A discrete-event simulation model to test multimodal strategies for a greener and more resilient wood supply
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Increasing occurrences of natural disturbances, including windstorms and high snow cover, and supply chain risks lead to severe irregularities in wood harvest and transport. To overcome resulting supply difficulties, innovative multimodal systems via rail terminals are promising options offering the potential to increase buffer capacity, improve supply chain resilience, and reduce greenhouse gas emissions. Therefore, a train terminal is included in a virtual simulation environment spanning the entire wood supply chain from forest to industry to test, analyze, and evaluate a complex multimodal system in different scenario settings. Furthermore, the simulation model provides intuitive decision support through animation and a cockpit of key performance indicators, facilitating hands-on workshops with supply chain managers. Results show the advantage of a combination of unimodal and multimodal transport in the wood supply chain of the observed case-study region. This combination proves to be resilient and outperforms other tested supply chain strategies by avoiding both bottlenecks and ill-timed plans and reducing carbon dioxide (CO 2 ) emissions. Furthermore, workshops conducted with industry experts indicate that adapting collaborative supply chain control strategies by means of a participatory simulation environment enhances the development of advanced risk management and therefore improves supply chain resilience, efficiency, and sustainability.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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 it