{"id":"W6884624144","doi":"10.11575/prism/42174","title":"Advancing Smart Cities through Novel Social Media Text Analysis: A Case Study of Calgary","year":2023,"lang":"en","type":"other","venue":"Open MIND","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Social media; Variety (cybernetics); Set (abstract data type); Bureaucracy; Population; Perception","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":[0.001062392,0.0005276491,0.0002678373,0.001364806,0.005833951,0.002439276,0.001176142,0.001640499,0.001471004],"category_scores_gemma":[0.002982654,0.0001900272,0.0002213695,0.002869065,0.002334617,0.001367246,0.001503813,0.001373968,0.0003523726],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00509678,"about_ca_system_score_gemma":0.002127647,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.2288319,"about_ca_topic_score_gemma":0.4558156,"domain_scores_codex":[0.9990935,0.0003960243,0.0000318408,0.0001333783,0.0001695672,0.0001756945],"domain_scores_gemma":[0.9981009,0.00103537,0.0001611013,0.0001093845,0.0003347882,0.0002584382],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"observational","study_design_scores_codex":[0.0003931513,0.001759238,0.1350289,0.0006062355,0.0001875321,0.05973303,0.5648108,0.008670807,0.01162399,0.01079271,0.04021911,0.1661745],"study_design_scores_gemma":[0.00005818455,0.0002259322,0.141017,0.000141716,0.00005822229,0.001567844,0.7371383,0.02113326,0.004113188,0.002590108,0.09186976,0.00008649347],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.989528,0.0001132205,0.001308672,0.001892194,0.00002847867,0.0001239085,0.0003249095,0.00005398261,0.006626741],"genre_scores_gemma":[0.9833219,0.0003952243,0.005272797,0.000871964,0.00005387994,0.00009132602,0.0005309327,0.00009666081,0.00936527],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7711681,"threshold_uncertainty_score":0.4549998,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0709479041161818,"score_gpt":0.343308528664639,"score_spread":0.2723606245484572,"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."}}