Evidence for associations between SilviScan-measured wood properties and intraring checking in a study of twenty-nine 6-year-old<i>Pinus radiata</i>
Bibliographic record
Abstract
Wood anatomical characteristics of twenty-nine 6-year-old Pinus radiata D. Don trees, selected to represent the extremes of intraring checking, were assessed with SilviScan. Derived ring-level summary variables were calculated from the SilviScan pith-to-bark wood property traces, based on ring means, standard deviations, and quantiles. Incidence of checking was assessed on discs that had been oven dried using a standardized method, and evidence for associations between wood characteristics and checking was evaluated at the tree and ring level using Bayesian statistical methods. Bayes factors of 39.1, 14.9, and 7.8 were obtained at the tree level, representing moderate to good evidence for associations between ring medians of wood density, tracheid radial diameter, and cell wall thickness, respectively. Increasing wood density, decreasing tracheid radial diameter, and increasing cell wall thickness were associated with reduced incidence of checking. These are parameters expected a priori to contribute to tracheid cells' resistance to collapse under drying stresses. A generalized linear model with radial diameter and cell wall thickness as independent variables had a concordance of 83% for predicting the checking status of trees.
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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 | 0.001 |
| 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".