{"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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.001558225,0.0001329939,0.0003601498,0.0001501064,0.0001492912,0.00009217508,0.0003307907,0.00006600704,0.01680628],"category_scores_gemma":[0.0007038087,0.0001083142,0.0001131831,0.0003295901,0.0001349503,0.0002621775,0.0000779623,0.00009817829,0.0100654],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002234789,"about_ca_system_score_gemma":0.000005902851,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005678742,"about_ca_topic_score_gemma":0.00001183094,"domain_scores_codex":[0.9987394,0.00004140756,0.0004406977,0.0002904847,0.00008384772,0.0004041776],"domain_scores_gemma":[0.9991661,0.000215562,0.0001623548,0.0002767457,0.00001155394,0.0001676232],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001607385,0.0006405567,0.009526931,0.00003737377,0.00004964691,0.000001872611,0.002334919,0.00001378551,0.00005393951,0.9038598,0.005849096,0.07761599],"study_design_scores_gemma":[0.0003066392,0.0001421229,0.03111034,0.00002176597,0.00002643356,0.000005035352,0.0002478598,0.01913722,0.0005062218,0.2211532,0.7268022,0.0005409399],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.01590025,0.0001590022,0.3296463,0.001126817,0.004136375,0.0002440448,0.0001592928,0.00007368216,0.6485541],"genre_scores_gemma":[0.7380581,0.00004708969,0.2089075,0.001612353,0.03224128,0.00007165998,0.00003319079,0.00004553526,0.01898326],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7221578,"threshold_uncertainty_score":0.9907054,"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."}}