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Record W2138490642 · doi:10.1111/conl.12157

The Challenges of Red Wolf Conservation and the Fate of an Endangered Species Recovery Program

2014· article· en· W2138490642 on OpenAlexafffund
Dennis L. Murray, Guillaume Bastille‐Rousseau, Jennifer R. Adams, Lisette P. Waits

Bibliographic record

VenueConservation Letters · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife Ecology and Conservation
Canadian institutionsTrent University
FundersNatural Sciences and Engineering Research Council of CanadaU.S. Fish and Wildlife ServiceCanada Research Chairs
KeywordsCanisEndangered speciesPopulationGeographyGray wolfEcologyPopulation declineDemographyFisheryBiology

Abstract

fetched live from OpenAlex

Abstract Endangered red wolves ( Canis rufus ) receive intense conservation efforts in the United States, and to date, population recovery has been challenged by hybridization with closely related coyotes ( C. latrans ) and illegal human‐caused mortality. Ongoing review of the red wolf program in the single recovery area in North Carolina prompted us to compare demography (survival, recruitment) and cause of death of red wolves and coyotes/hybrids. In most respects, canids had similar demographic rates, although sterilization was effective in controlling coyote reproduction. Comparison of previous (1999‐2007) to contemporary (2009‐2014) causes of death revealed that shooting mortality consistently accounted for ∼25% of wolf mortality. As evidenced by the lack of coyote deaths from strife with wolves, and stationary/declining wolf numbers during the last 15 years, current conditions are inadequate to establish a viable self‐sustaining wolf population. Accordingly, the program review should determine whether: (1) banning coyote hunting will sufficiently benefit wolf survival or recruitment; (2) the wolf population should be considered conservation‐reliant under revised recovery goals; or (3) the recovery program in North Carolina should be abandoned.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.174
Threshold uncertainty score0.422

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.017
GPT teacher head0.213
Teacher spread0.196 · 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.

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

Citations21
Published2014
Admission routes2
Has abstractyes

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