An application of linear programming to log allocation in the forest industry of British Columbia
Bibliographic record
Abstract
This thesis presents an application of linear programming to the question of efficient log allocation in the forest industry of British Columbia. Current procedures for allocating logs among alternative utilization processes are discussed and it is suggested that a more efficient allocation might be obtained through a systematic approach to the problem. The economic necessity of improving net returns to the log supply is emphasized. A linear programme log-allocation model is presented, based on an integrated-industry in the coastal region of British Columbia. The model encompasses three main categories of log-use, namely sawmilling, plywood production and pulp production, and demonstrates how a given supply of logs may be optimally distributed among these structurally different log-conversion processes. Emphasis throughout this study is on the structure of the linear programme model, although considerable effort was directed to obtaining realistic data. Solutions of the model, obtained through the services of the Computing Centre at the University of British Columbia, are discussed, and a superficial comparison is made with actual log allocation in the industry. Modifications of the model to suit the log-allocation problem faced by an individual firm in the short-run are discussed and normal comparative-statics applications are considered. It is pointed out that many of the simplifying assumptions in the model may be relaxed. However, the main limitation to its practical application by industry and government lies in the quality and type of data available. In this respect it is suggested that the linear programme model of this thesis provides a valuable guide to the production data required to improve economic efficiency in the forest industry.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".