{"id":"W3012993595","doi":"10.1039/d0cp00997k","title":"A hybrid theoretical method for predicting electrokinetic energy conversion in nanochannels","year":2020,"lang":"en","type":"article","venue":"Physical Chemistry Chemical Physics","topic":"Nanopore and Nanochannel Transport Studies","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"National Natural Science Foundation of China; U.S. Department of Energy","keywords":"Electrokinetic phenomena; Energy transformation; Energy (signal processing); Materials science; Nanotechnology; Chemistry; Chemical engineering; Thermodynamics; Physics; Engineering","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":[0.0004783201,0.0006265541,0.0006206142,0.0008817192,0.0004852592,0.0005256996,0.001179895,0.001206936,0.001329205],"category_scores_gemma":[0.0011023,0.0002873671,0.0008182216,0.0006030052,0.0005566942,0.0009019115,0.0005271774,0.0005674471,0.0002973781],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007253295,"about_ca_system_score_gemma":0.001300707,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004594954,"about_ca_topic_score_gemma":0.002383851,"domain_scores_codex":[0.9997844,0.00004225947,0.00001137862,0.00003027952,0.0001092396,0.00002241885],"domain_scores_gemma":[0.9995995,0.0002111968,0.00002459483,0.00003282779,0.0001104199,0.00002151239],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002474149,0.00006921554,0.0005432199,0.0001170063,0.00003143655,0.00009435845,0.00002807857,0.9438025,0.0124798,0.02696467,0.0003735695,0.01547135],"study_design_scores_gemma":[0.000001854462,0.000004861289,0.00003385695,0.000001875592,0.000001429128,0.000008738161,0.000001590923,0.9983727,0.0005554199,0.000856893,0.0001582136,0.000002496437],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05742869,0.0006203572,0.9327115,0.0001993859,0.000104808,0.000113631,0.0002112194,0.0005174819,0.008092807],"genre_scores_gemma":[0.7097446,0.0009801062,0.2803923,0.0002531396,0.00009343279,0.0008182696,0.0002973248,0.0002414964,0.007179354],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004594954,"threshold_uncertainty_score":0.009136438,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007753578462601918,"score_gpt":0.2158460642471097,"score_spread":0.2080924857845078,"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."}}