Backhauling in forest transportation: models, methods, and practical usage
Why this work is in the frame
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Bibliographic record
Abstract
In this paper, we consider a tactical transportation planning problem in forestry to decide the destination of logs. This problem is generally solved by finding the flow between a set of supply points and demand points. It can be formulated as a linear programming problem involving direct flows between supply and demand points. However, better solutions can be found by using additional flow variables representing flow in potential backhaul routes. However, the number of such variables is often very large. In this article, we provide the basis for backhaul flow planning in forestry. This includes defining the underlying operations research models for both the flow problem and the subproblem to find backhaul routes. The size of the problem in terms of the number of variables increases rapidly with the number of supplies and demands and we describe a column generation approach for its solution. We report on some case studies and industrial systems where the approach has been used.
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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 | 0.010 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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 it