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Record W2168098334 · doi:10.1002/jwmg.644

Decrease in horn size and increase in age of trophy sheep in Alberta over 37 years

2013· article· en· W2168098334 on OpenAlexafffundabout
Marco Festa‐Bianchet, Fanie Pelletier, Jon T. Jorgenson, Chiarastella Feder, Anne Hubbs

Bibliographic record

VenueJournal of Wildlife Management · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife Ecology and Conservation
Canadian institutionsAlberta Environment and Protected AreasUniversité de Sherbrooke
FundersNatural Sciences and Engineering Research Council of CanadaAlberta Conservation Association
KeywordsTrophyFrench hornOvis canadensisWildlifePopulationOvisAnimal scienceBiologyGeographyEcologyDemographyArchaeology

Abstract

fetched live from OpenAlex

ABSTRACT Long‐term data (1974–2011) from harvested bighorn rams ( Ovis canadensis ) in Alberta, Canada, suggested a reduction in horn size and in the proportion of trophy rams in the provincial population over time. Age at harvest increased over time, suggesting slower horn growth. Rams that experienced favorable environmental conditions early in life had rapid horn growth and were harvested at a younger age than rams with slower horn growth. Guided nonresident hunters did not harvest larger rams than residents, suggesting that few large rams were available. Resident hunter success declined in recent years. Despite an apparently stable population, successive cohorts produced a decreasing harvest of trophy rams. We suggest that unrestricted harvest based on a threshold horn size led to a decline in the availability of trophy rams. That decline is partly an inevitable consequence of selective hunting that removes larger rams. Although our analysis does not establish that evolution of smaller horns caused the observed decline in both horn size and harvest of trophy rams, we suggest that intensive trophy hunting may have artificially selected for a decrease in horn growth rate. © 2013 The Wildlife Society.

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 score0.003
Threshold uncertainty score0.948

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 head0.005
GPT teacher head0.197
Teacher spread0.192 · 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

Citations65
Published2013
Admission routes3
Has abstractyes

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