Analyzing the impact of implementing a logistics center for a complex forest network
Bibliographic record
Abstract
The challenges faced recently by the North American forest products industry have forced it to review many of its key operations. Implementing logistics centers for such a context may therefore help in allocating the wood fibre more efficiently and in reducing sorting and transportation costs. This paper aims to better understand the interaction between a forest logistics center and a complex forest network while exploring the business environment favoring the use of such a structure. A profit maximization model is proposed and applied to a real case in the Mauricie region in Quebec, Canada. A total of 18 groups of scenarios are tested, based on the use of a sort yard and of backhauling. Results show that a logistics center already in operation adds $0.52 in profits for each cubic metre of wood available for harvest (over 2 580 411 m 3 per year) for the network under study ($1.4 million annually). A sensitivity analysis also highlights that higher prices and sorting error rates have the greatest impact on the logistics center’s profitability.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".