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Record W2009506283 · doi:10.1139/x08-043

Occurrence, proportion, and vertical distribution of red heartwood in paper birch

2008· article· en· W2009506283 on OpenAlexafffundvenueabout
Guillaume Giroud, Alain Cloutier, Jérôme Alteyrac

Bibliographic record

VenueCanadian Journal of Forest Research · 2008
Typearticle
Languageen
FieldEnvironmental Science
TopicForest ecology and management
Canadian institutionsCentre de Géomatique du Québec
FundersCanada Economic Development for Quebec RegionsUniversité Laval
KeywordsCrown (dentistry)Diameter at breast heightRed pineForestryBotanyHorticultureMathematicsGeographyBiologyPinus <genus>

Abstract

fetched live from OpenAlex

Normal paper birch ( Betula papyrifera Marsh.) wood has a clear and uniform color. However, some paper birch trees contain reddish-brown-, discolored wood known as red heartwood. Its occurrence, proportion, and vertical distribution were investigated. One hundred and fifty trees were randomly sampled from three stands located at the Montmorency Forest, 75 km north of Quebec City, Quebec, Canada. A subsample of 18 trees showing occurrence of red heartwood at stump height were felled, and 5 cm thick disks were cut at every 0.5 m of height. Red heartwood volume, proportion, and vertical distribution were determined from the disks. Trees with larger diameter at breast height and lower tree height had a higher probability of red heartwood occurrence. Red heartwood starts occurring in 40-year-old trees on average in the stands studied. The volume of red heartwood was positively correlated with tree age, and the proportion of red heartwood was positively related to tree age, and negatively related to the amount of sunlight on the live crown. Red heartwood proportion was 13.3% of the tree merchantable volume, mostly located under the live crown.

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 score0.806
Threshold uncertainty score0.387

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 communication0.0000.000
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 head0.030
GPT teacher head0.282
Teacher spread0.252 · 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

Citations12
Published2008
Admission routes4
Has abstractyes

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