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Record W4293771244 · doi:10.3389/fevo.2022.980776

Casting a light on the shoreline: The influence of light pollution on intertidal settings

2022· article· en· W4293771244 on OpenAlexafffund
K. Devon Lynn, Pedro A. Quijón

Bibliographic record

VenueFrontiers in Ecology and Evolution · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicImpact of Light on Environment and Health
Canadian institutionsUniversity of Prince Edward Island
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsLight pollutionIntertidal zoneHabitatEcologyPollutionEnvironmental scienceShoreGeographyBiologyFishery

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.027
Threshold uncertainty score0.423

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.005
GPT teacher head0.203
Teacher spread0.198 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations25
Published2022
Admission routes2
Has abstractyes

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