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Record W596343905

Jake's World: Visit Seattle's Gritty Realm Near King Street Station

2007· article· en· W596343905 on OpenAlexaboutno aff
B. B. Bachman

Bibliographic record

VenueTrains · 2007
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicTransport and Economic Policies
Canadian institutionsnot available
Fundersnot available
KeywordsDowntownTrainTransport engineeringService (business)GeographyEngineeringArchaeologyEconomy
DOInot available

Abstract

fetched live from OpenAlex

This article focuses on the area surrounding King Street Station and Union Station in Seattle, Washington. It describes the gritty neighborhood as Downtown Railroad Space, a phenomenon found thoughout the United States. No longer requiring the presence of passenger trains or actual stations, Downtown Railroad Space remains persistent, a reminder of the time in the U.S. when passenger travel to almost anywhere involved at least one segment of rail transportation. Downtown Railroad Space is not just limited to large cities; it is present in smaller areas, even in tiny towns. The author relates that by the mid 1950s, Seattle railroad passenger operations were in a steady state of decline, but freight traffic remained relatively strong. Amtrak service saw a revival when it began to serve the Portland, Oregon to Vancouver, British Columbia corridor, as well as Sound Transit's commuter service from Everett and Tacoma. The author concludes by noting that Downtown Railroad Space will remain a part of U.S. history, serving as living link to the railroad past.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.053
Threshold uncertainty score0.178

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0060.001
Scholarly communication0.0020.002
Open science0.0000.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0530.006

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 head0.242
Teacher spread0.219 · 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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