{"id":"W3211229017","doi":"10.14778/3551793.3551804","title":"A scalable AutoML approach based on graph neural networks","year":2022,"lang":"en","type":"article","venue":"Proceedings of the VLDB Endowment","topic":"Machine Learning and Data Classification","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Scalability; Scripting language; Metadata; Pipeline (software); Artificial intelligence; Machine learning; Graph; Pipeline transport; Theoretical computer science; Database; Programming language; World Wide Web","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006170537,0.0001188687,0.0001217231,0.0001036997,0.0003857234,0.00009674426,0.001457579,0.00002077916,0.00001634946],"category_scores_gemma":[0.00005368016,0.00008634634,0.00009150135,0.000702279,0.00003395048,0.0001452332,0.0006012432,0.0003255756,0.000001575509],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006863532,"about_ca_system_score_gemma":0.00002117455,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003393227,"about_ca_topic_score_gemma":1.444718e-7,"domain_scores_codex":[0.9986434,0.0000287661,0.0001979763,0.0003581157,0.0005426779,0.0002290819],"domain_scores_gemma":[0.9993181,0.00003418854,0.0002216327,0.000310207,0.00006639535,0.00004943077],"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":[0.0001283811,0.001460698,0.01620446,0.0001512346,0.00005188561,9.512481e-7,0.0009407894,0.6324161,0.003257048,0.248615,0.03973371,0.05703972],"study_design_scores_gemma":[0.0002998419,0.0001360351,0.003024465,0.000007322508,0.000007282446,0.000005325504,0.00005402504,0.991061,0.000464435,0.000547872,0.004295875,0.00009646614],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1698421,0.0009190809,0.485265,0.08170652,0.005062535,0.006236517,0.00007373475,0.003000078,0.2478944],"genre_scores_gemma":[0.9910998,0.000002037745,0.007494811,0.0009179666,0.00003403399,0.0001599785,0.00000587282,0.000009044433,0.0002764007],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8212577,"threshold_uncertainty_score":0.3521101,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0127707688173583,"score_gpt":0.2149683934998161,"score_spread":0.2021976246824577,"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."}}