A model for predicting the value of forest stands in various market conditions in British Columbia
Bibliographic record
Abstract
A model for predicting the value of forest stands was developed at FPInnovations – Feric Division. The model uses standard cruise data collected in British Columbia (BC) and company sort descriptions to predict the distribution of wood volume by species and grade sort for each proposed harvest site. The model is capable of quickly analyzing different scenarios (i.e., different sort sets) to reflect changing market conditions. Within the model, a taper equation is applied to each tree to calculate the diameters inside the bark, and quality and pathological descriptors from cruise data are used. A Dynamic Programming algorithm is used to predict the combination of logs that maximizes the value of each stem, and results are summarized and extended from individual trees to cruise plots, then to harvest blocks and/or stands. The model was successfully tested for second-growth stands with relatively few defects on Coastal BC. Improvements are being implemented to enhance its applicability to more complex stands. Key words: value of forest stands, economic operability, optimal bucking of trees, Dynamic Programming, computer model, inventory systems
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".