Testing accuracy of log volume calculation procedures against water displacement techniques (xylometer)
Bibliographic record
Abstract
The accuracy of three traditional formulas to calculate log volumes (Smalian, Huber, and Newton) and three recent methods (cubic splines, centroid sampling, and overlapping bolts) were compared and tested against volumes determined by the water-displacement technique (xylometer). Fifty-two felled trees were measured in a Pinus elliottii Engelm. plantation. The accuracy of these six procedures was analyzed considering total and merchantable outside bark volumes with 1-, 2-, 4-, and 6-m log lengths. The results showed that Huber's formula was superior for all volumes and log lengths considered. Centroid and Newton had a similar performance to Huber but with some higher errors. As expected, Smalian was the procedure with the worst performance. When log lengths of 1 and 2 m were employed, all procedures used to calculate total volume presented errors lower than 4.77%. However, the errors increased when the merchantable volume involves a large top diameter (veneer volumes, for example) or for calculating volumes from trees with large diameters. The results of this and other research have shown that Huber's formula has been accurate for several species, ages, geographic regions, etc. Thus, Huber's formula could be used in the majority of circumstances with log lengths greater than 2 m, reducing drastically the costs and sampling effort.
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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 | 0.006 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".