{"id":"W2320878123","doi":"10.13063/2327-9214.1213","title":"Software-Enabled Distributed Network Governance: The PopMedNet Experience","year":2016,"lang":"en","type":"article","venue":"eGEMs (Generating Evidence & Methods to improve patient outcomes)","topic":"Scientific Computing and Data Management","field":"Decision Sciences","cited_by":35,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institutes of Health; Hamilton Health Sciences Foundation; Patient-Centered Outcomes Research Institute","keywords":"Workflow; Computer science; Corporate governance; Software; Variety (cybernetics); Knowledge management; Process management; Data science; Computer security; Database; Business","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0272912,0.0004191396,0.0003245995,0.001125637,0.001909515,0.006179516,0.0025049,0.001558702,0.0052401],"category_scores_gemma":[0.02830057,0.0003529608,0.0004156142,0.002118977,0.004186362,0.01049146,0.007938242,0.001780363,0.001076868],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002761701,"about_ca_system_score_gemma":0.00474763,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003187107,"about_ca_topic_score_gemma":0.003543904,"domain_scores_codex":[0.989445,0.006881772,0.0004324628,0.0007201313,0.002049881,0.000470831],"domain_scores_gemma":[0.9730316,0.01579257,0.001127483,0.004272174,0.00304177,0.002734375],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.0005835681,0.0005369472,0.01556889,0.0006830859,0.00008053965,0.00165276,0.01178363,0.04812605,0.002419518,0.5841159,0.06402646,0.2704225],"study_design_scores_gemma":[0.0001878837,0.0004281096,0.002886524,0.000494582,0.00003583058,0.000828924,0.003360205,0.06749076,0.003506301,0.1800768,0.7406173,0.00008678699],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2096961,0.003599025,0.4963355,0.05330412,0.001337433,0.001066314,0.001638269,0.006575037,0.2264482],"genre_scores_gemma":[0.6615833,0.004122308,0.2893707,0.00458813,0.0005139285,0.001094345,0.004869977,0.001232874,0.03262452],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9727088,"threshold_uncertainty_score":0.1443313,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1394144769260026,"score_gpt":0.4436966141641815,"score_spread":0.304282137238179,"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."}}