{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002114966,0.0009161308,0.0005622295,0.0007449591,0.0003072854,0.0009115792,0.001005084,0.0007047229,0.001756939],"category_scores_gemma":[0.00343189,0.0004707602,0.0009661862,0.0003883074,0.0003140685,0.0006465005,0.0006268247,0.0007522725,0.0003256798],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009768393,"about_ca_system_score_gemma":0.0006296433,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03377863,"about_ca_topic_score_gemma":0.02100908,"domain_scores_codex":[0.9995973,0.0001586589,0.00002638564,0.0001280813,0.00002276687,0.00006698701],"domain_scores_gemma":[0.997804,0.001476336,0.0003786844,0.00006369807,0.0001874079,0.00008980718],"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":[0.0002393454,0.0001303931,0.05791755,0.00002463901,0.0001408341,0.00005923485,0.00005327175,0.9328431,0.0005796234,0.0006292137,0.0004387382,0.006944029],"study_design_scores_gemma":[0.000008900436,0.0000304693,0.006084126,0.000003090192,0.00001367337,0.000005154563,0.0000189834,0.9934447,0.0000426867,0.0002687229,0.00007460763,0.000004981516],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.972858,0.0002860902,0.0238726,0.0002348845,0.00002681633,0.00005671037,0.001241274,0.0002231984,0.001200499],"genre_scores_gemma":[0.9948048,0.0001116385,0.002783398,0.00002156062,0.00001192638,0.00005672844,0.0008752933,0.00001527175,0.001319311],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03377863,"threshold_uncertainty_score":0.06716406,"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."}}