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Record W2793478281 · doi:10.3356/jrr-16-100.1

Raptor Interactions With Wind Energy: Case Studies From Around the World

2018· article· en· W2793478281 on OpenAlexaff
Richard T. Watson, Patrick S. Kolar, Miguel Ferrer, Torgeir Nygård, Naira N. Johnston, W. Grainger Hunt, Hanneline Smit‐Robinson, Christopher J. Farmer, Manuela Huso, Todd E. Katzner

Bibliographic record

VenueJournal of Raptor Research · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicAvian ecology and behavior
Canadian institutionsUniversity of Northern British Columbia
Fundersnot available
KeywordsElectrocutionWind powerForagingEndangered speciesEnvironmental scienceHabitatPopulationGeographyEnvironmental resource managementEcologyBiology

Abstract

fetched live from OpenAlex

The global potential for wind power generation is vast, and the number of installations is increasing rapidly. We review case studies from around the world of the effects on raptors of wind-energy development. Collision mortality, displacement, and habitat loss have the potential to cause population-level effects, especially for species that are rare or endangered. The impact on raptors has much to do with their behavior, so careful siting of wind-energy developments to avoid areas suited to raptor breeding, foraging, or migration would reduce these effects. At established wind farms that already conflict with raptors, reduction of fatalities may be feasible by curtailment of turbines as raptors approach, and offset through mitigation of other human causes of mortality such as electrocution and poisoning, provided the relative effects can be quantified. Measurement of raptor mortality at wind farms is the subject of intense effort and study, especially where mitigation is required by law, with novel statistical approaches recently made available to improve the notoriously difficult-to-estimate mortality rates of rare and hard-to-detect species. Global standards for wind farm placement, monitoring, and effects mitigation would be a valuable contribution to raptor conservation worldwide.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.322
Threshold uncertainty score0.997

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 studies0.0010.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.097
GPT teacher head0.411
Teacher spread0.314 · 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.

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

Citations82
Published2018
Admission routes1
Has abstractyes

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