Numerical transformation of geochemical data: 2. Stabilizing measurement error to facilitate data interpretation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Traditional uses of data transformations in geoscience have typically been motivated by three objectives: (1) creating normally distributed data; (2) creating data that are additive; and (3) making errors constant across the data range. Part 1 of this paper discussed the transformation applicability for the first two reasons in geochemical applications, and introduced a fourth motivation whereby transformation maximizes the variance (or geochemical contrast) in the transformed data; it did not discuss transformation to stabilize measurement error. Although transformation to stabilizing errors in geochemical data is not common, this is a useful attribute in geochemical data analysis. The transformation is dependent on the model describing the magnitude of measurement (sampling and analysis) error as a function of concentration. Models describing measurement error as a function of concentration can be used to derive a transformation that will stabilize the measurement error in the transformed variable. Poisson, binomial and hypergeometric models are typically used to describe sampling errors, whereas straight line (constant, proportional and affine) models are used to describe analytical errors. The associated variance stabilizing transformations, derived from these models, have constant propagated errors. As a result, these transformations create a ‘level playing field’ for subsequent data analysis, enabling the discovery of additional information in the data. Homoscedastic measurement error allows the geochemist to justify use of a specific transformation based not on the subsequent data analysis results (circular reasoning; ‘the end justifies the means’), but on optimal properties created by the transformation. In this way, objective results can be achieved scientifically, providing another motivation to collect geochemical quality control 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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