Unusual rainfall-induced cut-off slope failures and their countermeasures
Bibliographic record
Abstract
2004年と2009年に，異常な降雨の影響で北関東自動車道における切土斜面が13か所において損傷を起こし，交通に支障をきたした．原因は，表面水や地下水を集積しやすい地形だったこととこれに見合うような排水施設の機能が不十分だったたｔめに切土斜面の上部と下部でパイピングを起こしたためである．このような災害に対する対応策として，表面水と地下水の処理の両面作戦の排水施設を設置するとともに，浅層崩壊に対しては，布団籠マット工法，深層崩壊には布団籠枠工法で対応した．その対応が功を奏し，対策工が完成した2009年以降は，切土斜面において全く損傷を見ることはなかった．とりわけ，東北地方太平洋沖地震において，震度6弱を経験したにもかかわらず，当該補強切土区間は，何らの損傷も見られなかった．
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".