{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006689382,0.001535109,0.001327235,0.006745674,0.0007320501,0.001724174,0.001709791,0.0007347468,0.04304542],"category_scores_gemma":[0.004901327,0.0009412989,0.0007320073,0.01361481,0.000194572,0.001128384,0.001021898,0.001542005,0.04556864],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002378495,"about_ca_system_score_gemma":0.005123877,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.2233794,"about_ca_topic_score_gemma":0.2793276,"domain_scores_codex":[0.9992456,0.00006895053,0.0001213149,0.0001900859,0.000251023,0.0001231108],"domain_scores_gemma":[0.9966286,0.0003215226,0.0004053525,0.00039734,0.001968477,0.0002786501],"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":[0.00002829997,0.00001837867,0.002649645,0.0001622477,0.00001860425,0.00001288552,0.00002514403,0.0001293084,0.00002138346,0.0001754137,0.99437,0.002388653],"study_design_scores_gemma":[0.0001812295,0.00001966737,0.08746114,0.0003354535,0.00005730017,0.00006174892,0.0003839678,0.0005278913,0.0002927761,0.0003991556,0.9102373,0.00004253569],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0003304215,0.00002793985,0.00004458951,0.00003159886,0.00001256911,0.00001475034,0.9987382,0.00007592305,0.0007238389],"genre_scores_gemma":[0.0008045327,0.00008485719,0.0001470596,0.00001975379,0.00001000849,0.0001475076,0.9961959,0.0000298881,0.002560524],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.2233794,"threshold_uncertainty_score":0.4441583,"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."}}