{"id":"W3206808214","doi":"10.48550/arxiv.2110.08385","title":"Robust Correlation Clustering with Asymmetric Noise","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Complex Network Analysis Techniques","field":"Physics and Astronomy","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Cluster analysis; Correlation clustering; Mathematics; Clustering coefficient; Graph; Computer science; Graph partition; Theoretical computer science; Combinatorics; Algorithm; 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":[0.002858027,0.001264914,0.001412158,0.001319628,0.0009737923,0.001465176,0.00249322,0.001855565,0.001200326],"category_scores_gemma":[0.01159828,0.000718599,0.001206727,0.001686969,0.001760032,0.002038518,0.002275851,0.002256451,0.0007026932],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001939247,"about_ca_system_score_gemma":0.001639203,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003924589,"about_ca_topic_score_gemma":0.00436341,"domain_scores_codex":[0.9977691,0.0008475861,0.00008310143,0.0006793996,0.0004770824,0.0001436836],"domain_scores_gemma":[0.9942576,0.002930504,0.0007277434,0.001208273,0.0006939344,0.0001819062],"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":[0.0001437214,0.00005072519,0.001111052,0.00008064334,0.00008455477,0.0001042553,0.0001073191,0.8908954,0.00334794,0.05360408,0.002774724,0.04769555],"study_design_scores_gemma":[0.000005473753,0.0000100387,0.0001051646,0.00000423281,0.000004071524,0.00002041065,0.000009798043,0.9814209,0.0008640012,0.01720569,0.0003440629,0.000006229987],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01051883,0.00006965473,0.9881241,0.0001539862,0.00001467399,0.00002646323,0.00007520563,0.0002994712,0.0007176226],"genre_scores_gemma":[0.5112619,0.0002264181,0.4833293,0.0003261668,0.00008722754,0.0002099486,0.001030652,0.0003260402,0.003202353],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003924589,"threshold_uncertainty_score":0.0151149,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05486950618209576,"score_gpt":0.1791187569262068,"score_spread":0.124249250744111,"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."}}