Refining volume estimates of down woody debris
Bibliographic record
Abstract
Down woody debris (DWD) plays a vital role in forest ecosystem structure and function. Although volume is likely the most common metric used to characterize DWD, an evaluation of the formulae used for volume estimation on individual DWD pieces has received little attention. We determined actual volume of 155 diverse DWD pieces (types, species, lengths, and diameters) by detailed field measurements. By comparing the actual and calculated volumes from six commonly used formulae, we assessed their bias, precision, and accuracy. Based on observed DWD forms, we developed a new formula, namely the “conic−paraboloid”, which was included in the assessment. Among the formulae that require length and two end diameter measurements, the conic−paraboloid had the lowest bias, highest precision, and hence greatest accuracy. Newton’s and the centroid formulae had higher accuracy yet require more field measurements. Smalian’s, conical frustum, and average-of-ends formulae had poor performance relative to the others. Accuracy of all formulae decreased with increasing piece length. Thus, partitioning pieces into two, three, and four sections for additional measurement improved accuracy. As decay advances, pieces become progressively more elliptical in cross section. Using the cross-sectional area derived from only the long axis of the ellipse leads to substantial volume overestimates for well-decayed DWD.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".