{"id":"W4297878000","doi":"10.1016/j.jhydrol.2022.128465","title":"TPE-CatBoost: An adaptive model for soil moisture spatial estimation in the main maize-producing areas of China with multiple environment covariates","year":2022,"lang":"en","type":"article","venue":"Journal of Hydrology","topic":"Soil Moisture and Remote Sensing","field":"Environmental Science","cited_by":55,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"Yunnan Key Research and Development Program","keywords":"Covariate; Estimator; Environmental science; Water content; Spatial variability; Statistics; Estimation; Soil science; Agricultural engineering; Mathematics","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":[0.002376689,0.0008091785,0.001265213,0.0006081063,0.0004351172,0.0005988666,0.00323067,0.001239386,0.001533824],"category_scores_gemma":[0.002963459,0.0006590344,0.0008420768,0.0009878281,0.0005321783,0.001039781,0.001144152,0.001325108,0.0002903302],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007539406,"about_ca_system_score_gemma":0.002103788,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04459433,"about_ca_topic_score_gemma":0.04449741,"domain_scores_codex":[0.999541,0.0001772824,0.00002097198,0.0001554054,0.00004138083,0.00006402505],"domain_scores_gemma":[0.9988954,0.0006909511,0.00009394317,0.00007269162,0.000183446,0.00006343046],"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":[0.000272407,0.0001175626,0.007634342,0.00006943303,0.0001371185,0.0000604462,0.00003643577,0.9301629,0.0007076146,0.0020322,0.002113278,0.05665625],"study_design_scores_gemma":[0.000007276371,0.000007323487,0.0003681791,0.000001430187,0.00000469196,0.000003357228,0.00000209604,0.9991964,0.00004711328,0.0002755932,0.00008350331,0.000003098648],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2147509,0.0007219649,0.7801998,0.0005292824,0.0001231131,0.0001153326,0.001266378,0.00160284,0.0006903717],"genre_scores_gemma":[0.8656037,0.0003285978,0.1274137,0.0002268812,0.00008930356,0.0003469183,0.002406343,0.0001810218,0.003403621],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.04459433,"threshold_uncertainty_score":0.08866948,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00984223835389486,"score_gpt":0.2111653328654671,"score_spread":0.2013230945115722,"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."}}