertidal habitats, arguably the most exposed to anthropogenic light sources. As the number of light pollution studies on sandy beaches, rocky shores and other intertidal habitats raises, commonalities, research gaps and venues can be identified. Hence, the influence of light pollution on the behavior and ecology of a variety of intertidal macro-invertebrates and vertebrates are outlined by examining 54 published studies. To date, a large majority of the reported effects of light pollution are negative, as expected from the analysis of many species with circadian rhythms or nocturnal habits, although the severity of those effects ranges widely. Experimental approaches are well represented throughout but methodological limitations in measurement units and standardization continue to limit the proposal of general conclusions across species and habitats. In addition, studies targeting community variables and the explicit influence of skyglow are heavily underrepresented. Likewise, studies addressing the interaction between light pollution and other natural and anthropogenic stressors are critically needed and represent a key venue of research. The nature of those interactions (synergistic, additive, antagonistic) will likely dictate the impact and management of light pollution in the decades ahead.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".