{"id":"W1969516015","doi":"10.1007/s10651-007-0068-2","title":"A spatial clustering perspective on autocorrelation and regionalization","year":2007,"lang":"en","type":"article","venue":"Environmental and Ecological Statistics","topic":"Spatial and Panel Data Analysis","field":"Economics, Econometrics and Finance","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"York University; University of Toronto","funders":"","keywords":"Spatial analysis; Hierarchical clustering; A priori and a posteriori; Autocorrelation; Autoregressive model; Mathematics; Classifier (UML); Cluster analysis; Pattern recognition (psychology); Data mining; Computer science; Algorithm; Artificial intelligence; Statistics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001586158,0.00008006423,0.0001294643,0.00004711166,0.0001232112,0.0000233476,0.00002859301,0.00006156433,0.0007289166],"category_scores_gemma":[0.00004511648,0.00007730237,0.00001513152,0.00002745712,0.00008089373,0.00005155133,0.00004560841,0.00006782087,0.0001149131],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009511959,"about_ca_system_score_gemma":8.717852e-7,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002407226,"about_ca_topic_score_gemma":0.0001653122,"domain_scores_codex":[0.9994069,0.000007675285,0.0002006982,0.000234319,0.00002934571,0.0001211138],"domain_scores_gemma":[0.9996971,0.00009417271,0.00009188363,0.00005207026,0.000001949423,0.00006277719],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000123889,0.0002859714,0.5286545,0.00001100386,0.00005676048,0.00003115989,0.0006899766,0.0002799166,0.00005631212,0.450047,0.0003137145,0.01944992],"study_design_scores_gemma":[0.0002547454,0.0002141774,0.9413429,0.000001842836,0.000007580609,0.000004251706,0.0001595433,0.02046271,0.000003840525,0.03522297,0.002206286,0.0001190725],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3857496,0.0002038195,0.6088281,0.0002230837,0.0001121293,0.0001806808,0.0005937915,0.00001779825,0.004090962],"genre_scores_gemma":[0.9970596,0.0002042169,0.002057008,0.0002416056,0.0000518335,0.0000042654,0.0001382005,0.000004961876,0.000238255],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6113101,"threshold_uncertainty_score":0.7981126,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02197869175012091,"score_gpt":0.2153155627447375,"score_spread":0.1933368709946166,"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."}}