{"id":"W3046970120","doi":"10.21307/stattrans-2020-039","title":"Applying data synthesis for longitudinal business data across three countries","year":2020,"lang":"en","type":"article","venue":"Statistics in Transition New Series","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"HEC Montréal","funders":"","keywords":"Microdata (statistics); Confidentiality; Publication; Identification (biology); Profiling (computer programming); Aggregate data","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02877207,0.0003051877,0.000502335,0.002488633,0.001089359,0.00178053,0.0006840546,0.0008087754,0.003084559],"category_scores_gemma":[0.1141128,0.0002418622,0.001404751,0.003983167,0.001034685,0.0008493225,0.001751389,0.001106462,0.0002728826],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002223082,"about_ca_system_score_gemma":0.002287396,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02241046,"about_ca_topic_score_gemma":0.009684433,"domain_scores_codex":[0.9774227,0.01818806,0.001055556,0.001374085,0.001572748,0.0003869113],"domain_scores_gemma":[0.8739828,0.1003798,0.005028375,0.01198109,0.008037344,0.0005906033],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.004063328,0.001110465,0.4824052,0.001275941,0.003208177,0.001268651,0.004199837,0.2825921,0.003090922,0.07515014,0.01323139,0.1284038],"study_design_scores_gemma":[0.00178218,0.003586604,0.2896433,0.0005984126,0.002004637,0.0003719798,0.01546215,0.554125,0.01906809,0.05993563,0.05314036,0.0002816546],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8925613,0.0004575606,0.08773352,0.001495449,0.0001296733,0.00166119,0.01132169,0.0003726791,0.004266944],"genre_scores_gemma":[0.9706072,0.00007720095,0.02440376,0.000112548,0.00001644358,0.000754776,0.003673507,0.00002177515,0.0003328087],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02877207,"threshold_uncertainty_score":0.1521631,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.152565414613606,"score_gpt":0.3469894683376717,"score_spread":0.1944240537240658,"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."}}