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Record W7001196463

Intercity Passenger Rail: National Policy and Strategies Needed to Maximize Public Benefits from Federal Expenditures

2006· report· en· W7001196463 on OpenAlexaboutno aff

Bibliographic record

VenueUniversity of North Texas Digital Library (University of North Texas) · 2006
Typereport
Languageen
FieldBusiness, Management and Accounting
TopicTransport and Economic Policies
Canadian institutionsnot available
FundersMetro North Hospital and Health ServiceDelaware Department of TransportationAppalachian Regional CommissionState of Connecticut Department of TransportationU.S. Department of Transportation
KeywordsSubsidyPaceGovernment (linguistics)Service (business)AccountabilityPublic transportPublic policy
DOInot available

Abstract

fetched live from OpenAlex

A letter report issued by the Government Accountability Office with an abstract that begins "Intercity passenger rail service is at a critical juncture in the United States. Amtrak, the current service provider, requires $1 billion a year in federal subsidies to stay financially viable but cannot keep pace with its deteriorating infrastructure. At the same time, the federal government faces growing fiscal challenges. To assist the Congress, GAO reviewed (1) the existing U.S. system and its potential benefits, (2) how foreign countries have handled passenger rail reform and how well the United States is positioned to consider reform, (3) challenges inherent in attempting reform efforts, and (4) potential options for the federal role in intercity passenger rail. GAO analyzed data on intercity passenger rail performance and studied reform efforts in Canada, France, Germany, Japan, and the United Kingdom."

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)0.005
metaresearch head score (Gemma)0.010
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 score0.190
Threshold uncertainty score0.378

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0030.001
Scholarly communication0.0110.004
Open science0.0010.002
Research integrity0.0060.003
Insufficient payload (model declined to judge)0.0090.001

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 head0.023
GPT teacher head0.185
Teacher spread0.161 · 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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