高レベル放射性廃棄物地層処分にかかわる天然現象影響に関する研究
Bibliographic record
Abstract
Japan Atomic Energy Agency (JAEA) have developed a formal evaluation method to assess the potential impacts of natural phenomena (earthquakes and faulting; volcanism; uplift, subsidence, denudation and sedimentation; climatic and sea-level changes) on a high level radioactive waste (HLW) disposal system for the purpose of maintenance of evaluation method for potential impacts of natural phenomena within more realistic view. In this report, we developed a framework as a part of the total system performance assessment for two purposes: the first one is quantification of relationship of characteristic of natural phenomena between geological environmental conditions (T-H-M-C-G), and the other one is quantification of relationship of T-H-M-C-G condition between parameters of performance assessment. We tried to apply the framework to all natural phenomena to confirm the effectiveness of the framework. On the other hand, we applied an argument-diagram that was developed one of the methods of the knowledge management to elicit future tasks. As a result, to apply the framework, we could show that information integration could carry out efficiently, and information integration was attained by the common framework by combining information on geohistory and existing information of natural phenomena and geological environmental conditions as modern-analogue. We could show that suitable scenarios might be chosen by information integration. Furthermore, we were able to elicit future tasks to this study by applying the argument-diagram.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.005 | 0.012 |
| Insufficient payload (model declined to judge) | 0.004 | 0.029 |
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; both teacher heads 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".