{"id":"W3177253759","doi":"10.1080/15376494.2021.1928345","title":"Aspects on viscoelasticity modeling of HDPE using fractional derivatives: Interpolation procedures and efficient numerical scheme","year":2021,"lang":"en","type":"article","venue":"Mechanics of Advanced Materials and Structures","topic":"Fractional Differential Equations Solutions","field":"Mathematics","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Fundação de Amparo à Pesquisa do Estado de São Paulo","keywords":"Creep; High-density polyethylene; Viscoelasticity; Applied mathematics; Interpolation (computer graphics); Fractional calculus; Constitutive equation; Range (aeronautics); Stress (linguistics); Work (physics); Mathematics; Materials science; Computer science; Structural engineering; Finite element method; Polyethylene; Physics; Thermodynamics; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006914113,0.0001243133,0.0002632432,0.00007485212,0.0001051818,0.00002162572,0.00003634871,0.00006335931,0.0000843166],"category_scores_gemma":[0.0008623424,0.0001139474,0.00002811132,0.00008407451,0.00002790259,0.00008632962,0.00005899241,0.00006664512,1.065919e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002178171,"about_ca_system_score_gemma":0.00006585257,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009969537,"about_ca_topic_score_gemma":0.000004344086,"domain_scores_codex":[0.9991108,0.00004712353,0.0003377268,0.0001986842,0.0002049129,0.0001008094],"domain_scores_gemma":[0.9992476,0.000177298,0.000227783,0.00009992087,0.0002132786,0.00003408995],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00007198744,0.00004723448,0.000001608144,0.0001254768,0.00002395039,2.672317e-7,0.00009672024,0.01859947,0.5860562,0.3949341,5.862711e-7,0.00004238656],"study_design_scores_gemma":[0.0002427229,0.0000527598,0.0002016939,0.0001364587,0.00002918032,0.000009025399,0.000235945,0.2955317,0.2258478,0.4776227,6.170632e-7,0.0000894143],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6690723,0.00004554701,0.3305734,0.00001932348,0.0001236589,0.00009012012,0.0000478984,0.000009885768,0.00001778371],"genre_scores_gemma":[0.9104452,0.00001639063,0.08946913,0.000008797986,0.00003020942,0.000004263421,0.00001259427,0.00001205963,0.000001422232],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3602084,"threshold_uncertainty_score":0.4646641,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03782804564675355,"score_gpt":0.3176946710753326,"score_spread":0.279866625428579,"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."}}