{"id":"W7065691250","doi":"","title":"Ep.51 - How to Make Poor People Disappear (Census Edition)","year":2016,"lang":"en","type":"other","venue":"Bulletin of Miscellaneous Information (Royal Gardens Kew)","topic":"Radiation Therapy and Dosimetry","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Government (linguistics); Census; Poverty; Poor people","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001976365,0.001024802,0.0005590018,0.001702064,0.001648276,0.003298774,0.001363872,0.002944808,0.1763236],"category_scores_gemma":[0.007442999,0.0006404552,0.0006407739,0.003165332,0.0007737377,0.003824722,0.002188972,0.003083122,0.1408383],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002250622,"about_ca_system_score_gemma":0.007946835,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1300039,"about_ca_topic_score_gemma":0.140166,"domain_scores_codex":[0.9985785,0.0003122732,0.0001544759,0.0001006102,0.0006349321,0.0002191575],"domain_scores_gemma":[0.9973635,0.0004309863,0.0001995629,0.0001791496,0.001488248,0.0003386179],"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":[0.000004442585,0.000006397072,0.00008040498,0.0000689025,7.946564e-7,0.000007528117,0.00005681722,0.00001280251,0.000009758809,0.001364299,0.9839483,0.01443964],"study_design_scores_gemma":[0.000005682749,0.000005827762,0.001704707,0.0002928196,0.000001443169,0.00001515885,0.0001583186,0.000008486658,0.00002253918,0.0007613023,0.9970176,0.000006062452],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.000769918,0.03745389,0.0008746173,0.06507974,0.01474572,0.000923825,0.07454254,0.0007608837,0.8048488],"genre_scores_gemma":[0.004162475,0.03087558,0.001781748,0.02734621,0.002129297,0.001745703,0.02004272,0.0005913071,0.911325],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.8236764,"threshold_uncertainty_score":0.5898613,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005426694635117813,"score_gpt":0.2040720760653554,"score_spread":0.1986453814302375,"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."}}