Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
ABSTRACT In Alberta, oil sands bitumen is utilized for synthetic crude oil (SCO) production by surface mining, bitumen extraction followed by primary (coking) and secondary (catalytic hydrotreating) upgrading processes. SCO is further refined in specially designed or slightly modified conventional refineries into transportation fuels. Oil sands tailings, composed of water, sands, silt, clay and residual bitumen, is produced as a byproduct of the bitumen extraction process. The tailings have poor consolidation and water release characteristics. For twenty years, significant research has been performed to improve the consolidation and water release characteristics of the tailings. Several processes were developed for the management of oil sands tailings, resulting in different recovered water characteristics, consolidation rates and consolidated solid characteristics. These processes may affect the performance of the overall plant operations. Apex Engineering Inc. (AEI) has been developing a process for the same purpose. In this process oil sands tailings are treated with Ca(OH)2 lime and CO2 and thickened using a suitable thickener. The combination of chemical treatment and the use of a thickener results in the release of process water in short retention times without accumulation of any ions in the recovered water. This makes it possible to recycle the recovered water, probably after a chemical treatment, as warm as possible, which improves the thermal efficiency of the extraction process. The AEI Process can be applied in many different fashions for the management of different fractions of the tailings effluent, depending on the overall plant operating priorities.
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.
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 | 0.001 |
| 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 it