{"id":"W2129955232","doi":"10.1109/iembs.2009.5332499","title":"Model based clustering for tandem mass spectrum quality assessment","year":2009,"lang":"en","type":"article","venue":"","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"University of Saskatchewan; Université Laval","keywords":"Cluster analysis; Discriminative model; Computer science; Mixture model; Pattern recognition (psychology); Artificial intelligence; Probabilistic logic; Gaussian; Tandem; Expectation–maximization algorithm; Quality (philosophy); Posterior probability; Quality assessment; Mass spectrum; Cluster (spacecraft); Spectral line; Linear discriminant analysis; Mathematics; Statistics; Bayesian probability; Mass spectrometry; Chemistry; Maximum likelihood; Materials science; Physics; Engineering","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001825417,0.0001579462,0.0002597572,0.00006425524,0.0001010502,0.00005177623,0.0001811354,0.00009903051,0.001371834],"category_scores_gemma":[0.00003595094,0.0001426362,0.0001654705,0.0001767671,0.00001517762,0.00006783448,0.00001335698,0.0001171777,0.000004080649],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001888786,"about_ca_system_score_gemma":0.00007521824,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001596761,"about_ca_topic_score_gemma":0.00001502831,"domain_scores_codex":[0.9989036,0.000006005338,0.0002740325,0.0002943185,0.0002063653,0.0003156851],"domain_scores_gemma":[0.9993925,0.00009455485,0.00008579294,0.0003080654,0.00003205668,0.00008701994],"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":[0.0001439372,0.0006405002,0.008061904,0.0002436429,0.0001972505,0.000004593826,0.00004258321,0.0276556,0.9377473,0.01907407,0.003551675,0.002636943],"study_design_scores_gemma":[0.0007568435,0.00003690338,0.0004310365,0.000005822241,0.00007218651,4.786093e-7,0.0000904925,0.6385149,0.3507674,0.008885267,0.0002018665,0.0002368],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01868771,0.00003287047,0.8489716,0.00132859,0.0000170597,0.00005881917,0.00001390201,0.0001591792,0.1307303],"genre_scores_gemma":[0.9282027,0.000004869534,0.06406208,0.0007294598,0.00008815814,0.00001413688,0.00002421414,0.00001154762,0.006862829],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.909515,"threshold_uncertainty_score":0.999541,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06682164370623579,"score_gpt":0.3746624719290271,"score_spread":0.3078408282227913,"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."}}