Estimation of methylene blue index in oil sands tailings using hyperspectral data
Bibliographic record
Abstract
Abstract Clay minerals constitute a significant fraction of oil sands tailings; and clay mineralogy, abundance, and morphology can dramatically affect the strength of the tailings during consolidation. The ability of clays to expand or swell in the presence of water is of importance in establishing the post‐depositional strength and the geotechnical stability of a tailings deposit for reclamation purposes. The surface activity of oil sands tailings solids is typically determined using the methylene blue index (MBI), an analytical technique that quantifies the ionic absorption capacity of a sample. The present work explores the use of short‐wave infrared (SWIR) and long‐wave infrared (LWIR) hyperspectral observations for the estimation of the swelling potential or activity of oil sands tailings indicated by MBI. Spectral features in reflectance spectra were characterized in the SWIR and LWIR, in particular those attributed to the presence of quartz and clays. These features were employed to assess the correlation between MBI and remote sensing reflectance measurements collected in the laboratory from air‐dried tailings. In the SWIR, a band ratio of reflectance at 2.111 and 1.992 μm was highly correlated with MBI. For the potential estimation of MBI in outdoor settings, where the intervening atmosphere can impact the spectral measurements, a band ratio of reflectance at 1.773 and 1.307 μm provided an estimation of MBI when moisture of tailings did not exceed 0.20 g/g (20 wt%). The best predictions of MBI were obtained in the LWIR using the reflectance peaks at 9.67 µm and 11 µm attributable to clay minerals.
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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 | 0.000 |
| 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 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".