{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007096452,0.0004591128,0.000456357,0.00153728,0.003476185,0.002113623,0.001406517,0.0006982746,0.03471869],"category_scores_gemma":[0.006345412,0.0004618541,0.0003497052,0.001454961,0.0008244782,0.001520582,0.006755994,0.00111715,0.006418918],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003238961,"about_ca_system_score_gemma":0.01355817,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02701616,"about_ca_topic_score_gemma":0.02917302,"domain_scores_codex":[0.9948875,0.003346013,0.0001560922,0.0003949812,0.0005537031,0.000661698],"domain_scores_gemma":[0.9965602,0.0007456679,0.0002630388,0.0004626823,0.0008297467,0.001138738],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.001806703,0.001824572,0.07598198,0.00113197,0.0001479854,0.0003429023,0.01354509,0.001029473,0.0007577987,0.07162777,0.366022,0.4657816],"study_design_scores_gemma":[0.0008431383,0.0007834622,0.1446009,0.00105296,0.0001140542,0.0003660597,0.01559567,0.001773751,0.0008551633,0.01582092,0.8181089,0.00008504941],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.3026608,0.008884889,0.03588382,0.04760469,0.002702993,0.03341112,0.2144029,0.002623349,0.3518254],"genre_scores_gemma":[0.5183195,0.004874928,0.1010576,0.00824536,0.0005497803,0.1104841,0.09926207,0.000707405,0.1564992],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03471869,"threshold_uncertainty_score":0.1161456,"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."}}