{"id":"W4400889525","doi":"10.1080/01431161.2024.2377228","title":"Generating surface soil moisture at the 30 m resolution in grape-growing areas based on stacked ensemble learning","year":2024,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Soil Moisture and Remote Sensing","field":"Environmental Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"National Natural Science Foundation of China","keywords":"Environmental science; Water content; Moisture; Remote sensing; Surface (topology); Soil science; Geology; Meteorology; Geography; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001022167,0.0001818257,0.0001773316,0.0001422853,0.000206398,0.0001938223,0.0001996778,0.00009496787,0.00003799624],"category_scores_gemma":[0.0002914549,0.0001296998,0.0001728437,0.0002656655,0.00009354611,0.000238817,0.0001065165,0.0007505009,0.00005512501],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009255391,"about_ca_system_score_gemma":0.00005032799,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008197937,"about_ca_topic_score_gemma":0.001500234,"domain_scores_codex":[0.9978251,0.0002345426,0.0004627484,0.0002604921,0.0009601997,0.0002568654],"domain_scores_gemma":[0.9991053,0.0004003668,0.0002248974,0.0001314,0.00007133355,0.00006670302],"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":[0.0000703152,0.000009126918,0.0007117052,0.000004431694,0.00003308673,0.0006808346,0.0005964408,0.6740953,0.08005729,0.000003554971,0.000550535,0.2431873],"study_design_scores_gemma":[0.0003926402,0.00006224771,0.00455277,0.0007525183,0.00002457871,0.0006123547,0.0003339691,0.9742075,0.01083404,0.0002534852,0.007797682,0.000176226],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9747999,0.0003418457,0.01219925,0.004122371,0.00158386,0.00006996188,5.121915e-7,0.00003257681,0.006849693],"genre_scores_gemma":[0.9884796,0.00004356128,0.009748505,0.0007673268,0.0005019113,6.245763e-9,0.000003797644,0.00002951724,0.0004258333],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3001121,"threshold_uncertainty_score":0.5289006,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009725774006570844,"score_gpt":0.2456714917741203,"score_spread":0.2359457177675494,"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."}}