{"id":"W3172432183","doi":"10.3390/environments8060050","title":"An Investigation of Takagi-Sugeno Fuzzy Modeling for Spatial Prediction with Sparsely Distributed Geospatial Data","year":2021,"lang":"en","type":"article","venue":"Environments","topic":"Soil Geostatistics and Mapping","field":"Environmental Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Kriging; Data mining; Outlier; Cluster analysis; Fuzzy logic; Computer science; Fuzzy clustering; Weighting; Inverse distance weighting; Sampling (signal processing); Geospatial analysis; Mathematics; Machine learning; Statistics; Artificial intelligence; Remote sensing; Multivariate interpolation; Geography","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":[0.0001715938,0.0001243349,0.0001295314,0.00001349387,0.0001145929,0.00002211232,0.0002168632,0.00005545436,0.0001497822],"category_scores_gemma":[0.00003180163,0.0001238704,0.00001654984,0.00007886461,0.00009769697,0.0003150053,0.0001801982,0.00006391122,0.00001602327],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007796094,"about_ca_system_score_gemma":0.00002060053,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006052182,"about_ca_topic_score_gemma":0.0003141697,"domain_scores_codex":[0.9987281,0.00004175877,0.0002399938,0.0004540291,0.0003363146,0.0001998166],"domain_scores_gemma":[0.999196,0.00002073173,0.0001189454,0.0005521195,0.000005833831,0.0001063598],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000149678,0.0003476273,0.4103794,0.00003902518,0.00009503598,0.00001585488,0.0009684846,0.4765031,0.09332085,0.0001158932,0.0009496203,0.01711542],"study_design_scores_gemma":[0.0009068577,0.0002111649,0.2407729,0.00002333366,0.00008227412,0.000004788244,0.0001297636,0.7509888,0.004358066,0.001159204,0.001163215,0.0001996675],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5352489,0.000008151443,0.4633401,0.0000332123,0.00007615313,0.0001764878,0.001050986,0.00001076183,0.00005523984],"genre_scores_gemma":[0.9749343,0.00001829161,0.01858388,0.00004458825,0.00006150058,0.0000229314,0.006271818,0.00001864163,0.00004409291],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4447562,"threshold_uncertainty_score":0.5051287,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04478271326752056,"score_gpt":0.2349383176096091,"score_spread":0.1901556043420886,"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."}}