{"id":"W6958521110","doi":"10.6084/m9.figshare.19498047","title":"Additional file 5 of LANDMark: an ensemble approach to the supervised selection of biomarkers in high-throughput sequencing data","year":2022,"lang":"en","type":"article","venue":"Figshare","topic":"Genomics and Rare Diseases","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University; University of Guelph","funders":"","keywords":"Selection (genetic algorithm); Feature selection; Pattern recognition (psychology); Key (lock); DNA sequencing","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":[0.003352061,0.001899863,0.001476754,0.002699764,0.000906818,0.002484165,0.002662215,0.001599614,0.6893762],"category_scores_gemma":[0.03288424,0.0008062238,0.001719777,0.003583764,0.0004660333,0.001561456,0.001391506,0.001715991,0.1408946],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009051503,"about_ca_system_score_gemma":0.001869151,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003109008,"about_ca_topic_score_gemma":0.007685985,"domain_scores_codex":[0.9988954,0.0003049512,0.0001325119,0.000387279,0.0001838883,0.00009599706],"domain_scores_gemma":[0.9788665,0.01800945,0.0004458533,0.001070335,0.001304201,0.0003035666],"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":[0.0006363487,0.0001050826,0.003154526,0.00312066,0.0002339111,0.0001148411,0.00007812512,0.002971896,0.0004818296,0.001172148,0.9635435,0.02438724],"study_design_scores_gemma":[0.007443505,0.0005185533,0.02087774,0.001974842,0.0007228348,0.0008288528,0.0003450403,0.03889647,0.005200182,0.05299821,0.8698155,0.0003782675],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.000493516,0.00007298776,0.005359595,0.0001693369,0.00006592958,0.0001320843,0.9872158,0.005718815,0.0007718707],"genre_scores_gemma":[0.01321356,0.0001710135,0.03614912,0.0004600597,0.0001444624,0.002282694,0.9366401,0.005888517,0.005050385],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.6893762,"threshold_uncertainty_score":0.4430672,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04141544127412097,"score_gpt":0.2421792642417175,"score_spread":0.2007638229675965,"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."}}