Criteria and Indicators of Sustainable Forest Management
Bibliographic record
Abstract
This chapter uncovers different issues on SFM. In Section 14.2 , controversies surrounding the definition and application of SFM that led to the development of processes on criteria and indicators (C&I) are presented. The basic elements of C&I that are common to all processes are reviewed, and forest certification is briefly discussed. Then, the main processes that are most applied are reviewed. In Section 14.3 , the pan-European and Montréal Processes for temperate and boreal forests are described, and some examples of application are provided. The processes for tropical forests are covered in Section 14.4 , which include the International Tropical Timber Organization (ITTO), the African Timber Organization (ATO), and Tarapoto processes. In Section 14.5 , the importance of collaboration and cooperation among processes is highlighted. In particular, efforts to promote collaborations among organizations involved in the development of processes and to harmonize the different processes are discussed. The application of C&I must be based on different sources of information and data to ensure that they report the state of forests as exactly as possible. These sources are discussed in Section 14.6 . Regarding data sources, the importance of statistically based forest inventories and the use of remote sensing techniques are reviewed.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".