{"id":"W2952080999","doi":"10.1101/678995","title":"Improved Sensitivity in Low-Input Proteomics using Micro-Pillar Array-based Chromatography","year":2019,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Austrian Science Fund; European Commission","keywords":"Chromatography; Proteomics; Elution; Pillar; High-performance liquid chromatography; Dispersity; Chemistry; Materials science; Nanotechnology; Engineering","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":[0.0009506484,0.0005901876,0.0006546414,0.0005549362,0.0002381856,0.001234252,0.0007022724,0.0008291198,0.0007637132],"category_scores_gemma":[0.0009444242,0.000246957,0.000323786,0.0005024433,0.0003733291,0.0005740189,0.0004312963,0.0005368065,0.0008937457],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003767899,"about_ca_system_score_gemma":0.0003641841,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008750978,"about_ca_topic_score_gemma":0.001106835,"domain_scores_codex":[0.9987632,0.0002225287,0.00006693506,0.0003128136,0.0005277156,0.000106892],"domain_scores_gemma":[0.999517,0.0002460341,0.00004448206,0.0000578316,0.000103556,0.00003113414],"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":[0.00003877787,0.00001917922,0.0002580584,0.0000581991,0.00001680672,0.00002586045,0.00001049381,0.000218598,0.9945919,0.00005106704,0.0001024701,0.004608536],"study_design_scores_gemma":[0.00000459036,0.00009546102,0.001494048,0.000003746879,0.00001930053,0.0001023609,0.00000927785,0.003798043,0.9929843,0.00003817474,0.001437324,0.0000133783],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.8958166,0.008493553,0.08878806,0.0004840392,0.0002605251,0.000164938,0.001103437,0.002144176,0.002744723],"genre_scores_gemma":[0.8371376,0.002802081,0.1549107,0.000487745,0.00008812284,0.0001047012,0.0009440192,0.0001656001,0.003359446],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.001234252,"threshold_uncertainty_score":0.005027592,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0104377059306084,"score_gpt":0.2297930323340904,"score_spread":0.219355326403482,"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."}}