Bibliographic record
Abstract
sium on Oil Shale in Tallinn mark a milestone in production of both oil- shale-based power and shale oil in the Estonian Republic.Oil shale is certainly one of the most important mineral resources of Estonia.In the beginning of 1999, its mineable reserves were estimated to be 2203.4 million tons."'At the present level of oil shale consumption for producing oil and electricity, the reserves will last through some more generations.Several methods of retorting have been used to process Estonian oil shale.Retorts (gas generators) and solid heat carrier units in use require further development.During the last years much attention has been paid to the Alberta-Taciuk Processor (ATP) retort- ing technology elaborated in Canada and already tested there for pilot-scale processing of Estonian oil shale.The possibility to retort crushed run-of- mine oil shale without its previous beneficiation is, of course, tempting.However, some complicated scientific-technical problems are to be solved before large-scale application of ATP in Estonia.Over 90 % of electricity produced in Estonia is based оп ой shale.™Though many scientists and engineers have been optimistic about long-term use of oil-shale-based energetics in the future, their opponents are of the opinion that this branch of industry will die out within the next twenty-five years."Prof. E. Reinsalu founds his arguments on the fact that, on the one hand, mineable reserves are relatively limited, and, on the other hand, so are the reserves of the mining industry.In February 1998, the Parliament of the Estonian Republic accepted the long-term (1998-2018) development plan for Estonian fuel and power man- agement including tasks for the energy branch.It is perfectly clear that com- bustion of fossil fuels, oil shale among them, pollutes the environment.The international public pressure to extend the exploitation of the resources of renewable energy is constantly growing.Recultivation of mined-out areas of the oil-shale basins is a serious problem as well.
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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.402 | 0.181 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".