Determining the magnitude of true analytical error in geochemical analysis
Bibliographic record
Abstract
ABSTRACT Geochemical analysis of geological materials introduces errors at virtually every stage of sample preparation and analysis. Determining the actual analytical error (that error introduced during the analysis of prepared sub-samples of geological materials) is commonly difficult because many forms of analysis destroy the sub-sample. As a result, duplicate analysis cannot be undertaken to measure analytical error directly, and analytical error cannot be isolated from sub-sampling error. However, using replicate analyses of sub-samples of two different masses, and solving a system of three equations in three unknowns, the actual ‘analytical’ error can be deduced and distinguished from the sub-sampling error. This provides a means to estimate sub-sampling and analytical error magnitudes and to determine whether increasing sub-sample mass will result in an efficient reduction in overall error in geochemical analyses. It also provides a means to quantify sub-sampling error in reference materials so that they can be properly used in geochemical analysis to monitor and quantify analytical error.
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How this classification was reachedexpand
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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".