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Record W2158507413 · doi:10.2193/2006-133

Home Range and Population Density of Fishers in Eastern Ontario

2007· article· en· W2158507413 on OpenAlexafffundabout
Erin L. Koen, Jeff Bowman, C. Scott Findlay, Ligang Zheng

Bibliographic record

VenueJournal of Wildlife Management · 2007
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsTrent UniversityMinistry of Natural Resources and ForestryNatural Resources CanadaCarleton UniversityUniversity of Ottawa
FundersMinistry of Natural Resources
KeywordsHome rangePopulation densityRange (aeronautics)PopulationGeographyHabitatPopulation sizeDemographyEcologyPhysical geographyBiology

Abstract

fetched live from OpenAlex

Abstract: Fishers ( Martes pennanti ) were almost extirpated in Ontario, Canada, south of the French and Mattawa rivers by the 1940s but have recolonized much of their former range over the past several decades. We assessed the effect of the current harvest quota on a fisher population in eastern Ontario by estimating home range size and population density from a sample of radiocollared animals. Mean (± SD) adult home ranges (based on annual 95% min. convex polygons) were consistently smaller than those reported in the literature (M: 11 ± 4.4 km 2 ; F: 2.1 ± 0.8 km 2 ), with up to 71% overlap of adjacent intrasexual home ranges. This yielded an estimated adult fisher population density of 32.7/100 km 2 of suitable habitat, as defined by the habitat composition within observed home ranges. We further estimated that between 2003 and 2005, trappers harvested 17.8‐42.3% of the pretrapping population. These results suggest that although current fisher population density is high in our study area compared to reported densities in other areas, harvest rate is also high and an increase in quota is unwarranted.

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)0.000
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 score0.880
Threshold uncertainty score0.241

Distilled classifier scores by category (both heads)

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 head0.010
GPT teacher head0.212
Teacher spread0.202 · 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

Citations30
Published2007
Admission routes3
Has abstractyes

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