{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004410305,0.0001476124,0.0002592106,0.0001255734,0.0000595622,0.0005443768,0.001393043,0.0002275788,0.00005930244],"category_scores_gemma":[0.00004287608,0.0001440463,0.0001003391,0.000155872,0.00001114321,0.0007253163,0.004268151,0.0006651453,0.00001776878],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000117768,"about_ca_system_score_gemma":0.0001951331,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004623473,"about_ca_topic_score_gemma":0.002477631,"domain_scores_codex":[0.998467,0.0000210198,0.0003203943,0.0005496053,0.0004022756,0.0002397342],"domain_scores_gemma":[0.9992637,0.00006053062,0.00008460424,0.0005080953,0.00003987655,0.00004316026],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000001621642,0.00008286557,0.001435707,0.0002209528,0.00003504947,0.0002429823,0.04146384,0.006820588,0.0001045565,0.8949053,0.0002174983,0.05446903],"study_design_scores_gemma":[0.0002301787,0.000001586712,0.009153561,0.0001306822,0.000004240189,0.000007086642,0.0006687933,0.1460196,0.0001544745,0.8431459,0.00002896182,0.0004549819],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3154051,0.00004825339,0.6802295,0.0002576536,0.001283478,0.00005713942,5.612408e-7,0.0001088321,0.002609485],"genre_scores_gemma":[0.8727819,0.00001314254,0.1265863,0.0001828969,0.0003247045,0.00001120118,0.000007991887,0.00000749813,0.00008435364],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5573768,"threshold_uncertainty_score":0.5874039,"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."}}