SECOND BEST PRICING OF EXPRESSWAY WITH REFERENCE TO MAINTENANCE COSTS
Bibliographic record
Abstract
本研究では，高速道路と一般道路が並列する道路ネットワークを対象として，道路利用がもたらす混雑と構造物の劣化という外部不経済性を内部化することを目的とした高速道路の次善料金について理論的分析を試みる．その際，大型車と普通車によって道路構造物の劣化に及ぼす影響が異なること，および，一般道路と高速道路の間で構造物の耐荷力が異なることに着目し，車種別の高速道路料金設定による大型車の経路誘導を通じて，混雑費用，維持補修費用の双方を考慮した社会的費用を可能な限り抑制する次善高速道路料金について分析する．さらに，車種別料金を差別的に設定することは，料金設定による大型車の経路誘導を通じた道路ネットワークの維持補修費用の軽減効果の有効性を高めるうえで重要な意味を持つことを示す．
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".