Model development for lumber volume recovery of natural balsam fir trees in Quebec, Canada
Bibliographic record
Abstract
To improve the precision of sawing simulations, 4 regression models were developed to predict simulated lumber volume recovery using tree size variables. Simulated lumber volume recoveries from natural balsam fir trees based on the sawing simulator Optitek were different from real lumber volume recoveries from a stud sawmill because the simulation method only takes wane into consideration. Therefore, 2 methods were developed to correct estimated lumber volume recoveries. The results indicate that the lumber volume correction models for stem deformations could adjust the predictions of lumber volume recovery from the simulation and directly from the sawing simulator to obtain more accurate estimates. With the correction models, the lumber volume recovery from natural balsam fir trees could be estimated directly using easily measured tree DBH and height from the forest resource inventory. Key words: balsam fir, stem deformation, product recovery, sawing simulation, correction models, regression model
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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 | 0.000 | 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".