{"id":"W2129351949","doi":"10.1186/1477-5956-10-68","title":"A feedback framework for protein inference with peptides identified from tandem mass spectra","year":2012,"lang":"en","type":"article","venue":"Proteome Science","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Saskatoon City Hospital; University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Inference; Identification (biology); Computer science; Tandem mass spectrometry; Iterative and incremental development; Proteomics; Peptide; Computational biology; Data mining; Machine learning; Artificial intelligence; Mass spectrometry; Biology; Chemistry; Biochemistry; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002986078,0.0002064947,0.0001853963,0.000056131,0.0004420221,0.0001777103,0.0007514158,0.0001068496,0.0001984945],"category_scores_gemma":[0.0003162568,0.0001721918,0.00004702969,0.0005096801,0.0005146551,0.0006768277,0.0001075579,0.0002990931,0.00003869335],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001290604,"about_ca_system_score_gemma":0.0001412638,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002977709,"about_ca_topic_score_gemma":0.00000339708,"domain_scores_codex":[0.9981776,0.000005852665,0.0002360723,0.0005313103,0.0003923168,0.0006568596],"domain_scores_gemma":[0.9986882,0.00008029423,0.0002103092,0.0006423912,0.0001582985,0.0002205342],"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":[0.00002501929,0.00004929927,0.002331166,0.00003370879,0.00000517054,2.808411e-7,0.000154046,0.000006216701,0.9611316,0.03535938,0.000006476715,0.0008976195],"study_design_scores_gemma":[0.000144569,0.00002561474,0.0004721573,0.0001557226,0.000007869213,0.000001545312,0.00008201928,0.0001515184,0.8088433,0.1892459,0.000625246,0.0002445231],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3497442,0.00004476792,0.647042,0.0001918252,0.0000194366,0.001090083,0.0000403884,0.0001917163,0.001635601],"genre_scores_gemma":[0.5274932,0.000003381172,0.4701471,0.00002211154,0.0001225368,0.001899674,0.000004911455,0.00001571357,0.0002913136],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.1777491,"threshold_uncertainty_score":0.7021776,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02378883828556725,"score_gpt":0.3071759030604714,"score_spread":0.2833870647749041,"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."}}