{"id":"W2125332531","doi":"10.1007/s11004-012-9403-8","title":"Special Issue on Spatial Multivariate Methods","year":2012,"lang":"en","type":"article","venue":"Mathematical Geosciences","topic":"Spatial and Panel Data Analysis","field":"Economics, Econometrics and Finance","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"Geological Survey of Canada","funders":"","keywords":"Multivariate statistics; Computer science; Contrast (vision); Salient; Field (mathematics); Climate change; Climate model; Spatial analysis; Data science; Geography; Econometrics; Operations research; Mathematics; Artificial intelligence; Machine learning; Geology; Remote sensing","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003609443,0.001645211,0.002312767,0.004406068,0.001022659,0.003809507,0.001664407,0.002856028,0.07975733],"category_scores_gemma":[0.01234998,0.0006583187,0.001668653,0.003478849,0.001441273,0.003899859,0.002225743,0.003523162,0.02489424],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001085687,"about_ca_system_score_gemma":0.001626019,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007990043,"about_ca_topic_score_gemma":0.001648801,"domain_scores_codex":[0.9975625,0.0008312834,0.0001908037,0.0003780278,0.0009232858,0.0001141383],"domain_scores_gemma":[0.9922167,0.003791531,0.0003878325,0.001219366,0.001756305,0.0006281692],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001772247,0.00004417035,0.0002176726,0.0003129181,0.00005078626,0.0000628695,0.00002799321,0.0007442794,0.0001981158,0.0466093,0.8895427,0.06217154],"study_design_scores_gemma":[0.00001413452,0.00003481456,0.0008485269,0.0002780238,0.00004063815,0.000249136,0.00003386209,0.003849141,0.0002151824,0.1087073,0.885703,0.00002609545],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"editorial","genre_gemma":"editorial","genre_scores_codex":[0.001676589,0.09175488,0.1456415,0.05004971,0.580945,0.0001644207,0.001702861,0.000938122,0.127127],"genre_scores_gemma":[0.01492614,0.05599808,0.03158646,0.009644987,0.6288378,0.0003178355,0.00246592,0.001954542,0.2542683],"genre_candidate":"editorial","genre_consensus":"editorial","teacher_disagreement_score":0.07975733,"threshold_uncertainty_score":0.2668149,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08277531504908604,"score_gpt":0.3245440443483515,"score_spread":0.2417687292992655,"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."}}