{"id":"W4405483849","doi":"10.5430/wjel.v15n2p342","title":"Hedging in Medical Articles from Two Pandemics","year":2024,"lang":"en","type":"article","venue":"World Journal of English Language","topic":"Disaster Response and Management","field":"Health Professions","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Pandemic; Coronavirus disease 2019 (COVID-19); Computer science; Business; Medicine","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.01425552,0.0004061745,0.0006328265,0.019469,0.002944296,0.004884627,0.000662027,0.001200773,0.002804283],"category_scores_gemma":[0.09786288,0.0003714038,0.000532858,0.01899709,0.003418166,0.004389266,0.003739386,0.0009849679,0.0003213985],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002161871,"about_ca_system_score_gemma":0.002707244,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001214449,"about_ca_topic_score_gemma":0.00312641,"domain_scores_codex":[0.9849428,0.007525468,0.003002657,0.0009620081,0.003198014,0.0003690706],"domain_scores_gemma":[0.7609144,0.2058678,0.01891736,0.0041156,0.009286831,0.0008979131],"domain_codex":null,"domain_gemma":"reporting","domain_candidate":"reporting","domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"observational","study_design_scores_codex":[0.001038544,0.000160069,0.04736577,0.03400835,0.0004161084,0.006612119,0.5731133,0.000536781,0.009525793,0.01938331,0.02011871,0.2877212],"study_design_scores_gemma":[0.0000791358,0.0003434062,0.1872898,0.03496298,0.0007023055,0.005574508,0.3576551,0.00107691,0.009634215,0.0151225,0.3873,0.0002590373],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.89681,0.05241189,0.006676978,0.01135509,0.001958135,0.0005895484,0.004248456,0.0001362179,0.02581366],"genre_scores_gemma":[0.9570426,0.01946909,0.01335486,0.002439415,0.001212864,0.0005199711,0.003017744,0.0001352103,0.002808127],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9857445,"threshold_uncertainty_score":0.07539123,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02867111220755584,"score_gpt":0.3978342708519098,"score_spread":0.3691631586443539,"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."}}