{"id":"W2084017699","doi":"10.1111/j.0008-3658.2005.00087.x","title":"A framework for statistical inferential decisions in spatial pattern analysis","year":2005,"lang":"en","type":"article","venue":"Canadian Geographies / Géographies canadiennes","topic":"Spatial and Panel Data Analysis","field":"Economics, Econometrics and Finance","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"Wilfrid Laurier University; University of Toronto","funders":"","keywords":"Data science; Computer science; Perspective (graphical); Inference; Statistical inference; Spatial analysis; Representation (politics); Decision tree; Data mining; Management science; Artificial intelligence; Statistics; Mathematics; Engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003797002,0.0003754064,0.0008727972,0.01027089,0.0003730036,0.0002113665,0.0005698248,0.0002834248,0.001270673],"category_scores_gemma":[0.0005533455,0.0004462812,0.0006324264,0.006719292,0.0003991008,0.0002531912,0.00004676725,0.0002741468,0.00006829557],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000141945,"about_ca_system_score_gemma":0.00008088318,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9147421,"about_ca_topic_score_gemma":0.9991786,"domain_scores_codex":[0.9968173,0.00003219074,0.0009945108,0.000851645,0.00008360035,0.001220735],"domain_scores_gemma":[0.9975836,0.0004739634,0.0002403559,0.0006729611,0.00009001409,0.0009391378],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002210295,0.00005099543,0.8113954,0.00001263921,0.000790828,0.00002259451,0.0006104734,0.0003747643,3.00649e-7,0.1367108,0.001629921,0.04837921],"study_design_scores_gemma":[0.0007090141,0.0001220779,0.8086768,0.00003158114,0.0004207587,0.000004049507,0.0009697569,0.005769068,0.000002795008,0.07717526,0.1050881,0.001030712],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6903282,0.003012654,0.2735547,0.004924428,0.001029629,0.0009151483,0.02477592,0.0001004393,0.00135893],"genre_scores_gemma":[0.9925419,0.0009011083,0.003840288,0.000753821,0.0002939951,0.0001796839,0.001395917,0.00004107382,0.00005221337],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3022137,"threshold_uncertainty_score":0.9997989,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02264810158846387,"score_gpt":0.228270984270691,"score_spread":0.2056228826822271,"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."}}