{"id":"W2788613589","doi":"10.12688/f1000research.14048.2","title":"Predicting ionizing radiation exposure using biochemically-inspired genomic machine learning","year":2018,"lang":"en","type":"preprint","venue":"F1000Research","topic":"Molecular Biology Techniques and Applications","field":"Biochemistry, Genetics and Molecular Biology","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"Canada Excellence Research Chairs, Government of Canada; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"NS3; Biology; Protease","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0007323198,0.0004338562,0.0003974055,0.0008552567,0.0001444191,0.0006390684,0.0003220129,0.0004009031,0.001456509],"category_scores_gemma":[0.002075574,0.0001494432,0.0006340418,0.0005373209,0.0002403255,0.0002431264,0.0003729478,0.0004702998,0.0008110111],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003562423,"about_ca_system_score_gemma":0.0003771341,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006986823,"about_ca_topic_score_gemma":0.001309833,"domain_scores_codex":[0.9996904,0.00009409284,0.00002176579,0.00009988649,0.00006852369,0.00002529942],"domain_scores_gemma":[0.9992657,0.0003108961,0.000208633,0.0000890636,0.0000984927,0.00002723781],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0011716,0.0004170106,0.2622996,0.0006818193,0.0005321151,0.000358521,0.0001259039,0.1892705,0.2738396,0.002538648,0.004867283,0.2638974],"study_design_scores_gemma":[0.00005226866,0.0008411779,0.1608568,0.0001169804,0.0002891326,0.0009327859,0.0001523453,0.6536744,0.1648879,0.009239017,0.008868635,0.00008845217],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8085499,0.001682339,0.1766605,0.0007123225,0.00007264823,0.0001284183,0.007541416,0.00140445,0.003247908],"genre_scores_gemma":[0.9330795,0.000503312,0.05794045,0.0002099981,0.00003044702,0.0001026151,0.006924056,0.00006891772,0.001140708],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001456509,"threshold_uncertainty_score":0.004872501,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03036296142458492,"score_gpt":0.32772980077904,"score_spread":0.2973668393544551,"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."}}