Reuse of Iraqi Agricultural Drainage Water Using Nanofiltration
Bibliographic record
Abstract
Irrigated areas between Euphrates and Tigris rivers in Iraq suffer from salinity buildup in the root zone of corps. Agricultural drain water (ADW) from these areas is collected in a single main drain canal, in an annual flow rate of about 6 billion cubic meter. In the present work, a pilot-scale nanofiltration membranes unit was used to evaluate the feasibility of desalinating ADW from the main drain canal for further reuse. Bench scale experiments were conducted to determine the optimum anti-scale dosage values in the unit. These values were verified in a plate type laboratory scale NF membrane to visually monitor the onset of crystal appearance behavior. A method for calcium sulfate precipitation control is presented and an empirical correlation of anti-scale dosage as a function of concentration factor (CF) was obtained. A pilot-scale unit was used to investigate the performance of NF membrane. High rejection values for both cations and anions indicate that the use of NF membrane in desalinating ADW from the Iraqi main drain canal is promising. The treated drainage water is considered good for irrigation when classified according to Wilcox classification.
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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".