Determining the effectiveness of using acoustic velocity as an indirect measurement of branchiness in standing longleaf pine
Bibliographic record
Abstract
Branches reduce stem quality, and the level of the effect is determined by the geometry, size, and number, of associated knots. Quantifying branchiness is difficult, as visual estimates are subjective, and mechanical measurements are impractical. Acoustic velocity (AV) is a relatively novel measurement capturing the speed stress travels through wood. AV is correlated with wood stiffness and is affected by internal characteristics like knots. This project tested AV as an indirect branchiness metric by measuring AV, height, diameter, and counting branches classified by diameter on 255 standing 8 year old longleaf pine ( Pinus palustris). AV was highly correlated with height (r = 0.76, p < 0.0001) and slenderness (r = 0.52, p < 0.0001). AV was moderately correlated with large living branches (diameter > 2.54 cm) (r = −0.27, p < 0.0001), but not correlated with total branches. Height, slenderness, and the count of large living branches (diameter > 2.54 cm) were included in the selected model for AV. Inclusion of dead or small (diameter < 2.54 cm) branches reduced model power. The best model captured 11% of the variation in AV, of which branches explained 5%. We conclude that AV does not appropriately quantify individual tree branchiness, but may be suitable for comparing populations such as families or provenances.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".