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Record W587320459

Greyhound: Building a Better Breed of Bus Company

2006· article· en· W587320459 on OpenAlexaboutno aff
Matthew LeGrande

Bibliographic record

VenueMetrologia · 2006
Typearticle
Languageen
FieldEngineering
TopicTransportation and Mobility Innovations
Canadian institutionsnot available
Fundersnot available
KeywordsDemographicsLogo (programming language)AdvertisingSpring (device)BusinessMarketingComputer scienceSociologyEngineeringDemographyMechanical engineering
DOInot available

Abstract

fetched live from OpenAlex

This article presents a profile of Greyhound, the Dallas-based carrier, that is streamlining its route network, withdrawing from money-losing routes and creating a regionally based network. It is also upgrading terminals and adding new coaches aimed at niche passengers such as the Unleashed Spring Break 2006 tour with hip-hop decorations. A new logo is also designed to enhance Greyhound's image and try to re-focus it on the urban youth market. The article provides breakdowns of passenger demographics, which show that about 30 percent have college degrees and make more than $35,000 a year, while students make up 15 to 20 percent. Greyhound Canada is also aiming for the student audience. A Web-based rewards program offers discounts for people who ride frequently.

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)0.001
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: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.086
Threshold uncertainty score0.269

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.001
Scholarly communication0.0040.005
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0800.014

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 head0.008
GPT teacher head0.203
Teacher spread0.196 · 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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