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Record W2566941514

Degradation of Wood in Standing Lodgepole Pine Killed by Mountain Pine Beetle

2011· article· en· W2566941514 on OpenAlexfundno aff
Kathy J. Lewis, Doug Thompson

Bibliographic record

VenueWood and Fiber Science (Society of Wood Science and Technology) · 2011
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Insect Ecology and Management
Canadian institutionsnot available
FundersNatural Resources CanadaU.S. Forest ServiceCanadian Forest ServiceGovernment of Canada
KeywordsMountain pine beetleEnvironmental scienceWater contentDendrochronologyForestryWoody plantMoisturePinus contortaPine forestEcologyBiologyGeographyGeologyArchaeology
DOInot available

Abstract

fetched live from OpenAlex

Lodgepole pine is widely distributed throughout the Pacific Northwest and is an important commercial species.Although outbreaks of mountain pine beetle can kill extensive areas of pine stands, little attention was paid to postmortality rate of wood quality and quantity deterioration until the most recent outbreak, which because of its unprecedented size has resulted in extensive salvage harvesting.We used dendrochronology to determine the exact year of mortality and destructive sampling to quantify change in wood characteristics with time.We also estimated the fall-down rate of dead trees.Most trees did not start to fall until 8 yr postmortality.We found that change in wood moisture content was the main driver behind changes in wood properties.Dependent variables included checking (number and depth), blue-stain depth, saprot, and damage caused by wood borers and were explained by a small collection of biophysical variables.Biogeoclimatic unit and soil moisture regime were not important predictors of decay and degrade, except for development of saprot at the base of trees.Wood quality significantly changed within the first 1-2 yr postmortality and varied with position along the stem followed by a period of relative stability.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.139
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.004
Science and technology studies0.0000.008
Scholarly communication0.0000.001
Open science0.0010.001
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 head0.009
GPT teacher head0.210
Teacher spread0.201 · 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 designBench or experimental
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

Citations22
Published2011
Admission routes1
Has abstractyes

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