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Record W3130112245

ASSESSING AGE OF HARVESTED MOOSE PRIOR TO POPULATION DECLINES IN BRITISH COLUMBIA

2021· article· en· W3130112245 on OpenAlexaboutno aff
Gerald W. Kuzyk, Kaitlyn D. Schurmann, Shelley Marshall, Chris Procter

Bibliographic record

VenueAlces : A Journal Devoted to the Biology and Management of Moose · 2021
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsnot available
Fundersnot available
KeywordsPopulationDemographyAnimal scienceAge groupsBiologyGeography
DOInot available

Abstract

fetched live from OpenAlex

Moose populations in parts of British Columbia, Canada have been declining since about the mid-2000s with the licensed harvest dropping by more than half from 1987 to 2014. A tooth reporting program for harvested moose from 1982 to 2003 enabled us to assess the relationship between age of harvested moose and 1) time (1982–2003), 2) level of licensed harvest of bulls and cows, and 3) estimated populations prior to declines with age data collected after decline in the province. We used age data determined from cementum annuli of teeth collected from hunter returns from 72,888 moose (n = 57,376 bulls and n = 15,512 cows). We found average age of harvested bulls and cows to be 3.32 ± 0.02 and 4.99 ± 0.06 years, respectively, similar to ranges reported elsewhere in western North America. Age of bulls declined linearly by year, whereas age of cows declined in the latter half of the study period. The average age of cows harvested from 1983 to 2003 prior to the population decline (n = 2,016; mean = 3.84 years, SD = 3.03) was 7 years younger than that of a small sample of cows dying of multiple causes (harvest and natural) during the decline (n = 47; mean = 10.93 years, SD = 3.72). We acknowledge the logistical and financial constraints required to gather a representative sample of teeth from harvested moose, but recommend reimplementation of a tooth collection program to provide continuous information on the age structure of moose populations to help guide management decisions.

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 score0.527
Threshold uncertainty score0.995

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 studies0.0010.000
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 head0.044
GPT teacher head0.389
Teacher spread0.345 · 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

Citations1
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueAlces : A Journal Devoted to the Biology and Management of MooseSame topicIndigenous Studies and EcologyFrench-language works237,207