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Record W2724480407 · doi:10.1515/sg-2013-0032

Genetic Variation in Wood Property and Growth of Taiwania (Taiwania cryptomerioides Hayata)

2013· article· en· W2724480407 on OpenAlexaff
Jeng‐Der Chung, Chih-Ming Chiu, Gordon D. Nigh, Ching‐Te Chien, Cheng Ying

Bibliographic record

VenueSilvae genetica/Silvae Genetica · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicForest ecology and management
Canadian institutionsMinistry of Forests
Fundersnot available
KeywordsHeritabilityBiologyGenetic variationTree breedingVariance componentsBotanyTracheidEcologyForestryWoody plantEvolutionary biologyGeographyMathematicsStatistics

Abstract

fetched live from OpenAlex

Abstract The genetic variation in wood property and growth of Taiwania (Taiwania cryptomerioides) was investigated at two test sites in the same general location, but one at 1200 m (LK20) and another at 1900 m (LK24). A rangewide sample of 48 open-pollinated families in Taiwan was involved. The test plantations were 16 years old when the wood property data were collected. Traits studied include wood specific gravity, tracheid length, ring width. DBH at ages13, 16, and 22 and height at age 13 were also analyzed. Site environment had a dominant effect on growth and wood density, but showed little interaction with family. The site effect was evident in large between-site differences in means, range of family means, and size of family variance components and heritability estimates. These differences are attributable to the site characteristics of LK24 (high elevation, upper slope near a mountain crest) and an unfavourable local climate. Taiwania is fast growing and produces quality wood, and is also an interesting species from the perspective of evolutionary biology. Protection of its genetic variability should be a priority to be managed. We propose that the existing five progeny tests be protected since these tests well-represent the genetic variability of the species in Taiwan.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.260
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.004
GPT teacher head0.183
Teacher spread0.178 · 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; both teacher heads agree on what is shown here.

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

Citations1
Published2013
Admission routes1
Has abstractyes

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