{"id":"W3090042117","doi":"10.1109/jiot.2020.3028026","title":"Trajectory Penetration Characterization for Efficient Vehicle Selection in HD Map Crowdsourcing","year":2020,"lang":"en","type":"article","venue":"IEEE Internet of Things Journal","topic":"Mobile Crowdsensing and Crowdsourcing","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Crowdsourcing; Trajectory; Computer science; Maximization; Trajectory optimization; Selection (genetic algorithm); Data mining; Mathematical optimization; Real-time computing; Machine learning; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001036281,0.001143705,0.001109597,0.001083136,0.0006092637,0.001184448,0.002303587,0.001029217,0.00185095],"category_scores_gemma":[0.00642394,0.0004414165,0.0005906693,0.001306901,0.0006970017,0.001629652,0.002167436,0.0008050795,0.0007020319],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00102161,"about_ca_system_score_gemma":0.001205861,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01148081,"about_ca_topic_score_gemma":0.006886139,"domain_scores_codex":[0.9990333,0.0001733911,0.00004844189,0.0003055278,0.0002472117,0.0001920546],"domain_scores_gemma":[0.9982059,0.0006687363,0.0002330022,0.0003017553,0.0004059427,0.0001847662],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0003591843,0.000152403,0.02233297,0.0002516952,0.00008249964,0.0004060813,0.000562744,0.8848389,0.00831996,0.007333899,0.002892569,0.07246714],"study_design_scores_gemma":[0.000006072828,0.0000268426,0.001696346,0.000007738378,0.000007058874,0.00003921363,0.00008767431,0.9932359,0.001082854,0.003229473,0.0005702574,0.00001062933],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1896513,0.0005235848,0.8033329,0.0003744107,0.00005626176,0.0001948696,0.00110263,0.001314748,0.00344929],"genre_scores_gemma":[0.9564235,0.0001804013,0.04020385,0.00007970202,0.00004350454,0.0001674125,0.001199889,0.0001014415,0.001600277],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01148081,"threshold_uncertainty_score":0.02282798,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01766222007855258,"score_gpt":0.2314294887119462,"score_spread":0.2137672686333936,"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."}}