X‐ray radiation monitor for measuring solids content in fluid fine tailings
Bibliographic record
Abstract
Abstract The extraction of bitumen from oil sands produces fluid fine tailings (FFT) consisting mainly of water, sands, clay, and residual bitumen. Generally, devices with radioactive sources are used to measure the variation of FFT density or solids concentration inline, but to date there is no suitable device for in situ monitoring in tailings storage facilities such as large tailings ponds. In this study, an alternative method using high‐resolution spectrometry based on a low radiation intensity source and a cadmium telluride (CdTe) detector was used to measure the solids content in tailings samples based on X‐ray attenuation. The radiation source used in the experiment was a 1 μCi 133Ba. GEANT4, a Monte Carlo–based simulation code that calculates the transmission of radiation through matter, was used to simulate the results of this study and build calibration curves that can determine the solids content concentration based on measured sample composition. Experiments and simulations were performed on various concentrations of both actual FFT samples from tailings facilities and kaolin as a model material. Good agreement between the experimental and simulation results was observed, paving the way for a potential real‐time solids content measurement system that could be deployed over large areas to measure the settling of FFT in tailings ponds.
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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) | 0.001 | 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".