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Record W2796623113 · doi:10.1139/cjfr-2018-0140

Genetic control of transition from juvenile to mature wood with respect to microfibril angle in Norway spruce ( <i>Picea abies</i> ) and lodgepole pine ( <i>Pinus contorta</i> )

2018· article· en· W2796623113 on OpenAlexvenueno aff
Haleh Hayatgheibi, Nils Forsberg, Sven‐Olof Lundqvist, Tommy Mörling, Ewa J. Mellerowicz, Bo Karlsson, Harry X. Wu, María Rosario García‐Gil

Bibliographic record

VenueCanadian Journal of Forest Research · 2018
Typearticle
Languageen
FieldEngineering
TopicWood Treatment and Properties
Canadian institutionsnot available
FundersSkogforsk
KeywordsPinus contortaPicea abiesBiologyBotanyTree breedingJuvenileBark (sound)HeritabilityHorticultureWoody plantEcology

Abstract

fetched live from OpenAlex

Genetic control of microfibril angle (MFA) transition from juvenile wood to mature wood was evaluated in Norway spruce (Picea abies (L.) Karst) and lodgepole pine (Pinus contorta Douglas ex Loudon). Increment cores were collected at breast height (1.3 m) from 5664 trees in two 21-year-old Norway spruce progeny trials in southern Sweden and from 823 trees in two lodgepole pine progeny trials, aged 34–35 years, in northern Sweden. Radial variations in MFA from pith to bark were measured for each core using SilviScan. To estimate MFA transition from juvenile wood to mature wood, a threshold level of MFA 20° was considered, and six different regression functions were fitted to the MFA profile of each tree after exclusion of outliers, following three steps. The narrow-sense heritability estimates (h 2 ) obtained for MFA transition were highest based on the slope function, ranging from 0.21 to 0.23 for Norway spruce and from 0.34 to 0.53 for lodgepole pine, while h 2 were mostly non-significant based on the logistic function, under all exclusion methods. Results of this study indicate that it is possible to select for an earlier MFA transition from juvenile wood to mature wood in Norway spruce and lodgepole pine selective breeding programs, as the genetic gains (ΔG) obtained in direct selection of this trait were very high in both species.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.468
Threshold uncertainty score0.986

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.013
GPT teacher head0.227
Teacher spread0.214 · 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

Citations14
Published2018
Admission routes1
Has abstractyes

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