Embracing sustainability in public-owned forest resources management: Lessons learned and perspectives
Bibliographic record
Abstract
The growing interest in sustainability has pushed governments to elaborate new legislations and orientations and, in turn, has led companies and organizations to consider sustainability goals in their strategies, operations management, and decision-making processes. This is the case in natural resources supply chains, and particularly in forest products supply chains, where the row resource is publicly owned but exploited by private companies. While public owners are responsible for the sustainable management of the resource according to the three sustainability pillars, industrials tend to focus on the use of the allocated resources to maximize their utility, mainly from an economic perspective. The key question is how to rethink the whole public owners/industrials relationship to ensure consensual and satisfactory solutions for all the stakeholders. In this context, this paper reviews and analyzes sustainable supply chain planning approaches proposed in the forestry planning literature and, based on the model observed in the forestry sector in the province of Quebec, Canada, proposes and analyzes a new, performance-based approach to overcome its challenges and facilitate sustainability integration in the forestry planning processes.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.013 |
| Scholarly communication | 0.013 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".