Mineral potential evaluation based on airborne geophysical data
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Bibliographic record
Abstract
A prediction model, based on the differences between the distribution functions of geophysical survey data from mineralised and non-mineralised areas, has been developed to identify exploration targets. Exploration for mineral resources is conducted in a step-by-step approach, and the proposed prediction model is applicable to each of these steps. Empirical probabilities of discovering new deposits are estimated by a cross-validation technique. The cross-validation technique is central to the proposed methodology.The first study area, located in north-east Guinea, West Africa, covers 46000 km2. Magnetic and radiometric data from a 1 km line spacing airborne survey were used to create a mineral prediction map for lateritic-type gold deposits. This can be considered a reconnaissance study to identify further exploration areas likely to contain undiscovered deposits. These areas should be small enough to carry out further geologic study. From the prediction model, we have identified target areas, covering approximately 2300 km2 or 5% of the study area. We expect that the target areas contain 55% of all undiscovered lateritic gold deposits in the study region. The best prediction results are obtained when the total magnetic field, potassium abundance and uranium to thorium ratio data are used.The second example is from the Bathurst Mining Camp in New Brunswick, eastern Canada. Geophysical data are from a high-resolution helicopter-borne magnetic, electromagnetic and radiometric survey flown during the summer of 1995. This prediction study can be considered as a second step after a first reconnaissance study. The area covers approximately 4100 km2 and many volcanogenic massive sulphide (VMS) deposits are found in this region. The best prediction results are obtained using magnetic and electromagnetic data only, without radiometric data. The target areas delineated in the prediction map cover 41 km2 or 1% of the study area. From the cross-validation analysis these target areas are expected to contain 40% of all undiscovered VMS deposits in the Bathurst Mining Camp.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it