Review on catalysis related research at CanmetENERGY
Bibliographic record
Abstract
Abstract Canada's oil sands and heavy oil represent a major North American energy source. However, many technological, economic, and environmental challenges must be overcome in order to improve the effectiveness and efficiency required for converting these unconventional oils into clean and high‐quality transportation fuels that meet the ever‐increasing social licence associated with these energy resources. In oil sands bitumen and heavy oil upgrading, and petroleum refining, over 70 % of the processes involve catalysts and catalytic technologies. To advance new catalysts and catalytic technology development for bitumen upgrading and refining, fundamental and applied research has been conducted at CanmetENERGY on hydroprocessing (including catalyst development, reaction mechanism and kinetics, and process/reactor modelling and simulation), fluid catalytic cracking evaluation of bitumen‐derived feedstocks, and other related subjects. In addition, research collaborations have been established and maintained with a number of national and international universities, research organizations, and industrial companies to promote new upgrading and refining technology development. These research efforts have resulted in important impacts on academic R&D and industrial practice for catalytic conversion of Canadian oil sands bitumen and heavy oil. At the same time, reliable technical data and information have been generated to help government agencies in developing and implementing policies and regulations for oil sands development.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.006 |
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".