{"id":"W4316810597","doi":"10.21203/rs.3.rs-2471638/v1","title":"Artificial Benchmark for Community Detection with Outliers (ABCD+o)","year":2023,"lang":"en","type":"preprint","venue":"Research Square","topic":"Complex Network Analysis Techniques","field":"Physics and Astronomy","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Outlier; Benchmark (surveying); Community structure; Graph; Computer science; Artificial intelligence; Mathematics; Statistics; Theoretical computer science; Geography; Cartography","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":[0.004693516,0.0008032439,0.0009078109,0.002175532,0.0009649889,0.001450976,0.002163845,0.002152528,0.001533455],"category_scores_gemma":[0.02283416,0.0002789847,0.0008141646,0.00186568,0.001344138,0.001851731,0.00156899,0.001547909,0.0004082439],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001446564,"about_ca_system_score_gemma":0.000799856,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005930316,"about_ca_topic_score_gemma":0.005082983,"domain_scores_codex":[0.9960574,0.002174946,0.0001325374,0.0007472307,0.0006764545,0.0002115032],"domain_scores_gemma":[0.982381,0.01066372,0.001269319,0.002715452,0.002161311,0.0008091005],"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":[0.0007827664,0.0004500141,0.01111952,0.0004443193,0.0002017632,0.00028229,0.0001581134,0.890698,0.004624984,0.03217791,0.01340849,0.04565185],"study_design_scores_gemma":[0.00003317444,0.00007306662,0.000855781,0.00001315192,0.00000859609,0.00007405598,0.00002447295,0.9834742,0.001409896,0.012644,0.001377915,0.00001165054],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.5730585,0.001604022,0.4058259,0.002210648,0.0004736173,0.00031605,0.005084774,0.002981922,0.008444538],"genre_scores_gemma":[0.9002323,0.0001768779,0.09352978,0.0002571578,0.00007311133,0.0001472887,0.004097777,0.0002247718,0.001260928],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.005930316,"threshold_uncertainty_score":0.024822,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1446454096180996,"score_gpt":0.4256884955456726,"score_spread":0.2810430859275729,"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."}}