{"id":"W3130628100","doi":"10.1111/jep.13550","title":"<scp>COVID</scp>‐19 and the generation of novel scientific knowledge: Research questions and study designs","year":2021,"lang":"en","type":"article","venue":"Journal of Evaluation in Clinical Practice","topic":"Academic Publishing and Open Access","field":"Decision Sciences","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Public Health Ontario; University of Toronto","funders":"","keywords":"Pandemic; Context (archaeology); Coronavirus disease 2019 (COVID-19); Objectivity (philosophy); Quality (philosophy); Data science; Computer science; Medicine; Epistemology; History","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":["metaresearch","scholarly_communication"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.4160302,0.00007711851,0.0003695526,0.00045718,0.0004848718,0.001826547,0.0006588333,0.0001451835,0.00003706565],"category_scores_gemma":[0.8392911,0.00004729434,0.00007257187,0.002166336,0.0007268961,0.00321591,0.0003650864,0.001489243,0.000007136049],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007913863,"about_ca_system_score_gemma":0.002591751,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004417931,"about_ca_topic_score_gemma":0.0001766566,"domain_scores_codex":[0.9597352,0.02888888,0.004115463,0.0006786637,0.006327594,0.0002542144],"domain_scores_gemma":[0.5516192,0.4087217,0.005087696,0.001201475,0.03283087,0.0005391168],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0009757802,0.01170964,0.3645407,0.00004287946,0.0004010833,0.00007317594,0.05637078,0.01040309,0.001975834,0.02225658,0.3518977,0.1793528],"study_design_scores_gemma":[0.02212554,0.001172482,0.3670143,0.0002007996,0.0007213253,0.000642269,0.1138105,0.2886477,0.00009119183,0.09459002,0.1108127,0.0001711691],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9735325,0.0038306,0.008269857,0.009688365,0.001431279,0.0005175802,0.000002894169,0.000002400604,0.002724511],"genre_scores_gemma":[0.9942353,0.0002702625,0.003963232,0.0002757359,0.0004036629,0.000009708115,9.267607e-7,0.000005024573,0.0008361398],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4232609,"threshold_uncertainty_score":0.9992096,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.7713072359991014,"score_gpt":0.6851158347109702,"score_spread":0.0861914012881313,"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."}}