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Record W4283827222 · doi:10.14430/arctic75123

Population Characteristics, Morphometry, and Growth of Harvested Gray Wolves and Coyotes in Alaska

2022· article· en· W4283827222 on OpenAlexvenueno aff
Carl D. Mitchell, Roy C. Chaney, Ken Aho, R. Terry Bowyer

Bibliographic record

VenueARCTIC · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife Ecology and Conservation
Canadian institutionsnot available
Fundersnot available
KeywordsCanisGray wolfSympatric speciationBiologyArcticPopulationZoologyEcologyGeographyDemography

Abstract

fetched live from OpenAlex

Few concurrent studies exist of sympatric gray wolf (Canis lupus) and coyote (C. latrans) harvest at far northern latitudes. Moreover, no studies explicitly examine effects of concurrent harvest on phenotypes of wolves and coyotes. We documented changes in sex and age characteristics and morphology of gray wolves and coyotes harvested by hunters near Ptarmigan Lake, east-central Alaska, USA, between 1998 and 2001. We hypothesized that the harvest would result in larger, heavier canids, reduce densities, and increase young to adult ratios in both wolves and coyotes. We generated von Bertalanffy growth curves indicating that wolves and coyotes of both sexes increased in length or weight until 2 or 3 years old. No significant changes in either mean length or weight or length to weight ratios occurred during the 3-year study, except that coyote mean length was longer over the last winter of study. Catch-per-unit effort (CPUE) for wolves ranged from 0.061 to 0.112 killed/day and for coyotes from 0.552 to 0.11 killed/day over the study. CPUE indicated that coyotes but not wolves declined in abundance. Changes in male to female and young to adult ratios did not differ significantly for either canid. We posit that coyote populations were disproportionately affected by the conflation of the severe Arctic environment and sustained harvest. Our findings will be beneficial for managing sympatric canid populations and for understanding demographic responses to density-dependent processes in wolves and coyotes, especially at far northern latitudes.

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)0.000
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 score0.001
Threshold uncertainty score0.293

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 communication0.0000.000
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 head0.007
GPT teacher head0.194
Teacher spread0.187 · 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

Citations4
Published2022
Admission routes1
Has abstractyes

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