{"id":"W4403511241","doi":"10.1109/tnsm.2024.3483013","title":"A Data Completion Algorithm Based on Low-Rank Prior Knowledge for Data-Driven Applications","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Computer science; Algorithm; Data mining","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":[0.0002939013,0.0002165387,0.0001717251,0.0001224964,0.0005717467,0.0003673427,0.001607825,0.00005462898,0.00001210902],"category_scores_gemma":[2.33264e-7,0.0002041169,0.00004839788,0.0009463184,0.00002573445,0.0003368583,0.0000610456,0.0001809673,0.00008268328],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003566621,"about_ca_system_score_gemma":0.00003319055,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001152603,"about_ca_topic_score_gemma":0.0001153824,"domain_scores_codex":[0.9981294,0.0000433907,0.0002687267,0.00104867,0.0001927561,0.0003170467],"domain_scores_gemma":[0.9973929,0.0002612041,0.00004384412,0.00214169,0.00005074831,0.0001096277],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001219301,0.0002697199,1.698479e-7,0.0002543542,0.00007750692,0.000002583354,0.00003866714,0.1302719,0.000003766915,0.008104058,0.01230477,0.8486603],"study_design_scores_gemma":[0.0002382205,0.00004094457,0.00001367118,0.0001064115,0.00007455426,0.000001336025,0.000007984961,0.6868424,0.000003835134,0.0003804459,0.3121414,0.000148814],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00001207879,0.0001711077,0.9904932,0.005649266,0.000488654,0.001608512,0.0004323203,0.0003659695,0.0007789102],"genre_scores_gemma":[0.1982703,0.002427518,0.7743347,0.01240951,0.00198212,0.00591191,0.003002444,0.0001694126,0.001492067],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8485115,"threshold_uncertainty_score":0.8323644,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04574966949544952,"score_gpt":0.2960385690265718,"score_spread":0.2502888995311223,"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."}}