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Record W3096756660 · doi:10.1145/3417990.3421440

Toward client-agnostic hybrid model editor tools as a service

2020· article· en· W3096756660 on OpenAlexaff
Liam Walsh, Juergen Dingel, Karim Jahed

Bibliographic record

VenueModel Driven Engineering Languages and Systems · 2020
Typearticle
Languageen
FieldComputer Science
TopicModel-Driven Software Engineering Techniques
Canadian institutionsQueen's University
Fundersnot available
KeywordsComputer sciencePoint (geometry)Service (business)Software engineeringProgramming languageWorld Wide WebHuman–computer interaction

Abstract

fetched live from OpenAlex

General-purpose languages (GPLs) have reached a point where they can be easily learned with little background in computing. This has allowed them to cultivate users rapidly, and incentivized the creation of IDEs (Integrated Development Environments) and IDE tools for these languages.

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)0.013
metaresearch head score (Gemma)0.021
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 score0.013
Threshold uncertainty score0.067

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.021
Meta-epidemiology (narrow)0.0010.002
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0080.010
Open science0.0070.008
Research integrity0.0030.007
Insufficient payload (model declined to judge)0.0090.009

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 head0.025
GPT teacher head0.239
Teacher spread0.214 · 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

Citations1
Published2020
Admission routes1
Has abstractyes

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