A mixture model approach to analyzing major element chemistry data of the Changjiang (Yangtze River)
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Bibliographic record
Abstract
In this article we study the statistical distributions of major chemical compositions (HCO3, Ca; charges are neglected for simplicity) and the total dissolved solid (TDS) concentration in the river water of the Changjiang (Yangtze River) of China. We propose a Bayesian finite mixture model with an unknown number of components for the multi-year averages of continuously monitored data over the period 1958–1990 at 191 stations in the drainage basin. A discretization-based Monte Carlo sampling approach is used to estimate the posterior distributions of the parameters in the model. Two sub-populations are identified for the levels of TDS, HCO3 and Ca, and observations from the 191 stations are classified into two groups using the posterior classification probabilities. Copyright © 2005 John Wiley & Sons, Ltd.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.002 |
| 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