Prediction of water content and normalized evaporation from oil sands soft tailings surface using hyperspectral observations
Bibliographic record
Abstract
The paper addresses the challenge of measuring water content and evaporative fluxes from oil sands soft tailings surfaces using hyperspectral observations. Hyperspectral time-series laboratory observations were collected from four different mature fine tailings (MFT) samples displaying variations in swelling potential and bitumen concentration. The samples were allowed to evaporate from an initial state of water saturation to an air-dried state. From these data, several spectral features were evaluated to predict water content and normalized evaporation rate from the optically sensed portion of the tailings surface (less than a few hundred micrometres). For the samples tested, the best estimate of moisture content was achieved using the normalized soil moisture index (NSMI) index (coefficient of determination R2 = 0.97). The absolute reflectance at 1920 nm was found to be the best spectral estimator of normalized evaporation (R2 = 0.97), with the NSMI index also being valuable (R2 = 0.95). In both instances, the NSMI index may be of value for estimations attempted in the field. Remote estimation of moisture content and evaporation could help tailings managers assess the drying process to determine when the deposit has stopped drying at the surface and decide when the next lift should be deposited. In future efforts, the models obtained from this laboratory investigation will be assessed for their applicability in field settings and validated using concurrent sampling.
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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 | 0.000 | 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 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".