{"id":"W2979571350","doi":"10.1002/celc.201901394","title":"Identifying Nanoscale Pinhole Defects in Nitroaryl Layers with Scanning Electrochemical Cell Microscopy","year":2019,"lang":"en","type":"article","venue":"ChemElectroChem","topic":"Electrochemical Analysis and Applications","field":"Chemistry","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Mitacs","keywords":"Pinhole (optics); Pipette; Nanoscopic scale; Materials science; Microscopy; Electrochemistry; Scanning ion-conductance microscopy; Scanning probe microscopy; Nanotechnology; Scanning electrochemical microscopy; Optics; Scanning electron microscope; Analytical Chemistry (journal); Scanning confocal electron microscopy; Composite material; Chemistry; Electrode; Chromatography; Physics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001198669,0.0004782805,0.0005758004,0.0001458954,0.0001249634,0.0001023908,0.0005809825,0.0002782405,0.0004938704],"category_scores_gemma":[0.00001932219,0.0004579864,0.0002153976,0.001035838,0.00008447898,0.0001698312,0.0001011595,0.0009078818,0.0002517179],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003745772,"about_ca_system_score_gemma":0.0001565806,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002704774,"about_ca_topic_score_gemma":0.00002748781,"domain_scores_codex":[0.9968767,0.00001361516,0.0004903878,0.001008471,0.0004174941,0.001193329],"domain_scores_gemma":[0.9987825,0.00009889861,0.0001560668,0.0006613173,0.00008424836,0.0002170263],"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":[0.00009990948,0.0002337171,0.01222214,0.0001945502,0.00004698628,0.000004849307,0.0001185601,0.000004892715,0.9866984,0.00008836416,0.000223281,0.00006427527],"study_design_scores_gemma":[0.001260008,0.00005684247,0.00008218848,0.0001233295,0.00006424045,0.00001842092,0.0001701135,0.0002736492,0.9958248,0.0003155014,0.001224006,0.0005869127],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9782448,0.0008484977,0.000515214,0.00009759315,0.0000112241,0.0001774807,0.000002526724,0.0001697429,0.01993296],"genre_scores_gemma":[0.9953146,0.00006114285,0.001597061,0.0002037245,0.0001076854,0.0001597339,0.0001729948,0.00009099098,0.002292046],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01764091,"threshold_uncertainty_score":0.9997872,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004890651253451947,"score_gpt":0.223637663996031,"score_spread":0.218747012742579,"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."}}