{"id":"W4362600856","doi":"10.1007/s00500-023-08045-8","title":"Correction to: Class-biased sarcasm detection using BiLSTM variational autoencoder-based synthetic oversampling","year":2023,"lang":"en","type":"article","venue":"Soft Computing","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Oversampling; Sarcasm; Autoencoder; Class (philosophy); Artificial intelligence; Computer science; Pattern recognition (psychology); Artificial neural network; Linguistics; Philosophy; Telecommunications; Irony","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":[0.0008768695,0.0001796287,0.0002041233,0.0006200196,0.0006471166,0.0003618171,0.0003540105,0.00006228119,0.00001943932],"category_scores_gemma":[0.0003623809,0.0001982862,0.0001541917,0.00220298,0.00001721918,0.000246092,0.0002041998,0.0001760598,0.0001096735],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001982939,"about_ca_system_score_gemma":0.0001235825,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001006831,"about_ca_topic_score_gemma":0.00001241483,"domain_scores_codex":[0.9979942,0.0001112431,0.0003912442,0.0005938074,0.0004865566,0.0004229938],"domain_scores_gemma":[0.9984073,0.0008013392,0.0002034427,0.0003403715,0.0001355483,0.0001120116],"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":[0.000004965921,0.00002444139,0.002084147,0.00000842003,0.00002659175,0.000004492106,0.0003905756,0.9576615,0.005838563,0.0002852372,0.0001990229,0.03347205],"study_design_scores_gemma":[0.0002248786,0.00003833172,0.003619586,0.0001155576,0.00001705615,0.000006496628,0.00006913843,0.9931356,0.001978275,0.0001889447,0.0003806768,0.0002254591],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09871002,0.000008721114,0.8965927,0.0002423459,0.003560385,0.0001265765,8.226416e-7,0.0006370998,0.0001212911],"genre_scores_gemma":[0.9185815,3.262497e-7,0.08076154,0.000277215,0.0002867464,0.00000317598,0.00001061453,0.00001798091,0.00006092633],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8198714,"threshold_uncertainty_score":0.8085876,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04273798532063115,"score_gpt":0.3004413453508715,"score_spread":0.2577033600302403,"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."}}