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Record W3211079363

The Cascadia Corridor (Northwest of the United States): A limited territorial anchorage for a growing interurban rail service

2020· article· en· W3211079363 on OpenAlexaboutno aff
Matthieu Schorung

Bibliographic record

VenueFlux · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicTransport and Economic Policies
Canadian institutionsnot available
Fundersnot available
KeywordsInterurbanMetropolitan areaService (business)Modernization theoryGovernment (linguistics)Public transportTransport engineeringBusinessPublic administrationEnvironmental planningGeographyPolitical scienceEngineeringArchaeology
DOInot available

Abstract

fetched live from OpenAlex

The Northwest Corridor is part of the Cascadia Region, stretching from Portland, Oregon, through Seattle, Washington, to Vancouver, British Columbia in Canada. The Amtrak Cascades service is a high-speed rail service in which the managing states (Washington, Oregon) are heavily involved, in coordination with Amtrak and the freight company BNSF, which owns the infrastructure. It is important to understand why this corridor modernization programme is considered a model of its kind, both by Amtrak Cascades officials and by the federal government. This article therefore analyses US rail geography through the case study of the Cascades corridor and questions the process of territorialization of rail policies. The Cascades Corridor is a relevant case study for looking at a mixed-use corridor (freight and passengers) supported by highly committed public stakeholders despite the institutional and budgetary isolation of intercity rail transport. This analysis reveals a successful experience, welcomed by all public and private stakeholders, of modernizing an existing rail corridor, but only a partial territorialization process that does not take sufficient account of stations and station districts and ignores the metropolitan planning dimension of the transport project with regard to Seattle and Portland.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.312
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

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

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 head0.021
GPT teacher head0.201
Teacher spread0.181 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations0
Published2020
Admission routes1
Has abstractyes

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