Predicting decay and round-wood end use volume in trembling aspen (Populus tremuloides Michx.)
Bibliographic record
Abstract
$\bullet$ In Quebec (Canada), predicting net merchantable volume of standing trees is essential to adjust stumpage fees. Furthermore, round-wood end use is important in the provincial forest management context because it is used to split the allowable annual cut among the different mill types. $\bullet$ A method relying on linear, binomial and cumulative logit regressions is proposed to predict both decay volume and round-wood end use volume. Tree age, height and quality, as well as ecological region, stand origin and presence of Phellinus tremulae (Bond.) Bond. & Boriss. and Ceratocystis fimbriata (Ellis & Halst.) fungi are the main factors that contribute to the presence and the proportion of decayed merchantable volume. Once the net merchantable volume is estimated, its division into round-wood end use is estimated through a series of steps involving the presence of Phellinus tremulae, saw log height, stem quality and size as explanatory variables. The first step is a multinomial regression which predicts the number of end uses (pulp wood, low-grade saw logs, saw logs, low-grade veneer, and veneer) that are present in the stem. A series of logistic regressions then determines the presence of each end use, with linear regressions predicting the round-wood volume of each end use.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".