{"id":"W2138616250","doi":"10.1109/iembs.2010.5626766","title":"Accurate samples for testing mass spectrometry based peptide quantification algorithms","year":2010,"lang":"en","type":"article","venue":"","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Canadian Institutes of Health Research; McGill University","keywords":"Calibration; Algorithm; Set (abstract data type); Computer science; Mass spectrometry; Noise (video); Envelope (radar); Data set; Quantitative proteomics; Proteomics; Data mining; Chemistry; Mathematics; Artificial intelligence; Statistics; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01775995,0.001571385,0.0007476998,0.001771516,0.00120674,0.001785692,0.002113493,0.003055267,0.001391975],"category_scores_gemma":[0.07880244,0.0005655864,0.001045343,0.001968192,0.001631045,0.002048826,0.001447361,0.001849037,0.0006307151],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000990007,"about_ca_system_score_gemma":0.0009401073,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008726785,"about_ca_topic_score_gemma":0.0008159081,"domain_scores_codex":[0.9767221,0.008499213,0.002418232,0.002991059,0.00865501,0.000714319],"domain_scores_gemma":[0.9449804,0.03400943,0.00312907,0.009923246,0.007563959,0.0003939634],"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":[0.003535952,0.004605712,0.05296176,0.0009753747,0.0009506835,0.0004927927,0.0006492995,0.2833741,0.3952925,0.03049419,0.003356504,0.223311],"study_design_scores_gemma":[0.0002050831,0.00232161,0.01254549,0.00005626091,0.0001614478,0.0003458001,0.0001678652,0.5380243,0.4319606,0.01020924,0.003896877,0.0001053853],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2943672,0.0005854539,0.7004743,0.0002646349,0.0002492855,0.0007112153,0.0009751315,0.0011045,0.001268268],"genre_scores_gemma":[0.5010883,0.0002562917,0.493656,0.0002550758,0.00006200474,0.001588488,0.002230885,0.0003174571,0.0005454138],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01775995,"threshold_uncertainty_score":0.0939247,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05557987754729865,"score_gpt":0.3250148174039379,"score_spread":0.2694349398566392,"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."}}