{"id":"W4398988701","doi":"10.7910/dvn/sgfrya/ligqmt","title":"eb_2017_qd12_3.tab","year":2019,"lang":"en","type":"dataset","venue":"Harvard Dataverse","topic":"Economic and Technological Developments in Russia","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institute on Governance","funders":"","keywords":"Replication (statistics); Computer science; Artificial intelligence; Data science; Psychology; Biology; Virology","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":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0006012063,0.0002506023,0.0003760974,0.0001407274,0.0003600326,0.0001529607,0.001686294,0.0007800351,0.1054302],"category_scores_gemma":[0.0005106615,0.0002366857,0.0001006182,0.0001348514,0.0004084411,0.0002690008,0.0005940809,0.000520669,0.5881421],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002300694,"about_ca_system_score_gemma":0.0004325654,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001305128,"about_ca_topic_score_gemma":0.0006691181,"domain_scores_codex":[0.998225,0.0001000442,0.0003044379,0.0005333018,0.0003286108,0.0005086474],"domain_scores_gemma":[0.9983118,0.000116174,0.0002333945,0.001151979,0.00003654118,0.0001500815],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000005594266,0.00003429091,0.00002475642,0.00002062785,0.00004152592,0.00002358993,0.00004008426,2.238125e-7,1.866926e-7,0.002902582,0.9961554,0.0007511862],"study_design_scores_gemma":[0.0001696025,0.00001281943,0.00004505219,0.00004070648,0.00003438295,9.683431e-7,0.0002292397,6.033277e-7,0.000001082756,0.000536508,0.9985901,0.0003389433],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00001218196,0.000001446926,0.000008494768,0.00004128381,0.002282981,0.0003280642,0.9671848,0.0001153155,0.03002546],"genre_scores_gemma":[0.00002453721,0.001492238,0.0003375235,0.0005465061,0.0004470671,0.00002115166,0.9838356,0.00001304312,0.01328237],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.4827119,"threshold_uncertainty_score":0.9651763,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03770024541042623,"score_gpt":0.2933501955753289,"score_spread":0.2556499501649027,"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."}}