{"id":"W4287075205","doi":"10.5281/zenodo.5009008","title":"Zenodo Archive for \"Improved radiation expression profiling by sequential application of sensitive and specific gene signatures\"","year":2021,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Profiling (computer programming); Gene expression profiling; Computational biology; Radiation; Biology; Gene expression; Gene; Computer science; Genetics; Physics; Operating system","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":[0.00256855,0.0018228,0.002565998,0.00199356,0.001159191,0.004267464,0.003645467,0.001685731,0.2114582],"category_scores_gemma":[0.005721902,0.001220717,0.002220538,0.002921884,0.0005205158,0.002296831,0.00395156,0.002779521,0.1931234],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001195532,"about_ca_system_score_gemma":0.002035314,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003331127,"about_ca_topic_score_gemma":0.006176413,"domain_scores_codex":[0.9979191,0.0002298014,0.0001769767,0.000632714,0.0007565109,0.0002848921],"domain_scores_gemma":[0.9971796,0.0007659137,0.0002264773,0.001090685,0.0004821049,0.000255156],"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":[0.0005005638,0.00005877001,0.001126219,0.001476329,0.0001343204,0.00006790273,0.000069085,0.000647416,0.007349209,0.001680764,0.9729291,0.0139603],"study_design_scores_gemma":[0.0004151257,0.00008283944,0.005201178,0.0002482396,0.00009017134,0.0001640801,0.00005317096,0.002672261,0.009760458,0.004991248,0.9762176,0.0001035835],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0008370351,0.0004287492,0.009086871,0.0002697424,0.0004584698,0.0000918712,0.9289816,0.05344845,0.006397197],"genre_scores_gemma":[0.002982773,0.0003715109,0.01112928,0.0004323792,0.00007169987,0.000432001,0.9623876,0.01860736,0.003585399],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.2114582,"threshold_uncertainty_score":0.7073981,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01422459232364455,"score_gpt":0.2348702177670627,"score_spread":0.2206456254434182,"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."}}