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Record W3215880103 · doi:10.1898/1051-1733-102.3.232

DENSITY OF FISHERS (PEKANIA PENNANTI) AT THE SOUTHWESTERN EDGE OF THE SPECIES' RANGE IN BRITISH COLUMBIA

2021· article· en· W3215880103 on OpenAlexaffabout
Larry R. Davis, Richard D. Weir

Bibliographic record

VenueNorthwestern Naturalist · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife Ecology and Conservation
Canadian institutionsMinistry of EnvironmentASL Environmental Sciences (Canada)
Fundersnot available
KeywordsGeographyRange (aeronautics)Abundance (ecology)Population densityMark and recaptureEcologyPopulationFisheryBiologyDemography

Abstract

fetched live from OpenAlex

Fishers (Pekania pennanti) are a species of conservation concern in central British Columbia for which distribution and abundance information is needed to help guide conservation efforts. We conducted a DNA-based spatial capture-recapture study in the Bridge River watershed to gain a better understanding of their density in the dry forests at the southwestern edge of the species' range in the province. We established and monitored baited hair traps at 152 sites spread throughout 771.4 km2 over 4 mo in early 2012, detecting 8 individual Fishers (3 females, 5 males) at 16 different sites. We used spatially explicit capture-recapture methods to estimate the density of Fishers to be 13.1 Fishers/1000 km2 (95% CI: 6.3 to 27.4 Fishers/1000 km2) when we constrained the plausible sampling area to biogeoclimatic zones that are known to support Fishers. This study provides resource managers and trappers with a snapshot of local Fisher densities at the southern edge of the species range in British Columbia that will help estimate sustainable harvest levels and refine the estimate of the provincial population of Fishers.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.060
Threshold uncertainty score0.120

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.009
GPT teacher head0.190
Teacher spread0.181 · 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 source (direct Gemma or distilled Codex), 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

Citations2
Published2021
Admission routes2
Has abstractyes

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