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Record W642128041

Nobody Said This Was Going to Be Easy: Serving a Quarter-Million Impatient Commuters May Be a 'Can't Win': LIRR Does it Anyway

2006· article· en· W642128041 on OpenAlexaboutno aff
Joe Greenstein

Bibliographic record

VenueTrains · 2006
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicTransport and Economic Policies
Canadian institutionsnot available
Fundersnot available
KeywordsnobodyTrainMileOrder (exchange)Quarter (Canadian coin)Track (disk drive)Service (business)TicketBusinessTelecommunicationsOperations managementFinanceAdvertisingEngineeringHistoryMarketingComputer scienceGeographyComputer securityArchaeology
DOInot available

Abstract

fetched live from OpenAlex

This article describes in detail the Long Island Railroad (LIRR), which serves 262,00 passengers daily on its 324-mile system. From the strategies of Wall Street workers who position themselves at Penn Station in order to race to the correct track and claim a seat, to the railroad's 6,300 workers who complain about the red tape that tangles up the management of the system, this article explains the railroad's strengths and weaknesses. It includes the financing of the railroad, its replacement of diesel-powered trains, too small passenger seats, diminishing service to the far east end of Long Island, and the problems of Penn Station.

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)0.007
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: none
Teacher disagreement score0.035
Threshold uncertainty score0.119

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0070.002
Scholarly communication0.0070.006
Open science0.0010.002
Research integrity0.0050.006
Insufficient payload (model declined to judge)0.0350.008

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 head0.019
GPT teacher head0.219
Teacher spread0.199 · 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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