{"id":"W2590785525","doi":"10.1109/icci-cc.2016.7862044","title":"On cognitive foundations of big data science and engineering","year":2016,"lang":"en","type":"article","venue":"","topic":"Cognitive Computing and Networks","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Big data; Data science; Computer science; Property (philosophy); Set (abstract data type); Science and engineering; Cognitive science; Theoretical computer science; Epistemology; Data mining; Engineering ethics; Engineering; Psychology","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":[0.0004335489,0.00004782751,0.00005552522,0.0001102432,0.00007208325,0.00004655612,0.0005231348,0.00001045287,0.000003051896],"category_scores_gemma":[0.0007754285,0.00003131951,0.000005870952,0.0003857015,0.0001352846,0.0003280479,0.00069013,0.00002856822,0.000008757729],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000009473695,"about_ca_system_score_gemma":0.00007481717,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003810078,"about_ca_topic_score_gemma":0.000001886396,"domain_scores_codex":[0.9993252,0.00000845529,0.00007835031,0.0002884362,0.0001757609,0.0001238188],"domain_scores_gemma":[0.9986807,0.0007141006,0.0000269864,0.000317456,0.0002132528,0.0000475485],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000001801098,0.0000184272,0.0001392184,0.000003034965,0.000004749015,8.294909e-7,0.00004622356,0.000003289385,0.001747068,0.07662722,0.0001484953,0.9212596],"study_design_scores_gemma":[0.002311376,0.0006217118,0.05866734,0.001814687,0.00002706241,0.0000408913,0.00005311649,0.8921533,0.03037742,0.009920472,0.003173317,0.0008393102],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03866508,0.00002091655,0.9562874,0.0002688283,0.0002247148,0.0000448545,0.000004273664,0.00005603951,0.004427889],"genre_scores_gemma":[0.9959787,0.00001373341,0.003830817,0.00008069837,0.00003866688,8.551877e-7,4.998851e-7,0.000001914019,0.00005415292],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9573136,"threshold_uncertainty_score":0.1277173,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0670574970088479,"score_gpt":0.2803250133880988,"score_spread":0.2132675163792509,"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."}}