{"id":"W2939530943","doi":"10.1145/3322790.3330591","title":"Applicability Study of the PRIMAD Model to LIGO Gravitational Wave Search Workflows","year":2019,"lang":"en","type":"preprint","venue":"","topic":"Pulsars and Gravitational Waves Research","field":"Physics and Astronomy","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute of Standards and Technology; Sandia National Laboratories; York University; University of Pittsburgh; National Science Foundation","keywords":"LIGO; Gravitational wave; Observatory; Workflow; Computer science; Abstraction; Set (abstract data type); Interferometry; Gravitational-wave observatory; Process (computing); Software engineering; Systems engineering; Astronomy; Data science; Physics; Programming language; Engineering; Database","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01853629,0.0008117542,0.0007375358,0.002872656,0.0009659644,0.005040871,0.002662443,0.001241738,0.002568248],"category_scores_gemma":[0.05740942,0.0005454765,0.001701588,0.001628529,0.001259647,0.003483246,0.003845639,0.001943522,0.0005618834],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002548201,"about_ca_system_score_gemma":0.00428967,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01299524,"about_ca_topic_score_gemma":0.008929845,"domain_scores_codex":[0.9919193,0.004231233,0.0008275762,0.001031206,0.001678243,0.0003124114],"domain_scores_gemma":[0.9630752,0.0253735,0.001055019,0.006921933,0.002821228,0.0007531925],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00256902,0.001429996,0.06961562,0.00413834,0.0009393061,0.0008075812,0.003168633,0.6106962,0.009208098,0.1160481,0.0132269,0.1681521],"study_design_scores_gemma":[0.0001496532,0.0002698921,0.003451667,0.0002918747,0.0001951509,0.0001253928,0.0004707389,0.9423897,0.005495145,0.02600701,0.02109974,0.0000541193],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4517415,0.002112564,0.4975991,0.003531327,0.0004862031,0.002383597,0.007983843,0.01411633,0.02004539],"genre_scores_gemma":[0.6274567,0.001194195,0.359619,0.0005344226,0.00008237716,0.001189586,0.006954515,0.0009667074,0.00200261],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01853629,"threshold_uncertainty_score":0.09803051,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04788496993193429,"score_gpt":0.3723087086901615,"score_spread":0.3244237387582272,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}