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Record W2939530943 · doi:10.1145/3322790.3330591

Applicability Study of the PRIMAD Model to LIGO Gravitational Wave Search Workflows

2019· preprint· en· W2939530943 on OpenAlexfundno aff
Dylan Chapp, Danny Rorabaugh, D. Brown, Ewa Deelman, Karan Vahi, Von Welch, Michela Taufer

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldPhysics and Astronomy
TopicPulsars and Gravitational Waves Research
Canadian institutionsnot available
FundersNational Institute of Standards and TechnologySandia National LaboratoriesYork UniversityUniversity of PittsburghNational Science Foundation
KeywordsLIGOGravitational waveObservatoryWorkflowComputer scienceAbstractionSet (abstract data type)InterferometryGravitational-wave observatoryProcess (computing)Software engineeringSystems engineeringAstronomyData sciencePhysicsProgramming languageEngineeringDatabase

Abstract

fetched live from OpenAlex

The PRIMAD model with its six components (i.e., Platform, Research Objective, Implementation, Methods, Actors, and Data) provides an abstract taxonomy to represent computational experiments and promote reproducibility by design. In this paper, we employ a post-hoc assessment of the model applicability to a set of Laser Interferometer Gravitational-Wave Observatory (LIGO) workflows from literature sources (i.e., published papers). Our work outlines potential advantages and limitations of the model in terms of its levels of abstraction and means of application.

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)0.019
metaresearch head score (Gemma)0.057
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.019
Threshold uncertainty score0.098

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0190.057
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0030.002
Science and technology studies0.0010.001
Scholarly communication0.0050.003
Open science0.0030.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.048
GPT teacher head0.372
Teacher spread0.324 · 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
Published2019
Admission routes1
Has abstractyes

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