{"id":"W6920836559","doi":"10.6084/m9.figshare.21077625","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":"Open MIND","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; University of Toronto","funders":"Medical Research Council","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":[0.003552389,0.0007901731,0.001065604,0.002562993,0.001427223,0.002324159,0.001810086,0.001617213,0.8796379],"category_scores_gemma":[0.07132461,0.0005279711,0.0008328668,0.007181352,0.000329336,0.003558831,0.001548423,0.001645215,0.1417606],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002236415,"about_ca_system_score_gemma":0.005066066,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03237802,"about_ca_topic_score_gemma":0.04099392,"domain_scores_codex":[0.9981468,0.000461985,0.0002844814,0.000239804,0.0005829986,0.0002839049],"domain_scores_gemma":[0.9462063,0.03942784,0.00304911,0.001620728,0.008309011,0.001387006],"domain_codex":null,"domain_gemma":"evaluation","domain_candidate":"evaluation","domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00007572704,0.00002370196,0.0006652296,0.001204158,0.0000132937,0.00002069445,0.00004099208,0.0001591168,0.000009292549,0.001128309,0.9934691,0.003190404],"study_design_scores_gemma":[0.003336534,0.0001878599,0.02110105,0.00699482,0.0001545076,0.000302612,0.001579082,0.001491188,0.0002353043,0.01400295,0.9504926,0.0001214704],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"other","genre_scores_codex":[0.0001070961,0.00002663649,0.0001197554,0.0004995164,0.00004382226,0.0001268531,0.995738,0.00007428954,0.003264029],"genre_scores_gemma":[0.01480816,0.0006394908,0.004239501,0.002983816,0.0003550449,0.005484912,0.936219,0.0005633893,0.03470673],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.9964476,"threshold_uncertainty_score":0.1716819,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4565125178215523,"score_gpt":0.4791094295848147,"score_spread":0.02259691176326245,"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."}}