{"id":"W4386913035","doi":"10.1007/s10601-023-09355-2","title":"Constraint programming approaches to electric vehicle and robot routing problems","year":2023,"lang":"en","type":"article","venue":"Constraints","topic":"Transportation and Mobility Innovations","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Electric vehicle; Greenhouse gas; Robot; Mobile robot; Computer science; Battery (electricity); Variety (cybernetics); Investment (military); Electric motor; Vehicle routing problem; Automotive engineering; Routing (electronic design automation); Engineering; Electrical engineering; Embedded system; Artificial intelligence","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002076696,0.0001157029,0.0001172295,0.0001669429,0.00008235699,0.00005226844,0.00006287747,0.0000543251,0.00002559647],"category_scores_gemma":[0.00003180285,0.0001273803,0.00002462237,0.0008187222,0.00008985036,0.00006722492,0.000008523371,0.0001289467,0.00005605848],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000243601,"about_ca_system_score_gemma":0.00002795901,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003612043,"about_ca_topic_score_gemma":0.0000184463,"domain_scores_codex":[0.9991743,0.000008448998,0.0002323482,0.0001828159,0.00009417631,0.0003079001],"domain_scores_gemma":[0.9997063,0.00004025918,0.00001670042,0.000107112,0.0000260473,0.0001035769],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.000002956509,0.00004159626,0.01253072,0.000198747,0.00009383622,0.00001377588,0.003932466,0.02599864,0.03955151,0.0351837,0.0002472337,0.8822048],"study_design_scores_gemma":[0.003216446,0.0002737927,0.6175998,0.0004858957,0.0001433598,0.0001296509,0.009820149,0.3293091,0.01911747,0.002785861,0.01462648,0.002491981],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.963745,0.00004054159,0.02955095,0.0007291043,0.000128644,0.0007973432,0.0000167525,0.001611721,0.003379924],"genre_scores_gemma":[0.9975973,0.000005473875,0.002137727,0.00005595804,0.00002246758,0.0001003319,0.00002430104,0.00001982546,0.00003664817],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8797128,"threshold_uncertainty_score":0.5194419,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0801388474127351,"score_gpt":0.2332340308149371,"score_spread":0.153095183402202,"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."}}