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Record W2141016810

Proceedings of Formal Methods 2009 Doctoral Symposium, November 6, 2009, Eindhoven, The Netherlands

2009· article· en· W2141016810 on OpenAlexaff
Mohammad Reza Mousavi, Emil Sekerinski

Bibliographic record

VenueData Archiving and Networked Services (DANS) · 2009
Typearticle
Languageen
FieldComputer Science
TopicModel-Driven Software Engineering Techniques
Canadian institutionsMcMaster University
FundersRWTH Aachen UniversityRadboud Universiteit
KeywordsFormal methodsComputer scienceLibrary scienceSoftware engineering
DOInot available

Abstract

fetched live from OpenAlex

Welcome to the third Doctoral Symposium of the International Symposium on Formal Methods.This year, the Formal Methods Symposium and its Doctoral Symposium are organized in Eindhoven, the Netherlands.It is also part of the Formal Methods Week featuring a number of scientific events dedicated to Formal Methods and their application.The call for papers for the Doctoral Symposium was sent out in July 2009 and has attracted 20 papers from 13 different countries.The review committee then spent about one month reviewing the submitted papers and discussing them.The final decision was a particularly difficult one since 14 out of 20 papers received an average positive score from the reviewers; hence, many good submissions had to be rejected due to the limited time dedicated to the symposium and to guarantee sufficient room for discussion for the accepted papers.Finally, the review committee accepted 10 papers from 6 different countries, which are presented in this proceedings.We would like to thank several people and organizations which helped us in organizing this symposium.First and foremost, we would like to acknowledge the help and support provided by the FM 2009 organization committee and program co-chairs: Tijn Borghuis, Erik de Vink, Jos Baeten, Ana Cavalcanti and Dennis Dams.We are grateful to Formal Methods Europe association for providing generous travel grants and free tickets to the conference dinner for the participating students.Also, we would like to thank our Review and Examination Committees, as well as the additional sub-referees for their time and effort in reviewing and selecting among the submitted papers.Our best thanks go to Professor Cliff B. Jones for accepting our invitation to give an invited talk in this symposium.Finally we would like to thank the students who have

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)0.014
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: Other · Consensus signal: none
Teacher disagreement score0.087
Threshold uncertainty score0.290

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.014
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0020.002
Science and technology studies0.0010.002
Scholarly communication0.0070.006
Open science0.0020.003
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0870.024

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 head0.018
GPT teacher head0.277
Teacher spread0.259 · 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
GenreOther

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
Published2009
Admission routes1
Has abstractyes

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