Forest conversions in the United States from a certification and regulatory perspective
Bibliographic record
Abstract
Forest conversions are an important consideration of the forest products sector. Both third-party certifications, such as the Sustainable Forestry Initiative, and government regulation, such as the European Union Regulation on Deforestation Free Supply Chains, require knowledge of conversions in fiber supply regions. Here we develop a new approach to estimate some relevant forest conversion metrics for economic regions of the United States. Across economic regions, forest conversion rates were small. For example, gross annual forest loss to agriculture was <0.044%, gross annual natural forest loss to planted forest was <0.86%, net annual loss in natural forest was <0.41%, and net 10-year forest loss was <1.86%. Our results suggest three major conclusions. First, forest conversions to agriculture are not currently an issue in roundwood producing regions of the United States. Second, natural forest conversions to planted forest are offset by landowners choosing to use natural regeneration methods. Third, there are several economic regions where 10-year net forest loss approaches but does not exceed 1% net loss at p = 0.95. Finer-scale analyses, in these economic regions, will likely be necessary for the forest products sector to ensure compliance with forest certification standards.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".