{"id":"W1537873983","doi":"10.1007/s10110-004-0200-8","title":"Development similarity based on proximity: A case study of urban clusters in Canada","year":2004,"lang":"en","type":"article","venue":"Papers of the Regional Science Association","topic":"Urban Transport and Accessibility","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Similarity (geometry); Regional science; Geography; Economic geography; Computer science; Artificial intelligence","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001829506,0.00006311175,0.0001213065,0.00006043436,0.0004632578,0.00001292973,0.0003757424,0.00003259763,0.000004684835],"category_scores_gemma":[0.0003092329,0.00004835653,0.0000340177,0.0009360031,0.0001811007,0.0001720052,0.00002182304,0.00009517094,1.174666e-7],"about_ca_system_candidate":true,"about_ca_system_consensus":true,"about_ca_system_score_codex":0.004051198,"about_ca_system_score_gemma":0.00669868,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.8643299,"about_ca_topic_score_gemma":0.9920466,"domain_scores_codex":[0.9979309,0.00009861735,0.0002530857,0.0001942967,0.00130356,0.000219483],"domain_scores_gemma":[0.9992718,0.0001051164,0.0003028348,0.0001364789,0.0001284854,0.00005530033],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001170892,0.0002790802,0.9865826,0.000004539284,0.00000425821,0.000005404206,0.01084988,0.001829095,0.00006206171,0.0001032293,0.00001309872,0.0002550429],"study_design_scores_gemma":[0.0005651878,0.00002494681,0.9793409,0.00003468739,0.000007810993,1.819147e-7,0.01939741,0.00009760731,0.0002041814,0.00009761271,0.0001494303,0.00008004518],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9958095,0.000003069027,0.000001455637,0.002396714,0.00009606799,0.0003792121,0.00000209441,0.000004459664,0.00130741],"genre_scores_gemma":[0.9996687,4.857027e-7,0.0001011829,0.0001800496,0.0000106316,0.000007346982,5.135915e-7,0.000001805131,0.00002926093],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1277167,"threshold_uncertainty_score":0.9997721,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02331221110790765,"score_gpt":0.2693294263234275,"score_spread":0.2460172152155199,"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."}}