Modeling an integrated market for sawlogs, pulpwood, and forest bioenergy
Bibliographic record
Abstract
Traditionally, in the initial stage of the forest supply chain, most applications deal with sawlogs to sawmills, pulpwood to pulp mills, or forest residues to heating plants. In this paper, we develop a model that accounts for all raw materials in the forest, i.e., sawlogs, pulpwood, and forest residues, and byproducts from sawmills. They exist in an integrated market where pulpwood can be sent to heating plants as bioenergy. The model represents a multiperiod multicommodity network planning problem with multiple sources of supply, i.e., preselected harvest areas, and multiple types of destinations, i.e., sawmills, pulp mills, and heating plants. Different from the classic wood procurement problem, we take the unit purchasing costs of raw materials as variables on which the corresponding supplies of different assortments depend linearly. The objective of the problem is to minimize the total cost for the integrated market including the purchasing cost of raw materials. Therefore, it is a quadratic programming problem. A large case study in southern Sweden, under different scenario assumptions, is implemented to simulate the integrated market and to study how price restriction, market regulation, harvest flexibility, demand fluctuation, and exogenous change in the price for fossil fuel will influence the entire wood flows.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".