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Record W4319999521 · doi:10.23977/acss.2023.070104

A simple calculation model for transport of visitors on a closed circuit

2023· article· en· W4319999521 on OpenAlexvenueno aff
F. Tinebra

Bibliographic record

VenueAdvances in Computer Signals and Systems · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicTransportation Planning and Optimization
Canadian institutionsnot available
Fundersnot available
KeywordsSimple (philosophy)Realization (probability)Diophantine equationSet (abstract data type)KinematicsMathematical optimizationWork (physics)Applied mathematicsDynamical systems theoryComputer sciencePath (computing)Complex systemMathematicsStatistical physicsClassical mechanicsPhysicsEngineeringDiscrete mathematicsArtificial intelligenceMechanical engineering

Abstract

fetched live from OpenAlex

It is examined a very simple, geometrically closed configuration concerning transport systems. The problem analyzed is a multi-objective optimization problem, in which an ever increasing set of visitors is urged to visit and move from a given set of sites on a closed path. An essentially kinematic approach is developed and the performance evaluation is obtained by means of a combinations of variables. It is also examined a particular realization in which one makes use of C_0-functions instead of discrete variables, in the spirit of classical mathematical physics. Some features of the model show relevant differences with others concerned with traffic and transport problems, for the presence of Diophantine integral evolution equations in place of statistical and/or numerical complex evaluation methods. This work is the first part of a more thorough discussion on dynamical equations in transport systems, including simulations and optimization schemes.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.016
Threshold uncertainty score0.054

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0030.001
Research integrity0.0030.001
Insufficient payload (model declined to judge)0.0160.002

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 head0.041
GPT teacher head0.326
Teacher spread0.285 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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