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Record W2887326578

Perspectives on Probabilistic Assessment of Systems and Software

2016· preprint· en· W2887326578 on OpenAlexaff
Emmanuel Ledinot, Jean-Paul Blanquart, Jean Gassino, Bertrand Ricque, Philippe Baufreton, Jean‐Louis Boulanger, Jean‐Louis Camus, Cyrille Comar, Hervé Delseny, Philippe Quéré

Bibliographic record

VenueHAL (Le Centre pour la Communication Scientifique Directe) · 2016
Typepreprint
Languageen
FieldComputer Science
TopicSoftware Reliability and Analysis Research
Canadian institutionsSafran Electronics (Canada)
Fundersnot available
KeywordsProbabilistic logicComputer scienceSoftware constructionAvionics softwareSocial software engineeringSoftwareSoftware developmentSoftware systemSoftware metricRelevance (law)Software engineeringSoftware qualityArtificial intelligenceProgramming language
DOInot available

Abstract

fetched live from OpenAlex

International audience

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)0.022
metaresearch head score (Gemma)0.049
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.022
Threshold uncertainty score0.117

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0220.049
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0040.003
Bibliometrics0.0070.006
Science and technology studies0.0020.017
Scholarly communication0.0110.018
Open science0.0060.005
Research integrity0.0090.009
Insufficient payload (model declined to judge)0.0080.001

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 head0.019
GPT teacher head0.278
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 designTheoretical or conceptual
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

Citations2
Published2016
Admission routes1
Has abstractyes

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