{"id":"W4416540324","doi":"10.1016/j.rtbm.2025.101564","title":"Analyzing temporal and spatial freight activity considering truck types and restriction policy","year":2025,"lang":"en","type":"article","venue":"Research in Transportation Business & Management","topic":"Urban and Freight Transport Logistics","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Science Basic Research Program of Shaanxi Province; Shanxi Provincial Education Department; ShanXi Science and Technology Department; National Natural Science Foundation of China","keywords":"Truck; Variation (astronomy); Distribution (mathematics); Trip generation; Key (lock); Spatial variability","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":[0.002999278,0.000466806,0.0006182554,0.0007359277,0.0004097801,0.0026408,0.00133708,0.001060372,0.008588615],"category_scores_gemma":[0.008025596,0.0006049982,0.001598012,0.001491598,0.0006246712,0.001994235,0.0006632259,0.00133379,0.0003537393],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002702614,"about_ca_system_score_gemma":0.002637693,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.09131895,"about_ca_topic_score_gemma":0.09261817,"domain_scores_codex":[0.9988171,0.0005215701,0.00003894536,0.000168358,0.00007956841,0.0003745804],"domain_scores_gemma":[0.9910041,0.007204346,0.0008033913,0.0002462245,0.0003309675,0.0004108925],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.001645848,0.0009746335,0.2069051,0.0001107508,0.0009122074,0.0003737787,0.0002804591,0.7499154,0.002167398,0.02182507,0.0009646691,0.01392466],"study_design_scores_gemma":[0.00005133806,0.0003452137,0.08656035,0.00002260537,0.0005138448,0.00004858115,0.002331211,0.8982761,0.001122441,0.009223551,0.00146657,0.00003821625],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9915616,0.00009011108,0.00534907,0.0002893836,0.00001102063,0.00001695517,0.0003357142,0.0000189969,0.002327154],"genre_scores_gemma":[0.9974334,0.00006325395,0.0007810832,0.00001491791,0.000007420579,0.0000101582,0.0002401354,0.000008539438,0.001441067],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09131895,"threshold_uncertainty_score":0.1815749,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04439666264492017,"score_gpt":0.2993097208737571,"score_spread":0.2549130582288369,"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."}}