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Record W2057678386 · doi:10.3934/nhm.2015.10.1

Transferability of collective transportation line networks from a topological and passenger demand perspective

2015· article· en· W2057678386 on OpenAlexaff
Eva Barrena, Alicia De-Los-Santos, Gilbert Laporte, Juan A. Mesa

Bibliographic record

VenueNetworks and Heterogeneous Media · 2015
Typearticle
Languageen
FieldPhysics and Astronomy
TopicComplex Network Analysis Techniques
Canadian institutionsHEC Montréal
FundersJunta de AndalucíaMinisterio de Economía y Competitividad
KeywordsTransferabilityLinearizationPerspective (graphical)Computer scienceLine (geometry)Complex networkTopology (electrical circuits)MathematicsPhysicsArtificial intelligenceCombinatoricsMachine learningGeometryNonlinear system

Abstract

fetched live from OpenAlex

We analyze the transferability of collective transportation line networks (CTLN) with the help of hypergraphs, their linearization, and connectivity measures from Complex Network Theory. In contrast to other existing works in the literature, where transferability isanalyzed at a topological level, we are also concerned with passenger system level, introducing data on the travel patterns. This will allow us to have a more complete view of the functioning of the transfer system of a CTLN.

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)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.003
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.000

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 head0.022
GPT teacher head0.261
Teacher spread0.239 · 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 designSimulation or modeling
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

Citations12
Published2015
Admission routes1
Has abstractyes

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