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Record W293177673 · doi:10.1515/sg-2009-0001

Genetic Variation in Growth Curve Parameters of Konishii fir (Cunninghamia lanceolata (LAMB.) HOOK. var. konishii)

2009· article· en· W293177673 on OpenAlexaff
Jeng‐Der Chung, Ching‐Te Chien, Gordon D. Nigh, Cheng Ying

Bibliographic record

VenueSilvae genetica/Silvae Genetica · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicForest ecology and management
Canadian institutionsGovernment of British Columbia
Fundersnot available
KeywordsCunninghamiaWeibull distributionBiologyGrowth curve (statistics)TyphoonSelection (genetic algorithm)StatisticsTree (set theory)EcologyMathematicsBotanyComputer scienceMeteorology

Abstract

fetched live from OpenAlex

Abstract Cunninghamia konishii is the island race of the species complex C. lanceolata, and is native to Taiwan. It is a valuable timber species. A comprehensive provenance- family test was established in 1973. Height and diameter were measured periodically until age 26, which was close to the species’ harvest age of about 30. These data offered an opportunity to examine the species’ growth characteristics by fitting asymptotic growth functions. We adopted the concept of repeated measures data analyses, i.e., a combination of variance component analysis and growth curve fitting, the latter involved fitting the individual tree height and diameter data to a Weibull-based function. A severe typhoon in 1996 caused serious damage to the plantation, mostly to tree heights. To prevent this damage from influencing our results, we limited the analyses to those trees judged relatively free of typhoon damage, and focused on the diameter growth data. Fitting a Weibull function with parameters a, b, and c was statistically successful (e.g. the mean R2 for diameter was 0.98). Both analyses indicate substantial variation among provenances and families, and thus opportunities for genetic selection and breeding. We particularly expound on the practical applications of growth curve fitting as an analytical tool for elucidating the mechanistic process of tree growth to assist decisions on the age for selection, even retrospectively, and modeling the response of tree growth to future climate.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
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 score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.007
GPT teacher head0.207
Teacher spread0.200 · 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.

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

Citations8
Published2009
Admission routes1
Has abstractyes

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