{"id":"W4404755228","doi":"10.1111/tpj.17164","title":"Machine learning‐enhanced multi‐trait genomic prediction for optimizing cannabinoid profiles in cannabis","year":2024,"lang":"en","type":"article","venue":"The Plant Journal","topic":"Cannabis and Cannabinoid Research","field":"Medicine","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval; University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Cannabis; Cannabinoid; Trait; Genotyping; Computational biology; Biology; Single-nucleotide polymorphism; Genomic selection; Cannabis sativa; Population; Evolutionary biology; Machine learning; Computer science; Genetics; Gene; Psychology; Medicine; Psychiatry; Genotype; Botany","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009609313,0.0006075343,0.0005219397,0.0006319932,0.0002639027,0.0005133981,0.0003551152,0.0003470084,0.0006327812],"category_scores_gemma":[0.001783311,0.0001703428,0.0006804175,0.0006131653,0.0001945774,0.0003151901,0.0004094744,0.0005948517,0.0001918141],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004871905,"about_ca_system_score_gemma":0.0007343065,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006648849,"about_ca_topic_score_gemma":0.0074585,"domain_scores_codex":[0.9997718,0.00007977421,0.000009530261,0.00007867702,0.00003367075,0.00002651368],"domain_scores_gemma":[0.9994093,0.0004106843,0.00005407185,0.00002759172,0.00007521292,0.00002315516],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007136393,0.0003969698,0.04986298,0.0001802803,0.0003275987,0.000156201,0.000104087,0.7313984,0.05308982,0.001854931,0.0008902508,0.1610249],"study_design_scores_gemma":[0.00001968336,0.00008254656,0.005809824,0.000007453553,0.000051337,0.00001991914,0.00001990021,0.9875717,0.004969676,0.0009945777,0.0004361041,0.00001724721],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7108203,0.001049942,0.2839508,0.0003176105,0.00003202176,0.00007681724,0.001133203,0.001216849,0.001402446],"genre_scores_gemma":[0.9131114,0.0002028397,0.08445817,0.00008698522,0.00001258086,0.00004718303,0.001436133,0.00007377708,0.0005709596],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006648849,"threshold_uncertainty_score":0.01322025,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0256914918416382,"score_gpt":0.2888103087144351,"score_spread":0.2631188168727969,"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."}}