Forecasting Techniques Applied To Water Quality Time Series In View Of Data Quality Assessment
Bibliographic record
Abstract
The main advantage of continuous water quality measurement systems is the ability to capture dynamics in water and wastewater systems, which allows for the identification of critical events, the evaluation of impacts on receiving water bodies, the identification of cause and effect relationships and the ability to discern trends. However, the challenge associated with automatic monitoring systems is the collection of data with sufficient quality for the intended application. That is, useful monitoring is dependent on cautious data quality assessment. With particular attention to its practical implementation, this paper presents a method for data quality assessment that attempts to extract useful information from individual water quality measurement time series. Based on forecasting techniques that make use of the historical behavior of the data, raw measurements are evaluated for the detection of doubtful data and outliers. Posterior treatment is then applied to remove noise and detect potential sensor faults. The proposed tool has been successfully tested on water quality time series collected from different water and wastewater systems.
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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 | 0.003 | 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 | 0.000 |
| Open science | 0.001 | 0.003 |
| 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 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".