Applied science facilitates the large-scale expansion of protected areas in an Amazonian hot spot
Bibliographic record
Abstract
Meeting international commitments to protect 17% of terrestrial ecosystems worldwide will require >3 million square kilometers of new protected areas and strategies to create those areas in a way that respects local communities and land use. In 2000-2016, biological and social scientists worked to increase the protected proportion of Peru's largest department via 14 interdisciplinary inventories covering >9 million hectares of this megadiverse corner of the Amazon basin. In each landscape, the strategy was the same: convene diverse partners, identify biological and sociocultural assets, document residents' use of natural resources, and tailor the findings to the needs of decision-makers. Nine of the 14 landscapes have since been protected (5.7 million hectares of new protected areas), contributing to a quadrupling of conservation coverage in Loreto (from 6 to 23%). We outline the methods and enabling conditions most crucial for successfully applying similar campaigns elsewhere on Earth.
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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 | 0.011 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".