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Record W2899185753 · doi:10.1016/j.trpro.2018.10.054

A robust datawarehouse as a requirement to the increasing quantity and complexity of travel survey data

2018· article· en· W2899185753 on OpenAlexaff
Pierre-Léo Bourbonnais, Catherine Morency

Bibliographic record

VenueTransportation research procedia · 2018
Typearticle
Languageen
FieldComputer Science
TopicData Management and Algorithms
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsAdaptation (eye)Travel surveyComputer scienceTravel behaviorSurvey data collectionData scienceData processingData miningTransport engineeringEngineeringDatabaseMathematics

Abstract

fetched live from OpenAlex

This research proposes a travel datawarehouse using dimensional modelling for promoting a more understandable structure, generating comparable results, providing faster access to data and accelerating publication of highlights. The adaptation of dimensional modeling to travel data encourages a better structure while integrating, enriching and enhancing data. It provides automated data processing and validation. The proposal of a dimensional model for travel data follows the expected development of transportation planning tools.

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)0.006
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.666
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0020.000
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 head0.515
GPT teacher head0.430
Teacher spread0.086 · 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 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

Citations5
Published2018
Admission routes1
Has abstractyes

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