The United States’ Implementation of the Montréal Process Indicator of Forest Fragmentation
Bibliographic record
Abstract
The United States’ implementation of the Montréal Process indicator of forest fragmentation presents a case study in the development and application of science within a criteria and indicator framework to evaluate forest sustainability. Here, we review the historical evolution and status of the indicator and summarize the latest empirical results. While forest cover fragmentation is increasing, the rate of increase has slowed since 2006. Most of the fragmentation in the western United States is associated with changes in semi-natural land cover (e.g., shrub and grass) while most of the eastern fragmentation is associated with changes in agriculture and developed (including roads) land covers. Research conducted pursuant to indicator implementation exemplifies the role of a criteria and indicator framework in identifying policy-relevant questions and then focusing research on those questions, and subsequent indicator reporting exemplifies the value of a common language and developed set of metrics to help bridge the gaps between science and policy at national and international scales.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".