Discrimination between diffuse and point sources of arsenic at Zimapán, Hidalgo state, Mexico
Bibliographic record
Abstract
There are two principal sources of arsenic in Zimapán. Point sources are linked to mining and smelting activities and especially to mine tailings. Diffuse sources are not well defined and are linked to regional flow systems in carbonate rocks. Both sources are caused by the oxidation of arsenic-rich sulfidic mineralization. Point sources are characterized by Ca-SO(4)-HCO(3) ground water type and relatively enriched values of deltaD, delta(18)O, and delta(34)S(SO(4)). Diffuse sources are characterized by Ca-Na-HCO(3) type of ground water and more depleted values of deltaD, delta(18)O, and delta(34)S(SO(4)). Values of deltaD and delta(18)O indicate similar altitude of recharge for both arsenic sources and stronger impact of evaporation for point sources in mine tailings. There are also different values of delta(34)S(SO(4)) for both sources, presumably due to different types of mineralization or isotopic zonality in deposits. In Principal Component Analysis (PCA), the principal component 1 (PC1), which describes the impact of sulfide oxidation and neutralization by the dissolution of carbonates, has higher values in samples from point sources. In spite of similar concentrations of As in ground water affected by diffuse sources and point sources (mean values 0.21 mg L(-1) and 0.31 mg L(-1), respectively, in the years from 2003 to 2008), the diffuse sources have more impact on the health of population in Zimapán. This is caused by the extraction of ground water from wells tapping regional flow system. In contrast, wells located in the proximity of mine tailings are not generally used for water supply.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".