Weighted sums – knowledge based empirical indices for use in exploration geochemistry
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
A background to the use of empirical indices (i.e. ratios and sums) in exploration geochemistry is presented, together with commentary on more sophisticated statistical multivariate procedures. Multivariate statistical procedures can assist in developing geochemical models upon which further investigations can be based, and in identifying geochemically anomalous samples. A case is made for a simple method, weighted sums, that is based on prior knowledge concerning the mineralogy and geochemistry of sought-after mineral resources. This procedure avoids many of the complications and pit-falls of more sophisticated multivariate statistical methods. Although weighted sums were introduced to exploration geochemistry over 20 years ago, they don’t appear to have been used extensively. The objective of this paper is to reintroduce them, with a simple but clear exposition, as a tool worthy of consideration in the knowledge-based 21st century.
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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 | 0.002 |
| Open science | 0.001 | 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