{"id":"W2951048064","doi":"10.1103/physreve.99.062701","title":"Machine learning topological defects of confined liquid crystals in two dimensions","year":2019,"lang":"en","type":"article","venue":"Physical review. E","topic":"Data Visualization and Analytics","field":"Computer Science","cited_by":48,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Compute Canada","keywords":"Artificial neural network; Computer science; Lattice (music); Liquid crystal; Sorting; Topology (electrical circuits); Topological sorting; Artificial intelligence; Topological defect; Square lattice; Field (mathematics); Algorithm; Physics; Statistical physics; Optics; Condensed matter physics; Mathematics; Combinatorics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0002229446,0.00008745405,0.0003396932,0.00003592908,0.00001841297,0.00001385947,0.0003194718,0.0000110965,0.00007335893],"category_scores_gemma":[0.0002947875,0.00006541062,0.00008662567,0.0004005581,0.00002823506,0.0001182518,0.0002218479,0.0001252127,0.0001435434],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000009161665,"about_ca_system_score_gemma":0.0000224561,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001507616,"about_ca_topic_score_gemma":0.000002749623,"domain_scores_codex":[0.9990935,0.0001502047,0.0002253262,0.0002131499,0.0001763016,0.0001415548],"domain_scores_gemma":[0.999332,0.0001749989,0.0001004209,0.0002882142,0.00004609783,0.00005830413],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000008220049,0.000365845,0.003694582,0.0003721474,0.00001137967,0.000007362774,0.0001069981,0.0004447516,0.02627416,0.9666707,0.0002502411,0.001793608],"study_design_scores_gemma":[0.001879281,0.001350782,0.002791398,0.002843024,0.00006743458,0.000009465514,0.00001980298,0.892633,0.01193688,0.01952355,0.06615229,0.0007931568],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9121895,0.007732959,0.06481244,0.002079115,0.0002055405,0.0008361106,0.00001359865,0.0001950416,0.01193568],"genre_scores_gemma":[0.9971353,0.00142648,0.0004597599,0.0008517755,0.00001530445,0.000003326674,0.00001341115,0.000003984861,0.00009065663],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9471471,"threshold_uncertainty_score":0.2667367,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02610218709758682,"score_gpt":0.3703123720436928,"score_spread":0.344210184946106,"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."}}