{"id":"W4406038213","doi":"10.5376/lgg.2024.15.0029","title":"Optimizing Soybean Yield Through Integrated Agronomic Management","year":2024,"lang":"en","type":"article","venue":"Legume Genomics and Genetics","topic":"Soybean genetics and cultivation","field":"Agricultural and Biological Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Yield (engineering); Agronomy; Business; Agricultural engineering; Engineering; Biology; Materials science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009192505,0.0001456744,0.0001060393,0.00001206121,0.0001319481,0.00025057,0.0001337288,0.00007667603,0.0001112806],"category_scores_gemma":[0.000002180689,0.00006780813,0.00005556225,0.0001470301,0.00003756148,0.00004242613,0.00009148574,0.00009075841,0.00003770838],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003022279,"about_ca_system_score_gemma":0.000006085928,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004185742,"about_ca_topic_score_gemma":0.00006446127,"domain_scores_codex":[0.9991841,0.00001366472,0.0001838012,0.0003253358,0.00007657404,0.0002165722],"domain_scores_gemma":[0.9997931,0.00004164379,0.00002784825,0.00006108786,0.00001919202,0.00005715297],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001536105,0.00004232848,0.000458886,0.00004417015,0.0001170762,0.00001574268,0.001087382,0.0002281619,0.3329988,0.007068457,0.002404089,0.6555195],"study_design_scores_gemma":[0.000309242,0.0004730542,0.03603027,0.0001755123,0.0001614068,0.00002861412,0.004241576,0.02338303,0.02681066,0.01432667,0.8929443,0.001115702],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9898018,0.005525641,0.0005733299,0.0006992571,0.0003104367,0.0001888016,0.00003089764,0.00006829773,0.002801562],"genre_scores_gemma":[0.9914941,0.001896586,0.005045163,0.0002984906,0.0002742425,0.00001047759,0.00007125086,0.000003391153,0.0009063128],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8905402,"threshold_uncertainty_score":0.2765135,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02188184319290345,"score_gpt":0.2145820466332349,"score_spread":0.1927002034403315,"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."}}