{"id":"W4287280698","doi":"10.48550/arxiv.2103.05173","title":"PCOR: Private Contextual Outlier Release via Differentially Private\\n Search","year":2021,"lang":"","type":"preprint","venue":"arXiv (Cornell University)","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Outlier; Anomaly detection; Context (archaeology); Computer science; Metric (unit); Population; Differential privacy; Data mining; Artificial intelligence; Geography; Engineering; Medicine","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003628533,0.0009776684,0.00274956,0.001290595,0.001714621,0.002827192,0.005091053,0.002556934,0.005219447],"category_scores_gemma":[0.01735085,0.0005601107,0.001542273,0.002892687,0.001892764,0.005398949,0.01055185,0.002812323,0.002251332],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001993343,"about_ca_system_score_gemma":0.004782763,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003911051,"about_ca_topic_score_gemma":0.005087579,"domain_scores_codex":[0.993739,0.001267142,0.0004096982,0.001267878,0.002314374,0.001001923],"domain_scores_gemma":[0.9908429,0.002349466,0.0007665455,0.004874927,0.0007340619,0.000431965],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.006127118,0.0008504271,0.009689171,0.0008741506,0.0005542825,0.002207646,0.001201721,0.1540395,0.03709937,0.1693988,0.06458359,0.5533744],"study_design_scores_gemma":[0.0004599399,0.0004709508,0.001920761,0.00006692025,0.0001235591,0.001359441,0.0003498658,0.8254933,0.01322876,0.1355089,0.02088798,0.0001297398],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.05082949,0.002468471,0.9231147,0.002388052,0.0004231172,0.0006853371,0.002414087,0.009040956,0.008635756],"genre_scores_gemma":[0.7628226,0.0009982336,0.2195634,0.001568586,0.0004651213,0.0005355492,0.003481691,0.0005733781,0.009991417],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005219447,"threshold_uncertainty_score":0.01918977,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06299439755621525,"score_gpt":0.2031978242395544,"score_spread":0.1402034266833392,"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."}}