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Record W3024298485

The ATLAS Publication Process Supported by Continuous Integration and Web Framework.

2020· preprint· en· W3024298485 on OpenAlexaff
Juan Pedro Araque Espinosa, Gabriel Baldi Levcovitz, R. M. Bianchi, I. Brock, T. Carli, N. F. Castro, A. Ciocio, Maurizio Colautti, Ana Carolina Da Silva Menezes, G Fonseca, Leandro Domingues Macedo Alves, A. Hoecker, Bruno Lange Ramos, Gabriela Lemos Lúcidi Pinhão, C. Maidantchik, F. Malek, R. A. McPherson, Gianluca Picco, Marcelo Texeira Dos Santos

Bibliographic record

VenuearXiv (Cornell University) · 2020
Typepreprint
Languageen
FieldComputer Science
TopicDistributed and Parallel Computing Systems
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsAtlas (anatomy)CorrectnessComputer scienceProcess (computing)SoftwareWorld Wide WebWorkspaceSoftware engineeringEngineering managementData scienceEngineering
DOInot available

Abstract

fetched live from OpenAlex

The ATLAS Collaboration defines methods, establishes procedures, and organises advisory groups to manage the publication processes of scientific papers, conference papers, and public notes. All stages are managed through web systems, computing programs, and tools that are designed and developed by the Collaboration. The Phase 0 system was implemented using the FENCE framework and is integrated into the CERN GitLab software repository, to automatically configure workspaces where the analysis can be documented and used by the analysis team and managed by the conveners. Continuous integration is used to guide the writers in applying accurate format and valid statements when preparing papers to be submitted to scientific journals. Additional software assures the correctness of other aspects such as lists of collaboration authors, funding agencies, and foundations. The ATLAS Physics and Committees Office provides support to the researchers and facilitates each phase of the publication process, allowing authors to focus on the article's contents that describe the results of the ATLAS experiment.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.925
Threshold uncertainty score0.878

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0020.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.039
GPT teacher head0.201
Teacher spread0.162 · 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 teacher head, 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

Citations1
Published2020
Admission routes1
Has abstractyes

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