{"id":"W6945825889","doi":"10.25549/wpacards-m78169","title":"WPA household census for 1704 MIRAMAR ST, Los Angeles","year":2012,"lang":"en","type":"dataset","venue":"University of Southern California Digital Library","topic":"Research Data Management Practices","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Census; Population; Quarter (Canadian coin); Work (physics); Data collection","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00021053,0.0003318432,0.0003713426,0.0003571561,0.0001759197,0.001351571,0.00454321,0.0002208839,0.0001384326],"category_scores_gemma":[0.00007160051,0.0003411116,0.0002388879,0.0003335191,0.000269556,0.01277537,0.003234207,0.0003170352,0.0003797895],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004618075,"about_ca_system_score_gemma":0.0001464993,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006256722,"about_ca_topic_score_gemma":0.000006446733,"domain_scores_codex":[0.9979084,0.00008197878,0.0002453186,0.0006268252,0.0005841139,0.0005534013],"domain_scores_gemma":[0.9975138,0.0003322166,0.0004330113,0.001380407,0.00004798102,0.0002925406],"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":[0.00008538714,0.0002015686,0.0001014335,0.0003685377,0.0001108285,0.00006478628,0.000003602482,0.000003835483,4.891365e-7,0.0001944382,0.9965198,0.00234528],"study_design_scores_gemma":[0.0003552953,0.00004395317,0.000004598104,0.00007686297,0.0000586777,0.000003659952,0.0002714192,0.00006729439,0.000006782005,0.0002300632,0.9985288,0.0003526218],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00002601469,0.000183887,0.01130244,0.0003191711,0.0000636219,0.0004339973,0.9866819,0.000173741,0.0008152396],"genre_scores_gemma":[0.00003328217,0.0001853917,0.002733979,0.0001016871,0.0001172255,0.000001034335,0.9958342,0.00003313887,0.0009600866],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.0114238,"threshold_uncertainty_score":0.9999041,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04073202564207833,"score_gpt":0.2265696540278979,"score_spread":0.1858376283858195,"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."}}