{"id":"W2009478957","doi":"10.1109/icbbe.2009.5162855","title":"Feature Selection for Tandem Mass Spectrum Quality Assessment via Sparse Logistical Regression","year":2009,"lang":"en","type":"article","venue":"","topic":"Morphological variations and asymmetry","field":"Mathematics","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada; University of Saskatchewan; Université Laval","keywords":"Computer science; Feature selection; Tandem; Selection (genetic algorithm); Quality (philosophy); Artificial intelligence; Pattern recognition (psychology); Regression; Feature (linguistics); Machine learning; Quality assessment; Data mining; Statistics; Mathematics; Evaluation methods; Reliability engineering; 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":[],"consensus_categories":[],"category_scores_codex":[0.0006097545,0.0001455929,0.0002532093,0.00004146609,0.0001581705,0.00004374048,0.00009216155,0.0002034858,0.0004216431],"category_scores_gemma":[0.0003463573,0.00009367175,0.000108772,0.0001675604,0.00001818462,0.00005745512,0.00001458895,0.0002111382,0.000009797152],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001022961,"about_ca_system_score_gemma":0.00002596152,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005922423,"about_ca_topic_score_gemma":0.00001097855,"domain_scores_codex":[0.9989319,0.00009474323,0.0002376877,0.0002739088,0.0002128053,0.000248899],"domain_scores_gemma":[0.9991454,0.0003932023,0.0001214483,0.0001954946,0.0000616562,0.00008275912],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00003026934,0.0003382781,0.001939296,0.00001770437,0.00001494779,0.000002020096,0.000004647411,0.000005717657,0.005345714,0.9423945,0.04753789,0.002369031],"study_design_scores_gemma":[0.000729934,0.0004397013,0.04047546,0.00002217501,0.00004927671,0.00001113298,0.00003302388,0.0115502,0.003099409,0.938645,0.004682928,0.0002617769],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004700761,0.000007048432,0.9748104,0.008055609,0.0001102788,0.0003607666,0.000007603486,0.0001570665,0.0117905],"genre_scores_gemma":[0.5966565,0.000002822984,0.3997057,0.0003366015,0.0001638235,0.00001328728,0.00001674236,0.000006627935,0.003097884],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5919557,"threshold_uncertainty_score":0.4616697,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1058880085282851,"score_gpt":0.4000656543438159,"score_spread":0.2941776458155307,"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."}}