{"id":"W4388096351","doi":"10.1111/sum.12981","title":"Estimating the spatial distribution of soil volumetric water content in an agricultural field employing remote sensing and other auxiliary data under different tillage management practices","year":2023,"lang":"en","type":"article","venue":"Soil Use and Management","topic":"Soil Geostatistics and Mapping","field":"Environmental Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"Národní Agentura pro Zemědělský Výzkum; Technology Agency of the Czech Republic","keywords":"Tillage; Environmental science; Water content; Terrain; Soil water; Remote sensing; Soil management; Soil science; Vegetation (pathology); Sampling (signal processing); Precision agriculture; Hydrology (agriculture); Agriculture; Computer science; Geography; Cartography; Agronomy; Geology","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":[0.0004118517,0.0001284933,0.0001231571,0.00005118938,0.0001614952,0.0001318905,0.0001479094,0.00002787347,0.00001329392],"category_scores_gemma":[0.00004017791,0.00007756124,0.00001466913,0.0001619877,0.00005563777,0.0002129875,0.001066565,0.00007301066,0.000007334165],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003781572,"about_ca_system_score_gemma":8.386102e-7,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01287573,"about_ca_topic_score_gemma":0.005602751,"domain_scores_codex":[0.9988772,0.00006941117,0.0002245379,0.0003506517,0.0002317047,0.0002465252],"domain_scores_gemma":[0.9994171,0.00009247457,0.0001228279,0.0003177508,0.000006866018,0.00004299424],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00009207024,0.0001471011,0.1048599,0.0006137257,0.0002636728,0.0001450747,0.002427466,0.01270746,0.001163519,0.0005274392,0.003455504,0.8735971],"study_design_scores_gemma":[0.0003668688,0.00004169747,0.7205726,0.00009039695,0.00008236192,0.000002901844,0.001993414,0.2754626,0.00009756759,0.0004181128,0.0007218533,0.0001496287],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9857755,0.00001705789,0.01291589,0.0004921815,0.0001189593,0.000323746,0.0000155058,0.00002746268,0.000313711],"genre_scores_gemma":[0.9982267,0.0001499447,0.001029466,0.0002137469,0.00002283083,0.000002890962,0.0001078391,0.000008659058,0.0002379123],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8734475,"threshold_uncertainty_score":0.9936976,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0790000684437063,"score_gpt":0.2812207149433505,"score_spread":0.2022206464996442,"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."}}