{"id":"W2951059495","doi":"10.1145/3292500.3330765","title":"FDML","year":2019,"lang":"en","type":"article","venue":"","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":163,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Raw data; Computation; Artificial intelligence; Scale (ratio); Architecture; Training set; Machine learning; Human–computer interaction; Distributed computing; Algorithm; Programming language","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":["open_science","insufficient_payload"],"consensus_categories":["open_science"],"category_scores_codex":[0.0001110112,0.00004779703,0.0000542252,0.00004541955,0.0000166013,0.0000654987,0.03135593,0.00003952873,0.0001908419],"category_scores_gemma":[0.001915324,0.00003892017,0.00001609079,0.0002090965,0.00001461458,0.0004436265,0.09960532,0.00007532279,0.002805409],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001430611,"about_ca_system_score_gemma":0.00001384333,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001140181,"about_ca_topic_score_gemma":0.000001147694,"domain_scores_codex":[0.9994171,0.000008095159,0.00006467161,0.0002354939,0.0001214825,0.0001532121],"domain_scores_gemma":[0.9909253,0.00004242995,0.00001746717,0.00898139,0.00001450538,0.00001886987],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[4.112462e-7,0.00001359303,0.003690261,0.000004321567,0.000004553416,0.00000395647,0.000007929902,0.00000106786,0.001128187,0.1453955,0.8155861,0.03416412],"study_design_scores_gemma":[0.0001800589,0.00004682848,0.002727755,0.000007732697,6.353031e-7,0.00001123994,0.00000976295,0.1602761,0.01537974,0.7518331,0.06933271,0.0001943497],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03112083,0.00004813684,0.728942,0.0563399,0.0006998124,0.0001354343,0.000001208245,0.002942668,0.1797701],"genre_scores_gemma":[0.3407924,0.000005632988,0.6570878,0.0005214803,0.000008597183,0.000002573016,7.584763e-7,0.0000031303,0.001577595],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.7462534,"threshold_uncertainty_score":0.997971,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02165854612277613,"score_gpt":0.2565101633071002,"score_spread":0.2348516171843241,"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."}}