Bibliographic record
Abstract
The pollution of heavy metals from coal mine spoils piled perennially in diggings soil in Huainan where have been exploited for more than 100 years was studied. Soil samples were collected from 7 sections near spoil heaps from different coal mine region with a variety of mining history. Heavy metals(Cu、Ni、Pb、Zn、Sn、Cr、Co)in samples were analyzed by IRIS Intrepid Inductively coupled plasma-atomic emission spectrometry(ICP-AES) instrument in Institute of Soil Science, CAS, and the certified sample(LKSD-1) from Canada was used for analytical quality control and its results are valid with respect to accuracy and precision. The contribution of heavy metals in soil was mainly caused by the weathering and leaching of coal mining spoils, which show that the soil near heaps were polluted and the content of its metals was also accumulated. The difference of long-term accumulation and mobility of individual metal was clearly illustrated through the gradually reduce trend of their concentration in soil samples from mine districts with different mining history from 100 years to 25 years. Cu、Co、Zn、Ni、Pb appeared stronger mobile from spoils in the research mine area and their contents in several soil sections were more than the first level of national pollution standard of soil, but all metals were lower than the second, Environmental Protection Agency of China. It means that the mobility of heavy metals from spoils transferring to soil is a slow procedure.
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 | 0.001 | 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".