Classification of water for production using parameters in real time
Bibliographic record
Abstract
In this paper, a new classification method for production water is proposed, based on so real-time measured parameters. The classification method consists of three steps: 1) An initial classification of the Water Quality Index is computed using the method proposed by KUMAR; 2) Feature selection based on random forest (specifically based on the method varSelRF); and 3) Training of classifiers using different configurations of heuristic decision trees. A total of 4 datasets (5090 instances of 8 features each) representative of water samples from Portugal, Canada, Mexico, and Romania were used for method validation. The dataset was group in two families of different classes: binary (good and regular water) and multiclass (good, regular and bad water). Final classification accuracy reached 94.85% for the binary family and 91.73% for the multiclass family. The contribution consists of a continuous monitoring system to detect (in real time) dramatic changes in water quality and provide tools for historical studies behaviour in strategic points.
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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 | 0.000 | 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 | 0.000 |
| 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".