{"id":"W2183854145","doi":"","title":"FLOW AND TEXTURE MODELING OF LIQUID CRYSTALLINE MATERIALS","year":2013,"lang":"en","type":"article","venue":"","topic":"Hermeneutics and Narrative Identity","field":"Arts and Humanities","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Rheology; Materials science; Viscoelasticity; Texture (cosmology); Nucleation; Shear flow; Flow (mathematics); Anisotropy; Mesophase; Shear thinning; Mechanics; Liquid crystal; Thermodynamics; Composite material; Physics; Optics; Computer science; Artificial intelligence","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":[0.0002028306,0.0004905961,0.000596515,0.0008001061,0.0002520949,0.00102422,0.0007053084,0.0008302681,0.001392295],"category_scores_gemma":[0.0004924001,0.0002512486,0.0005190537,0.0008443145,0.000595135,0.0009946063,0.0002529576,0.0003749884,0.0003674648],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006425615,"about_ca_system_score_gemma":0.0004745918,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003171299,"about_ca_topic_score_gemma":0.001139086,"domain_scores_codex":[0.9998968,0.0000185666,0.000007194951,0.00001864627,0.00004568244,0.00001324801],"domain_scores_gemma":[0.9998925,0.00004089579,0.0000188466,0.00001323233,0.00002800455,0.000006489322],"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":[0.00005300701,0.00005640265,0.0006717539,0.000411541,0.00003338128,0.0001879868,0.0000710703,0.82388,0.03597969,0.08335581,0.001714661,0.05358479],"study_design_scores_gemma":[0.000005730187,0.00001040033,0.0002464709,0.00001699373,0.000003714344,0.00004642964,0.000008269351,0.982314,0.003165559,0.009034616,0.00513926,0.000008554385],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04692782,0.01542768,0.9081863,0.0004782705,0.0001891978,0.0001169731,0.0007852836,0.000781863,0.02710668],"genre_scores_gemma":[0.7636652,0.02448466,0.1851232,0.0002197907,0.0004220745,0.0003133455,0.001763469,0.0004725779,0.02353567],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003171299,"threshold_uncertainty_score":0.006305695,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02498588465352493,"score_gpt":0.2152002502042859,"score_spread":0.190214365550761,"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."}}