Bibliographic record
Abstract
The Horonobe Underground Research Laboratory (URL) Project is being pursued by the Japan Atomic Energy Agency (JAEA) to enhance the reliability of relevant disposal technologies for geological disposal of High-level Radioactive Waste through investigations of the deep geological environment within the host sedimentary rock at Horonobe Town in Hokkaido, north Japan. The investigations will be conducted in three phases, namely "Phase 1: Surface-based investigations", "Phase 2: Construction phase" (investigations during construction of the underground facilities) and "Phase 3: Operation phase" (research in the underground facilities). According to the research plan described in the 3rd Mid- and Long- term Plan of JAEA, "Demonstration of EBS in geological environment", "Demonstration of disposal concept", and "Validation of buffer capacity of the sedimentary rock to tectonism" are important issues of the Horonobe URL Project, and schedule of future research and backfill plans of the URL will be decided by the end of 2019 Fiscal Year. JAEA summarizes the research and development activities of the important issues carried out during the 3rd Mid- and Long-term Plan, and set out three important issues after 2020 fiscal year. After consultation with Hokkaido and Horonobe town, JAEA formulated the Horonobe underground research plan after 2020 fiscal year within the 3rd and 4th Mid- and Long-term Plan. This report summarizes the investigation program for the 2020 fiscal year (2020/2021).
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".