{"id":"W6888002023","doi":"10.18130/9kar-xn17","title":"Artificial Intelligence in the City: Building Civic Engagement and Public Trust","year":2022,"lang":"en","type":"book","venue":"Libra","topic":"Smart Cities and Technologies","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Public engagement; Civic engagement; Multidisciplinary approach; Corporate governance; Work (physics); Civil society; Big data; Public space; Space (punctuation)","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":[0.0002118316,0.0001698719,0.0001698511,0.0002233331,0.00009934302,0.0001620004,0.0004150675,0.0001297837,0.0005849393],"category_scores_gemma":[0.00003315624,0.0001460446,0.00004014986,0.0001577374,0.00006288145,0.0001057844,0.0002923757,0.0008038452,0.000005690652],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007397735,"about_ca_system_score_gemma":0.0000275947,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005789002,"about_ca_topic_score_gemma":0.00001933939,"domain_scores_codex":[0.9992071,0.00002468109,0.0002009357,0.0001762355,0.000160884,0.000230174],"domain_scores_gemma":[0.9995117,0.0001593324,0.00002735593,0.0002810568,0.000003280566,0.00001731867],"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":[0.000003040641,0.00002048571,0.0003667088,0.0002280326,0.0000734643,0.00007886376,0.001476385,0.0004804083,0.00001394186,0.7213546,0.02670842,0.2491956],"study_design_scores_gemma":[0.00001936752,0.0000342247,0.0001313385,0.00004443872,0.00001310314,0.000008107009,0.002621969,0.002006214,0.00008739955,0.1061939,0.8885547,0.0002852454],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.07513398,0.01204164,0.004187852,0.003478451,0.003044521,0.001511536,0.00012488,0.003100895,0.8973762],"genre_scores_gemma":[0.9584085,0.003677559,0.001414934,0.0005740535,0.0009403508,0.0004804278,0.0001291461,0.0002096674,0.03416531],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8832746,"threshold_uncertainty_score":0.6404675,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04704171566425736,"score_gpt":0.2266591275764701,"score_spread":0.1796174119122127,"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."}}