Impact of independent and non-independent parameters on various elements’ rejection by nanofiltration employed in groundwater treatment
Bibliographic record
Abstract
Nanofiltration is one of the widely used robust methods in water sweetening throughout the world. This work highlights the comparison of the impact of independent and non-independent parameters on element rejection to treat groundwater of a region located in Qazvin province, Iran. A pilot-scale FILMTEC-NF90-4040 nanofilter membrane was applied. Samples were collected from three various wells and the volume of 50 cc was tested, each of which was tested three times, and then cations and anions were measured in raw water and treated water by ion chromatography. The data collection and operation of the system was performed by the 761 compact IC version 1.1 software where statistical analyses were performed with SPSS, version 19. Results showed that the degradation efficiency of divalent ions was greater than that of monovalent cations. The content of ion rejection in the nanofilter membrane was not an independent phenomenon. For instance, the results revealed that the presence of high magnesium ion is very effective in disposing sodium ion. At the same time, the rejection rate of ions does not always follow the Van’t Hoff series. Optimal pressure was determined to be 10 bar.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".