{"id":"W2808327574","doi":"10.1094/pdis-02-18-0245-re","title":"Validating <i>Sclerotinia sclerotiorum</i> Apothecial Models to Predict Sclerotinia Stem Rot in Soybean (<i>Glycine max</i>) Fields","year":2018,"lang":"en","type":"article","venue":"Plant Disease","topic":"Plant pathogens and resistance mechanisms","field":"Agricultural and Biological Sciences","cited_by":28,"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; Ascocarp; Biology; Stem rot; Growing season; Agronomy; Weather station; Crop; Horticulture; 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":[0.001772394,0.000771994,0.0003201939,0.0002601919,0.0002118137,0.0005928656,0.0006311447,0.0004072391,0.0005420446],"category_scores_gemma":[0.001749585,0.0002442934,0.0007304526,0.0001839766,0.0001962616,0.0004620397,0.0003755805,0.0004864418,0.0002387362],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009318215,"about_ca_system_score_gemma":0.0007959969,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03165189,"about_ca_topic_score_gemma":0.0315288,"domain_scores_codex":[0.9996147,0.0001128198,0.0000265563,0.0001582414,0.00004332231,0.00004425343],"domain_scores_gemma":[0.9989777,0.0005153958,0.0001414774,0.00007836011,0.0002226597,0.00006448659],"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":[0.0004053242,0.0005057694,0.2155806,0.00008155093,0.000221589,0.00008183796,0.00007996872,0.7474535,0.01666844,0.0002593196,0.0009296256,0.01773251],"study_design_scores_gemma":[0.00005016424,0.0002422657,0.05561642,0.00001215984,0.00003932235,0.00001733286,0.000043179,0.93856,0.004747913,0.0001506385,0.0005028659,0.00001769114],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9960646,0.00006384419,0.002760485,0.00004984606,0.000007782636,0.00001721432,0.0004804576,0.0001274456,0.0004283517],"genre_scores_gemma":[0.9945562,0.00003846925,0.003656493,0.00003220572,0.000004782813,0.00002057935,0.001375552,0.00001757804,0.0002982028],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03165189,"threshold_uncertainty_score":0.06293529,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04106694893772488,"score_gpt":0.2095192979649385,"score_spread":0.1684523490272136,"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."}}