{"id":"W4214847508","doi":"10.1109/tits.2022.3143861","title":"Lightweight Tensor Deep Computation Model With Its Application in Intelligent Transportation Systems","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Transportation Systems","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"St. Francis Xavier University","funders":"National Natural Science Foundation of China","keywords":"Computer science; Tensor (intrinsic definition); Computation; Intelligent transportation system; Artificial intelligence; Big data; Modal; Deep learning; Data modeling; Machine learning; Data mining; Algorithm; 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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003298728,0.0004472968,0.000440156,0.0009340886,0.0002818427,0.00007531972,0.0002667278,0.0001480667,0.00002894298],"category_scores_gemma":[5.601134e-7,0.0004765537,0.0001460673,0.001007626,0.00003556654,0.0003821259,3.795198e-7,0.0005536061,0.00003777238],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005681868,"about_ca_system_score_gemma":0.00004216484,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001873131,"about_ca_topic_score_gemma":0.0006540379,"domain_scores_codex":[0.996857,0.0001031702,0.001212028,0.0005919791,0.0008338787,0.0004019448],"domain_scores_gemma":[0.9991336,0.00005915629,0.0001889738,0.0003158789,0.0001550417,0.0001473184],"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":[0.0001479443,0.0002895127,0.00008427787,0.0003290859,0.0001145352,0.00001325478,0.002220643,0.9903102,0.0005408721,0.001583711,0.0003036356,0.004062346],"study_design_scores_gemma":[0.0005903748,0.0002158584,0.0002821767,0.0001173292,0.0001142977,0.000008482977,0.002273991,0.9881273,0.004415974,0.00001685236,0.003330949,0.000506448],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02916313,0.0003741554,0.962954,0.00004168273,0.001216507,0.002638512,0.0003642399,0.002944829,0.000302984],"genre_scores_gemma":[0.9942776,0.0003580418,0.000468317,0.00003857199,0.00003681929,0.003984665,0.0004592126,0.0001163613,0.0002604445],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9651144,"threshold_uncertainty_score":0.9997686,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01547082909206232,"score_gpt":0.2215133860601879,"score_spread":0.2060425569681256,"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."}}