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Record W2736849143 · doi:10.1155/2017/7590389

Definition of an On-Board Comfort Index (Rail) for the Railway Transport

2017· article· en· W2736849143 on OpenAlexvenueno aff
Domenico Walter Edvige Mongelli, Antonio Tassitani

Bibliographic record

VenueJournal of Advanced Transportation · 2017
Typearticle
Languageen
FieldHealth Professions
TopicNoise Effects and Management
Canadian institutionsnot available
Fundersnot available
KeywordsAttractivenessPublic transportQuality (philosophy)Index (typography)Service (business)Computer scienceTransport engineeringService qualityProcess (computing)ModalLevel of serviceEngineeringBusinessPhysicsPsychologyMarketing

Abstract

fetched live from OpenAlex

The use of collective transport is strongly influenced by the quality of offered service. One of the overriding factors that affect the modal shift process is the quality of transport systems. To increase the attractiveness of collective transport services and therefore reduce the use of cars, authorities in collaboration with transport companies should take steps to ensure a high level of service quality in the public transport system. The provided quality is the level of quality achieved on daily basis and measured by the customer/user point of view. This research aims to relate service quality perceived by the user to measurements of two environmental indicators, that is, vibration, in reference to which the acceleration transmitted to the body by the vehicle motion and by its vibration will be measured, and noise, in reference to which the Equivalent Sound Pressure Level—Leq (<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:mrow><mml:mi>A</mml:mi></mml:mrow></mml:math>)—will be measured. Finally, a Comfort Index (CI) (rail) is specified, calibrated, and validated.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.768
Threshold uncertainty score0.517

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.051
GPT teacher head0.395
Teacher spread0.344 · 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

Citations7
Published2017
Admission routes1
Has abstractyes

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