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Record W641422844

Market Power: Transit Locomotive Purchases Hold Steady as Agencies Bulk Up Fleets, Launch New Service

2007· article· en· W641422844 on OpenAlexaboutno aff
Angela Cotey

Bibliographic record

VenueProgressive railroading · 2007
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicTransport and Economic Policies
Canadian institutionsnot available
Fundersnot available
KeywordsMetropolitan areaTransport engineeringTransit (satellite)PurchasingService (business)Fleet managementBusinessFreight trainsEngineeringPublic transportTrainOperations managementMarketing
DOInot available

Abstract

fetched live from OpenAlex

This article looks at what types of locomotive transit agencies are purchasing. The number of locomotives in service in the commuter-rail market has risen in recent years, with more than 700 active locomotives in 2007. Some of the reasons cited for purchasing new locomotives include improved reliability, increased train speed, and expanded service. The article discusses fleet acquisition plans for the MTA Metro-North Railroad, Ontario’s GO Transit, the Southern California Regional Rail Authority, Metropolitan Council of Minnesota, and the Utah Transit Authority. The article also notes that some agencies, such as the Massachusetts Bay Transportation Authority, plan to purchase hybrid locomotives. These locomotives are advantageous in that they can help reduce NOx and particulate matter emissions, and their use can also lead to a 35-50 percent reduction in fuel usage.

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)0.005
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 score0.072
Threshold uncertainty score0.241

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0060.006
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0720.011

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 head0.024
GPT teacher head0.247
Teacher spread0.223 · 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
Published2007
Admission routes1
Has abstractyes

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