{"id":"W2063386367","doi":"10.1177/0969776407077188","title":"Observing Regularities in Location Patterns","year":2007,"lang":"en","type":"article","venue":"European Urban and Regional Studies","topic":"Regional Economics and Spatial Analysis","field":"Economics, Econometrics and Finance","cited_by":45,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"","keywords":"Economic geography; Metropolitan area; Economies of agglomeration; Census; Geography; Population; Crowding; Regional science; Demographic economics; Econometrics; Demography; Economics; Economic growth; Sociology; Psychology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008118813,0.0001174608,0.0002747547,0.0001950074,0.0001185544,0.00003563706,0.000103968,0.00002471109,0.00001217986],"category_scores_gemma":[0.00004437536,0.0001190581,0.00006192658,0.0001422834,0.0001059502,0.0001046098,0.00008509223,0.00007775569,0.00005250083],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006260365,"about_ca_system_score_gemma":0.000003579705,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003465775,"about_ca_topic_score_gemma":0.0006520149,"domain_scores_codex":[0.9990054,0.00001768735,0.0004648103,0.0002935916,0.00002846889,0.0001899949],"domain_scores_gemma":[0.9995728,0.00006134848,0.0001590641,0.0001229398,0.00003507319,0.0000488036],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002000946,0.00004756958,0.5494133,0.00005705769,0.0001480909,0.00003336538,0.002200595,0.0001020085,0.00000470869,0.4437389,0.002769599,0.001464761],"study_design_scores_gemma":[0.0003224478,0.00003009174,0.9250848,0.00006443314,0.000005144492,0.00000440675,0.001317855,0.0003170437,0.000003302588,0.01412022,0.05850229,0.0002279316],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9287203,0.03913223,0.001072172,0.004204283,0.0002150349,0.0000996404,0.00001409959,0.00002819205,0.02651404],"genre_scores_gemma":[0.9938759,0.003442529,0.0001350704,0.0005879636,0.0002165771,0.00000205524,0.00001109547,0.00001542337,0.001713446],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4296187,"threshold_uncertainty_score":0.4855049,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08253045674241577,"score_gpt":0.2339375312185381,"score_spread":0.1514070744761223,"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."}}