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Record W3131559485 · doi:10.5558/tfc2021-008

Growth and yield of lodgepole pine stands disturbed by mountain pine beetle in the Lower Foothills of Alberta

2021· article· en· W3131559485 on OpenAlexafffundvenueabout
W. R. Dempster, Sharon Meredith

Bibliographic record

VenueThe Forestry Chronicle · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicFire effects on ecosystems
Canadian institutionsWestern Forest Products
FundersForest Resource Improvement Association of AlbertaUniversity of AlbertafRI Research
KeywordsFoothillsMountain pine beetlePinus contortaClearcuttingForestryWood productionSite indexStand developmentAgroforestryDisturbance (geology)GeographyEnvironmental scienceEcologyBiologyForest management

Abstract

fetched live from OpenAlex

Changes in the structure and dynamics of lodgepole pine stands following the 2006 outbreak of mountain pine beetle in western Alberta were investigated by monitoring attacked permanent sample plots over the following decade and projecting future stand development with locally validated growth models. Results suggest that a wide range in growth and yield of attacked stands is likely, with projected impacts varying from minor and inconsequential modifications to full stand replacement. Severely disturbed stands are unlikely to naturally regenerate to pine. The degree to which timber production is naturally restored in such stands will depend to a large extent on the amount and composition of non-pine advance growth and regeneration. Variation in attack severity and tree species composition will, in the absence of clearcutting following disturbance, result in increased diversity in stand and forest structure. Recommendations are made for scheduling salvage and restoration operations to ameliorate losses in timber supply.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.079
Threshold uncertainty score0.159

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
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 head0.005
GPT teacher head0.204
Teacher spread0.199 · 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

Citations5
Published2021
Admission routes4
Has abstractyes

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