Study on the use of the Indonesian water quality index method, CCME, pollution index and storet in determining water quality status - Case study of the Cirarab River
Bibliographic record
Abstract
Water Quality Index (WQI) is a number without unit to show quality of water body based on the value of several weighted monitoring parameters. Several methods have been developed to calculate the value of Water Quality Index, which are Pollution Index (PI), STORET, Canadian Council of Ministers of Environment Water Quality Index (CCME-WQI) and National Sanitation Foundation Water Quality Index (NSF-WQI). In Indonesia, the method commonly used to determine WQI is pollution index (PI) and STORET. The Indonesian Water Quality Index (WQI-INA) is the latest WQI calculation method developed from NSF-WQI method in 2017 thus providing a weighting value that approaches river conditions in tropical countries. The purpose of this study is to compare WQI values calculated based on WQI-INA method with WQI values that calculate based on Pollution Index, STORET and CCME methods using Cirarab River monitoring data (2015-2018).The Study result indicates that using WQI-INA method gives consistent results in each monitoring location while STORET method gives the same value results even though the monitoring results data is very different. PI method also gives quite different result to WQI-INA value because of range of the PI values is too narrow, so it does not reflect the actual river condition. The CCME WQI method results are the closest to WQI-INA value but require more parameter input rather than WQI–INA. Based on this study, the WQI-INA method is very good to be developed further because it is easy to use and simple but gives good results for WQI assessment.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".