{"id":"W6969166502","doi":"10.5683/sp3/tcfb5e","title":"RDCs and Disclosure Analysis","year":2004,"lang":"en","type":"dataset","venue":"Borealis","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Microdata (statistics); Session (web analytics); Data quality; Data collection; Public information; Data retention","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"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":[0.0002659197,0.0004573322,0.0007865509,0.0007845499,0.00009763113,0.0001482972,0.0004530758,0.0004329663,0.0002214802],"category_scores_gemma":[0.0001297792,0.0004000076,0.0002940484,0.001178863,0.0001762083,0.00009323464,0.0001799823,0.0003679231,0.000238394],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001788345,"about_ca_system_score_gemma":0.0001390948,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.2530168,"about_ca_topic_score_gemma":0.3636075,"domain_scores_codex":[0.9979542,0.00009591292,0.0003424435,0.000650503,0.0005752251,0.0003816553],"domain_scores_gemma":[0.9978455,0.00004543764,0.0002886682,0.001507519,0.0000805068,0.0002323716],"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":[0.00001623462,0.00006311179,0.0001440856,0.00006641585,0.001858361,0.0001068347,0.00002012542,0.00003249864,0.000001204246,0.0000235699,0.9976196,0.00004795273],"study_design_scores_gemma":[0.0002868483,0.00002561276,0.006473068,0.00003602119,0.007686282,0.0000102369,0.00001181428,0.0000029422,0.000003374819,0.0001198149,0.9848912,0.0004527744],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00001512634,0.0005849067,0.00000364551,0.00007657609,0.00003130085,0.0001804736,0.9986669,0.00009109985,0.0003499141],"genre_scores_gemma":[0.00001920262,0.0003347554,0.00006180842,0.00009441059,0.0002157435,0.0000298967,0.9991139,0.00007230676,0.00005804267],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1105907,"threshold_uncertainty_score":0.9998452,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01251168036968799,"score_gpt":0.2702754467861486,"score_spread":0.2577637664164606,"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."}}