{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004648193,0.001647613,0.002050403,0.004387114,0.001169533,0.001624312,0.003181312,0.001679078,0.001704305],"category_scores_gemma":[0.01096183,0.000769345,0.00248342,0.003918326,0.001076804,0.001955779,0.001697885,0.0017885,0.001245398],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00210656,"about_ca_system_score_gemma":0.001434373,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007751502,"about_ca_topic_score_gemma":0.007688899,"domain_scores_codex":[0.9964274,0.001317482,0.0001893837,0.0007627551,0.001152135,0.0001510309],"domain_scores_gemma":[0.9956124,0.001751065,0.0006055741,0.0008140983,0.001126809,0.00009011435],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001574367,0.0001444483,0.002725637,0.000264231,0.000436771,0.00009049238,0.0002451662,0.7466488,0.005115236,0.0190226,0.004658155,0.220491],"study_design_scores_gemma":[0.000006812619,0.00001690337,0.0005127054,0.00001026544,0.00001522159,0.00004267428,0.00002150567,0.986605,0.001097198,0.0107604,0.000889176,0.00002212332],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003554711,0.0001994949,0.9948465,0.00007939574,0.00001332093,0.00006354968,0.0001110326,0.0007721769,0.0003598125],"genre_scores_gemma":[0.1382003,0.0003447144,0.8580656,0.0001206345,0.00004909144,0.0003690875,0.00127295,0.0003557093,0.001221853],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007751502,"threshold_uncertainty_score":0.02458227,"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."}}