{"id":"W5984741","doi":"","title":"A refined multisite fungal protein localizer","year":2008,"lang":"en","type":"article","venue":"International conference on Artificial intelligence and applications","topic":"Machine Learning in Bioinformatics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Subcellular localization; Classifier (UML); Protein subcellular localization prediction; Computer science; Artificial intelligence; Computational biology; Pattern recognition (psychology); Data mining; Biology; Cytoplasm; Biochemistry; Gene","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":[0.00008297441,0.0001171307,0.00007751596,0.00005593099,0.0001619383,0.00004190506,0.0002258706,0.00007924822,0.0001432485],"category_scores_gemma":[0.00006391948,0.0001122611,0.00003754792,0.00007051861,0.0001639121,0.000009848046,0.00007208593,0.0001277515,0.0001688308],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001024686,"about_ca_system_score_gemma":0.00003953212,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002094556,"about_ca_topic_score_gemma":0.00002983004,"domain_scores_codex":[0.999196,0.00001961038,0.0002532638,0.0002410587,0.0001627351,0.0001273485],"domain_scores_gemma":[0.9994532,0.00001122425,0.00008640265,0.0002099382,0.0001725775,0.00006659279],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001747398,0.0002647723,0.001045761,0.00001380394,0.00005743551,0.000004833305,0.0002058684,0.0005602824,0.1165024,0.7719933,0.0005133163,0.1086635],"study_design_scores_gemma":[0.0002837414,0.0008306942,0.002399474,0.0001023506,0.00002277683,0.0001818458,0.0007416127,0.07771805,0.5407725,0.02799153,0.34783,0.00112539],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3846633,0.00006914449,0.5412112,0.004127115,0.000174831,0.001068887,0.00009781431,0.00009414693,0.06849346],"genre_scores_gemma":[0.9951153,0.0001318506,0.002505208,0.000402596,0.0002159208,0.0001923271,0.0001377659,0.000009788436,0.001289262],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7440018,"threshold_uncertainty_score":0.4577874,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06156228484602595,"score_gpt":0.3292272926304756,"score_spread":0.2676650077844497,"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."}}