Comparison of data classification procedures in applied geochemistry using Monte Carlo simulation
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Bibliographic record
Abstract
In geochemical applications, data classification commonly involves 'mapping' continuous variables into discrete descriptive categories, and often is achieved using thresholds to define specific ranges of data as separate groups which then can be compared with other categorical variables. This study compares several classification methods used in applied geochemistry to select thresholds and discriminate between populations or to recognize anomalous observations. The comparisons were made using monte carlo simulation to evaluate how well different techniques perform using different data set structures. A comparison of maximum likelihood parameter estimates of a mixture of normal distributions using class interval frequencies versus raw data was undertaken to study the quality of the corresponding results. The more time consuming raw data approach produces optimal parameter estimates while the more rapid class interval approach is the approach in common use. Results show that provided there are greater than 50 observations per distribution and (on average) 10 observations per class interval, the maximum likelihood parameter estimates by the two methods are practically indistinguishable. Univariate classification techniques evaluated in this study include the 'mean plus 2 standard deviations', the '95th percentile', the gap statistic and probability plots. Results show that the 'mean plus 2 standard deviations' and '95th percentile' approaches are inappropriate for most geochemical data sets. The probability plot technique classifies mixtures of normal distributions better than the gap statistic; however, the gap statistic may be used as a discordancy test to reveal the presence of outliers. Multivariate classification using the background characterization approach was simulated using several different functions to describe the variation in the background distribution. Comparisons of principal components, ordinary least squares regression and reduced major axis regression indicate that reduced major axis regression and principal components are not only consistent with assumptions about geochemical data, but are less sensitive to varying degrees of data set truncation than is ordinary least squares regression. Furthermore, correcting the descriptive statistics of a truncated data set and calculating the background functions using these statistics produces residuals and scores which are predictable and thus can be distinguished easily from residuals and scores calculated for data from another distribution.
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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 | 0.000 | 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 it