JAEA収着データベース(JAEA-SDB)の開発; 2017年度における収着データ/信頼度情報の拡充
Bibliographic record
Abstract
Sorption and diffusion of radionuclides in buffer materials (bentonites), rocks and cementitious materials are the key processes in the safe geological disposal of radioactive waste, because migration of radionuclides in these barrier materials is expected to be diffusion-controlled and retarded by sorption processes. It is therefore necessary to understand the sorption and diffusion processes and develop databases compiling reliable data and mechanistic/predictive models, so that reliable parameters can be set under a variety of geochemical conditions relevant to performance assessment (PA). The present report focuses on updating of the sorption database (JAEA-SDB) as basis of integrated approach for PA-related K$_{d}$ setting and mechanistic sorption model development. This includes an overview of database structure and contents. K$_{d}$ data and their QA results are updated by focusing our recent activities on the K$_{d}$ setting and mechanistic model development. As a result, 4,256 K$_{d}$ data from 30 references were added, total number of K$_{d}$ values in the JAEA-SDB reached about 63,000. The QA/classified K$_{d}$ data reached about 69\% for all K$_{d}$ data in JAEA-SDB. The updated JAEA-SDB is expected to make it possible to obtain quick overview of the available data, and to have suitable access to the respective data for PA-related K$_{d}$ setting in effective, traceable and transparent manner.
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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 | 0.013 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.042 | 0.069 |
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".