Use of Multi-Channel Gamma-Gamma Logs to Improve the Accuracy of Log-Derived Densities of Massive Sulfides
Bibliographic record
Abstract
In-situ rock densities derived from borehole gamma-gamma (y - y) density measurements may be in error for two reasons: 1) the ratio of atomic number to atomic weight (Z/A) varies for different chemical elements; and 2) photoelectric absorption, formerly neglected in calibration, becomes significant when heavier elements are present. For most rocks, the atomic number of the constituent elements is fairly low (Z 4.0 g/cm 3 have high effective atomic numbers (Z > 26) and the Z/A ratios are appreciably less than 0.5. In these cases, the assumption of a constant Z/A ratio is not valid and the standard density calibration procedures result in densities that are underestimated. The second major problem of density determinations in massive sulfides is that photoelectric absorption considerably perturbs the count rate in the density window that spans the energy range over which Compton scattering is assumed to be the dominant γ-ray interaction with the rock mass. This becomes severe when the sulfides contain a significant percentage of galena (lead sulfide). Lead has an extremely high atomic number (Z = 82) compared to other base metals (Z < 30), and photoelectric absorption, rather than Compton scattering, becomes the dominant interaction in the density window. The Geological Survey of Canada (GSC) has calibrated the density-logging tool for use in high density/high Z sulfide zones. Spectral y - γ density logging data were acquired in several underground boreholes at Brunswick No. 12 mine, New Brunswick. Core densities were also measured in the field on several drill core sections that were selected to be representative of waste rock, massive pyrite and Pb-Zn-Cu sulfide mineralization. A multiple regression analysis of core density against six energy windows covering the γ-ray energy spectrum from 0.05 to 0.5 MeV gave significantly improved density estimates in both the low and high-Z media.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| 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 | 0.002 |
| 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 teacher head, 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".