Particularities of 5-component magnetotelluric soundings application for mineral exploration
Bibliographic record
Abstract
In the application of electroprospecting for mineral exploration, there are few clearly observed trends based on the development of electroprospecting technologies, hardware, software and computer technologies aimed at: a) the increase of electroprospecting application in comparison with other EM methods; b) application of electroprospecting at all stages of the exploration cycle; c) the increase of application of induction electroprospecting methods and, first of all, these which are based on the study of the natural EM field of the Earth (NEMFE). A special role here is played by the method of Broadband Magnetovariational Profiling (BMVP).Three stages in the application of electroprospecting are quite clearly distinguished: a) exploration for new mining provinces according to the distribution of resistivity in the Earth’s crust and upper mantle (the AusLAMP project, a revolutionary idea proposed by Australian scientists; deep MT, scale 1 : 5,000 000 - 1 : 1,000 000); b) exploration for large conductive ore bodies, areas with a prospecting survey square area of more than 100 km2 by airborne geophysics, for areas with smaller size - 5-component AMT on a scale of 1 : 200,000 - 1 : 50,000; c) detailization and support of drilling operations, mapping of veins and dikes - 5-component AMT on the scale 1 : 20,000 - 1 : 5,000 in complex areas with induction and geometric soundings using control source if Induced Polarization is an exploration factor.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".