Estimation of Censored Data Water Quality Values Using Decomposable Markov Networks
Why this work is in the frame
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Bibliographic record
Abstract
While application of probabilistic inference modeling to large and complex datasets has been limited both as a result of computational difficulties, and implicit/explicit assumptions of normality and lognormality, an alternative is developed herein, based on advancements in graphical modeling using decomposable Markov networks (DMNs). Uncertainties in estimates for censored and/or missing data, are reduced by quantifying dependencies among quality attributes using DMNs. The dependence structure is modeled by a DMN, and established using training data. The improvement from learning DMNs employing the training data is demonstrated using water quality information from water distribution systems. The approach provides a general alternative to traditional techniques for estimating values for censored data.
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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 | 0.002 |
| 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 it