Identification of a potential metabolic marker, inositol, for the inherently fast growth trait by stems of <i>Pinus densiflora</i> via a retrospective approach
Bibliographic record
Abstract
A retrospective metabolomics approach was utilized as a tool in genomics-assisted selection for forest tree improvement. Key metabolic components were thus correlated with the inherently rapid stem growth of Pinus densiflora Siebold & Zucc. trees, including water soluble metabolites and amino acids found in the inner bark (phloem) of cambial region tissues that were harvested in midsummer (July) from 34-year-old trees. Metabolites were assessed in individual genotypes from seven open-pollinated, half-sibling families. Four of the families were classed as fast growing, and three of the families were significantly slower growing. This metabolomics approach was also used to assess metabolite profiles in similar cambium–phloem region tissues from 4-year-old trees representing 12 unrelated families. Initially by Pearson’s correlation analysis, and subsequently by stepwise linear modeling, we assessed the interactions between stem growth parameters (stem diameters and a stem volume index) and metabolite contents, and we did this via a retrospective approach. Among the metabolites identified, inositol was a consistent, positive, and highly significant correlate with the stem growth of P. densiflora trees at two developmental stages (ages 4 and 34 years). Inositol also exhibited these significant correlations in an environment-independent manner, i.e., on two very different field progeny test sites. We conclude that inositol may be a useful metabolic biomarker for early selection of rapid tree stem growth traits of this conifer species. In so doing, it could appreciably enhance the breeding and selection of inherently rapid-growing families of P. densiflora.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".