Ore detection and grade estimation in the Sudbury mines using thermal infrared reflectance spectroscopy
Bibliographic record
Abstract
Abstract This pilot study investigated the usefulness of thermal infrared reflectance (TIR) spectroscopy to estimate ore grade in an underground environment and to separate ore-bearing samples from their host rocks. Work was carried out under laboratory conditions to test the initial concept; all samples had naturally broken faces to mimic the situation in a freshly blasted underground opening. A total of 26 samples, including massive and disseminated ores, were collected from eight mines around the Sudbury basin in Ontario. Rock surfaces were measured wet and dry to address environmental conditions encountered underground. To separate barren rocks from ores and for ore-grade estimation, an important finding of this research is that, in the region of 1319 cm−1, most known silicate minerals converge to a common reflectance minima (< 1.5%), but massive and disseminated sulfides have distinctly higher reflectance. Individual sulfide minerals (chalcopyrite, pyrrhotite, pentlandite), however, do not reveal diagnostic features in this spectral region. When sulfides are disseminated in the host rock, the average reflectance of the rock increases but the correlation with abundance is not systematic. However, sulfide concentration as a function of continuum-removed reflectance (CRR) is systematic. The empirical correlation between CRR at 1319 cm−1 versus the total sulfide concentration, estimated via thin-section point counts, gives a coefficient of determination value (R2) of 0.93 for measurement of dry and wet surfaces when averaged. Similar results are observed when dry and wet locations are analyzed separately. The relationship demonstrates the feasibility to estimate total sulfide concentration from TIR reflectance data even when samples are wet.
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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".