{"meta":{"page":1,"per_page":50,"max_per_page":100,"total":1187,"total_is_capped":false,"direct_labels_cover":1,"predictions_cover":1187,"direct_label_status":"direct model label, unvalidated","prediction_status":"machine_predicted_unvalidated (Codex and Gemma teacher distillation)","score_status":"score_only:v0-immature-baseline (scores rank; they never assert a category)","snapshot":{"source":"OpenAlex, pinned release, all 482 partitions","release":"2026-06-24","frame_built":"2026-07-12"},"query_hash":"c746e7dad116","filters":{"topic":"Vehicle Routing Optimization Methods"}},"results":[{"id":"W2039568841","doi":"10.1007/s10479-005-3971-7","title":"Metaheuristics in Combinatorial Optimization","year":2005,"lang":"en","type":"article","venue":"Annals of Operations Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":2341,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Université de Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Metaheuristic; Theory of computation; Combinatorial optimization; Vehicle routing problem; Computer science; Scheduling (production processes); Mathematical optimization; Operations research; Job shop scheduling; Parallel metaheuristic; Optimization problem; Management science; Routing (electronic design automation); Mathematics; Artificial intelligence; Algorithm; Engineering; Meta-optimization","retraction":null,"screen_n_in":null,"score":{"opus":0.1739552011743806,"gpt":0.4498479164768863,"spread":0.2758927153025057,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001806021,0.00006721008,0.0001295994,0.0004075045,0.00007860686,0.00005094167,0.0001606528,0.00007223764,0.0001278967],"category_scores_gemma":[0.0007425012,0.00007442567,0.00002467669,0.0008637326,0.00005392783,0.0002294562,0.00003386732,0.0002451775,0.00002360497],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003257729,"about_ca_system_score_gemma":0.00005382407,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002899226,"about_ca_topic_score_gemma":0.00002564732,"domain_scores_codex":[0.998749,0.0002483758,0.0003235797,0.0001098283,0.0003298387,0.0002393589],"domain_scores_gemma":[0.9990183,0.0001584609,0.000007775972,0.0002081037,0.0005536016,0.00005382813],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000003795724,0.00005095291,0.0001131219,0.00001217642,0.000008325391,5.83909e-7,0.0001989639,0.989912,0.0003328483,0.006137669,0.000705043,0.002524525],"study_design_scores_gemma":[0.000208221,0.00002460924,0.0002093537,0.00001289949,0.000001236254,5.801477e-7,0.00003546771,0.9876408,0.01052601,0.0000933867,0.001180302,0.00006712203],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2050064,0.00116394,0.7450065,0.004615061,0.0006410421,0.001123278,0.00004384453,0.0003361799,0.04206377],"genre_scores_gemma":[0.8192676,0.0003657105,0.1800403,0.000019953,0.0001109574,0.00002476209,0.00001803813,0.0000216911,0.0001310062],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6142612,"threshold_uncertainty_score":0.3034991,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2471190594","doi":"10.1109/tsmc.2016.2582745","title":"Vehicle Routing Problems for Drone Delivery","year":2016,"lang":"en","type":"article","venue":"IEEE Transactions on Systems Man and Cybernetics Systems","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":1245,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"York University; University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Drone; Payload (computing); Computer science; Energy consumption; Mathematical optimization; Simulation; Mathematics; Engineering; Computer network; Network packet; Electrical engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.01946075877356595,"gpt":0.224061717961391,"spread":0.2046009591878251,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005720535,0.0002798998,0.000370894,0.0001582559,0.0001919888,0.0001539845,0.0001398121,0.0001946417,0.00000611833],"category_scores_gemma":[0.000007372527,0.0002311108,0.00008693745,0.0001672608,0.00004960238,0.0001395334,0.000001583734,0.0001273958,0.00003614256],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001475089,"about_ca_system_score_gemma":0.00001705496,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007884951,"about_ca_topic_score_gemma":0.00001179737,"domain_scores_codex":[0.9982769,0.0001338821,0.0005906231,0.0003436411,0.000237538,0.0004173781],"domain_scores_gemma":[0.9989587,0.0003266154,0.00009972926,0.0003321653,0.0001210765,0.0001616908],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002290308,0.00005248746,0.0001123704,0.0008050668,0.0001828667,0.000002317652,0.0005422461,0.9570262,0.02445816,0.0008236251,0.000382852,0.01558889],"study_design_scores_gemma":[0.001696376,0.0001821105,0.00005686441,0.001078885,0.00008593097,0.00005537043,0.0004273639,0.9850734,0.00527397,0.00001944717,0.005471085,0.0005791957],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05203975,0.0006322786,0.9428071,0.00003705993,0.002071858,0.001011682,0.00008222451,0.0005690553,0.000748977],"genre_scores_gemma":[0.994726,0.0001880102,0.001194703,0.00001012045,0.0001722382,0.0003127455,0.000001706854,0.000106843,0.00328766],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9426862,"threshold_uncertainty_score":0.9424424,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2066086285","doi":"10.1287/trsc.1030.0056","title":"Vehicle Routing Problem with Time Windows, Part I: Route Construction and Local Search Algorithms","year":2005,"lang":"en","type":"article","venue":"Transportation Science","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":1112,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Université de Montréal","funders":"Emil Aaltosen Säätiö","keywords":"Vehicle routing problem; Heuristic; Benchmark (surveying); Pareto principle; Mathematical optimization; Set (abstract data type); Metaheuristic; Point (geometry); Routing (electronic design automation); Computer science; Interval (graph theory); Local search (optimization); Algorithm; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.0132602200802881,"gpt":0.2520301703643327,"spread":0.2387699502840446,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007404433,0.0001201089,0.0001204386,0.0001133213,0.0002095225,0.00008479146,0.0001174594,0.00004272021,0.00004222411],"category_scores_gemma":[0.000006815329,0.0001139555,0.00001391182,0.0007650325,0.0004358123,0.0007519018,0.000003638809,0.0001394307,0.00001927977],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007582192,"about_ca_system_score_gemma":0.00008055997,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001644033,"about_ca_topic_score_gemma":0.00001874969,"domain_scores_codex":[0.9987337,0.00002388795,0.000246797,0.0002811087,0.0004132899,0.0003011994],"domain_scores_gemma":[0.9995419,0.00003719411,0.00003737911,0.0001255969,0.0001310725,0.0001267949],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000006576277,0.000006806341,0.006975449,0.00001583638,0.00000526089,0.00000162278,0.001144413,0.877481,0.004984897,0.0004044141,0.00000449177,0.1089692],"study_design_scores_gemma":[0.000454652,0.00003124717,0.02131861,0.00004100857,0.00001279156,0.00001440519,0.0002463672,0.9617651,0.01560047,0.00001719409,0.0003091231,0.0001890893],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4034589,0.00002220584,0.5952296,0.0001089262,0.00003664822,0.00016147,0.000008547915,0.0002711708,0.0007025945],"genre_scores_gemma":[0.6993262,0.000009007581,0.3005314,0.00002072971,0.00003375479,0.000006456874,0.000008338528,0.00001452268,0.00004963214],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.2958674,"threshold_uncertainty_score":0.4646972,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1970355999","doi":"10.1287/opre.1050.0234","title":"Selected Topics in Column Generation","year":2005,"lang":"en","type":"article","venue":"Operations Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":1084,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Group for Research in Decision Analysis; HEC Montréal","funders":"","keywords":"Column generation; Column (typography); Perspective (graphical); Integer programming; Computer science; Dual (grammatical number); Linear programming; Decomposition; Point (geometry); Mathematical optimization; Operations research; Mathematics; Artificial intelligence; Algorithm","retraction":null,"screen_n_in":null,"score":{"opus":0.08886315424571074,"gpt":0.393188984849525,"spread":0.3043258306038143,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007835589,0.00005455227,0.00006665767,0.000246072,0.0001519469,0.0001260219,0.000101832,0.00006981999,0.0002623021],"category_scores_gemma":[0.0002814986,0.00006312932,0.000009377187,0.001123759,0.00002099784,0.0001931561,0.00002032444,0.0002886563,0.0001183875],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002127083,"about_ca_system_score_gemma":0.00007154743,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004849921,"about_ca_topic_score_gemma":0.002064953,"domain_scores_codex":[0.9990349,0.0002089316,0.000179281,0.0001220008,0.0002177014,0.0002371774],"domain_scores_gemma":[0.9994796,0.00004006122,0.000002208469,0.0001693464,0.0002638216,0.00004496438],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[5.654483e-7,0.00001710715,0.0004029769,0.000003592906,0.000002626748,6.679471e-7,0.0002772106,0.9683754,0.01675965,0.0006213547,0.002049029,0.01148978],"study_design_scores_gemma":[0.0001516816,0.000009975788,0.001544549,0.00000409481,5.749879e-7,0.000001464266,0.00002119201,0.9789404,0.01023522,0.000004089602,0.009021394,0.00006537659],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8878965,0.000194939,0.09517416,0.001994589,0.0001362279,0.0005137227,0.000005060995,0.0002610029,0.01382385],"genre_scores_gemma":[0.8462967,0.00007394569,0.1492654,0.00002663841,0.0004123849,0.0000882588,0.0000391667,0.00002269407,0.003774885],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05409123,"threshold_uncertainty_score":0.2872024,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2168210873","doi":"10.1287/trsc.1090.0301","title":"Fifty Years of Vehicle Routing","year":2009,"lang":"en","type":"article","venue":"Transportation Science","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":981,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"HEC Montréal","funders":"","keywords":"Vehicle routing problem; Heuristics; Truck; Metaheuristic; Routing (electronic design automation); Mathematical optimization; Decomposition; Computer science; Operations research; Engineering; Mathematics; Computer network","retraction":null,"screen_n_in":null,"score":{"opus":0.01620746114889389,"gpt":0.2787206248230034,"spread":0.2625131636741095,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004577014,0.00004739383,0.00007605713,0.00008807798,0.00003951664,0.00001389926,0.0001518233,0.00002168614,0.00002009904],"category_scores_gemma":[0.000037628,0.00005626586,0.00002154758,0.0007880446,0.00007153179,0.0002594677,7.825607e-7,0.00005203587,0.000004811629],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001822521,"about_ca_system_score_gemma":0.00002689197,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004308285,"about_ca_topic_score_gemma":0.000001694494,"domain_scores_codex":[0.999293,0.000007545733,0.0002013117,0.0001096396,0.0002467599,0.0001417658],"domain_scores_gemma":[0.9997098,0.00002640215,0.00003442731,0.0001184896,0.00006531381,0.00004554295],"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":[0.00000278648,0.00001293286,0.005281716,0.000009848149,0.000001944148,0.000001684409,0.002410843,0.7427246,0.1811836,0.00310606,0.00001143861,0.06525254],"study_design_scores_gemma":[0.0001565906,0.00001855073,0.7357615,0.00001545533,0.000004696985,3.652756e-7,0.00006670622,0.2095639,0.05406232,0.000205719,0.00005112025,0.0000930516],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8815113,0.00001543364,0.116992,0.00002573815,0.00007386498,0.00004755947,0.00000303565,0.0001526196,0.00117838],"genre_scores_gemma":[0.9524834,0.000005465827,0.04745594,0.00002764932,0.000008816617,6.560352e-7,0.000002013822,0.000004855401,0.00001120215],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7304798,"threshold_uncertainty_score":0.2294455,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2116675171","doi":"10.1287/trsc.1030.0057","title":"Vehicle Routing Problem with Time Windows, Part II: Metaheuristics","year":2005,"lang":"en","type":"article","venue":"Transportation Science","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":826,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Université de Montréal","funders":"","keywords":"Vehicle routing problem; Metaheuristic; Heuristics; Benchmark (surveying); Mathematical optimization; Set (abstract data type); Routing (electronic design automation); Computer science; Point (geometry); Interval (graph theory); Operations research; Mathematics; Computer network","retraction":null,"screen_n_in":null,"score":{"opus":0.0140215721847575,"gpt":0.2471341688413525,"spread":0.233112596656595,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007909327,0.0001373166,0.0001406621,0.0001084851,0.0002946833,0.00006350031,0.0002323891,0.0000367554,0.0001151464],"category_scores_gemma":[0.00002612254,0.0001278262,0.00002431628,0.0010132,0.00016321,0.000615305,0.000004109257,0.0001261396,0.00005783337],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006120914,"about_ca_system_score_gemma":0.00007633153,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004090481,"about_ca_topic_score_gemma":0.00001343158,"domain_scores_codex":[0.9986026,0.00001790558,0.0003143534,0.0002611702,0.0004691232,0.000334829],"domain_scores_gemma":[0.9994215,0.00004291369,0.00006264754,0.0002005345,0.000153311,0.0001190603],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000004458878,0.00001607478,0.001828876,0.00001007261,0.000007027676,0.000001749974,0.001242292,0.9810138,0.008563874,0.0008517219,0.00006044873,0.006399646],"study_design_scores_gemma":[0.0005137626,0.00005220836,0.01482659,0.00004751552,0.00003268174,0.000004089486,0.0000642402,0.9491122,0.02973916,0.00004837042,0.005207277,0.000351939],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6929827,0.00004758595,0.2986113,0.0002661647,0.0001010447,0.0003192539,0.00001857727,0.0009288674,0.006724519],"genre_scores_gemma":[0.7557557,0.00000676918,0.2438504,0.00005013641,0.00004741929,0.000009840936,0.0000101882,0.00002198456,0.0002475348],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06277303,"threshold_uncertainty_score":0.5212602,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2114939271","doi":"10.1111/j.1475-3995.2000.tb00200.x","title":"Classical and modern heuristics for the vehicle routing problem","year":2000,"lang":"en","type":"article","venue":"International Transactions in Operational Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":705,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Université de Montréal; Group for Research in Decision Analysis; Computer Research Institute of Montréal; HEC Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Heuristics; Tabu search; Metaheuristic; Mathematical optimization; Vehicle routing problem; Computer science; Routing (electronic design automation); Algorithm; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.07150300697132775,"gpt":0.386507307404209,"spread":0.3150043004328812,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001163916,0.00008524054,0.00007718474,0.0001351493,0.0003115859,0.0001731646,0.0002105369,0.00006835868,0.0006134824],"category_scores_gemma":[0.0001212248,0.00007517303,0.00003228779,0.0002341034,0.0001057845,0.0001955254,0.000007123721,0.0004157814,0.00001444842],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001511571,"about_ca_system_score_gemma":0.00006088019,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002436858,"about_ca_topic_score_gemma":0.00005253287,"domain_scores_codex":[0.9987934,0.00009152726,0.000260005,0.0001824635,0.0004448376,0.0002277604],"domain_scores_gemma":[0.9982911,0.001313375,0.00000749729,0.0001049388,0.00023678,0.00004629691],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002667485,0.00003332095,0.0001457039,0.00000835779,0.00002804323,7.99019e-7,0.0003038615,0.8876732,0.0004025424,0.004055379,0.000132241,0.1071899],"study_design_scores_gemma":[0.0003888449,0.00001528605,0.001001631,0.00001831756,0.00000334467,0.000008441475,0.00005940757,0.9856815,0.0002336279,0.001725729,0.01078607,0.00007779853],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01542884,0.0001418669,0.9697949,0.005813821,0.0002025041,0.0005383169,0.00006353728,0.00008793925,0.007928283],"genre_scores_gemma":[0.9497411,0.0002905143,0.04622711,0.00005765443,0.0001602425,0.0002733788,0.00001632275,0.00002732799,0.003206415],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9343122,"threshold_uncertainty_score":0.6717201,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2082772228","doi":"10.1007/s11750-007-0009-0","title":"Static pickup and delivery problems: a classification scheme and survey","year":2007,"lang":"en","type":"article","venue":"Top","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":698,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"HEC Montréal","funders":"","keywords":"Pickup; Scheme (mathematics); Computer science; Vehicle routing problem; Class (philosophy); Classification scheme; Routing (electronic design automation); Field (mathematics); Operations research; Mathematical optimization; Artificial intelligence; Data science; Computer network; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.04328352270591027,"gpt":0.2781970216619149,"spread":0.2349134989560046,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000821259,0.00006036316,0.0000699782,0.00004923469,0.00002987634,0.00002478075,0.00002736546,0.00004146662,0.00001077055],"category_scores_gemma":[0.00009217707,0.00006434755,0.000004928948,0.0001151301,0.00002187026,0.00007223141,0.00001260769,0.0000645386,0.00000429745],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002099979,"about_ca_system_score_gemma":0.000006006006,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003039256,"about_ca_topic_score_gemma":0.00003351197,"domain_scores_codex":[0.9995663,0.00003670699,0.0001286931,0.00009534163,0.00005676445,0.0001162567],"domain_scores_gemma":[0.9996618,0.0001516912,0.00001820456,0.00008659063,0.00003348055,0.00004822787],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00004105147,0.00005612211,0.6493103,0.000719587,0.0001019025,0.000007489278,0.004311624,0.03094401,0.04412964,0.001801744,0.001349975,0.2672265],"study_design_scores_gemma":[0.0002530332,0.00001502879,0.5746883,0.00001976379,0.000005844161,0.00000478994,0.0000772764,0.4233328,0.000726707,0.00007691541,0.0006716143,0.0001279939],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7882364,0.0002062223,0.2106024,0.00002999015,0.00003856149,0.0000887835,0.000002744263,0.0001283726,0.0006665544],"genre_scores_gemma":[0.8928355,0.00008988332,0.1069117,0.0000311578,0.00001629278,0.000002966538,0.000008487296,0.00001564674,0.00008832739],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3923888,"threshold_uncertainty_score":0.2624017,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2119601963","doi":"10.1287/opre.1120.1048","title":"A Hybrid Genetic Algorithm for Multidepot and Periodic Vehicle Routing Problems","year":2012,"lang":"en","type":"article","venue":"Operations Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":685,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Polytechnique Montréal; Université du Québec à Montréal; Université de Montréal","funders":"","keywords":"Vehicle routing problem; Benchmark (surveying); Metaheuristic; Mathematical optimization; Computer science; Population; Genetic algorithm; Routing (electronic design automation); Algorithm; Mathematics; Medicine","retraction":null,"screen_n_in":null,"score":{"opus":0.06420246300526486,"gpt":0.3580434042946693,"spread":0.2938409412894044,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001445568,0.0001025818,0.0001145636,0.0001537299,0.0005590562,0.000213889,0.0001064852,0.00005070542,0.00004130988],"category_scores_gemma":[0.000286841,0.0001062317,0.00002522896,0.0002401513,0.0000681301,0.0002522123,0.00006170103,0.000232042,0.00002827939],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007823614,"about_ca_system_score_gemma":0.00003756439,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000484222,"about_ca_topic_score_gemma":0.000008423943,"domain_scores_codex":[0.9986751,0.0001625999,0.0002173605,0.0001702949,0.0002271585,0.0005474593],"domain_scores_gemma":[0.9992352,0.0001925059,0.000006368768,0.000198518,0.0002080002,0.0001594322],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000002239299,0.00006905315,0.003985792,0.0001050225,0.00003898013,0.000001311914,0.002484212,0.703074,0.02358387,0.000342437,0.00034305,0.2659701],"study_design_scores_gemma":[0.000300904,0.00002761648,0.003623697,0.00001777149,0.000004901511,0.000014461,0.0001279318,0.9907183,0.003872706,0.00001214586,0.001156287,0.0001232455],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3355439,0.0007417302,0.6623821,0.00008227894,0.0000975229,0.0006761429,0.0000194949,0.0001385734,0.0003182714],"genre_scores_gemma":[0.5581167,0.0000606455,0.4410846,0.000008284718,0.0001579684,0.0002542058,0.00001210783,0.00003729462,0.0002682672],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.2876444,"threshold_uncertainty_score":0.4332005,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1985290529","doi":"10.1016/s0191-2615(02)00045-0","title":"A tabu search heuristic for the static multi-vehicle dial-a-ride problem","year":2003,"lang":"en","type":"article","venue":"Transportation Research Part B Methodological","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":676,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Group for Research in Decision Analysis; HEC Montréal","funders":"","keywords":"Tabu search; Heuristic; Computer science; Duration (music); Set (abstract data type); Mathematical optimization; Vehicle routing problem; Operations research; Transport engineering; Engineering; Routing (electronic design automation); Mathematics; Computer network; Algorithm; Artificial intelligence","retraction":null,"screen_n_in":null,"score":{"opus":0.4602205926685543,"gpt":0.4768557581067919,"spread":0.01663516543823756,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01654468,0.0002296613,0.0003584999,0.0001614785,0.0004817815,0.00009602624,0.0003584555,0.0001846929,0.0003674778],"category_scores_gemma":[0.003574356,0.0001676062,0.0001422126,0.000885106,0.0002953094,0.0001220362,0.000008302298,0.0008052832,0.00004281534],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007894028,"about_ca_system_score_gemma":0.0001023141,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003311054,"about_ca_topic_score_gemma":0.00004920021,"domain_scores_codex":[0.9937841,0.003486276,0.0006264859,0.0004479219,0.0007295714,0.0009256756],"domain_scores_gemma":[0.9846877,0.01420358,0.00004590456,0.0003878408,0.0004470557,0.0002278961],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001457709,0.0001689064,0.004414084,0.000418674,0.0001325383,0.00002477914,0.002236089,0.9584997,0.006105458,0.01598201,0.001838763,0.01003329],"study_design_scores_gemma":[0.003530121,0.0004689099,0.04431538,0.00009600708,0.0001222457,0.000008458177,0.002267803,0.8897229,0.02164114,0.007018863,0.02998966,0.0008185403],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.05334874,0.0002806599,0.9433785,0.0003988748,0.0001781048,0.001750762,0.00006527526,0.0003384219,0.0002606505],"genre_scores_gemma":[0.2489043,0.0002545943,0.7492056,0.00006211994,0.00007860465,0.0009538771,0.00004859882,0.00007000002,0.0004223381],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1955555,"threshold_uncertainty_score":0.6834783,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2051358678","doi":"10.1287/opre.1060.0283","title":"A Branch-and-Cut Algorithm for the Dial-a-Ride Problem","year":2006,"lang":"en","type":"article","venue":"Operations Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":672,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"HEC Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Branch and cut; Travelling salesman problem; Vehicle routing problem; Computer science; Integer programming; Mathematical optimization; Set (abstract data type); Routing (electronic design automation); Branch and price; 2-opt; Traveling purchaser problem; Algorithm; Mathematics; Computer network","retraction":null,"screen_n_in":null,"score":{"opus":0.04499379721732293,"gpt":0.3588639218862965,"spread":0.3138701246689736,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00131969,0.00007237522,0.00007705641,0.0001001703,0.0005786,0.0002474624,0.0001390992,0.00005021537,0.00004148289],"category_scores_gemma":[0.0001269086,0.00005594532,0.00002514383,0.0003682992,0.00007633612,0.000115517,0.00003906368,0.0002053596,0.00002153515],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004661217,"about_ca_system_score_gemma":0.00004283148,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002052139,"about_ca_topic_score_gemma":0.0002083557,"domain_scores_codex":[0.9990987,0.0001099655,0.0001606114,0.0001387428,0.0002130572,0.0002789523],"domain_scores_gemma":[0.9990296,0.0005066628,0.000003732073,0.0002031978,0.0002192307,0.00003757239],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000001512193,0.00001529487,0.00004325329,0.00001984279,0.00001561773,5.008947e-7,0.0001732721,0.8714188,0.002853279,0.003893735,0.003926619,0.1176382],"study_design_scores_gemma":[0.0002114469,0.00001539019,0.0002627453,0.000007405521,0.000004345769,0.000004332659,0.0000339765,0.9854132,0.002867009,0.0003565242,0.01075242,0.00007120999],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004125947,0.0004089767,0.9898782,0.001148059,0.00007325748,0.0008260225,0.00002457889,0.0001299088,0.003385094],"genre_scores_gemma":[0.214568,0.0001895901,0.7757881,0.00003309386,0.0005866443,0.001116139,0.0000431839,0.00007318611,0.007602034],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.21409,"threshold_uncertainty_score":0.4450182,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2029341065","doi":"10.1002/net.20033","title":"An exact algorithm for the elementary shortest path problem with resource constraints: Application to some vehicle routing problems","year":2004,"lang":"en","type":"article","venue":"Networks","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":665,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Université de Montréal","funders":"","keywords":"Column generation; Shortest path problem; Path (computing); Routing (electronic design automation); Vehicle routing problem; Computer science; Mathematical optimization; Computation; K shortest path routing; Longest path problem; Constrained Shortest Path First; Algorithm; Mathematics; Theoretical computer science; Graph","retraction":null,"screen_n_in":null,"score":{"opus":0.008733564536156346,"gpt":0.2388332489582275,"spread":0.2300996844220712,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007728843,0.0002061825,0.0001682821,0.00004077988,0.0002431107,0.00009913483,0.0002651525,0.00009484238,0.000006740194],"category_scores_gemma":[0.00000908758,0.0001651213,0.00003632824,0.0003287758,0.00005374839,0.0001857295,0.00002636866,0.0002434186,0.000002891615],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000142795,"about_ca_system_score_gemma":0.00002603792,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002751359,"about_ca_topic_score_gemma":0.00001994812,"domain_scores_codex":[0.9986975,0.00004427959,0.0003120204,0.0003159485,0.0001759472,0.0004543141],"domain_scores_gemma":[0.9992006,0.0001484839,0.00007014357,0.0003878121,0.00006421468,0.00012878],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000004499133,0.00002091642,0.0002176422,0.000007220614,0.00002596023,4.948296e-7,0.0002362995,0.7256218,0.00018961,0.0002394921,0.0000664521,0.2733696],"study_design_scores_gemma":[0.0006201129,0.0001481803,0.0003773007,0.0000797341,0.00003541831,0.000007515926,0.0002315664,0.9965013,0.0002178151,0.0001455896,0.001389282,0.000246178],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003101211,0.0001138979,0.9941126,0.0002302868,0.00005529507,0.001699423,0.00001451507,0.0005013429,0.0001714919],"genre_scores_gemma":[0.5673158,0.00001182286,0.4312564,0.000360296,0.0004087675,0.0004814431,0.00007564282,0.00008265812,0.000007154552],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5642146,"threshold_uncertainty_score":0.6733452,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1991149726","doi":"10.1287/trsc.1030.0079","title":"Traveling Salesman Problems with Profits","year":2005,"lang":"en","type":"article","venue":"Transportation Science","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":617,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Université de Montréal","funders":"","keywords":"Travelling salesman problem; Mathematical optimization; Profit (economics); Generalization; Vertex (graph theory); Heuristic; Traveling purchaser problem; Operations research; Computer science; Combinatorial optimization; Bottleneck traveling salesman problem; Mathematics; Economics; Combinatorics; Microeconomics; Graph","retraction":null,"screen_n_in":null,"score":{"opus":0.01684428022553878,"gpt":0.2527487551312724,"spread":0.2359044749057337,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004343075,0.00008831146,0.0000764706,0.00008554287,0.0001093423,0.0000419231,0.000159369,0.00002419204,0.00002867898],"category_scores_gemma":[0.000008334488,0.00007925955,0.00001280054,0.0008153219,0.0001142337,0.0005080436,8.120352e-7,0.00008166708,0.00001936369],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004077157,"about_ca_system_score_gemma":0.00004842343,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002381956,"about_ca_topic_score_gemma":0.00003660411,"domain_scores_codex":[0.9990821,0.000006995478,0.0001864771,0.0001838833,0.0003233145,0.0002171613],"domain_scores_gemma":[0.9996554,0.00001676168,0.00002804415,0.0001239733,0.00009792182,0.00007786317],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00000160085,0.000005534977,0.00293701,0.0000180303,0.000001933304,5.562813e-7,0.00160397,0.9713183,0.01527006,0.0005484516,0.00000401386,0.008290523],"study_design_scores_gemma":[0.0005188194,0.00003640343,0.1061895,0.0000694744,0.00001587132,0.000005470345,0.0001297188,0.831332,0.05999776,0.00004390246,0.001319898,0.0003412364],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5230128,0.00002080558,0.473717,0.00007783555,0.00004963461,0.0001550378,0.000002611222,0.0003777014,0.002586611],"genre_scores_gemma":[0.8000012,0.000006286718,0.199855,0.00002575025,0.00002472202,0.00001181787,0.000004395094,0.00001422508,0.0000566223],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2769884,"threshold_uncertainty_score":0.3232111,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2593704234","doi":"10.1016/j.trb.2017.02.004","title":"The electric vehicle routing problem with nonlinear charging function","year":2017,"lang":"en","type":"article","venue":"Transportation Research Part B Methodological","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":583,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Computer Research Institute of Montréal","funders":"Agence Nationale de la Recherche; Universidad EAFIT; Universidad de Antioquia; Departamento Administrativo de Ciencia, Tecnología e Innovación (COLCIENCIAS)","keywords":"Testbed; Nonlinear system; Electric vehicle; Vehicle routing problem; Mathematical optimization; Routing (electronic design automation); Computer science; Metaheuristic; Range (aeronautics); Battery (electricity); Function (biology); Nonlinear programming; Engineering; Algorithm; Mathematics; Power (physics)","retraction":null,"screen_n_in":null,"score":{"opus":0.2329196114939603,"gpt":0.4196467031617125,"spread":0.1867270916677522,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.01061197,0.0001828239,0.0002406199,0.0001015731,0.0022293,0.0003490433,0.0004919341,0.0001509522,0.00005876724],"category_scores_gemma":[0.0009741738,0.0001205159,0.00006661802,0.0004033894,0.0002290947,0.0002831041,0.00001438993,0.0009084249,0.00003186366],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006338847,"about_ca_system_score_gemma":0.00005159356,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003279547,"about_ca_topic_score_gemma":0.00007478063,"domain_scores_codex":[0.9966031,0.001204229,0.0004039987,0.0003566118,0.0006903172,0.0007417415],"domain_scores_gemma":[0.9966263,0.002228637,0.0001133345,0.0005557514,0.0003302187,0.0001457673],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001553357,0.0002022535,0.1304914,0.0003337405,0.0004795336,0.0001229087,0.001858166,0.475168,0.06832265,0.03532095,0.001362132,0.284785],"study_design_scores_gemma":[0.00147079,0.000545951,0.3786292,0.0001233042,0.0000659845,0.000005962996,0.0004705469,0.5842375,0.01573455,0.00254063,0.01559165,0.0005840185],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.436929,0.0001879173,0.5565362,0.001050437,0.0002338368,0.0008183786,0.00000795917,0.0006557007,0.003580634],"genre_scores_gemma":[0.7920495,0.0002638701,0.2067792,0.00002775225,0.0002838994,0.0001917107,0.00002397197,0.00006046745,0.0003195959],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.3551205,"threshold_uncertainty_score":0.9990697,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2127654349","doi":"10.1287/trsc.2013.0472","title":"Thirty Years of Inventory Routing","year":2013,"lang":"en","type":"article","venue":"Transportation Science","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":578,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"HEC Montréal; Université Laval","funders":"","keywords":"Vehicle routing problem; Operations research; Scheduling (production processes); Computer science; Routing (electronic design automation); Metaheuristic; Class (philosophy); Categorization; Operations management; Engineering; Artificial intelligence; Computer network","retraction":null,"screen_n_in":null,"score":{"opus":0.01910265373136389,"gpt":0.2644134794584833,"spread":0.2453108257271194,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004052101,0.00004540052,0.00006905553,0.00009625927,0.0000330375,0.00001764358,0.0001552798,0.00002193619,0.0001007482],"category_scores_gemma":[0.00003985066,0.00005104213,0.00001980977,0.0005987892,0.0001230544,0.0003974046,0.000001702537,0.00005226794,0.00002424895],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002008953,"about_ca_system_score_gemma":0.00003073493,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003081963,"about_ca_topic_score_gemma":0.000003466263,"domain_scores_codex":[0.9993116,0.000009191207,0.0002025603,0.0001004266,0.0002438013,0.0001323686],"domain_scores_gemma":[0.9996722,0.00002574444,0.00003816839,0.0001158659,0.00009711171,0.00005089642],"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":[0.000001028659,0.00001546811,0.08948469,0.00005559321,0.000007088391,9.657416e-7,0.006097064,0.7041531,0.1613896,0.003586648,0.00007562576,0.03513318],"study_design_scores_gemma":[0.0001158819,0.000007122742,0.6794265,0.00001875882,0.000004208225,2.295222e-7,0.0001766183,0.2983409,0.02155913,0.0002064289,0.00004626948,0.00009797162],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9194403,0.00001225,0.07885283,0.00001107524,0.0001239469,0.0000806247,0.000001569012,0.0001299221,0.001347537],"genre_scores_gemma":[0.9577026,0.000004924182,0.04222933,0.00001592262,0.000007898501,0.000004871434,0.000001866304,0.000007554927,0.00002498438],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5899418,"threshold_uncertainty_score":0.2081438,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W311217234","doi":"10.1007/0-387-25486-2_2","title":"Shortest Path Problems with Resource Constraints","year":2006,"lang":"en","type":"book-chapter","venue":"","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":573,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Polytechnique Montréal; Group for Research in Decision Analysis","funders":"","keywords":"Shortest path problem; Computer science; Column generation; Resource (disambiguation); Mathematical optimization; Scheduling (production processes); Path (computing); Vehicle routing problem; Operations research; Resource constraints; Routing (electronic design automation); Distributed computing; Engineering; Mathematics; Theoretical computer science; Graph; Computer network","retraction":null,"screen_n_in":null,"score":{"opus":0.01383972761132562,"gpt":0.2045635052047349,"spread":0.1907237775934093,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001766285,0.0003731139,0.0003336806,0.0001126492,0.00004032636,0.00004956128,0.0001452971,0.0003154097,0.0008900351],"category_scores_gemma":[0.000006593001,0.0003310273,0.00005897738,0.00003736449,0.0001285688,0.00003933614,0.00002456552,0.0004020171,0.00007492803],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007402596,"about_ca_system_score_gemma":0.00003066,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004288561,"about_ca_topic_score_gemma":0.000009293411,"domain_scores_codex":[0.9988833,0.0000107799,0.0003234743,0.0002860969,0.0002461081,0.0002502543],"domain_scores_gemma":[0.999368,0.0000689847,0.00006545691,0.0003537481,0.00006002879,0.00008377055],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000002506015,0.00000677991,0.00006960961,0.00009808774,0.0001067614,0.00003892697,0.00004215699,0.9496067,0.0000250587,0.01829628,0.02037865,0.01132854],"study_design_scores_gemma":[0.0007398713,0.0001185587,0.00006494656,0.001049251,0.0001764499,0.0001705816,0.00001666849,0.1680217,0.0001184383,0.001100426,0.8264869,0.00193624],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.000004651322,0.0000968095,0.217999,0.00001401931,0.00004325645,0.0002397649,0.00002346126,0.0008581349,0.7807209],"genre_scores_gemma":[0.003573169,0.00003032132,0.1554652,0.00007501178,0.0002070433,0.00001520584,0.0001926368,0.0003669006,0.8400745],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.8061082,"threshold_uncertainty_score":0.9999142,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2070637511","doi":"10.1007/s10489-006-6926-z","title":"Multi-Objective Genetic Algorithms for Vehicle Routing Problem with Time Windows","year":2006,"lang":"en","type":"article","venue":"Applied Intelligence","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":510,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Brock University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Vehicle routing problem; Benchmark (surveying); Computer science; Set (abstract data type); Pareto principle; Mathematical optimization; Genetic algorithm; Extension (predicate logic); Ranking (information retrieval); Multi-objective optimization; Routing (electronic design automation); Algorithm; Mathematics; Artificial intelligence","retraction":null,"screen_n_in":null,"score":{"opus":0.01597300508623297,"gpt":0.2513967951754557,"spread":0.2354237900892227,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002726626,0.0002498548,0.0002323148,0.00007831919,0.0001294093,0.00006181784,0.0002203356,0.000104093,0.00003176225],"category_scores_gemma":[0.00001766712,0.000243499,0.0000457042,0.0003723114,0.00006199177,0.00007557638,0.0000315088,0.0001683867,0.0001008218],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001014171,"about_ca_system_score_gemma":0.0000262775,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000319639,"about_ca_topic_score_gemma":0.00000817078,"domain_scores_codex":[0.9986377,0.00002190745,0.0003616456,0.0003644883,0.000165681,0.0004485732],"domain_scores_gemma":[0.9993222,0.0001876736,0.00007486621,0.0002523577,0.0001009284,0.00006199565],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001507758,0.00003227991,0.0002239196,0.00003388226,0.0000276675,0.000001453803,0.0002512301,0.9470985,0.01742842,0.0005005853,0.00006159161,0.03432541],"study_design_scores_gemma":[0.0002595725,0.00004489756,0.0006370613,0.00002623495,0.00002247758,0.000006240626,0.00007919167,0.8911179,0.1067639,0.0005045849,0.000211073,0.000326884],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.009020726,0.00007527585,0.9850879,0.00001103483,0.00005149195,0.0009536278,0.00000978617,0.0005864311,0.004203681],"genre_scores_gemma":[0.3802112,0.000003706234,0.6192089,0.00002072486,0.00009089439,0.0001916718,0.00001041585,0.00007021191,0.0001922755],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.3711904,"threshold_uncertainty_score":0.9929599,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2146004814","doi":"10.1016/j.cor.2012.04.007","title":"An adaptive large neighborhood search heuristic for Two-Echelon Vehicle Routing Problems arising in city logistics","year":2012,"lang":"en","type":"article","venue":"Computers & Operations Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":506,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Université du Québec à Montréal; HEC Montréal; Transport Canada","funders":"Natural Sciences and Engineering Research Council of Canada; Austrian Science Fund","keywords":"Vehicle routing problem; Heuristic; Computer science; Context (archaeology); Routing (electronic design automation); Mathematical optimization; Scheme (mathematics); Local search (optimization); Operations research; Mathematics; Algorithm; Artificial intelligence; Computer network","retraction":null,"screen_n_in":null,"score":{"opus":0.1379065368142567,"gpt":0.4106109157336956,"spread":0.2727043789194389,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005336909,0.0002004346,0.0002567739,0.0004512585,0.0006191744,0.0003329168,0.0004045558,0.0001375909,0.00001972439],"category_scores_gemma":[0.0004932036,0.0002246528,0.00004742201,0.001078597,0.00008663005,0.0005865743,0.0001635103,0.0008237339,0.00001964844],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004621042,"about_ca_system_score_gemma":0.0001261678,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001375854,"about_ca_topic_score_gemma":0.0001258552,"domain_scores_codex":[0.9967758,0.0007623157,0.0004546292,0.0003579623,0.0004530255,0.001196257],"domain_scores_gemma":[0.9979612,0.0008102485,0.00001676131,0.0004467932,0.0005009957,0.0002640025],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001261619,0.0001860996,0.004961028,0.0000481864,0.00001818827,0.000001804453,0.002791628,0.9776144,0.001865091,0.006767613,0.0000601808,0.005673167],"study_design_scores_gemma":[0.000775975,0.0001259052,0.002555204,0.00007293684,0.000005972256,0.000003625648,0.0005354655,0.9942925,0.001231782,0.0001154608,0.00004849716,0.0002366983],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08073785,0.0001538169,0.9171859,0.00008696344,0.0002556024,0.0009390454,0.00002967819,0.0002236864,0.000387467],"genre_scores_gemma":[0.7870376,0.00001019352,0.2123972,0.00002266471,0.0002913105,0.00009660565,0.0000605962,0.0000586258,0.00002520809],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7062997,"threshold_uncertainty_score":0.9161075,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2119097848","doi":"10.1287/trsc.1090.0272","title":"Branch and Cut and Price for the Pickup and Delivery Problem with Time Windows","year":2009,"lang":"en","type":"article","venue":"Transportation Science","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":462,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"HEC Montréal","funders":"","keywords":"Column generation; Pickup; Mathematical optimization; Set (abstract data type); Branch and cut; Relaxation (psychology); Linear programming relaxation; Lagrangian relaxation; Computer science; Path (computing); Integer programming; Linear programming; Shortest path problem; Branch and price; Vehicle routing problem; Column (typography); Mathematics; Routing (electronic design automation); Theoretical computer science","retraction":null,"screen_n_in":null,"score":{"opus":0.01091285829767171,"gpt":0.2396820738027333,"spread":0.2287692155050615,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003883927,0.00006603267,0.00006613821,0.00004194886,0.0001609655,0.00006141806,0.00005842203,0.00001812607,0.00000271603],"category_scores_gemma":[0.000009582536,0.0000475064,0.000005183922,0.0002553434,0.0001395514,0.0003056581,0.000001071671,0.00004092613,2.883282e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000006562871,"about_ca_system_score_gemma":0.00002066584,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000324957,"about_ca_topic_score_gemma":0.000007943318,"domain_scores_codex":[0.9995002,0.000005592808,0.00009909627,0.0001509386,0.0001199875,0.0001241826],"domain_scores_gemma":[0.9997118,0.00009239167,0.00002188296,0.00006679288,0.00005803681,0.00004908766],"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":[0.0001215209,0.00003272762,0.0329287,0.0002514535,0.00003307662,0.000002798426,0.01713081,0.632996,0.09547345,0.003888609,0.00007786271,0.217063],"study_design_scores_gemma":[0.0007357716,0.0001108025,0.5604447,0.00003719052,0.00003384678,0.000009906421,0.00008122937,0.4341531,0.00356541,0.0003456165,0.0002951769,0.0001872722],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6775494,0.0001832901,0.3215188,0.0001990593,0.00001146454,0.0002967013,0.000004316721,0.00006728969,0.0001696236],"genre_scores_gemma":[0.9228811,0.00006746071,0.07691897,0.00007249648,0.000007593977,0.000008389613,0.000001480964,0.000005662807,0.00003686295],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5275159,"threshold_uncertainty_score":0.1937255,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2040075495","doi":"10.1016/j.ejor.2013.08.002","title":"The bi-objective Pollution-Routing Problem","year":2013,"lang":"en","type":"article","venue":"European Journal of Operational Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":442,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"HEC Montréal","funders":"University of Southampton","keywords":"Weighting; Mathematical optimization; Computer science; Normalization (sociology); Vehicle routing problem; Set (abstract data type); Routing (electronic design automation); Minification; Constraint (computer-aided design); Extension (predicate logic); Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.05395561392824383,"gpt":0.3389584209137308,"spread":0.285002806985487,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008382143,0.00008873171,0.00009838456,0.0001700608,0.0006011438,0.0004516336,0.0003935686,0.00001850004,0.0001685544],"category_scores_gemma":[0.001287012,0.00006047003,0.00005591024,0.0004279917,0.000111394,0.000380041,0.00007039839,0.0007000036,0.0002955001],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001379245,"about_ca_system_score_gemma":0.0001318591,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004609836,"about_ca_topic_score_gemma":7.97861e-7,"domain_scores_codex":[0.9968314,0.001506424,0.0004927564,0.00008983911,0.0007661122,0.0003134969],"domain_scores_gemma":[0.9975724,0.0005902085,0.00006715637,0.0001386328,0.001507995,0.0001236608],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002786083,0.00004409257,0.0006712497,0.00001961597,0.0001652705,0.00004105341,0.001818887,0.8523622,0.0257733,0.01098735,0.04111831,0.06697085],"study_design_scores_gemma":[0.002657887,0.0009014104,0.1304321,0.0004393444,0.00002580758,0.0006667879,0.003859307,0.769015,0.01114298,0.003006512,0.07705238,0.0008005665],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3036712,0.002206209,0.3461417,0.01525903,0.001472383,0.001476429,0.00001081622,0.0002394581,0.3295228],"genre_scores_gemma":[0.9555721,0.0001078,0.04275326,0.00004636657,0.0005118414,0.000004924719,0.00000110034,0.00004132644,0.0009612485],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6519009,"threshold_uncertainty_score":0.4623573,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2136871443","doi":"10.1287/trsc.1060.0188","title":"A Branch-and-Cut Algorithm for a Vendor-Managed Inventory-Routing Problem","year":2007,"lang":"en","type":"article","venue":"Transportation Science","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":426,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"HEC Montréal","funders":"","keywords":"Mathematical optimization; Stockout; Integer programming; Operations research; Vendor; Time horizon; Purchasing; Computer science; Economic order quantity; Linear programming; Set (abstract data type); Order (exchange); Vendor-managed inventory; Routing (electronic design automation); Branch and cut; Supply chain; Vehicle routing problem; Product (mathematics); Supply chain management; Mathematics; Operations management; Economics; Business","retraction":null,"screen_n_in":null,"score":{"opus":0.02032142200990912,"gpt":0.2890078836574357,"spread":0.2686864616475266,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002009491,0.000104135,0.0001138451,0.0001787241,0.0001962249,0.0000511731,0.0001487127,0.00003984186,0.000006410761],"category_scores_gemma":[0.00004412441,0.0001142856,0.00003202151,0.000723274,0.0001298066,0.0003544675,0.000002734656,0.00008608729,0.000002425478],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000484565,"about_ca_system_score_gemma":0.00003312047,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008994615,"about_ca_topic_score_gemma":0.00003000298,"domain_scores_codex":[0.9988627,0.000009434393,0.0002996196,0.0002384567,0.0002587336,0.0003310249],"domain_scores_gemma":[0.9995298,0.00008988586,0.0000522956,0.0001128641,0.0001053398,0.0001098468],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001990157,0.0000399215,0.01002302,0.0002790896,0.00003080353,0.000008822553,0.0129037,0.150538,0.04697737,0.00880303,0.0000505317,0.7703258],"study_design_scores_gemma":[0.0008905999,0.00003695191,0.03891905,0.000059054,0.00002722189,0.000003857834,0.0003034193,0.9332026,0.0246431,0.0008615832,0.0007227837,0.000329837],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1324579,0.00003404282,0.8662319,0.00002580487,0.0001393666,0.0002873233,0.000007614211,0.0002675178,0.000548568],"genre_scores_gemma":[0.6410026,0.000005742243,0.3588545,0.00003469192,0.00002818887,0.00001550157,0.000006564468,0.00001571241,0.00003647392],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7826645,"threshold_uncertainty_score":0.4660432,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2123789618","doi":"","title":"Construcción de aplicativo web para la gestión y prestación del servicio de ambulancias en la ciudad de Pereira, utilizando inteligencia artificial","year":2021,"lang":"es","type":"article","venue":"Repository Technological University of Pereira (Technological University of Pereira)","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":353,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Université de Montréal; University of New Brunswick","funders":"","keywords":"Arc routing; Arc (geometry); Routing (electronic design automation); Computer science; Mathematical optimization; Operations research; Mathematics; Computer network; Geometry","retraction":null,"screen_n_in":null,"score":{"opus":0.02668394925971116,"gpt":0.2532070197623499,"spread":0.2265230705026387,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts","research_integrity"],"consensus_categories":["research_integrity"],"category_scores_codex":[0.00170178,0.000854505,0.001535642,0.0005724225,0.0007807665,0.00009796995,0.002563347,0.004337493,0.000485486],"category_scores_gemma":[0.001668448,0.001060063,0.0007579528,0.00171954,0.004354479,0.0003044679,0.001855823,0.002824799,0.00002749127],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001321947,"about_ca_system_score_gemma":0.001043232,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003370096,"about_ca_topic_score_gemma":0.00004917707,"domain_scores_codex":[0.993957,0.001913668,0.0007716794,0.00138587,0.0006785518,0.001293253],"domain_scores_gemma":[0.9952022,0.001538048,0.0006585103,0.001476303,0.0006580916,0.0004667762],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0009062861,0.002301008,0.152649,0.001458059,0.001129086,0.009602267,0.001797717,0.01829691,0.5258554,0.2363491,0.0003028694,0.04935222],"study_design_scores_gemma":[0.00773279,0.001916792,0.2649147,0.003183864,0.00296503,0.005614896,0.07559165,0.2774247,0.2952816,0.01468338,0.04481686,0.005873737],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8220032,0.0007149459,0.1634701,0.0007068574,0.00005376306,0.0004360221,0.0001595362,0.00190385,0.01055174],"genre_scores_gemma":[0.8799331,0.002070404,0.1172673,0.00002726834,0.00003766599,9.866786e-7,0.00002157257,0.00005888346,0.0005827579],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2591278,"threshold_uncertainty_score":0.9994757,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2008806816","doi":"10.1016/s0377-2217(02)00915-3","title":"Real-time vehicle routing: Solution concepts, algorithms and parallel computing strategies","year":2003,"lang":"en","type":"article","venue":"European Journal of Operational Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":347,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Group for Research in Decision Analysis; HEC Montréal","funders":"","keywords":"Computer science; Vehicle routing problem; Fleet management; Time horizon; Field (mathematics); Routing (electronic design automation); Operations research; Routing algorithm; Algorithm; Emphasis (telecommunications); Mathematical optimization; Telecommunications; Computer network; Routing protocol; Engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.06723250682678367,"gpt":0.3664937950685185,"spread":0.2992612882417349,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008181138,0.0001193769,0.000174861,0.0001769148,0.0003198588,0.0003350879,0.0001790397,0.00003150777,0.0001064494],"category_scores_gemma":[0.0006478077,0.0001140335,0.00004145014,0.0002735643,0.0001285631,0.0004071197,0.00004551709,0.0005212091,0.00004366726],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009117485,"about_ca_system_score_gemma":0.0001686577,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000293289,"about_ca_topic_score_gemma":2.354476e-7,"domain_scores_codex":[0.9964715,0.001930306,0.0005198328,0.0001413922,0.0006164682,0.0003205611],"domain_scores_gemma":[0.9985932,0.0004074637,0.00007746373,0.0001100278,0.0006535304,0.0001583089],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002473563,0.00004150307,0.0005426837,0.00002868243,0.00007171644,0.0001055281,0.001491784,0.913367,0.06223197,0.01218682,0.002069191,0.007838325],"study_design_scores_gemma":[0.001290931,0.0002636543,0.008963018,0.000115812,0.000009756664,0.0002570584,0.0009807989,0.9837227,0.001631863,0.0002131627,0.002306313,0.0002448587],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4498955,0.0006700851,0.484542,0.0004016523,0.0002849577,0.0002280251,0.000004773066,0.0001135896,0.06385941],"genre_scores_gemma":[0.8012952,0.0001748613,0.1980412,0.0000116201,0.0002189395,4.203172e-7,0.000003199219,0.00003808062,0.0002163827],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.3513997,"threshold_uncertainty_score":0.465015,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2046280257","doi":"10.1002/nav.20261","title":"What you should know about the vehicle routing problem","year":2007,"lang":"en","type":"article","venue":"Naval Research Logistics (NRL)","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":324,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"HEC Montréal","funders":"","keywords":"Vehicle routing problem; Heuristics; Metaheuristic; Computer science; Set (abstract data type); Operations research; Mathematical optimization; Routing (electronic design automation); Mathematics; Algorithm; Computer network","retraction":null,"screen_n_in":null,"score":{"opus":0.1509400784947585,"gpt":0.4200791947934686,"spread":0.26913911629871,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01430049,0.0002571266,0.0002589873,0.0002494139,0.0005913327,0.0006333965,0.0008330612,0.0002909801,0.00008531537],"category_scores_gemma":[0.004324782,0.0002079382,0.00009169793,0.001248796,0.0004091556,0.0003098428,0.000315441,0.001830959,0.0002088022],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000394202,"about_ca_system_score_gemma":0.0001183085,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008059432,"about_ca_topic_score_gemma":0.00007257233,"domain_scores_codex":[0.9957039,0.00044545,0.0005861902,0.0004085063,0.001400484,0.001455467],"domain_scores_gemma":[0.9955072,0.002751015,0.00006227359,0.0007348369,0.0006627813,0.000281855],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001020417,0.0001404766,0.003928984,0.0003043136,0.0001400923,0.0001493855,0.002929871,0.5787121,0.0121654,0.03591925,0.007047293,0.3584608],"study_design_scores_gemma":[0.00078398,0.000171486,0.003898328,0.000375621,0.00003119236,0.0000241601,0.002227345,0.9255081,0.01330748,0.005287958,0.04774976,0.0006346184],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02302049,0.004814268,0.9003319,0.001808621,0.001851382,0.001427105,0.00002215483,0.001289502,0.06543458],"genre_scores_gemma":[0.9477387,0.001292119,0.04750071,0.0001156921,0.0007446971,0.00003608082,0.00001611662,0.0001386846,0.002417191],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9247182,"threshold_uncertainty_score":0.8479473,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2126043710","doi":"10.1111/j.1937-5956.2012.01338.x","title":"Analysis of Travel Times and CO <sub>2</sub> Emissions in Time‐Dependent Vehicle Routing","year":2012,"lang":"en","type":"article","venue":"Production and Operations Management","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":319,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"HEC Montréal","funders":"","keywords":"Vehicle routing problem; Fuel efficiency; Greenhouse gas; Context (archaeology); Computer science; Scheduling (production processes); Limiting; Operations research; Environmental economics; Transport engineering; Routing (electronic design automation); Environmental science; Automotive engineering; Operations management; Economics; Engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.01247377138778854,"gpt":0.2547284891803219,"spread":0.2422547177925333,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006684029,0.00009008833,0.0001516086,0.0003770051,0.0000993545,0.00003737019,0.00003976201,0.00003168351,0.00003400957],"category_scores_gemma":[0.00004697386,0.00009395393,0.00002156941,0.0005737076,0.00002480855,0.0002088185,0.00003485422,0.00006743841,0.000004543143],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000340832,"about_ca_system_score_gemma":0.000002904745,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000493853,"about_ca_topic_score_gemma":0.000008259308,"domain_scores_codex":[0.9992661,0.00006437345,0.0002490579,0.0001590794,0.0001193219,0.0001420249],"domain_scores_gemma":[0.9997351,0.00001646448,0.00002301905,0.0001432331,0.00002703157,0.00005515408],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000004170464,0.0001023345,0.0277724,0.00006317472,0.0003093161,3.63891e-7,0.001561382,0.909518,0.04300512,0.0006666828,0.000131054,0.01686601],"study_design_scores_gemma":[0.0002302961,0.000009501712,0.1156586,0.00003111316,0.0003532629,0.000002038722,0.0006143521,0.8308186,0.05202026,0.000007790926,0.00007150539,0.0001826764],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9631579,0.0002182277,0.03437366,0.0002101662,0.00009775695,0.0002961723,0.000004798638,0.00007659285,0.001564729],"genre_scores_gemma":[0.9913799,0.0002945266,0.007860703,0.00001510724,0.00003244449,0.00002322879,0.00001715627,0.00001186908,0.0003651229],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08788622,"threshold_uncertainty_score":0.383133,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3122956505","doi":"10.1002/net.20177","title":"Models and branch‐and‐cut algorithms for pickup and delivery problems with time windows","year":2007,"lang":"en","type":"article","venue":"Networks","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":310,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"HEC Montréal","funders":"","keywords":"Pickup; Computer science; Branch and cut; Vehicle routing problem; Set (abstract data type); Mathematical optimization; Limit (mathematics); Algorithm; Time limit; Running time; Integer programming; Mathematics; Routing (electronic design automation); Computer network; Engineering; Artificial intelligence","retraction":null,"screen_n_in":null,"score":{"opus":0.01662834278793372,"gpt":0.2314751382640148,"spread":0.2148467954760811,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004200549,0.0001339094,0.0001621408,0.00004323646,0.00007361988,0.00004594747,0.00003721015,0.0001157976,0.000003018215],"category_scores_gemma":[0.000005907091,0.000124383,0.00001230796,0.00009506296,0.0000418133,0.0001413663,0.00002296474,0.000125194,3.661185e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001449382,"about_ca_system_score_gemma":0.00000501518,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005689141,"about_ca_topic_score_gemma":0.00001126962,"domain_scores_codex":[0.9993294,0.00001431153,0.0001488838,0.0001797449,0.00006632401,0.0002613632],"domain_scores_gemma":[0.9995797,0.0001678992,0.00002573227,0.00009508422,0.00003733944,0.00009423259],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001771915,0.000003441802,0.0004586689,0.00003696135,0.00002571896,9.607946e-7,0.0001787444,0.9220534,0.00005122831,0.00004854455,0.00009655994,0.07702807],"study_design_scores_gemma":[0.0006792974,0.00005263753,0.0005760846,0.00005076668,0.00002384202,0.00002196384,0.00000919026,0.997842,0.00004786065,0.0002255902,0.0003079682,0.0001627767],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05054317,0.001567201,0.9468042,0.00001882629,0.00005270404,0.0003347335,0.000002561524,0.0001634142,0.0005131664],"genre_scores_gemma":[0.6592078,0.000394867,0.3397602,0.00009284737,0.0002406924,0.00002513463,0.00001118296,0.00007446707,0.000192811],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6086646,"threshold_uncertainty_score":0.5072193,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2059559546","doi":"10.1287/trsc.35.4.375.10432","title":"Benders Decomposition for Simultaneous Aircraft Routing and Crew Scheduling","year":2001,"lang":"en","type":"article","venue":"Transportation Science","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":308,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Polytechnique Montréal; Group for Research in Decision Analysis; HEC Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Crew scheduling; Crew; Column generation; Scheduling (production processes); Integer programming; Mathematical optimization; Benders' decomposition; Engineering; Routing (electronic design automation); Branch and bound; Decomposition; Heuristic; Computer science; Iterated local search; Operations research; Metaheuristic; Aeronautics; Mathematics; Computer network","retraction":null,"screen_n_in":null,"score":{"opus":0.02012691727956976,"gpt":0.3079237455336067,"spread":0.287796828254037,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005019603,0.00009001057,0.00009012685,0.0001117119,0.0002390692,0.00006616906,0.00009685444,0.00003634206,0.000009207227],"category_scores_gemma":[0.0000896295,0.0001025435,0.00002219672,0.0004716431,0.00008984158,0.0003741619,0.000001606303,0.00005931958,0.000001726127],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003989193,"about_ca_system_score_gemma":0.00002615614,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005764126,"about_ca_topic_score_gemma":0.00001392876,"domain_scores_codex":[0.9991583,0.000008387513,0.0002132406,0.0002104632,0.0001804588,0.0002291606],"domain_scores_gemma":[0.9995102,0.0001770129,0.00003403766,0.00008591256,0.0001091591,0.00008369231],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000005347201,0.000003891032,0.002027422,0.00001878122,0.000002525111,0.000001771205,0.0007489995,0.9693616,0.01673604,0.0004978445,8.154981e-7,0.01059494],"study_design_scores_gemma":[0.0002631284,0.00001576422,0.006347263,0.00002220792,0.00001172784,0.00000498601,0.0002181171,0.9878444,0.004919173,0.0001596935,0.0000630818,0.0001304309],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4290377,0.00002539646,0.5704123,0.00004119256,0.00007339731,0.00010642,0.000003192452,0.0001668946,0.0001335096],"genre_scores_gemma":[0.7344263,0.00002199059,0.2654615,0.00003385512,0.00001873677,0.000007302664,0.000009227675,0.00001204357,0.00000907601],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.3053885,"threshold_uncertainty_score":0.4181604,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1650780448","doi":"10.1109/tase.2015.2461213","title":"Planning Paths for Package Delivery in Heterogeneous Multirobot Teams","year":2015,"lang":"en","type":"article","venue":"IEEE Transactions on Automation Science and Engineering","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":295,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo","funders":"","keywords":"Travelling salesman problem; Truck; Scheduling (production processes); Computer science; Job shop scheduling; Motion planning; Mathematical optimization; Heuristic; Vehicle routing problem; Traveling purchaser problem; Task (project management); Graph; Shortest path problem; 2-opt; Distributed computing; Operations research; Routing (electronic design automation); Robot; Engineering; Artificial intelligence; Computer network; Theoretical computer science; Mathematics; Algorithm","retraction":null,"screen_n_in":null,"score":{"opus":0.03005034896176269,"gpt":0.2773482127765395,"spread":0.2472978638147768,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006634278,0.0001220112,0.0001180332,0.0004188341,0.00008813857,0.00007927127,0.0001003235,0.00005599553,0.00000183914],"category_scores_gemma":[0.00005077761,0.00013501,0.00002264136,0.00056408,0.00003393228,0.0004150287,0.00000115968,0.000110549,0.00000391003],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001989962,"about_ca_system_score_gemma":0.0000423548,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004917503,"about_ca_topic_score_gemma":0.000001790924,"domain_scores_codex":[0.9991227,0.00001018312,0.0002064036,0.0001839717,0.0002258963,0.0002508474],"domain_scores_gemma":[0.9995654,0.00008899377,0.00001823849,0.0001227875,0.00008152025,0.0001230348],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000003750845,0.000008156988,0.00001648285,0.0000218293,0.000003046506,0.000001203834,0.0007051574,0.9733056,0.01212546,0.000005779063,0.000005335812,0.01379814],"study_design_scores_gemma":[0.0003807792,0.00003524517,0.0002186903,0.00004530355,0.000004198312,0.00001004103,0.0001145836,0.969269,0.02971094,0.000005977557,0.00005912634,0.0001460828],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2922614,0.00002963128,0.706841,0.00001195786,0.000341854,0.0001344351,0.000004040775,0.000341482,0.00003418412],"genre_scores_gemma":[0.9302793,0.000008992291,0.06959709,0.00001952244,0.00001613662,0.00004937606,8.545602e-7,0.00002191505,0.000006880426],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6380178,"threshold_uncertainty_score":0.5505548,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2152304735","doi":"10.1023/a:1011336210885","title":"Variable Neighborhood Decomposition Search","year":2001,"lang":"en","type":"article","venue":"Journal of Heuristics","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":291,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"HEC Montréal","funders":"","keywords":"Variable neighborhood search; Metaheuristic; Local search (optimization); Descent (aeronautics); Mathematical optimization; Variable (mathematics); Node (physics); Gradient descent; Decomposition; Computer science; Local optimum; Algorithm; Mathematics; Artificial intelligence; Artificial neural network","retraction":null,"screen_n_in":null,"score":{"opus":0.01601429448935615,"gpt":0.2909555204022765,"spread":0.2749412259129203,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004991315,0.00007229276,0.0001441092,0.0001100077,0.00004038112,0.00003883074,0.0001156618,0.00005345431,0.0001089353],"category_scores_gemma":[0.0001245007,0.00007080853,0.00003941756,0.0002270674,0.00001067146,0.0001044996,0.00001226176,0.0002510256,0.00001240503],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007007556,"about_ca_system_score_gemma":0.00003176382,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":7.329583e-7,"about_ca_topic_score_gemma":9.148894e-8,"domain_scores_codex":[0.9992149,0.00005598082,0.0003352086,0.00004207838,0.0002005193,0.0001512923],"domain_scores_gemma":[0.9994135,0.0001287382,0.00006553129,0.00009326408,0.000211074,0.00008782445],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001492585,0.00003514975,0.002889034,0.00002582634,0.00003931824,0.00006559135,0.00007530704,0.9858889,0.002767125,0.001344682,0.001928641,0.004925492],"study_design_scores_gemma":[0.0006069971,0.0001164299,0.002428524,0.00007001795,0.00005385628,0.0008565596,0.00004219021,0.9850467,0.001643674,0.002251484,0.00671958,0.000163925],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02359594,0.0002021853,0.9690291,0.00006977293,0.0004968909,0.0000276573,0.000001935268,0.00004651056,0.006530045],"genre_scores_gemma":[0.5567801,0.0001972975,0.4425572,0.00003605966,0.0003375441,2.266746e-7,0.000001399741,0.00002436106,0.00006588699],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5331841,"threshold_uncertainty_score":0.2887488,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2023482164","doi":"10.1287/opre.50.3.415.7751","title":"An Integer <i>L</i>-Shaped Algorithm for the Capacitated Vehicle Routing Problem with Stochastic Demands","year":2002,"lang":"en","type":"article","venue":"Operations Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":282,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Université de Montréal; HEC Montréal","funders":"","keywords":"Vehicle routing problem; Integer (computer science); Mathematical optimization; Computer science; Integer programming; Routing (electronic design automation); Operations research; TRIPS architecture; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.07063186204221393,"gpt":0.3441733096093075,"spread":0.2735414475670935,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001546688,0.0001389514,0.0001300001,0.0001548801,0.000957034,0.0003677683,0.0003046616,0.0000752634,0.0001716873],"category_scores_gemma":[0.0001873732,0.00009912931,0.0000285006,0.0008448548,0.0001212069,0.0003174107,0.00002749583,0.0004505597,0.00003910012],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009766914,"about_ca_system_score_gemma":0.00003532798,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005307667,"about_ca_topic_score_gemma":0.00009989234,"domain_scores_codex":[0.998422,0.0002272442,0.0002400555,0.0002456471,0.0003741906,0.0004908951],"domain_scores_gemma":[0.9983677,0.0005438496,0.00001036675,0.0003856616,0.0005829768,0.0001094556],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000003632693,0.00003656496,0.00001265725,0.00001020364,0.00003461438,8.971105e-7,0.001455946,0.9706272,0.002986887,0.0003296262,0.0002603169,0.0242414],"study_design_scores_gemma":[0.0004566754,0.0001347881,0.00002782895,0.00002678158,0.00001220868,0.000007830081,0.0006293818,0.9978009,0.0005598681,0.00001806629,0.0001839187,0.0001417126],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007858652,0.0001108771,0.9896825,0.0004177349,0.00004854471,0.000972114,0.00002081251,0.0002448043,0.0006439884],"genre_scores_gemma":[0.7281141,0.00001447529,0.2703485,0.00002897449,0.000120127,0.0005377062,0.00002240674,0.00006782935,0.0007459347],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7202554,"threshold_uncertainty_score":0.7360829,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2041183433","doi":"10.1287/opre.48.1.129.12455","title":"A Tabu Search Heuristic for the Capacitated arc Routing Problem","year":2000,"lang":"en","type":"article","venue":"Operations Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":278,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"HEC Montréal","funders":"","keywords":"Arc routing; Tabu search; Heuristics; Benchmark (surveying); Mathematical optimization; Routing (electronic design automation); Computer science; Heuristic; Arc (geometry); Vehicle routing problem; Guided Local Search; Operations research; Mathematics; Computer network","retraction":null,"screen_n_in":null,"score":{"opus":0.09081247834877422,"gpt":0.380172694943396,"spread":0.2893602165946217,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003029615,0.0001115167,0.0001141921,0.0001462669,0.001126055,0.0003552973,0.0003313551,0.00007057533,0.0008759877],"category_scores_gemma":[0.0004544606,0.00008892971,0.00004319878,0.0009566742,0.0001234157,0.0001457033,0.00003152834,0.0005158729,0.000206395],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001212725,"about_ca_system_score_gemma":0.00011258,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001707429,"about_ca_topic_score_gemma":0.00009223954,"domain_scores_codex":[0.9981822,0.0003375899,0.0002649246,0.0002180195,0.0004210994,0.0005761629],"domain_scores_gemma":[0.9981133,0.0009680744,0.000003950563,0.0003787194,0.0004454508,0.00009046114],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000007227137,0.00001643984,0.00003171516,0.00002797679,0.0000248789,9.068692e-7,0.001387736,0.971073,0.001880674,0.001498958,0.001192892,0.02285756],"study_design_scores_gemma":[0.0002525307,0.00003331952,0.0001528361,0.00002237306,0.000005732682,0.000005623011,0.0003644028,0.9934278,0.001193445,0.0000698679,0.004365942,0.0001062],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1746136,0.0005036002,0.7836055,0.00336959,0.0001828404,0.004075391,0.0001005107,0.0007646176,0.03278438],"genre_scores_gemma":[0.8628458,0.000152025,0.1267423,0.00002987289,0.0001871141,0.0006285801,0.00003909149,0.00007428656,0.009300929],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6882322,"threshold_uncertainty_score":0.9591451,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2098090443","doi":"10.1287/trsc.1040.0103","title":"A Tabu Search Algorithm for the Split Delivery Vehicle Routing Problem","year":2006,"lang":"en","type":"article","venue":"Transportation Science","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":273,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Tabu search; Vehicle routing problem; Benchmark (surveying); Algorithm; Computer science; Mathematical optimization; Routing (electronic design automation); Set (abstract data type); Residual; Route planning; Mathematics; Computer network","retraction":null,"screen_n_in":null,"score":{"opus":0.01970802792664043,"gpt":0.2744546307225553,"spread":0.2547466027959149,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001306915,0.00009606065,0.00008538162,0.00008284735,0.0003764944,0.0001017317,0.0002836445,0.00003301445,0.00001700894],"category_scores_gemma":[0.00001301827,0.00008097542,0.00004231634,0.0008929421,0.0001539985,0.0003442825,0.000003468747,0.0000982553,0.000007469143],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005569974,"about_ca_system_score_gemma":0.00007224539,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001197365,"about_ca_topic_score_gemma":0.00003365344,"domain_scores_codex":[0.9987956,0.00001490572,0.0002636415,0.0002171286,0.0003740411,0.0003347221],"domain_scores_gemma":[0.9993652,0.0001880892,0.00003395364,0.0001652816,0.0002016747,0.00004579897],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000001896534,0.000008540977,0.001434827,0.0000173277,0.000003589146,6.431303e-7,0.0005752073,0.9104491,0.01052518,0.002038107,0.00003568406,0.07490982],"study_design_scores_gemma":[0.0002179252,0.000009224444,0.039293,0.00001085792,0.00001086126,7.176306e-7,0.0001359206,0.9496039,0.01018211,0.000144141,0.0002836572,0.0001076586],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1392037,0.00006239297,0.8595569,0.00008883684,0.0001074461,0.0003437385,0.00002057086,0.0002302301,0.0003862369],"genre_scores_gemma":[0.6573957,0.00000678427,0.3423711,0.00002364373,0.00005229669,0.00003633793,0.00001028206,0.00001602575,0.00008782493],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5181921,"threshold_uncertainty_score":0.3302082,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2099860183","doi":"10.1016/j.ejor.2009.06.034","title":"An exact algorithm for a vehicle routing problem with time windows and multiple use of vehicles","year":2009,"lang":"en","type":"article","venue":"European Journal of Operational Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":272,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Université de Montréal","funders":"","keywords":"Vehicle routing problem; Column generation; Mathematical optimization; Benchmark (surveying); Computer science; Routing (electronic design automation); Shortest path problem; Lagrangian relaxation; Set (abstract data type); Linear programming; Dynamic programming; Mathematics; Computer network","retraction":null,"screen_n_in":null,"score":{"opus":0.06359111993787328,"gpt":0.3323522610403489,"spread":0.2687611411024756,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004128777,0.0001058066,0.0001856613,0.0002098188,0.0001566964,0.0001896112,0.0001794701,0.00002362518,0.00001365531],"category_scores_gemma":[0.00040041,0.00008744001,0.00003478644,0.0002319258,0.00006551816,0.0006332279,0.00001916371,0.0003055828,0.000002726229],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004178917,"about_ca_system_score_gemma":0.00008057009,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001746352,"about_ca_topic_score_gemma":3.420783e-7,"domain_scores_codex":[0.9980466,0.0006389552,0.0004423163,0.0001303368,0.00050476,0.0002370173],"domain_scores_gemma":[0.9981675,0.0005410778,0.00008691329,0.0001246853,0.0009323222,0.0001475063],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001458218,0.0001039624,0.001306328,0.00002251197,0.00005537567,0.00003315548,0.000698733,0.7455714,0.1144578,0.0001251578,0.0003124075,0.1371674],"study_design_scores_gemma":[0.001231776,0.001129718,0.01349472,0.0001129929,0.000008327923,0.00006293878,0.00005940915,0.976464,0.006857392,0.00002179124,0.0004474687,0.0001094188],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6098316,0.0001068815,0.3889534,0.000248793,0.00002169047,0.0003763297,0.00002212,0.00003625351,0.0004028926],"genre_scores_gemma":[0.5798408,0.00001374262,0.4199263,0.0000171604,0.0001124532,7.245367e-7,0.000004864396,0.00002740337,0.00005652455],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2308927,"threshold_uncertainty_score":0.35657,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2884929083","doi":"10.1016/j.ijpe.2018.07.016","title":"Electric vehicle routing problem with recharging stations for minimizing energy consumption","year":2018,"lang":"en","type":"article","venue":"International Journal of Production Economics","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":269,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Manitoba; McMaster University","funders":"","keywords":"Energy consumption; Computer science; Heuristics; Vehicle routing problem; Greenhouse gas; Electric vehicle; Routing (electronic design automation); Mathematical optimization; Automotive engineering; Engineering; Computer network; Electrical engineering; Mathematics; Power (physics)","retraction":null,"screen_n_in":null,"score":{"opus":0.02196577315842007,"gpt":0.2677034716093187,"spread":0.2457376984508987,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006124823,0.0001055344,0.000139644,0.0003151448,0.00009022273,0.00008818851,0.0001673174,0.00004486423,0.00002325493],"category_scores_gemma":[0.0001091985,0.0001104424,0.0000513005,0.0001086453,0.0000306463,0.0005456128,0.00001132475,0.0001084797,0.000003407634],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003142314,"about_ca_system_score_gemma":0.00007094244,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002478545,"about_ca_topic_score_gemma":0.000007940756,"domain_scores_codex":[0.9990649,0.00003239587,0.0005075667,0.0001396368,0.0001039714,0.0001515794],"domain_scores_gemma":[0.9986015,0.00008728175,0.0003711826,0.00008497848,0.0008082542,0.00004679522],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002326361,0.000051734,0.00225053,0.00002557319,0.0004396655,0.000001525072,0.0008071134,0.8722615,0.02727402,0.005869238,0.0007332739,0.09005314],"study_design_scores_gemma":[0.001462128,0.0002657567,0.0007984288,0.0001467315,0.00007291567,0.0003891559,0.0002129124,0.8023887,0.1839803,0.001612555,0.008321089,0.0003493131],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3300759,0.00005318186,0.6668308,0.0006532966,0.001859943,0.0001047063,0.000005746265,0.00007155685,0.0003448559],"genre_scores_gemma":[0.801421,0.0001786693,0.1966915,0.00005155604,0.001527659,0.000009379679,0.000007553575,0.00003690001,0.00007569872],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.4713451,"threshold_uncertainty_score":0.4503712,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2332834184","doi":"10.1016/j.ejor.2016.03.040","title":"An adaptive large neighborhood search for the two-echelon multiple-trip vehicle routing problem with satellite synchronization","year":2016,"lang":"en","type":"article","venue":"European Journal of Operational Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":266,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Polytechnique Montréal","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada","keywords":"Vehicle routing problem; Heuristics; Computer science; Synchronization (alternating current); Routing (electronic design automation); Set (abstract data type); Mathematical optimization; Real-time computing; Operations research; Computer network; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.0590928165349166,"gpt":0.340670755225449,"spread":0.2815779386905324,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01164114,0.0001539576,0.0001642881,0.0002167256,0.0005340619,0.0002558902,0.0004939969,0.00003130135,0.00005759904],"category_scores_gemma":[0.0006239368,0.00008703938,0.00005648539,0.0004947542,0.00009921734,0.0006723507,0.0000576971,0.0004804095,0.00002620408],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002310143,"about_ca_system_score_gemma":0.0002469092,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002249881,"about_ca_topic_score_gemma":0.000005546371,"domain_scores_codex":[0.9962,0.001639443,0.0005138787,0.0002071501,0.0009472609,0.0004922159],"domain_scores_gemma":[0.9957244,0.001810274,0.00009127669,0.0002453973,0.001955049,0.000173596],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005424445,0.0001191411,0.007245197,0.00003438722,0.000172193,0.00004239656,0.001610687,0.8223668,0.03468287,0.007256199,0.0001567327,0.125771],"study_design_scores_gemma":[0.003439461,0.001045255,0.007317424,0.0002464811,0.0000179888,0.0000664129,0.0005759452,0.9776703,0.008197859,0.00004816814,0.001169301,0.0002054794],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02674543,0.0002705262,0.9701048,0.0008574836,0.00007515882,0.0005001739,0.00002213009,0.00004877083,0.001375593],"genre_scores_gemma":[0.9217843,0.0001196019,0.07726195,0.00003101496,0.0005598101,0.000009896176,0.000007287259,0.00009058121,0.0001356017],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8950388,"threshold_uncertainty_score":0.4107626,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2081363577","doi":"10.1016/j.trb.2014.09.008","title":"The fleet size and mix pollution-routing problem","year":2014,"lang":"en","type":"article","venue":"Transportation Research Part B Methodological","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":266,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"HEC Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Vehicle routing problem; Fuel efficiency; Benchmark (surveying); Routing (electronic design automation); Computer science; Pollution; Homogeneous; Transport engineering; Operations research; Metaheuristic; Environmental economics; Environmental science; Engineering; Economics; Automotive engineering; Mathematics; Computer network","retraction":null,"screen_n_in":null,"score":{"opus":0.2067050982487565,"gpt":0.4203752284752951,"spread":0.2136701302265386,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01605242,0.0001564443,0.0002329728,0.00006554272,0.0006147832,0.0001097452,0.0002218036,0.0001642686,0.00008123548],"category_scores_gemma":[0.003456439,0.0001113364,0.00005735907,0.0004931172,0.0003199139,0.0001032845,0.00001309593,0.0006066613,0.00001768491],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003392423,"about_ca_system_score_gemma":0.00002132292,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001778656,"about_ca_topic_score_gemma":0.00003832826,"domain_scores_codex":[0.9951538,0.002983212,0.0004565564,0.0003093996,0.000506502,0.0005905444],"domain_scores_gemma":[0.9891241,0.01022476,0.00004404188,0.0002497468,0.0001841699,0.0001731967],"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":[0.0003406278,0.0001228665,0.03136209,0.0004513875,0.0002485006,0.00002363492,0.004188222,0.417574,0.04033038,0.2661936,0.006029428,0.2331352],"study_design_scores_gemma":[0.002240824,0.0005084452,0.5387901,0.000166285,0.00007396996,0.00001426425,0.00151863,0.2700926,0.00978567,0.03845085,0.1372636,0.001094757],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2506961,0.0003116941,0.7407006,0.002391088,0.0002542973,0.0006974873,0.00001527221,0.0007156978,0.004217736],"genre_scores_gemma":[0.6277002,0.0002847612,0.3714009,0.000068402,0.0001452907,0.00009980322,0.00001094343,0.00003056931,0.0002591629],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.507428,"threshold_uncertainty_score":0.556348,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2136611127","doi":"10.1287/trsc.1060.0187","title":"Solving the Capacitated Location-Routing Problem by a Cooperative Lagrangean Relaxation-Granular Tabu Search Heuristic","year":2007,"lang":"en","type":"article","venue":"Transportation Science","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":260,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"HEC Montréal; Université Laval","funders":"","keywords":"Tabu search; Mathematical optimization; Vehicle routing problem; Routing (electronic design automation); Heuristics; Metaheuristic; Computer science; Relaxation (psychology); Heuristic; Local search (optimization); Mathematics; Computer network","retraction":null,"screen_n_in":null,"score":{"opus":0.01782215380448123,"gpt":0.2770757188440929,"spread":0.2592535650396117,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003811379,0.0001700723,0.0001386039,0.0001741588,0.0007193388,0.0001478084,0.0003684656,0.00006008611,0.00003418447],"category_scores_gemma":[0.0002262158,0.0001452679,0.00003070006,0.002986173,0.0003997285,0.000560518,0.000004333228,0.0002836626,0.00002195777],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001298579,"about_ca_system_score_gemma":0.0001253338,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001116428,"about_ca_topic_score_gemma":0.0001426109,"domain_scores_codex":[0.9979303,0.0000678931,0.000510613,0.0003268518,0.0006841929,0.0004801649],"domain_scores_gemma":[0.9985245,0.0003465587,0.00009011396,0.0002471368,0.0006639967,0.000127721],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000009718014,0.00002377718,0.00622691,0.00005305617,0.00001269305,0.000003706718,0.0210366,0.895071,0.0698673,0.003628121,0.00013668,0.003930387],"study_design_scores_gemma":[0.0007075767,0.00004862585,0.07594886,0.0001517828,0.00004529825,0.000006458899,0.004665728,0.8582441,0.05913224,0.0001740863,0.0003082346,0.0005670081],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2706774,0.0001127165,0.7273492,0.0001342556,0.0001219339,0.0003606796,0.00001343639,0.0002935267,0.0009368859],"genre_scores_gemma":[0.965493,0.00001960122,0.03420775,0.00007271863,0.00002847687,0.0000176851,0.00005113587,0.00002950033,0.00008010636],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6948156,"threshold_uncertainty_score":0.5923854,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2943956374","doi":"10.1155/2019/5075671","title":"A Survey on the Electric Vehicle Routing Problem: Variants and Solution Approaches","year":2019,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":259,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"European Regional Development Fund; Hrvatska Zaklada za Znanost","keywords":"Vehicle routing problem; Heuristics; Computer science; Algorithm; Electric vehicle; Greenhouse gas; Operations research; Routing (electronic design automation); Mathematics; Physics","retraction":null,"screen_n_in":null,"score":{"opus":0.02666724517741675,"gpt":0.2407231984742481,"spread":0.2140559532968313,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001006325,0.0000953113,0.0001485046,0.00008495156,0.00005126449,0.0000223794,0.00007095695,0.00004784003,0.000006568617],"category_scores_gemma":[0.00005536663,0.0000734696,0.00003558941,0.0002894092,0.000008252396,0.0002603761,0.000001013654,0.0002320809,0.000002169328],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004130756,"about_ca_system_score_gemma":0.00001836054,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004138627,"about_ca_topic_score_gemma":0.00001059646,"domain_scores_codex":[0.9991423,0.0001004559,0.0003508953,0.00008684141,0.0001819993,0.0001374412],"domain_scores_gemma":[0.9993813,0.0002364584,0.0001825242,0.00007926505,0.00008529051,0.00003516674],"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":[0.0000532329,0.00001668715,0.01215809,0.00002787013,0.00002929737,0.000001132569,0.0007136365,0.9574275,0.01795312,0.0006248874,0.000008851539,0.01098569],"study_design_scores_gemma":[0.0006126541,0.0001239265,0.8084256,0.00007313159,0.00002178147,0.000003947808,0.00009990575,0.1882793,0.001938566,0.0003004163,0.0000139546,0.0001067761],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.934568,0.0001194819,0.06467986,0.00009087804,0.0001432601,0.0001783929,0.000003167782,0.00003260508,0.0001843617],"genre_scores_gemma":[0.983245,0.00006577694,0.01659451,0.00001862086,0.00003072755,0.000002185147,0.000006800691,0.00002131433,0.00001504255],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7962675,"threshold_uncertainty_score":0.2996003,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2108911946","doi":"10.1287/ijoc.2013.0550","title":"Formulations and Branch-and-Cut Algorithms for Multivehicle Production and Inventory Routing Problems","year":2013,"lang":"en","type":"article","venue":"INFORMS journal on computing","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":258,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Group for Research in Decision Analysis; HEC Montréal","funders":"","keywords":"Vehicle routing problem; Mathematical optimization; Heuristic; Computer science; Routing (electronic design automation); Index (typography); Production (economics); Branch and cut; Algorithm; Order (exchange); Enumeration; Integer programming; Operations research; Mathematics; Economics","retraction":null,"screen_n_in":null,"score":{"opus":0.02831864026295009,"gpt":0.2741303086561243,"spread":0.2458116683931742,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008480389,0.0001478671,0.0001648379,0.000146012,0.0004954819,0.000253033,0.00005001009,0.00006844722,0.000002869488],"category_scores_gemma":[0.0003417741,0.0001308027,0.00002981127,0.0001141523,0.00003625707,0.0006793909,0.00004112873,0.0003276082,0.000001738606],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005595202,"about_ca_system_score_gemma":0.00001172839,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006508903,"about_ca_topic_score_gemma":0.000001496994,"domain_scores_codex":[0.9990503,0.00002593935,0.0004057011,0.0001356933,0.0001209321,0.0002614694],"domain_scores_gemma":[0.9993364,0.0001822939,0.0001411519,0.00007133682,0.0001436081,0.0001252692],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000005174528,0.00001148519,0.01232471,0.000134447,0.00005097962,3.366834e-7,0.003354513,0.4913399,0.001091643,0.000268518,0.00007746506,0.4913409],"study_design_scores_gemma":[0.000499277,0.00006207439,0.01357075,0.000197481,0.00001011877,0.0001166867,0.0001603873,0.9839265,0.0004406099,0.0006668238,0.0001804233,0.0001689157],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.678395,0.0001155465,0.3204764,0.0001484444,0.0002494594,0.0003677779,8.100854e-7,0.0001201891,0.0001264163],"genre_scores_gemma":[0.8961689,0.0000438473,0.1033954,0.00005942924,0.0002623188,0.000008232647,0.000001581413,0.00002770816,0.00003254446],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4925866,"threshold_uncertainty_score":0.533398,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2088243692","doi":"10.1007/s10288-008-0089-1","title":"Variable neighbourhood search: methods and applications","year":2008,"lang":"en","type":"article","venue":"4OR","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":258,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Group for Research in Decision Analysis; HEC Montréal","funders":"","keywords":"Metaheuristic; Heuristics; Neighbourhood (mathematics); Mathematical optimization; Computer science; Variable neighborhood search; Tabu search; Variable (mathematics); Heuristic; Simulated annealing; Local search (optimization); Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.03730997085608331,"gpt":0.3194639916222266,"spread":0.2821540207661433,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003262591,0.00006992342,0.00009397366,0.00005017252,0.0001024553,0.00001497579,0.00007115699,0.00004964714,0.0001377251],"category_scores_gemma":[0.00004102667,0.00007452644,0.00001386598,0.0002458149,0.00003038365,0.00006337685,0.00002617231,0.0001057887,0.00002402973],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001932738,"about_ca_system_score_gemma":0.00001561407,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005976393,"about_ca_topic_score_gemma":1.395586e-7,"domain_scores_codex":[0.999501,0.00006481849,0.0001089034,0.0001126769,0.00006218191,0.0001503955],"domain_scores_gemma":[0.9995874,0.0001250665,0.000009226621,0.0001762366,0.00002874028,0.00007329177],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000005550363,0.00006360917,0.003768069,0.0002006616,0.0001105543,0.000005999771,0.001338086,0.5954016,0.03152893,0.04647585,0.001628396,0.3194727],"study_design_scores_gemma":[0.0004661327,0.00002049312,0.006489823,0.00001234258,0.00002370638,0.0001012692,0.00006076237,0.9047142,0.01369496,0.00180463,0.07226645,0.0003452086],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001639507,0.0003176656,0.9750223,0.00003539567,0.00004621734,0.0001254172,0.000003729021,0.0003252714,0.02248446],"genre_scores_gemma":[0.02848443,0.00009282793,0.9707927,0.00004443954,0.00008747129,0.00004008429,0.000003600685,0.00002694506,0.0004274812],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.3191275,"threshold_uncertainty_score":0.30391,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1481088856","doi":"10.1016/s0927-0507(06)14006-2","title":"Chapter 6 Vehicle Routing","year":2006,"lang":"en","type":"book-chapter","venue":"Handbooks in operations research and management science","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":257,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"HEC Montréal","funders":"","keywords":"Computer science; Computer network","retraction":null,"screen_n_in":null,"score":{"opus":0.0595897319446535,"gpt":0.3357477211135783,"spread":0.2761579891689248,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002078666,0.0001871111,0.0001671123,0.00105471,0.0005631481,0.0004376925,0.0003731463,0.00008854613,0.00008968946],"category_scores_gemma":[0.00003458623,0.0001969047,0.00002156125,0.00022451,0.0006192462,0.0002705475,0.0003365865,0.0004382847,0.00005188618],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001872552,"about_ca_system_score_gemma":0.00003212468,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003434578,"about_ca_topic_score_gemma":0.0001334592,"domain_scores_codex":[0.9979862,0.00002104861,0.0003087611,0.0004689205,0.0007124096,0.0005026566],"domain_scores_gemma":[0.999305,0.00004993237,0.00001300791,0.0003751774,0.0001475213,0.0001093995],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000003378158,0.00001243071,0.00004268664,0.0001062006,0.00001517255,0.00003457181,0.0001746162,0.2146659,0.0006164707,0.7622906,0.0006285814,0.02140942],"study_design_scores_gemma":[0.0005205818,0.00006949342,0.0002906127,0.0006744452,0.00001072759,0.000005283216,0.00007054555,0.9573606,0.0008078219,0.00550699,0.03411439,0.0005684584],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.0005212852,0.0003898649,0.02525808,0.00008283681,0.0001221654,0.0006605998,0.000006282376,0.0001292408,0.9728296],"genre_scores_gemma":[0.1366947,0.003712986,0.06115662,0.00007200819,0.0001692619,0.0001413221,0.000024311,0.0001214706,0.7979074],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.7567836,"threshold_uncertainty_score":0.8029539,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3125883116","doi":"10.1287/mnsc.2020.3741","title":"On-Time Last-Mile Delivery: Order Assignment with Travel-Time Predictors","year":2020,"lang":"en","type":"article","venue":"Management Science","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":252,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto","funders":"National Natural Science Foundation of China; National Science Foundation","keywords":"Computer science; Heuristics; Unobservable; Vehicle routing problem; Operations research; Order (exchange); Last mile (transportation); Analytics; Big data; Service provider; Routing (electronic design automation); Mathematical optimization; Service (business); Data mining; Mile; Econometrics; Engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.008366144430866654,"gpt":0.2046425094906613,"spread":0.1962763650597946,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004174818,0.0001666502,0.0001381342,0.0001254269,0.000145216,0.0001100834,0.0004560276,0.00002322018,0.0004451048],"category_scores_gemma":[0.00003636905,0.0001473843,0.00001991287,0.001333689,0.0001321051,0.0001906652,0.000118098,0.0000948758,0.0005445014],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001026595,"about_ca_system_score_gemma":0.00001768242,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":8.599208e-7,"about_ca_topic_score_gemma":1.438724e-7,"domain_scores_codex":[0.9984244,0.00002894344,0.0001601478,0.0003856349,0.0006415941,0.0003593567],"domain_scores_gemma":[0.9994634,0.00003860091,0.00003188711,0.0002543431,0.00003474005,0.0001769982],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001011373,0.00002210005,0.0002179135,0.0000312621,0.00003175593,0.00001304163,0.0002941347,0.9895285,0.003263607,0.0006010647,0.003004997,0.002981452],"study_design_scores_gemma":[0.0003740054,0.0001389007,0.001999129,0.0000316185,0.00002337193,7.778999e-7,0.00006974269,0.9928426,0.002108156,0.00001764434,0.002161587,0.0002325192],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07935435,0.00001477501,0.7678305,0.0007883662,0.0002016152,0.0007844979,0.00001031069,0.001119972,0.1498956],"genre_scores_gemma":[0.7935189,0.00001226722,0.2034867,0.0009279108,0.00005586918,0.00003740974,0.000006234046,0.00005787087,0.00189691],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7141645,"threshold_uncertainty_score":0.6998645,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2955472062","doi":"10.1016/j.trb.2019.06.006","title":"The electric vehicle routing problem with energy consumption uncertainty","year":2019,"lang":"en","type":"article","venue":"Transportation Research Part B Methodological","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":249,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"HEC Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Vehicle routing problem; Mathematical optimization; Heuristic; Computer science; Energy consumption; Context (archaeology); Electric vehicle; Routing (electronic design automation); Operations research; Robust optimization; Engineering; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.1688924189623227,"gpt":0.3911624713733028,"spread":0.2222700524109801,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007427437,0.0001810144,0.0002559309,0.0001213476,0.0003642927,0.00009709484,0.0002799807,0.0001641726,0.0002522609],"category_scores_gemma":[0.0002421263,0.0001178809,0.00006298147,0.0008795431,0.0001399771,0.0001338074,0.00000812833,0.0006530571,0.00006411495],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009922209,"about_ca_system_score_gemma":0.00005636807,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004330031,"about_ca_topic_score_gemma":0.00007958116,"domain_scores_codex":[0.9955899,0.002187145,0.0004333184,0.0003636053,0.0007103435,0.0007157495],"domain_scores_gemma":[0.9945564,0.004648251,0.00006252428,0.0003153247,0.0002837856,0.0001337243],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003343302,0.00004385758,0.04124712,0.00009794625,0.00009683646,0.000013918,0.0003708502,0.8704417,0.02137557,0.03294532,0.0004289244,0.03260367],"study_design_scores_gemma":[0.001886973,0.0007016129,0.1298481,0.000127121,0.00004565058,0.000009136441,0.000511313,0.825379,0.01817858,0.003349005,0.01924795,0.0007155403],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6763245,0.0002845754,0.3194714,0.0003175781,0.0001380613,0.0006502203,0.000007028972,0.0005871822,0.002219421],"genre_scores_gemma":[0.9277926,0.0004677903,0.07094433,0.00004195182,0.00007247834,0.000178405,0.00004196187,0.00004564783,0.0004148069],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2514681,"threshold_uncertainty_score":0.4807044,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2233419640","doi":"10.1016/j.cor.2015.06.001","title":"Time-dependent routing problems: A review","year":2015,"lang":"en","type":"review","venue":"Computers & Operations Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":244,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Tree traversal; Arc routing; Computer science; Time horizon; Routing (electronic design automation); Graph traversal; Arc (geometry); Point (geometry); Graph; Vehicle routing problem; Point of interest; Field (mathematics); Operations research; Node (physics); Mathematical optimization; Algorithm; Artificial intelligence; Theoretical computer science; Computer network; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.1917023661751183,"gpt":0.4491348750788929,"spread":0.2574325089037747,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.007057812,0.0004816455,0.001596091,0.0006263035,0.0003507756,0.0004139046,0.001069125,0.0003117438,0.0001748317],"category_scores_gemma":[0.0005357014,0.0004447872,0.0002595306,0.001914478,0.0001104723,0.0002242421,0.0004819832,0.001600495,0.002103906],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008609124,"about_ca_system_score_gemma":0.0007726791,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001144982,"about_ca_topic_score_gemma":0.000004378861,"domain_scores_codex":[0.9944676,0.002036219,0.001108988,0.0006078926,0.001026964,0.0007523395],"domain_scores_gemma":[0.9972813,0.0004623408,0.00005084805,0.001038014,0.0008611045,0.0003063548],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[1.445715e-7,0.00001969172,3.531519e-8,0.02285389,0.00009426305,0.000008361607,0.00009095002,0.2189155,3.479218e-7,0.00004302849,0.02378195,0.7341918],"study_design_scores_gemma":[0.00007107875,0.00002023852,1.201e-8,0.03641974,0.00008586357,0.00004334818,0.00000423964,0.3131282,2.660809e-7,0.000002805845,0.6499253,0.0002989029],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[8.2169e-8,0.916354,0.07774267,0.00006758,0.0003270125,0.002510461,0.00002898513,0.0005473095,0.002421875],"genre_scores_gemma":[2.6264e-7,0.9533417,0.04361435,0.00003161877,0.0003353702,0.0005362658,0.0003041401,0.0001906635,0.001645615],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.7338929,"threshold_uncertainty_score":0.9998004,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2108538642","doi":"10.1287/trsc.34.4.426.12325","title":"Diversion Issues in Real-Time Vehicle Dispatching","year":2000,"lang":"en","type":"article","venue":"Transportation Science","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":238,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Université de Montréal; Computer Research Institute of Montréal","funders":"","keywords":"Tabu search; Computer science; Scheduling (production processes); Heuristic; Vehicle routing problem; Operations research; Routing (electronic design automation); Service (business); Engineering; Computer network; Operations management; Algorithm","retraction":null,"screen_n_in":null,"score":{"opus":0.0119139913638161,"gpt":0.2780669214422083,"spread":0.2661529300783922,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004779069,0.00007458808,0.00008756237,0.00012224,0.0000944839,0.00003468776,0.0001747615,0.00002885911,0.0005795666],"category_scores_gemma":[0.00001059816,0.0000804625,0.00001764494,0.0008761818,0.00007470615,0.0005738688,0.000001551604,0.00007043833,0.0001103352],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004901179,"about_ca_system_score_gemma":0.00001776762,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001359141,"about_ca_topic_score_gemma":0.00002500551,"domain_scores_codex":[0.9991424,0.00001822852,0.0001865233,0.000178724,0.0002734587,0.0002006333],"domain_scores_gemma":[0.9997445,0.00003055769,0.00001449067,0.0001237028,0.00002756824,0.00005920851],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000005430078,0.00001094587,0.01722393,0.00001042043,9.577618e-7,0.000004474439,0.002976432,0.9049954,0.05278736,0.0001028065,0.00002426348,0.02185751],"study_design_scores_gemma":[0.0002452884,0.000009672055,0.4469493,0.00003209801,0.000003346811,3.661364e-7,0.00007835457,0.5433169,0.008855324,0.00009483474,0.000261241,0.0001532451],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9921557,0.00001691867,0.00364185,0.00006745427,0.00005113672,0.00007540004,0.000004138682,0.0002664549,0.003720948],"genre_scores_gemma":[0.9752172,0.0001267554,0.02431828,0.00001621553,0.0000119428,0.000003007943,0.000007587314,0.00001057609,0.0002884785],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4297254,"threshold_uncertainty_score":0.6345848,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2157158960","doi":"10.1287/opre.1110.0965","title":"Benders Decomposition for Large-Scale Uncapacitated Hub Location","year":2011,"lang":"en","type":"article","venue":"Operations Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":237,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"HEC Montréal; Concordia University","funders":"","keywords":"Benders' decomposition; Mathematical optimization; Benchmark (surveying); Robustness (evolution); Heuristic; Computer science; Decomposition; Set (abstract data type); Algorithm; Reduction (mathematics); Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.1269248190035982,"gpt":0.4001214067080162,"spread":0.273196587704418,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001264072,0.00008225231,0.00008427608,0.0002514188,0.0004303397,0.0000826777,0.0001390075,0.00008632309,0.0002095027],"category_scores_gemma":[0.0001711009,0.00009031874,0.00002786715,0.0006775821,0.00003563026,0.0002616386,0.00002048956,0.0001892027,0.00008582722],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001231662,"about_ca_system_score_gemma":0.00005260226,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000323302,"about_ca_topic_score_gemma":0.0001569243,"domain_scores_codex":[0.9989226,0.0001640412,0.0001986044,0.0001725276,0.0002091514,0.0003330418],"domain_scores_gemma":[0.9989133,0.00009714744,0.000005903191,0.0002350707,0.0006700245,0.0000785533],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002014331,0.0001140771,0.0002291639,0.00007290855,0.00003734541,6.331503e-7,0.005441788,0.9647171,0.01543632,0.008785401,0.002478873,0.002666247],"study_design_scores_gemma":[0.0003229033,0.0000500809,0.0007088865,0.0000152595,0.000005354138,0.00000168759,0.0007833117,0.9786235,0.0184791,0.0002540915,0.0006475152,0.0001083122],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04329809,0.00004106715,0.9486955,0.0001048911,0.00012809,0.0005624461,0.00002328553,0.0002256828,0.006920987],"genre_scores_gemma":[0.6991071,0.00001845706,0.3000026,0.00001504859,0.00004949008,0.0002272527,0.0001280469,0.00003331044,0.0004187139],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.655809,"threshold_uncertainty_score":0.3683091,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1980175144","doi":"10.1016/j.trb.2003.09.002","title":"Waiting strategies for the dynamic pickup and delivery problem with time windows","year":2003,"lang":"en","type":"article","venue":"Transportation Research Part B Methodological","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":229,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"HEC Montréal; Simon Fraser University","funders":"","keywords":"Pickup; Computer science; Dynamic problem; Algorithm; Artificial intelligence","retraction":null,"screen_n_in":null,"score":{"opus":0.1976879323996146,"gpt":0.3989842575124488,"spread":0.2012963251128342,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006453147,0.0001537957,0.0002234333,0.00008345443,0.0003186442,0.0001002168,0.0001329931,0.0001172958,0.0001163113],"category_scores_gemma":[0.0003143403,0.000100053,0.00004383753,0.0003552959,0.000227884,0.0001653331,0.000003312422,0.0003961778,0.000005376117],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002939121,"about_ca_system_score_gemma":0.0000679719,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008814767,"about_ca_topic_score_gemma":0.00003990925,"domain_scores_codex":[0.9975144,0.001135706,0.0002953799,0.000279738,0.0003261626,0.0004486243],"domain_scores_gemma":[0.9937834,0.005709461,0.00003566115,0.000165349,0.0002142101,0.00009187684],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001815833,0.00003662392,0.001663115,0.0002495399,0.0001347954,0.00001250057,0.001304011,0.951036,0.008396451,0.02599743,0.0003087683,0.01067923],"study_design_scores_gemma":[0.002003425,0.0006172723,0.01842372,0.0001190551,0.0001137433,0.0000177716,0.004481223,0.9471421,0.003316701,0.01186763,0.01127943,0.0006179109],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.230846,0.0002989795,0.7667577,0.000222405,0.0000283342,0.0009241782,0.00001439699,0.0002199273,0.000688046],"genre_scores_gemma":[0.3963862,0.0001534692,0.6028509,0.00003187635,0.00002554036,0.0003258105,0.0000255073,0.00003619746,0.0001644264],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1655402,"threshold_uncertainty_score":0.4080045,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2046064436","doi":"10.1016/j.cor.2011.09.021","title":"A parallel iterated tabu search heuristic for vehicle routing problems","year":2011,"lang":"en","type":"article","venue":"Computers & Operations Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":228,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"HEC Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Tabu search; Vehicle routing problem; Mathematical optimization; Heuristics; Guided Local Search; Iterated local search; Heuristic; Incremental heuristic search; Computer science; Mathematics; Algorithm; Routing (electronic design automation); Beam search; Search algorithm","retraction":null,"screen_n_in":null,"score":{"opus":0.1569513745849268,"gpt":0.3647560928907695,"spread":0.2078047183058427,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002038768,0.0001780172,0.0002093331,0.000365653,0.0006431495,0.0003258364,0.0004535939,0.0001111405,0.00008561829],"category_scores_gemma":[0.0002343324,0.0001880436,0.000061921,0.0009754251,0.0001066457,0.0002747878,0.0001419686,0.000486211,0.00009185354],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001513525,"about_ca_system_score_gemma":0.0001107444,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009551538,"about_ca_topic_score_gemma":0.00002618815,"domain_scores_codex":[0.9977123,0.0003910021,0.0004068328,0.0003716707,0.0003763614,0.0007418652],"domain_scores_gemma":[0.9982624,0.0003662044,0.00001069451,0.0004249707,0.0007500992,0.0001856425],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001339827,0.00006269795,0.0002516045,0.00008537772,0.00004262885,0.000004245191,0.004324763,0.9836853,0.002419654,0.002487828,0.001021521,0.005600997],"study_design_scores_gemma":[0.0005721003,0.0001062344,0.0006363356,0.0000645438,0.000005495867,0.0000060164,0.000159777,0.9959787,0.001814357,0.00009570843,0.0003570467,0.000203668],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05789894,0.0001190231,0.9386768,0.0001218253,0.0002329527,0.00112147,0.00001480464,0.0004387395,0.001375399],"genre_scores_gemma":[0.6178319,0.00002273211,0.3813332,0.00002637081,0.0001144234,0.0002337872,0.00004544564,0.00006357762,0.000328603],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5599329,"threshold_uncertainty_score":0.7668197,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2135680984","doi":"10.1287/trsc.1070.0223","title":"Tabu Search, Partial Elementarity, and Generalized <i>k</i>-Path Inequalities for the Vehicle Routing Problem with Time Windows","year":2008,"lang":"en","type":"article","venue":"Transportation Science","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":227,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Polytechnique Montréal; Group for Research in Decision Analysis","funders":"","keywords":"Column generation; Vehicle routing problem; Tabu search; Mathematical optimization; Benchmark (surveying); Generalization; Heuristic; Routing (electronic design automation); Path (computing); Set (abstract data type); Computer science; Relaxation (psychology); Shortest path problem; Mathematics; Graph; Theoretical computer science","retraction":null,"screen_n_in":null,"score":{"opus":0.03526509438761437,"gpt":0.2763590414941016,"spread":0.2410939471064872,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001077044,0.0001123808,0.0001224617,0.00005602002,0.0005357256,0.00006436481,0.0001731067,0.00002429538,0.00002127016],"category_scores_gemma":[0.00001815835,0.00008422788,0.00002162185,0.0004464596,0.0003276221,0.0003935876,0.000005348725,0.00008619832,0.000001892493],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002962169,"about_ca_system_score_gemma":0.00009005233,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009405001,"about_ca_topic_score_gemma":0.00003777423,"domain_scores_codex":[0.9988167,0.00003115987,0.0002537287,0.0002162041,0.0003778682,0.0003043029],"domain_scores_gemma":[0.9994735,0.0001446698,0.00004422565,0.0001389256,0.000125317,0.0000734382],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004895938,0.00001802092,0.03505726,0.00005923133,0.0000224835,0.000002808441,0.01314132,0.9169374,0.02996005,0.00263386,0.00005571728,0.00206296],"study_design_scores_gemma":[0.001122268,0.00006766857,0.03717532,0.0000237147,0.00002734129,0.00000613979,0.0003774705,0.9379273,0.02267597,0.00006040123,0.0003166034,0.0002198054],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7052914,0.00003385648,0.2938739,0.0001273376,0.00003135904,0.0003977469,0.00002274722,0.0001381275,0.00008350238],"genre_scores_gemma":[0.8933262,0.00002734797,0.1063803,0.0000748908,0.00003300517,0.0000551323,0.00001515961,0.00001798587,0.00006997936],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1880349,"threshold_uncertainty_score":0.4120423,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2055819286","doi":"10.1007/s00291-008-0135-6","title":"Dynamic transportation of patients in hospitals","year":2008,"lang":"en","type":"article","venue":"OR Spectrum","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":224,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"HEC Montréal","funders":"","keywords":"Computer science; Heuristic; Context (archaeology); Scheme (mathematics); Tabu search; Service (business); Quality (philosophy); Operations research; Simple (philosophy); Phase (matter); Mathematical optimization; Algorithm; Artificial intelligence; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.006537182786802548,"gpt":0.2320161659181334,"spread":0.2254789831313309,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00004853898,0.00005891799,0.000103882,0.0000789831,0.0000110088,0.000001612452,0.0000496866,0.00003541131,0.00007870727],"category_scores_gemma":[0.00001190125,0.00005871341,0.00002041606,0.0002242914,0.00001289794,0.00006274759,0.000001449652,0.00005345307,0.00000549301],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004011325,"about_ca_system_score_gemma":0.000007972475,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001367074,"about_ca_topic_score_gemma":0.00008445877,"domain_scores_codex":[0.9995466,0.00001123637,0.0001872331,0.00006763847,0.00008246595,0.0001047735],"domain_scores_gemma":[0.9998457,0.00001739371,0.00002526865,0.00008343591,0.000009949457,0.00001819406],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002262199,0.00006001337,0.5448043,0.00006164886,0.00001234917,0.00001318957,0.001521791,0.4503245,0.0004231593,0.00008908704,0.00004892938,0.002618379],"study_design_scores_gemma":[0.0003908701,0.00004904502,0.9405306,0.00001317858,0.000002725635,5.25233e-7,0.0000163119,0.05602889,0.002790213,0.00004441564,0.00004656291,0.00008664486],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9871818,0.00002323467,0.0120713,0.000009984858,0.0001011828,0.00009948211,0.000009964706,0.00007573279,0.0004272439],"genre_scores_gemma":[0.9823849,0.00009278028,0.01741171,0.000003554379,0.000004049223,0.000005084763,0.00001712729,0.00001773586,0.00006312845],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3957263,"threshold_uncertainty_score":0.2394263,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null}]}