{"id":"W6991460468","doi":"","title":"The great urban techno shift: Are central neighborhoods the next Silicon Valleys? Evidence from three Canadian metropolitan areas","year":2016,"lang":"en","type":"other","venue":"EspaceINRS (National Institute for Scientific Research (Canada))","topic":"","field":"","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Metropolitan area; Service (business); Geographic information system; Econometric model; Point (geometry); Silicon valley","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":[0.0008621473,0.0002805542,0.0002837462,0.002103033,0.00416431,0.00217088,0.001300382,0.0003818892,0.003038835],"category_scores_gemma":[0.002815396,0.0002542056,0.0004015204,0.005571837,0.002252206,0.0007190768,0.001834929,0.0005824674,0.0001828962],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01815738,"about_ca_system_score_gemma":0.03442003,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9954211,"about_ca_topic_score_gemma":0.9987319,"domain_scores_codex":[0.9992842,0.0000598459,0.00002251609,0.000127158,0.0002376453,0.0002685949],"domain_scores_gemma":[0.9969617,0.0002981636,0.0005451394,0.0001709788,0.001416704,0.0006073963],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000122644,0.00004773668,0.954383,0.0001070644,0.00006591314,0.0001819526,0.01269387,0.0002535877,0.0003096565,0.002418051,0.005856359,0.02356013],"study_design_scores_gemma":[0.000004709025,0.000008823312,0.9746053,0.00007293151,0.00002767207,0.00002103352,0.02013471,0.0002265009,0.00009263384,0.000140407,0.004654228,0.00001110251],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9859881,0.001040999,0.0001431402,0.00117146,0.00001449585,0.00002643349,0.002629051,0.000007431261,0.00897908],"genre_scores_gemma":[0.9958475,0.0007801337,0.0002040912,0.0001359468,0.000006011727,0.00001243834,0.001078001,0.000007540481,0.001928222],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01815738,"threshold_uncertainty_score":0.1317416,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0697174498991547,"score_gpt":0.3248494163201192,"score_spread":0.2551319664209645,"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."}}