{"id":"W3215341501","doi":"10.2991/assehr.k.211122.086","title":"Corpus Analysis of the Terms Denoting Covid-19","year":2021,"lang":"en","type":"article","venue":"Advances in Social Science, Education and Humanities Research/Advances in social science, education and humanities research","topic":"Linguistics, Language Diversity, and Identity","field":"Arts and Humanities","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Social Science Fund of China","keywords":"Coronavirus disease 2019 (COVID-19); Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); 2019-20 coronavirus outbreak; Coronavirus; Corona (planetary geology); Linguistics; Term (time); Corpus linguistics; Computer science; Natural language processing; History; Geography; Physics; Virology; Astronomy; Medicine; Philosophy; Pathology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","sts","scholarly_communication"],"consensus_categories":["sts"],"category_scores_codex":[0.006332125,0.0002331076,0.0004584631,0.003471808,0.01390965,0.002143947,0.001397739,0.00008782687,0.0007932918],"category_scores_gemma":[0.009487495,0.0002148479,0.0001149693,0.004780776,0.0309615,0.00312423,0.0008812848,0.0008401275,0.000005423929],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001453077,"about_ca_system_score_gemma":0.008379546,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003100263,"about_ca_topic_score_gemma":0.04674667,"domain_scores_codex":[0.9937787,0.0007386074,0.000644632,0.0008985189,0.002785872,0.001153676],"domain_scores_gemma":[0.9921763,0.0006914168,0.0002790224,0.0003423712,0.006280997,0.0002299238],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"qualitative","study_design_scores_codex":[0.00001277835,0.0003658261,0.02086105,0.000152365,0.00001068112,0.000001128625,0.2623364,0.000002312087,0.00001990735,0.7055813,0.0006298967,0.01002641],"study_design_scores_gemma":[0.0002025469,0.00003792197,0.0153737,0.00005913511,0.0000256958,6.3954e-7,0.6193789,0.0000220141,0.00004304514,0.07711726,0.2875034,0.0002357417],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.3916133,0.01014914,0.000003672722,0.0003709133,0.006221459,0.0006136619,0.00008029117,0.00003235741,0.5909152],"genre_scores_gemma":[0.9717041,0.004666665,0.00003876145,0.000583768,0.004162351,0.00008137683,0.00002506087,0.00001511406,0.01872279],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.628464,"threshold_uncertainty_score":0.9988919,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.119198080654489,"score_gpt":0.4633425578320097,"score_spread":0.3441444771775208,"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."}}