{"id":"W7133055943","doi":"","title":"Deep Learning Approaches for Modeling Spatio-Temporal Dynamics in Evolving Networks","year":2024,"lang":"","type":"dissertation","venue":"TSpace","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Missing data; Deep learning; Representation (politics); Focus (optics); Artificial neural network; Variety (cybernetics); Intelligent transportation system; Temporal database; Data modeling; Feature learning","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.00120334,0.001024192,0.0008070059,0.0009832731,0.000437015,0.001271278,0.001937579,0.001449397,0.001568842],"category_scores_gemma":[0.003875439,0.0008296959,0.001096701,0.001210122,0.0009168311,0.00238521,0.001333404,0.003103442,0.0002772448],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001930431,"about_ca_system_score_gemma":0.0009873877,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01757649,"about_ca_topic_score_gemma":0.01889592,"domain_scores_codex":[0.999689,0.00009012008,0.00002211139,0.0000997742,0.00005538609,0.00004357885],"domain_scores_gemma":[0.9989552,0.0006494875,0.0001435754,0.00007601317,0.0001272726,0.00004837048],"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":[0.00001251101,0.00001507099,0.0006741052,0.00003314931,0.00003048259,0.00002655414,0.00003496573,0.9663359,0.0003167182,0.017145,0.0005236057,0.01485199],"study_design_scores_gemma":[7.371456e-7,0.000001779462,0.000036543,0.000001938678,0.00000132996,0.000001953457,0.00000224784,0.9945345,0.00003135359,0.005284502,0.0001019627,0.000001143242],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02137449,0.0009793674,0.9750997,0.0006081904,0.00004494499,0.00003071668,0.0003239043,0.0003023806,0.001236398],"genre_scores_gemma":[0.7513951,0.002799544,0.2360645,0.0003649499,0.0001950272,0.0003034519,0.001357245,0.000161677,0.007358533],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01757649,"threshold_uncertainty_score":0.03494835,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02253789959995486,"score_gpt":0.271179873990714,"score_spread":0.2486419743907591,"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."}}