DETECTION METHOD OF LOCALIZED SURFACE DISTRESS FOR PAVEMENT SURFACE MONITORING BASED ON A QUARTER-CAR ALGORITHM
Bibliographic record
Abstract
舗装の維持管理上，優先的に補修が必要となる箇所を的確に把握することはきわめて重要である．本研究では，舗装路面の損傷について，クォーターカー(QC)アルゴリズムを用い，機能的側面から評価が必要なひび割れ箇所の検出方法について，ウェーブレット理論に基づき検討した．その結果，Lifting Schemeにより，QCフィルタ適用後のプロファイルから，機能評価上重要なひび割れ損傷箇所およびその類似箇所が検出できることを確認した．本研究成果は，車両振動応答に基づく路面モニタリングの高効率化に寄与するものと期待できる．一方，特異点検出時の閾値については，路面モニタリングデータを蓄積するとともに，舗装の管理目的に応じた設定が必要である．
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".