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Record W806196319

Experience Counts Aboard the Ocean: In Eastern Canada, VIA Rail is Transforming What a Passenger Train Can Be

2006· article· en· W806196319 on OpenAlexaboutno aff
Bob Johnston

Bibliographic record

VenueTrains · 2006
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicTransport and Economic Policies
Canadian institutionsnot available
Fundersnot available
KeywordsService (business)TourismTransport engineeringPassenger trainBusinessWork (physics)Stock (firearms)EngineeringAeronauticsAdvertisingMarketingGeographyAutomotive engineering
DOInot available

Abstract

fetched live from OpenAlex

This article describes how eastern Canada’s premier passenger train, VIA Rail, is reinventing itself and transforming passenger rail service. The article explains how belt-tightening led the rail service to purchase a conglomeration of hand-me-down railroad car stock that required repurposing. To enhance tourist business, the rail service put volunteers to work as “learning coordinators,” who provide informative talks and stories to passengers up in the dome cars in between serving drinks and taking meal reservations. The rail service also established different classes of fares, and the dining service was changed. Attention to details of service has made substantial differences in the rail service’s bottom line.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.091
Threshold uncertainty score0.183

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0230.009
Scholarly communication0.0070.003
Open science0.0010.005
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0100.001

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.

Opus teacher head0.017
GPT teacher head0.201
Teacher spread0.184 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2006
Admission routes1
Has abstractyes

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