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Record W4396624169 · doi:10.3390/f15050808

Pre-Commercial Thinning Increases Tree Size and Reduces Western Gall Rust Infections in Lodgepole Pine

2024· article· en· W4396624169 on OpenAlexafffundabout
Francis Scaria, Bradley D. Pinno

Bibliographic record

VenueForests · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicForest ecology and management
Canadian institutionsUniversity of Alberta
FundersForest Resource Improvement Association of Alberta
KeywordsThinningPinus contortaCrown (dentistry)SilvicultureRust (programming language)GallForestryStand developmentBiologyAgroforestryEnvironmental scienceGeographyEcologyMedicine

Abstract

fetched live from OpenAlex

Alberta’s forest industry is predicted to be impacted by a medium-term decline in timber supply. Intensive silviculture tools, such as pre-commercial thinning, have been shown to increase individual tree growth, shorten rotation lengths, and improve stand merchantability in important commercial species such as lodgepole pine. However, lodgepole pine stands are susceptible to western gall rust infections, and thinning at an early stage may increase infection rates. This study collected tree and stand level data from 33 operational harvest origin lodgepole pine stands consisting of 11 stands thinned at age 17–19 years (PCT_18), 11 stands thinned at age 23–25 (PCT_24), and 11 unthinned stands. Approximately 40 years after pre-commercial thinning, merchantable volume is similar in all stands but thinned stands, regardless of timing, had greater individual tree size (~15% higher) compared to unthinned stands. Pre-commercially thinned stands also had a higher potential for commercial thinning since they have lower variability in tree size and longer live crown lengths. In addition, delayed thinning (PCT_24) reduced western gall rust infections and the severity of infections compared to both PCT_18 and unthinned stands. In conclusion, pre-commercial thinning should be considered for lodgepole pine stands in order to address timber supply issues in Alberta.

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 score0.101
Threshold uncertainty score0.913

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)0.0010.000

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 head0.007
GPT teacher head0.246
Teacher spread0.239 · 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

Citations4
Published2024
Admission routes3
Has abstractyes

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