Numerical transformation of geochemical data: 1. Maximizing geochemical contrast to facilitate information extraction and improve data presentation
Bibliographic record
Abstract
Data transformation in geoscience has typically been motivated by three objectives: (1) creating normally distributed data; (2) creating data that are additive; and (3) making errors constant across the range of the data. Historically, transformation of geochemical concentrations has been undertaken to achieve normality. Unfortunately, most geochemical distributions are multi-modal and derived from several geological sources. Thus no continuous, monotonic transformations exist that can convert these into (even approximately) normal distributions, and thus transformation for this purpose is neither generally achievable nor justified. Transformations that create additivity are rare in geochemical applications, although they are important in error treatment and lithogeochemical data analysis. These transformations effectively convert data into a form that can be sensibly manipulated, and thus facilitate subsequent data analysis. Transformation to stabilizing errors in geochemical data is also not common, although it is a useful attribute in subsequent geochemical data analysis. Another type of data transformation, designed to maximize geochemical contrast (or maximize data variance), may be achieved by raising geochemical concentrations to a power after transforming the data to the 0 ↔ 1 interval. The power that produces the maximum variance in the transformed result creates the maximum geochemical contrast, affording the geochemist an opportunity to extract the most information from the geochemical data. The ‘maximum data variance’ transformation is based not on the subsequent data analysis result (e.g. recognizable geochemical patterns; circular reasoning where ‘the end justifies the means’), but on an optimal property created by the transform. As a result, this transformation provides significant advantage in subsequent data analysis because results achieved are not subjective.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.006 |
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 source (direct Gemma or distilled Codex), 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".