{"id":"W2043628394","doi":"10.1114/1.1561293","title":"Recognition of Adenosine Triphosphate Binding Sites Using Parallel Cascade System Identification","year":2003,"lang":"en","type":"article","venue":"Annals of Biomedical Engineering","topic":"Protein Structure and Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":8,"is_retracted":false,"has_abstract":false,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"k-nearest neighbors algorithm; Test set; Conventional PCI; Cascade; Linear discriminant analysis; Artificial intelligence; Pattern recognition (psychology); Computer science; Chemistry; Biological system; Algorithm; Biology; Chromatography","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002709147,0.00009619862,0.0001562244,0.00009277564,0.00002041223,0.000005758868,0.00007954297,0.0001451932,0.00000440861],"category_scores_gemma":[0.0002060923,0.00009483648,0.00007166111,0.0001746622,0.00004069212,0.00000625049,0.00002004497,0.00005248059,0.000001012095],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000006893854,"about_ca_system_score_gemma":0.00002615396,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008885204,"about_ca_topic_score_gemma":3.119316e-7,"domain_scores_codex":[0.9991996,0.00002165391,0.0003323249,0.000152145,0.0001401323,0.000154188],"domain_scores_gemma":[0.9995539,0.00001168646,0.0001415552,0.0001407911,0.00008579001,0.00006626152],"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":[0.00001631795,0.0000183762,0.00005219558,0.0002191719,0.00004874235,0.000001768187,0.00001420752,0.0008796572,0.9973934,0.0002111527,0.00003443282,0.001110606],"study_design_scores_gemma":[0.0002508607,0.00008081242,0.0002258134,0.0001280025,0.00002096978,0.00002499577,0.00003602135,0.009378223,0.9891475,0.00003560157,0.0005487378,0.0001224249],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.904954,0.001343572,0.09336786,0.00001910455,0.0001382573,0.0001001384,0.00003808731,0.00001221592,0.00002677746],"genre_scores_gemma":[0.9936915,0.0001360211,0.005928354,0.00001148241,0.00005998202,0.000004403912,0.000143802,0.00001338063,0.00001107012],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08873752,"threshold_uncertainty_score":0.3867319,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0262684065009927,"score_gpt":0.2693162666368714,"score_spread":0.2430478601358787,"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."}}