{"id":"W2772469707","doi":"10.4018/ijcini.2017100103","title":"NBPMF","year":2017,"lang":"en","type":"article","venue":"International Journal of Cognitive Informatics and Natural Intelligence","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Computer science; Matching (statistics); Inference; Function (biology); Filter (signal processing); Bipartite graph; Probability mass function; Mass spectrometry; Data mining; Algorithm; Artificial intelligence; Probability density function; Statistics; Chromatography; Theoretical computer science; Mathematics; 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":[0.0001185014,0.00008459319,0.0001114833,0.00005932405,0.0001388758,0.0002043886,0.0005537681,0.00004376525,0.00009257557],"category_scores_gemma":[0.0004163712,0.00006798881,0.00006415705,0.00001332516,0.000152915,0.0006124195,0.0001395299,0.0002867692,0.000006936018],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002388251,"about_ca_system_score_gemma":0.00002604446,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000461669,"about_ca_topic_score_gemma":0.000001189414,"domain_scores_codex":[0.9992105,0.000002115482,0.0004303492,0.00004326831,0.0002303834,0.00008335933],"domain_scores_gemma":[0.9980462,0.0001230858,0.000817456,0.0000975661,0.0008617194,0.00005398831],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002832779,0.00007954138,0.003073125,0.00003750893,0.0002589283,0.00003840265,0.001045894,0.00002306295,0.00461419,0.02571574,0.0001449381,0.9646854],"study_design_scores_gemma":[0.001039479,0.0001474827,0.002179659,0.001831789,0.00008873092,0.00135014,0.003108298,0.01280845,0.8586944,0.09979527,0.01832971,0.0006265952],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.690353,0.0006324525,0.2787901,0.0007876863,0.0005534625,0.00009952438,0.00006635147,0.00002540321,0.02869199],"genre_scores_gemma":[0.982795,0.001108863,0.01558157,0.0001442965,0.000161795,0.000003154298,0.000004741555,0.000004778642,0.000195786],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9640588,"threshold_uncertainty_score":0.2772503,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01954277042598941,"score_gpt":0.3476818837583908,"score_spread":0.3281391133324014,"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."}}