{"id":"W4414015725","doi":"10.11159/mvml25.002","title":"Human AI - How Big Data is Big Enough?","year":2025,"lang":"en","type":"article","venue":"Proceedings of the World Congress on Electrical Engineering and Computer Systems and Science","topic":"Big Data and Business Intelligence","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Big data; Computer science; Data science; Data mining","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0004076639,0.0001675784,0.0002270503,0.0004638713,0.0003208772,0.001141194,0.00119319,0.00003606676,6.423447e-7],"category_scores_gemma":[0.00006359911,0.000114666,0.00002278325,0.001998632,0.0001853933,0.0006472894,0.0009059,0.0001880063,7.986072e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001434372,"about_ca_system_score_gemma":0.00002134435,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009454729,"about_ca_topic_score_gemma":0.000003749207,"domain_scores_codex":[0.9987269,0.00000120034,0.0001943769,0.0004770905,0.0003162225,0.0002842263],"domain_scores_gemma":[0.9993404,0.00002718232,0.0001194635,0.0002592567,0.0002320888,0.00002164758],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000026794,0.0001460551,0.01447302,0.00209422,0.00008023604,0.000002091578,0.00004279112,0.000273269,0.01012156,0.7680559,0.06592354,0.1387606],"study_design_scores_gemma":[0.0002533193,0.00002426665,0.008618333,0.001025078,0.00004347177,0.000007512418,0.00001226315,0.8105842,0.001495324,0.0002970031,0.1773122,0.0003271088],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.911323,0.004897529,0.01364039,0.02761789,0.02915793,0.002031944,0.00003796876,0.0007411086,0.0105522],"genre_scores_gemma":[0.9973765,0.00001395257,0.00004808115,0.0005751345,0.0007067784,0.000006511965,7.437486e-7,0.00000722698,0.001265049],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8103109,"threshold_uncertainty_score":0.9998957,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0409612776379997,"score_gpt":0.2537377033176493,"score_spread":0.2127764256796496,"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."}}