Bibliographic record
Abstract
Because of world's high crude oil price, oil shale business is being paid more attention in China, just as in Estonia and in some other countries. Recently Chinese National Oil Shale Association was established in Fushun. Last year, Fushun shale oil plant under Fushun Bureau of Mines produced 180,000 tons shale oil; now the plant is building 20 Fushun-type retorts, so that it will operate totally 140 Fushun retorts in this year; daily processing capacity of each retort is 100 t oil shale, thus the yearly capacity of oil shale processing in Fushun will totally reach about 4.5 million tons. Due to the fact that in Fushun oil shale is the by-product of coal mining, the production cost of shale oil is low, about 1500 Chinese yuan per ton, while its selling price reaches 3000 yuan per ton, therefore the plant earns much money and is willing to expand its shale oil production. Fushun Bureau of Mines signed a contract with Krupp Co. and Canadian Taciuk to build an ATP retort, with the daily processing capacity of 6000 tons oil shale, the project was reviewed and passed by me, as the head of reviewing group, nominated by China's National Development Committee. Besides, Longkow Coal Mine, Shangdong Province, and Huadian, Jilin Province, are intended to build oil shale plants, each with the yearly production of 200,000 t shale oil. The projects are being reviewed.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.025 | 0.007 |
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".