Using Performance Indicators to Evaluate an Environmental Education Program in Artisanal Gold Mining Communities in the Brazilian Amazon
Bibliographic record
Abstract
The Tapajos River Basin in the Brazilian Amazon is the location of one of the largest concentrations of artisanal and small-scale miners in the world. Today, 40,000 miners produce 8 t of gold annum(-1) and at least double this amount of mercury is released into the environment. This region was selected under the Global Mercury Project, a United Nations program that aims to reduce the environmental and health impacts caused by mercury through the application of cleaner technologies and increased awareness. A group of educators provided support to miners, training 4200 people in 141 mining locations. The effectiveness of this training was evaluated based on 20 performance indicators. After 120 days of training, an absolute improvement of approximately 29% had been achieved. As a result of this training, it is estimated that annual mercury emissions in Tapajos have been reduced by 1762 kg or around 10% of the total mercury released in the region.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".