{"id":"W6950525927","doi":"10.5683/sp3/bozfpy","title":"Data Use and PUMFs","year":2003,"lang":"en","type":"dataset","venue":"Borealis","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta; University of New Brunswick","funders":"","keywords":"Microdata (statistics); Session (web analytics); Population; Public access; Public use; Data collection; Public health","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004301526,0.0003756875,0.0003953485,0.0001844681,0.00007421939,0.0003275718,0.001203118,0.0003391231,0.0001611529],"category_scores_gemma":[0.001102092,0.0003562608,0.00002869056,0.0001692092,0.0001489977,0.0005272425,0.0007820654,0.0003558525,0.0005614312],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005172547,"about_ca_system_score_gemma":0.0001240838,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.1540294,"about_ca_topic_score_gemma":0.1485597,"domain_scores_codex":[0.9979834,0.0001615808,0.000271538,0.0008156458,0.0004039294,0.0003639262],"domain_scores_gemma":[0.9940487,0.0001155879,0.0002057051,0.00535961,0.00006688406,0.0002035059],"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":[0.00001222691,0.00004400376,0.00001891968,0.00004888973,0.00007462071,0.0001156552,0.000002483006,8.63211e-8,0.000001680286,0.00001626433,0.9996083,0.00005685008],"study_design_scores_gemma":[0.0002503893,0.00001420357,0.000541685,0.00004484146,0.0002645713,0.0000618739,0.000003296863,0.000003437515,0.000001811793,0.00002322725,0.9983971,0.0003935977],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.000002261573,0.0003726126,7.429916e-7,0.00002768276,0.00009023453,0.0002440817,0.9989561,0.00006887726,0.0002374448],"genre_scores_gemma":[5.632194e-8,0.0007606404,0.0002303735,0.0003413003,0.0001693541,0.00001275243,0.9982619,0.0001054044,0.000118244],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.005469707,"threshold_uncertainty_score":0.999889,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1025708630731416,"score_gpt":0.3188067703922392,"score_spread":0.2162359073190976,"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."}}