{"id":"W4388714572","doi":"10.1017/9781108995825.009","title":"Conclusion","year":2023,"lang":"en","type":"book-chapter","venue":"Cambridge University Press eBooks","topic":"Law, AI, and Intellectual Property","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Harm; Leverage (statistics); Internet privacy; Reputation; Public relations; Business; Law and economics; Accountability; Liability; Big data; Profit (economics); Political science; Economics; Law; Computer science; Accounting","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001266043,0.0003450857,0.0003657491,0.0001921478,0.0003330222,0.0001168851,0.001845904,0.000367152,0.000009460666],"category_scores_gemma":[0.00001884638,0.0003491216,0.0002397451,0.0000180819,0.0002932709,0.0002102748,0.002352764,0.0004419316,0.0003572727],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001759731,"about_ca_system_score_gemma":0.0001607981,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001029062,"about_ca_topic_score_gemma":0.00000192426,"domain_scores_codex":[0.9983255,0.00004106445,0.0001846185,0.0007260158,0.0003862046,0.0003365713],"domain_scores_gemma":[0.9983898,0.000112433,0.0001454972,0.0009551417,0.0001996815,0.0001974199],"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":[0.00001406233,0.000003603124,6.106282e-8,0.00002732486,0.00004754208,0.0002555934,0.00009329194,9.31286e-7,0.00005212623,0.7963464,0.1998782,0.003280844],"study_design_scores_gemma":[0.0002864076,0.00008930701,0.000001022678,0.0001259875,0.00003987306,0.00001520484,0.000006947152,0.002501648,0.0005241105,0.0001221375,0.9958236,0.000463753],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.000004636178,0.00006355443,0.0257449,0.00005104671,0.0008138526,0.0002790111,0.00004893757,0.000676456,0.9723176],"genre_scores_gemma":[0.0002975907,0.0001948051,0.0002656748,0.0002210136,0.0001457126,3.749043e-7,0.00001752534,0.00004378947,0.9988135],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.7962242,"threshold_uncertainty_score":0.999896,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03818860533374172,"score_gpt":0.1961087895127599,"score_spread":0.1579201841790182,"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."}}