The Mandatory Forest Certification Scheme as a Tool for Sustainable Forest Management in Russia
Bibliographic record
Abstract
The Certification Law in the Russian Federation regulates both voluntary and mandatory forest certification. The Mandatory Forest Certification Scheme (MFCS) was developed observing the principles, criteria and indicators of the Helsinki and Montreal processes, as well as the Russian list of criteria and indicators. Also the principles of the Forest Stewardship Council and the International Organization for Standardization Standard 14001 were used as reference. The scheme has been tested in five regions, and an auditing of a large North-American forest company will be carried out during the summer of 2001 in Karelia. \n \nThe mandatory scheme differs in some respects from the certification systems developed elsewhere. One of the major distinguishing features is that the set of criteria are presented in the form of 24 normative documents, including the Forest Code. In addition, the applicant of the MFCS certificate is the forest user, instead of the forest owner, which is the state in the Russian Federation. \n \nThe scheme is aimed to cover the ecological, economical, social and cultural aspects of sustainable forestry, and an independent certification body issues the certificate. The scheme includes third party auditing and provides the possibility for the state or public organizations to supervise forest loggings, and request non-scheduled auditing from the Forest Certification Center if deemed necessary. \n \nThe scheme is aimed to complement the Helsinki and Montreal processes by putting the general forest policy into action at the operational level in the leskhozes.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".