Traditional and novel geochemical extractions applied to a Cu–Zn soil anomaly: a quantitative comparison of exploration accuracy and precision
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
ABSTRACT Partial extractions have been employed and promoted as geochemical exploration tools, but assessment of performance is infrequently undertaken. Minimum probability statistics were used to quantitatively compare twelve extractions and document the level of exploration precision and accuracy. These methods were valuable in comparing the different digestion methods, identifying the best performance level and determining an appropriate geochemical threshold for future exploration. Eight ‘conventional’ reagents were tested, namely ‘four-acid’, aqua regia, bacterial leach (LocatOre ® ), Genalysis ® proprietary leach (TL3), cold hydroxylamine hydrochloride, Mehlich I reagent, hydrogen peroxide, deionized water, together with four new digestible digests (Coke ® , Pepsi ® , Diet Coke ® and a Tempranillo red wine). These tests involved analysis of thirty ® leach and the hydroxylamine hydrochloride leach performed the best in terms of both accuracy and precision. The new extractions were particularly effective for Cu, whereas the hydroxylamine hydrochloride leach was best for Zn. The Coke ® , Pepsi ® and Diet Coke ® extractions exhibited some buffering effects. In contrast, the Tempranillo wine is exceptionally well buffered and the most robust of the new digestions. Results indicate that even over marginally anomalous soils, many partial extraction techniques will confidently identify the location of mineralization. The use of expensive and proprietary procedures may not produce any better result than using standard reagents or even common beverage solutions as soil extraction reagents.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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