Value-adding through silvicultural flexibility: an operational level simulation study
Bibliographic record
Abstract
Forest products industry's competitiveness is influenced by the agility of wood procurement systems in delivering raw material to support downstream manufacturing activities. However, in a hierarchical forest management planning context, silvicultural treatments are prescribed and set as constraints for supply chain managers, restricting supply flexibility and consequently value-adding potential. This study was conducted with an objective of quantifying the benefits of improving wood procurement systems agility through flexibility in the choice of silvicultural treatments at the operational level. The aim was also to determine the range of conditions under which benefits from flexibility can be realized while accounting for the impact on long-term supply. We present a novel approach that integrates silvicultural options into operational-level decision-making to solve the multi-product, multi-industry problem with divergent flow. The approach entails solving a mixed integer programming model in a rolling planning horizon framework. Subsequently, we demonstrate benefits associated with integrating supply chain and silvicultural decisions through a case study. Future impact of exercising flexibility on long-term supply was accounted through incorporating costs associated with applying different silvicultural regimes. The presented approach will prove to be useful in implementing an adaptive forest management system that integrates the complexity of social, economic and ecological dimensions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".