Development and testing of a criteria and indicators system for sustainable forest management at the local level. Case study at the Haliburton Forest Wild Reserve Ltd., Canada
Bibliographic record
Abstract
The concept of criteria and indicators (C&I) for sustainable forest management (SFM) requires further development and testing at the local level. Suitable case studies of applying advanced local level C&I are lacking. The study was based on the hypothetical suitability of C&I for defining, measuring, assessing and monitoring sustainability. The research objectives were to develop a C&I system, which consists of an optimal and minimum generic C&I set for the local level, which has applicability for the SFM of temperate forests internationally, and can be utilized as an adaptive management system. This case study took place at the Haliburton Forest & Wild Life Reserve Ltd. in Ontario, Canada. The selected forest of approximately 24,000 hectares is highly suitable because of its multiple use and nature-oriented forest management system. The methods applied for the development and testing of the C&I system included: the development of the optimal and minimum generic set of C&I for SFM at the level; the identification of verifiers and norms; the testing of the generic C&I set as a case study; the development of indicator measurement databases including a geographic information system; the assessment of the state of the forest and its management; and the application of adaptive management procedures. The generic C&I set consists of four principles, 16 criteria and 58 indicators. The C&I system consists of a sequence of generic modules which can be applied internationally in temperate forests while specifying and addressing local conditions. These modules are: the formulation of goals and objectives; the identification of local forest management standards; the application of the generic C&I set; the assessment of C&I performance; and the application of adaptive management procedures. The Haliburton Forest shows a very good sustainability assessment result with a sustainability assessment score of 89.9%. 41 indicators out of 58 show a positive, the remaining 17 indicators a neutral sustainability trend. The C&I system for SFM at the local level is suitable for defining, measuring, assessing and monitoring the sustainability of forest management. This study contributes to the definition, promotion, implementation and evaluation of SFM at the local level internationally.
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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 | 0.009 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".