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Ecology and Habitat Selection of a Woodland Caribou Population in West-central Manitoba, Canada

2007· article· en· W2040644001 on OpenAlexfundaboutno aff
Juha M. Metsaranta, Frank F. Mallory

Bibliographic record

VenueNortheastern Naturalist · 2007
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife Ecology and Conservation
Canadian institutionsnot available
FundersManitoba Hydro
KeywordsWoodland caribouHabitatWoodlandEcologyGeographyLoggingRange (aeronautics)PopulationSnagForest managementHome rangeWildlifeForestryBiology

Abstract

fetched live from OpenAlex

This study examines the ecology of Rangifer tarandus caribou (woodland caribou) in the Naosap range in west-central Manitoba, Canada. This population is considered to be of high conservation concern because of potential resource-development impacts; therefore, baseline data are required to guide and evaluate the management of this species in this area. Radio-telemetry data were collected every two weeks from February 1998 to April 2001 and used in combination with forest-inventory data to evaluate habitat selection, site fidelity, movement, and grouping patterns. In both summer and winter, selected habitats were mature upland spruce and pine forests, as well as treed muskeg. Hardwood forests were least selected at all scales. Mature coniferous forest was preferred over immature coniferous forests in a pair-wise comparison in winter, but not in summer. Home-range sizes were within expected ranges of variation. Animals used distinct areas in summer and winter, showing broad fidelity to seasonal ranges. However, small shifts in the core areas were observed, particularly in winter. Movement rates and grouping behavior were typical of other caribou. Habitats used in winter were common in the study area, but the ability of the animals to disperse to alternate winter areas is not known. Management efforts could focus on protecting known calving and winter-use areas, and regenerating coniferous forests after logging, which is consistent with regional forest-management objectives.

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 score0.222
Threshold uncertainty score0.279

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 head0.006
GPT teacher head0.197
Teacher spread0.191 · 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

Citations23
Published2007
Admission routes2
Has abstractyes

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