{"id":"W3112231827","doi":"10.1016/j.actbio.2020.12.027","title":"Superviscous properties of the in vivo brain at large scales","year":2020,"lang":"en","type":"article","venue":"Acta Biomaterialia","topic":"Elasticity and Material Modeling","field":"Engineering","cited_by":39,"is_retracted":false,"has_abstract":false,"ca_institutions":"Western University","funders":"Horizon 2020; Deutsche Forschungsgemeinschaft; European Commission","keywords":"Magnetic resonance elastography; Materials science; Biomedical engineering; Viscoelasticity; Elastography; In vivo; Stiffness; Nuclear magnetic resonance; Acoustics; Physics; Medicine; Composite material; Ultrasound; Biology","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":[0.00006235769,0.0001046087,0.0001558977,0.00001986084,0.00003422519,0.00002613516,0.0001814945,0.00006354026,0.000119092],"category_scores_gemma":[0.00003100597,0.00007348318,0.00003098191,0.00008806357,0.00003345485,0.00008121216,0.0001355409,0.00002505195,0.00001472817],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002244459,"about_ca_system_score_gemma":0.000006088275,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005656301,"about_ca_topic_score_gemma":0.0001201851,"domain_scores_codex":[0.9993917,0.00002937805,0.0002116703,0.0001005987,0.0000844205,0.0001822726],"domain_scores_gemma":[0.9998094,0.000005836386,0.00001956746,0.0001217457,0.00001012024,0.00003329554],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00003018592,0.000008664996,0.0001542851,0.0002558519,0.00000806797,0.000001175521,0.0009681061,0.0002356844,0.9972743,0.00005213772,0.001005916,0.000005654681],"study_design_scores_gemma":[0.0002477167,0.00001492144,0.0003298766,0.00008590384,0.000004724873,0.000001603865,0.00004972972,0.006079904,0.9908078,0.00001563616,0.002261874,0.000100337],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.997901,0.00005394321,0.00004217376,0.0007681138,0.0008785122,0.0001184063,0.00006725809,0.00009767854,0.00007287603],"genre_scores_gemma":[0.9996616,0.000008632331,0.00004049071,0.0001402343,0.00009505278,0.000007530623,0.000002305861,0.00002125019,0.0000229113],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006466498,"threshold_uncertainty_score":0.2996557,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01994541480754318,"score_gpt":0.1911268993009724,"score_spread":0.1711814844934293,"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."}}