Arrow IFPA Series: Note 2 of 8: Developing criteria and indicators of sustainable forest management in the Arrow Forest District
Bibliographic record
Abstract
This extension note is the second in a series of eight that describes a set of tools and processes developed to support sustainable forest management (SFM) planning and its pilot application in the Arrow Timber Supply Area (TSA). It outlines the development of criteria and indicators (C&I), which focus on explicitly defined goals and an objective means of determining success in meeting these goals. Criteria and indicators are used to evaluate the long-term sustainability of forest management through decision support in planning processes and through monitoring and adaptive management activities. The C&I for the Arrow TSA were based on the Canadian Council of Forest Ministers framework and were refined to address specific local issues through an iterative process that included input and review by professionals, academics, and forestry practitioners, and evaluation by stakeholders. The development process was guided by two directives: that performance-based indicators be emphasized and that these indicators should be credible, measurable, cost-effective, and connected to forestry. The resulting C&I are preliminary—their evolution is shaped by testing and application in forest management planning, and by continuing public review.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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".