Bibliographic record
Abstract
Abstract The article contains sections titled: 1. Introduction 2. Definitions 3. Chemistry 4. Viscous Oil Origins, Geological Setting and Resource Base Estimates 4.1. Viscous Oil Origins 4.2. Geographical Distribution and Resource Base Estimates 4.3. Canadian Oil Sands Deposits 4.3.1. The Athabasca Oil Sands 4.3.2. The Cold Lake Oil Sands 4.3.3. The Peace River Viscous Oil Sands 4.3.4. The Wabiskaw Viscous Oil Sands 4.3.5. The Heavy Oil Belt 4.3.6. Venezuelan Viscous Oil Deposits 4.4. Other Major Viscous Oil Deposits 4.4.1. Russia 4.4.2. Kazakhstan 4.4.3. Kuwait 4.4.4. China 4.4.5. Iran 5. In situ Production Technologies 5.1. Historical Development 5.2. Surface Mining 5.3. New Oil Production Technologies 5.3.1. Technical Screening Criteria for VO Production 5.3.2. VO Production Cost Estimates 5.3.3. Nonthermal Commercialized Methods 5.3.3.1. Cold Production 5.3.3.2. Cold Heavy Oil Production With Sand 5.3.3.3. Pressure Pulse Stimulation Technology 5.3.4. Commercialized Thermal Methods 5.3.4.1. In Situ Combustion 5.3.4.2. Conventional Steam Processes 5.3.4.3. Vertical Well Cyclic Steam Stimulation 5.3.4.4. Horizontal Well Cyclic Steam Stimulation 5.3.4.5. Steam Assisted Gravity Drainage 5.3.5. Emerging Methods 5.3.5.1. Vapor‐Assisted Petroleum Production 5.3.5.2. Toe‐to‐Heel‐Air Injection 5.3.5.3. CAPRI 5.3.5.4. Deep Miscible CO2Injection 5.3.6. Hybrid Approaches and Sequencing of Technologies 5.3.7. Geomechanics of Thermal VO Production 5.3.8. Steam Generation 5.3.9. Further Technical Issues 6. Upgrading and Transportation 6.1. Noncatalytic Processes in VO Upgrading 6.1.1. Solvent Deasphaltening 6.1.2. Thermal Conversion 6.1.2.1. Gasification 6.1.2.2. Delayed Coking 6.1.2.3. Fluid Coking and Flexicoking 6.1.2.4. Visbreaking 6.2. Catalytic Processes in VO Upgrading 6.2.1. Fluid Catalytic Cracking 6.2.2. Hydroprocessing/Hydrogenation 6.3. Hydrogen Sources 6
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.261 | 0.135 |
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".