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Record W6987404389

Spatial scaling in northern landscapes : habitat selection by small mammals

2017· other· en· W6987404389 on OpenAlexaboutno aff

Bibliographic record

VenueKnowledge Commons (Lakehead University) · 2017
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsHabitatPopulationAbundance (ecology)Selection (genetic algorithm)Population densitySpatial ecologyRange (aeronautics)Sampling (signal processing)
DOInot available

Abstract

fetched live from OpenAlex

I examined a series of simple and repeated northern landscapes in the Hudson Bay
\nLowland of Ontario to document regional and local patterns of population abundance
\nof red-backed voles {Clethrionomys gapperi). I tested whether a spatially-explicit
\necological process, density-dependent habitat selection, could account for population
\nregulation of voles across a range of spatial scales. Over a large regional scale,
\nmultiple regression analysis indicated that population density of voles was primarily
\npredicted by location of sampling and measures of microhabitat. Regional abundance
\npatterns, therefore, appear to be independent of nonadditive landscape effects and
\nprobably result from large-scale biogeographic influences or differences in average
\nhabitat quality between sites. At a local scale, my analysis identified density-dependent
\nhabitat selection as a universal process structuring abundance patterns,
\nregardless of regional differences in population density. Habitat selection, at the
\ndispersal and perhaps microhabitat scales, thereby provides a feasible mechanism
\nlinking landscape structure directly to population regulation.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.991
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0040.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.004

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 head0.021
GPT teacher head0.231
Teacher spread0.211 · 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 designNot applicable
Domainnot available
GenreOther

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
Published2017
Admission routes1
Has abstractyes

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