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Record W3143182863 · doi:10.82308/51405

The abundance and distribution of beavers (Castor canadensis) in Québec, Canada /

2006· article· en· W3143182863 on OpenAlexaboutno aff
STACEY I. JAREMA

Bibliographic record

VenueeScholarship@McGill (McGill) · 2006
Typearticle
Languageen
FieldEnvironmental Science
TopicEcology and biodiversity studies
Canadian institutionsnot available
Fundersnot available
KeywordsCastor canadensisAbundance (ecology)Distribution (mathematics)GeographyForestryEcologyBeaverBiologyMathematics

Abstract

fetched live from OpenAlex

The importance of spatial variation in abundance for the assessment of climate change impacts was examined using the North American beaver ( Castor canadensis) in Quebec as a model species. A preliminary characterization of the beavers' range edge improved the core-sampling bias and revealed that beavers are present at low densities, in shrubby riparian habitats as far north as the communities of Tasiujaq and Umiujaq. Spatial variation in beaver abundance across the province follows a roughly logistic pattern, with abundance peaking in southern Quebec, declining steeply around 49°N, and remaining uniformly low as far as 58°N. Although climate sensitivity of beaver abundance and the greatest changes in future beaver density are predicted to occur near the middle of their range, beavers are expected to occupy most of the province by 2055. These results highlight the value of incorporating density estimates from across a species' range into climate envelope models.

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 score0.010
Threshold uncertainty score0.075

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.160
Teacher spread0.155 · 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

Citations0
Published2006
Admission routes1
Has abstractyes

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