{"id":"W7111043055","doi":"10.57638/3034-8234paritoro","title":"Exploring high-tech specializations with the use of metadata: evidence from the metropolitan clusters of Paris and Toronto","year":2024,"lang":"en","type":"article","venue":"Università Politecnica delle Marche (Università Politecnica delle Marche)","topic":"Global Urban Networks and Dynamics","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Metropolitan area; Core (optical fiber); Point (geometry); Technological change; Simple (philosophy); Information technology","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":[0.0006736741,0.0002594369,0.0002091763,0.004144787,0.001576015,0.001550295,0.0004955227,0.0003313102,0.001988173],"category_scores_gemma":[0.005107785,0.0001921892,0.0002254101,0.01109394,0.001154913,0.000912322,0.001493533,0.0002458371,0.000221133],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006387594,"about_ca_system_score_gemma":0.004139217,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.8305674,"about_ca_topic_score_gemma":0.9271658,"domain_scores_codex":[0.9994304,0.0001498626,0.0000364812,0.0001143937,0.0001423803,0.0001264792],"domain_scores_gemma":[0.9945459,0.001590498,0.001844406,0.0004343922,0.001066277,0.0005183978],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00008983233,0.00002634754,0.9581994,0.0001833046,0.0001055731,0.0006619679,0.01806291,0.001242088,0.0006362232,0.002669409,0.003664799,0.01445794],"study_design_scores_gemma":[0.000003165059,0.00001092393,0.9758871,0.00006665972,0.00003572259,0.00008473721,0.01697776,0.0008145598,0.0002612835,0.0002055239,0.005637864,0.00001477204],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9901999,0.0005255404,0.0005130036,0.0003133049,0.000006106167,0.00002239101,0.004127737,0.0000172529,0.004274679],"genre_scores_gemma":[0.996718,0.0002638531,0.0004548328,0.00001900293,0.000005139285,0.0000161336,0.001787982,0.000006177684,0.0007289971],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1694326,"threshold_uncertainty_score":0.3408608,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1145134930564448,"score_gpt":0.2729923154861992,"score_spread":0.1584788224297544,"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."}}