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Record W2132290576 · doi:10.14430/arctic482

Population Estimate for the Bluenose-East Caribou Herd Using Post-calving Photography

2004· article· en· W2132290576 on OpenAlexvenueno aff
Brent R. Patterson, Benjamin T. Olsen, Damien O. Joly

Bibliographic record

VenueARCTIC · 2004
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife Ecology and Conservation
Canadian institutionsnot available
Fundersnot available
KeywordsHerdGeographyWoodland caribouIce calvingPopulationAerial surveyAbundance (ecology)FisheryPhysical geographyEcologyBiologyDemographyPredationCartography

Abstract

fetched live from OpenAlex

Genetic and spatial analyses suggest that what was previously described as the Bluenose herd of barren-ground caribou (Rangifer tarandus groenlandicus) comprises three separate populations. Of these, the Bluenose-East caribou herd (BECH) has received little coverage in past surveys. Existing estimates of abundance suggested that current harvest rates of Bluenose-East caribou (~5000 animals/year) might be excessive. We used post-calving photography in June-July 2000 to estimate the size of the BECH. A maximum of 33 radio-collared caribou were available for location in June 2000. We located 30 of these caribou and photographed distinct groups containing 23 collared individuals. Excluding caribou assumed to belong to the neighboring Bluenose-West herd, we photographed a minimum of 84 412 adult and 4193 calf caribou. Using a simple mark-recapture model to account for caribou associated with collared individuals not photographed, we calculated an estimate of 104 000 ± 22 100 (95% CI 84 412 - 126 100) non-calf caribou. A recently published stochastic model produced a considerably higher and more variable estimate of 208 700 (95% CI 112 600 - 474 700). In March 2001, we deployed seven more radio collars in anticipation of repeating the census in 2001, but poor weather conditions precluded the formation of large aggregations. Present densities of Bluenose-East caribou seem high, and we recommend regular monitoring of body condition to assess the potential for a forage-induced population crash.

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 score0.025
Threshold uncertainty score0.997

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 head0.016
GPT teacher head0.251
Teacher spread0.234 · 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

Citations3
Published2004
Admission routes1
Has abstractyes

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