{"id":"W7002137254","doi":"","title":"MLI Report: Understanding and Responding to the Threat of Russian Disinformation","year":2019,"lang":"en","type":"other","venue":"Bulletin of Miscellaneous Information (Royal Gardens Kew)","topic":"Financial Literacy, Pension, Retirement Analysis","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Disinformation; Foreign policy; Diplomacy; Espionage; The Internet","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002337847,0.0003550454,0.0001987455,0.001065729,0.002220338,0.00363378,0.0005485493,0.00139357,0.1141455],"category_scores_gemma":[0.006729109,0.0001677712,0.0001402058,0.000978083,0.000563707,0.002743819,0.002161852,0.001811211,0.03894746],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002561189,"about_ca_system_score_gemma":0.003307919,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03299079,"about_ca_topic_score_gemma":0.07555687,"domain_scores_codex":[0.9992218,0.0001617209,0.00003247489,0.00005320924,0.0004006889,0.000130056],"domain_scores_gemma":[0.9967864,0.0008456138,0.000172433,0.0001609349,0.001243196,0.0007915339],"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":[0.000008196053,0.00001363907,0.0004125724,0.00002909763,6.098581e-7,0.00002375754,0.0009952737,0.00001209557,0.00005026939,0.001716971,0.9839756,0.01276182],"study_design_scores_gemma":[0.000003992505,0.00001734523,0.004727476,0.0001323877,0.000001891232,0.000050069,0.004864593,0.00006727408,0.000219497,0.000723409,0.9891849,0.000007275113],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.009722251,0.004222483,0.0004449446,0.1212114,0.007726494,0.0001422669,0.009046105,0.0005132056,0.8469708],"genre_scores_gemma":[0.04395592,0.004141014,0.0007532819,0.003885273,0.001488706,0.0001269034,0.004908293,0.0003146641,0.9404261],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.1141455,"threshold_uncertainty_score":0.3818546,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01044392088360641,"score_gpt":0.1939656506368279,"score_spread":0.1835217297532215,"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."}}