{"id":"W1997606224","doi":"10.1115/imece2014-36785","title":"A Hybrid Model for Characterizing Pre-Yield Properties of MR Fluids","year":2014,"lang":"en","type":"article","venue":"","topic":"Vibration Control and Rheological Fluids","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Magnetorheological fluid; Magnetic field; Finite element method; Materials science; Excitation; Magnet; Shear modulus; Vibration; Mechanics; Stiffness; Beam (structure); Timoshenko beam theory; Acoustics; Physics; Composite material; Mechanical engineering; Structural engineering; Engineering; Optics","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":[0.0002605389,0.0006391735,0.0004068988,0.0005116694,0.000177207,0.0004978214,0.001146088,0.001216964,0.001149907],"category_scores_gemma":[0.0005682422,0.000288484,0.0005208617,0.0002469422,0.00053695,0.001128259,0.000433745,0.0005991871,0.0005899129],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002649864,"about_ca_system_score_gemma":0.0003486269,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000960346,"about_ca_topic_score_gemma":0.0006801219,"domain_scores_codex":[0.999801,0.00004023642,0.000009274478,0.00005453419,0.00007544553,0.00001953678],"domain_scores_gemma":[0.9998289,0.00006206704,0.00003536521,0.00002678396,0.00003967808,0.0000070919],"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":[0.00009228639,0.0001153009,0.0008848707,0.0002024746,0.00003860686,0.00025555,0.0001862765,0.8115065,0.148645,0.01966189,0.0005310553,0.01788016],"study_design_scores_gemma":[0.000004071228,0.00004721637,0.0001760826,0.000003961587,0.000005022834,0.00004466151,0.00001070371,0.993699,0.004007347,0.001064317,0.0009298875,0.000007803465],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05129199,0.0004630826,0.9409147,0.0001646259,0.00005331408,0.00009169679,0.0001555854,0.0003431608,0.006521964],"genre_scores_gemma":[0.9152982,0.0009762844,0.06647483,0.0001368157,0.0000629551,0.0004069999,0.0002530911,0.00008679912,0.01630404],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001216964,"threshold_uncertainty_score":0.003846765,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02374792879408957,"score_gpt":0.1989288089431684,"score_spread":0.1751808801490788,"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."}}