{"id":"W4236304318","doi":"10.12688/f1000research.14048.1","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":7,"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":"Biology","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":[0.0007557437,0.0004401623,0.0004028674,0.00089923,0.0001492775,0.0006604657,0.0003292277,0.0004126323,0.001469811],"category_scores_gemma":[0.002139921,0.0001535246,0.000647655,0.0005556891,0.0002504001,0.000249494,0.0003867438,0.0004818995,0.0007997528],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003728614,"about_ca_system_score_gemma":0.000390196,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007310755,"about_ca_topic_score_gemma":0.001361628,"domain_scores_codex":[0.999676,0.0001002887,0.00002249651,0.000103422,0.00007074203,0.00002698644],"domain_scores_gemma":[0.9992434,0.0003240402,0.0002123643,0.00009114366,0.0001009677,0.00002810183],"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":[0.001160922,0.0004241848,0.2659996,0.0006914804,0.0005406172,0.0003632039,0.0001298979,0.1943266,0.2699735,0.002632991,0.004815042,0.258942],"study_design_scores_gemma":[0.00005252919,0.0008550655,0.1593848,0.0001193589,0.0002913659,0.0009003914,0.0001600172,0.6551469,0.1647527,0.009385717,0.008860861,0.00009036827],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8088712,0.001698789,0.1762653,0.0007320846,0.00007324998,0.0001326283,0.00757168,0.001363404,0.003291667],"genre_scores_gemma":[0.9308817,0.00051706,0.05998109,0.0002150261,0.0000311943,0.0001053854,0.007016495,0.00006919876,0.001182887],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001469811,"threshold_uncertainty_score":0.004917026,"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."}}