오일샌드 역청 개질 기술의 특허정보 분석
Bibliographic record
Abstract
Oil sands had not received enough attention due to high production cost. However, as oil price significantly increases, oil sands are receiving more and more interest as unconventional crude oil. The value and applicability of oil sands can be enhanced by upgrading oil sands bitumen to produce synthetic crude oil (SCO). This study analyzed 213 oil sands upgrading patents applied between 1969 and 2006 in US, Canada, Japan, Europe, and Korea. The upgrading technologies could be classified into 9 detailed technologies; hydrocracking, coking, thermal cracking, deasphalting, supercritical technology, bio-technology, hydrotreating, gasification, and others. The number of patents applied for oil sands upgrading increased after 1970, reached a maximum in the early 1980, and slowly increases again in recent years. Korea has a lack of technologies for oil sands. Therefore, the technologies for oil sands production and application, specially, upgrading technologies based on accumulated oil refinery technologies need to be developed to increase self-development ratio of energy resource.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".