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Record W587689293

Aftermath: It'll Take Money, Materials, Manpower- and Months- for Railroads to Rebuild in Hurricane Katrina's Wake

2005· article· en· W587689293 on OpenAlexaboutno aff
Jeff Stagl

Bibliographic record

VenueProgressive railroading · 2005
Typearticle
Languageen
FieldEngineering
TopicInfrastructure Resilience and Vulnerability Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsHurricane katrinaEconomic shortageRevenueService (business)Transport engineeringFront (military)BusinessEngineeringFinanceNatural disasterGeographyMarketingGovernment (linguistics)Meteorology
DOInot available

Abstract

fetched live from OpenAlex

This article describes a wide range of responses by rail operators and transit agencies in the wake of Hurricane Katrina. The area most damaged is a major interchange point for the five U.S. Class I railroads and Canadian National Railway, as well as home to three short lines and a streetcar system. It also feeds traffic to more than 20 small roads on the Gulf Coast and is a major destination area for Amtrak. The article relates how railroads are facing repairs, lost revenues, and a shortage of skilled workers and materials On the freight front, interruptions have been minimal, though there have been local traffic problems. The repairs have given impetus to plans to realign rail service to the area, perhaps reducing the number of cars that stop in New Orleans and redesigning routes, though most Class I executives are doubtful major changes will occur.

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)0.001
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 score0.018
Threshold uncertainty score0.056

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0040.001
Scholarly communication0.0030.002
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0170.003

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 head0.007
GPT teacher head0.252
Teacher spread0.246 · 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
Published2005
Admission routes1
Has abstractyes

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