Assessment of water quality in Hawkesbury-Nepean River in Sydney using water quality index and multivariate analysis
Bibliographic record
Abstract
This study has assessed the water quality status of the Hawkesbury-Nepean River (HNR) in Sydney, Australia. It is based on the collected data of 12 water quality parameters at four monitoring stations along the river. HNR is one of the most important rivers in Australia which supplies over 90% of Sydney's potable water for more than 4.8 million people. Canadian Water Quality Index (CWQI) is adopted in this study to summarise the water quality status at the individual stations by categorising the water quality in five divisions; poor, marginal, fair, good and excellent. In addition, water quality parameters are regressed with the calculated CWQI to identify the significant parameters. Based on the calculated CWQI, only one station is found to fall in 'fair' category, two in 'marginal' and one in 'poor' water quality categories. No significant trend is observed in the CWQI for the stations during the period of data collection; however, one station shows slight trend of decreasing water quality. The preliminary results of the regression analysis demonstrate that not all the water quality parameters are significant in explaining the CWQI at the stations. The results of this study are expected to provide useful information for water quality management, and to form the basis for further investigation.
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".