{"id":"W3096959275","doi":"10.1007/978-3-030-63128-4_58","title":"Learning Reddit User Reputation Using Graphical Attention Networks","year":2020,"lang":"en","type":"book-chapter","venue":"Advances in intelligent systems and computing","topic":"Topic Modeling","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Reputation; Embedding; Predictive power; Focus (optics); Task (project management); Graph; Machine learning; Set (abstract data type); Artificial intelligence; Attention network; Feature (linguistics); Feature engineering; Simple (philosophy); Deep learning; Feature learning; Graphical model; Human–computer interaction; Theoretical computer science","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000485358,0.0003253556,0.0004875273,0.000209585,0.0001946726,0.0002599925,0.0003698913,0.0002478123,0.000002154489],"category_scores_gemma":[0.00003577358,0.0003433646,0.0001055991,0.0001370267,0.00004512185,0.000430182,0.0003703096,0.0007724423,0.000005121053],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001115923,"about_ca_system_score_gemma":0.00002548068,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003577137,"about_ca_topic_score_gemma":0.000006054302,"domain_scores_codex":[0.9975182,0.00008846378,0.000885253,0.0008756596,0.000338495,0.0002939293],"domain_scores_gemma":[0.9987603,0.0001804013,0.0005761684,0.0002957439,0.00009287965,0.00009444048],"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":[0.000003477921,0.000003844326,0.000509196,0.0001368599,0.00001824369,0.00003564077,0.0001987236,0.647274,0.000004339943,0.2630934,0.000004008669,0.08871838],"study_design_scores_gemma":[0.00008255049,0.00005376142,0.00001564499,0.001628312,0.00001240581,0.00005304124,0.000076394,0.9777432,0.000001300573,0.005453338,0.01454684,0.000333185],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0005706437,0.01177323,0.9791917,0.0000443504,0.001691534,0.0003097617,4.451221e-7,0.0001530256,0.006265348],"genre_scores_gemma":[0.9615234,0.004015262,0.02683464,0.0001133554,0.00146192,0.000008530193,0.00002469945,0.00009208971,0.005926143],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9609527,"threshold_uncertainty_score":0.9999018,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02528109185902576,"score_gpt":0.2691474554260198,"score_spread":0.243866363566994,"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."}}