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Record W2990632407 · doi:10.15273/pnsis.v50i1.8874

Testing efficacy of bird deterrents at wind turbine facilities: a pilot study in Nova Scotia, Canada

2019· article· en· W2990632407 on OpenAlexafffundvenueabout
Katherine Dorey, S.A. Dickey, Tony R. ‎Walker

Bibliographic record

VenueProceedings of the Nova Scotian Institute of Science · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicAvian ecology and behavior
Canadian institutionsDalhousie University
FundersMitacs
KeywordsWind powerTurbineNova scotiaRenewable energyEnvironmental scienceElectricityEngineeringGeographyEcologyBiology

Abstract

fetched live from OpenAlex

Wind energy has become one of the fastest-growing renewable electricity sources globally, and this trend is expected to continue. However, wind turbines cause avian mortality when birds collide with these structures. Although regulatory agencies in many jurisdictions require post-construction bird mortality monitoring at turbine sites, resulting mortality estimates are often imprecise and under-reported. This uncertainty is often attributed to searcher inefficiencies or scavenger losses. Furthermore, data regarding the effectiveness of active bird mortality mitigation at these facilities are also lacking. This pilot study assessed mitigation effectiveness of visual and audio deterrents, using predator owl deterrent models and bioacoustic alarm and predator calls deployed at a wind turbine facility in Nova Scotia, Canada. These deterrents did not deter birds from wind turbines in statistically significant ways, in comparison to control sites. Whilst results were inconclusive, it would be prudent to continue assessing mitigative options to minimize impacts on birds, considering the expected growth of the wind energy sector in Canada.Keywords: Wind energy development; Wind turbines; Bird mortality monitoring; Bird deterrents; Bird mortality 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 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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.236
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.003
Scholarly communication0.0000.001
Open science0.0020.001
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 head0.036
GPT teacher head0.250
Teacher spread0.214 · 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

Citations3
Published2019
Admission routes4
Has abstractyes

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