{"id":"W2806307264","doi":"10.1051/e3sconf/20183804013","title":"The Fractal Characterization of Mechanical Surface Profile Based on Power Spectral Density and Monte-Carlo Method","year":2018,"lang":"en","type":"article","venue":"E3S Web of Conferences","topic":"Adhesion, Friction, and Surface Interactions","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Fundamental Research Funds for the Central Universities; Natural Science Foundation of Hubei Province","keywords":"Fractal; Fractal dimension; Monte Carlo method; Spectral density; Fractal derivative; Statistical physics; Parametric statistics; Fractal analysis; Fractal dimension on networks; Fractal landscape; Mathematics; Surface (topology); Characterization (materials science); Range (aeronautics); Geometry; Mathematical analysis; Physics; Optics; Materials science; Statistics","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":[0.0006806069,0.0002983644,0.0004322316,0.001634596,0.0003578511,0.0005640295,0.0006314234,0.0005754881,0.001039027],"category_scores_gemma":[0.003115581,0.0002215786,0.0005261992,0.000769829,0.0005416065,0.00128484,0.0003259892,0.0004364723,0.0001757107],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005956273,"about_ca_system_score_gemma":0.0003593604,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00218604,"about_ca_topic_score_gemma":0.000872617,"domain_scores_codex":[0.9994311,0.0001272061,0.00002551587,0.0000775897,0.0002967438,0.00004177706],"domain_scores_gemma":[0.9988316,0.0006039416,0.00009003421,0.0001227164,0.0003232438,0.00002847405],"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":[0.0001982161,0.0001569808,0.008288758,0.0005763032,0.0001104911,0.0004947867,0.0005613251,0.5386222,0.06487817,0.1660854,0.002445721,0.2175817],"study_design_scores_gemma":[0.000004048141,0.00001554628,0.001255664,0.0000107771,0.000006495257,0.0001256477,0.000016789,0.9874195,0.00489738,0.005341863,0.0008840578,0.00002217064],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03982031,0.0005675984,0.9564434,0.00008889443,0.00003280777,0.00004834452,0.00005075293,0.0003586753,0.002589239],"genre_scores_gemma":[0.8392712,0.0006231882,0.1580551,0.00004733762,0.00004386461,0.0001284413,0.0001513822,0.00009029776,0.001589272],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00218604,"threshold_uncertainty_score":0.004346669,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01193247490907755,"score_gpt":0.2456199806488734,"score_spread":0.2336875057397958,"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."}}