{"id":"W4295308628","doi":"10.1109/access.2022.3206384","title":"Travel Time Prediction Using Hybridized Deep Feature Space and Machine Learning Based Heterogeneous Ensemble","year":2022,"lang":"en","type":"article","venue":"IEEE Access","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"Khalifa University of Science, Technology and Research","keywords":"Computer science; Artificial intelligence; Ensemble learning; Feature (linguistics); Machine learning; Feature vector; Pattern recognition (psychology)","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":[0.0001291391,0.0001379196,0.0001386502,0.0001561138,0.000225404,0.00008378737,0.0001495389,0.00003971552,0.00006819447],"category_scores_gemma":[0.000005112014,0.0001582596,0.00004000977,0.0001712509,0.00001384825,0.0001742843,0.0000646872,0.0002791721,0.000002046818],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008637054,"about_ca_system_score_gemma":0.00000680499,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002794846,"about_ca_topic_score_gemma":0.000005503585,"domain_scores_codex":[0.9992938,0.00005095056,0.0001142333,0.0001890742,0.0001723693,0.0001795482],"domain_scores_gemma":[0.9997529,0.00001936413,0.00003372912,0.0001273434,0.00001183989,0.00005482602],"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":[0.00003205708,0.0000236535,0.0002424753,0.00005238647,0.00004791326,0.00002998736,0.0000744265,0.9523922,0.03780491,0.00000704125,0.00490229,0.004390721],"study_design_scores_gemma":[0.0004368969,0.00004164097,0.0002850303,0.000009122921,0.00003576604,0.00004096727,0.00001310036,0.9706296,0.02388582,0.00001120981,0.00447086,0.0001399777],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2791543,0.0005625162,0.7127098,0.0001071691,0.0006386024,0.0004318852,0.00005000913,0.005260409,0.001085264],"genre_scores_gemma":[0.9987355,0.00004877786,0.0008055907,0.0000898456,0.00005828584,0.00004157577,0.0000388346,0.00004153902,0.0001400282],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7195812,"threshold_uncertainty_score":0.645364,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01503526924804175,"score_gpt":0.2241375017620856,"score_spread":0.2091022325140438,"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."}}