{"id":"W4367181665","doi":"10.1007/s12145-023-01007-6","title":"Spatial heterogeneity and partitioning of soil health indicators in the Northern Great Plains using self-organizing map and change point methods","year":2023,"lang":"en","type":"article","venue":"Earth Science Informatics","topic":"Soil Geostatistics and Mapping","field":"Environmental Science","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Prince Edward Island","funders":"","keywords":"Watershed; Cluster analysis; Geostatistics; Self-organizing map; Artificial neural network; Spatial variability; Computer science; Hydrology (agriculture); Environmental science; Statistics; Mathematics; Geology; Artificial intelligence","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":[0.001229275,0.0001572804,0.0003059708,0.00213146,0.0003943819,0.0007935279,0.0004030808,0.0002248548,0.0003656402],"category_scores_gemma":[0.003765462,0.0001917545,0.0004223792,0.001701729,0.0008864335,0.0005340663,0.0007812931,0.0001716457,0.00004445374],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006336757,"about_ca_system_score_gemma":0.0005412991,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.07157736,"about_ca_topic_score_gemma":0.07108023,"domain_scores_codex":[0.999222,0.0003752809,0.00004896381,0.0001690303,0.0001029627,0.00008173085],"domain_scores_gemma":[0.9974219,0.001583961,0.0003766173,0.0002286298,0.0002763762,0.0001126254],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000227966,0.00005401017,0.9833617,0.00002643695,0.0001919421,0.00009524923,0.0008817175,0.005499824,0.001364382,0.0004290549,0.000161431,0.007706224],"study_design_scores_gemma":[0.000007173084,0.0000126404,0.987255,0.000003191158,0.00002583228,0.00003165058,0.0005083748,0.01172053,0.00008341354,0.0002537482,0.00009197515,0.000006401735],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9995307,0.00002184178,0.0002484468,0.00001871183,5.78485e-7,0.000002944355,0.00006365362,0.000004844293,0.0001083508],"genre_scores_gemma":[0.9995804,0.000008600759,0.0002256005,0.000002501007,0.000001667826,0.000003967694,0.0001217487,0.00000154119,0.00005394069],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07157736,"threshold_uncertainty_score":0.1423215,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03912788753362337,"score_gpt":0.3079607464347933,"score_spread":0.2688328589011699,"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."}}