MétaCan
Menu
Back to cohort
Record W2951830357 · doi:10.3390/f10060500

Primary and Secondary Branch Growth in Black Spruce and Balsam Fir after Careful Logging around Small Merchantable Stems (CLASS)

2019· article· en· W2951830357 on OpenAlexafffundabout
Audrey Lemay, Cornélia Krause, Alexis Achim

Bibliographic record

VenueForests · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicForest ecology and management
Canadian institutionsUniversité LavalUniversité du Québec à Chicoutimi
FundersNatural Sciences and Engineering Research Council of CanadaMinistère des Forêts, de la Faune et des Parcs
KeywordsBalsamCrown (dentistry)Abies balsameaBlack spruceForestryLoggingThinningTaigaOld-growth forestHorticultureSilvicultureBotanyMathematicsGeographyBiologyMaterials science

Abstract

fetched live from OpenAlex

Careful logging around small merchantable stems (CLASS) is a partial cutting treatment that consists of the harvest of 70%–90% of the merchantable volume of an irregular coniferous stand. In this treatment, regeneration, saplings and small merchantable stems (DBH < 15 cm) are preserved and can continue to grow and develop into the dominant layer of the new stand. The aim of this project was to examine the effects of CLASS on the primary and secondary growth of branches, as well as on branch diameter in black spruce and balsam fir trees in the boreal forest of Quebec, Canada. Primary and secondary growth were measured on five branches per tree while branch diameter was analysed from 15 whorls distributed within the crown of the 48 black spruce and 48 balsam fir trees sampled. Branch primary and secondary growth significantly increased after CLASS in the lower part of the crown in both species, and both types of growth increased proportionally. These findings suggest that CLASS may delay crown recession as the lower branches tend to survive and grow for a longer period. However, although radial growth increased in the years post-CLASS, this did not significantly influence the final branch diameter and should not lead to lumber downgrade.

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 score0.016
Threshold uncertainty score0.929

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 head0.006
GPT teacher head0.188
Teacher spread0.183 · 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

Citations3
Published2019
Admission routes3
Has abstractyes

Explore more

Same venueForestsSame topicForest ecology and managementFrench-language works237,207