Quantitative assessment of the success of geochemical exploration techniques using minimum probability methods
Bibliographic record
Abstract
Hypergeometric statistics have been used to establish a quantitative measure of performance for geochemical exploration techniques over known mineral showings. An alternative and complementary measure of exploration performance is geochemical contrast, which determines how convincing or compelling a geochemical result is. Using the identical philosophy employed to assess exploration ‘accuracy’, the Student's t distribution is used to create a quantitative measure of ‘geochemical contrast’. First, thresholds are selected to separate anomalous and background populations. Then, Student's t test statistics for each of these sets of anomalous and background samples are calculated, and the Student's t probability is determined for the highest test statistic. This probability describes the chance that the anomalous and background populations were derived from the same underlying distribution. If this probability is low, then the concentrations of the anomalous and background samples are very different, and high geochemical contrast exists. If this probability is high, the alternative is true. As a result, this approach can be used to quantitatively compare the geochemical contrast of competing exploration techniques. Furthermore, because both of these ‘accuracy’ and ‘geochemical contrast’ measures are probabilities that vary inversely with exploration performance, their joint probability (their product) can be used to collectively rate the performance of exploration techniques.
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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 | 0.028 | 0.117 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".