{"id":"W7095155380","doi":"","title":"Theme D CITY AND INNOVATION: DIFFERENT SIZE, DIFFERENT STRATEGY.","year":2003,"lang":"en","type":"article","venue":"","topic":"Regional Economics and Spatial Analysis","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Theme (computing); Distribution (mathematics); Path (computing); Public sector; Base (topology)","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":[0.001527992,0.0001558197,0.0003592582,0.001720775,0.0006057592,0.00214456,0.000779075,0.0006275855,0.007976252],"category_scores_gemma":[0.008575365,0.0001262181,0.0009346237,0.003418276,0.001666452,0.001194435,0.0026748,0.0008388638,0.0006138454],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001676007,"about_ca_system_score_gemma":0.002369119,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04812699,"about_ca_topic_score_gemma":0.07197741,"domain_scores_codex":[0.9984623,0.0003707608,0.00006375317,0.0003026153,0.0004504619,0.0003499576],"domain_scores_gemma":[0.9891468,0.003042711,0.004171125,0.000902825,0.0008310343,0.001905464],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001219268,0.00004960469,0.9633424,0.00007320073,0.0002060523,0.0001172903,0.001016408,0.0003977956,0.0004067796,0.008923839,0.001832215,0.02351252],"study_design_scores_gemma":[0.00002017257,0.00006981223,0.9844316,0.00004586198,0.0001800544,0.0002164664,0.002080933,0.0008904935,0.0002903394,0.003044817,0.008712552,0.00001704216],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"other","genre_scores_codex":[0.969932,0.002027611,0.003076322,0.003177205,0.00007652374,0.00004608724,0.001599908,0.00003580601,0.02002856],"genre_scores_gemma":[0.9959793,0.0003326395,0.0006218227,0.0001375189,0.00002075113,0.00001711099,0.0004065236,0.000013197,0.002471095],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.04812699,"threshold_uncertainty_score":0.09569371,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05451459103375373,"score_gpt":0.2207624113405827,"score_spread":0.1662478203068289,"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."}}