{"id":"W4243839871","doi":"10.32920/ryerson.14654334.v1","title":"Structural Classification of Proteins Using Image Based Machine Learning","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Machine Learning in Bioinformatics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Structural Classification of Proteins database; Convolutional neural network; Computer science; Artificial intelligence; Pattern recognition (psychology); Visualization; Machine learning; Rendering (computer graphics); Artificial neural network; Contextual image classification; Class (philosophy); Image (mathematics); Protein structure; Chemistry","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002340379,0.0002526724,0.000270891,0.00007303333,0.00007127415,0.00006667223,0.0002720836,0.0003750815,0.0001631433],"category_scores_gemma":[0.0003324129,0.0002394706,0.0001731653,0.00007026442,0.00008290495,0.000003686504,0.0005447373,0.000577739,0.000001425134],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002801077,"about_ca_system_score_gemma":0.0002869346,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001781212,"about_ca_topic_score_gemma":0.00003828289,"domain_scores_codex":[0.9986532,0.0001547479,0.0004530316,0.0003430405,0.000214015,0.000181995],"domain_scores_gemma":[0.9985898,0.00001097362,0.0005137095,0.0005818054,0.0002517595,0.00005196742],"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":[0.0000374046,0.00002588552,0.01384596,0.0007219799,0.000080069,0.00000147669,0.00008082066,0.03596874,0.9483976,0.00003710406,0.00002607039,0.0007768254],"study_design_scores_gemma":[0.0002733873,0.00007919442,0.004708855,0.00008791733,0.00003962194,0.000007835164,0.00008113862,0.7825313,0.2115102,0.000009370288,0.0003818452,0.0002893752],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8956051,0.0001825552,0.101605,0.00006651487,0.0001402587,0.0003458711,0.00002749447,0.00002932526,0.001997872],"genre_scores_gemma":[0.8411732,0.00001296983,0.1562997,0.00004322032,0.00008665281,0.00001073742,0.002103793,0.00003215882,0.0002375984],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7465625,"threshold_uncertainty_score":0.9765328,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02176540601397041,"score_gpt":0.2890749217975099,"score_spread":0.2673095157835395,"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."}}