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Record W2762573618

WOOD DENSITY AND EXTRACTIVE CONTENT VARIATION AMONG JAPANESE LARCH (LARIX KAEMPFERI, [LAMB.]CARR.) PROGENIES/PROVENANCES TRIALS IN EASTERN CANADA

2017· article· en· W2762573618 on OpenAlexaffabout
Claudia B. Cáceres, Roger E. Hernández, Yves Fortin, Michel Beaudoin

Bibliographic record

VenueWood and Fiber Science (Society of Wood Science and Technology) · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicForest ecology and management
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsLarchLarix kaempferiHorticultureSawdustWater contentProvenanceSoftwoodBotanyBark (sound)Dry weightBiologyAnimal scienceEnvironmental scienceEcology
DOInot available

Abstract

fetched live from OpenAlex

Twelve years old Japanese larch ( Larix kaempferi , [Lamb.]Carr.) stems of 20 different progenies and/or provenances were obtained. Two disks of 5 cm thickness were cut at approximately 0.25 m and 2.7 m from the ground. Two wedges were cut from each disk to determine basic density at these two heights. The remaining log was used to obtain standard samples for the determination of basic and oven-dry densities closest to the bark. The adjacent material of standard samples was used to produce sawdust for the determination of hot-water extractive content. Basic and oven-dry densities were then corrected by the mass of extractives. Wedge basic density showed a significant variation along the stem. Density was higher at 0.4 m than at 2.75 m in height. However, no significant effect of progeny/provenance was found, nor for basic and oven-dry densities. Once these densities were corrected, the progeny/provenance showed a significant effect which allowed a progeny grouping by density. Hot-water extractive content was also significantly affected by the progeny/provenance and it varied between 2.9 to 6.9%. Progeny 7280 would have an interesting potential among progeny/provenance for lumber and pulping uses as it showed the lowest extractive content, the highest corrected densities and high growth rate. In general, corrected densities and extractive content would be more appropriate for a preliminary selection of the progenies/provenances according to the final utilization. Further studies of other wood properties would be necessary to confirm these results.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.784
Threshold uncertainty score0.429

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 communication0.0000.000
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 head0.019
GPT teacher head0.244
Teacher spread0.225 · 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

Citations10
Published2017
Admission routes2
Has abstractyes

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