High thallium content in rocks associated with Au–As–Hg–Tl and coal mineralization and its adverse environmental potential in SW Guizhou, China
Bibliographic record
Abstract
Very few investigations have dealt with the environmental impact of the highly toxic metal thallium (Tl), and its subsequent dispersion through natural processes and human activities such as mining and farming. This study is focused on high concentrations of Tl in rocks in SW Guizhou, China, that are related to several widely scattered disseminated gold–mercury–arsenic and coal deposits, and a primary Tl deposit within an Au–As–Hg–Tl metallogenic belt of the Huijiabao anticline. The Tl, Hg and As in the Lanmuchang Hg–Tl deposit area are associated with the abundant occurrence of sulfide minerals such as lorandite, realgar, orpiment and cinnabar. Concentrations of Tl range from 100 to 35 000 ppm in sulfide ores, and 39–490 ppm in host rocks. The enrichment of Au, Tl, Hg, As, and Sb in the Yanshang gold mineralized area reflects the occurrence of Au mineralization and its mineral assemblage of Tl–Hg–As–Sb sulfides. Thallium ranges from 0.22 to 16 ppm in Au ores and host rocks. Thallium in coals is enriched up to 46 ppm within the Au–As–Hg–Tl metallogenic belt, and is derived from the regional Au–As–Hg–Tl mineralization. Mercury and As show a similar distribution to Tl with high concentrations in sulfide ores, coals and host rocks. Human populations living near and downstream of Tl deposits and Tl-bearing ore deposits are susceptible to Tl contamination because of its high toxicity and high uptake rate by crops. The dispersion of Tl, Hg and As associated with the primary mineralization of Au–As–Hg–Tl can be traced through physical erosion and chemical weathering, producing secondary dispersion into soils, groundwater and surface water and crops. Mining activities compound the natural processes, readily dispersing Tl into the surface environment. The Lanmuchang area illustrates Tl contamination related to a Tl-rich deposit due to both natural processes and the impact of mining. The Yanshang area demonstrates Tl contamination related toa Tl-bearing gold deposit, caused by natural processes in the absence of mining activity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 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) | 0.000 | 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 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".