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Record W2119534554 · doi:10.22230/jem.2006v7n2a538

Rate of deterioration, degrade, and fall of trees killed by mountain pine beetle

2006· article· en· W2119534554 on OpenAlexafffund
Kathy J. Lewis, Ian D. Hartley

Bibliographic record

VenueJournal of Ecosystems and Management · 2006
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicForest Ecology and Biodiversity Studies
Canadian institutionsUniversity of Northern British Columbia
FundersCanadian Forest ServiceNatural Resources CanadaU.S. Forest Service
KeywordsMountain pine beetleDendroctonusPinus contortaEnvironmental scienceForestryEcologyBark beetleBiologyGeographyBark (sound)

Abstract

fetched live from OpenAlex

The information presented in this paper results from a review of published articles on deterioration of dead wood, and interviews of people with forestry and (or) mill experience from the 1980s Cariboo Plateau mountain pine beetle outbreak. The literature review focussed on mountain pine beetle (Dendroctonus ponderosae) and lodgepole pine (Pinus contorta Dougl. ex Loud. var. latifolia Engelm.), but also included papers on other conifer species. Most of the existing research has focussed on utilization of trees that have been dead for less than 5 years. The general conclusion was that reduced moisture content, checking (related to moisture content), and bluestain were the most important factors involved in loss of product opportunities and quality. Decay of standing pine was slow (at least in the regions studied), and trees were more likely to fall over before significant losses of wood volume due to decay fungi. Once trees were on the ground, decay rates accelerated substantially.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
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 head0.008
GPT teacher head0.177
Teacher spread0.169 · 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

Citations47
Published2006
Admission routes2
Has abstractyes

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