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Record W3010100483 · doi:10.1139/cjz-2019-0066

Not all ponds are created equal: long-term beaver (<i>Castor canadensis</i>) lodge occupancy in a heterogeneous landscape

2020· article· en· W3010100483 on OpenAlexaffvenue
Glynnis A. Hood

Bibliographic record

VenueCanadian Journal of Zoology · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicEcology and biodiversity studies
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsOccupancyHabitatEcologyBeaverSpatial ecologyBiologySpatial variabilityCastor canadensisStatistics

Abstract

fetched live from OpenAlex

Modeling habitat selection often represents single spatial scales and short time periods. However, for species that dramatically modify environments upon initial colonization, non-linear interactions change original habitat to one where habitat selection is confounded by subsequent modifications by the organism itself. The ability of beavers (Castor canadensis Kuhl, 1820) to dramatically alter habitat components over time makes them a model species for examining habitat selection spatially and temporally. This study identified habitat features selected by a relatively long-lived, colonial species, and how selection influenced site occupancy over varying spatial scales. Remote sensing and field data provided spatial and temporal coverage of four measures of occupancy. Models of occupancy over an 11-year period, combined with environmental variables, identified features influencing occupancy, abandonment, and recolonization at 30, 50, and 100 m scales. Unlike many studies, % deciduous was a poor predictor of occupancy. Instead, pond area, % grassland, distance to a pond that was active at least once, and standard deviation (SD) of slope were important variables. SD of slope was the only variable not influenced by beavers. Examining species–habitat relationships over broader spatial and temporal scales, particularly for ecosystem engineers, enhances understanding of non-linear effects and how they influence perceptions of habitat selection.

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 score0.712
Threshold uncertainty score0.999

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)0.0020.000

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 head0.028
GPT teacher head0.217
Teacher spread0.189 · 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

Citations18
Published2020
Admission routes2
Has abstractyes

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