{"id":"W3127137997","doi":"10.12688/f1000research.47618.1","title":"Using DHS and MICS data to complement or replace NGO baseline health data: an exploratory study","year":2021,"lang":"en","type":"preprint","venue":"F1000Research","topic":"Global Maternal and Child Health","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"UNICEF; Global Affairs Canada; United States Agency for International Development","keywords":"Baseline (sea); Psychological intervention; Geography; Demography; Statistics; Biology; Medicine; Mathematics; Sociology","routes":{"ca_aff":true,"ca_fund":true,"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"],"consensus_categories":[],"category_scores_codex":[0.07480862,0.001030004,0.0009407086,0.006072806,0.001411095,0.003101243,0.002357827,0.001308899,0.002722642],"category_scores_gemma":[0.1321355,0.001014107,0.001754456,0.01058956,0.001711566,0.00438554,0.004478214,0.001844164,0.001116257],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002382512,"about_ca_system_score_gemma":0.005132475,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01868447,"about_ca_topic_score_gemma":0.02134869,"domain_scores_codex":[0.9042978,0.07968203,0.003921145,0.003067552,0.006366631,0.002664865],"domain_scores_gemma":[0.7896992,0.168217,0.01506151,0.0127901,0.0128604,0.00137192],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0008548023,0.002672146,0.8484792,0.003369681,0.00104353,0.001834088,0.05945481,0.001188414,0.001304922,0.00564226,0.005886857,0.06826942],"study_design_scores_gemma":[0.0004482781,0.004965718,0.6524209,0.004703784,0.001443348,0.001764018,0.2591507,0.01262059,0.002822848,0.003800784,0.05549524,0.0003637346],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9637924,0.0009384025,0.01836436,0.0008122476,0.00008112314,0.005854996,0.005942275,0.00007175709,0.004142434],"genre_scores_gemma":[0.9285476,0.0006704524,0.05329523,0.00102923,0.00007105542,0.008147337,0.007514764,0.0001306239,0.0005936011],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9251914,"threshold_uncertainty_score":0.3956305,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.6656451477071305,"score_gpt":0.5732227961106765,"score_spread":0.09242235159645396,"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."}}