{"id":"W4388196638","doi":"10.20944/preprints202310.1673.v1","title":"AI and Regulation an Analysis","year":2023,"lang":"en","type":"preprint","venue":"Preprints.org","topic":"Legal and Policy Issues","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Section (typography); Misrepresentation; Cognition; Set (abstract data type); Human intelligence; Cognitive science; Natural (archaeology); Illusion; Psychology; Computer science; Artificial intelligence; Sociology; Cognitive psychology; Political science; Law; History","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005759796,0.0004235374,0.0005428388,0.002366454,0.004862182,0.009826269,0.001461454,0.005616102,0.008100152],"category_scores_gemma":[0.007141784,0.0002658146,0.001078613,0.001895946,0.02890202,0.01036132,0.003717946,0.00632391,0.000852871],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01104328,"about_ca_system_score_gemma":0.005373567,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008021437,"about_ca_topic_score_gemma":0.003096654,"domain_scores_codex":[0.9932325,0.002981755,0.00022197,0.001043883,0.001703006,0.0008168918],"domain_scores_gemma":[0.9952018,0.002941867,0.0003448839,0.000555795,0.000766334,0.0001893109],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[7.089987e-7,0.000001504473,0.00002634354,0.000004190533,9.386528e-7,0.000007114212,0.0001650184,0.00007782856,0.000009047633,0.9983861,0.0007523398,0.0005687297],"study_design_scores_gemma":[0.000002666982,0.000004218669,0.0001791283,0.00007462168,0.000003337366,0.00002631054,0.0003330568,0.0005896945,0.00005686189,0.9238131,0.07491046,0.000006581557],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"other","genre_gemma":"commentary","genre_scores_codex":[0.01180394,0.02086763,0.05527867,0.1600732,0.001129683,0.0000720669,0.0001668924,0.0001227438,0.7504852],"genre_scores_gemma":[0.8748072,0.01585177,0.01164308,0.02867623,0.003135891,0.0004687587,0.000173345,0.0001865392,0.06505724],"genre_candidate":"commentary","genre_consensus":null,"teacher_disagreement_score":0.01104328,"threshold_uncertainty_score":0.08012497,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2571769257992574,"score_gpt":0.4681825355088599,"score_spread":0.2110056097096024,"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."}}