{"id":"W6977586687","doi":"10.6084/m9.figshare.21077625.v1","title":"Additional file 1 of Saving millions of lives but some resources squandered: emerging lessons from health research system pandemic achievements and challenges","year":2022,"lang":"en","type":"article","venue":"Figshare","topic":"Academic Publishing and Open Access","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Michael Smith Health Research BC","funders":"","keywords":"Pandemic; Coronavirus disease 2019 (COVID-19); Public health; Government (linguistics); Healthcare system; Systems research; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)","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":["metaresearch","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00326925,0.0008317822,0.001099064,0.002726257,0.001355125,0.002369355,0.001858653,0.001591224,0.8976314],"category_scores_gemma":[0.06268077,0.0005460955,0.0009526659,0.007666335,0.0003415042,0.003723127,0.001584396,0.001609867,0.164527],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002124469,"about_ca_system_score_gemma":0.004685159,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02963349,"about_ca_topic_score_gemma":0.03741569,"domain_scores_codex":[0.9982051,0.0004532568,0.0002629568,0.0002480875,0.0005409703,0.0002896975],"domain_scores_gemma":[0.9519409,0.03598499,0.002474115,0.001534523,0.006819973,0.00124556],"domain_codex":null,"domain_gemma":"evaluation","domain_candidate":"evaluation","domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"qualitative","study_design_scores_codex":[0.00007372937,0.00002180995,0.0005784582,0.001241327,0.00001448356,0.00001946429,0.00003477933,0.0001833493,0.000008969356,0.001071801,0.9937614,0.002990493],"study_design_scores_gemma":[0.003744687,0.0001829982,0.01905567,0.006857845,0.0001690115,0.0002895448,0.001352499,0.001633742,0.0002351037,0.01509596,0.9512577,0.0001252425],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.00008643228,0.00002328784,0.0001099517,0.0003823186,0.00003853131,0.0001101352,0.9960837,0.00008107653,0.00308454],"genre_scores_gemma":[0.01400442,0.0005754016,0.00425376,0.002577572,0.000301925,0.004827396,0.9397262,0.000687669,0.03304551],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9967307,"threshold_uncertainty_score":0.1460163,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4677752085452835,"score_gpt":0.4506118585499627,"score_spread":0.01716334999532082,"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."}}