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Record W2133930683 · doi:10.1139/x08-013

Age trends in genetic parameters of wood density components in 46 half-sibling families of <i>Pinus pinaster</i>

2008· article· en· W2133930683 on OpenAlexvenueno aff
Maria João Gaspar, José Louzada, Maria Emília Silva, Alexandre Aguiar, Maria Helena Almeida

Bibliographic record

VenueCanadian Journal of Forest Research · 2008
Typearticle
Languageen
FieldEngineering
TopicWood Treatment and Properties
Canadian institutionsnot available
FundersJohns Hopkins University
KeywordsPithHeritabilityPinus pinasterPinus <genus>BotanyBiologyHorticultureAnimal scienceMaximum densityVariance componentsMathematicsPhysicsStatistics

Abstract

fetched live from OpenAlex

This study contributes to the Pinus pinaster Ait. breeding programme, which is reaching the third generation by adding information on wood quality of 46 open-pollinated families from a progeny trial located in Leiria, Portugal, that originated from seed collected in a clonal seed orchard. A total of 552 seventeen-year-old trees were sampled at 2 m height. Trends were studied from the pith outward in variance components and narrow-sense heritability (h 2 ) of wood density components and ring-width characteristics as well as genetic correlations between cambial ages. Mean ring density (RD), minimum density (MND), maximum density (MXD), earlywood density (EWD), latewood density (LWD), earlywood width, latewood width, ring width, latewood percentage, and heterogeneity index were determined using X-ray densitometry procedures. RD had higher genetic control (h 2 = 0.63), and heritability values of earlywood components (h 2 MND = 0.54, h 2 EWD = 0.60) exceeded those of latewood components (h 2 MXD = 0.34, h 2 LWD = 0.26). Heritabilities increased with ring number from pith for almost all wood density components, and there were high age–age genetic correlations for wood density traits (r g &gt; 0.98).

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 score0.277
Threshold uncertainty score0.972

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
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 head0.101
GPT teacher head0.271
Teacher spread0.170 · 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

Citations40
Published2008
Admission routes1
Has abstractyes

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