Capacity Modeling Guidebook for Shared-Use Passenger and Freight Rail Operations
Bibliographic record
Abstract
This report provides technical guidance for state departments of transportation (DOTs) who are starting or expanding passenger rail service on privately-owned and shared-use rail corridors. The guidance is designed to aid in the DOTs’ understanding of the methods host railroads use to calibrate and apply capacity models to determine if adequate capacity exists to support new or increased passenger rail service or if infrastructure improvements may be necessary. A shared understanding of these methods will aid all parties—including state DOTs—in the negotiation of service outcome agreements. After an introductory chapter, the individual chapters present a synthesis of stakeholder input, analytical approaches to line capacity in shared-use corridors, best practices, and a discussion of recent and ongoing planning for the Chicago-Saint Louis high speed rail implementation on the Union Pacific Railroad and Canadian National Railway line. This report should be of immediate use to transportation professionals charged with the responsibility for planning passenger rail service and negotiating shared-corridor service agreements with host railroads.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.154 | 0.062 |
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".