A decision support system for harvest scheduling and log processing at the Malcolm Knapp Research Forest in Maple Ridge, British Columbia
Bibliographic record
Abstract
This essay develops prototypes of linear programming-based models to project the harvesting level, lumber production, and net revenue of a harvesting plan in order to support forest operation decision-making in the Malcolm Knapp Research Forest (MFRF) in Maple Ridge, British Columbia. Field data were collected from 3 different biogeoclimatic zones within the research forest. Growth and yield of the forest was modeled by Table Interpolation Program for Stand Yields (TIPSY) which is a stand- and forest-level model developed by the British Columbia Ministry of Forest and Range. Then, the growth and yield data were imported into a forest estate linear programming model to forecast the volume of logs harvested from the forest. A sawing pattern model was used to design the sawing pattern and generate Lumber Recovery Factors by log diameter. Subsequently, a log-to-product linear programming model was used to estimate the final lumber production, mill working hours and the net profit for the forest. The results predict that the mill can earn $3,110,141 by producing 141,229 MBF of lumber and 298,818 m³ of chips over a 60-year planning horizon. Sensitivity analysis included different sawing patterns, price fluctuations and limited mill operating hours. Some potential improvements in model structure, efficiency, availability for big diameter log, and carbon credits are also discussed. Although the forest data and the sawmill configuration used in the models are different from reality, it will not be difficult to modify the model for MKRF.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".