Mineralogy and spectral reflectance of soils and tailings from historical gold mines, Nova Scotia
Bibliographic record
Abstract
Gold was mined in 64 districts in southern Nova Scotia between 1861 and the early 1940s, followed by limited, intermittent production up to the present. There is extensive dispersion of arsenic- and mercury-bearing mine tailings in the receiving environment downstream from many of these sites. Elevated mercury concentrations, highest near old stamp mill foundations, occur because of the mercury amalgamation process used to extract gold until the 1940s. Arsenic, on the other hand, occurs naturally in arsenopyrite, which is associated with the gold-bearing quartz veins and host rocks. Tailings are composed of fine sand- to silt-sized quartz, feldspar, illite and chlorite, and represent the primary rock-forming minerals in the metasedimentary host rocks of the Cambro-Ordovician Meguma Supergroup. Carbonate and sulphide minerals occur in minor to trace amounts, along with secondary minerals such as scorodite (FeAsO 4 ·2H 2 O). The extent of tailings dispersal can be mapped through hyperspectral remote sensing methods, as these major mineral components provide an identifiable spectral signature through visible, near infrared and short-wave infrared regions. This paper examines the mineralogy of soils, tills and tailings in the Upper and Lower Seal Harbour gold districts of Nova Scotia. Ground-truthing of space-borne hyperspectral data demonstrates the potential for remote mapping of the spatial extent of these historical mine wastes.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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 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".