{"id":"W3022817842","doi":"10.1007/978-3-030-47358-7_42","title":"An Energy-Efficient Method with Dynamic GPS Sampling Rate for Transport Mode Detection and Trip Reconstruction","year":2020,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Human Mobility and Location-Based Analysis","field":"Social Sciences","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Computer science; Global Positioning System; Energy consumption; Real-time computing; Sampling (signal processing); Energy (signal processing); Mode (computer interface); Reduction (mathematics); Particle filter; Path (computing); Filter (signal processing); Algorithm; Simulation; Statistics; Telecommunications; Computer vision; Mathematics; Electrical 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003867257,0.0007996373,0.0008511433,0.0008890511,0.0003579753,0.0006033404,0.001330485,0.0007267669,0.004739058],"category_scores_gemma":[0.001319662,0.0004961111,0.0007828701,0.001095808,0.0002250244,0.0007713395,0.0008899824,0.0006924724,0.002557449],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003117393,"about_ca_system_score_gemma":0.0009246974,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004688814,"about_ca_topic_score_gemma":0.006251772,"domain_scores_codex":[0.9996617,0.0000508411,0.0000231731,0.00007779263,0.0001541793,0.00003231466],"domain_scores_gemma":[0.9995947,0.0001384802,0.00002393802,0.00006145903,0.0001645573,0.00001686144],"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":[0.0002960547,0.0001222679,0.0009087685,0.0001496016,0.00008902857,0.0001188235,0.00007990264,0.07291096,0.04519666,0.004339709,0.004458846,0.8713295],"study_design_scores_gemma":[0.00001754957,0.00003753791,0.0006929714,0.00001177379,0.00003187472,0.0002024328,0.00002668002,0.9837565,0.01039751,0.001542525,0.003258391,0.00002424046],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003176339,0.0001498773,0.9954407,0.00002456614,0.00004896019,0.0000266424,0.00005946564,0.0004967714,0.0005766879],"genre_scores_gemma":[0.06398178,0.0002966204,0.9300443,0.0000602626,0.00006835534,0.000123355,0.0005288728,0.0001419586,0.004754515],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004739058,"threshold_uncertainty_score":0.0158537,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0197763188208626,"score_gpt":0.2985340471191392,"score_spread":0.2787577282982766,"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."}}