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Record W4308144529 · doi:10.1080/17477778.2022.2080009

Editorial: Special issue on modelling and simulation in the cloud computing era

2022· editorial· en· W4308144529 on OpenAlexaff
Alberto Falcone, Alfredo Garro, Navonil Mustafee, Muaz A. Niazi, Gabriel Wainer

Bibliographic record

VenueJournal of Simulation · 2022
Typeeditorial
Languageen
FieldComputer Science
TopicCloud Computing and Resource Management
Canadian institutionsCarleton University
Fundersnot available
KeywordsComputer scienceCloud computingData scienceComputational scienceManagement scienceDistributed computingOperating system

Abstract

fetched live from OpenAlex

Modelling and Simulation (M&S) represents one of the fundamental methods to design and study complex systems in many industrial and scientific domains such as transport, energy, and aerospace. M&S ...

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)0.006
metaresearch head score (Gemma)0.024
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.019
Threshold uncertainty score0.064

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.024
Meta-epidemiology (narrow)0.0050.001
Meta-epidemiology (broad)0.0050.004
Bibliometrics0.0050.002
Science and technology studies0.0030.002
Scholarly communication0.0100.004
Open science0.0030.001
Research integrity0.0140.015
Insufficient payload (model declined to judge)0.0190.013

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 head0.016
GPT teacher head0.280
Teacher spread0.264 · 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 designNot applicable
Domainnot available
GenreEditorial

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

Citations1
Published2022
Admission routes1
Has abstractno

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