Modeling of wood properties from parameters obtained in nursery seedlings
Bibliographic record
Abstract
Early selection of trees allows acceleration of genetic improvement, as well as processes related to forest management, to improve the quality of the wood produced; however, to reach this objective, it is necessary to know which parameters can be used as predictors of a tree’s aged condition. The objective of this research was to study parameters that are measurable in nursery seedlings and that could be used in prediction models of basic density (BDt), modulus of elasticity (EMt), and strength (fmt) of wood from trees. The tests were performed in 240 seedlings (3 and 6 months old) and in 52 trees (72 months old) from seven genetic units of two species: three Eucalyptus clones and four Pinus pinaster progenies. In the seedlings, measurements of longitudinal velocity of ultrasonic waves (VLs), basic density (BDs), height (Hs), diameter (Ds), strength (fts), and modulus of elasticity (Ets) in tension parallel to the grain were obtained. The EMt and fmt can be predicted by parameters obtained in seedlings of the same genetic unit. Thus, the use of these parameters, in association with others already used in selection programs, may increase the positive results of the early selection, with economic gains and time reductions in forest management.
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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 | 0.000 | 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".