{"id":"W2999940786","doi":"10.29012/jpc.697","title":"Program for TPDP 2018","year":2018,"lang":"en","type":"article","venue":"Journal of Privacy and Confidentiality","topic":"Legal and Policy Issues","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Differential privacy; Differential (mechanical device); Computer science; Library science; Data science; Engineering; Data mining","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":[],"consensus_categories":[],"category_scores_codex":[0.001146071,0.00005329409,0.0001429034,0.0000345218,0.0003362958,0.0001654211,0.0002058731,0.00005828893,0.000164137],"category_scores_gemma":[0.0002789547,0.00004208392,0.00008807326,0.00006976667,0.0003968712,0.0002873921,0.00002925165,0.00007316926,0.00000783643],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001645953,"about_ca_system_score_gemma":0.0001350905,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002060967,"about_ca_topic_score_gemma":0.0005561039,"domain_scores_codex":[0.9991921,0.0001048256,0.0002542118,0.00007005965,0.0002061276,0.0001726516],"domain_scores_gemma":[0.9992173,0.00007544507,0.0002132905,0.00007417623,0.0003158048,0.0001039941],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001004125,0.0006017669,0.008053843,0.0001376941,0.0003556522,0.000009682007,0.1110165,8.327218e-8,0.001841568,0.2774196,0.1523069,0.4472527],"study_design_scores_gemma":[0.0003490982,0.0004061045,0.003196688,0.00001757844,0.00003759472,0.000005016872,0.000985969,0.000002508605,0.0008650452,0.02872706,0.9653445,0.00006285703],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9723813,0.0003847636,0.001132433,0.008650334,0.001542483,0.0003465381,0.000007364219,0.00002929431,0.01552548],"genre_scores_gemma":[0.9936867,0.0001022956,0.001121469,0.000250325,0.003276463,0.000003005774,2.964845e-7,0.000003289741,0.001556155],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8130376,"threshold_uncertainty_score":0.3115579,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05281254402730334,"score_gpt":0.4200759570956735,"score_spread":0.3672634130683702,"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."}}