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Record W2150513951 · doi:10.2193/2009-560

Thermal Properties of Tree Cavities During Winter in a Northern Hardwood Forest

2010· article· en· W2150513951 on OpenAlexafffund
Andrea B. Coombs, Jeff Bowman, Colin J. Garroway

Bibliographic record

VenueJournal of Wildlife Management · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicSpecies Distribution and Climate Change
Canadian institutionsMinistry of Natural Resources and ForestryTrent University
FundersNatural Sciences and Engineering Research Council of CanadaTrent University
KeywordsSnagDiameter at breast heightHardwoodAtmospheric sciencesEnvironmental scienceThermoregulationTree (set theory)ThermalMean radiant temperatureClimate changeEcologyBiologyMeteorologyGeographyGeologyMathematics

Abstract

fetched live from OpenAlex

ABSTRACT Tree cavities likely vary in their thermal quality for cavity‐nesting animals, which could be especially important during winter. We conducted a winter field experiment to test whether cavities vary either in their buffering capacity or in their mean temperature according to predictable characteristics. We found that cavities buffered temperature and that there was a lag effect in temperature that appeared to be related to heating and cooling. Diameter at breast height was the most important variable influencing cavity temperature during the day, with smaller trees warming up more. During the night, diameter at breast height and tree decay class were important, such that larger, live trees cooled down less. Maintaining live trees with cavities in managed forests should be considered in addition to snag retention, because live trees appear to provide warmer structures during winter.

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 score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.015
GPT teacher head0.205
Teacher spread0.190 · 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

Citations97
Published2010
Admission routes2
Has abstractyes

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