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Record W2078497438 · doi:10.1115/jrc2011-56029

Shared Corridors, Strange Bedfellows: Understanding the Interface Between Freight and Passenger Rail

2011· article· en· W2078497438 on OpenAlexaffabout
Mario Iacobacci

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicTransport and Economic Policies
Canadian institutionsAecom (Canada)
Fundersnot available
KeywordsTrainTransport engineeringPublic transportTrack (disk drive)Freight trainsBusinessService (business)Passenger trainRail freight transportPassenger transportEngineeringMarketingAutomotive engineering

Abstract

fetched live from OpenAlex

This paper aims to clarify issues regarding shared rail corridors from a public policy perspective. It presents an overview of the relationships between the main stakeholders operating trains on North America’s rail networks: the railway companies that own the rail infrastructure and use it to provide freight services to shippers, and the passenger service operators—which are primarily public agencies that pay railway companies for track access and other services required to operate commuter and intercity passenger trains. The issues at stake are of concern to the policy and business community alike, because congestion on railway lines affects commuter rail, intercity passenger trains, and long-distance freight trains. In addition to the obvious economic costs of delays or less-reliable transit times in passenger and freight rail, respectively, adverse environmental and social impacts (e.g., higher accident rates on roadways) arise if either freight or passenger traffic shifts from rail to roadways. An earlier version of this paper was published by the Conference Board of Canada in September 2010.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.205
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.097
GPT teacher head0.227
Teacher spread0.129 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2011
Admission routes2
Has abstractyes

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