Probabilistic versus Regression Modeling for Disinfection Byproducts
Bibliographic record
Abstract
Probabilistic network approaches, including Bayesian networks (BN) and decomposable Markov networks (DMN) are graphical models in which a problem is structured as a set of parameters and probabilistic relationships between them. Probabilistic analyses have been effectively used to incorporate expert knowledge and historical data for revising the prior belief in the light of new evidence. In this chapter, the probabilistic approach is compared to traditional methods such as regression analysis for water quality predictions. The capabilities and advantages/disadvantages of DMN approach are described. A DMN through machine learning on the basis of historical data from the experiments is constructed. The results indicate that DMN is a better prediction model than multiple regression both theoretically and experimentally for these applications. Drinking water utilities face a challenge in recognizing, characterizing, and responding to potential and actual contamination events, while
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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 | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 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".