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Record W4415752063 · doi:10.1139/cjfr-2025-0027

The effects of commercial thinning treatments on the growth response of black spruce plantations in Northwestern Ontario

2025· article· en· W4415752063 on OpenAlexaffvenueabout
Douglas E.B. Reid, Dave Morris, Shes Kanta Bhandari, Cameron Leitrants

Bibliographic record

VenueCanadian Journal of Forest Research · 2025
Typearticle
Languageen
FieldEngineering
TopicForest Biomass Utilization and Management
Canadian institutionsNOSM UniversityMinistry of the Environment, Conservation and ParksMinistry of Natural Resources and Forestry
Fundersnot available
KeywordsThinningBasal areaBlack spruceSite indexYield (engineering)Volume (thermodynamics)Growing season

Abstract

fetched live from OpenAlex

This study examined the temporal growth, yield and mortality responses of mid-rotation, black spruce ( Picea mariana (Mill.) B.S.P.) plantations to varying intensities of commercial thinning. We established 32 one-hectare experimental thinning plots that included replicates of four thinning levels (0%, 25%, 35%, and 50% basal area removals) at two sites that varied in soil type and site quality. Permanent fixed area (400 m 2 ) plots were established, and standard mensuration measurements taken at year 0, 5, 9, 14. Thinning had a variable effect on growth and yield depending on site, thinning intensity, and time after thinning. Periodic annual increment (PAI) of diameter and gross total volume were highest within 5 years of thinning, then declined gradually. Growth responses in diameter of individual trees were positively correlated with tree size, and larger diameter classes contributed more toward increased volume increments in the thinned plots. Although the growth response was relatively short-lived and PAI of gross total volume was negative in thinned stands on the better-quality site by the third measurement period, there was an overall positive effect on yield. Larger diameter trees in thinned stand should allow for size class upgrades and greater economic returns from harvested wood products at final harvest.

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)0.001
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 score0.891
Threshold uncertainty score0.954

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.024
GPT teacher head0.282
Teacher spread0.258 · 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

Citations2
Published2025
Admission routes3
Has abstractyes

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