Bibliographic record
Abstract
The paper presents a general method for predicting the stem curve, volume, and merchantable height of a tree if breast height diameter (DBH) is measured, or if DBH and total height (H) as well as diameters at any heights are measured. Estimates for prediction variances are obtained both for diameters and volumes. The approach is multivariate and nonparametric. At the estimation stage, a multivariate model is developed for the total height and a fixed set of diameters: four diameters at absolute heights below breast height and eight diameters at relative distances between the breast height and the top of the tree. The expected values and variances of the dimensions and the correlations between dimensions are expressed as functions of DBH. These functions were estimated using smoothing splines. The model is applied by predicting unobserved dimensions from the observed dimensions using a linear predictor. If total height is not measured, then prediction is done using an approach based on two-point distributions. Correlation of total heights of different trees in the same stand is also modeled, and with this model, measured total heights in a stand can be used to predict unmeasured total heights. The approach provides both a detailed analysis of variation and covariation of stem curves and a practical prediction method.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".