{"id":"W4283212128","doi":"10.1111/jac.12614","title":"Improving the estimation of soil water evaporation based on days after wetting","year":2022,"lang":"en","type":"article","venue":"Journal of Agronomy and Crop Science","topic":"Plant Water Relations and Carbon Dynamics","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Conselho Nacional de Desenvolvimento Científico e Tecnológico; Fundação de Apoio à Pesquisa do Distrito Federal; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior; Empresa Brasileira de Pesquisa Agropecuária; University of the Fraser Valley","keywords":"Lysimeter; Evaporation; Wetting; Environmental science; Soil science; Pan evaporation; Soil water; Hydrology (agriculture); Materials science; Geotechnical engineering; Geology; Composite material; Meteorology; Geography","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005525413,0.0004698712,0.0007967938,0.0006918919,0.000115938,0.0005166784,0.0003758314,0.0003501943,0.0003143229],"category_scores_gemma":[0.000968791,0.0002530488,0.0004737233,0.0008105985,0.0001286424,0.0007490873,0.0002936731,0.000356892,0.0002331267],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002462559,"about_ca_system_score_gemma":0.0002145679,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001524315,"about_ca_topic_score_gemma":0.002488481,"domain_scores_codex":[0.9995443,0.00007569538,0.0000375113,0.0001247212,0.0001896192,0.00002813365],"domain_scores_gemma":[0.9995106,0.0002116836,0.00009619387,0.00005115692,0.0001143739,0.00001600439],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0006246815,0.0002624124,0.1336216,0.0006379774,0.000290103,0.0001416159,0.0001319132,0.06022591,0.7052013,0.0001929718,0.0002922568,0.09837729],"study_design_scores_gemma":[0.00003121844,0.000396851,0.222322,0.00002377972,0.0001430566,0.00020579,0.00005937381,0.4339079,0.3417538,0.0001681787,0.0009035777,0.00008454541],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8929862,0.0007383967,0.1044,0.00001976201,0.00002462698,0.00004836254,0.0004846599,0.0004069052,0.0008910321],"genre_scores_gemma":[0.9792251,0.0002689123,0.01983865,0.000008827668,0.000006141801,0.00004205637,0.0002616541,0.00002575687,0.000322865],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001524315,"threshold_uncertainty_score":0.003030896,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003867761261336808,"score_gpt":0.1815296321791751,"score_spread":0.1776618709178383,"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."}}