Mineralogy and geochemical investigation of Cambrian and Ordovician–Silurian shales in South China: Implication for potential environment pollutions
Bibliographic record
Abstract
Rapid expansion of shale gas development in China raises environmental and human health concerns. Several studies present related information on these concerns in the United States and Canada, yet they are few in China at present. This paper presents a series of original and published mineralogical and trace elements data from two gas shales (Niutitang shale and Longmaxi shale) considered as producing gas shale in China. Mineralogical and geochemical data surveyed can be applied to evaluate the potential environment pollutions during shale weathering and hydraulic fracturing. After compilations of the mineralogical and trace elements data, we can conclude that (a) there is generally more pyrite compared with carbonate for Niutitang shales, indicating that Niutitang shales are more prone to generate the hydrogen ions than Longmaxi shales; (b) many of the environmental hazardous trace elements considered here show an association with organic matter and/or pyrite, indicating that these trace elements are prone to mobilize and release during shale weathering and hydraulic fracturing; (c) some environmental hazardous elements like As, Ni, and Ba that have extremely high concentrations in some regions as compared with the screening limits for soil and drinking water should attract more attention; and (d) clay‐rich shale with abundant organic matter may be the most favourable shale gas reservoir for the Niutitang shale, especially for those shales in Lower Yangtze region. Moreover, more mineralogical and geochemical data will be required for a comprehensive environmental impacts assessment.
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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 | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".