Forest management certification around the world –Progress and problems
Bibliographic record
Abstract
Certification to approved forest management standards is a recognized business practice. There are two international forest certification programs: the Programme for the Endorsement of Forest Certification (PEFC) and the Forest Stewardship Council (FSC). The common objective of both is to improve forest management and provide assurance to the public and customers that forest products come from sustainably managed forests and not from illegal operations. As of June 2011 there were approximately 372 million ha of certified forests around the world. There are 234 million ha of forests in 26 countries that have been certified to standards approved by PEFC. There are143 million ha of forest certified to FSC standards in 81 countries. In 20 of these countries, with101 million ha (70%), the forests have been certified to standards approved by FSC. In the remaining 61 countries, 42 million ha (30%), the forests have been certified to draft or “interim standards” that have not gone through the FSC approval process. Consumers have no way of knowing whether the wood or paper products with an FSC label are from forests certified to FSC-approved standards or to “interim standards” developed by FSC certification bodies.
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 | 0.023 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.011 | 0.018 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.026 | 0.006 |
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".