{"id":"W7131227352","doi":"10.25549/wpacards-c8-208","title":"WPA blocklist of household censuses for Whittier, Herbert, Gage, Olympic Streets","year":2021,"lang":"en","type":"dataset","venue":"University of Southern California Digital Library","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Census; Quarter (Canadian coin); Data collection; Administration (probate law); Value (mathematics); Variety (cybernetics)","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":[0.0007529911,0.00137489,0.00116246,0.004385773,0.0005807246,0.001977921,0.00134193,0.0006557738,0.1232494],"category_scores_gemma":[0.005387245,0.0007744639,0.0005461419,0.01250966,0.0002377314,0.001437681,0.001324406,0.001548496,0.1548316],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001383341,"about_ca_system_score_gemma":0.002622557,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05231814,"about_ca_topic_score_gemma":0.07656312,"domain_scores_codex":[0.9991797,0.0001009153,0.0001157568,0.0002374714,0.0002580748,0.0001079962],"domain_scores_gemma":[0.997343,0.000476689,0.0003024122,0.0004184395,0.001240344,0.0002191058],"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":[0.000008995937,0.000006131152,0.000545265,0.0001379944,0.000006656141,0.000006499893,0.00001554711,0.00005894768,0.00002215189,0.0003308461,0.9972675,0.001593476],"study_design_scores_gemma":[0.00005505606,0.000004632956,0.008163687,0.0001935229,0.00001006061,0.00002048883,0.00009715623,0.0001538435,0.0001106981,0.0004840641,0.9906946,0.00001211506],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00006151693,0.00001908123,0.00004350077,0.0000199256,0.000008189299,0.000007009035,0.999032,0.00005961055,0.0007491428],"genre_scores_gemma":[0.0001979644,0.00004317772,0.0001245136,0.00001578753,0.000003988794,0.00004936749,0.9983481,0.00003907894,0.001178044],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1232494,"threshold_uncertainty_score":0.4123105,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01263275570478227,"score_gpt":0.1773405943997166,"score_spread":0.1647078386949344,"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."}}