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Record W2032225812 · doi:10.1139/x06-256

Winter conditions in eastern hemlock and mixed-hardwood deer wintering areas of Vermont

2007· article· en· W2032225812 on OpenAlexvenueno aff
Shane C. Lishawa, D. R. Bergdahl, Scott D. Costa

Bibliographic record

VenueCanadian Journal of Forest Research · 2007
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Insect Ecology and Management
Canadian institutionsnot available
Fundersnot available
KeywordsTsugaHardiness (plants)SnowMicroclimateEcologyBiologyOdocoileusForestryGeographyAgronomy

Abstract

fetched live from OpenAlex

In regions experiencing harsh winter, eastern hemlock ( Tsuga canadensis (L.) Carrière) is thought to moderate winter conditions and provide cover for white-tailed deer ( Odocoileus virginianus Zimmerman, 1780). In Vermont, USA, eastern hemlock is the dominant tree in many white-tailed deer wintering areas. Hemlock woolly adelgid ( Adelges tsugae Annand; HWA), an insect introduced from Asia, is causing widespread mortality of eastern hemlock. HWA cold-hardiness research and cold-hardiness zone climate models predict that HWA is capable of winter survival in portions of southern Vermont. This study quantifies the effects of eastern hemlock cover on winter temperatures and snow depths and assesses the effect of hemlock dominance on the winter severity values for white-tailed deer and on cold hardiness zone designations. Within site-paired eastern hemlock and hardwood stands, temperatures were recorded at 30 min intervals and snow depths were recorded every 2 weeks in the winters of 2003–2004 and 2004–2005. Extreme temperatures and snow depths were significantly moderated within hemlock stands. In southern Vermont, eastern hemlock mortality caused by HWA will alter these pockets of moderated microclimate, adding additional stress to white-tailed deer. Furthermore, the magnitude of temperature buffering in hemlock stands suggests that HWA may be capable of winter survival farther north than previously estimated.

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 score0.675
Threshold uncertainty score0.947

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 studies0.0000.000
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 head0.027
GPT teacher head0.292
Teacher spread0.265 · 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

Citations20
Published2007
Admission routes1
Has abstractyes

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