Applying of water quality indices methods for assessment of 9-Nissan Water Treatment Plant
Bibliographic record
Abstract
In this research different methods for measuring water quality indices were conducted to investigate the performance of the newly designed, constructed and operated 9-Nissan water treatment plant, Iraq. Data gathering and implementation took place throughout winter and summer. Water samples were taken periodically, according to the standard method, the re-search was carried out by collecting different random samples for eight months (Jun. 2015–Jan. 2016) and measuring (tur-bidity, total hardness, pH, total dissolved solids, suspended solids, Cl–, Mg2+, Fe2+,NO3–, NH3+) for each sample. Five dif-ferent approaches and methodologies of calculating the water index were applied. The results revealed that the Water Qual-ity Indices varied from 70.55 to 88.24, when applying Canadian Council of Ministers of the Environment Water Quality Index (CCMEWQI) and British Columbia water quality index (BCWQI) geometric weighted mean respectively. All the results, from the five approaches indicated good water quality, multiple regression analyses were conducted for turbidity, total hardness and suspended solids, they found that these parameters are strongly related to each other and to other pa-rameters.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".