Quebec provincial government evaluates the potential of OR in the midst of its forest regime renewal
Bibliographic record
Abstract
In implementing a new forest management regime, the provincial government of Quebec is evaluating the use of operational research (OR) to develop plans. This article presents findings from one such study. The main objectives of this project were to quantify the potential reduction in wood procurement cost through (1) the inclusion of an optimization routine in the development of the annual plans and (2) a greater integration and coordination during delineation of harvest blocks and planning forest operations among companies. A wood procurement planning problem was formulated and solved as a mixed integer program. Inclusion of an optimization routine in the planning process led to a cost reduction of 0.88 $/m3 (2.6%) in comparison to the benchmark scenario. Greater integration and coordination coupled with the optimization routine reduced wood procurement costs by 1.52 $/m3 (4.5%), which translates into potential annual cost savings of $ 950, 000.
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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 | 0.003 | 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 | 0.001 |
| 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".