Relationships between acoustic variables and different measures of stiffness in standing <i>Pinus taeda</i> trees
Bibliographic record
Abstract
Acoustic tools are increasingly used to estimate standing-tree (dynamic) stiffness; however, such techniques overestimate static stiffness, the standard measurement for determining modulus of elasticity (MOE) of wood. This study aimed to identify correction methods for standing-tree estimates making dynamic and static stiffness comparable. Sixty Pinus taeda L. trees, ranging from 14 to 19 years old, obtained from genetic tests established in the southeastern United States, were analyzed. Standing-tree acoustic velocities were measured using the TreeSonic tool. Acoustic velocities were also recorded in butt logs cut from the same trees using the Director HM200. A strong but biased relationship between tree and log velocities was observed, with tree velocities 32% higher (on average) than the corresponding log velocities. Two correction methods, one for calibrating tree velocities and one for accounting for differences in wood moisture content, were used to determine an adjusted MOE. After correction, adjusted MOE estimates were in good agreement with static longitudinal MOE values measured on clearwood specimens obtained from the trees, and no systematic bias was observed. The results of this study show that acoustic estimates of MOE on standing trees largely depend on how the data are processed and the reference method used.
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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".