Occurrence of Acid Mine Drainage and its Treatment by Successive Alkalinity Producing System (SAPS): An Overview
Bibliographic record
Abstract
Acid mine drainage (AMD) is one of the most important environmental problem faced by some of the coal and many metal mines which is required to be treated and managed in economical and efficient manner.The AMD is harmful for aquatic life and corrodes the pumps, pipes and machineries in the mines.The AMD problem originates from active mines, abandoned mines, coal waste spoils and stripped area.About 40% of the AMD pollution problems originates from active mines both surface and underground and rest from others.The discharged acidic water from the mines is detrimental to environment in general and water quality in particular, because if their high acidity, high metal concentration and high sulfate content.Lots of researches are going on throughout the worlds to tackle and minimize the problems of AMD in coal mines of USA, Canada, Australia and India.Therefore it requires neutralization up to acceptable limit.Now days, various treatment systems are available for treatment of AMD.Active treatment system is costly and requires continuous supervision, whereas passive treatment system is long process.The application of SAPS utilizes the advantage of active treatment system and passive treatment system.In present paper, an attempt has been made to highlight AMD generation and application of SAPS for treatment of AMD.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".