Track Wear and Tear Cost by Traffic Class: Functional Form, Zero-Output Levels and Marginal Cost Pricing Recovery on the French Rail Network
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Bibliographic record
Abstract
We address the issue of the allocation of railway track maintenance (wear-and-tear) costs to traffic output classes and consider a very general function relating maintenance cost C to a set of technical production characteristics K used to produce traffic output vector T. We neglect other rail cost categories, such as traffic control and track renewal. The data base pertains to over 1500 sections of the French rail infrastructure in 1999, representing about 90% of the total network of 30000 km of lines in regular service. In addition to the maintenance cost C, it provides by track section 15 technical characteristics (both state S and quality Q) and 4 train traffic outputs T. Input prices, assumed to be uniform in space, disappear from the analysis, as in other national cross-sectional cases. With database subsets of approximately 1000 observations, several functional forms are tested: Linear, Log-Log, trans-Log and generalized Box-Cox. All are embedded in an unrestricted extension (U-GBC) of Khaled's seminal restricted generalized Box-Cox (R-GBC) functional specification. The U-GBC architecture, compared with its 4 principal nested variants, turns out to be by far the most appropriate, in particular when some observed zero traffic sample values are included - an issue rather neglected previously in the literature. It appears that several technical characteristics, such as maximum allowed speed and number of switches, are highly significant maintenance cost factors, which gives a hint that derived marginal costs are short term; also, the relation between maintenance costs and traffic is non linear and differs significantly by train category. Implications of different specifications for marginal infrastructure cost charges by traffic type are outlined.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it