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Record W4230509233 · doi:10.17771/pucrio.acad.33331

ANÁLISE DA MULTIMODALIDADE DO TRANSPORTE DE CARGA NO ESTADO DO RIO DE JANEIRO ATRAVÉS DA TÉCNICA DE PREFERÊNCIA DECLARADA

2017· dissertation· pt· W4230509233 on OpenAlexaff
Felipe Lobo Umbelino de Souza

Bibliographic record

Venuenot available
Typedissertation
Languagept
FieldEngineering
TopicUrban and Freight Transport Logistics
Canadian institutionsAdidas (Canada)
Fundersnot available
KeywordsWelfare economicsMode (computer interface)Reliability (semiconductor)Transport engineeringService (business)BusinessGeographyEngineeringComputer scienceEconomicsMarketing

Abstract

fetched live from OpenAlex

Freight mode choice is a critical part in modeling freight demand. This study uses the stated preference techniques to analyze cargo transportation in the State of Rio de Janeiro, aiming to identify the relevant factors in the mode choice (road and railroad) by companies operating in the State in the category of General Cargo products. The study used the Multinominal Logit model in order to verify the importance of factors (cost, time, service, reliability, availability and cargo theft risk) in the mode choice by the companies, and to indicate which measures may be adopted to promote multimodality in freight transport in the State of Rio de Janeiro.

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), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Research integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.380
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0020.000
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0030.000

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 head0.032
GPT teacher head0.257
Teacher spread0.225 · 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 designObservational
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
Published2017
Admission routes1
Has abstractyes

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