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Record W2170301086 · doi:10.1017/s1466046610000542

Environmental Reviews & Case Studies: Integrating High-Speed Rail into North America's Next Mobility Transition

2011· article· en· W2170301086 on OpenAlexaff
Anthony Perl, John Calimente

Bibliographic record

VenueEnvironmental Practice · 2011
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicAviation Industry Analysis and Trends
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsTrainTransport engineeringInvestment (military)Adaptation (eye)BusinessRail transitEngineeringGeographyPolitical science

Abstract

fetched live from OpenAlex

Although the American Recovery and Reinvestment Act of 2009 provided $8 billion in federal grants for passenger rail development, no national policy framework yet exists to guide this investment. Integrating new high-speed rail lines with existing railroad infrastructure and connecting them with air, road, and transit systems will be of utmost importance. As most of the world's cheap and accessible oil has already been consumed, transportation modes that depend exclusively on oil can be expected to decline, while energy-efficient, low-impact modes such as passenger trains will advance. A new model railroad that shifts more passenger travel to rail will develop through an incremental adaptation of current passenger services, a comprehensive transformation using high-speed train technology, or a combination of these two trajectories. North American designs for high-speed rail will need to incorporate the European innovation of providing multiple air, auto, and rail connections in the form of four station types: the city-center and airport stations seen in Europe, as well as suburban stations at business parks and at suburban commercial centers. To create an effective synergy between transportation and local land use, three broad categories of policy tools will need to be deployed.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.882
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.004

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 head0.088
GPT teacher head0.271
Teacher spread0.183 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreEmpirical

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

Citations2
Published2011
Admission routes1
Has abstractyes

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