Rapid assessment of wood density of live trees using the Resistograph for selection in tree improvement programs
Bibliographic record
Abstract
Wood density is traditionally determined by a volumetric method that is accurate but expensive for large-scale sampling. A new device called the Resistograph was investigated for rapid assessment of relative wood density of live trees in progeny trials. Fourteen full-sib families of loblolly pine (Pinus taeda L.) produced by a six-parent half-diallel mating design were tested at four sites. For each family, wood density was measured with the traditional volumetric method and then compared with the Resistograph readings (amplitude). Amplitude had weak (0.29) to moderate (0.65) phenotypic correlations with wood density on an individual-tree basis over the four sites. The family mean correlation between the two measurements, however, was much stronger (0.92). The additive genetic correlation between the two measures was also high (0.95). Individual-tree breeding values of amplitude yielded more accurate rankings than phenotypic values. The rankings of the parental, general-combining abilities were identical for the two measures. Both wood density and amplitude were under strong genetic control at the family level (full-sib family heritability (h2fs) = 0.95 for wood density and h2fs = 0.85 for amplitude). The efficiency of using the Resistograph as a means of indirect selection for improvement of wood density was 87% at the family level. Results from this study suggest that the Resistograph could be used reliably and efficiently to assess relative wood density of live trees for selection in tree improvement programs. The method is rapid, nondestructive, and much cheaper than the traditional volumetric method.
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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 | 0.001 | 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".