{"id":"W4323319191","doi":"10.3390/proteomes11010010","title":"Optimized Proteome Reduction for Integrative Top–Down Proteomics","year":2023,"lang":"en","type":"article","venue":"Proteomes","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Brock University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Proteome; Dithiothreitol; Reduction (mathematics); Proteomics; Protocol (science); Computer science; Chemistry; Computational biology; Biochemistry; Biology; Enzyme; Medicine; Mathematics","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002667168,0.0002979235,0.0003277078,0.0001392877,0.0003277292,0.00007467255,0.000353929,0.0002102179,0.0003299015],"category_scores_gemma":[0.0002106746,0.0002633155,0.0001805318,0.0004381189,0.0001143366,0.0001998769,0.0001166407,0.0003117473,0.000116485],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001419303,"about_ca_system_score_gemma":0.00009003802,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001461217,"about_ca_topic_score_gemma":4.701314e-7,"domain_scores_codex":[0.9983426,0.00001477554,0.0004076274,0.0005794255,0.0001820342,0.0004734947],"domain_scores_gemma":[0.9988686,0.00006114376,0.000236208,0.0005241737,0.0002062065,0.0001036911],"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":[0.0002185528,0.00008458114,0.0000322252,0.0002752688,0.00005605687,0.000001621209,0.0004568631,0.0003844991,0.9795355,0.006970881,0.002452557,0.009531426],"study_design_scores_gemma":[0.0007204186,0.00006016457,0.000006223205,0.00008676958,0.00001883958,0.00000894359,0.0003054357,0.002597073,0.9260291,0.05601976,0.01379975,0.0003474801],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2893324,0.0001035158,0.6739689,0.005259284,0.0001876851,0.01567713,0.0004824304,0.004945311,0.01004327],"genre_scores_gemma":[0.05522649,0.000169686,0.8614676,0.00004543945,0.0005607306,0.06684586,0.0003673215,0.0001409793,0.01517591],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.234106,"threshold_uncertainty_score":0.9999819,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01907385947959687,"score_gpt":0.3078943647640642,"score_spread":0.2888205052844673,"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."}}