{"id":"W2753542768","doi":"10.1094/pdis-04-17-0504-re","title":"Weather-Based Models for Assessing the Risk of <i>Sclerotinia sclerotiorum</i> Apothecial Presence in Soybean (<i>Glycine max</i>) Fields","year":2017,"lang":"en","type":"article","venue":"Plant Disease","topic":"Plant pathogens and resistance mechanisms","field":"Agricultural and Biological Sciences","cited_by":47,"is_retracted":false,"has_abstract":true,"ca_institutions":"Agriculture and Agri-Food Canada","funders":"National Institute of Food and Agriculture; Wisconsin Soybean Marketing Board; University of Wisconsin-Madison; United Soybean Board; North Central Soybean Research Program; U.S. Department of Agriculture","keywords":"Sclerotinia sclerotiorum; Sclerotinia; Horticulture; Leaf wetness; Biology; Ascocarp; Relative humidity; Dew; Crop; Growing season; Agronomy; Mathematics; Botany; Meteorology; Geography","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":[0.0003824692,0.0001351446,0.0001864806,0.00001026745,0.0005445699,0.0001242866,0.0005225047,0.00006400332,0.00002649988],"category_scores_gemma":[0.0001472194,0.0000462776,0.0001311204,0.00005061998,0.00007997868,0.0001539922,0.00005275417,0.00009992071,0.000001511419],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000007602553,"about_ca_system_score_gemma":0.00002412837,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000188403,"about_ca_topic_score_gemma":0.001422882,"domain_scores_codex":[0.998995,0.00006848288,0.0002102278,0.0002538651,0.0001993308,0.0002730665],"domain_scores_gemma":[0.999104,0.0003302343,0.0002482192,0.0001737798,0.00003672243,0.0001071101],"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":[0.003635011,0.002030016,0.2871015,0.0004006959,0.0001176431,0.0001703403,0.0004670586,0.01044067,0.6317189,0.007853757,0.003418074,0.05264635],"study_design_scores_gemma":[0.001368815,0.0001809625,0.85979,0.0005908853,0.000130059,0.000002039319,0.0002589039,0.1121283,0.007534246,0.01656212,0.0009339965,0.0005196657],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9941624,0.00009518698,0.0008232122,0.001043183,0.0001514404,0.0005180981,0.002918104,0.00002064263,0.00026769],"genre_scores_gemma":[0.9992771,0.00005576559,0.0001776599,0.0001241422,0.0001638246,0.00005494909,0.00007650017,0.00000157975,0.00006845529],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6241846,"threshold_uncertainty_score":0.4188447,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03703630607439156,"score_gpt":0.2438125823220483,"score_spread":0.2067762762476567,"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."}}