{"id":"W4394325540","doi":"10.6084/m9.figshare.19367264","title":"Additional file 8 of Healthcare system resilience in Bangladesh and Haiti in times of global changes (climate-related events, migration and Covid-19): an interdisciplinary mixed method research protocol","year":2022,"lang":"en","type":"dataset","venue":"Figshare","topic":"Healthcare Systems and Reforms","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Resilience (materials science); Coronavirus disease 2019 (COVID-19); Protocol (science); Health care; Healthcare system; Geography; Computer science; Economic growth; Medicine; Economics","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.002173641,0.0009591733,0.0009572551,0.002717451,0.0008854783,0.001665278,0.001978627,0.001494139,0.4833209],"category_scores_gemma":[0.01900048,0.0007539209,0.001070127,0.005687808,0.0003538217,0.001553495,0.00153928,0.001480199,0.0721978],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002251276,"about_ca_system_score_gemma":0.004199384,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05012748,"about_ca_topic_score_gemma":0.08218376,"domain_scores_codex":[0.9988505,0.0002603641,0.0002740479,0.0002137094,0.000202481,0.0001988934],"domain_scores_gemma":[0.9899396,0.004708685,0.001096574,0.0008103352,0.00296588,0.0004789471],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00007534051,0.00003541975,0.001889545,0.00116764,0.00003116028,0.00001594585,0.0000533796,0.0002413351,0.0000168049,0.0006414575,0.993943,0.001888814],"study_design_scores_gemma":[0.002522776,0.0001125183,0.03956341,0.003568177,0.0001750073,0.00009786025,0.001044471,0.0008327483,0.0002628795,0.004554572,0.9471583,0.0001073232],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0000796306,0.000009949285,0.00003509193,0.00005032605,0.000006389032,0.00006538009,0.9993825,0.00002134851,0.0003493405],"genre_scores_gemma":[0.002750139,0.00007373779,0.0008725948,0.0002110842,0.00001832764,0.003669886,0.9883474,0.00007662744,0.00398028],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.4833209,"threshold_uncertainty_score":0.7369801,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09818737665171369,"score_gpt":0.4020405904867528,"score_spread":0.3038532138350391,"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."}}