{"id":"W4286208906","doi":"10.3390/plants11141887","title":"Identification of Spring Wheat with Superior Agronomic Performance under Contrasting Nitrogen Managements Using Linear Phenotypic Selection Indices","year":2022,"lang":"en","type":"article","venue":"Plants","topic":"Wheat and Barley Genetics and Pathology","field":"Agricultural and Biological Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Agriculture and Agri-Food Canada; University of Alberta","funders":"Saskatchewan Wheat Development Commission; Alberta Wheat Commission; Western Grains Research Foundation; Agriculture and Agri-Food Canada; Alberta Crop Industry Development Fund","keywords":"Selection (genetic algorithm); Spring (device); Identification (biology); Agronomy; Biology; Nitrogen; Botany; Computer science; Engineering; Chemistry; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0001382955,0.00006042351,0.00008536517,0.00001706297,0.0002801762,0.00001470952,0.00008976075,0.00001863391,0.00008520709],"category_scores_gemma":[0.000001130355,0.00002902787,0.00001619771,0.0001015537,0.0000168415,0.00005222757,0.00004253106,0.00005707001,0.000002741143],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001931083,"about_ca_system_score_gemma":0.000005145695,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001503625,"about_ca_topic_score_gemma":0.0001108835,"domain_scores_codex":[0.9994581,0.00003343246,0.0001397231,0.0001379231,0.00009789148,0.0001328875],"domain_scores_gemma":[0.9998421,0.00001773837,0.0000857958,0.00002144423,0.0000140217,0.00001888439],"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":[0.00004762329,0.00002557455,0.2386609,0.000007594655,0.00001435197,6.405999e-7,0.00005651292,0.002036946,0.7543036,0.000007736926,0.000001662132,0.004836826],"study_design_scores_gemma":[0.0002601855,0.0003034196,0.8980193,0.00001775909,0.00003558829,0.00002475356,0.0006286062,0.01794994,0.08242846,0.00004309643,0.0001291916,0.0001597336],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9996136,0.00007447196,0.00003484366,0.00001987555,0.00007218934,0.0001052433,0.00002129763,0.00001238605,0.00004609938],"genre_scores_gemma":[0.9997486,0.00001813466,0.00008504779,0.00002427984,0.00006299259,0.000006933857,0.00002475825,8.982491e-7,0.00002838894],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6718751,"threshold_uncertainty_score":0.2154917,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02121949589281704,"score_gpt":0.2099779357608529,"score_spread":0.1887584398680358,"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."}}