{"id":"W3136533050","doi":"","title":"Geostatistics with infinite dimensional data: a generalization of cokriging and multivariable spatial prediction","year":2011,"lang":"en","type":"article","venue":"Matemática","topic":"Soil Geostatistics and Mapping","field":"Environmental Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Multivariable calculus; Kriging; Geostatistics; Mathematics; Context (archaeology); Multivariate statistics; Generalization; Sampling (signal processing); Statistics; Parametric statistics; Functional data analysis; Computer science; Spatial variability; Geography; Mathematical analysis; Engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001196168,0.00006748328,0.00008078615,0.00001899038,0.00006366914,0.00001095102,0.000070121,0.00002381176,0.0004020527],"category_scores_gemma":[0.00004198831,0.00005653015,0.000003290099,0.00006863596,0.0001133667,0.0001159631,0.000163397,0.00003385346,0.00001222704],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001285657,"about_ca_system_score_gemma":0.00000887493,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002611334,"about_ca_topic_score_gemma":0.0001092152,"domain_scores_codex":[0.9993934,0.00001791158,0.0001454273,0.0001796365,0.0001513342,0.0001123128],"domain_scores_gemma":[0.9996549,0.00003585422,0.00007274721,0.0001776508,0.0000134384,0.00004540164],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000276619,0.0003100598,0.9412313,0.0001825393,0.00009506945,0.00003973044,0.002886323,0.005690943,0.011396,0.016465,0.004234025,0.01719241],"study_design_scores_gemma":[0.0007242689,0.0001773116,0.5530601,0.00006807172,0.00007227886,0.00002057668,0.00006782059,0.4395559,0.0009334617,0.001826379,0.003300108,0.0001938045],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5631859,0.00002186872,0.4273282,0.00002316426,0.0001638109,0.0002753757,0.0005863018,0.00004875319,0.008366568],"genre_scores_gemma":[0.9242343,0.00001105645,0.07547303,0.00003624295,0.00001327871,0.000003805115,0.0001528516,0.000008593864,0.00006679953],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4338649,"threshold_uncertainty_score":0.4402196,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02709286903258501,"score_gpt":0.2204231985392186,"score_spread":0.1933303295066336,"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."}}