Mineralogy and computer-orientated study of mineral deposits in Slocan City Camp, Nelson Mining Division, British Columbia.
Bibliographic record
Abstract
Slocan City mineral deposits are "dry" fissure types (Cairnes, 1934, p.114) consisting of high grade silver veins in quartz, with minor amounts of lead and zinc. These veins, almost all in Nelson plutonic rocks, occur in an area of approximately 100 square miles along the eastern margin of Slocan Lake. Mineralogical analysis revealed a definite concentric zoning in the camp; a pyrite halo with high gold values surrounds a core of galena and sphalerite with high silver values. The most commonly occurring minerals in order of deposition are: pyrite, sphalerite, chalcopyrite, gold, tetrahedrite, galena, silver, ruby silvers, and argentite. Quartz is the dominant gangue mineral, with small amounts of calcite, siderite, barite, and fluorite generally concentrated in the central zone. Publically available production data for 73 mineral deposits, and geological and mineralogical data obtained from field and laboratory studies,were organized in a computer-processible data file. Methods used to investigate the usefulness of such a file for both academic and practical purposes include: computer generated plots and contour maps, correlation studies, trend surface analysis, multiple regression, and chi square analysis. Computer contour plots and trend surface analysis were rapid means of analyzing lateral zoning of average metal grades and ratios. Patterns obtained substantiated the mineral zoning which was based on data from appreciably fewer mineral deposits. Multiple stepwise regression showed that value of a deposit (estimated by total production in tons) is dependent on average grades of lead and zinc, and volume percentage total sulphides. Consequently, the tonnage potential of a prospect might be predictable within specified limits from a single bulk sample and a brief geological examination. Chi square analysis showed that relatively large deposits are characterized by a more-or-less northeasterly strike and the presence of small amounts of barite and carbonate gangue. The ease and rapidity with which proven statistical techniques can be applied to the mass of informal ion in a computer-processible data file gives great scope and practicality to the concept.
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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 | 0.004 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".