Prediction and Assessment for Metals and Petroleum, Pergamon Press, Oxford. Statistical Pattern Integration for Mineral Exploration*
Bibliographic record
Abstract
The method of statistical pattern integration used in this paper consists of reducing each set of mineral deposit indicator features on a map to a pattern of relatively few discrete states. In its simplest form the pattern for a feature is binary representing its presence or absence within a small unit cell; for example, with area of 1 km 2 on a 1:250,000 map. The feature of interest need not occur within the unit cell; its "presence " may indicate that the unit cell occurs within a given distance from a linear or curvilinear feature on a geoscience map. By using Bayes ’ rule, two probabilities can be computed that the unit cell contains a deposit. The log odds of the unit cell's posterior probability is obtained by adding weights W + or W- for presence or absence of the feature to the log odds of the prior probability. If a binary pattern is positively correlated with deposits, W+ is positive and the contrast C=W +-W- provides a measure of the strength of this correlation. Weights for patterns with more than two states also can be computed and special consideration can be given to unknown data. Addition of weights from several patterns results in an integrated pattern of posterior probabilities. This final map subdivides the study region into areas of unit cells with different probabilities of containing a mineral deposit. In this paper, statistical pattern integration is applied to occurrence of gold mineralization in Meguma Terrane, eastern mainland Nova Scotia, Canada.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.109 | 0.043 |
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".