{"id":"W6920769586","doi":"10.6084/m9.figshare.23590429.v1","title":"Additional file 2 of ExplaiNN: interpretable and transparent neural networks for genomics","year":2023,"lang":"en","type":"article","venue":"Figshare","topic":"Machine Learning in Bioinformatics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"BC Children's Hospital","funders":"","keywords":"Artificial neural network; Genomics; Feature (linguistics); File format; Pattern recognition (psychology)","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00142767,0.001764224,0.001540717,0.001818441,0.0008921475,0.002305456,0.003232876,0.001689959,0.8547018],"category_scores_gemma":[0.01609081,0.0009772629,0.001245447,0.002823449,0.0005023396,0.002390474,0.001525852,0.001834038,0.2971043],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009673905,"about_ca_system_score_gemma":0.001447951,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003856242,"about_ca_topic_score_gemma":0.007296938,"domain_scores_codex":[0.9993927,0.0001105902,0.00005026292,0.0002126853,0.0001533151,0.00008030865],"domain_scores_gemma":[0.9919003,0.006226677,0.0002458711,0.0006011518,0.0007584523,0.0002676471],"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":[0.0002032336,0.00005590981,0.0008066095,0.001940845,0.00005306647,0.00006703843,0.00003777002,0.001515595,0.0003846524,0.002046701,0.9845052,0.008383313],"study_design_scores_gemma":[0.002027094,0.0001581004,0.005498153,0.001176307,0.0001789105,0.0003896882,0.0001492985,0.009308339,0.003035403,0.04214643,0.9357507,0.0001816349],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0001963526,0.00005044012,0.003130046,0.000119728,0.00007606265,0.00005376543,0.9902284,0.004014069,0.002131042],"genre_scores_gemma":[0.008520766,0.0001930446,0.0171172,0.0005476876,0.0001305214,0.0008909759,0.9502385,0.01200546,0.01035584],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.8547018,"threshold_uncertainty_score":0.2072502,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01977128704959072,"score_gpt":0.2491596745858259,"score_spread":0.2293883875362351,"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."}}