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Record W2067070436 · doi:10.1080/10871209.2012.661028

Wildlife Sightings at Western Canadian Regional Airports: Implications for Risk Analyses

2012· article· en· W2067070436 on OpenAlexaffabout
Gayle Hesse, Roy V. Rea, Annie L. Booth, Cuyler J. Green

Bibliographic record

VenueHuman Dimensions of Wildlife · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicAvian ecology and behavior
Canadian institutionsPositive Living NorthUniversity of Northern British ColumbiaRaincoast Conservation Foundation
Fundersnot available
KeywordsWildlifeDue diligenceGeographyUngulateWildlife managementEnvironmental resource managementFisheryBusinessEcologyHabitatEnvironmental scienceFinance

Abstract

fetched live from OpenAlex

Aircraft collisions with wildlife result in substantial personal and economic losses, requiring airport authorities to utilize all available resources to develop effective management strategies. We surveyed 16 western Canadian regional airports to document the use of wildlife strike and sighting records (WSSRs). Ninety-four percent of airports kept wildlife strike records, 19% kept bird sighting records and 25% kept animal sighting records. Of 12 airports, 33% used WSSRs to identify problem species or trends and 25% used WSSRs for risk analysis and management planning. Our findings suggest that WSSRs are underutilized in risk analyses and ungulate strike risk may be underestimated at most respondent airports. Airport mangers must stress due diligence in record keeping and the application of wildlife data to support risk analyses and sound wildlife management practices at airports.

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)0.002
metaresearch head score (Gemma)0.010
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 score0.060
Threshold uncertainty score0.121

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.010
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0000.001
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 head0.065
GPT teacher head0.325
Teacher spread0.260 · 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

Citations2
Published2012
Admission routes2
Has abstractyes

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