{"id":"W4210797700","doi":"10.14778/3489496.3489504","title":"LargeEA","year":2021,"lang":"en","type":"article","venue":"Proceedings of the VLDB Endowment","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Scalability; Benchmark (surveying); Exploit; Process (computing); Channel (broadcasting); Partition (number theory); Competitor analysis; Feature (linguistics); Data mining; Artificial intelligence; Database; Programming language","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001034315,0.00009638671,0.0001196222,0.00002710934,0.00008373565,0.0000525752,0.0009073472,0.00002777057,0.000008543218],"category_scores_gemma":[0.0000551032,0.0000671169,0.0001027169,0.0005610397,0.00003701969,0.0002890772,0.0007651329,0.0001211279,0.00000646813],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002534772,"about_ca_system_score_gemma":0.00002053895,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000174737,"about_ca_topic_score_gemma":8.04051e-7,"domain_scores_codex":[0.9990408,0.000004567143,0.0001716215,0.0002622998,0.0002969787,0.0002237249],"domain_scores_gemma":[0.9994027,0.00002552372,0.0001267309,0.0002131002,0.0001836263,0.00004833081],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00000755201,0.0001775877,0.005824821,0.00006630682,0.00005163523,0.000006228023,0.000536922,0.0001593115,0.175773,0.787488,0.01019289,0.01971574],"study_design_scores_gemma":[0.000397177,0.0000410523,0.002931396,0.00007699507,0.0000116321,0.00005935696,0.00006508303,0.002185934,0.8963573,0.07911799,0.01857285,0.0001832127],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7001984,0.006081498,0.07495213,0.05669829,0.006572707,0.002146313,0.00001428393,0.001274939,0.1520614],"genre_scores_gemma":[0.9685709,0.00006409425,0.02967015,0.0007024539,0.00005700923,0.00001853051,2.003391e-7,0.000008141623,0.0009084475],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7205843,"threshold_uncertainty_score":0.2736948,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007208047789126523,"score_gpt":0.2039609482731143,"score_spread":0.1967529004839878,"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."}}