{"id":"W3159042761","doi":"10.3390/su13094986","title":"Effects of the COVID-19 Pandemic on Classrooms: A Case Study on Foreigners in South Korea Using Applied Machine Learning","year":2021,"lang":"en","type":"article","venue":"Sustainability","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Sentiment analysis; Timeline; Pandemic; Coronavirus disease 2019 (COVID-19); Social media; Coping (psychology); Government (linguistics); Creativity; Public opinion; Psychology; Data science; Computer science; Public relations; Artificial intelligence; World Wide Web; Political science; Geography; Social psychology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008542332,0.0003502402,0.0002428582,0.0007825181,0.001152205,0.0008716908,0.0004413674,0.0007270849,0.001637734],"category_scores_gemma":[0.001875576,0.0001258833,0.0003517532,0.0008105105,0.0007514284,0.0009508491,0.0009433333,0.0008696537,0.0002841438],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001331544,"about_ca_system_score_gemma":0.0009155217,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02776911,"about_ca_topic_score_gemma":0.06008739,"domain_scores_codex":[0.9994603,0.0002419264,0.00002174045,0.00006820186,0.00005733776,0.0001506219],"domain_scores_gemma":[0.9984031,0.0007117153,0.0003116059,0.00008050635,0.0002255827,0.0002675817],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000339089,0.001023407,0.8688436,0.0004800662,0.0001228429,0.01019177,0.03534821,0.007246932,0.004449415,0.001423886,0.007805249,0.06272554],"study_design_scores_gemma":[0.00003766548,0.0006583208,0.6783072,0.0002444668,0.00008930432,0.001243445,0.2816235,0.01939236,0.002651331,0.001002297,0.01466109,0.00008893963],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9969158,0.00008675001,0.0003921643,0.0007155539,0.00001695835,0.0000488439,0.000313017,0.00001196946,0.001498773],"genre_scores_gemma":[0.9980186,0.0001354945,0.0006486796,0.0001788756,0.00001379939,0.00003249282,0.0002279586,0.000006897178,0.0007372695],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02776911,"threshold_uncertainty_score":0.05521494,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03491010426566622,"score_gpt":0.3180392873903146,"score_spread":0.2831291831246484,"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."}}