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Record W2413293918

GENETIC VARIATION IN TRACHEID LENGTH AND RELATIONSHIPS WITH GROWTH AND WOOD TRAITS IN EASTERN WHITE SPRUCE (PICEA GLAUCA )

2007· article· en· W2413293918 on OpenAlexfundno aff
Jean Beaulieu

Bibliographic record

VenueWood and Fiber Science (Society of Wood Science and Technology) · 2007
Typearticle
Languageen
FieldEngineering
TopicWood Treatment and Properties
Canadian institutionsnot available
FundersCanadian Forest ServiceNatural Resources CanadaU.S. Forest Service
KeywordsTracheidHeritabilityBiologyBotanyTraitGenetic gainGenetic variationWoody plantTree breedingSelection (genetic algorithm)HorticultureEvolutionary biologyXylemGeneticsGene
DOInot available

Abstract

fetched live from OpenAlex

Wood traits affect the quality of wood products, which is especially true for tracheid length regarding paper quality.While variation in tracheid length in white spruce is well known, estimates of genetic control over that trait as well as its relationships with growth traits and other wood characteristics have not yet been reported.Thus, the objectives of this study were: (1) to determine the extent of the differences in tracheid length among 30-year-old white spruce open-pollinated families; (2) to estimate the narrow sense heritability at both the individual and family levels for tracheid length; and (3) to estimate the phenotypic, genetic, and family mean correlations between height, diameter, volume, and wood specific gravity.We have shown that more than 90% of the variation in tracheid length is due to differences among trees within family plots.Heritabilities at both the family and the individual levels are low, so significant genetic gain could only be obtained from selection and vegetative propagation of the trees with the longest tracheids.Tracheid length in white spruce is negatively correlated to growth traits but appears to be independent of wood specific gravity.Effects of selection for growth traits on tracheid length are discussed.

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 score0.177
Threshold uncertainty score0.694

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.002
Scholarly communication0.0000.001
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 head0.011
GPT teacher head0.193
Teacher spread0.182 · 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

Citations24
Published2007
Admission routes1
Has abstractyes

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Same venueWood and Fiber Science (Society of Wood Science and Technology)Same topicWood Treatment and PropertiesFrench-language works237,207