{"id":"W2067125310","doi":"10.1109/wivec.2014.6953225","title":"Frame-based mobility estimation via compressive sensing in delay-tolerant vehicular networks","year":2014,"lang":"en","type":"article","venue":"","topic":"Sparse and Compressive Sensing Techniques","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Mobility model; Frame (networking); Real-time computing; Scheme (mathematics); TRACE (psycholinguistics); Trajectory; Computer network","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001732161,0.0001772483,0.0002315669,0.00008987053,0.00004474464,0.00004017455,0.0000905276,0.0001473475,0.00001526959],"category_scores_gemma":[0.00002130588,0.0001738513,0.00005463932,0.0001505378,0.00003661313,0.00008247205,0.00002249937,0.0002531573,0.000008270194],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006064484,"about_ca_system_score_gemma":0.000006377913,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001714341,"about_ca_topic_score_gemma":0.00005701294,"domain_scores_codex":[0.9990911,0.00007332651,0.0002550207,0.0002022333,0.0001261418,0.0002522185],"domain_scores_gemma":[0.9993789,0.0001293231,0.00003433246,0.0003493124,0.00005388648,0.00005428139],"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":[0.000006482154,0.00001725704,0.0002084481,0.00001018737,0.000007267085,0.000009058628,0.00002496382,0.9738501,0.002604414,0.0000631517,0.0001641482,0.02303448],"study_design_scores_gemma":[0.000228526,0.00001958443,0.001572047,0.0001119691,0.000008602142,0.000005459002,0.000004334473,0.9716121,0.02500958,0.001076297,0.000149511,0.0002019693],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2894851,0.00005745833,0.7086322,0.00002215592,0.0001026872,0.0001455697,3.014513e-7,0.0006733469,0.0008811495],"genre_scores_gemma":[0.9582487,0.000002585651,0.041463,0.0001915808,0.00004606628,0.000005217596,0.00001252157,0.00002796939,0.000002398137],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6687635,"threshold_uncertainty_score":0.7089449,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006849446075307106,"score_gpt":0.2074744636671596,"score_spread":0.2006250175918525,"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."}}