{"id":"W6893094553","doi":"10.5281/zenodo.14531667","title":"Scoring Information Integration with Statistical Quality Control Enhanced Cross-Run Analysis of Data-Independent Acquisition Proteomics Data (BioRxiv version, Deprecated)","year":2024,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Data integration; Quality (philosophy); Control (management); Data acquisition; Statistical process control; Data quality; Statistical analysis","routes":{"ca_aff":true,"ca_fund":false,"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":[0.01163469,0.003165072,0.002417595,0.004355569,0.001646779,0.003732486,0.002835613,0.00128464,0.02945339],"category_scores_gemma":[0.01979931,0.001302341,0.00177547,0.00421211,0.0008213944,0.001783163,0.002673502,0.002760135,0.01366895],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001148761,"about_ca_system_score_gemma":0.003614627,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003834492,"about_ca_topic_score_gemma":0.006848294,"domain_scores_codex":[0.9914317,0.001466295,0.001270372,0.001935203,0.003261646,0.0006347682],"domain_scores_gemma":[0.9855069,0.003116965,0.00130482,0.003321621,0.006411038,0.0003385709],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.004954929,0.001023963,0.02504355,0.003688469,0.001386524,0.0006606372,0.00121711,0.00662345,0.1941203,0.01005488,0.2077834,0.5434428],"study_design_scores_gemma":[0.000440162,0.0006119005,0.05341172,0.0003764303,0.0006220108,0.001230627,0.0001872429,0.1906794,0.5745959,0.008850462,0.168208,0.0007862385],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02842073,0.0006440468,0.8322633,0.000349453,0.000564937,0.001157193,0.01794199,0.1148261,0.003832249],"genre_scores_gemma":[0.06959222,0.0003656775,0.8487043,0.0003961652,0.0001817837,0.003355032,0.04537397,0.02418907,0.007841823],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02945339,"threshold_uncertainty_score":0.09853142,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04635587143436949,"score_gpt":0.3313152526722325,"score_spread":0.284959381237863,"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."}}