Accelerated learning for wood supply managers – the next generation of on-line training tools
Bibliographic record
Abstract
The Virtual Wood Supply Arena is an on-line training environment for managing roundwood purchase, production and transport in cut-to-length supply systems. The purpose of its development was accelerated training for coordination of these functions under realistic operating conditions. It offers 8- and 12-week scenarios for supplying five mills. Weekly planning is done for 10 harvesting teams and 10 trucks in a Swedish case geography while tracking mill delivery fulfillment under weekly trafficability restrictions. The purpose of this paper is to introduce the training environment and report the progression of student performance after 2 years of use in university-level training. Student teams reached full delivery fulfillment within three training runs. After familiarization during an introductory run, a complete 12-week scenario took four effective hours to complete. Delivery fulfillment increased from 82 to 95 and 100% between the first, second and third training runs. The progression of team performance included a 36% reduction of relocation distances for harvesting teams and 11% reduction of transport distances for hauling from forest to mill. By the third training run these performance levels were attained with less than 2 weeks of inventory for both the purchase bank and roadside stocks.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.025 | 0.005 |
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".