{"id":"W4394558804","doi":"10.6084/m9.figshare.23590435","title":"Additional file 4 of ExplaiNN: interpretable and transparent neural networks for genomics","year":2023,"lang":"en","type":"dataset","venue":"Figshare","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"BC Children's Hospital; Canadian Institute for Advanced Research; University of British Columbia","funders":"","keywords":"Computer science; Artificial neural network; Genomics; Artificial intelligence; Computational biology; Biology; Genome; Genetics; Gene","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":[0.001447573,0.002214039,0.001746597,0.002290559,0.001016743,0.002487573,0.003357488,0.002521214,0.498173],"category_scores_gemma":[0.01119692,0.0008051812,0.001596756,0.003702657,0.0005762862,0.001702667,0.001415962,0.002167581,0.177263],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001260475,"about_ca_system_score_gemma":0.001997669,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00884355,"about_ca_topic_score_gemma":0.02242951,"domain_scores_codex":[0.9992459,0.0001390734,0.0000844633,0.000289829,0.0001279425,0.0001128616],"domain_scores_gemma":[0.9953685,0.002954434,0.0002220636,0.0006000893,0.0006372135,0.0002178455],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001330723,0.00005554099,0.0011661,0.001392738,0.00006063161,0.00002874474,0.0000244359,0.0007052812,0.0001354179,0.0006848257,0.9926271,0.002986092],"study_design_scores_gemma":[0.001854721,0.000117185,0.007562169,0.0009460549,0.0001828428,0.000216918,0.000154988,0.002357953,0.001013965,0.01300302,0.9724833,0.0001068833],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00008922025,0.00002972657,0.0001463065,0.00003751729,0.00001737109,0.00001887809,0.9989814,0.0003358385,0.000343736],"genre_scores_gemma":[0.001139325,0.00004879696,0.001033387,0.0001097704,0.0000149693,0.0003606645,0.9956185,0.000316383,0.00135836],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.498173,"threshold_uncertainty_score":0.7157953,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04664963765654526,"score_gpt":0.2588137362297308,"score_spread":0.2121640985731855,"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."}}