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Record W4304890913 · doi:10.1002/wlb3.01056

Regulations and hunter preference affect mountain goat harvest and horn length

2022· article· en· W4304890913 on OpenAlexafffundabout
Chad Rice, Benjamin Larue, Bill Jex, Marco Festa Bianchet

Bibliographic record

VenueWildlife Biology · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife Ecology and Conservation
Canadian institutionsGovernment of British ColumbiaUniversité de Sherbrooke
FundersNatural Sciences and Engineering Research Council of CanadaUniversité de Sherbrooke
KeywordsWildlifeHunting seasonOvis canadensisFrench hornPopulationGeographyBiologyWildlife managementDemographyEcology

Abstract

fetched live from OpenAlex

Wildlife management attempts to balance consumptive and non‐consumptive values to manage hunting opportunities, considering population resilience. Mountain goats Oreamnos americanus are particularly sensitive to harvest. Using data from 33 792 mountain goats harvested in British Columbia (BC), Canada, between 1977 and 2019, we performed Bayesian regressions to examine the effect of regulations (limited entry hunting (LEH) or general open season (GOS)) on yearly harvest and harvest sex ratios. We also investigated temporal trends and the effect of licensed hunter residency (resident in British Columbia, or not) on harvest sex ratios. We then examined how horn length of harvested mountain goats was influenced by sex, year of harvest, age and mountain range. The more restrictive LEH regulations generally reduced harvest of mountain goats. The annual proportion of males harvested appeared independent of regulation and increased over time. Non‐resident hunters harvested a greater proportion of males compared to resident hunters. The combined length of the first and second horn growth increments decreased slightly with age at harvest for males but increased for females, suggesting a possible very weak hunter selection for males with rapid early horn growth and possibly against lactating females. Our study supports LEH regulations and hunter education to distinguish sex and age as key tools for mountain goat harvest management. Similar tools could be considered to manage other ungulates that are sensitive to harvest.

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 score0.048
Threshold uncertainty score0.880

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)0.0010.000

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 head0.019
GPT teacher head0.229
Teacher spread0.210 · 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 routes3
Has abstractyes

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