{"id":"W3083949612","doi":"10.1038/s41598-020-71755-8","title":"Case studies of clinical hemodialysis membranes: influences of membrane morphology and biocompatibility on uremic blood-membrane interactions and inflammatory biomarkers","year":2020,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Membrane Separation Technologies","field":"Environmental Science","cited_by":79,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University; St. Paul's Hospital; University of Alberta; University of Saskatchewan","funders":"Social Sciences and Humanities Research Council; Social Sciences and Humanities Research Council of Canada; Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; University of Saskatchewan","keywords":"Membrane; Hemodialysis; Medicine; Chemistry; Internal medicine; Biochemistry","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.002189179,0.0002031037,0.0006530065,0.0001655816,0.0001963593,0.0000335439,0.00016781,0.0001175138,0.0002297325],"category_scores_gemma":[0.002474658,0.0001682761,0.0001129801,0.0006773475,0.004258086,0.000294312,0.0003430426,0.0001836871,0.000008546213],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001826419,"about_ca_system_score_gemma":0.00004084351,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000118889,"about_ca_topic_score_gemma":0.0001264978,"domain_scores_codex":[0.996637,0.000212406,0.001516436,0.001010652,0.0004227069,0.0002007911],"domain_scores_gemma":[0.997568,0.000506716,0.0009824248,0.0007162335,0.00007289714,0.0001537881],"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":[0.0001043435,0.0001846941,0.2088167,0.0004000505,0.0002581423,0.001216703,0.001774088,0.0007937057,0.7831612,0.00001892775,0.0004288301,0.002842615],"study_design_scores_gemma":[0.0006520325,0.0002705956,0.01891949,0.00007220322,0.0002785513,0.002473128,0.002515462,0.0009686201,0.9720614,0.0008068839,0.0006393924,0.0003422546],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9981121,0.0002021063,0.000004519217,0.0002746859,0.0006216386,0.0004033872,0.000008374307,0.00006895838,0.000304266],"genre_scores_gemma":[0.999078,0.0001149225,0.0006693932,0.00005298907,0.00001379529,0.00001475259,0.000005613952,0.000008281702,0.00004222936],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1898972,"threshold_uncertainty_score":0.9984518,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05882472592481697,"score_gpt":0.338570244673895,"score_spread":0.279745518749078,"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."}}