{"id":"W4405975676","doi":"10.1093/bib/bbag270","title":"CLCNet: a contrastive learning and chromosome-aware network for genomic prediction in plants","year":2024,"lang":"en","type":"preprint","venue":"Briefings in Bioinformatics","topic":"Genetic Mapping and Diversity in Plants and Animals","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Institute of Genetics; National Key Research and Development Program of China; Peking University Health Science Center; Peking University; Chinese Academy of Sciences","keywords":"Artificial intelligence; Epistasis; Machine learning; Feature selection; Computer science; Biology; Genetics; Gene","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"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":[0.0002994294,0.000229066,0.0002850381,0.0001014,0.00007058946,0.00009674369,0.0001218521,0.0004110645,0.000004749488],"category_scores_gemma":[0.00005693846,0.0002372387,0.00006576848,0.00004621878,0.00005264594,0.00000310845,0.0005510493,0.0003831995,0.000004701553],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002946229,"about_ca_system_score_gemma":0.0001068112,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008979092,"about_ca_topic_score_gemma":0.00007237066,"domain_scores_codex":[0.9988679,0.00002070939,0.0004073226,0.0002954025,0.00008878668,0.0003198996],"domain_scores_gemma":[0.9996185,0.00003254498,0.0001534925,0.0001085649,0.0000313,0.00005558095],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.003625078,0.0006800133,0.2482704,0.02959145,0.002232737,0.0001993309,0.02847094,0.2282788,0.0319884,0.001228222,0.3039284,0.1215061],"study_design_scores_gemma":[0.009162542,0.002329219,0.06521456,0.009763976,0.0003587308,0.000297397,0.005878193,0.5855868,0.003047022,0.01028476,0.304475,0.003601759],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.989645,0.003290902,0.002963961,0.0001874766,0.0005186722,0.000863299,0.001097781,0.00003716559,0.001395766],"genre_scores_gemma":[0.9888528,0.003055674,0.00477293,0.0004219894,0.0004251567,0.00006920871,0.001780277,0.00003158096,0.0005903601],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.357308,"threshold_uncertainty_score":0.9674312,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01042217855830364,"score_gpt":0.2297369446228695,"score_spread":0.2193147660645659,"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."}}