Methodological Framework for Analyzing Ability of Freight Rail Customers to Forecast Short-Term Volumes Accurately
Why this work is in the frame
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Bibliographic record
Abstract
The freight transport business is extremely challenging for railways because transport by truck has intrinsic advantages in flexibility and quality. Providing freight customers with flexible scheduling is particularly difficult because optimizing an interconnected rail operating plan is more difficult than arranging for shipment by truck. In this environment it would be helpful if shippers could provide railways with accurate demand forecasts. However, the ability to forecast rail freight transport differs strongly by shipper and commodity type. The goal of this research is to develop a methodological framework to understand better the characteristics that influence the ability of freight shippers to prepare accurate forecasts of rail demand. This information will help railways increase productivity by improving their ability to develop optimized schedules. It will help railways decide when to rely on shipper forecasts and provide a benchmark for identifying shippers that can provide accurate forecasts. The paper describes the methodological framework and presents results from a case study application to illustrate the practical applicability of the proposed framework.
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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 | 0.008 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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