北米地域のウラン廃棄物処分に関する調査; 米国ユタ州,テキサス州及びカナダオンタリオ州における処分及び規制の現状
Bibliographic record
Abstract
Uranium bearing waste in Japan is not included in Category-2 radioactive waste disposal in NSCRG: F-RW-I.02 (published in August 2010, NSC Japan). Therefore, disposal of uranium bearing waste should be considered in institutionalization. In charge of the consideration, it is thought effective to refer to the proven tactics of the uranium waste disposal in overseas and the information on a safe regulatory system. Since the view of regulations and enterprises in this field are progressing day by day, renewal of the existing information of disposal of the uranium waste in each country is required. Furthermore, amendment of the U.S. federal rule aiming at safety disposal of depleted uranium is in progress. It is important to collect and arrange the latest information on the two above-mentioned points. Therefore, it visited the disposal responsible organization and regulatory agency of the disposal site of the uranium waste in the U.S. and Canada, and held institution investigations and interviews paying attention to the following four items: (1) "amendment of the U.S. 10CFR61", (2) "Safety evaluation of uranium bearing waste", (3) "Disposal site design", (4) "Stakeholder involvement".
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 0.008 |
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".