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Record W1548420013 · doi:10.22621/cfn.v123i2.690

Home Ranges and Spatial Organization of Fishers, <em>Martes pennanti</em>, in Central British Columbia

2009· article· en· W1548420013 on OpenAlexaffvenueabout
Richard D. Weir, Alton S. Harestad, Fraser B. Corbould

Bibliographic record

VenueThe Canadian Field-Naturalist · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife Ecology and Conservation
Canadian institutionsSimon Fraser University
FundersMinistry of Environment
KeywordsHome rangeGeographyRange (aeronautics)HabitatDemographySpatial distributionEcologyPhysical geographyBiology

Abstract

fetched live from OpenAlex

We described the size and spatial arrangement of aggregate and seasonal home ranges for 17 radio-tagged resident Fishers (Martes pennanti) that were >1.5 years old in two areas of central British Columbia during 1990-1993 and 1996-2000. We estimated home range size for each Fisher from the 95% isopleth of the utilization distribution generated using a fixed kernel model with smoothing selected by least-squares cross-validation (95% FK). For comparison to previous studies, we also calculated the minimum convex polygon estimate of home range size (MCP) for each animal. The aggregate home ranges (95% FK) of female Fishers (mean = 37.9 km², SD = 18.5, range = 10.5 – 81.2, n = 11) were significantly smaller than those of males (mean = 161.3 km², SD = 100.0, range = 46.0 – 225.2, n = 3; P = 0.019). We observed minor overlap among 95% FK home ranges of Fishers of the same sex, but considerable overlap among home ranges of males and females. Home ranges (95% FK or MCP) that we observed in central British Columbia were larger than those reported elsewhere in North America, particularly for males. We suggest that the distribution of resources for Fishers may occur at lower gross densitiesin central British Columbia than in other portions of the Fisher’s range and that suitable habitat in which Fishers can establish home ranges is not found uniformly across the landscape.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.400
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)0.0040.000

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 head0.174
Teacher spread0.169 · 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.

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

Citations12
Published2009
Admission routes3
Has abstractyes

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