{"id":"W3075656738","doi":"10.1007/978-3-030-57811-4_16","title":"An Ensemble Deep Learning Technique to Detect COVID-19 Misleading Information","year":2020,"lang":"en","type":"book-chapter","venue":"Advances in intelligent systems and computing","topic":"Misinformation and Its Impacts","field":"Social Sciences","cited_by":18,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Computer science; Preprocessor; Misinformation; Word (group theory); Artificial intelligence; Word embedding; Ensemble learning; Deep learning; Coronavirus disease 2019 (COVID-19); Word error rate; Machine learning; Embedding; Data mining; Computer security; 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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001250795,0.0002468126,0.0003729137,0.0003523178,0.0005592095,0.0003595176,0.0002598256,0.0002361822,0.00004225895],"category_scores_gemma":[0.0006177889,0.0002588728,0.00005239934,0.0001443178,0.00006622698,0.001276644,0.0000734922,0.0004255865,0.00008046266],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003781142,"about_ca_system_score_gemma":0.0001383332,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003674936,"about_ca_topic_score_gemma":0.000363329,"domain_scores_codex":[0.998142,0.0001118731,0.0007597807,0.0002457305,0.0004172928,0.0003233187],"domain_scores_gemma":[0.9985672,0.0002316621,0.0004744418,0.0001382734,0.00009972022,0.0004887202],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00002867073,0.000004399345,0.0000692924,0.0006814308,0.00001689825,0.000008911925,0.0962304,0.07316029,0.00001844388,0.2851616,0.0001486805,0.544471],"study_design_scores_gemma":[0.00008020134,0.0001311497,0.000002077235,0.000638006,0.000006879017,0.00001104995,0.01640147,0.01039973,0.00003109415,0.00244797,0.9694703,0.0003801005],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00008844394,0.001410634,0.6528568,0.0001263993,0.0004623936,0.0009882464,0.000003041371,0.0002027234,0.3438613],"genre_scores_gemma":[0.9773996,0.004417555,0.00501693,0.002130345,0.0007908488,0.00002348466,0.00005844321,0.00005898332,0.01010379],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9773112,"threshold_uncertainty_score":0.9999864,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03000525690212222,"score_gpt":0.3360283573853521,"score_spread":0.3060231004832299,"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."}}