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Record W3101541863 · doi:10.1111/csp2.319

Evaluating potential effects of solar power facilities on wildlife from an animal behavior perspective

2020· article· en· W3101541863 on OpenAlexaff
Rachel Y. Chock, Barbara Clucas, Elizabeth K. Peterson, Bradley F. Blackwell, Daniel T. Blumstein, Kathleen Church, Esteban Fernández‐Juricic, Gabriel Francescoli, Alison L. Greggor, Paul S. Kemp, Gabriela Medeiros de Pinho, Peter M. Sanzenbacher, Bruce A. Schulte, Pauline Toni

Bibliographic record

VenueConservation Science and Practice · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicPhotovoltaic Systems and Sustainability
Canadian institutionsUniversité de SherbrookeUniversity of Windsor
FundersNational Wildlife Research CenterU.S. Fish and Wildlife ServiceAnimal Behavior SocietyU.S. Department of Agriculture
KeywordsWildlifeRenewable energySolar powerEnvironmental resource managementSolar energyPopulationWildlife conservationEcologyEnvironmental planningBusinessEnvironmental sciencePower (physics)BiologySociology

Abstract

fetched live from OpenAlex

Abstract Solar power is a renewable energy source with great potential to help meet increasing global energy demands and reduce our reliance on fossil fuels. However, research is scarce on how solar facilities affect wildlife. With input from professionals in ecology, conservation, and energy, we conducted a research‐prioritization process and identified key questions needed to better understand impacts of solar facilities on wildlife. We focused on animal behavior, which can be used to identify population responses before mortality or other fitness consequences are documented. Behavioral studies can also offer approaches to understand the mechanisms leading to negative interactions (e.g., collision, singeing, avoidance) and provide insight into mitigating effects. Here, we review how behavioral responses to solar facilities, including perception, movement, habitat use, and interspecific interactions are priority research areas. Addressing these themes will lead to a more comprehensive understanding of the effects of solar power on wildlife and guide future mitigation.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.047
GPT teacher head0.356
Teacher spread0.309 · 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 source (direct Gemma or distilled Codex), 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

Citations48
Published2020
Admission routes1
Has abstractyes

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