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Record W2041864840 · doi:10.1139/x02-193

Population dynamics of <i>Glaucomys</i> <i>sabrinus</i> and <i>Tamiasciurus douglasii</i> in old-growth and second-growth stands of coastal coniferous forest

2003· article· en· W2041864840 on OpenAlexvenueaboutno aff
Douglas B. Ransome, Thomas P. Sullivan

Bibliographic record

VenueCanadian Journal of Forest Research · 2003
Typearticle
Languageen
FieldEnvironmental Science
TopicAnimal Ecology and Behavior Studies
Canadian institutionsnot available
FundersMinistry of Environment
KeywordsEcologyBiologyPopulationOld-growth forestHabitatPopulation growthDemography

Abstract

fetched live from OpenAlex

Habitat preferences and population dynamics of northern flying squirrels (Glaucomys sabrinus Shaw) and Douglas squirrels (Tamiasciurus douglasii Audubon and Bachman) were examined in old-growth and mature second-growth stands in British Columbia, Canada. Using mark–recapture techniques to estimate population dynamics, we tested the hypothesis that old-growth stands provided higher-quality habitat than second-growth stands for these species. Populations were monitored in two old-growth and two mature second-growth stands from August 1995 to May 1999. We were unable to detect major differences in movement, density, recruitment, mass of males, survival, percentage of the population breeding, and the duration that individuals remained on the study plots between stand types for G. sabrinus. Similarly, with the exception of recruitment, we were unable to detect major differences in these parameters between stand types for T. douglasii. Recruitment of T. douglasii was higher in second-growth than in old-growth stands. Old-growth stands were not higher-quality habitat than second-growth stands for either species for the period of enquiry and the parameters we measured. We also presented evidence of late fall – early winter breeding for G. sabrinus, as well as seasonal fluctuations in mass and trappability, larger movement by males than females, and the age of some squirrels exceeding 3.5 years.

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)0.001
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 score0.797
Threshold uncertainty score0.968

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.017
GPT teacher head0.262
Teacher spread0.245 · 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
Published2003
Admission routes2
Has abstractyes

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