{"id":"W4285464655","doi":"10.32920/ryerson.14660694.v1","title":"Identity matching in social media platforms","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Topic Modeling","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University; York University","funders":"","keywords":"Computer science; Matching (statistics); Social media; Identity (music); Set (abstract data type); Task (project management); World Wide Web; Information retrieval; Similarity (geometry); Process (computing); Social Semantic Web; String (physics); Semantic Web; Artificial intelligence; Engineering; Mathematics","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":[0.005950136,0.0005011194,0.0007329164,0.006718991,0.002753103,0.004633079,0.001647309,0.001869404,0.002709099],"category_scores_gemma":[0.01934926,0.0005228519,0.001535803,0.005886081,0.001482881,0.009702651,0.004405284,0.001056668,0.00131768],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001916901,"about_ca_system_score_gemma":0.001636804,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005532076,"about_ca_topic_score_gemma":0.003371578,"domain_scores_codex":[0.9916154,0.003149416,0.0005086066,0.001683374,0.002372494,0.0006706715],"domain_scores_gemma":[0.9931945,0.00312127,0.001292716,0.001274414,0.000878325,0.0002389366],"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":[0.0004346688,0.0005115312,0.04961312,0.0005080687,0.0003284834,0.001846108,0.005694241,0.0498074,0.008649423,0.5280575,0.009806506,0.3447429],"study_design_scores_gemma":[0.00002606032,0.0001176478,0.01553979,0.0002235876,0.0001329792,0.001198767,0.004829665,0.5192714,0.0121092,0.4041783,0.04226056,0.0001119586],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1724203,0.001019554,0.8017762,0.001588277,0.0001740469,0.0006201903,0.001215536,0.00102522,0.0201606],"genre_scores_gemma":[0.8355941,0.0006506011,0.1551838,0.0001982454,0.0001381293,0.0003368989,0.00145034,0.0001230783,0.006324733],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006718991,"threshold_uncertainty_score":0.03146774,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05234093860938426,"score_gpt":0.2957890022646055,"score_spread":0.2434480636552213,"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."}}