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Record W2464487016 · doi:10.1111/conl.12280

Socioeconomic Benefits of Large Carnivore Recolonization Through Reduced Wildlife‐Vehicle Collisions

2016· article· en· W2464487016 on OpenAlex
Sophie L. Gilbert, Kelly J. Sivy, Casey B. Pozzanghera, Adam J. Dubour, Kelly S Overduijn, Matthew M. Smith, Jiake Zhou, Joseph Little, Laura R. Prugh

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueConservation Letters · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife Ecology and Conservation
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsCarnivoreWildlifeGeographyEcosystem servicesSocioeconomic statusHerbivoreTrophic cascadePopulationEcosystemEcologyBiologyDemographyPredation

Abstract

fetched live from OpenAlex

Abstract The decline of top carnivores has released large herbivore populations around the world, incurring socioeconomic costs such as increased animal–vehicle collisions. Attempts to control overabundant deer in the Eastern United States have largely failed, and deer–vehicle collisions (DVCs) continue to rise at alarming rates. We present the first valuation of an ecosystem service provided by large carnivore recolonization, using DVC reduction by cougars as a case study. Our coupled deer population models and socioeconomic valuations revealed that cougars could reduce deer densities and DVCs by 22% in the Eastern United States, preventing 21,400 human injuries, 155 fatalities, and $2.13 billion in avoided costs within 30 years of establishment. Recently established cougars in South Dakota prevent $1.1 million in collision costs annually. Large carnivore restoration could provide valuable ecosystem services through such socio‐ecological cascades, and these benefits could offset the societal costs of coexistence.

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.

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)0.000
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.079
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.013
GPT teacher head0.225
Teacher spread0.212 · 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