Transforming Waste Oil Sand Ash into Construction Material Using Net-Zero Energy
Bibliographic record
Abstract
The aim of the chapter is to measure the potential of transforming waste oil sand ash into construction materials using net-zero energy. For this purpose, waste oil sand ash was collected from a manufacturer in Canada. In order to test the effectiveness of the proposed method, waste oil sand ash was collected from a manufacturer in Canada. In order to demonstrate the efficacy of the tested methods, particle size distribution, scanning electron microscopy (SEM) observation, energy dispersive spectroscopy (EDS), and X-ray diffraction (XRD) were employed. Furthermore, comprehensive information about the production technique of oil sand waste (OSW) and its economic value was included in the study. Additionally, to extract bitumen from oil sand, a new method containing a distillation process was suggested. It is possible to conclude that OSW can be grinded until it is finer than 3 tm and also that OSW contains about 85% silicon dioxide (Si0 2 ) and displays various geometric as well as amorphous shapes. Moreover, it is possible to utilize waste oil sand ash in making construction materials using net-zero energy in terms of its fineness, content of SiO 2 , and various geometric and amorphous shapes.
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 | 0.000 |
| 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) | 0.005 | 0.002 |
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".