<title>Application of complex trace analysis for improved target identification in gem-tourmaline-bearing pegmatites in the Himalaya mine, San Diego County, California</title>
Bibliographic record
Abstract
Gem bearing miarolitic cavities in rare metal, lithium enriched, granitic pegmatites pose challenging exploration targets, as they are not readily identifiable using normal geophysical methods. Application of ground penetrating radar (GPR) has been successful in delineating gem-bearing zones in the Himalaya pegmatite mine in San Diego County, California. Careful setup and data processing, using complex signal analysis, have so far allowed us to distinguish between gem- bearing pockets, non-gem bearing pockets, and barren/frozen dike. Each of us independently hypothesized, in 1995, that GPR could be an appropriate tool for gemstone exploration in the subsurface exposures in adits, drifts and stopes of many gem mines (Patterson, 1996; Cook, 1997). The culmination of these efforts was reached in June, 1998, with the first documented discovery of gem tourmaline pockets using this technique.
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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) | 0.002 | 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".