{"id":"W7118700935","doi":"10.25545/jv022s","title":"Completely noninvasive viscosity characterization using a portable magnetic resonance sensor","year":2025,"lang":"","type":"dataset","venue":"UNB Dataverse","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Inviscid flow; SIGNAL (programming language); Sensitivity (control systems); Viscosity; Magnetic field; Magnet; Ferrofluid; Magnetic resonance imaging; Characterization (materials science)","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":["metaepi_narrow","sts","scholarly_communication","research_integrity","insufficient_payload"],"consensus_categories":["metaepi_narrow","research_integrity","insufficient_payload"],"category_scores_codex":[0.001284389,0.002764109,0.002975712,0.002078273,0.001768174,0.001147443,0.003398376,0.001485123,0.01886327],"category_scores_gemma":[0.001487938,0.003556858,0.0005387305,0.004107777,0.001161325,0.002448744,0.003949468,0.00241793,0.02662003],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002124012,"about_ca_system_score_gemma":0.003495079,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003230951,"about_ca_topic_score_gemma":0.0007119522,"domain_scores_codex":[0.9861075,0.001490275,0.002937937,0.004466468,0.002195992,0.002801883],"domain_scores_gemma":[0.9862977,0.0004697756,0.003029827,0.008126166,0.001239361,0.0008371347],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001256768,0.001335631,0.0002631601,0.001688465,0.0002994236,0.003166581,0.0001543524,0.0001414716,0.02527356,0.00005522378,0.9654484,0.0009170074],"study_design_scores_gemma":[0.003302384,0.000418073,0.001251691,0.004075137,0.002876951,0.0004154411,0.0002102923,0.01294293,0.000841765,0.000005669175,0.9707897,0.002869918],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.006034144,0.0003449399,0.0006396254,0.00002616829,0.002593546,0.003840577,0.9861399,0.000228581,0.0001525447],"genre_scores_gemma":[0.0001232573,0.00220435,0.003228751,0.0008742523,0.000792729,0.0001080423,0.9897535,0.0002917427,0.002623416],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.0244318,"threshold_uncertainty_score":0.9998894,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0296591349554236,"score_gpt":0.2690526198386221,"score_spread":0.2393934848831985,"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."}}