{"id":"W6930030106","doi":"10.5281/zenodo.10087651","title":"DeepRFQC: automating quality control for P-wave receiver function analysis using a U-net inspired network","year":2023,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Protein Kinase Regulation and GTPase Signaling","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Quality (philosophy); Function (biology); Control (management); Control system; Artificial neural network","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":[0.001701314,0.001264973,0.0008817498,0.001011067,0.000739207,0.001654284,0.002868929,0.001410611,0.009851735],"category_scores_gemma":[0.004821691,0.0007025188,0.0009433734,0.0007818331,0.0007725835,0.00163589,0.001772794,0.001984356,0.002902646],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001212068,"about_ca_system_score_gemma":0.001465831,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008180737,"about_ca_topic_score_gemma":0.01114571,"domain_scores_codex":[0.999335,0.0001283876,0.0000328512,0.0001946474,0.000230282,0.00007877422],"domain_scores_gemma":[0.9987611,0.0005184686,0.00009171406,0.0002573276,0.0002935938,0.00007786655],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001196632,0.0003115663,0.003154521,0.0003796628,0.0003109433,0.0003331176,0.0001914078,0.2076758,0.05621129,0.02574308,0.04362456,0.6608675],"study_design_scores_gemma":[0.00002240718,0.00002942034,0.000190765,0.000006996104,0.00001202019,0.00002753281,0.00000859744,0.9812133,0.01130764,0.005486838,0.001681334,0.0000131917],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008554145,0.000136202,0.9655069,0.000127642,0.00007868889,0.00005151554,0.0003999541,0.02424145,0.0009034856],"genre_scores_gemma":[0.2165211,0.0001459825,0.7713927,0.0002784662,0.00007907738,0.0001660774,0.002271357,0.004126286,0.005018923],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009851735,"threshold_uncertainty_score":0.03295732,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05344360839335015,"score_gpt":0.2820693527860704,"score_spread":0.2286257443927203,"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."}}