{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000306166,0.0001832079,0.0002612496,0.0006548729,0.0001561227,0.0004687984,0.0003666102,0.0003468792,0.0004522307],"category_scores_gemma":[0.002260798,0.0001379678,0.0001610033,0.0003981632,0.0004376432,0.0005385387,0.0002704977,0.0002484775,0.00005181245],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004748423,"about_ca_system_score_gemma":0.0002915674,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002184519,"about_ca_topic_score_gemma":0.001828911,"domain_scores_codex":[0.9999115,0.00002827212,0.000006921347,0.00001805241,0.00002322602,0.00001196997],"domain_scores_gemma":[0.999005,0.0006126119,0.0001425794,0.00008877541,0.0001153699,0.00003566446],"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":[0.0002851523,0.000128984,0.01383856,0.0001398126,0.00004475769,0.000192109,0.0001594848,0.8935646,0.02289268,0.006489766,0.0009021449,0.06136196],"study_design_scores_gemma":[0.000005205125,0.00001332619,0.001134403,0.000001831062,0.000001249755,0.000007660398,0.00001190765,0.9950884,0.00227851,0.001388636,0.00006495263,0.00000393014],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9366756,0.00009606985,0.0614815,0.0001960644,0.00001211956,0.00002630028,0.0002250122,0.0006237476,0.0006635594],"genre_scores_gemma":[0.9813866,0.00003068857,0.01822596,0.000009757501,0.000004494333,0.00001583675,0.0001740029,0.00001441615,0.0001382824],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002184519,"threshold_uncertainty_score":0.004343569,"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."}}