Synchrotron Based TXRF for Assessment of Treated Wastewater
Bibliographic record
Abstract
The use of wastewater for diverse applications is gaining popularity for protecting scarce freshwater resources. The global supply of freshwater is limited and is threatened by the masses. Communities are competing over the allocation of limited freshwater resources to meet the increasing demand for water for agriculture, industry and cities. Wastewater treatment units are being used to treat wastewater for irrigation, firefighting, and other domestic purposes. The environment and human health can be adversely affected if wastewater is not accurately treated. Treated wastewater if free from toxicity can help in preserving the natural environment. In the present work, the synchrotron-based Total Reflection X-ray Fluorescence (TXRF) has been used to assess the trace elements present in the treated wastewater collected from a sewerage treatment plant in the study area. The results are compared with the World Health Organization (WHO) recommended values and concluded that the concentration of all detected elements (Cr, Mn, Ni, Cu, Zn and Pb) are within permissible limits (except iron). Investigations are further incorporated in calculations of the water quality index (WQI) that is used for the treated water standards. The present WQI 82.70 lies in the good quality range 80-94 by Canadian Council of Ministers of the Environment (CCME 2001) standards and does not pose any hazard to the environment, therefore, recommended for irrigation, toilet flushing, firefighting etc.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".