Equations for predicting tree height, total volume, and product recovery for black spruce (<i>Picea mariana</i>) plantations in northeastern Quebec
Bibliographic record
Abstract
Several regression models with different independent variables were studied for their ability to predict total tree height, total stem volume, and product recoveries (lumber volume, chip volume, lumber value, and total product value) from a sawing simulator. A sample of 172 trees from black spruce plantations was used to fit model parameters and another independent sample of 139 trees was used for model evaluation. The sample encompassed large variations in tree characteristics and tree product recovery. All the fitted models were suitable for predicting their corresponding response variables. Model validation through actual product recovery data from a real stud mill further indicated that the general tree-level models for the product recovery were able to accurately predict product recovery, especially from small- and medium-sized trees, using measured tree characteristics. These models provide a valuable tool for forest managers in determining appropriate management strategies (e.g., stand volume and optimizing stand value). Key words: black spruce, regression analysis, tree characteristics, product recovery, sawing simulation
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".