Predicting volumes by log grades in standing sugar maple and yellow birch trees in southern Quebec, Canada
Bibliographic record
Abstract
Modelling volumes by log grades in standing hardwood trees is often hindered by the nature of the response variable. In this paper, we used a two-part conditional model to account for the excess of zero responses for some log grades. Moreover, this approach was used as a framework to compare three different tree classifications in their ability to predict volumes by log grades in standing yellow birch ( Betula alleghaniensis Britton) and sugar maple ( Acer saccharum Marsh.) trees. A tree grade classification was compared with two preharvest tree classifications based on mortality risk assessment. A cross-validation was also carried out to evaluate the two parts of the model. The results showed that the two-part conditional approach was efficient in this case study. Compared with a general model, the three classifications improved the maximum likelihood. According to the Akaike and Bayesian information criteria, the tree grade classification was the “best” one. All three classifications proved to be better able to distinguish log grade occurrence than log grade volume. Although it implies additional cost, the implementation of the tree grade classification into the preharvest inventories would improve the prediction of volumes by log grades for yellow birch and sugar maple trees.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".