{"id":"W4413751440","doi":"10.1145/3760214","title":"Why AI Cannot Learn South Asian Cities","year":2025,"lang":"en","type":"article","venue":"interactions","topic":"Smart Cities and Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"South asia; Computer science; History; Ancient history","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":[0.00001147662,0.00007346066,0.00007323845,0.0001677928,0.00007825485,0.00005278387,0.00009008135,0.00004104302,0.0002501574],"category_scores_gemma":[0.00002725572,0.00007510612,0.00004360755,0.0001475535,0.00003254281,0.0001064764,0.00002873427,0.000223313,0.00006836719],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005634132,"about_ca_system_score_gemma":0.000009712758,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001067668,"about_ca_topic_score_gemma":0.0005561639,"domain_scores_codex":[0.9996685,0.000003754281,0.00009267023,0.00007181591,0.00003543143,0.0001278295],"domain_scores_gemma":[0.9997744,0.00002625167,0.000007607407,0.0001553289,0.00002306007,0.00001334623],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00000455679,0.00001496115,0.002867596,0.00004725884,0.0001711284,0.000007278039,0.001239267,0.003314422,0.0005891086,0.01781486,0.9561744,0.01775514],"study_design_scores_gemma":[0.00005963684,0.000006880455,0.001112502,0.00004231931,0.00001112713,0.000004120577,0.004890146,0.001537107,0.002477348,0.001535833,0.9882352,0.00008783092],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.136996,0.0005344489,0.03870152,0.0315135,0.009833572,0.0002450381,0.0001354299,0.005925393,0.7761151],"genre_scores_gemma":[0.9926646,0.00001281208,0.0001448828,0.0006998013,0.00005703008,0.00003193528,0.000005412186,0.00001054288,0.006372973],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8556686,"threshold_uncertainty_score":0.3062739,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0102810375945805,"score_gpt":0.2400530766967854,"score_spread":0.229772039102205,"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."}}