Traditional and novel geochemical extractions applied to a Cu–Zn soil anomaly: a quantitative comparison of exploration accuracy and precision
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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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".