{"id":"W2752663088","doi":"10.18653/v1/s17-2080","title":"UWaterloo at SemEval-2017 Task 8: Detecting Stance towards Rumours with Topic Independent Features","year":2017,"lang":"en","type":"article","venue":"","topic":"Misinformation and Its Impacts","field":"Social Sciences","cited_by":47,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"SemEval; Computer science; Classifier (UML); Gradient boosting; Task (project management); Artificial intelligence; Boosting (machine learning); Social media; Natural language processing; Machine learning; World Wide Web; Random forest","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":["sts"],"consensus_categories":[],"category_scores_codex":[0.0005559054,0.0001081183,0.0001223993,0.00003955754,0.002175277,0.0006600848,0.000450954,0.00009018397,0.0006165959],"category_scores_gemma":[0.0002964418,0.00007579117,0.00003696768,0.00004128531,0.0001601286,0.0008819076,0.00009257964,0.0001258796,0.00009259511],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001587174,"about_ca_system_score_gemma":0.0001564739,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003517208,"about_ca_topic_score_gemma":0.0284771,"domain_scores_codex":[0.998719,0.00004318152,0.0001311259,0.0001493553,0.0005873691,0.000369916],"domain_scores_gemma":[0.9991311,0.00001937131,0.0001926893,0.0003700021,0.00009538364,0.0001914777],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"observational","study_design_scores_codex":[0.0003914219,0.0001275562,0.07205367,0.0001373619,0.0001941435,0.00009097882,0.4551623,0.00008480487,0.001853938,0.02798525,0.06870783,0.3732107],"study_design_scores_gemma":[0.002132626,0.0002345249,0.7794547,0.0001499965,0.00003903572,0.00003085328,0.0478237,0.00007057821,0.01720274,0.0009301832,0.1510673,0.0008637827],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5941011,0.00002324297,0.0001083837,0.002229817,0.0002506289,0.0001435435,0.000002819711,0.00007043467,0.40307],"genre_scores_gemma":[0.9318817,0.00003827778,0.0002991664,0.0003761052,0.0001562827,0.000001658664,0.000001318275,0.000006085352,0.0672394],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.707401,"threshold_uncertainty_score":0.9991238,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03221418869156419,"score_gpt":0.3298032894109148,"score_spread":0.2975891007193506,"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."}}