{"id":"W4384525924","doi":"10.3390/s23146443","title":"Empowering Patient Similarity Networks through Innovative Data-Quality-Aware Federated Profiling","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Data Quality and Management","field":"Decision Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Humber Polytechnic","funders":"","keywords":"Computer science; Data mining; Data quality; Quality assurance; Profiling (computer programming); Outlier; Classifier (UML); Data integrity; Metadata; Machine learning; Artificial intelligence; Database; Engineering","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":[],"consensus_categories":[],"category_scores_codex":[0.005883192,0.0002266393,0.0004001411,0.0001851532,0.0004995663,0.0006167264,0.001139014,0.00011014,0.0001683316],"category_scores_gemma":[0.004023704,0.0001841444,0.00005596383,0.003597221,0.0001250241,0.000830128,0.002088137,0.0003305246,0.0007761669],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004599751,"about_ca_system_score_gemma":0.00005711291,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002360544,"about_ca_topic_score_gemma":0.0001380584,"domain_scores_codex":[0.9951067,0.0007063752,0.001118685,0.001057679,0.001476131,0.0005344088],"domain_scores_gemma":[0.9961143,0.001221309,0.0003844685,0.001671043,0.0005230107,0.00008594356],"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":[0.0003948204,0.0005133611,0.009585452,0.0001479829,0.0005220016,0.0005078879,0.01512261,0.1534747,0.0003493101,0.0475928,0.5779588,0.1938303],"study_design_scores_gemma":[0.00108754,0.0001963884,0.01185293,0.0001114539,0.00003414716,0.00000546964,0.07800837,0.3692501,0.001927836,0.01670636,0.5194705,0.001348999],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9355175,0.00004312475,0.04274582,0.00475237,0.002162087,0.0009981672,0.001115762,0.0007847605,0.01188035],"genre_scores_gemma":[0.9931757,0.00003529715,0.001671183,0.001880881,0.0001451712,0.0000153241,0.001659758,0.00002664185,0.001390009],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2157754,"threshold_uncertainty_score":0.9976313,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3662454189758328,"score_gpt":0.4956535517202481,"score_spread":0.1294081327444152,"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."}}