{"id":"W3191705065","doi":"10.1109/tits.2021.3093061","title":"Applications of Passive GPS Data to Characterize the Movement of Freight Trucks—A Case Study in the Calgary Region of Canada","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Transportation Systems","topic":"Urban and Freight Transport Logistics","field":"Engineering","cited_by":31,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Truck; Global Positioning System; Transport engineering; Geography; Computer science; Engineering; Telecommunications; Automotive engineering","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0002231929,0.0001794941,0.0003406293,0.0001222191,0.00006737451,0.000009454046,0.0003923466,0.00005955073,0.0000237066],"category_scores_gemma":[0.000002554452,0.0001332224,0.00006661394,0.0007815154,0.00004741537,0.00006355395,5.203297e-7,0.0002034833,6.560622e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007131061,"about_ca_system_score_gemma":0.0001827571,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.08022285,"about_ca_topic_score_gemma":0.4567311,"domain_scores_codex":[0.9980083,0.00008234504,0.001068428,0.0002453449,0.000434001,0.0001615752],"domain_scores_gemma":[0.9984607,0.0002081966,0.0001429057,0.0009070194,0.0002238897,0.00005725073],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"qualitative","study_design_scores_codex":[0.0002436713,0.003740364,0.005226353,0.00255118,0.001604808,0.002668863,0.05409979,0.9026163,0.007236959,0.004738249,0.001002615,0.01427081],"study_design_scores_gemma":[0.006537874,0.002081048,0.08654891,0.002375263,0.004192514,0.001080762,0.4231239,0.08983018,0.3525254,0.0002599701,0.027925,0.003519157],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1611734,0.0001495086,0.8354615,0.0000494536,0.0004368904,0.001413426,0.001213544,0.00001896656,0.00008328453],"genre_scores_gemma":[0.9992666,0.00009229677,0.00008879551,0.00003424684,0.00002074515,0.0003167525,0.0001188179,0.0000218142,0.00003992513],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8380932,"threshold_uncertainty_score":0.925902,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0620549908609711,"score_gpt":0.2424573531655858,"score_spread":0.1804023623046147,"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."}}