{"id":"W4393139240","doi":"10.1016/j.jbc.2024.106172","title":"Abstract 1527 Informing models of in-silico DNA digestion","year":2024,"lang":"en","type":"article","venue":"Journal of Biological Chemistry","topic":"Genetics, Bioinformatics, and Biomedical Research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"In silico; Digestion (alchemy); DNA; Computational biology; Chemistry; Biology; Biochemistry; Gene; Chromatography","routes":{"ca_aff":true,"ca_fund":false,"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":[0.001002364,0.0007044661,0.001105704,0.0008490054,0.0008514228,0.001597863,0.001500659,0.002165894,0.0105294],"category_scores_gemma":[0.005411887,0.0006977744,0.0009586443,0.0006290397,0.001171971,0.001728095,0.0008361649,0.001318743,0.001249204],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002049811,"about_ca_system_score_gemma":0.001053261,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01296995,"about_ca_topic_score_gemma":0.008763616,"domain_scores_codex":[0.9996791,0.0001305367,0.00001617168,0.00005733948,0.00006237542,0.00005452687],"domain_scores_gemma":[0.9968418,0.002233552,0.0002417637,0.0002247771,0.0002688496,0.0001893402],"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":[0.00002407641,0.00002573823,0.001120734,0.00002583717,0.00001243989,0.00004586586,0.00003632907,0.9772502,0.000515895,0.01934504,0.0006118184,0.0009860123],"study_design_scores_gemma":[0.000009751635,0.000006184528,0.000125547,0.000004889743,0.000003407512,0.00000779544,0.00001018833,0.9904585,0.000133761,0.008705908,0.0005284568,0.000005585141],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3954835,0.0008253976,0.5034124,0.003599647,0.0006002433,0.0002648583,0.003988308,0.001554254,0.09027146],"genre_scores_gemma":[0.9253613,0.0005739438,0.05510097,0.0005708136,0.0001053386,0.0005027602,0.001518697,0.0005582324,0.0157078],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01296995,"threshold_uncertainty_score":0.03522438,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03146337495118756,"score_gpt":0.2955870854263418,"score_spread":0.2641237104751543,"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."}}