Optimizing geochemical threshold selection while evaluating exploration techniques using a minimum hypergeometric probability method
Bibliographic record
Abstract
Hypergeometric statistics have recently been used to establish a quantitative measure of performance for geochemical exploration techniques over known mineral occurrences. Using this method, the effectiveness of new exploration techniques can be compared objectively with conventional approaches. Sample site classification is the basis on which survey results are compared. This performance measure requires prior knowledge of the location of mineralization and a model for element dispersion from the primary into the secondary environment. This information allows assignment of sample sites that should give ‘anomalous’ (e.g. those overlying mineralized zones) or ‘background’ results to be identified prior to the orientation survey. Previous application of hypergeometric statistics requires that a high contrast exists between ‘anomalous’ and ‘background’ subpopulations in the geochemical orientation data so that there is no uncertainty in the classification of samples. In this paper, a refinement is developed that allows consideration of geochemical variables that do not exhibit this required high level of geochemical contrast, that is, where ‘anomalous’ and ‘background’ subpopulations exhibit significant overlap. This refinement involves determining the hypergeometric probabilities of obtaining the same result at random ( P ( x )) for a range of geochemical thresholds, instead of only one threshold (i.e. the one used to classify anomalous and background samples in cases with high geochemical contrast). At each threshold, different numbers of samples will be classified as ‘anomalous’ and different numbers of ‘anomalous’ samples will occur at ‘anomalous’ sites. As a consequence, the resulting random hypergeometric probabilities will change with threshold level. Using a range of thresholds to classify the geochemical orientation survey results allows identification of the minimum hypergeometric probability (MHP) for the dataset. Using this threshold, the classification of anomalies will bear the least resemblance to what would be expected if the survey results were generated at random. Employing this refined MHP approach, one can simultaneously evaluate the effectiveness of an exploration method, and select the threshold that optimally classifies anomalous and background samples.
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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 | 0.008 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".