{"id":"W4390483843","doi":"10.1109/ssci52147.2023.10371940","title":"Advancing Smart Cities Through Novel Social Media Text Analysis: A Case Study of Calgary","year":2023,"lang":"en","type":"article","venue":"","topic":"Human Mobility and Location-Based Analysis","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Social media; Data science; Computer science; Variety (cybernetics); Smart city; Set (abstract data type); Urban planning; Bureaucracy; Sentiment analysis; Population; Perception; Artificial intelligence; World Wide Web; Political science; Sociology; Politics; Engineering; Psychology; Internet of Things","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001290172,0.0001006199,0.0003442591,0.0003021082,0.0009362074,0.00004414374,0.0001783052,0.00007139426,0.0008678208],"category_scores_gemma":[0.0003953186,0.00009512415,0.0002150714,0.003499062,0.0002645309,0.0001865587,0.00004808705,0.00009767125,0.00002811759],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007823394,"about_ca_system_score_gemma":0.0001983984,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.1958249,"about_ca_topic_score_gemma":0.7888733,"domain_scores_codex":[0.998273,0.0002507761,0.0003772614,0.000269938,0.0005442589,0.0002848385],"domain_scores_gemma":[0.9987333,0.0007043993,0.0001095208,0.0001935098,0.0001919622,0.00006732448],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"qualitative","study_design_scores_codex":[0.00001269288,0.0008205949,0.04302174,0.00002646497,0.001316985,0.0001495895,0.9389107,0.0008976674,0.00004120663,0.007976331,0.0007070618,0.006118937],"study_design_scores_gemma":[0.0003243282,0.00003390102,0.01123324,0.000003563666,0.0008866548,7.701335e-7,0.98317,0.003136529,0.00001357727,0.0004809849,0.0005600924,0.0001563183],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9893587,0.00001693212,0.002815636,0.0003373754,0.00006882865,0.0001844413,0.000007554035,0.0001383265,0.007072222],"genre_scores_gemma":[0.9986287,0.000008926605,0.0001176744,0.00007575863,0.0001368525,0.00003314317,0.00001692203,0.00000649467,0.0009755627],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5930484,"threshold_uncertainty_score":0.9502029,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04882676916612464,"score_gpt":0.3442873849231619,"score_spread":0.2954606157570372,"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."}}