{"id":"W2948279515","doi":"10.1016/j.mex.2019.05.037","title":"The Niakhar Social Networks and Health Project","year":2019,"lang":"en","type":"article","venue":"MethodsX","topic":"Health disparities and outcomes","field":"Social Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Université de Montréal","funders":"National Institute of General Medical Sciences; National Institutes of Health","keywords":"Engineering; Computer science; Data science","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":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.005239036,0.00005509391,0.000144229,0.00001401795,0.001493198,0.00009245107,0.0001248318,0.00006101154,0.00003083641],"category_scores_gemma":[0.0001252792,0.00003726846,0.00003582901,0.0001360472,0.0001536003,0.00005700416,0.00003571496,0.0001361472,0.000009433629],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006498513,"about_ca_system_score_gemma":0.0003177285,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01184369,"about_ca_topic_score_gemma":0.002707049,"domain_scores_codex":[0.998235,0.0008846659,0.0001422623,0.0001240339,0.000162307,0.0004517343],"domain_scores_gemma":[0.9991795,0.0005725464,0.00006409515,0.00008361805,0.0000215902,0.0000786578],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001694691,0.00001335206,0.06425145,0.0000834442,0.00001624827,6.222411e-7,0.02773563,0.000003500456,3.647779e-7,0.3408449,0.04234835,0.5246851],"study_design_scores_gemma":[0.0001109152,0.00001768689,0.08073044,0.000008221014,0.000001946453,2.131921e-7,0.005587724,0.0001145919,3.407656e-7,0.00175261,0.9116135,0.00006178349],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.1322203,0.01947709,0.01570676,0.6326773,0.009848043,0.005201054,0.00001655687,0.0004774951,0.1843754],"genre_scores_gemma":[0.7358928,0.01804539,0.05798762,0.1014427,0.008267115,0.000211437,0.000009110195,0.0001073307,0.07803646],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8692652,"threshold_uncertainty_score":0.9998067,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1183404259640972,"score_gpt":0.4973341848429472,"score_spread":0.37899375887885,"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."}}