{"id":"W4411334025","doi":"10.1016/j.ijtst.2025.06.001","title":"EcoRouteQ: A Reinforcement Learning Framework for Green Route Recommendations","year":2025,"lang":"en","type":"article","venue":"International Journal of Transportation Science and Technology","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"National Research Council Canada; Natural Sciences and Engineering Research Council of Canada","keywords":"Reinforcement; Reinforcement learning; Business; Engineering; Transport engineering; Computer science; Operations management; Artificial intelligence; Structural engineering","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009442027,0.00005761622,0.0001026928,0.000909781,0.0004175079,0.00007244971,0.0003726762,0.00008992966,0.00002417368],"category_scores_gemma":[0.000531334,0.00005779437,0.00003588599,0.0008361368,0.0003561891,0.0005182733,0.000003876416,0.0001574779,6.205129e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001009728,"about_ca_system_score_gemma":0.0005751891,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009198146,"about_ca_topic_score_gemma":0.000247289,"domain_scores_codex":[0.9989139,0.00001449967,0.0003862259,0.0001263932,0.0004234795,0.0001354742],"domain_scores_gemma":[0.9974035,0.0001342379,0.0002952668,0.00004652659,0.002074835,0.00004562349],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00006041855,0.00003110888,0.03315831,0.000006465167,0.0000565037,0.000003940207,0.004669376,0.002931813,0.0002078751,0.9000139,0.0002214969,0.05863879],"study_design_scores_gemma":[0.002732929,0.0003873145,0.03693518,0.0006679338,0.0001829489,0.000009782758,0.02699409,0.003126589,0.002711682,0.2372605,0.6885763,0.0004146919],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07963109,0.0001238982,0.8352991,0.0808733,0.001817432,0.00025542,0.00001923163,0.00008715882,0.001893428],"genre_scores_gemma":[0.9769179,0.0002951192,0.02195105,0.0002497913,0.00005717723,0.00001022044,0.00001898796,0.000002985998,0.0004967384],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8972868,"threshold_uncertainty_score":0.3211176,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01782883525290078,"score_gpt":0.3585817779203076,"score_spread":0.3407529426674068,"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."}}