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Record W4394993052 · doi:10.1139/cjfr-2023-0095

Radial distribution of heartwood extractives in second-growth western redcedar

2024· article· en· W4394993052 on OpenAlexafffundvenueabout
Hanna McDonnell, Cosmin N. Filipescu, Rod Stirling, Ignacio Barbeito

Bibliographic record

VenueCanadian Journal of Forest Research · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicForest Ecology and Biodiversity Studies
Canadian institutionsFPInnovationsNatural Resources CanadaCanadian Forest ServiceUniversity of British Columbia
FundersUniversity of British Columbia
KeywordsForestryDistribution (mathematics)BotanyEnvironmental scienceBiologyMathematicsGeography

Abstract

fetched live from OpenAlex

Western redcedar has high economic value and has been traditionally used for cultural purposes by Indigenous communities. Second-growth redcedar is potentially growing faster due to lower planting densities, fertilization, and tree breeding. Little quantitative information is available about the impact of management practices on wood quality, particularly heartwood extractives. This study evaluated the effects of growth rate and site on heartwood extractives at two locations aged 70–90 years in British Columbia, Canada. A three-parameter sigmoid model was fit to the data using nonlinear mixed effects to analyze the relationship between heartwood extractives relative to cambial age, growth rate, and sampling site. The southern site had significantly higher cumulative extractive concentrations, while all extractive concentrations increased faster. This study shows that smaller trees will reach their peak concentrations earlier than larger trees. Results show that faster growth through active management of western redcedar may lead to increased and more uniformly distributed content of heartwood extractives.

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 score0.455
Threshold uncertainty score0.904

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 studies0.0000.000
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 head0.050
GPT teacher head0.285
Teacher spread0.235 · 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

Citations3
Published2024
Admission routes4
Has abstractyes

Explore more

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