{"id":"W4200361938","doi":"10.1016/j.ins.2021.12.091","title":"PR-LTTE: Link travel time estimation based on path recovery from large-scale incomplete trip data","year":2021,"lang":"en","type":"article","venue":"Information Sciences","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":false,"ca_institutions":"York University","funders":"Natural Science Foundation of Shandong Province; National Natural Science Foundation of China; Shandong University","keywords":"Computer science; Travel time; Path (computing); Scale (ratio); Transport engineering; Estimation; Travel behavior; Data mining; Geography; Computer network; 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":[],"consensus_categories":[],"category_scores_codex":[0.000481075,0.0000890126,0.00009466264,0.0001604762,0.0001243464,0.0002269346,0.000326541,0.00004895688,0.0001401985],"category_scores_gemma":[0.00007684823,0.00008469592,0.00002393158,0.0003745093,0.00003154518,0.002242732,0.00006004329,0.00008206321,0.0002527262],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003373492,"about_ca_system_score_gemma":0.0000467114,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009522952,"about_ca_topic_score_gemma":0.000007098168,"domain_scores_codex":[0.9990778,0.00002115271,0.0002823415,0.000130909,0.0003485803,0.0001392108],"domain_scores_gemma":[0.9994729,0.00006355393,0.00005924598,0.0003276904,0.00003632563,0.00004025627],"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":[0.00001530807,0.00005053085,0.00009167093,0.00006861927,0.00002336426,0.000002382389,0.0008398746,0.2978934,0.0004817561,0.000595824,0.207489,0.4924482],"study_design_scores_gemma":[0.0002059124,0.00002459177,0.002467625,0.0000373527,0.000005985191,5.311276e-7,0.0001110336,0.9604874,0.0006980863,0.0001161701,0.03574718,0.00009812997],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003041261,0.00001557003,0.9634067,0.0005602112,0.0003777877,0.0001552626,0.0004144494,0.001467706,0.0305611],"genre_scores_gemma":[0.9443753,0.00004435994,0.05174549,0.001545968,0.00007619936,0.00002092904,0.002123665,0.000007459651,0.00006056474],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9413341,"threshold_uncertainty_score":0.3453799,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02206821534800807,"score_gpt":0.2427786270319392,"score_spread":0.2207104116839312,"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."}}