Quantifying total sulfide content of cores and cut-rock surfaces using thermal infrared reflectance
Bibliographic record
Abstract
Abstract In this study, core samples of sulfide-rich zones were delineated from their host rock and a quantitative estimation of sulfide content on cut-rock faces was made using thermal infrared reflectance (TIR; 3.0–30μm region) spectroscopy. Core sections and rocks were collected from mines in the Sudbury basin, Ontario, Canada. The TSC% (areal percentage of total sulfide content) of each sample was then estimated by summing the modal abundance of all sulfide minerals (chalcopyrite, pyrrhotite, and pentlandite). TIR at 1319cm−1(TIR1319) is linearly related to TSC%. The average of the spectral ratios TIR995/TIR1319 and TIR1180/TIR1319(TIRratio) is related exponentially to TSC%. The coefficients of determination (R2) based on a calibration data set are 0.90 and 0.87, respectively. Two models were tested, the TIR1319 model and the TIRindex model; the latter combines TIR1319 and TIRratio. The value for R2 for predicted TSC% versus observed TSC% (from thin sections) is 0.98 and 0.97, respectively, with little or no bias. The standard deviation of the residuals is 3.1 and 3.5 TSC%, respectively. The TIR1319 model is preferred over the TIRindex model because it is linear and therefore does not have the problem with insensitivity at low TSC% that the exponential TIRratio model has. In addition, from the viewpoint of developing an instrument for automated core logging, the TIR1319 model requires measurements at only one wavelength rather than three and hence requires a less expensive instrument.
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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 | 0.000 | 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 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".