FSC forest management certification analysis in Lithuaniua and Russia
Bibliographic record
Abstract
First time name of certification were mentioned 1990s concerning a problems with bad forest practices, hard improvement of governmental regulations especial in tropics. Later this concern were growing to 1992 Rio de Janeiro conference. And so, need of strict forest system in 1993 established Forest Stewardship Council (FSC). Main activities started later 1996 in Canada with small group of people which started developing countries regional standards (Claros, 2009). Now FSC program is one of the biggest forest certification and accreditation providing company providing wood and their products and certification service. This program supports LEED Lumber, IKEA, biggest companies buying wood in the world, non governamental organisations World wild Fund (WWF), Green peace (www.fsc.org).\nThe curiosity of how FSC forest certification impact forest management in Lithuania and Russia and lack of FSC standard studies with national law encouraged to create such study. We want to analyze FSC certification annual public reports raised CAR’s (Corrective action request) from Forest Management Units (FMU) - enterprises, leaseholders in Lithuania and Russia. The first aim was to find, what main CAR’s in Lithuania, Russia and distribute CAR’s to environmental, economical, social type aspects. In later stages analyze Lithuanian and Russian FSC standards Smart Wood, SGS Qualifor and Russian national. In the last step to compare FSC standards with state law for each country.\nAnalysis of... [to full text]
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".