Bibliographic record
Abstract
JNC has conducted the international joint project "Tunnel Sealing Experiment (TSX)" with AECL at Underground Research Laboratory-(URL) in Canada. Full-scale sealing technologies are applied to the underground tunnel in the TSX. Sealing technologies contain backfilling, plugging, grouting and so on. Since development of the plugging technology has impact to the whole sealing technology, understanding of the sealing performance of the plug is one of the most important problems. The TSX has full-scale concrete plug and clay plug. The sealing performance of the plugs has been monitored with the various sensors in and around the plugs, and assessed by the monitored data. This report summaries the design and construction technology of the plugs in the TSX. The design of the plugs focused on the impervious (seepage control). The concrete plug selected the shape and the size of the body and the size of the key considering the attachment between plug and rock mass. The clay plug selected the shape and the size of the key considering the volume of the EDZ. The material of the concrete plug was Low-Heat High-Performance Concrete. The material of the clay plug was pre-compacted bentonite blocks with sand. Feasibility of design and construction of both plugs were demonstrated with engineer scale and in-situ full-scale plugs.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.020 | 0.017 |
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".