Sensitivity of predictions of merchantable tree height, log production, and lumber recovery to tree taper
Bibliographic record
Abstract
Tree taper models characterize the change in diameter from the bottom to the top of a tree, thereby contributing to the estimation of tree volume. This paper examines the sensitivity of predictions of merchantable height defined as the tree height at a given top diameter inside bark (DIB) determined by the utilization standard, log production, and lumber recovery to the eight parameters in Kozak’s (1988) tree taper model. We found that predictions of merchantable height and log production were sensitive to two parameters, whereas predictions of the percentage of lumber recovery were sensitive to one parameter. Because the three measures examined in this study are not very sensitive to tree taper, especially the percentage of lumber recovery that is of most concern to the forest industry, together with the relatively small variations in tree taper parameters across Canada and the limited contribution of tree taper to characterizing the value of lumber recovery at the stand scale, one could infer that it may be possible to develop a single Canadian national softwood tree taper model for predicting forest product variables such as log production and percentage of lumber recovery from forest inventory.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".