Comparative analysis of weighted arithmetic and CCME Water Quality Index estimation methods, accuracy and representation
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.
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 | 0.007 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".