GENETIC VARIATION IN WOOD MECHANICAL PROPERTIES OF CALYCOPHYLLUM SPRUCEANUM AT AN EARLY AGE IN THE PERUVIAN AMAZON
Bibliographic record
Abstract
Calycophyllum spruceanum (Benth.)Hook.f. ex Shum. is an important timber species of the Peruvian Amazon Basin.Due to overexploitation in natural populations, users are turning to young trees of potentially lower quality.Therefore, variation in juvenile wood properties should be investigated to determine whether wood quality can be maintained or, if necessary, improved by breeding.A provenance/ progeny test was established to evaluate genetic variation in growth and wood properties of young trees, the strength of their genetic control, as well as their interrelationships both at the genetic and phenotypic levels.This paper presents results obtained for ultimate crushing strength ( L ), the static compliance coefficient (s 11 ) in longitudinal compression, the dynamic s 11 in the longitudinal direction (determined by ultrasound), and air-dry density at 39 months.Results indicate that the mechanical properties of juvenile wood of this species are adequate for structural uses.There was significant variation in all wood properties due to families within provenances, and in all but dynamic s 11 due to provenances.Families accounted for a larger percentage of the total phenotypic variance than provenances.Heritability estimates were higher for L and static s 11 than for dynamic s 11 and density.Genetic correlations indicate that selecting trees with denser wood and/or faster growth would have a positive effect on some mechanical properties.A non-destructive ultrasonic method appeared suitable for estimating juvenile wood strength and stiffness of this species.
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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 | 0.001 |
| 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) | 0.001 | 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".