{"id":"W2101695352","doi":"10.1016/j.agrformet.2004.05.009","title":"Operational exposure of leaf wetness sensors","year":2004,"lang":"en","type":"article","venue":"Agricultural and Forest Meteorology","topic":"Irrigation Practices and Water Management","field":"Agricultural and Biological Sciences","cited_by":86,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Guelph","funders":"Conselho Nacional de Desenvolvimento Científico e Tecnológico","keywords":"Leaf wetness; Dew; Environmental science; Biometeorology; Remote sensing; Hydrology (agriculture); Meteorology; Horticulture; Engineering; Geology; Geotechnical engineering; Geography; Biology","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":[0.0001057619,0.0001016213,0.0001424203,0.000008052461,0.000128543,0.0000264408,0.00009539616,0.00007102787,0.0001238049],"category_scores_gemma":[0.00001584674,0.00003041548,0.00004645709,0.0001266084,0.00007420536,0.0001958521,0.00005812655,0.00006411837,0.00001611218],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000007284539,"about_ca_system_score_gemma":0.00000252625,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003552239,"about_ca_topic_score_gemma":0.0005784432,"domain_scores_codex":[0.9993202,0.00004134346,0.0001880751,0.0001832634,0.0001104216,0.0001566758],"domain_scores_gemma":[0.9997042,0.00005422863,0.00008964072,0.00002755367,0.0000733883,0.00005096602],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0002683326,0.0005376221,0.1050361,0.00006483665,0.0002538416,0.00002789838,0.001253447,0.004667853,0.4996199,0.3501206,0.001184833,0.03696473],"study_design_scores_gemma":[0.0003177623,0.0004777643,0.9845132,0.000005657692,0.00002433741,0.00004259924,0.0004458537,0.000006853556,0.00282271,0.003818521,0.007395755,0.0001290297],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9914602,0.000104962,0.000003577767,0.007198141,0.00009722829,0.0001258667,0.00001110863,0.00002113053,0.0009777809],"genre_scores_gemma":[0.9989239,0.00004647131,0.0001736949,0.0002577672,0.0001199737,0.00001135207,0.00008898909,4.011585e-7,0.0003774807],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.879477,"threshold_uncertainty_score":0.1355577,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01227816524957097,"score_gpt":0.2022182913477789,"score_spread":0.189940126098208,"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."}}