{"meta":{"query_hash":"2fec6a3f013b","filters":{"venue":"EURO Journal on Transportation and Logistics"},"cohort_total":12,"direct_labels_cover":0,"predictions_cover":12,"exported":12,"export_cap":100000,"truncated":false,"label_status":"direct model label, unvalidated","prediction_status":"machine_predicted_unvalidated (Codex and Gemma teacher distillation)","score_status":"score_only:v0-immature-baseline","snapshot":{"source":"OpenAlex, pinned release, all 482 partitions","release":"2026-06-24","frame_built":"2026-07-12"},"permalink":"https://metacan.xera.ac/q/2fec6a3f013b","api":"https://metacan.xera.ac/api/v1/cohort?venue=EURO+Journal+on+Transportation+and+Logistics"},"results":[{"id":"W1974861471","doi":"10.1007/s13676-012-0005-x","title":"Districting for routing with stochastic customers","year":2012,"lang":"en","type":"article","venue":"EURO Journal on Transportation and Logistics","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":72,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"National University of Defense Technology; National Natural Science Foundation of China","keywords":"Heuristic; Mathematical optimization; Routing (electronic design automation); Vehicle routing problem; Computer science; Compact space; Operations research; Mathematics","score_opus":0.028451525478535144,"score_gpt":0.2688076632308992,"score_spread":0.24035613775236409,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1974861471","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04088952,0.0000667962,0.9582531,0.000029688883,0.00028954822,0.00008279174,0.000016086306,0.00008234808,0.00029011563],"genre_scores_gemma":[0.9508703,0.000023658027,0.048814904,0.000053686184,0.00016803484,0.0000026670803,0.000014813256,0.00003105346,0.000020881696],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99935627,0.000021500511,0.00022341662,0.00006474929,0.000116995114,0.00021705989],"domain_scores_gemma":[0.9994627,0.00022454468,0.00007802802,0.000043701017,0.000059116934,0.00013186128],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033147694,0.000107378095,0.000110488756,0.00006601057,0.00014527228,0.00003777313,0.000036233105,0.000035538473,0.0000068971717],"category_scores_gemma":[0.00010532333,0.00009185658,0.000026029918,0.00010949393,0.000022033038,0.00010007854,4.0716938e-7,0.00019759829,0.0000012671761],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000029348523,0.000014988917,0.0027215884,0.00003098034,0.000022299735,0.000003062866,0.00063930685,0.98877275,0.00012492319,0.0028991483,0.00003431176,0.0047072833],"study_design_scores_gemma":[0.0038583083,0.00059575506,0.09877473,0.00028950837,0.00046518605,0.00018337046,0.0018086483,0.88765657,0.00074538856,0.00018486871,0.0043862164,0.0010514766],"about_ca_topic_score_codex":3.756689e-7,"about_ca_topic_score_gemma":6.6473956e-7,"teacher_disagreement_score":0.9099808,"about_ca_system_score_codex":0.000025754764,"about_ca_system_score_gemma":0.000008843631,"threshold_uncertainty_score":0.37458023},"labels":[],"label_agreement":null},{"id":"W1990106667","doi":"10.1007/s13676-012-0007-8","title":"A note on logit choices in strategy transit assignment","year":2012,"lang":"en","type":"article","venue":"EURO Journal on Transportation and Logistics","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Logit; Transit (satellite); Computation; Mathematical optimization; Computer science; Representation (politics); Tree (set theory); Path (computing); Sensitivity (control systems); Centroid; Mixed logit; Operations research; Public transport; Logistic regression; Mathematics; Algorithm; Transport engineering; Engineering; Artificial intelligence; Machine learning; Combinatorics; Computer network","score_opus":0.05427746854326112,"score_gpt":0.3282431087095666,"score_spread":0.2739656401663055,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1990106667","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.60447323,0.0007546813,0.3226752,0.0061630174,0.0027265565,0.0008274249,0.00018670918,0.00025581452,0.061937366],"genre_scores_gemma":[0.9977473,0.00061497337,0.00056246616,0.0005467841,0.00018650113,0.0000025219852,0.000038140646,0.000010169942,0.0002911333],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9987731,0.000132906,0.00032529345,0.00012191161,0.000375123,0.00027165262],"domain_scores_gemma":[0.9993936,0.000166847,0.00013410058,0.00004729554,0.000052750624,0.00020540236],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00059565593,0.000115485316,0.00013130187,0.0001418869,0.00027421556,0.00007257104,0.0000699586,0.00009278633,0.0001267315],"category_scores_gemma":[0.000053150783,0.000105052044,0.000039320377,0.00017505651,0.000084519255,0.00022028793,1.8149622e-7,0.00030563446,0.0000119431825],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006528164,0.0010457365,0.19994935,0.00005212982,0.000040712996,0.00021409505,0.055504445,0.48113468,0.000173643,0.23353435,0.0005386217,0.02715941],"study_design_scores_gemma":[0.0011790078,0.00033928832,0.9617232,0.00009453612,0.000045551427,0.0000027200044,0.0022173263,0.00025155442,0.000062926476,0.00072417565,0.033092573,0.0002671548],"about_ca_topic_score_codex":0.00007031016,"about_ca_topic_score_gemma":0.0005630067,"teacher_disagreement_score":0.7617738,"about_ca_system_score_codex":0.0000462427,"about_ca_system_score_gemma":0.00006398342,"threshold_uncertainty_score":0.4283898},"labels":[],"label_agreement":null},{"id":"W2116517820","doi":"10.1007/s13676-013-0020-6","title":"Vehicle routing: historical perspective and recent contributions","year":2013,"lang":"en","type":"article","venue":"EURO Journal on Transportation and Logistics","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":59,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"","keywords":"Perspective (graphical); Vehicle routing problem; Set (abstract data type); Routing (electronic design automation); Computer science; Operations research; Engineering; Management science; Artificial intelligence; Computer network","score_opus":0.023715562606487994,"score_gpt":0.26785541019620596,"score_spread":0.24413984758971796,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2116517820","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08632912,0.0008950102,0.90562534,0.0038642476,0.0006753033,0.00020756973,0.000029774326,0.00021971921,0.002153925],"genre_scores_gemma":[0.98873353,0.0013123673,0.009548638,0.00018409187,0.000093632734,0.000002694958,0.0000064193982,0.000018425008,0.00010020739],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99933696,0.00005475315,0.00024630217,0.000100193094,0.00011865903,0.0001431312],"domain_scores_gemma":[0.99937886,0.00011841386,0.00005420782,0.000051373994,0.0002416571,0.00015549577],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018842187,0.00010031267,0.00012522795,0.00006285424,0.00014498294,0.00006628008,0.000035347264,0.000055395307,0.000071556584],"category_scores_gemma":[0.00021797372,0.000094706615,0.000022713702,0.00010635832,0.000032536955,0.00008593837,9.725878e-7,0.0003068175,0.000006587493],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013625027,0.00038731287,0.027783524,0.00011347494,0.00030522892,0.00020444163,0.009599621,0.6318665,0.010657354,0.22949438,0.014717447,0.07473445],"study_design_scores_gemma":[0.0040507023,0.00067754067,0.5646937,0.00011409189,0.00023789828,0.00015948601,0.001928704,0.36520287,0.0012023598,0.011836585,0.048862364,0.0010336721],"about_ca_topic_score_codex":0.000010857624,"about_ca_topic_score_gemma":0.0000025953402,"teacher_disagreement_score":0.9024044,"about_ca_system_score_codex":0.00018784727,"about_ca_system_score_gemma":0.000014275997,"threshold_uncertainty_score":0.38620237},"labels":[],"label_agreement":null},{"id":"W2225208082","doi":"10.1007/s13676-016-0101-4","title":"The vehicle routing problem with hard time windows and stochastic service times","year":2016,"lang":"en","type":"article","venue":"EURO Journal on Transportation and Logistics","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":70,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal; Polytechnique Montréal; Group for Research in Decision Analysis; École de Technologie Supérieure","funders":"","keywords":"Vehicle routing problem; Mathematical optimization; Probabilistic logic; Set (abstract data type); Stochastic dominance; Computer science; Service (business); Dimension (graph theory); Resource constraints; Routing (electronic design automation); Mathematics; Distributed computing; Artificial intelligence; Economics","score_opus":0.014529178717328553,"score_gpt":0.22272130247434482,"score_spread":0.20819212375701626,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2225208082","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07686471,0.00013327633,0.92050606,0.0015841237,0.00009921256,0.00015539794,0.000019146735,0.0001415982,0.00049649813],"genre_scores_gemma":[0.9826524,0.00015183771,0.016618652,0.00017049274,0.00006901153,0.0000024753726,0.000002950895,0.000035851284,0.00029627618],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992791,0.00005430796,0.00023888588,0.000104355975,0.00015394788,0.00016942967],"domain_scores_gemma":[0.99929583,0.00035303392,0.00008074224,0.000073522446,0.00009836323,0.00009851399],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003622695,0.000121764984,0.00011112671,0.00003607678,0.00023408748,0.000091216316,0.00006311928,0.000037273792,0.000019164667],"category_scores_gemma":[0.0000516499,0.000067851324,0.000014352914,0.00010126455,0.000053432334,0.000091899754,0.0000014566184,0.00017124847,0.000008961212],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017313192,0.000022565406,0.0029057374,0.000055884528,0.00009507776,0.000039110644,0.0011132811,0.9481926,0.0045912345,0.0041003446,0.00021972913,0.038491316],"study_design_scores_gemma":[0.0061403047,0.00085145544,0.18768351,0.0009721879,0.00033051358,0.00029022555,0.0004131911,0.79352444,0.0012169611,0.0023572198,0.0051367013,0.0010832712],"about_ca_topic_score_codex":9.070453e-7,"about_ca_topic_score_gemma":0.000005694441,"teacher_disagreement_score":0.90578777,"about_ca_system_score_codex":0.000016095713,"about_ca_system_score_gemma":0.000016664166,"threshold_uncertainty_score":0.27668965},"labels":[],"label_agreement":null},{"id":"W2943778955","doi":"10.1016/j.ejtl.2020.100004","title":"A tutorial on recursive models for analyzing and predicting path choice behavior","year":2020,"lang":"en","type":"preprint","venue":"EURO Journal on Transportation and Logistics","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Intuition; Computer science; Path (computing); Discrete choice; Inverse; Theoretical computer science; Mathematical optimization; Artificial intelligence; Machine learning; Mathematics; Cognitive science","score_opus":0.08301966753873334,"score_gpt":0.33858687997178055,"score_spread":0.2555672124330472,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2943778955","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20121342,0.00042399752,0.78414714,0.0036814644,0.0053616106,0.0018010068,0.0019281077,0.00026143258,0.0011818341],"genre_scores_gemma":[0.9896263,0.0013759494,0.006766367,0.00031686312,0.0012869155,0.00002790705,0.0004884389,0.00003083759,0.000080427664],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9984524,0.00011738545,0.00047912874,0.0003537881,0.000389729,0.00020756433],"domain_scores_gemma":[0.9985028,0.00042029252,0.00047690573,0.00006939529,0.00027483178,0.00025579924],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045678727,0.00020872046,0.00027702088,0.00012724326,0.00065346074,0.00023818995,0.00010847516,0.00023952001,0.00000886138],"category_scores_gemma":[0.000373749,0.00021241322,0.00009512868,0.000098856166,0.00010344041,0.00012241444,0.0000019359152,0.0007131643,4.326259e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011591868,0.00031285526,0.046892837,0.00041674942,0.00021209788,0.00022719322,0.077909455,0.71208334,0.00003638958,0.14981675,0.0012557756,0.009677338],"study_design_scores_gemma":[0.015540437,0.0051309858,0.67418116,0.0049412423,0.0068005347,0.000019739964,0.016991623,0.09607141,0.000090289774,0.11408385,0.06149936,0.00464938],"about_ca_topic_score_codex":0.000095003525,"about_ca_topic_score_gemma":0.00017326526,"teacher_disagreement_score":0.78841287,"about_ca_system_score_codex":0.000046567853,"about_ca_system_score_gemma":0.00019615247,"threshold_uncertainty_score":0.8661959},"labels":[],"label_agreement":null},{"id":"W3009897573","doi":"10.1016/j.ejtl.2020.100020","title":"Machine learning in airline crew pairing to construct initial clusters for dynamic constraint aggregation","year":2020,"lang":"en","type":"article","venue":"EURO Journal on Transportation and Logistics","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Mila - Quebec Artificial Intelligence Institute; Université de Montréal; Group for Research in Decision Analysis","funders":"Natural Sciences and Engineering Research Council of Canada; Institut de Valorisation des Données","keywords":"Solver; Crew; Heuristics; Computer science; Pairing; Sequence (biology); Aggregate (composite); Reduction (mathematics); Constraint (computer-aided design); Baseline (sea); Mathematical optimization; Set (abstract data type); Mathematics; Engineering","score_opus":0.032987308182205494,"score_gpt":0.28849050009934984,"score_spread":0.2555031919171443,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3009897573","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05464844,0.000055943052,0.9437858,0.00090399326,0.0001810113,0.00016812276,0.00004245056,0.000102664344,0.00011159685],"genre_scores_gemma":[0.9206169,0.00010375613,0.078507185,0.0006256881,0.000059107442,0.000002651823,0.000052993993,0.000026707565,0.0000049580453],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991476,0.000060745606,0.00040752557,0.00012252288,0.00011245329,0.00014916276],"domain_scores_gemma":[0.99949694,0.0001840468,0.000075848126,0.000031650063,0.000060447248,0.00015104536],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029045067,0.00012254223,0.00016747632,0.00011406107,0.000075676915,0.000044833123,0.00004837981,0.00005014795,0.000018832252],"category_scores_gemma":[0.00034049674,0.00013119809,0.000035880155,0.00015643952,0.000030315106,0.000060291597,0.0000012244152,0.00035341294,0.0000015791359],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007745767,0.000006116321,0.0017714289,0.00006157907,0.000010558117,0.000029471741,0.0008234709,0.96376985,0.00035558478,0.00044318996,0.000010064612,0.03264122],"study_design_scores_gemma":[0.0012925564,0.000230125,0.0061037396,0.00008380461,0.000022741942,0.000018069652,0.00021786691,0.99074346,0.00021553323,0.00009046792,0.0008160684,0.00016554965],"about_ca_topic_score_codex":0.0000012586551,"about_ca_topic_score_gemma":0.0000149488005,"teacher_disagreement_score":0.8659685,"about_ca_system_score_codex":0.000035384954,"about_ca_system_score_gemma":0.000022001295,"threshold_uncertainty_score":0.5350103},"labels":[],"label_agreement":null},{"id":"W3108778051","doi":"10.1016/j.ejtl.2020.100024","title":"Introduction to the special issue on combining optimization and machine learning: Application in vehicle routing, network design and crew scheduling","year":2020,"lang":"en","type":"article","venue":"EURO Journal on Transportation and Logistics","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Group for Research in Decision Analysis; HEC Montréal","funders":"Agence Nationale de la Recherche","keywords":"Vehicle routing problem; Crew; Computer science; Crew scheduling; Scheduling (production processes); Routing (electronic design automation); Engineering; Embedded system; Aeronautics; Operations management","score_opus":0.02748217703064264,"score_gpt":0.2509191413029552,"score_spread":0.22343696427231252,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3108778051","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008918674,0.00009657975,0.98669606,0.003777806,0.00018003142,0.00019620688,0.0000018296698,0.00006242222,0.00007039889],"genre_scores_gemma":[0.8436503,0.00074666494,0.15142132,0.0007550906,0.0033422862,0.000004954793,0.00002546718,0.000040045237,0.0000138692585],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990852,0.00016294801,0.0003069663,0.00017654999,0.00013094586,0.00013737392],"domain_scores_gemma":[0.9995444,0.00016162745,0.00008993905,0.000050698523,0.000042604664,0.00011075895],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006413602,0.00012441044,0.0001384374,0.0000613124,0.00020255757,0.00009700129,0.000046857527,0.00005249753,0.000021056743],"category_scores_gemma":[0.00022498803,0.00011275974,0.000012109472,0.00025938018,0.00002399523,0.00007009848,0.0000026884002,0.00045037002,0.000002637724],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000076959484,0.000007803236,0.0014892262,0.000013636183,0.000006679246,0.000003489039,0.0011977729,0.9822689,0.00007875049,0.0006192403,0.00014944233,0.014088071],"study_design_scores_gemma":[0.00045869543,0.00020257045,0.0063654794,0.000023922183,0.000019741725,0.0000053544563,0.0001170784,0.9881327,0.000079682046,0.000040119965,0.004440232,0.00011443171],"about_ca_topic_score_codex":0.000001857617,"about_ca_topic_score_gemma":0.0000034402087,"teacher_disagreement_score":0.83527476,"about_ca_system_score_codex":0.000020397287,"about_ca_system_score_gemma":0.000007308714,"threshold_uncertainty_score":0.45982087},"labels":[],"label_agreement":null},{"id":"W4402615264","doi":"10.1016/j.ejtl.2024.100143","title":"A metaheuristic for a time-dependent vehicle routing problem with time windows, two vehicle fleets and synchronization on a road network","year":2024,"lang":"en","type":"article","venue":"EURO Journal on Transportation and Logistics","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Université de Montréal; Transport Canada","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Vehicle routing problem; Metaheuristic; Synchronization (alternating current); Computer science; Arrival time; Travel time; Time synchronization; Routing (electronic design automation); Fleet management; Real-time computing; Engineering; Transport engineering; Computer network; Artificial intelligence","score_opus":0.013122357018105244,"score_gpt":0.24962707871012074,"score_spread":0.2365047216920155,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402615264","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0427496,0.0005651257,0.9548364,0.0002351205,0.00021612593,0.00045937006,0.000054133685,0.00036039468,0.00052371825],"genre_scores_gemma":[0.96672106,0.00013793033,0.03238757,0.00012916519,0.00025044335,0.00001241422,0.000049894774,0.00008879405,0.00022275327],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998736,0.000091062924,0.00040727525,0.00024813527,0.00024202338,0.00027548865],"domain_scores_gemma":[0.99925965,0.00032247955,0.00009015686,0.00008893336,0.000098935256,0.00013985204],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00064042635,0.00022231815,0.0002369655,0.000112781076,0.00021140819,0.00024302593,0.00006180187,0.000067597095,0.000029381425],"category_scores_gemma":[0.00006452072,0.00019272909,0.000041675685,0.00023380885,0.00004019441,0.00012994139,0.00000207772,0.00033770487,0.000013534971],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012843883,0.000023663868,0.0002981761,0.00012790298,0.00009075161,0.000070501286,0.00036515118,0.9797353,0.000519114,0.0013287879,0.00014390063,0.017168306],"study_design_scores_gemma":[0.0011175754,0.0005276068,0.002474643,0.0003775401,0.00019755385,0.000048944315,0.000018956169,0.99385995,0.00014467062,0.00042311096,0.0005600483,0.00024939465],"about_ca_topic_score_codex":0.0000019644604,"about_ca_topic_score_gemma":0.0000040102964,"teacher_disagreement_score":0.9239714,"about_ca_system_score_codex":0.000057334288,"about_ca_system_score_gemma":0.000039476978,"threshold_uncertainty_score":0.78592646},"labels":[],"label_agreement":null},{"id":"W4402827990","doi":"10.1016/j.ejtl.2024.100146","title":"Stability metrics for a maritime inventory routing problem under sailing time uncertainty","year":2024,"lang":"en","type":"article","venue":"EURO Journal on Transportation and Logistics","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"","keywords":"Routing (electronic design automation); Stability (learning theory); Vehicle routing problem; Operations research; Computer science; Marine engineering; Environmental science; Engineering; Computer network; Machine learning","score_opus":0.04209613403359662,"score_gpt":0.28176092578484135,"score_spread":0.23966479175124472,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402827990","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018145742,0.0004197114,0.9798648,0.0001692957,0.00038341345,0.00018819937,0.00006946898,0.00027785008,0.00048151767],"genre_scores_gemma":[0.91726184,0.00019699644,0.08196922,0.00013639146,0.00016504372,0.0000058728842,0.00006554129,0.000059118658,0.00013997176],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989055,0.00007101058,0.00045261765,0.00017234456,0.0001887277,0.00020981632],"domain_scores_gemma":[0.99912524,0.00050924125,0.000054958815,0.00007464937,0.00011798255,0.00011794668],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010926194,0.00015136345,0.00017322296,0.0001628538,0.0001298768,0.00016006337,0.000065681554,0.00008186427,0.00006407544],"category_scores_gemma":[0.00018407217,0.00014581138,0.00007941029,0.0002962237,0.00003724312,0.00010828043,0.0000014325836,0.00037227917,0.000005530768],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000024955241,0.000021880962,0.000452868,0.00029018108,0.000057271638,0.000019755404,0.00050141686,0.9796565,0.0006046669,0.0076599987,0.00021237186,0.010498129],"study_design_scores_gemma":[0.00029264032,0.00009474937,0.001046492,0.00012529558,0.00008725281,0.000012388012,0.00010291252,0.99199045,0.0002290731,0.0028772582,0.0029428846,0.00019859789],"about_ca_topic_score_codex":0.0000017317694,"about_ca_topic_score_gemma":0.0000039104543,"teacher_disagreement_score":0.8991161,"about_ca_system_score_codex":0.00010931544,"about_ca_system_score_gemma":0.00005017224,"threshold_uncertainty_score":0.5946016},"labels":[],"label_agreement":null},{"id":"W4404633920","doi":"10.1016/j.ejtl.2024.100147","title":"Dynamic rebalancing for Bike-sharing systems under inventory interval and target predictions","year":2024,"lang":"en","type":"article","venue":"EURO Journal on Transportation and Logistics","topic":"Transportation and Mobility Innovations","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal; Polytechnique Montréal; Group for Research in Decision Analysis; Transport Canada","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada","keywords":"Bike sharing; Interval (graph theory); Environmental science; Computer science; Engineering; Mathematics; Transport engineering","score_opus":0.025212255535546114,"score_gpt":0.26165148600368143,"score_spread":0.2364392304681353,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404633920","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11012363,0.0010561721,0.8860991,0.00023100447,0.0016243418,0.00018062409,0.00026812623,0.00024925292,0.0001677653],"genre_scores_gemma":[0.99812394,0.00038295554,0.0009822154,0.00008154898,0.00007489356,0.000014346198,0.00014988278,0.000028389257,0.00016184397],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992498,0.000008190816,0.0003712431,0.00013841421,0.00010382338,0.00012851454],"domain_scores_gemma":[0.9996928,0.00006329344,0.00003220231,0.00005813328,0.000065450244,0.00008811321],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018349914,0.00011631684,0.00011867823,0.00018091564,0.00012226704,0.0001344494,0.000039693936,0.000056252367,0.000012228574],"category_scores_gemma":[0.000013802851,0.00011254163,0.000042142638,0.00012793503,0.00003843468,0.00013204469,5.465944e-7,0.00025932313,0.0000015750821],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004478468,0.00005238104,0.0064185616,0.0012902507,0.00025738924,0.000056245033,0.0020912478,0.77428526,0.0013096792,0.21180485,0.0010484479,0.0013408826],"study_design_scores_gemma":[0.00077111006,0.00020764988,0.13916658,0.00053180003,0.00019392335,0.00006775282,0.0010879118,0.8267025,0.000031672407,0.0050498084,0.025856083,0.00033321045],"about_ca_topic_score_codex":0.0000031664651,"about_ca_topic_score_gemma":0.00003801481,"teacher_disagreement_score":0.8880003,"about_ca_system_score_codex":0.000043251166,"about_ca_system_score_gemma":0.000021440655,"threshold_uncertainty_score":0.45893145},"labels":[],"label_agreement":null},{"id":"W4409885303","doi":"10.1016/j.ejtl.2025.100157","title":"50 years of Operations Research for the tactical planning of consolidation-based freight transportation","year":2025,"lang":"en","type":"article","venue":"EURO Journal on Transportation and Logistics","topic":"Urban and Freight Transport Logistics","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Fonds de recherche du Québec; Université de Montréal","keywords":"Consolidation (business); Transport engineering; Operations research; Engineering; Business; Finance","score_opus":0.09736906072891235,"score_gpt":0.3346004612744917,"score_spread":0.23723140054557937,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409885303","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04572737,0.00049069966,0.9520913,0.00031145557,0.00035038355,0.0002709107,0.00036270716,0.000026165631,0.00036906163],"genre_scores_gemma":[0.9952704,0.00023255992,0.004193579,0.00007060338,0.00005039303,0.000009802554,0.00012087419,0.000014779981,0.000037055514],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99893963,0.00003591073,0.0005334506,0.00009971086,0.00024106792,0.00015025011],"domain_scores_gemma":[0.9985038,0.0009871039,0.000048018203,0.00011016506,0.00029755454,0.000053370244],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041648006,0.00009892161,0.00018284953,0.0002122454,0.0001453568,0.000025233578,0.00011190396,0.000079306395,0.000026417629],"category_scores_gemma":[0.000109707726,0.000083377956,0.00007073964,0.00024059309,0.00022763514,0.000048328006,2.867772e-7,0.00033376017,4.748609e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002148567,0.000104508115,0.0037830258,0.00026642153,0.00011652283,0.000013872787,0.00074153254,0.86189955,0.0023558643,0.12829353,0.0008981368,0.0013121496],"study_design_scores_gemma":[0.004473322,0.00087984424,0.7321339,0.0006491374,0.00078640966,0.00000416434,0.0011969374,0.19526649,0.013405368,0.0053865723,0.04533339,0.0004844434],"about_ca_topic_score_codex":0.000010483426,"about_ca_topic_score_gemma":0.000046847774,"teacher_disagreement_score":0.949543,"about_ca_system_score_codex":0.000017190174,"about_ca_system_score_gemma":0.00009282358,"threshold_uncertainty_score":0.34000543},"labels":[],"label_agreement":null},{"id":"W4413332685","doi":"10.1016/j.ejtl.2025.100162","title":"Optimal departure time choices as quantiles and expectiles of the travel time distribution","year":2025,"lang":"en","type":"article","venue":"EURO Journal on Transportation and Logistics","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Quantile; Travel time; Distribution (mathematics); Environmental science; Computer science; Statistics; Operations research; Econometrics; Engineering; Mathematics; Transport engineering","score_opus":0.012376710756746798,"score_gpt":0.27956546266229604,"score_spread":0.26718875190554925,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413332685","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96801215,0.00043069271,0.024140788,0.0025087334,0.00034838152,0.00021887492,0.0002436101,0.000044102937,0.0040526823],"genre_scores_gemma":[0.9974045,0.00048480718,0.00030662716,0.00014869255,0.00004251584,0.000001177211,0.00007440675,0.000004570483,0.001532688],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.99919933,0.00009716922,0.00026274784,0.00011149652,0.00022187708,0.00010736876],"domain_scores_gemma":[0.9994227,0.00017056846,0.00017246355,0.00004951111,0.00012386037,0.000060843937],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002475796,0.00008594976,0.000126523,0.000051231786,0.00047351373,0.00006284746,0.00008046256,0.00007741496,0.000055738146],"category_scores_gemma":[0.00014363074,0.000065534055,0.000044819666,0.00018271306,0.00022912577,0.00009342186,7.9881414e-7,0.00015745428,0.0000022250345],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014328846,0.0007719245,0.1709006,0.00028080118,0.00036866506,0.0000924231,0.056555044,0.11532632,0.0051166355,0.62577116,0.0097735245,0.013610016],"study_design_scores_gemma":[0.0007757914,0.00014010374,0.97635365,0.00020356056,0.0001772876,0.000004846665,0.0025672966,0.0015511641,0.0006017408,0.001213876,0.016230956,0.00017971279],"about_ca_topic_score_codex":0.00004866038,"about_ca_topic_score_gemma":0.000081562066,"teacher_disagreement_score":0.80545306,"about_ca_system_score_codex":0.00001253765,"about_ca_system_score_gemma":0.000093940864,"threshold_uncertainty_score":0.36419326},"labels":[],"label_agreement":null}]}