{"id":"W3213380372","doi":"10.18280/ria.350506","title":"Q3 Accuracy and SOV Measure Analysis of Application of GA in Protein Secondary Structure Prediction","year":2021,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"Machine Learning in Bioinformatics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Soft computing; Artificial intelligence; Fuzzy logic; Measure (data warehouse); Machine learning; Class (philosophy); Value (mathematics); Data mining; Artificial neural network; Genetic algorithm; Face (sociological concept)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002031348,0.00007825505,0.0001712866,0.00009695334,0.00002265253,0.000007385569,0.00009383412,0.000106993,0.00005382881],"category_scores_gemma":[0.0002988103,0.00008035362,0.0000589237,0.0004669925,0.00005646149,0.000006600499,0.00006062846,0.0001114029,9.079653e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00000736542,"about_ca_system_score_gemma":0.00004891789,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002454574,"about_ca_topic_score_gemma":0.0001122069,"domain_scores_codex":[0.9991665,0.00004836176,0.0004212611,0.0001850972,0.00008818127,0.00009052165],"domain_scores_gemma":[0.9992794,0.0000211032,0.0002070167,0.0003259105,0.0001416162,0.00002494999],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00002324053,0.00004166481,0.01384651,0.0001651283,0.00008968971,3.809934e-7,0.0004255093,0.04045225,0.9222831,0.0002770056,0.00001119901,0.02238436],"study_design_scores_gemma":[0.00004011144,0.00005745098,0.00602159,0.00003136248,0.00005750151,0.000004428253,0.0003714025,0.1906883,0.8017627,0.0001195149,0.0007754226,0.00007023235],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9421427,0.000454336,0.05657352,0.00005616271,0.00001970504,0.0001774516,0.0000546339,0.000004220749,0.0005172364],"genre_scores_gemma":[0.9976363,0.00005816377,0.001954888,0.00001562092,0.00001714106,0.000009034632,0.0002089299,0.000005839657,0.00009414995],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.150236,"threshold_uncertainty_score":0.3276725,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009462598032123463,"score_gpt":0.2562440670326762,"score_spread":0.2467814690005528,"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."}}