Investigating casi Responses to Different Levels of Tailing Oxidation: Inco Copper Cliff Tailings area, Northern Ontario, Canada
Bibliographic record
Abstract
Sulphide-rich tailings cause acid mine drainage (AMD) when they are in contact with oxygen and water. AMD can contaminate surface and ground water, which in turn can degrade the quality of the environment. Knowledge of the progress of oxidation and also the spatial distribution of tailings materials are of considerable importance when developing programs to provide effective control of tailings sites. In this paper, the potential of Compact Airborne Spectrographic Imager (casi) data for providing information on variations in the oxidation of tailings is investigated. Spectral-mode casi data were acquired on August 19, 1998 in 72 bands covering the wavelength range of 400 nm to 950 nm. Twenty-one tailings samples were collected concurrently with the casi overflight. Reflectance was measured at all the sample locations using a GER 3700 spectrometer. Sample spectra from the GER were matched with the minerals from the USGS spectral library. Spectral matching revealed that the most likely minerals on this site are pyrite, pyrrhotite, jarosite, and goethite. Image spectra extracted from the casi data over the same sample locations were also matched to the USGS spectral library. Similar results were found in spite of the limited spectral coverage of the casi. Spectral unmixing of a test site within the casi imagery demonstrated the potential of this instrument to provide information on separating the zones of varying oxidation in the tailings. However, no direct relationships were found between the oxidation phases and the age of the tailings.
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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 | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 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".