Chemistry of surface water, precipitation, throughfall, leaves, sediment, soil, and air near a gold mining region in <scp>P</scp>eru
Bibliographic record
Abstract
Artisanal and small-scale gold mining (ASGM) is the primary global source of anthropogenic mercury (Hg) emissions and a large source of landscape change. ASGM occurs throughout the world, including in the Peruvian Amazon. This data set contains measurements of surface water, precipitation, throughfall, leaves, sediment, soil, and air samples from across the Madre de Dios region of Peru, in locations near and remote from ASGM. These data were collected to determine the fate and transport of Hg across the landscape. Samples were collected in 2018 and 2019. Data predominantly included total Hg and methyl Hg concentrations in surface water, precipitation, throughfall, leaves, sediment, soil, and air. Additional water and soil parameters were also measured to better characterize their chemistry. There are no copyright restrictions; please cite this data paper when the data are used in publication.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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 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".