Bitumen content estimation of Athabasca oil sand from broad band infrared reflectance spectra
Bibliographic record
Abstract
Abstract Oil sand is a mixture of quartz grains, clay minerals, bitumen, water, and minor accessory minerals. There is a need in oil sands mining operations for a robust method to estimate total bitumen content in real time; and so modelling of the total bitumen content (TBC) in Athabasca oil sands of Western Canada was undertaken on the basis of hyperspectral reflectance spectra. A selection of different bitumen, water, and clay mineral spectral features (3.0–30.0 µm) was used to develop broad‐band TBC predictive models that have good accuracy, with less than 1.5% error with respect to laboratory methods of bitumen assay. These models are also robust, in that they are independent of mine location. Simple broad band models, based upon previously identified Gaussian features or wavelet features, provide an incremental improvement over the currently deployed industry two‐band ratio model. An improved two‐band model was also developed, which makes use of a combination of the same two bands but normalised to their mean. A wavelet‐based, broad‐band model comprised of indices and five bands, where the bands are normalised to the mean of the bands, adequately addresses the influence of water, clay, and textural variation on selected bitumen features. This five‐band model appears to produce the most robust estimator of TBC, with a dispersion of ∼1.1–1.5%, which can be applied to different sites within a mine and to different mines without additional tuning or calibration, as evidenced by regression slopes of 0.99–1.0 for modelling, validation, and blind data sets.
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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 | 0.001 |
| 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 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".