{"id":"W4408498771","doi":"10.1186/s13007-025-01355-y","title":"A Bayesian framework to model variance of grain yield response to plant density","year":2025,"lang":"en","type":"article","venue":"Plant Methods","topic":"Wheat and Barley Genetics and Pathology","field":"Agricultural and Biological Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Agriculture and Agri-Food Canada","funders":"Kansas Wheat Commission","keywords":"Yield (engineering); Plant density; Variance (accounting); Statistics; Grain yield; Probability density function; Mathematics; Agronomy; Seeding; Crop yield; Constant (computer programming); Biology; Computer science; Sowing; Physics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001364248,0.0001110892,0.0002461221,0.00003180479,0.00009157974,0.00001732229,0.0002360137,0.0001442936,0.00004330303],"category_scores_gemma":[0.0006270632,0.00004694561,0.00006161604,0.0003749248,0.00001925377,0.00001122769,0.0001100054,0.0001170423,0.000005470921],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008318042,"about_ca_system_score_gemma":0.00001786488,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009350746,"about_ca_topic_score_gemma":0.0001962641,"domain_scores_codex":[0.9988556,0.0003509136,0.0001992866,0.0002719428,0.0000886562,0.0002335878],"domain_scores_gemma":[0.9984089,0.001310689,0.00003345986,0.00009450256,0.00003603303,0.0001164072],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0008472573,0.00004248704,0.00067212,0.000006967021,0.00001001557,0.000009328551,0.0002691643,0.0001923115,0.9634081,0.002701711,0.002315992,0.02952451],"study_design_scores_gemma":[0.0002864262,0.001868287,0.1364784,0.0006297068,0.00009661065,0.00005584121,0.0003642288,0.0161381,0.6812664,0.09765461,0.06414987,0.001011498],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6893274,0.0000829224,0.3044292,0.004795027,0.0002673107,0.0002032752,0.000303234,0.00002528464,0.0005662893],"genre_scores_gemma":[0.6495318,0.00001939166,0.3472369,0.002451211,0.00006637858,0.00001657802,0.00001502408,7.41741e-7,0.0006619339],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2821417,"threshold_uncertainty_score":0.1914386,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04098873725036353,"score_gpt":0.3189808942295952,"score_spread":0.2779921569792316,"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."}}