L'aménagement des produits forestiers non ligneux et des ressources ligneuses des forêts canadiennes : besoins d'intégration et de recherche
Bibliographic record
Abstract
Non timber forest products (NTFP) are emerging globally as a tool for the establishment of sustainable forest communities. They provide employment to various sectors of society, draw on local expertise and culture, and increase the outputs of forests. In recent years, NTFP have received accrued interest by the general public, governments and the private sectors of Canada. However, for the NTFP industry to enter mainstream Canadian industrial culture it is now critical to attempt the integration of the timber industry with the NTFP industry to benefit both sectors. NTFP can be harvested from four types of environment: wild stocks from timber-productive forests, wild stocks from non-timber-productive forests or lands, managed stocks from intensively managed forests, and domesticated stocks from agricultural systems. A large body of evidence suggests that NTFP management and harvest can serve the forest industry in many ways. There are four possible types of interaction between the NTFP and timber industries: independent resource use, competition for resources, complementary resource use and symbiotic resource use. Integration of both industries in a sustainable manner will need to be supported with research that address economic, social, policy and ecological questions. Key words: NTFP, sustainability, biodiversity, community forestry
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".