Comparative analysis of weighted arithmetic and CCME Water Quality Index estimation methods, accuracy and representation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract This paper aims to investigate and evaluate the difference in the computed WQI using the weighted arithmetic method (WAM) and Canadian Council of Ministers of the Environment (CCME) and the reasons of the exaggeration and permissive of these WQIs. In addition, it also aims to specify the suitable WQI computation method in Iraq. Al-Shula City, Baghdad, Iraq was considered as the case study. The results of estimating the WQI in the Al-Shula City using WA and (CCME) methods for each month fluctuated between 0.103 to 8645 and 8.53 to 58.56, respectively. Hence the WQ fluctuated between excellent to unsuitable for drinking (excellent to poor). However, the range of the computed accumulated WA and CCME WQI was between 8 to 3886 and 9 to 59. Consequently, for the two methods, the class of WQ is fluctuated between excellent to unsuitable and excellent to poor. In addition, the calculated CCME WQIs were always lower than the computed WA WQIs. Therefore, the CCME method is permissive or somewhat lenient in contrast to the WA method. Consequently, the WA WQI is more sensitive to presence of toxic contaminants than the CCME WQI. Therefore, the WA WQI is more suitable to use in Iraq because of the high fluctuation in the level and types of pollution sources.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it