The Estimation of Mineralized Veins: A Comparative Study of Direct and Indirect Approaches D. MARCOTTE and A. BOUCHER*
Bibliographic record
Abstract
Abstract — The accepted practice for the estimation of thin (2D) vein deposits recommends the use of the grade x thickness service variable (i.e., the accumulation). Grade estimates are obtained indi-rectly by the estimated accumulation/estimated thickness ratio. This practice stems from the varying support (thickness) problem and the resulting non-additive nature of the grade variable. We compare the actual performance of the direct grade estimation approach used by some practitioners to that of the indirect approach using accumulation. Our simulated and real data indicate that the direct approach is more accurate for point grade estimation where the grade-thickness correlation coeffi-cient is positive (and vice-versa). Moreover, the relative gain of the direct method increases with the (positive) correlation coefficient. This finding contradicts common thinking that the indirect approach should be the preferred method where grade-thickness correlation is strongly positive. Also, for a given positive grade-thickness correlation, the relative gain of the direct method increases with the coefficient of variation of the grade and thickness. © 2003 Canadian Institute of Mining, Metallurgy and Petroleum. All rights reserved. Résumé — L’estimation de veines minces (2D) minéralisées est normalement réalisée à l’aide de la variable auxiliaire épaisseur x teneur (i.e., accumulation). Les estimés des teneurs sont alors obtenus
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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 | 0.006 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".