{"id":"W4285505181","doi":"10.1109/tii.2022.3190549","title":"Jointly Low-Rank Tensor Completion for Estimating Missing Spatiotemporal Values in Logistics Systems","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Industrial Informatics","topic":"Tensor decomposition and applications","field":"Mathematics","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"St. Francis Xavier University","funders":"","keywords":"Computer science; Imputation (statistics); Missing data; Exploit; Data mining; Rank (graph theory); Trajectory; Matrix completion; Artificial intelligence; Machine learning; Mathematics","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":[0.003319183,0.001845148,0.001818018,0.00121856,0.0006288071,0.001251761,0.001462388,0.001162467,0.001473272],"category_scores_gemma":[0.008596398,0.0007725577,0.001517483,0.001814038,0.001218724,0.002268102,0.001427917,0.002708356,0.0007011901],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000953937,"about_ca_system_score_gemma":0.002736271,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01232359,"about_ca_topic_score_gemma":0.009052137,"domain_scores_codex":[0.9983214,0.0007287697,0.0001224407,0.000372286,0.0002749338,0.0001801506],"domain_scores_gemma":[0.996801,0.001678927,0.000436245,0.0003394997,0.000560146,0.0001842101],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002878333,0.0001353324,0.003045119,0.0005382313,0.0002023871,0.0002964608,0.0002572872,0.8361663,0.00519549,0.01792419,0.005352806,0.1305986],"study_design_scores_gemma":[0.000005535398,0.00002581808,0.0002724696,0.000009610952,0.000009385172,0.00002064243,0.00002147628,0.9928421,0.0005552797,0.005726806,0.0004964671,0.00001442218],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009790272,0.0004636432,0.9887001,0.0002137297,0.0000370802,0.00003073227,0.0001815352,0.0003055205,0.0002774441],"genre_scores_gemma":[0.430189,0.001944089,0.5602849,0.0002555113,0.0002669604,0.0002766849,0.003048698,0.0002892259,0.003444846],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01232359,"threshold_uncertainty_score":0.02450371,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1619201742071927,"score_gpt":0.3428514528192143,"score_spread":0.1809312786120216,"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."}}