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Record W3130814477 · doi:10.5558/tfc2021-006

Climate, radial growth, and mortality associated with conifer regeneration infected by root disease (<i>Armillaria ostoyae)</i>

2021· article· en· W3130814477 on OpenAlexafffundvenue
Michael P. Murray, Adrian Leslie

Bibliographic record

VenueThe Forestry Chronicle · 2021
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicTree-ring climate responses
Canadian institutionsMinistry of Forests
FundersCanadian Forest ServiceU.S. Forest ServiceSimon Fraser University
KeywordsArmillariaLarchPinus contortaBiologyEcologyForestryBotanyGeography

Abstract

fetched live from OpenAlex

Tree root disease caused by the pathogen Armillaria ostoyae (Romagn.) Herink has notable influence on bio-economic systems of southern British Columbia (BC) and the northwestern United States. Annual radial growth and mortality trends of regeneration associated with A. ostoyae during the first 21 years of a plantation were investigated. Our dendrochronological approach focused on a plantation established in 1991 with Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco), western larch (Larix occidentalis Nutt.), and lodgepole pine (Pinus contorta Dougl. ex Loud. var. latifolia Engelm. ex S. Wats.). We examined tree rings for reduced growth onset (RGO) to estimate infection periods. Among trees studied, we found that the number of RGO events peaked in 2006 followed by a steady decline in frequency. A similar trend was seen in annual mortality rates which reached maximum values in 2007 followed by an uninterrupted decrease. The average number of years from RGO to death based on host species ranged from 2.3 to 3.6 years. According to on logistic regression modeling, values associated with drought codes and duff moisture codes were significant in determining the year of death for infected trees. The predicted increase in frequency of summer droughts may lead to elevated mortality associated with A. ostoyae in young plantations.

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)0.000
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 score0.009
Threshold uncertainty score0.535

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.000

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 head0.011
GPT teacher head0.218
Teacher spread0.207 · 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

Citations13
Published2021
Admission routes3
Has abstractyes

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