{"id":"W2762566518","doi":"10.1139/cjp-2017-0070","title":"Approximate analytical solutions to nonlinear peristaltic flow with temperature-dependent viscosity parameters: Application of multi-step differential transform method (MsDTM)","year":2017,"lang":"en","type":"article","venue":"Canadian Journal of Physics","topic":"Nanofluid Flow and Heat Transfer","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Mechanics; Physics; Reynolds number; Viscosity; Pressure gradient; Nonlinear system; Flow (mathematics); Newtonian fluid; Ordinary differential equation; Wavelength; Boundary value problem; Constant (computer programming); Thermodynamics; Partial differential equation; Differential equation; Mathematical analysis; Mathematics; Optics; Turbulence","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000137979,0.0001748829,0.000358244,0.0001069796,0.0002340975,0.0001048062,0.0003016864,0.00008728894,0.00001055906],"category_scores_gemma":[0.000009974989,0.0001506929,0.000146675,0.00008673003,0.00007636415,0.0001996439,0.000005570945,0.0003112019,0.000003424545],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001382514,"about_ca_system_score_gemma":0.0002355243,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001100498,"about_ca_topic_score_gemma":0.01144793,"domain_scores_codex":[0.9990221,0.00001992872,0.0003132658,0.000127941,0.0001889292,0.0003278476],"domain_scores_gemma":[0.9989169,0.00001800751,0.00004816705,0.0003137478,0.0001654547,0.0005377421],"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":[0.0005355798,0.0008197681,0.003890609,0.001453235,0.002571464,0.0002563215,0.008548785,0.4236491,0.3028983,0.006941734,0.0008587171,0.2475763],"study_design_scores_gemma":[0.001995547,0.0003353729,0.004261069,0.0001981403,0.0004565511,0.000057931,0.0001745604,0.9364937,0.05493337,0.0001231229,0.0004750041,0.0004956315],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1912472,0.0000466654,0.8079826,0.0001278374,0.0001487756,0.00021035,0.0001609453,0.00001016201,0.00006553659],"genre_scores_gemma":[0.962655,0.000009527525,0.03709735,0.00001586491,0.0001593162,0.00000678095,0.000008896172,0.00003233868,0.00001494622],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7714078,"threshold_uncertainty_score":0.6388215,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02115481614556626,"score_gpt":0.2554041243254437,"score_spread":0.2342493081798775,"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."}}