{"id":"W4224119702","doi":"10.1002/elsa.202100185","title":"Accurately predicting transport properties of porous fibrous materials by machine learning methods","year":2022,"lang":"en","type":"article","venue":"Electrochemical Science Advances","topic":"Fuel Cells and Related Materials","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Thermal diffusivity; Support vector machine; Gradient boosting; Permeability (electromagnetism); Porosity; Artificial neural network; Computer science; Artificial intelligence; Mass transport; Biological system; Electrolyte; Materials science; Porous medium; Regression; Machine learning; Membrane; Chemistry; Composite material; Mathematics; Statistics; Random forest; Engineering; Physics; Thermodynamics; Biochemical engineering","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":[0.001243819,0.0005500381,0.0003812147,0.000866295,0.0001290701,0.0005021034,0.00041358,0.0005784933,0.0003696765],"category_scores_gemma":[0.002995871,0.000188207,0.0004704552,0.000442704,0.0002304083,0.0006016592,0.0001900964,0.00034149,0.0002012876],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005279231,"about_ca_system_score_gemma":0.0003989354,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001693876,"about_ca_topic_score_gemma":0.00127017,"domain_scores_codex":[0.9997926,0.00006520768,0.00001813524,0.00005889214,0.00005033941,0.00001487281],"domain_scores_gemma":[0.9988261,0.00071695,0.000181248,0.00007902102,0.0001788387,0.00001791274],"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":[0.0001854744,0.0001276389,0.008605477,0.000116056,0.00006302679,0.00006409555,0.00001628852,0.9176223,0.03110488,0.0006850506,0.0002890351,0.04112071],"study_design_scores_gemma":[0.00000268226,0.00001639976,0.0005381131,0.00000223298,0.000002898058,0.000005858226,0.000001580651,0.994212,0.005033798,0.0001285014,0.0000534536,0.000002504657],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8163407,0.0005103706,0.1804052,0.00009205144,0.00002680155,0.00005218136,0.0008746917,0.0008954083,0.0008025888],"genre_scores_gemma":[0.9679275,0.0001097948,0.03120019,0.00001083758,0.000007208761,0.00003479467,0.0005199064,0.00001309813,0.0001767673],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001693876,"threshold_uncertainty_score":0.006578028,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00978925184812256,"score_gpt":0.2501576819452567,"score_spread":0.2403684300971342,"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."}}