{"id":"W2980477204","doi":"10.1101/805770","title":"3D projection electrophoresis for single-cell immunoblotting","year":2019,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Microfluidic and Bio-sensing Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Cancer Moonshot; Moonshot Research and Development Program; National Science Foundation; National Institutes of Health; National Cancer Institute; Natural Sciences and Engineering Research Council of Canada","keywords":"Lysis; Digital micromirror device; Single-cell analysis; Electrophoresis; Throughput; Gel electrophoresis; Chemistry; Confocal; Microfluidics; Deconvolution; Cell; Molecular biology; Computer science; Chromatography; Biology; Physics; Materials science; Nanotechnology; Biochemistry; Optics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003934255,0.0007080717,0.0006520228,0.0004185163,0.0001610897,0.0002436103,0.00057245,0.001073159,0.000009201603],"category_scores_gemma":[0.0001468908,0.0007704168,0.000235783,0.0004329011,0.00008353007,0.0001089704,0.0003134802,0.0008905429,0.00006492396],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006011411,"about_ca_system_score_gemma":0.0002113097,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002285402,"about_ca_topic_score_gemma":5.224314e-7,"domain_scores_codex":[0.9974505,0.0000396984,0.000578426,0.0008523126,0.0002406892,0.0008383798],"domain_scores_gemma":[0.9980167,0.00008249247,0.0002465606,0.001254845,0.0003186385,0.00008079578],"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":[0.00002175787,0.00006561867,0.0002032432,0.001146516,0.0001104966,0.000006614566,0.000004685573,0.0002314967,0.9953412,0.00004649862,0.002803094,0.00001880525],"study_design_scores_gemma":[0.0003884163,0.0001070589,0.0004160688,0.0003532059,0.0001276153,3.94306e-8,0.000005620043,0.004867428,0.9801793,0.000002071084,0.01263186,0.0009213559],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9385267,0.0112704,0.03854155,0.00008083348,0.002895152,0.002282431,0.0002132186,0.006079968,0.0001097218],"genre_scores_gemma":[0.9743696,0.001243447,0.02347083,0.00003436164,0.0003862201,0.0001899599,0.000001021153,0.0002910201,0.00001357413],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03584284,"threshold_uncertainty_score":0.9994747,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01142993804107245,"score_gpt":0.1853192297489951,"score_spread":0.1738892917079227,"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."}}