Evaluation of mine water inflow quality based on multiple methods
Bibliographic record
Abstract
To reduce the negative impact of mine water gushing on the environment, it is necessary to evaluate the water quality. Taking the Pingdingshan coalfield as the research object, six components – namely, chroma, turbidity, total dissolved solids, total hardness, chloride (Cl−) and sulfate (SO4 2−) – were selected as index factors. The composite weight was calculated using the variable-weight theory, and the water quality of mine gushing water was evaluated using the matter-element extension model, fuzzy variable set model, Bayesian theory and Nemerow index method. The deviation of the four evaluation methods was calculated based on the specially constructed mathematical model. The research results show that the order of the deviation of the evaluation method from small to large is as follows: fuzzy variable set method (5.5) < matter-element extension method (8) < Bayesian statistical method (10) < Nemerow index method (17). The fuzzy variable set method is more suitable for Pingdingshan coalfield mine gushing water quality assessment. The authors hope that this research can provide a certain reference value for the evaluation of mine water quality in the future.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".