{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003851108,0.0002975652,0.0003216001,0.00004592158,0.0003180904,0.0001173758,0.0004597383,0.0001249089,0.0002733962],"category_scores_gemma":[0.00003278028,0.0001381328,0.0001166996,0.0004267103,0.0000529779,0.0001890348,0.0001577065,0.0001713088,0.0001348551],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003810765,"about_ca_system_score_gemma":0.00002817334,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001171766,"about_ca_topic_score_gemma":0.002123813,"domain_scores_codex":[0.9976969,0.0001054979,0.0004387745,0.0006344896,0.0004348828,0.0006894523],"domain_scores_gemma":[0.9989862,0.0001104217,0.0001165778,0.0001519684,0.00006805752,0.0005668073],"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":[0.003473635,0.001302838,0.04060869,0.0001609567,0.00007609677,0.0005047455,0.001017681,0.0009995298,0.8863714,0.005086296,0.01539032,0.04500782],"study_design_scores_gemma":[0.006176147,0.004159472,0.82632,0.0043151,0.0003309081,0.00008920511,0.002607281,0.05179439,0.06425062,0.01394314,0.01997597,0.0060378],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9944454,0.00005006037,0.0002779692,0.0007542451,0.0003627058,0.0007552447,0.002028054,0.0001514224,0.001174886],"genre_scores_gemma":[0.9971547,0.00002952761,0.0002064192,0.001070998,0.0009655575,0.00007984814,0.0002176744,0.00000471279,0.0002705744],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8221208,"threshold_uncertainty_score":0.5632891,"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."}}