{"id":"W4224273657","doi":"10.22163/fteval.2022.538","title":"On your marks, get set, fund! Rapid responses to the Covid-19 pandemic","year":2022,"lang":"en","type":"report","venue":"","topic":"COVID-19 epidemiological studies","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Core Research for Evolutional Science and Technology; Ministry of Science and Technology, Taiwan; Nederlandse Organisatie voor Wetenschappelijk Onderzoek; UK Research and Innovation; Deutsche Forschungsgemeinschaft; National Research Council Canada; ZonMw; Japan Science and Technology Agency; National Institute for Health and Care Research; National Science Foundation","keywords":"Coronavirus disease 2019 (COVID-19); Multidisciplinary approach; Pandemic; Set (abstract data type); Theme (computing); Public relations; Political science; Quality (philosophy); Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); 2019-20 coronavirus outbreak; Key (lock); Business; Medicine; Computer science; Computer security; Disease","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"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":[0.04327754,0.0003464659,0.0004577743,0.002768259,0.002969179,0.009637584,0.00139961,0.00130112,0.01990816],"category_scores_gemma":[0.1159285,0.0002913642,0.0005210018,0.004028938,0.002268799,0.004660456,0.01092238,0.002168753,0.003287203],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005466505,"about_ca_system_score_gemma":0.01177833,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01101425,"about_ca_topic_score_gemma":0.01561528,"domain_scores_codex":[0.9694303,0.01950006,0.0008734457,0.000817193,0.006107443,0.003271607],"domain_scores_gemma":[0.930664,0.0389585,0.007880955,0.004033451,0.009905997,0.008557054],"domain_codex":null,"domain_gemma":"incentives","domain_candidate":"incentives","domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0008482734,0.0002496714,0.1142003,0.001115213,0.0001296919,0.001699253,0.04431206,0.003300016,0.001553442,0.2663216,0.3221761,0.2440945],"study_design_scores_gemma":[0.00006859127,0.0002105764,0.07771987,0.0007519815,0.00003634665,0.00059721,0.05346896,0.001894952,0.001016484,0.03035492,0.833788,0.00009215029],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.3255358,0.005459366,0.02038963,0.298129,0.003028449,0.001452392,0.007177058,0.0008881227,0.3379401],"genre_scores_gemma":[0.8467315,0.005349091,0.03655444,0.0251217,0.001308392,0.001623609,0.003159966,0.0008323063,0.07931906],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.9567224,"threshold_uncertainty_score":0.2288763,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.6791661677150954,"score_gpt":0.5303359910264156,"score_spread":0.1488301766886798,"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."}}