MegaFlorestais 2024 Meeting Synopsis: The Evolution of Forest Management on the Path to 2030
Bibliographic record
Abstract
Since 2006, forest agency leaders from the most forested countries in the world have been gathering for annual meetings under the MegaFlorestais banner. This group of top forest agency leadership manages more than 50 percent of the world’s forests and is uniquely positioned to influence and accelerate change within the forestry sector. This year, leaders gathered in the state of Pará in northcentral Brazil. It was hosted by the Brazilian Forest Service, led by Director Garo Batmanian; MegaFlorestais co-chairs Sally Collins and Herman Sundqvist; and RRI President and Coordinator Solange Bandiaky-Badji. The meeting’s goals were well delivered in the Brazilian Amazon as we considered the progress and challenges of elevating the role of community-led conservation, preventing forest loss, and promoting restoration and reforestation around the world. Forest agency leaders and resource advisors participated from Australia, Brazil, Canada, China, Democratic Republic of the Congo (DRC), Republic of Congo, Indonesia, Nepal, Peru, Sweden, and the United States.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.096 | 0.034 |
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".