Impacts of inter-firm collaboration for supply networks harvesting mixed forests
Bibliographic record
Abstract
In Quebec, Canada, 11% of forests are mixed, meaning they consist of a significant variety of trees, with half of them being hardwood and the other half softwood. Due to the diverse range of species, which necessitates the involvement of at least two different factories in harvesting each site, such networks foster interdependence among mills. To enhance the resilience of forest logistics networks in mixed forests, the study conducted in this article evaluated various collaborative approaches aimed at minimizing logistics costs and thereby maximizing the sustainability of such networks. The study showed that resource sharing enabled the greatest savings, closely followed by transport consolidation among mills. However, the study revealed that to minimize logistics costs in such industrial networks, it was ideal to both share resources within the territory and fully consolidate transportation fleets. Doing so reduced transportation distances by more than 3%, and the number of trucks needed by 21%. Despite the fact that the success of wood processing plants originating from mixed forest environments is influenced by the success of other plants within the same network, examples of successful partnerships in such networks are rare. This article therefore highlights the impact of collaboration on forest supply networks harvesting mixed forests, opening the door to future research focusing on a real case study.
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 | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".