{"id":"W2031896295","doi":"10.1016/j.electacta.2014.02.120","title":"Dynamic electrochemical impedance spectroscopy, for electrocatalytic reactions","year":2014,"lang":"en","type":"article","venue":"Electrochimica Acta","topic":"Electrochemical Analysis and Applications","field":"Chemistry","cited_by":56,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Victoria","funders":"Natural Sciences and Engineering Research Council of Canada; Norges Forskningsråd; Norges Teknisk-Naturvitenskapelige Universitet","keywords":"Dielectric spectroscopy; Electrical impedance; Subtraction; Measure (data warehouse); SIGNAL (programming language); Computer science; Consistency (knowledge bases); Electrochemistry; Spectroscopy; Electronic engineering; Analytical Chemistry (journal); Materials science; Chemistry; Electrode; Electrical engineering; Physics; Engineering; Mathematics; Artificial intelligence; Data mining; Physical chemistry","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008851295,0.001675111,0.0008994412,0.001697279,0.0006566275,0.001361544,0.002073844,0.002196515,0.006689467],"category_scores_gemma":[0.002051706,0.0006432566,0.0004224938,0.001803033,0.000473113,0.002010958,0.0007600877,0.002254787,0.0040678],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006915886,"about_ca_system_score_gemma":0.0003718042,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005240296,"about_ca_topic_score_gemma":0.001613597,"domain_scores_codex":[0.9982198,0.0002073496,0.0001247552,0.0006665144,0.0006688994,0.0001127002],"domain_scores_gemma":[0.9993351,0.0002862278,0.00008129441,0.0000914929,0.000169242,0.00003662308],"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":[0.0001314904,0.00005847625,0.0003590158,0.0006926715,0.00003045581,0.000127384,0.0001078231,0.0001716084,0.948624,0.002151899,0.003474809,0.04407034],"study_design_scores_gemma":[0.00001412148,0.0001109817,0.001035744,0.00003810995,0.00005110201,0.0008747401,0.00008433143,0.003114271,0.9608385,0.001056265,0.03275122,0.00003059909],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0805312,0.03809396,0.8244498,0.002849366,0.003287792,0.0005418396,0.009183916,0.005996638,0.03506548],"genre_scores_gemma":[0.5835795,0.03182437,0.3143519,0.003063974,0.0007089216,0.000638549,0.008558108,0.0005943837,0.05668023],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006689467,"threshold_uncertainty_score":0.02237844,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00471473898946095,"score_gpt":0.2450671129329037,"score_spread":0.2403523739434427,"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."}}