{"id":"W6977083925","doi":"10.6084/m9.figshare.15079883","title":"Additional file 1 of Combining evidence and values in priority setting: testing the balance sheet method in a low-income country","year":2021,"lang":"en","type":"article","venue":"Figshare","topic":"History of Computing Technologies","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Balance sheet; Balance (ability); Key (lock); Sample (material)","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":["metaresearch","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.008547005,0.001176342,0.001799648,0.004160578,0.001191552,0.002391723,0.002610574,0.001490762,0.9028288],"category_scores_gemma":[0.2010326,0.001031035,0.002102387,0.007269396,0.0005167223,0.003646494,0.001745831,0.001498444,0.06684417],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001961918,"about_ca_system_score_gemma":0.004938208,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01238809,"about_ca_topic_score_gemma":0.01651283,"domain_scores_codex":[0.9963264,0.001543362,0.0008534339,0.0004600204,0.0005373196,0.0002794345],"domain_scores_gemma":[0.7411538,0.2354505,0.008099648,0.004576127,0.009374239,0.001345687],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.001129328,0.000134574,0.004065696,0.02004665,0.0003148924,0.0001091332,0.00021251,0.0009798243,0.00004502496,0.002494241,0.9578049,0.01266326],"study_design_scores_gemma":[0.08052611,0.001607533,0.07692119,0.05454291,0.003649535,0.001665102,0.002917112,0.008809752,0.001022784,0.05395427,0.7137796,0.0006041334],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0003155692,0.00003885551,0.0002991422,0.0003066192,0.00004179758,0.0007055828,0.9966126,0.0001176725,0.001562139],"genre_scores_gemma":[0.06066599,0.0009362311,0.02557266,0.003584421,0.0005917851,0.0616557,0.8181373,0.001607217,0.02724871],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.991453,"threshold_uncertainty_score":0.138603,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04637372375393044,"score_gpt":0.2826735906364066,"score_spread":0.2362998668824762,"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."}}