{"id":"W2036213444","doi":"10.1094/phyto-97-12-1608","title":"A Distributed Lag Analysis of the Relationship Between <i>Gibberella zeae</i> Inoculum Density on Wheat Spikes and Weather Variables","year":2007,"lang":"en","type":"article","venue":"Phytopathology","topic":"Wheat and Barley Genetics and Pathology","field":"Agricultural and Biological Sciences","cited_by":44,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Gibberella zeae; Lag; Biology; Regression analysis; Linear regression; Polynomial regression; Distributed lag; Gibberella; Statistics; Mathematics; Ecology; Botany","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005194738,0.0001191786,0.0002922588,0.00003414177,0.0001800478,0.00001160022,0.0001528999,0.000164949,0.00003135865],"category_scores_gemma":[0.0001032905,0.00004389072,0.0001206928,0.0007066983,0.0001738962,0.00001682129,0.00008031194,0.000141054,0.000003664012],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000007991324,"about_ca_system_score_gemma":0.000003921541,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001058019,"about_ca_topic_score_gemma":0.001243478,"domain_scores_codex":[0.9990174,0.0001539477,0.0002346543,0.0002565878,0.000102158,0.000235237],"domain_scores_gemma":[0.9989732,0.0007114943,0.0001021828,0.000109741,0.00004728338,0.000056086],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001916057,0.00003600804,0.9402922,0.00000268583,0.00001986645,0.000004172374,0.00005606946,0.000006437956,0.05554973,0.002308932,0.00006039771,0.001644274],"study_design_scores_gemma":[0.00008447524,0.0001460837,0.9884434,0.000006301665,0.000213073,0.000008632594,0.00003693188,0.000006894333,0.006752931,0.003085873,0.001123309,0.00009204778],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9976498,0.0001403615,0.000168513,0.0009162431,0.0000917514,0.00009836521,0.000134719,0.00001510192,0.0007851557],"genre_scores_gemma":[0.9993677,0.00001690169,0.00006790402,0.0002474751,0.0001218594,0.000002313845,0.00007231283,9.405462e-7,0.0001026524],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0487968,"threshold_uncertainty_score":0.1789812,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0277360168187548,"score_gpt":0.2430624550210367,"score_spread":0.2153264382022819,"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."}}