The Utilization of Quality Control Chart to Examine the Chemical Properties and Bacterial Water in Al-Dlail Area in the Zarqa Governorate
Bibliographic record
Abstract
This study focuses on the amount of chemical properties and microbiology (T.D.S ,T.H,MPN) into the water provided by the environmental health from the private wells Al-Dlail laboratories in a Zarqa Governorate. The research problem focuses on the chemical properties and microbiology (T.D.S, T.H, MPN) of the amount of the water provided by the environmental health of the private wells of Al-Dlail laboratories in a Zarqa Governorate. The research object was to develop the necessary treatments for reducing the presence of unauthorized descent from T.D.S, T.H and MPN.The analytical research methodology was adopted for the case study analysis to achieve the purpose of this research which was to develop necessary treatments to reduce the presence of unauthorized descent from T.D.S, T.H and MPN. To achieve the study objectives, the study uses (Minitab). Based on the statistical analysis, the main results are:The Control Limits for the Total Dissolved Solid (T.D.S) and the Total Hardness (T.H) are within acceptable limits In the Control Limits for the Most Probable Number (MPN), some readings are beyond the extent of control. Also, there are two samples (15,16) that breach the specification and the bacterium Bacillus colon(MPN) limits, which means exceeding the allowed percentage of the limit.The researcher concluded that the Control Limits for the Total Dissolved Solid (T.D.S) are within acceptable limits and the Control Limits for the Total Hardness (T.H) are within acceptable limits. The researcher recommends that the international standard must be adopted in water treatment.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".