Patterns of distribution of hard-to-recover oils with high content of resins and asphaltenes
Bibliographic record
Abstract
The spatial distribution of world reserves of high-asphaltene and high-resin oils as an important source of hydrocarbon raw materials in the future is studied. It is shown that one third of the world's oil and gas basins contains reserves of these oils. More than 88% of the world's reserves are located in Canada and Russia. About 94% of all Russian reserves of high asphaltene and high resinous oils are located in three basins: Timan-Pechora, West Siberian and Volga-Ural basins. It has been established on the basis of statistical investigations that high asphaltene and high resinous oils are characterized by increased density and viscosity, high content of sulfur, nitrogen, and oxygen, as well as vanadium and nickel. Regularities of changes in the content of resin-asphaltene components depending on the age of deposits, depth and lithological characteristics of reservoirs have been revealed. Maps of oil and gas basins were constructed on the basis of information from the database. These maps reflect the patterns of distribution of oil fields with high asphaltene and high resin oils on the territory of the basins and allow identifying areas of preferential localization of reserves of the oils under consideration.
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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 | 0.003 | 0.002 |
| 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) | 0.002 | 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".