{"id":"W4255460625","doi":"10.1016/j.bpj.2015.11.1754","title":"Graphene Nanopores for Protein Sequencing","year":2016,"lang":"en","type":"article","venue":"Biophysical Journal","topic":"Nanopore and Nanochannel Transport Studies","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"","keywords":"Nanopore; Nanopore sequencing; Graphene; Peptide; Hydrostatic pressure; Amino acid; Biophysics; Ionic bonding; Transmembrane protein; Chemistry; Nanotechnology; DNA sequencing; DNA; Biochemistry; Ion; Biology; Materials science; Physics","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":[0.0003606219,0.000410705,0.0002070389,0.0003951939,0.0005485731,0.0003336059,0.0003099582,0.0005734802,0.001247664],"category_scores_gemma":[0.0003103622,0.000188883,0.0001981865,0.0002809932,0.0003308765,0.0007037152,0.0003629331,0.0006291809,0.0002715051],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003943349,"about_ca_system_score_gemma":0.0002596363,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001277907,"about_ca_topic_score_gemma":0.002736012,"domain_scores_codex":[0.9998109,0.00004759131,0.000008347069,0.00003323705,0.00006631774,0.00003371072],"domain_scores_gemma":[0.999832,0.0000701884,0.00001415927,0.00002996668,0.00002905933,0.00002445751],"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":[0.00005595266,0.00003074265,0.00007253606,0.0000789321,0.00001174499,0.00003303569,0.00004244707,0.0005232712,0.9937764,0.001053446,0.0001761708,0.004145296],"study_design_scores_gemma":[0.000004284587,0.00009415608,0.000444161,0.000008924518,0.00001446353,0.000055521,0.00003082168,0.003233302,0.9919973,0.0004272681,0.003678706,0.00001111025],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9389902,0.008692035,0.04316135,0.0005946537,0.0002152412,0.0001087719,0.0005017556,0.0003116088,0.007424322],"genre_scores_gemma":[0.9640021,0.002420402,0.02915072,0.0002031956,0.00002840262,0.00005040276,0.0003719393,0.00005281539,0.003720114],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001277907,"threshold_uncertainty_score":0.004173875,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01638733408970761,"score_gpt":0.2171500881805966,"score_spread":0.200762754090889,"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."}}