Successful application of ground‐penetrating radar in the exploration of gem tourmaline pegmatites of southern California
Bibliographic record
Abstract
ABSTRACT Application of ground‐penetrating radar has been successful in delineating gem‐bearing zones in the Himalaya pegmatite mine of the Mesa Grande district of southern California. The high frequency of the electromagnetic signal allows features as small as a few centimetres to be resolved within 1–2 m of the surface of a mine wall. Careful initial set‐up consisted of: (i) selection of antennae with sufficiently high central frequencies; (ii) recording with a short time of scan to reduce end‐of‐scan noise levels; (iii) choosing appropriate colour schemes to highlight extreme amplitude variations. Operation during data collection consisted of pre‐painting marking points on the mine face and air launching the signal to reduce false anomalies caused by rocking of the antenna on the rough surfaces. Data processing using the Hilbert transform provided images of the cavity geometry that were then used by the blasting captain for accurate placement of explosives. The instantaneous frequency plot was found to be effective in distinguishing air‐filled from clay‐filled pockets, and the instantaneous phase plot was helpful in selecting potential targets where the amplitude was less than the maximum range. When carefully used in conjunction with good knowledge of the geological conditions, the method promises to provide an important tool for mapping internal structures of pegmatites, thus assisting future mining activities.
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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 | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".