{"id":"W2263300136","doi":"","title":"Intelligent Agents: Authors, Makers, and Owners of Computer-Generated Works in Canadian Copyright Law","year":2005,"lang":"en","type":"article","venue":"eYLS (Yale Law School)","topic":"Law, AI, and Intellectual Property","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Copyright law; Law; Law and economics; Business; Economics; Internet privacy; Computer science; Political science; Intellectual property","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008928697,0.0006229117,0.0005681533,0.005040952,0.02639607,0.02614757,0.003228787,0.009957123,0.008544622],"category_scores_gemma":[0.03082277,0.0007440222,0.00052754,0.006460422,0.03936769,0.01561014,0.006699407,0.006976268,0.0005687922],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.1091586,"about_ca_system_score_gemma":0.1311527,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9284764,"about_ca_topic_score_gemma":0.9446917,"domain_scores_codex":[0.9831593,0.002711478,0.0006399384,0.00123337,0.00897476,0.003281277],"domain_scores_gemma":[0.9857392,0.00676823,0.001149483,0.001052323,0.00391061,0.001380199],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000004683173,0.000005772858,0.0004342127,0.00001466769,0.000001790684,0.00008436495,0.003339053,0.0001943441,0.00003802743,0.9867198,0.004237641,0.004925688],"study_design_scores_gemma":[0.0000471014,0.00001485119,0.004938138,0.0003459066,0.00005536922,0.0002478326,0.009643533,0.004735053,0.0006087134,0.5719886,0.4072191,0.0001559101],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.04992153,0.007443844,0.01381593,0.1316374,0.0008537857,0.0002070414,0.0001668708,0.000110757,0.7958429],"genre_scores_gemma":[0.8859833,0.005278629,0.007908232,0.009781786,0.0004296038,0.000116775,0.000096852,0.00005088996,0.09035396],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.1091586,"threshold_uncertainty_score":0.7920046,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02009103383232158,"score_gpt":0.2394592447975779,"score_spread":0.2193682109652563,"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."}}