Bibliographic record
Abstract
The ultrasonic pulse velocity (UPV) method has been widely used as a nondestructive testing (NDT) in Civil Engineering to evaluate the quality of concrete, soils, and other materials.However, this method has never been used to evaluate the properties of the oil sands tailings.The oil sands tailings have slow consolidation, low density and stiffness, which often lead to the continuous accumulation of the FFT; leading to reclamation problem due to lack of strength to support the FFT .The main objective of this thesis is to assess whether the UPV method can be used as an on-site measurement application in the oil sand tailings in order to detect changes and trends in tailings' properties such as strength, density and structuration as a function of time.The ultrasonic equipment is calibrated, verified, and then used to capture P-waves and S-waves when the waves interact with the column filled with flocculated fluid fine tailings (fFFT).To increase the reliability of the UPV test, special "holders" were designed and fabricated to improve the stabilization of the piezoelectrical transducers.Inferred wave velocities and changes in wave attenuation determined from the p-wave data showed correlations with the expected changes in density in the column; data using s-wave or p-wave-s-wave combinations did not show useful trends.iiiDedication To my parents, sister and brother I cannot thank you enough for your countless supports, understanding and believing in me.I am grateful for the sacrifices you made to get me where I am today.Coming to Canada to
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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) | 0.001 | 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".