{"meta":{"query_hash":"c746e7dad116","filters":{"topic":"Vehicle Routing Optimization Methods"},"cohort_total":1187,"direct_labels_cover":1,"predictions_cover":1187,"exported":1187,"export_cap":100000,"truncated":false,"label_status":"direct model label, unvalidated","prediction_status":"machine_predicted_unvalidated (Codex and Gemma teacher distillation)","score_status":"score_only:v0-immature-baseline","snapshot":{"source":"OpenAlex, pinned release, all 482 partitions","release":"2026-06-24","frame_built":"2026-07-12"},"permalink":"https://metacan.xera.ac/q/c746e7dad116","api":"https://metacan.xera.ac/api/v1/cohort?topic=Vehicle+Routing+Optimization+Methods"},"results":[{"id":"W1006079010","doi":"","title":"Specific Multi-trip Operators for Vehicle Routing Problems","year":2014,"lang":"en","type":"article","venue":"ORBi (University of Liège)","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"","keywords":"Vehicle routing problem; Computer science; Routing (electronic design automation); Transport engineering; Computer network; Engineering","score_opus":0.023581155891998794,"score_gpt":0.21911758689420915,"score_spread":0.19553643100221035,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1006079010","genre_codex":"editorial","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0012643121,0.03563774,0.015483447,0.059721507,0.85024464,0.00008407797,0.0005125968,0.00015610123,0.036895465],"genre_scores_gemma":[0.029711714,0.060582906,0.013266684,0.016549623,0.70483595,0.000119891454,0.0010319902,0.0003901164,0.17351106],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991431,0.00020399957,0.00007732922,0.00017266837,0.00034891214,0.000053966585],"domain_scores_gemma":[0.99497956,0.0015010365,0.00022279649,0.0001697298,0.0028464843,0.00028034466],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017523284,0.0008567774,0.0006833688,0.0012125185,0.0006261345,0.0019044807,0.0012076072,0.0026861199,0.0142564075],"category_scores_gemma":[0.005519153,0.00029056944,0.0007935287,0.0006835079,0.000821571,0.0014774096,0.00044892842,0.0039452156,0.0044341944],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026485173,0.000013650845,0.00005105428,0.00039568933,0.000020541107,0.00008597178,0.000012314248,0.00082705775,0.0002475849,0.006723842,0.96132636,0.030269405],"study_design_scores_gemma":[0.00002350014,0.000022273263,0.00031859783,0.00030642204,0.000027048418,0.00015490568,0.00003563384,0.0024716726,0.0003980487,0.0069952025,0.98923177,0.000014917895],"about_ca_topic_score_codex":0.001269994,"about_ca_topic_score_gemma":0.0024438156,"teacher_disagreement_score":0.0142564075,"about_ca_system_score_codex":0.00082135614,"about_ca_system_score_gemma":0.0009757018,"threshold_uncertainty_score":0.047692478},"labels":[],"label_agreement":null},{"id":"W101593720","doi":"","title":"Le problème d'approvisionnement des stations d'essence","year":2000,"lang":"fr","type":"article","venue":"Journal Européen des Systèmes Automatisés","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Heuristics; Heuristic; Welfare economics; Operations research; Computer science; Engineering; Economics; Artificial intelligence","score_opus":0.021054518884501036,"score_gpt":0.26712978482939653,"score_spread":0.24607526594489548,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W101593720","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.068933465,0.0004074577,0.92380077,0.0003614529,0.000046425303,0.00013903962,0.000114810944,0.00053784903,0.0056586005],"genre_scores_gemma":[0.59178156,0.0006617624,0.39290527,0.00011871412,0.000075156284,0.00019998621,0.00033838252,0.00029156797,0.013627638],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993443,0.00023572535,0.000020036405,0.00017319877,0.00012354547,0.000103249935],"domain_scores_gemma":[0.9986817,0.0009566861,0.00009285771,0.000092461465,0.000103890525,0.000072331175],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014260114,0.00097278773,0.0009989205,0.00070846727,0.0009337016,0.0021650533,0.0014012228,0.002048468,0.0088275485],"category_scores_gemma":[0.0041366033,0.0006013534,0.0009391118,0.001105711,0.001108953,0.0020106176,0.0012667797,0.0014274971,0.0009033598],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00043190227,0.00008597339,0.0024478897,0.00031024968,0.00005380642,0.00022046601,0.00043674637,0.7457983,0.0055740154,0.046277147,0.0029605858,0.19540286],"study_design_scores_gemma":[0.00015372375,0.00030380173,0.00096801895,0.000039038998,0.000054217242,0.00032697557,0.00044304726,0.9328301,0.006300131,0.041155305,0.017394433,0.000031296113],"about_ca_topic_score_codex":0.004519003,"about_ca_topic_score_gemma":0.0031237986,"teacher_disagreement_score":0.0088275485,"about_ca_system_score_codex":0.00097523985,"about_ca_system_score_gemma":0.0010039762,"threshold_uncertainty_score":0.029531062},"labels":[],"label_agreement":null},{"id":"W104314163","doi":"10.1023/a:1020759332183","title":"Minmax p-Traveling Salesmen Location Problems on a Tree","year":2002,"lang":"en","type":"article","venue":"Annals of Operations Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"The Scarborough Hospital; University of Toronto","funders":"","keywords":"Theory of computation; Order (exchange); Time complexity; Mathematics; Combinatorics; Mathematical optimization; Computer science; Tree (set theory); Algorithm","score_opus":0.31706850734061026,"score_gpt":0.4308538352548084,"score_spread":0.11378532791419815,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W104314163","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09350814,0.0015266014,0.87090343,0.002736328,0.0001438979,0.00038901743,0.0019205315,0.00030786695,0.028564183],"genre_scores_gemma":[0.53760135,0.0023421005,0.42043847,0.00042704857,0.0003835003,0.00080679066,0.0020884373,0.00043817543,0.035474207],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.999215,0.00036234868,0.000044386936,0.00017437006,0.00008130441,0.00012243031],"domain_scores_gemma":[0.997632,0.001838371,0.00018972356,0.00007890653,0.00013306711,0.00012790645],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001728914,0.0015218484,0.0021908684,0.0011990954,0.0009466071,0.0025365164,0.0022472665,0.002881407,0.0115095],"category_scores_gemma":[0.005531079,0.001020918,0.001321882,0.0026622482,0.0010392284,0.004952258,0.002103436,0.0019487891,0.0012014507],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00042560778,0.00028758953,0.0012650972,0.000671021,0.00012486512,0.00026581113,0.0001611051,0.74597234,0.0016506888,0.15396166,0.0141822165,0.081032],"study_design_scores_gemma":[0.00005088052,0.00012425488,0.00028078363,0.00004137997,0.000033194963,0.00008705474,0.00009123893,0.87986314,0.0006568612,0.115747675,0.0030093822,0.000014185825],"about_ca_topic_score_codex":0.0026703964,"about_ca_topic_score_gemma":0.0034153848,"teacher_disagreement_score":0.0115095,"about_ca_system_score_codex":0.0015704915,"about_ca_system_score_gemma":0.0012061662,"threshold_uncertainty_score":0.03850311},"labels":[],"label_agreement":null},{"id":"W106072245","doi":"10.1007/978-3-319-13132-0_9","title":"Investigating Metaheuristics Applications for Capacitated Location Allocation Problem on Logistics Networks","year":2014,"lang":"en","type":"book-chapter","venue":"Studies in computational intelligence","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Metaheuristic; Tabu search; Computer science; Simulated annealing; Operations research; Ant colony optimization algorithms; Facility location problem; Genetic algorithm; Service (business); Service provider; Revenue; Mathematical optimization; Business; Engineering; Artificial intelligence; Mathematics","score_opus":0.12223287670963154,"score_gpt":0.35506640463304245,"score_spread":0.23283352792341092,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W106072245","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.061157178,0.028645122,0.76012355,0.002239305,0.0007597005,0.00015124472,0.00018139971,0.00031214923,0.14643037],"genre_scores_gemma":[0.45834765,0.02922538,0.45439425,0.0005793446,0.000749867,0.00025501882,0.00043148574,0.000367431,0.05564956],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996948,0.00014267741,0.000009062215,0.0000375362,0.00007428937,0.00004164661],"domain_scores_gemma":[0.99950993,0.00036124318,0.000024258177,0.000024859555,0.00006328905,0.000016429753],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006782279,0.0010654401,0.0005854598,0.0008186856,0.00037989864,0.0018934086,0.0012023872,0.0013744949,0.0043087653],"category_scores_gemma":[0.001809055,0.0004094533,0.0010194052,0.0026157731,0.00041111282,0.0014199364,0.0008035792,0.0015513713,0.0006061687],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009101244,0.00016809642,0.00054915884,0.0005360143,0.00009804586,0.00016110639,0.00014235689,0.68181765,0.0030526854,0.14107817,0.011093577,0.16121216],"study_design_scores_gemma":[0.000010194833,0.000050787643,0.00025182875,0.00009281453,0.00002615885,0.000058822287,0.00010433577,0.93860894,0.0011691556,0.044538412,0.015078422,0.00001013537],"about_ca_topic_score_codex":0.0027810626,"about_ca_topic_score_gemma":0.0024088086,"teacher_disagreement_score":0.0043087653,"about_ca_system_score_codex":0.0010989372,"about_ca_system_score_gemma":0.00061477686,"threshold_uncertainty_score":0.014414251},"labels":[],"label_agreement":null},{"id":"W1063455699","doi":"10.1007/s10601-015-9196-8","title":"A hybrid constraint programming approach to a wood procurement problem with bucking decisions","year":2015,"lang":"en","type":"article","venue":"Constraints","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"FPInnovations","keywords":"Procurement; Flexibility (engineering); Constraint (computer-aided design); Constraint programming; Integer programming; Mathematical optimization; Computer science; Set (abstract data type); Programming paradigm; Operations research; Mathematics; Stochastic programming; Business","score_opus":0.05045296916200682,"score_gpt":0.2725449922231356,"score_spread":0.2220920230611288,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1063455699","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0046338243,0.00025074702,0.9875848,0.00017691775,0.000053262327,0.0000744913,0.00013469387,0.00006539225,0.007025835],"genre_scores_gemma":[0.20812333,0.0009369044,0.7707762,0.00034879736,0.00017225234,0.0007516433,0.00044103872,0.00025907555,0.01819075],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998735,0.0005898099,0.000047861267,0.00020307457,0.0003231358,0.00010108266],"domain_scores_gemma":[0.9974674,0.0020148095,0.00012395225,0.00009435637,0.00022177272,0.00007782653],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022578838,0.0015386035,0.0016800979,0.001629709,0.0007956184,0.0026941807,0.0035042844,0.0027800023,0.010315926],"category_scores_gemma":[0.0040937443,0.0017366533,0.0018497964,0.0034030005,0.0011274215,0.0021386815,0.001617188,0.0021024484,0.00069213496],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015118505,0.000038632166,0.000094565585,0.000075848526,0.000030839663,0.00006868234,0.000022801327,0.9673378,0.00030227297,0.021146256,0.0008134349,0.010053766],"study_design_scores_gemma":[0.0000084794865,0.000012439231,0.00002992723,0.000011452696,0.000008157583,0.000014445017,0.00001106059,0.9923028,0.000106254345,0.006459611,0.001029021,0.00000641363],"about_ca_topic_score_codex":0.013245713,"about_ca_topic_score_gemma":0.013309553,"teacher_disagreement_score":0.013245713,"about_ca_system_score_codex":0.0015021904,"about_ca_system_score_gemma":0.0018857698,"threshold_uncertainty_score":0.034510195},"labels":[],"label_agreement":null},{"id":"W115258892","doi":"10.1007/978-94-017-8899-1_8","title":"Transportation and Routing","year":2014,"lang":"en","type":"book-chapter","venue":"Managing forest ecosystems","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Procurement; Operations research; Vehicle routing problem; Mode (computer interface); Transport engineering; Routing (electronic design automation); Selection (genetic algorithm); Computer science; Flow network; Transportation planning; Strategic planning; Business; Engineering; Marketing","score_opus":0.010805493972880869,"score_gpt":0.20601056618323377,"score_spread":0.1952050722103529,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W115258892","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00077981537,0.021146785,0.08584452,0.003326944,0.0031278406,0.000056496585,0.00043876123,0.00060465175,0.8846742],"genre_scores_gemma":[0.009462258,0.018634237,0.017565068,0.0006104721,0.00091224734,0.00007854013,0.00046229325,0.00039434293,0.9518805],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997234,0.000040692812,0.000010267934,0.000063822,0.00013785408,0.000023955812],"domain_scores_gemma":[0.99988675,0.000027579556,0.0000068847303,0.000026756323,0.000040766707,0.000011245817],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027411108,0.0015104861,0.0006776403,0.001118898,0.0008138449,0.0029373989,0.0009120465,0.0013576548,0.06784556],"category_scores_gemma":[0.0007215494,0.0005085228,0.00040559022,0.002113879,0.0009943169,0.0037016494,0.0013006761,0.0021683027,0.02883082],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009145836,0.000029488454,0.000083868945,0.00017359862,0.000012153737,0.000029431534,0.00011774724,0.0076291994,0.0006032238,0.34901485,0.27864927,0.36364803],"study_design_scores_gemma":[0.000001497728,0.00000573438,0.00007283899,0.00010054993,0.0000035808835,0.00003688489,0.00004493056,0.0025444923,0.00017657349,0.067915335,0.92909116,0.0000063120965],"about_ca_topic_score_codex":0.005526271,"about_ca_topic_score_gemma":0.008265783,"teacher_disagreement_score":0.06784556,"about_ca_system_score_codex":0.0018815993,"about_ca_system_score_gemma":0.001343607,"threshold_uncertainty_score":0.22696596},"labels":[],"label_agreement":null},{"id":"W1153261897","doi":"10.71781/10668","title":"Models and algorithms for the capacitated location-routing problem","year":2011,"lang":"en","type":"dissertation","venue":"","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Fonds Québécois de la Recherche sur la Nature et les Technologies","keywords":"Computer science; Routing (electronic design automation); Vehicle routing problem; Algorithm; Mathematical optimization; Mathematics; Computer network","score_opus":0.05035413735251719,"score_gpt":0.28820919084725705,"score_spread":0.23785505349473984,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1153261897","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0063531674,0.0019975195,0.9746431,0.0012024536,0.00015591958,0.000120797,0.00044288285,0.00035753474,0.014726595],"genre_scores_gemma":[0.31283557,0.0071169003,0.6393744,0.00070599467,0.0005676166,0.0013476374,0.0023871197,0.0006524852,0.035012286],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99787617,0.00076802,0.00011229547,0.0004276667,0.00049972185,0.0003161194],"domain_scores_gemma":[0.99646556,0.0024808599,0.00029432506,0.0002545651,0.00035797927,0.00014664941],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018220561,0.0024391424,0.0017271389,0.0013841637,0.00094589114,0.0047391434,0.0037611073,0.0036388552,0.010826524],"category_scores_gemma":[0.008089509,0.0014384148,0.0025723835,0.0031090735,0.0015083554,0.0051384363,0.0026487038,0.005202263,0.002389253],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006439796,0.00007511543,0.00035540422,0.0002626859,0.000047351306,0.00006206164,0.000112616326,0.8172061,0.00033563966,0.15545662,0.0046116863,0.021410348],"study_design_scores_gemma":[0.000020834792,0.00002772712,0.000074791715,0.00003772938,0.00001483902,0.00004379298,0.000042719814,0.896865,0.00016161434,0.09660716,0.00609007,0.000013796615],"about_ca_topic_score_codex":0.009209896,"about_ca_topic_score_gemma":0.010651526,"teacher_disagreement_score":0.010826524,"about_ca_system_score_codex":0.0038623966,"about_ca_system_score_gemma":0.003378097,"threshold_uncertainty_score":0.036218345},"labels":[],"label_agreement":null},{"id":"W117190991","doi":"10.1007/978-0-387-77778-8_16","title":"One-to-Many-to-One Single Vehicle Pickup and Delivery Problems","year":2008,"lang":"en","type":"book-chapter","venue":"Operations research, computer science. Interface series","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":42,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Group for Research in Decision Analysis; HEC Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Norges Forskningsråd","keywords":"Pickup; Computer science; Artificial intelligence","score_opus":0.0818470942801059,"score_gpt":0.3078218029519631,"score_spread":0.2259747086718572,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W117190991","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.029087208,0.005389752,0.9095117,0.0020439932,0.0010375082,0.00030159712,0.00067631685,0.00038569525,0.0515662],"genre_scores_gemma":[0.44699648,0.011609838,0.35535324,0.00093719305,0.0016252267,0.0007670426,0.0016268783,0.00043889138,0.1806452],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99860734,0.00040145888,0.00007942888,0.00036390623,0.0003096473,0.00023832427],"domain_scores_gemma":[0.99803466,0.0013239112,0.00015649233,0.00017131254,0.0001783201,0.00013527663],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017274217,0.0021935897,0.0038737769,0.00094381836,0.0015652529,0.0031249481,0.005035043,0.0038171962,0.019175116],"category_scores_gemma":[0.004346045,0.0009776517,0.0028053296,0.0025441914,0.001713695,0.0048652603,0.0028672528,0.0031136612,0.0024294655],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002655443,0.0002453698,0.00044555354,0.0011793377,0.00022226352,0.00048229742,0.00018314921,0.41428122,0.0012391687,0.39280078,0.03026453,0.15839075],"study_design_scores_gemma":[0.00008162191,0.00012824983,0.00022105443,0.000063670595,0.000065237466,0.00034305663,0.000082120285,0.65277123,0.00082478544,0.3334811,0.011895495,0.000042300937],"about_ca_topic_score_codex":0.0015828083,"about_ca_topic_score_gemma":0.0017876958,"teacher_disagreement_score":0.019175116,"about_ca_system_score_codex":0.0014855016,"about_ca_system_score_gemma":0.0015357301,"threshold_uncertainty_score":0.064147115},"labels":[],"label_agreement":null},{"id":"W120760926","doi":"10.1007/978-1-4419-1306-7_10","title":"Variable Intensity Local Search","year":2009,"lang":"en","type":"book-chapter","venue":"Annals of information systems","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Integer programming; Solver; Variable neighborhood search; Local search (optimization); Mathematical optimization; Heuristic; Variable (mathematics); Guided Local Search; Mathematics; Computer science; Integer (computer science); Metaheuristic","score_opus":0.04959168020910995,"score_gpt":0.2827534968050394,"score_spread":0.23316181659592947,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W120760926","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004374589,0.007390223,0.84832865,0.00097113184,0.00054478913,0.00005590172,0.0001984309,0.00089129753,0.13724495],"genre_scores_gemma":[0.19007093,0.0071164914,0.47057202,0.0009196186,0.0006070568,0.00038882397,0.00088719564,0.001364274,0.32807356],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997061,0.000080596576,0.000010703978,0.00006475635,0.000112445836,0.000025315941],"domain_scores_gemma":[0.99973696,0.00011995079,0.000017012704,0.000057298577,0.00005466676,0.0000139800495],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00054102397,0.00075833395,0.0012060112,0.0007314141,0.0005171778,0.0012438986,0.0012473939,0.0012373031,0.017689856],"category_scores_gemma":[0.0017996732,0.0005061667,0.0005303116,0.0013953036,0.00094653445,0.0015463753,0.00133483,0.0016883388,0.005358323],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007636253,0.00007502837,0.00022359753,0.00028376817,0.000060911305,0.00006670063,0.00010084027,0.1445629,0.0021286034,0.34600428,0.07832705,0.42809],"study_design_scores_gemma":[0.00004525459,0.00006767608,0.00033555567,0.0001358812,0.00004504997,0.00022281634,0.00005230345,0.58461577,0.0025998014,0.29432082,0.117516264,0.000042784774],"about_ca_topic_score_codex":0.001760054,"about_ca_topic_score_gemma":0.0029374198,"teacher_disagreement_score":0.017689856,"about_ca_system_score_codex":0.0008908826,"about_ca_system_score_gemma":0.00068887556,"threshold_uncertainty_score":0.05917847},"labels":[],"label_agreement":null},{"id":"W1421911707","doi":"10.1016/j.ejor.2015.07.048","title":"The traveling salesman problem with time-dependent service times","year":2015,"lang":"en","type":"article","venue":"European Journal of Operational Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":49,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; HEC Montréal","funders":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Travelling salesman problem; Service (business); Computer science; Mathematical optimization; Quadratic equation; Duration (music); Computation; Measure (data warehouse); Mathematics; Algorithm; Data mining","score_opus":0.07773001912674993,"score_gpt":0.3329230441880737,"score_spread":0.2551930250613238,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1421911707","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07363591,0.0022082168,0.9111008,0.0019083115,0.00038709867,0.00010949757,0.00055171445,0.00014985824,0.009948649],"genre_scores_gemma":[0.76238173,0.0046718237,0.18227528,0.00045681206,0.0007884353,0.0003956442,0.0011207871,0.00036216024,0.04754741],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99825114,0.0007613818,0.00007671042,0.00037882448,0.00027140888,0.00026046953],"domain_scores_gemma":[0.9953526,0.0033943586,0.0004707408,0.00014340732,0.00035026585,0.00028862766],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029271217,0.0018386933,0.0022195978,0.0012761154,0.0008707731,0.002855198,0.004174516,0.003733887,0.0050366223],"category_scores_gemma":[0.012191675,0.0018560755,0.0017947032,0.0027616287,0.0018263488,0.0046654525,0.0018581279,0.0030734362,0.00061821355],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016568552,0.000094468254,0.0004921231,0.0002454298,0.00010995285,0.0002806901,0.00010651589,0.8352083,0.0008239859,0.1486334,0.0028941117,0.010945292],"study_design_scores_gemma":[0.000019659146,0.000023510189,0.000094566334,0.000010572495,0.000023360959,0.000037000194,0.00003080972,0.9665837,0.00012799328,0.03215285,0.00088333763,0.0000127421645],"about_ca_topic_score_codex":0.013613285,"about_ca_topic_score_gemma":0.006462944,"teacher_disagreement_score":0.013613285,"about_ca_system_score_codex":0.0026522144,"about_ca_system_score_gemma":0.0032603445,"threshold_uncertainty_score":0.027068079},"labels":[],"label_agreement":null},{"id":"W1428828117","doi":"10.1016/j.ejor.2015.06.073","title":"The multi-vehicle traveling purchaser problem with pairwise incompatibility constraints and unitary demands: A branch-and-price approach","year":2015,"lang":"en","type":"article","venue":"European Journal of Operational Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":69,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Column generation; Mathematical optimization; Pairwise comparison; Vehicle routing problem; Purchasing; Computer science; Branch and bound; Scheduling (production processes); Traveling purchaser problem; Shortest path problem; Branch and price; Branch and cut; Integer programming; Graph; Operations research; Routing (electronic design automation); Mathematics; Combinatorial optimization; 2-opt; Economics","score_opus":0.10674453579939322,"score_gpt":0.3311792832241026,"score_spread":0.2244347474247094,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1428828117","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.045983445,0.0015499124,0.93165815,0.0014353334,0.0001488859,0.00032453338,0.0004071275,0.00014833629,0.018344175],"genre_scores_gemma":[0.58955216,0.0026261066,0.37876472,0.00037939835,0.00040900626,0.0006990686,0.0007131679,0.0003714117,0.026484942],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983419,0.0008693913,0.00005654942,0.0002585997,0.00024181897,0.000231693],"domain_scores_gemma":[0.99482274,0.004319007,0.00023421389,0.00010779394,0.00021661699,0.00029958988],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003529325,0.0020855903,0.0052086096,0.0025286472,0.0012642521,0.0038073563,0.005052066,0.0054079904,0.013323732],"category_scores_gemma":[0.008070973,0.0030644932,0.0025683455,0.003951815,0.0027351438,0.0061861146,0.0027184002,0.0035076095,0.00094404374],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001665936,0.00021069529,0.0005051026,0.00028073112,0.000115333634,0.0002900798,0.00007945618,0.9125244,0.0004995706,0.06681696,0.00287012,0.01564091],"study_design_scores_gemma":[0.000031331714,0.000037754,0.000093990086,0.000016218091,0.00002724354,0.000035510024,0.00003407613,0.9735108,0.00008663687,0.02563074,0.0004827019,0.0000130082835],"about_ca_topic_score_codex":0.009222946,"about_ca_topic_score_gemma":0.008013498,"teacher_disagreement_score":0.013323732,"about_ca_system_score_codex":0.0026041463,"about_ca_system_score_gemma":0.002735205,"threshold_uncertainty_score":0.044572294},"labels":[],"label_agreement":null},{"id":"W1446708373","doi":"10.1016/j.ifacol.2015.06.122","title":"A Column Generation Based Heuristic for the Capacitated Vehicle Routing Problem with Three-dimensional Loading Constraints","year":2015,"lang":"en","type":"article","venue":"IFAC-PapersOnLine","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Vehicle routing problem; Column generation; Tabu search; FIFO and LIFO accounting; Heuristic; Mathematical optimization; Routing (electronic design automation); Computer science; Shortest path problem; Cuboid; Path (computing); Algorithm; Mathematics; FIFO (computing and electronics); Theoretical computer science","score_opus":0.0472159441833659,"score_gpt":0.2604255402504211,"score_spread":0.2132095960670552,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1446708373","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03378044,0.0005058534,0.9559677,0.00020126162,0.000111343696,0.0002764567,0.00022721133,0.0008505779,0.008079061],"genre_scores_gemma":[0.35170487,0.00040458963,0.6430487,0.00024196572,0.00005990397,0.0004942823,0.0007073923,0.0002078716,0.0031303917],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996911,0.00010483277,0.000010700328,0.0000436127,0.00007596476,0.000073838455],"domain_scores_gemma":[0.99935156,0.0004017845,0.00006255117,0.00004326979,0.00009677154,0.00004407341],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003881366,0.0010291262,0.000914557,0.0011724544,0.00071832683,0.00078620366,0.0010349641,0.0009629205,0.0049837474],"category_scores_gemma":[0.0010838588,0.00054578856,0.00070896774,0.0015510678,0.00051614875,0.0006315413,0.0006599082,0.0007433032,0.00060730154],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007507843,0.00012488091,0.00035249852,0.00010730271,0.000032792334,0.00013623605,0.00005735831,0.898484,0.002825136,0.0058903024,0.0034438432,0.08847061],"study_design_scores_gemma":[0.000021983305,0.000054966837,0.000073511466,0.00000815782,0.000010849261,0.00003843889,0.000023081146,0.9960674,0.00079630566,0.0018786478,0.0010175719,0.00000906872],"about_ca_topic_score_codex":0.0071285055,"about_ca_topic_score_gemma":0.008686782,"teacher_disagreement_score":0.0071285055,"about_ca_system_score_codex":0.0008121774,"about_ca_system_score_gemma":0.0014287124,"threshold_uncertainty_score":0.016672313},"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,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"","keywords":"Computer science; Computer network","score_opus":0.0595897319446535,"score_gpt":0.3357477211135783,"score_spread":0.2761579891689248,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1481088856","genre_codex":"other","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0006330682,0.011709714,0.061588272,0.000994273,0.0034709775,0.00013005309,0.0011560761,0.0017997924,0.9185177],"genre_scores_gemma":[0.004648298,0.0094892355,0.016129231,0.0004007593,0.00049735914,0.00008956215,0.0015834467,0.00051085633,0.9666512],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99965537,0.00002461629,0.000011451766,0.00006418522,0.00021590636,0.000028460867],"domain_scores_gemma":[0.99987197,0.000016735192,0.0000051069205,0.000026142383,0.0000668057,0.000013215645],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020563787,0.001533766,0.00076363166,0.0015194541,0.0008534372,0.0021369061,0.0014551039,0.0013030737,0.17996114],"category_scores_gemma":[0.00054839795,0.00063630217,0.00076235784,0.0016387766,0.00038040022,0.0021635776,0.0011467536,0.0019289182,0.10512632],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002321645,0.00009560903,0.00007081114,0.00037005416,0.000013192517,0.000066153974,0.00009829709,0.007034966,0.0027023053,0.09330861,0.45880198,0.43741482],"study_design_scores_gemma":[0.00000262791,0.000014766953,0.000073165225,0.000105011844,0.0000052812516,0.00007629902,0.000024063587,0.0011685087,0.00077213865,0.017381588,0.98036903,0.0000074266504],"about_ca_topic_score_codex":0.0019941032,"about_ca_topic_score_gemma":0.0034135382,"teacher_disagreement_score":0.17996114,"about_ca_system_score_codex":0.0009041068,"about_ca_system_score_gemma":0.0010861399,"threshold_uncertainty_score":0.60203004},"labels":[],"label_agreement":null},{"id":"W1488405589","doi":"10.1007/3-540-45105-6_80","title":"A Hybrid Genetic Algorithm for the Capacitated Vehicle Routing Problem","year":2003,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":102,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Defence Research and Development Canada","funders":"","keywords":"Vehicle routing problem; Computer science; Benchmark (surveying); Genetic algorithm; Mathematical optimization; Diversification (marketing strategy); Key (lock); Routing (electronic design automation); Mathematics; Machine learning; Computer network","score_opus":0.01650154669074892,"score_gpt":0.23885929483689414,"score_spread":0.22235774814614523,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1488405589","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01793873,0.00036999318,0.97334856,0.00017421418,0.00010621769,0.00005936288,0.000045148885,0.0005043834,0.0074533867],"genre_scores_gemma":[0.29052097,0.0003877998,0.69971436,0.00022770304,0.000098273755,0.00030165148,0.00019962774,0.0001680845,0.008381546],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997098,0.00008407615,0.000008469724,0.000042092975,0.00012009552,0.000035455578],"domain_scores_gemma":[0.9996741,0.00018446743,0.00001798484,0.000024969571,0.00007987223,0.000018667168],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005632811,0.0007150079,0.0008861016,0.0007314807,0.00041384067,0.0008636111,0.0016206523,0.0018103136,0.0036135763],"category_scores_gemma":[0.0011884136,0.00041280733,0.0005586439,0.0011223621,0.0005248287,0.000742929,0.0008718004,0.0009135285,0.00062920817],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007271447,0.000078342266,0.00025753374,0.00004266111,0.0000408106,0.00005134833,0.00003859863,0.8618046,0.0020523102,0.011697883,0.0019984737,0.12186466],"study_design_scores_gemma":[0.000021811778,0.000022273924,0.00004024814,0.0000043161444,0.0000068140357,0.000012640229,0.00000423738,0.996806,0.00020043149,0.002122043,0.00075564394,0.0000035608516],"about_ca_topic_score_codex":0.004927002,"about_ca_topic_score_gemma":0.0043844655,"teacher_disagreement_score":0.004927002,"about_ca_system_score_codex":0.00063860323,"about_ca_system_score_gemma":0.0008473509,"threshold_uncertainty_score":0.012088597},"labels":[],"label_agreement":null},{"id":"W1493213455","doi":"10.1111/j.1538-4632.2009.00746.x","title":"The Maximal Covering Problem with Some Negative Weights","year":2009,"lang":"en","type":"article","venue":"Geographical Analysis","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":39,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Simulated annealing; Integer programming; Heuristic; Mathematical optimization; Computer science; Integer (computer science); Algorithm; Set (abstract data type); Mathematics","score_opus":0.004907159280361349,"score_gpt":0.2124023879436228,"score_spread":0.20749522866326145,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1493213455","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.25663626,0.00096841424,0.70717967,0.00095081533,0.00016029576,0.00012089678,0.0005085699,0.00028978573,0.03318525],"genre_scores_gemma":[0.84216726,0.0007248177,0.14916861,0.00015297685,0.00015132013,0.00015948545,0.0006542062,0.00016296947,0.00665846],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99850345,0.00067054946,0.00006469992,0.0002560538,0.00027486522,0.00023034636],"domain_scores_gemma":[0.9981342,0.0012920417,0.00015290722,0.00017682454,0.00013545666,0.00010860619],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015145788,0.001028374,0.0008842596,0.0006210403,0.00092896924,0.0014683302,0.0011948023,0.001185618,0.0033671614],"category_scores_gemma":[0.005952666,0.00052329514,0.0009899989,0.0012339843,0.0010442113,0.0032485735,0.0010987635,0.0010087115,0.00031506244],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00038951111,0.00016184892,0.0020886553,0.00046220512,0.00016597884,0.0009732046,0.00027293744,0.62499815,0.0094923675,0.22364801,0.009842934,0.12750421],"study_design_scores_gemma":[0.00006164811,0.00015337784,0.0009254196,0.00006472804,0.00007093226,0.0009897734,0.00014948788,0.70457435,0.0056239595,0.27312413,0.014221256,0.000040933475],"about_ca_topic_score_codex":0.0011657076,"about_ca_topic_score_gemma":0.0009172706,"teacher_disagreement_score":0.0033671614,"about_ca_system_score_codex":0.0006744639,"about_ca_system_score_gemma":0.0005752405,"threshold_uncertainty_score":0.011264265},"labels":[],"label_agreement":null},{"id":"W1498455135","doi":"10.1109/icec.1994.350017","title":"Improving a vehicle routing heuristic through genetic search","year":2002,"lang":"en","type":"article","venue":"","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Vehicle routing problem; Computer science; Heuristic; Genetic algorithm; Routing (electronic design automation); Mathematical optimization; Computer network; Artificial intelligence; Machine learning; Mathematics","score_opus":0.0310083192560953,"score_gpt":0.2584753643480404,"score_spread":0.22746704509194512,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1498455135","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05647755,0.0005927575,0.9291532,0.000256946,0.000098084594,0.00013365825,0.000043574946,0.00083638885,0.012407933],"genre_scores_gemma":[0.44504115,0.00060915755,0.54859126,0.00019090451,0.000055317225,0.00021886306,0.00015932113,0.00023449407,0.0048994757],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99928516,0.0002325442,0.000025581363,0.00010702844,0.00024652146,0.00010321823],"domain_scores_gemma":[0.9993506,0.00031690823,0.00007355717,0.00008634605,0.00014439861,0.00002829155],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001241163,0.0009905017,0.0009216772,0.001722504,0.0007693168,0.0011684357,0.0010966533,0.0015583587,0.002002462],"category_scores_gemma":[0.0033671681,0.0005706634,0.00085536053,0.0013122027,0.0009879387,0.00094575237,0.0010197442,0.0009707684,0.00056973845],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000039098384,0.00006103224,0.00044752518,0.000036697635,0.00003873567,0.00007184815,0.000058553134,0.92583984,0.0023583863,0.011727016,0.0011318106,0.058189467],"study_design_scores_gemma":[0.000022927248,0.000036772264,0.0000926892,0.000014213644,0.000023660903,0.0000264446,0.000016786464,0.9927643,0.0009959067,0.0045125275,0.0014833901,0.000010401077],"about_ca_topic_score_codex":0.0076838946,"about_ca_topic_score_gemma":0.006828762,"teacher_disagreement_score":0.0076838946,"about_ca_system_score_codex":0.001306628,"about_ca_system_score_gemma":0.001623538,"threshold_uncertainty_score":0.015278339},"labels":[],"label_agreement":null},{"id":"W1503619557","doi":"10.1007/978-3-540-28646-2_1","title":"A Comparison Between ACO Algorithms for the Set Covering Problem","year":2004,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":113,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"","keywords":"Benchmark (surveying); Computer science; Ant colony optimization algorithms; Set (abstract data type); Heuristic; Algorithm; Mathematical optimization; Artificial intelligence; Mathematics","score_opus":0.04490335325099004,"score_gpt":0.3128812745333813,"score_spread":0.2679779212823913,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1503619557","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21909827,0.039683275,0.59107673,0.0025410666,0.0021480322,0.00057811575,0.00080890546,0.0023805094,0.14168514],"genre_scores_gemma":[0.52576894,0.015636355,0.44108692,0.00050745416,0.0005146919,0.00028537682,0.0010717631,0.00087751914,0.014251015],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99767834,0.00077840796,0.00010180097,0.00020363505,0.0010445616,0.00019324123],"domain_scores_gemma":[0.9923314,0.0057373317,0.0002577446,0.0005849243,0.00090954895,0.00017897788],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002048858,0.00090929726,0.0015047509,0.0021059217,0.0008034513,0.0020608893,0.0021459667,0.0018821213,0.0052605327],"category_scores_gemma":[0.010546617,0.0004094841,0.001176185,0.004625464,0.00052117556,0.0024718046,0.0009288861,0.0012909527,0.0007119539],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010265089,0.00051664293,0.0014034458,0.0005907035,0.0002721528,0.00005064748,0.00008755875,0.41110116,0.0018637775,0.02744416,0.0071163243,0.5485269],"study_design_scores_gemma":[0.00017221458,0.00047095012,0.0022507494,0.00009408122,0.0001297578,0.00019829812,0.00012676405,0.97326964,0.0017479975,0.012386934,0.009121326,0.00003131001],"about_ca_topic_score_codex":0.004261206,"about_ca_topic_score_gemma":0.006104406,"teacher_disagreement_score":0.0052605327,"about_ca_system_score_codex":0.0017754516,"about_ca_system_score_gemma":0.0016365375,"threshold_uncertainty_score":0.017598212},"labels":[],"label_agreement":null},{"id":"W1504989502","doi":"10.1007/0-306-48058-1_13","title":"Long-Haul Freight Transportation","year":2006,"lang":"en","type":"book-chapter","venue":"Kluwer Academic Publishers eBooks","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":202,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Business; Product (mathematics); Consumption (sociology); Production (economics); Transport engineering; Commerce; Industrial organization; Economics; Engineering; Microeconomics","score_opus":0.016011050632956377,"score_gpt":0.22887835145311022,"score_spread":0.21286730082015384,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1504989502","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0058255326,0.026822995,0.10595507,0.0012994641,0.001074346,0.00009213135,0.000907784,0.0014314622,0.8565912],"genre_scores_gemma":[0.08012194,0.029135494,0.029153464,0.00041468066,0.00041070342,0.000080190584,0.0014677001,0.00040954474,0.8588063],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998142,0.000016260457,0.0000070164765,0.0000308614,0.00010421823,0.000027534008],"domain_scores_gemma":[0.99986136,0.000024832605,0.000011796763,0.000028138867,0.00005595052,0.000017931294],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022223934,0.00061881484,0.00023629615,0.00075006305,0.0007175502,0.002211277,0.0010433209,0.0007781442,0.054251455],"category_scores_gemma":[0.0003110906,0.00017805911,0.0002398327,0.0020509015,0.0003790623,0.0026787745,0.00093354593,0.0006177315,0.029572632],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000035048306,0.00004720972,0.00028984263,0.00037095702,0.00001251754,0.0001875007,0.00013126235,0.0065180054,0.0030204023,0.1285545,0.11791266,0.74292016],"study_design_scores_gemma":[0.0000024144479,0.00001982648,0.00032140134,0.000104316685,0.0000040656496,0.00022844778,0.00006829919,0.003288693,0.0010684539,0.014379923,0.9805032,0.0000108467375],"about_ca_topic_score_codex":0.002633512,"about_ca_topic_score_gemma":0.003974691,"teacher_disagreement_score":0.054251455,"about_ca_system_score_codex":0.0008452052,"about_ca_system_score_gemma":0.0005914949,"threshold_uncertainty_score":0.18148923},"labels":[],"label_agreement":null},{"id":"W1506688052","doi":"10.3233/fi-2014-990","title":"Heuristics to Solve a Real-world Asymmetric Vehicle Routing Problem with Side Constraints","year":2014,"lang":"en","type":"article","venue":"Fundamenta Informaticae","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Trois-Rivières","funders":"","keywords":"Heuristics; Vehicle routing problem; Computer science; Routing (electronic design automation); Mathematical optimization; Mathematics; Computer network","score_opus":0.013558364460848333,"score_gpt":0.25216539241031305,"score_spread":0.23860702794946473,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1506688052","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14166713,0.0013842927,0.84444714,0.000463094,0.00015007537,0.00038804163,0.00038381573,0.00052768004,0.010588744],"genre_scores_gemma":[0.5167043,0.0005579905,0.4781316,0.00014603592,0.000073271076,0.00053516286,0.00052697095,0.00010921304,0.0032155153],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99943227,0.00031567473,0.00002411196,0.00007241964,0.000055033153,0.00010056371],"domain_scores_gemma":[0.9982255,0.0013822214,0.0001447136,0.00006972013,0.00010709088,0.00007074044],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012528569,0.0012903227,0.0009838917,0.0011011872,0.00055549346,0.0009932942,0.0014779008,0.0015559995,0.0025691716],"category_scores_gemma":[0.0023166554,0.0006281015,0.00076670287,0.0017417745,0.0006425734,0.00078368257,0.0006262306,0.00097456924,0.00024210215],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006688071,0.00009193161,0.0002748099,0.00008273525,0.000040592407,0.00007940721,0.000035339108,0.9796118,0.00047400908,0.004231563,0.0008019377,0.014209014],"study_design_scores_gemma":[0.000053894062,0.00006602596,0.00011627426,0.000015323785,0.00001815482,0.000032263048,0.000040982384,0.9953257,0.00034609242,0.0032987085,0.00067998725,0.0000065853123],"about_ca_topic_score_codex":0.0041242056,"about_ca_topic_score_gemma":0.004303299,"teacher_disagreement_score":0.0041242056,"about_ca_system_score_codex":0.00090119295,"about_ca_system_score_gemma":0.0013201878,"threshold_uncertainty_score":0.008594751},"labels":[],"label_agreement":null},{"id":"W1513860408","doi":"10.1016/j.scient.2013.04.010","title":"The incomplete hub-covering location problem considering imprecise location of demands","year":2013,"lang":"en","type":"article","venue":"ORCA Online Research @Cardiff (Cardiff University)","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":28,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Facility location problem; Computer science; Variable neighborhood search; Location model; Fuzzy logic; 1-center problem; Variable (mathematics); Operations research; Order (exchange); Mathematical optimization; Covering problems; Metaheuristic; Mathematics; Set (abstract data type); Artificial intelligence; Business","score_opus":0.039065567892745706,"score_gpt":0.2893826163711615,"score_spread":0.25031704847841585,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1513860408","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13166991,0.0015534265,0.8559661,0.0011766406,0.00013765319,0.000114785726,0.0019668168,0.00024270515,0.007171944],"genre_scores_gemma":[0.8751012,0.0015216332,0.11206481,0.00015810558,0.00020637814,0.00022220066,0.0017658597,0.00023512526,0.008724712],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9973597,0.001135063,0.00012934256,0.0006811319,0.00036023543,0.00033445086],"domain_scores_gemma":[0.99069023,0.006820709,0.0009861062,0.0006116636,0.000490119,0.0004012134],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0039721075,0.0018738294,0.005234266,0.0016948276,0.0010170605,0.0037656573,0.004176457,0.0040258896,0.004776206],"category_scores_gemma":[0.0130372355,0.0029083746,0.002252055,0.0051201936,0.0022885676,0.005494918,0.002407561,0.0023066609,0.00045804976],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000075114636,0.000016158447,0.00029970505,0.00011039661,0.00004923832,0.00017145557,0.000053543958,0.98752636,0.00019539653,0.0070700375,0.00059034186,0.0038421042],"study_design_scores_gemma":[0.000020246365,0.00003590673,0.00026639385,0.00002662553,0.000039154802,0.000076118056,0.000066232686,0.98104364,0.00019545502,0.017632851,0.0005773533,0.000020024616],"about_ca_topic_score_codex":0.012556094,"about_ca_topic_score_gemma":0.007890076,"teacher_disagreement_score":0.012556094,"about_ca_system_score_codex":0.0029318975,"about_ca_system_score_gemma":0.001538766,"threshold_uncertainty_score":0.024966061},"labels":[],"label_agreement":null},{"id":"W1515177345","doi":"10.1023/a:1009621410177","title":"Solving Vehicle Routing Problems Using Constraint Programming and Metaheuristics","year":2000,"lang":"en","type":"article","venue":"Journal of Heuristics","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":178,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kellogg's (Canada)","funders":"European Commission","keywords":"Guided Local Search; Mathematical optimization; Tabu search; Local search (optimization); Constraint programming; Maxima and minima; Computer science; Hill climbing; Heuristics; Iterated local search; Benchmark (surveying); Local optimum; Beam search; Search algorithm; Algorithm; Mathematics","score_opus":0.023579582177976975,"score_gpt":0.26421585828604544,"score_spread":0.24063627610806845,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1515177345","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.024535507,0.0010423319,0.96286535,0.00042405844,0.00011865534,0.00022016148,0.00015714331,0.00025187558,0.010384866],"genre_scores_gemma":[0.20485692,0.0011376883,0.79013646,0.00021887445,0.00010380184,0.0005180677,0.00025017306,0.00011619093,0.0026617798],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99910223,0.00046536684,0.00004663569,0.000086953616,0.00019431168,0.00010457726],"domain_scores_gemma":[0.99772793,0.0017932041,0.00016197345,0.00008623603,0.0001877646,0.00004279118],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018647513,0.0012618538,0.0012954873,0.0016730671,0.00063985,0.0019456057,0.001755632,0.0021926432,0.0029305525],"category_scores_gemma":[0.0050882017,0.0010455501,0.0011799588,0.003222766,0.0007084743,0.0014603671,0.0008097127,0.001698728,0.00028467504],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000044054264,0.000063168045,0.00022175076,0.00012678685,0.000069952934,0.000042154745,0.00002767699,0.9475182,0.00047476354,0.014960805,0.0011376371,0.035312984],"study_design_scores_gemma":[0.00002756155,0.000020521717,0.000046899695,0.000018402812,0.000017741662,0.000011919314,0.000018203093,0.9922903,0.000313639,0.006306694,0.00092241046,0.000005640254],"about_ca_topic_score_codex":0.0150071485,"about_ca_topic_score_gemma":0.012912485,"teacher_disagreement_score":0.0150071485,"about_ca_system_score_codex":0.0012013905,"about_ca_system_score_gemma":0.002591121,"threshold_uncertainty_score":0.029839575},"labels":[],"label_agreement":null},{"id":"W1527580169","doi":"","title":"Optimization of forest vehicle routing using the metaheuristics: Reactive tabu search and extended great deluge","year":2013,"lang":"en","type":"article","venue":"International Conference on Industrial Engineering and Systems Management","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec en Abitibi-Témiscamingue; École de Technologie Supérieure","funders":"","keywords":"Tabu search; Metaheuristic; Vehicle routing problem; Computer science; Mathematical optimization; Routing (electronic design automation); Guided Local Search; Algorithm; Mathematics; Computer network","score_opus":0.07620791561631605,"score_gpt":0.28324155534683326,"score_spread":0.2070336397305172,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1527580169","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1040799,0.0010275177,0.8883048,0.00022219532,0.000060678874,0.000100693054,0.00007906806,0.0003933636,0.005731723],"genre_scores_gemma":[0.73757577,0.00046326392,0.2589749,0.00009727794,0.000020803462,0.00017102435,0.00013598631,0.00007548644,0.0024854227],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99970454,0.00014607263,0.000010003714,0.000028191422,0.00006698013,0.000044128778],"domain_scores_gemma":[0.9997112,0.00015140441,0.000048105783,0.00002330287,0.00005090242,0.00001498535],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00071467884,0.0005407203,0.0005377562,0.000579549,0.00034376886,0.0005276934,0.0007985396,0.00063116587,0.000864399],"category_scores_gemma":[0.00095103483,0.0002662785,0.0006964769,0.0006024434,0.0003515048,0.0006257294,0.00043319873,0.0004753683,0.00011845394],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005381998,0.000040776406,0.00036271717,0.00005165846,0.000041027495,0.0000335087,0.000028358243,0.9655943,0.0018132926,0.0046426267,0.00048626756,0.026851641],"study_design_scores_gemma":[0.000010497841,0.00004442902,0.00010298403,0.0000041539033,0.000006653503,0.000019007573,0.00000944908,0.9976532,0.00048135876,0.0012193904,0.00044493176,0.000004052983],"about_ca_topic_score_codex":0.0052895923,"about_ca_topic_score_gemma":0.0057068947,"teacher_disagreement_score":0.0052895923,"about_ca_system_score_codex":0.0005177638,"about_ca_system_score_gemma":0.0007687895,"threshold_uncertainty_score":0.010517597},"labels":[],"label_agreement":null},{"id":"W1528193418","doi":"10.1002/9780470400531.eorms0863","title":"Symmetry Handling in Mixed‐Integer Programming","year":2011,"lang":"en","type":"other","venue":"Wiley Encyclopedia of Operations Research and Management Science","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Integer programming; Integer (computer science); Symmetry (geometry); Branch and cut; Linear programming; Set (abstract data type); Branch and bound; Mathematics; Mathematical optimization; Integer points in convex polyhedra; Branch and price; Computer science; Combinatorics; Discrete mathematics; Geometry","score_opus":0.03418941115303403,"score_gpt":0.32785334334267485,"score_spread":0.2936639321896408,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1528193418","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012601024,0.00021500936,0.97790045,0.00019928245,0.000052528652,0.00009234738,0.000052405565,0.00018590534,0.008700931],"genre_scores_gemma":[0.3380979,0.00044988736,0.6564838,0.00017297361,0.00012563042,0.0003386678,0.0002055343,0.00019467763,0.0039309263],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9969103,0.0016559517,0.00014947221,0.0002481795,0.0008408655,0.00019522387],"domain_scores_gemma":[0.9947253,0.003296137,0.00065888895,0.00065597973,0.00057036383,0.000093327675],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0039179916,0.00081800687,0.0010002294,0.0009064974,0.000769701,0.0018422717,0.0013955968,0.000522827,0.0038763336],"category_scores_gemma":[0.009722997,0.00057858677,0.0012002371,0.0011574163,0.001245377,0.002059797,0.0020292422,0.0017123716,0.0006906096],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002376545,0.00015899939,0.001151298,0.00033806486,0.000088800756,0.00045111202,0.00025709323,0.3872233,0.005801155,0.42472756,0.0037233823,0.17584154],"study_design_scores_gemma":[0.000038314884,0.000058882226,0.00012993962,0.000047554036,0.000020270207,0.00008621187,0.00004154926,0.833664,0.0038790253,0.15612464,0.0058954265,0.000014125455],"about_ca_topic_score_codex":0.0009368833,"about_ca_topic_score_gemma":0.00084635604,"teacher_disagreement_score":0.0039179916,"about_ca_system_score_codex":0.0008290202,"about_ca_system_score_gemma":0.0011511625,"threshold_uncertainty_score":0.020720541},"labels":[],"label_agreement":null},{"id":"W1533840006","doi":"10.1007/978-3-540-24664-0_26","title":"Dispatching and Conflict-Free Routing of Automated Guided Vehicles: A Hybrid Approach Combining Constraint Programming and Mixed Integer Programming","year":2004,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Group for Research in Decision Analysis","funders":"","keywords":"Computer science; Integer programming; Constraint programming; Constraint (computer-aided design); Routing (electronic design automation); Mathematical optimization; Linear programming; Parallel computing; Theoretical computer science; Algorithm; Embedded system; Mathematics; Stochastic programming","score_opus":0.020408232098208974,"score_gpt":0.2582533827822211,"score_spread":0.23784515068401213,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1533840006","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0068668346,0.00041658574,0.9876858,0.00014950932,0.00006698687,0.000060082268,0.00007483944,0.00014586971,0.0045335134],"genre_scores_gemma":[0.23005567,0.000931031,0.75652,0.0001308042,0.00013288001,0.00035583455,0.00024876586,0.00030639203,0.011318691],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991375,0.00032760482,0.00004092895,0.000113824004,0.0002879183,0.00009214796],"domain_scores_gemma":[0.9989404,0.00073164294,0.000089018475,0.00006219857,0.00013511689,0.00004152365],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011973566,0.0015816718,0.0016835588,0.0010075421,0.00085334206,0.002620814,0.0028688142,0.0018866243,0.0037871515],"category_scores_gemma":[0.0025543165,0.0014821056,0.001462525,0.0024975534,0.000835847,0.002408395,0.0010559608,0.0017169833,0.00038550587],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000040841936,0.000034194658,0.00008097083,0.00008107785,0.000028010485,0.000045139448,0.000037489714,0.94960004,0.0008908236,0.018236795,0.000996448,0.029928077],"study_design_scores_gemma":[0.000005204532,0.000007743246,0.000018864215,0.000005142014,0.000005394538,0.000007957753,0.0000064690025,0.9925328,0.00028512094,0.0064642304,0.00065651373,0.000004526306],"about_ca_topic_score_codex":0.009907756,"about_ca_topic_score_gemma":0.01052204,"teacher_disagreement_score":0.009907756,"about_ca_system_score_codex":0.0012466421,"about_ca_system_score_gemma":0.0019523351,"threshold_uncertainty_score":0.01970017},"labels":[],"label_agreement":null},{"id":"W1539384798","doi":"10.1007/978-1-4615-1507-4_26","title":"Analysing the Run-Time Behaviour of Iterated Local Search for the Travelling Salesman Problem","year":2002,"lang":"en","type":"book-chapter","venue":"Operations research, computer science. Interface series","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Travelling salesman problem; Iterated function; Iterated local search; Computer science; Mathematical optimization; Local search (optimization); Mathematics; Algorithm","score_opus":0.06213274038974058,"score_gpt":0.3305188659932833,"score_spread":0.2683861256035427,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1539384798","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6378053,0.0024782177,0.3372469,0.0010342643,0.000076594304,0.000069632646,0.00009325496,0.0005380555,0.020657785],"genre_scores_gemma":[0.96574,0.00045202428,0.028896132,0.00004908397,0.000031832536,0.000055103283,0.00009475639,0.00020593,0.0044750557],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994006,0.0002869203,0.000023808214,0.00006418417,0.00011929052,0.00010524631],"domain_scores_gemma":[0.9903923,0.00822259,0.00057232403,0.00026510484,0.0003791392,0.00016848647],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020617926,0.0005721658,0.00087209995,0.0008635025,0.00047490653,0.0010842464,0.001459109,0.0010568383,0.0034281472],"category_scores_gemma":[0.015240517,0.00046862438,0.00084107573,0.00093432853,0.0012561555,0.0021457297,0.00081406324,0.001622,0.00031729214],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015451561,0.000105655665,0.0025431926,0.00014535194,0.000090908696,0.0002021392,0.00034733734,0.90594035,0.0026503797,0.06814609,0.0014826752,0.018191403],"study_design_scores_gemma":[0.000008907342,0.00001918703,0.00044943413,0.000006248614,0.000010609391,0.00001550178,0.000028884017,0.98478264,0.0002385203,0.014269352,0.00016385273,0.000006948181],"about_ca_topic_score_codex":0.009314856,"about_ca_topic_score_gemma":0.005431153,"teacher_disagreement_score":0.009314856,"about_ca_system_score_codex":0.0011184008,"about_ca_system_score_gemma":0.001034216,"threshold_uncertainty_score":0.018521309},"labels":[],"label_agreement":null},{"id":"W1547752062","doi":"10.1023/a:1009609820093","title":"An Evolution Program for Non-Linear Transportation Problems","year":2001,"lang":"en","type":"article","venue":"Journal of Heuristics","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Initialization; Heuristic; Mathematical optimization; Computer science; Mating; Fitness function; Function (biology); Convergence (economics); Flow network; Integer (computer science); Genetic algorithm; Mathematics; Biology; Genetics","score_opus":0.019223679010467735,"score_gpt":0.30678315734194855,"score_spread":0.2875594783314808,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1547752062","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021516277,0.00027498446,0.9692798,0.00041130476,0.000052739342,0.0000628159,0.000067308516,0.00008242236,0.008252335],"genre_scores_gemma":[0.51194483,0.0011650319,0.46598315,0.00020425128,0.000108881046,0.00047461165,0.00028533212,0.00017590812,0.019658027],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996815,0.00014137509,0.000011674819,0.000032223805,0.00009900154,0.00003420351],"domain_scores_gemma":[0.99898547,0.00078104174,0.000047052425,0.000025002442,0.00012561405,0.000035734305],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010042278,0.0005117301,0.0007057717,0.00051902625,0.0003506864,0.0007385661,0.0007570862,0.001030567,0.002878586],"category_scores_gemma":[0.0031056216,0.00036592127,0.0004960924,0.00081223156,0.00077714043,0.00089316134,0.0008496257,0.0013796602,0.00021105121],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004844695,0.00008152304,0.00037589754,0.00008476602,0.00002114118,0.00006998397,0.00010656565,0.81943965,0.0008283835,0.12874405,0.0019128057,0.04828684],"study_design_scores_gemma":[0.000007727412,0.000012541237,0.000033468565,0.0000061161104,0.000003479249,0.000006898019,0.0000066232856,0.9849183,0.00008822612,0.0141343735,0.0007804488,0.0000019044325],"about_ca_topic_score_codex":0.004004979,"about_ca_topic_score_gemma":0.0030968632,"teacher_disagreement_score":0.004004979,"about_ca_system_score_codex":0.00076658675,"about_ca_system_score_gemma":0.0009233166,"threshold_uncertainty_score":0.009629786},"labels":[],"label_agreement":null},{"id":"W1549969148","doi":"10.1007/978-3-540-45198-3_10","title":"Approximating the Degree-Bounded Minimum Diameter Spanning Tree Problem","year":2003,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Minimum spanning tree; Spanning tree; Combinatorics; Gomory–Hu tree; Degree (music); k-minimum spanning tree; Bounded function; Steiner tree problem; Kruskal's algorithm; Node (physics); Mathematics; Approximation algorithm; Discrete mathematics; Graph; Distributed minimum spanning tree; Undirected graph; Minimum degree spanning tree; Shortest-path tree; Enhanced Data Rates for GSM Evolution; Tree (set theory); Computer science; K-ary tree; Tree structure; Binary tree; Artificial intelligence","score_opus":0.03657327550404496,"score_gpt":0.2512355080131645,"score_spread":0.21466223250911956,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1549969148","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08222497,0.000948021,0.90634555,0.0007747538,0.00013366382,0.000051983738,0.00031891582,0.00044808915,0.0087539805],"genre_scores_gemma":[0.6291297,0.0010195146,0.36281148,0.00018503424,0.0001457911,0.00012412971,0.0007905459,0.00026868485,0.0055251163],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993418,0.0002279732,0.000022978782,0.000119343575,0.00020404307,0.00008376203],"domain_scores_gemma":[0.9979633,0.0013278932,0.00013598286,0.00022323646,0.0002428463,0.00010674375],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010458694,0.0007956761,0.0012001381,0.0007024485,0.0004128648,0.0010780052,0.0018915649,0.001269026,0.0026930177],"category_scores_gemma":[0.008006296,0.00044405094,0.0005473081,0.0012853293,0.0005568842,0.0020639286,0.0014666043,0.0012352772,0.00056425855],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002624864,0.00007407498,0.000824457,0.00018733797,0.00003130776,0.000069699585,0.00008493598,0.87712586,0.0032630519,0.03548914,0.0055998247,0.07698789],"study_design_scores_gemma":[0.000013510559,0.000020457343,0.00012593354,0.000008532697,0.000006650476,0.000035002875,0.000015690783,0.9785209,0.00046490124,0.020042678,0.0007432704,0.0000025903933],"about_ca_topic_score_codex":0.0020903104,"about_ca_topic_score_gemma":0.0025747837,"teacher_disagreement_score":0.0026930177,"about_ca_system_score_codex":0.0010205368,"about_ca_system_score_gemma":0.00066470605,"threshold_uncertainty_score":0.009009063},"labels":[],"label_agreement":null},{"id":"W1554092380","doi":"10.1023/a:1021953714975","title":"Near-optimal design of Global Positioning System (GPS) networks using the Tabu Search technique","year":2003,"lang":"en","type":"article","venue":"Journal of Global Optimization","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"European Commission","keywords":"Tabu search; Global Positioning System; Flexibility (engineering); Schedule; Mathematical optimization; Heuristic; Computer science; Mathematics","score_opus":0.01881942778388785,"score_gpt":0.28006170985629253,"score_spread":0.2612422820724047,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1554092380","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0791078,0.0006867458,0.9072151,0.00022240645,0.000090328685,0.00013095455,0.00010621064,0.0005070115,0.011933433],"genre_scores_gemma":[0.8251715,0.00042475635,0.17003895,0.00010332797,0.00003711281,0.00029300514,0.00013352734,0.00015086109,0.0036469656],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99949837,0.00024966698,0.000013664471,0.00005527075,0.00011272125,0.00007023675],"domain_scores_gemma":[0.9992105,0.000502752,0.00009992207,0.000033106564,0.00012106194,0.000032659256],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009523391,0.0009572132,0.0011319875,0.0011661751,0.00064729864,0.0011673749,0.00096023805,0.0010740535,0.0034785415],"category_scores_gemma":[0.0024634288,0.000959049,0.00085700804,0.0007739423,0.0009349073,0.0009700006,0.0007348225,0.0006805926,0.00035855456],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017750051,0.000008118842,0.00008266243,0.000018501596,0.000008568176,0.000011052212,0.00001438219,0.99281055,0.00030122226,0.0012548863,0.00015107339,0.005321241],"study_design_scores_gemma":[0.000009802773,0.00002874238,0.000041009127,0.0000042205893,0.0000065152935,0.0000048793754,0.000010892432,0.9982566,0.00015945765,0.0012627708,0.00021239203,0.0000026846867],"about_ca_topic_score_codex":0.0063106082,"about_ca_topic_score_gemma":0.005588822,"teacher_disagreement_score":0.0063106082,"about_ca_system_score_codex":0.00096565875,"about_ca_system_score_gemma":0.0014001415,"threshold_uncertainty_score":0.012547731},"labels":[],"label_agreement":null},{"id":"W1559962598","doi":"10.1002/9780470400531.eorms1034","title":"The Vehicle Routing Problem with Time Windows: State‐of‐the‐Art Exact Solution Methods","year":2011,"lang":"en","type":"other","venue":"Wiley Encyclopedia of Operations Research and Management Science","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal; Polytechnique Montréal; Group for Research in Decision Analysis","funders":"","keywords":"Vehicle routing problem; Solver; Benchmark (surveying); Integer programming; Mathematical optimization; Set (abstract data type); Computer science; Integer (computer science); Variable (mathematics); State (computer science); Routing (electronic design automation); Algorithm; Mathematics","score_opus":0.0235637405185882,"score_gpt":0.318285204207394,"score_spread":0.2947214636888058,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1559962598","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01031249,0.009235655,0.9634757,0.00035232017,0.00014625948,0.00007761289,0.0002139983,0.00038068276,0.015805367],"genre_scores_gemma":[0.21934138,0.012102809,0.75724924,0.00019159634,0.00025722294,0.00034081197,0.00050357025,0.00027929488,0.009734099],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.99928707,0.00025847278,0.000034380773,0.00009968625,0.00026268215,0.00005768248],"domain_scores_gemma":[0.9990133,0.0007067925,0.00006791296,0.000071763505,0.00011617027,0.000024122548],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009690833,0.0012594693,0.0011133505,0.00092564325,0.00035840587,0.0015795624,0.0014914165,0.0010024122,0.005266065],"category_scores_gemma":[0.0029805766,0.00058747415,0.000785401,0.0020484487,0.0005249357,0.0016694758,0.0010742254,0.001509241,0.0010453869],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006760789,0.000091418704,0.00033950526,0.00034418536,0.00005648005,0.00003278599,0.000045940906,0.75613713,0.000514622,0.04329994,0.003918396,0.19515194],"study_design_scores_gemma":[0.000018067678,0.000016304895,0.00006652172,0.00004160886,0.00000861671,0.000012550064,0.000015817044,0.9785459,0.00026877705,0.017098991,0.0039009927,0.000005816884],"about_ca_topic_score_codex":0.0072829975,"about_ca_topic_score_gemma":0.00482411,"teacher_disagreement_score":0.0072829975,"about_ca_system_score_codex":0.00064672146,"about_ca_system_score_gemma":0.0012897238,"threshold_uncertainty_score":0.017616749},"labels":[],"label_agreement":null},{"id":"W1566150287","doi":"10.1007/978-3-642-04244-7_28","title":"Solving a Location-Allocation Problem with Logic-Based Benders’ Decomposition","year":2009,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":26,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Integer programming; Mathematical optimization; Tabu search; Computer science; Benders' decomposition; Constraint programming; Decomposition; Truck; Integer (computer science); Set (abstract data type); Facility location problem; Constraint (computer-aided design); Stochastic programming; Mathematics; Programming language; Engineering","score_opus":0.015102974138954636,"score_gpt":0.2520780912799707,"score_spread":0.23697511714101607,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1566150287","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0071098236,0.00006131938,0.9876494,0.00013765556,0.000029485125,0.000050135895,0.00006345657,0.00013736836,0.004761348],"genre_scores_gemma":[0.16525082,0.00018626271,0.8270737,0.00015844617,0.000051923595,0.00020496012,0.00025495733,0.00016649305,0.0066524385],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993765,0.0002311684,0.00002885327,0.00011873955,0.00014598711,0.00009887538],"domain_scores_gemma":[0.99930656,0.0004873994,0.000045251083,0.000048328136,0.000086312604,0.000026101798],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013967302,0.0013383172,0.0013731774,0.00079873414,0.00063549035,0.0015252823,0.001315337,0.0016280753,0.008715083],"category_scores_gemma":[0.0025537836,0.0011435584,0.0017737318,0.0010829299,0.0009105153,0.0016258268,0.0012488407,0.002157885,0.0011096124],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000100713056,0.00010865776,0.00021001916,0.0001805814,0.000045404366,0.00006254048,0.000071854905,0.91117454,0.0029338968,0.034578722,0.0019191769,0.048613906],"study_design_scores_gemma":[0.000035789348,0.000047102123,0.000059150556,0.000015348216,0.000018631516,0.000026321497,0.00003490552,0.9644474,0.00088914856,0.033340935,0.0010759654,0.000009254062],"about_ca_topic_score_codex":0.0034202975,"about_ca_topic_score_gemma":0.0038747045,"teacher_disagreement_score":0.008715083,"about_ca_system_score_codex":0.0009544261,"about_ca_system_score_gemma":0.0014849756,"threshold_uncertainty_score":0.029154897},"labels":[],"label_agreement":null},{"id":"W1574334008","doi":"10.1007/978-3-642-13036-6_27","title":"On Column-Restricted and Priority Covering Integer Programs","year":2010,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":43,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Integer (computer science); Column (typography); Cover (algebra); Computer science; Column generation; Omega; Tree (set theory); Approximation algorithm; Combinatorics; Logical matrix; Matrix (chemical analysis); Constraint (computer-aided design); Type (biology); Discrete mathematics; Mathematics; Mathematical optimization; Group (periodic table); Programming language","score_opus":0.013578718181496757,"score_gpt":0.2464335359269424,"score_spread":0.23285481774544564,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1574334008","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015975593,0.0035329792,0.86612976,0.0015535626,0.0006021806,0.000107715314,0.00034636064,0.0003875324,0.11136416],"genre_scores_gemma":[0.32557303,0.008467102,0.56390643,0.001239464,0.002253341,0.00054037984,0.0018127199,0.001009352,0.09519821],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99893636,0.0003704991,0.000047053116,0.00012666186,0.00040130632,0.000118165466],"domain_scores_gemma":[0.99806637,0.0013450001,0.00009391427,0.0002350356,0.00018609272,0.00007359292],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014299463,0.0013627406,0.0010194635,0.00079222844,0.00055981014,0.0022211254,0.0013317062,0.0009892461,0.010338793],"category_scores_gemma":[0.00631321,0.00083688856,0.0011046886,0.002472643,0.0014337698,0.0040737153,0.0017432402,0.0048013083,0.0017353303],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010328013,0.00013945816,0.0002327005,0.00031015655,0.00003368071,0.00012084565,0.00016114175,0.09188569,0.0016511588,0.78050405,0.01863852,0.106219254],"study_design_scores_gemma":[0.000016853368,0.000029304436,0.00013611339,0.000055883414,0.0000119654715,0.00008132307,0.000027715765,0.1298418,0.0004892056,0.85439897,0.014898209,0.000012722415],"about_ca_topic_score_codex":0.0014534066,"about_ca_topic_score_gemma":0.0015073488,"teacher_disagreement_score":0.010338793,"about_ca_system_score_codex":0.0011994118,"about_ca_system_score_gemma":0.00085201,"threshold_uncertainty_score":0.034586668},"labels":[],"label_agreement":null},{"id":"W1581729816","doi":"10.1007/s10107-015-0947-5","title":"On integrality ratios for asymmetric TSP in the Sherali–Adams hierarchy","year":2015,"lang":"en","type":"article","venue":"Mathematical Programming","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Combinatorics; Mathematics; Travelling salesman problem; Digraph; Linear programming relaxation; Relaxation (psychology); Linear programming; Discrete mathematics; Algorithm","score_opus":0.06647276221161304,"score_gpt":0.3280434873934523,"score_spread":0.26157072518183927,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1581729816","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13862306,0.0041180826,0.6623603,0.004071866,0.0004218732,0.00011556713,0.00020807542,0.00024377246,0.18983747],"genre_scores_gemma":[0.882364,0.0028492222,0.089281,0.0006501186,0.0007800565,0.00018670202,0.0002032585,0.0004238712,0.02326173],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984029,0.0006526828,0.000051228064,0.00014548801,0.000467501,0.00028028947],"domain_scores_gemma":[0.9914083,0.006351111,0.00054206094,0.0003483439,0.00071161974,0.00063857593],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0037364033,0.0011628368,0.0013415612,0.0030243997,0.0016150991,0.0029412725,0.0025181738,0.0013849199,0.009732161],"category_scores_gemma":[0.02147795,0.000601354,0.0011055276,0.0020519842,0.0030712688,0.005249979,0.0028598025,0.004524783,0.00096883264],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000022959877,0.00003349578,0.00022724082,0.000062058985,0.0000095739215,0.000034049972,0.000102028374,0.01968241,0.00036023345,0.9701788,0.0017239372,0.007563242],"study_design_scores_gemma":[0.00001494076,0.000021198719,0.00023748109,0.000039996394,0.00001614006,0.00006038002,0.00007520783,0.24871519,0.00027951304,0.74781007,0.002715757,0.000014130726],"about_ca_topic_score_codex":0.0026114685,"about_ca_topic_score_gemma":0.002360416,"teacher_disagreement_score":0.009732161,"about_ca_system_score_codex":0.003169861,"about_ca_system_score_gemma":0.0013744399,"threshold_uncertainty_score":0.03255731},"labels":[],"label_agreement":null},{"id":"W1583403835","doi":"10.1016/j.ejor.2015.06.082","title":"The fleet size and mix location-routing problem with time windows: Formulations and a heuristic algorithm","year":2015,"lang":"en","type":"article","venue":"European Journal of Operational Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":97,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"Engineering and Physical Sciences Research Council","keywords":"Computer science; Vehicle routing problem; Benchmark (surveying); Mathematical optimization; Metaheuristic; Routing (electronic design automation); Heuristic; Dimensioning; Integer programming; Algorithm; Mathematics; Engineering; Computer network","score_opus":0.05021214779436636,"score_gpt":0.32654926189105693,"score_spread":0.2763371140966906,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1583403835","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.033901315,0.000620053,0.9545934,0.0005574084,0.00010450295,0.00023308568,0.0003413796,0.00025091413,0.009397891],"genre_scores_gemma":[0.4033879,0.00096475484,0.57998246,0.00015804407,0.00021596566,0.0007837042,0.00043002111,0.000247996,0.01382908],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994535,0.0002269037,0.000020366468,0.00010881665,0.00010276648,0.000087581015],"domain_scores_gemma":[0.99833435,0.001334408,0.00012645824,0.000051485298,0.0000791323,0.00007414731],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017516962,0.0013666241,0.001908861,0.0013773732,0.0006779475,0.0021068258,0.0022254982,0.0028696614,0.006156778],"category_scores_gemma":[0.004002104,0.0014555737,0.0013709638,0.0022185144,0.00081146683,0.0036405588,0.001553707,0.0017389735,0.00049689226],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007175444,0.00006498746,0.0001812098,0.000060732833,0.000028231701,0.000054492655,0.00002502092,0.9689174,0.00046187575,0.0133892605,0.0011951188,0.0155498665],"study_design_scores_gemma":[0.000018864679,0.000024615161,0.000070045135,0.0000065398704,0.00001159708,0.000022328879,0.000016062946,0.99245304,0.00018443915,0.0067257127,0.0004604166,0.000006340353],"about_ca_topic_score_codex":0.006835087,"about_ca_topic_score_gemma":0.0062932647,"teacher_disagreement_score":0.006835087,"about_ca_system_score_codex":0.0018174943,"about_ca_system_score_gemma":0.0017738541,"threshold_uncertainty_score":0.020596445},"labels":[],"label_agreement":null},{"id":"W1583951675","doi":"10.1007/978-3-540-73545-8_54","title":"Approximation Algorithms for the Black and White Traveling Salesman Problem","year":2007,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Travelling salesman problem; Combinatorics; Triangle inequality; Bounded function; Approximation algorithm; Mathematics; Undirected graph; Hamiltonian (control theory); Hamiltonian path; Graph; Discrete mathematics; Algorithm; Mathematical optimization","score_opus":0.03269764042657921,"score_gpt":0.27462067247672894,"score_spread":0.24192303205014973,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1583951675","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012534924,0.0018218587,0.96878004,0.0008750123,0.00027284506,0.00008798114,0.0002087012,0.0009022175,0.014516497],"genre_scores_gemma":[0.24877883,0.0025940444,0.71756613,0.00060454686,0.00048054926,0.00043778948,0.0011752323,0.0007606374,0.027602157],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99877363,0.0003749591,0.00004219332,0.0002469368,0.0003295052,0.00023280607],"domain_scores_gemma":[0.99765575,0.0015477478,0.0001465466,0.00026364104,0.00024270106,0.00014362323],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021005846,0.0017385929,0.0022203266,0.0013300267,0.0011917992,0.0027907381,0.004158433,0.0024355692,0.010651154],"category_scores_gemma":[0.0075139944,0.0011404195,0.0013936032,0.0033735123,0.0012438555,0.005107198,0.0020977168,0.0039143995,0.0022439684],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00042856467,0.00040568126,0.00049020234,0.0002785077,0.00012786033,0.00007297208,0.00016941132,0.5438083,0.0009031106,0.18734583,0.028996529,0.23697309],"study_design_scores_gemma":[0.000065662796,0.000029584184,0.00006481832,0.00002082208,0.000022673075,0.00002682284,0.000034171553,0.89588124,0.00024818582,0.10071168,0.0028855826,0.000008747296],"about_ca_topic_score_codex":0.007474968,"about_ca_topic_score_gemma":0.006833023,"teacher_disagreement_score":0.010651154,"about_ca_system_score_codex":0.0024644088,"about_ca_system_score_gemma":0.0020122027,"threshold_uncertainty_score":0.035631597},"labels":[],"label_agreement":null},{"id":"W1588856286","doi":"10.1023/a:1013661617536","title":"Using Constraint-Based Operators to Solve the Vehicle Routing Problem with Time Windows","year":2002,"lang":"en","type":"article","venue":"Journal of Heuristics","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":162,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Université de Montréal; Computer Research Institute of Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Maxima and minima; Pruning; Mathematical optimization; Computer science; Reduction (mathematics); Descent (aeronautics); Constraint programming; Variable neighborhood search; Constraint (computer-aided design); Mathematics; Metaheuristic; Stochastic programming","score_opus":0.0272116258951607,"score_gpt":0.2542900346914363,"score_spread":0.2270784087962756,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1588856286","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016387621,0.0001858339,0.98038703,0.000107901506,0.000058411188,0.00007679931,0.000057949772,0.00014219912,0.0025962545],"genre_scores_gemma":[0.19952337,0.00035804012,0.79763573,0.00011082799,0.00005160571,0.00023660538,0.00015589476,0.0001448518,0.0017830641],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994124,0.00023685195,0.00003617926,0.00006348144,0.00016105217,0.00008993993],"domain_scores_gemma":[0.9982457,0.0013240266,0.000112288006,0.0000676988,0.0001898379,0.000060391176],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015336828,0.00095746043,0.0008322761,0.00065600825,0.0004975816,0.0010109893,0.0013060911,0.0008460629,0.002599214],"category_scores_gemma":[0.003912565,0.00046726904,0.000764237,0.0015225586,0.00056237506,0.0015451448,0.0008311594,0.0016857786,0.00026447553],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00027679594,0.00016076287,0.000378031,0.00016546725,0.000075480806,0.00015385577,0.00011020182,0.8262054,0.004387077,0.06856456,0.0031134586,0.096408986],"study_design_scores_gemma":[0.00003669258,0.000038914044,0.000034398035,0.000008556674,0.00001296098,0.000018595589,0.000013146241,0.98614234,0.0008278803,0.0119379265,0.0009209997,0.0000075282433],"about_ca_topic_score_codex":0.008087788,"about_ca_topic_score_gemma":0.005157164,"teacher_disagreement_score":0.008087788,"about_ca_system_score_codex":0.00059329014,"about_ca_system_score_gemma":0.0018087993,"threshold_uncertainty_score":0.016081393},"labels":[],"label_agreement":null},{"id":"W1590764060","doi":"10.15837/ijccc.2011.1.2210","title":"Heuristic Algorithms for Solving the Generalized Vehicle Routing Problem","year":2011,"lang":"en","type":"article","venue":"International Journal of Computers Communications & Control","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":47,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Science North","funders":"","keywords":"Vehicle routing problem; Computer science; Constructive; Generalization; Mathematical optimization; Heuristic; Partition (number theory); Routing (electronic design automation); Combinatorial optimization; Node (physics); Algorithm; Mathematics; Combinatorics; Process (computing)","score_opus":0.04705773568698275,"score_gpt":0.2997911099384948,"score_spread":0.25273337425151204,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1590764060","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00998611,0.0008490529,0.983185,0.00021776056,0.00006568037,0.00010436844,0.000057519443,0.00034242572,0.0051921518],"genre_scores_gemma":[0.21031755,0.0013255378,0.78420085,0.0002156296,0.00011771326,0.0005210931,0.00028292433,0.0001360496,0.0028827088],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991867,0.00038122927,0.000029849818,0.00010890801,0.00019423304,0.00009900993],"domain_scores_gemma":[0.99897254,0.0007044132,0.00010772415,0.00007775309,0.00010353093,0.000034036748],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001136058,0.0012496505,0.0012689142,0.0010299496,0.00064048695,0.0011753025,0.0016103712,0.0013284936,0.0030906673],"category_scores_gemma":[0.0035082295,0.0004935786,0.00080816215,0.001526702,0.0009466517,0.0011457577,0.0009317998,0.001392024,0.00058399024],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000050077368,0.00006393264,0.0002024966,0.00013003396,0.00003628224,0.000068688685,0.000071213464,0.8922832,0.000775338,0.035200432,0.002482445,0.06863594],"study_design_scores_gemma":[0.000053410615,0.00003825014,0.00006012486,0.000021043927,0.000014847625,0.000042981923,0.000042037092,0.970463,0.0004078221,0.026569447,0.002278226,0.000008970654],"about_ca_topic_score_codex":0.003200082,"about_ca_topic_score_gemma":0.0034107831,"teacher_disagreement_score":0.003200082,"about_ca_system_score_codex":0.00091600773,"about_ca_system_score_gemma":0.0015949082,"threshold_uncertainty_score":0.010339379},"labels":[],"label_agreement":null},{"id":"W159706599","doi":"","title":"A Hyper-heuristic Approach to the Home Care Scheduling Problem","year":2009,"lang":"en","type":"article","venue":"","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Vehicle routing problem; Scheduling (production processes); Heuristic; Computer science; Job shop scheduling; Nurse scheduling problem; Operations research; Mathematical optimization; Routing (electronic design automation); Mathematics; Artificial intelligence; Computer network; Flow shop scheduling","score_opus":0.015172345127862091,"score_gpt":0.2429631302065686,"score_spread":0.2277907850787065,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W159706599","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.042275954,0.0028711944,0.91495687,0.0014870686,0.00024571008,0.0006852288,0.000540846,0.00041914405,0.036518034],"genre_scores_gemma":[0.4903781,0.0024997497,0.48772967,0.00057903357,0.00032564832,0.0010941708,0.00092304737,0.00012993682,0.016340544],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99888915,0.0006898949,0.000032728873,0.0001086015,0.00016471847,0.00011491594],"domain_scores_gemma":[0.9992855,0.00045716946,0.00007173501,0.000033791315,0.000079739446,0.00007214543],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009631183,0.0010536611,0.0008924412,0.0013187351,0.0006980048,0.0016168647,0.0019462558,0.0017800939,0.007714305],"category_scores_gemma":[0.0017324423,0.0006591094,0.0007972228,0.0019840195,0.00084793614,0.00093646476,0.0011175377,0.0012212755,0.0005572495],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000083811814,0.00012709245,0.0003501914,0.00019872577,0.00007082408,0.00020585925,0.000072010575,0.935282,0.0005299993,0.02989865,0.0038147906,0.029366063],"study_design_scores_gemma":[0.000037842663,0.00003820955,0.0001072997,0.000023312032,0.00001453066,0.000031868633,0.000042708598,0.9828372,0.00013708587,0.013938298,0.0027831276,0.000008425749],"about_ca_topic_score_codex":0.006388999,"about_ca_topic_score_gemma":0.006623147,"teacher_disagreement_score":0.007714305,"about_ca_system_score_codex":0.0019963337,"about_ca_system_score_gemma":0.0017643421,"threshold_uncertainty_score":0.025806904},"labels":[],"label_agreement":null},{"id":"W1601807516","doi":"10.1007/978-3-642-17514-5_17","title":"Approximation Algorithms for the Multi-Vehicle Scheduling Problem","year":2010,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Scheduling (production processes); Algorithm; Mathematical optimization; Mathematics","score_opus":0.029951197751429275,"score_gpt":0.2773958598174451,"score_spread":0.2474446620660158,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1601807516","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005128119,0.0022180525,0.9847948,0.0005205401,0.00020136459,0.00004661713,0.00011145826,0.00038879647,0.00659022],"genre_scores_gemma":[0.23353003,0.004226608,0.74731827,0.00037921502,0.0005408103,0.00036062184,0.000842283,0.00048620132,0.012315889],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99876094,0.00047335066,0.00004837546,0.00018717175,0.00034518237,0.00018508662],"domain_scores_gemma":[0.99737203,0.0018744173,0.0001447292,0.00026743184,0.00022425741,0.000117233445],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021606816,0.0017340322,0.0019687393,0.0011271155,0.0007872799,0.0022470152,0.0034103894,0.002212184,0.005860883],"category_scores_gemma":[0.0072462494,0.0009103588,0.0014164068,0.003062792,0.0009666778,0.003370205,0.0017559981,0.0040213205,0.0013694278],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00028158032,0.00018772593,0.00034160318,0.00023255878,0.000073419105,0.00004568079,0.000083609775,0.73504734,0.00059977156,0.09029766,0.01213536,0.16067365],"study_design_scores_gemma":[0.000038953396,0.000020177942,0.000053671727,0.000020833515,0.000012628094,0.000024075467,0.000017434666,0.94219476,0.00016578696,0.055304285,0.0021418035,0.000005609099],"about_ca_topic_score_codex":0.005778394,"about_ca_topic_score_gemma":0.0049330206,"teacher_disagreement_score":0.005860883,"about_ca_system_score_codex":0.0026762495,"about_ca_system_score_gemma":0.0018046817,"threshold_uncertainty_score":0.01960665},"labels":[],"label_agreement":null},{"id":"W1601856531","doi":"10.1007/s00453-002-0986-1","title":"TSP Heuristics: Domination Analysis and Complexity","year":2002,"lang":"en","type":"article","venue":"Algorithmica","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":60,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Combinatorics; Travelling salesman problem; Mathematics; Heuristics; Digraph; Heuristic; Theory of computation; Time complexity; Constant (computer programming); Discrete mathematics; Graph; Mathematical optimization; Algorithm; Computer science","score_opus":0.029251424219865128,"score_gpt":0.25060173871491265,"score_spread":0.22135031449504752,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1601856531","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06623892,0.018392831,0.85747826,0.0050112796,0.00029695904,0.00017036148,0.00045851473,0.0003867912,0.05156603],"genre_scores_gemma":[0.7752115,0.014613208,0.18548186,0.0006168673,0.0013894388,0.00040984395,0.0006955166,0.0004235981,0.021158218],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99800116,0.0008909208,0.000053347,0.00020352559,0.0006455988,0.00020544275],"domain_scores_gemma":[0.98199534,0.015729442,0.00055629114,0.00071777974,0.0007911029,0.00021006765],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0034141901,0.0011706538,0.0015280189,0.0028229388,0.0013280407,0.0039978833,0.0021462925,0.0019948294,0.0073416824],"category_scores_gemma":[0.02223738,0.0010047604,0.0013064619,0.004558725,0.0028522413,0.0073354864,0.0016167308,0.0035906958,0.0005366036],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001597548,0.00018637824,0.0015259283,0.00036967776,0.00010737961,0.00007841202,0.00033997998,0.43314517,0.0007275761,0.468238,0.014661455,0.08046036],"study_design_scores_gemma":[0.000029446239,0.000024305487,0.0005347524,0.00004999191,0.00003599656,0.000060158844,0.00006990751,0.64140594,0.00028935983,0.35376698,0.003716671,0.0000163672],"about_ca_topic_score_codex":0.0067400015,"about_ca_topic_score_gemma":0.007844958,"teacher_disagreement_score":0.0073416824,"about_ca_system_score_codex":0.0042609246,"about_ca_system_score_gemma":0.0023633207,"threshold_uncertainty_score":0.03091532},"labels":[],"label_agreement":null},{"id":"W1602449607","doi":"10.1109/icec.1995.489116","title":"Generalization and refinement of route construction heuristics using genetic algorithms","year":2002,"lang":"en","type":"article","venue":"","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Heuristics; Generalization; Computer science; Genetic algorithm; Algorithm; Theoretical computer science; Machine learning; Mathematics","score_opus":0.030225867247348527,"score_gpt":0.25142448154052444,"score_spread":0.22119861429317592,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1602449607","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.071782246,0.00032991148,0.9226247,0.0001346325,0.00007626988,0.00015183794,0.00005913137,0.0005495675,0.0042917076],"genre_scores_gemma":[0.48245037,0.00032582862,0.5148426,0.000120985635,0.00005603388,0.00017760365,0.00022264,0.00020812968,0.001595883],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989341,0.0004862172,0.000064547276,0.0001922226,0.00021152876,0.000111491],"domain_scores_gemma":[0.9953348,0.0027647684,0.00036124603,0.00081014493,0.0006315757,0.00009743736],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0033029544,0.00092066615,0.0013450743,0.0018047205,0.0006670061,0.00077370554,0.0021059709,0.0012211076,0.0015631217],"category_scores_gemma":[0.011155812,0.00076998747,0.0012608027,0.00138302,0.0012446695,0.0014306675,0.0012785303,0.001541008,0.00027168277],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006327036,0.00009510006,0.001069881,0.000052507676,0.000043117598,0.00005214091,0.0001386678,0.92725384,0.0015446772,0.0074786237,0.00064155675,0.061566588],"study_design_scores_gemma":[0.000016100297,0.000030593816,0.0001508761,0.000011115494,0.000016038033,0.0000143993275,0.000018582577,0.9952295,0.00042332857,0.003689592,0.00039473202,0.000005098371],"about_ca_topic_score_codex":0.011596263,"about_ca_topic_score_gemma":0.009813241,"teacher_disagreement_score":0.011596263,"about_ca_system_score_codex":0.0012283934,"about_ca_system_score_gemma":0.0015739168,"threshold_uncertainty_score":0.02305752},"labels":[],"label_agreement":null},{"id":"W1605874004","doi":"10.1002/9780470400531.eorms0118","title":"Branch‐Price‐and‐Cut Algorithms","year":2011,"lang":"en","type":"other","venue":"Wiley Encyclopedia of Operations Research and Management Science","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":89,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"","keywords":"Column generation; Branch and cut; Linear programming relaxation; Discretization; Branch and bound; Branch and price; Integer programming; Relaxation (psychology); Branching (polymer chemistry); Integer (computer science); Cutting-plane method; Mathematics; Tree (set theory); Polyhedron; Column (typography); Mathematical optimization; Algorithm; Decomposition; Computer science; Combinatorics; Geometry","score_opus":0.03414338929212809,"score_gpt":0.32532305928127525,"score_spread":0.2911796699891472,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1605874004","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0029515438,0.00083887187,0.9824225,0.00043238248,0.00010028885,0.00009514619,0.0000953103,0.00033510535,0.012728788],"genre_scores_gemma":[0.12438241,0.0012707186,0.8563005,0.000394446,0.00018416703,0.00039595945,0.00047867114,0.00032815046,0.016265066],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.9985127,0.000691471,0.00006736535,0.00019441643,0.00037310264,0.00016097611],"domain_scores_gemma":[0.9977963,0.0015880446,0.00013606985,0.00019171035,0.0002150944,0.00007287512],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018966609,0.0014756416,0.0016796739,0.0011329137,0.00073153334,0.0023099785,0.0018564332,0.0018755221,0.016145479],"category_scores_gemma":[0.004794223,0.0007570678,0.0009120191,0.0025616419,0.0011778998,0.0025076785,0.0016276303,0.003239552,0.0032196075],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011658941,0.00016918193,0.00033348167,0.00026770093,0.00009174057,0.00009537154,0.00006915293,0.6116681,0.0009087533,0.15956353,0.013944787,0.2127716],"study_design_scores_gemma":[0.000036231366,0.000028994991,0.000046104615,0.000037912287,0.000014729781,0.000033844757,0.000014980842,0.9038864,0.00061429536,0.08859155,0.0066872635,0.0000076935985],"about_ca_topic_score_codex":0.0030435114,"about_ca_topic_score_gemma":0.003197949,"teacher_disagreement_score":0.016145479,"about_ca_system_score_codex":0.0014444463,"about_ca_system_score_gemma":0.001551206,"threshold_uncertainty_score":0.05401194},"labels":[],"label_agreement":null},{"id":"W1606603715","doi":"10.6000/2371-1647.2015.01.02","title":"A Unified Framework for Integer Programming Formulation of Graph Matching Problems","year":2015,"lang":"en","type":"article","venue":"Journal of Advances in Management Sciences & Information Systems","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Integer programming; Computer science; Matching (statistics); Graph; Mathematical optimization; Theoretical computer science; Mathematics; Algorithm; Statistics","score_opus":0.029323131710985835,"score_gpt":0.31592017883428625,"score_spread":0.2865970471233004,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1606603715","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000506197,0.00053592573,0.99195194,0.00048602672,0.00007122847,0.000054587093,0.00009306579,0.00006044142,0.0062406743],"genre_scores_gemma":[0.058684614,0.0037603653,0.9273424,0.00060580036,0.00070571323,0.00090782915,0.00050579914,0.0002504901,0.0072369073],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9962889,0.0018058006,0.00022330508,0.00047351475,0.00088200613,0.00032655583],"domain_scores_gemma":[0.99773514,0.0013798786,0.0002264164,0.00019429358,0.00035542014,0.000108765154],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0050513106,0.0027200824,0.0017874722,0.002170747,0.0010036267,0.0040907613,0.003557411,0.0021296912,0.006926361],"category_scores_gemma":[0.007769574,0.0010558461,0.002813802,0.0041032247,0.001831422,0.0054191737,0.0028860592,0.007078704,0.0021415134],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014009348,0.00007776379,0.00014277818,0.00019851659,0.000026619167,0.00009077005,0.00012994834,0.1372553,0.00049088243,0.83006495,0.0055575673,0.02595097],"study_design_scores_gemma":[0.000021251848,0.000046933812,0.00008244786,0.000086655484,0.000023860073,0.00007934093,0.000074069416,0.5434049,0.0002737621,0.4345243,0.021361921,0.000020604284],"about_ca_topic_score_codex":0.0029724936,"about_ca_topic_score_gemma":0.0037547268,"teacher_disagreement_score":0.006926361,"about_ca_system_score_codex":0.002492672,"about_ca_system_score_gemma":0.0029896037,"threshold_uncertainty_score":0.026714206},"labels":[],"label_agreement":null},{"id":"W1608301497","doi":"10.1109/ccece.2015.7129191","title":"Generalized formulation for trajectory optimization in patrolling problems","year":2015,"lang":"en","type":"article","venue":"","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Patrolling; Trajectory; Mathematical optimization; Viewpoints; Sequence (biology); Computer science; Optimization problem; Trajectory optimization; Mathematics; Optimal control","score_opus":0.049368986062342884,"score_gpt":0.28055857626401254,"score_spread":0.23118959020166965,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1608301497","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.001632954,0.00049331464,0.9928716,0.0001520372,0.00007898524,0.000057661542,0.000105993226,0.000079157246,0.004528329],"genre_scores_gemma":[0.20615582,0.0036576719,0.7628426,0.0004381252,0.00048523286,0.0009291525,0.0011445059,0.00033959263,0.024007257],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99946684,0.00021414716,0.00002909779,0.00010559955,0.0001377919,0.000046630114],"domain_scores_gemma":[0.9996886,0.00012525228,0.000035387537,0.000042579075,0.000085392894,0.000022708602],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006618406,0.001268991,0.0009109461,0.00047796752,0.00040969398,0.001084544,0.0014826928,0.0012874661,0.0071797096],"category_scores_gemma":[0.001145658,0.00034598517,0.0011626452,0.0010647157,0.0005715883,0.0012345362,0.0010731177,0.0019254495,0.0014245136],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000035607893,0.000057640216,0.00020595861,0.00039634947,0.00004562962,0.00023714804,0.00010494385,0.72926784,0.002376271,0.20919637,0.0071788407,0.050897453],"study_design_scores_gemma":[0.000014302693,0.000052023137,0.00008672644,0.00003436746,0.000011923061,0.00007365721,0.00003929853,0.93255424,0.00037861627,0.049183093,0.017561557,0.00001016216],"about_ca_topic_score_codex":0.0030698383,"about_ca_topic_score_gemma":0.0031635708,"teacher_disagreement_score":0.0071797096,"about_ca_system_score_codex":0.0008415888,"about_ca_system_score_gemma":0.0011502638,"threshold_uncertainty_score":0.024018526},"labels":[],"label_agreement":null},{"id":"W163313786","doi":"10.1080/03155986.2005.11732712","title":"Evaluation of The Contract Or-Patch Heuristic Eor The Asymmetric Tsp<sup>1</sup>","year":2005,"lang":"en","type":"article","venue":"INFOR Information Systems and Operational Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Heuristics; Travelling salesman problem; Robustness (evolution); Mathematical optimization; Computer science; Heuristic; Algorithm; Variety (cybernetics); Running time; Mathematics; Artificial intelligence","score_opus":0.08325847113931187,"score_gpt":0.3756591253218196,"score_spread":0.2924006541825077,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W163313786","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.886926,0.0016463342,0.095884286,0.0004069493,0.00013371416,0.00042811717,0.0002992995,0.0007460417,0.013529275],"genre_scores_gemma":[0.888684,0.00044597042,0.10853315,0.000109840505,0.000040399704,0.00010044536,0.0004902637,0.00014387502,0.0014520916],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99862933,0.0006394265,0.00006409268,0.0001865425,0.00031154839,0.00016917122],"domain_scores_gemma":[0.9951735,0.0033903527,0.00028181236,0.00044730876,0.00052087585,0.00018619362],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024532205,0.0008042449,0.0011240435,0.0008258912,0.0005127953,0.0007895462,0.0021592316,0.0011746136,0.0027588934],"category_scores_gemma":[0.0065518357,0.00025250672,0.000563133,0.0008569317,0.00064773276,0.001482995,0.00058110245,0.0007457127,0.00026952696],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010009095,0.0009608648,0.00800765,0.00044409052,0.00020475892,0.00015181876,0.00009900266,0.8237283,0.0041688276,0.005108293,0.002542155,0.15358323],"study_design_scores_gemma":[0.00008882822,0.0005549298,0.0010841775,0.000008843725,0.0000318654,0.000055950484,0.00006714417,0.99320906,0.003280044,0.00074882625,0.00086193613,0.000008400351],"about_ca_topic_score_codex":0.0054528345,"about_ca_topic_score_gemma":0.004548881,"teacher_disagreement_score":0.0054528345,"about_ca_system_score_codex":0.0014973561,"about_ca_system_score_gemma":0.0011011049,"threshold_uncertainty_score":0.012974024},"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,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_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","score_opus":0.03005034896176269,"score_gpt":0.2773482127765395,"score_spread":0.24729786381477678,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1650780448","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.051487003,0.00012442695,0.9454908,0.00011157018,0.000021206117,0.00008517473,0.00011485027,0.0002361166,0.0023289165],"genre_scores_gemma":[0.5069221,0.00027671104,0.48683006,0.000045772962,0.00001990595,0.00029702196,0.00048079094,0.00016299178,0.004964559],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997247,0.000089293106,0.000011296832,0.00007714111,0.00004528608,0.000052199077],"domain_scores_gemma":[0.9995782,0.0002566393,0.000055226737,0.000035755438,0.000035030378,0.000039246454],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005876368,0.0008102757,0.0005694993,0.0006589835,0.0008734018,0.000723739,0.0009970816,0.000801007,0.0039077234],"category_scores_gemma":[0.0016595679,0.00048939785,0.000756306,0.0007253368,0.0006279902,0.0010299236,0.0012814545,0.00058398605,0.00046364675],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000039493923,0.000021904983,0.00029505359,0.000038328904,0.000013427036,0.0000789174,0.00006720654,0.9764574,0.00056042057,0.0056774085,0.0004822827,0.016268244],"study_design_scores_gemma":[0.000012352708,0.00003336437,0.00010200212,0.000004800464,0.0000060806765,0.000019786878,0.00006702451,0.9901446,0.0005470666,0.007944616,0.0011130911,0.0000052389423],"about_ca_topic_score_codex":0.006788511,"about_ca_topic_score_gemma":0.0059557958,"teacher_disagreement_score":0.006788511,"about_ca_system_score_codex":0.00093569775,"about_ca_system_score_gemma":0.0009470446,"threshold_uncertainty_score":0.013498008},"labels":[],"label_agreement":null},{"id":"W166422795","doi":"10.1007/978-0-387-71722-7_1","title":"Planned Route Optimization For Real-Time Vehicle Routing","year":2008,"lang":"en","type":"book-chapter","venue":"","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":50,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; Computer Research Institute of Montréal; Université Laval","funders":"","keywords":"Vehicle routing problem; Anticipation (artificial intelligence); Computer science; Consolidation (business); Focus (optics); Operations research; Routing (electronic design automation); Work (physics); Transport engineering; Engineering; Computer network; Artificial intelligence; Business","score_opus":0.021255838836052872,"score_gpt":0.24185218910682443,"score_spread":0.22059635027077157,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W166422795","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00071553653,0.00413083,0.95963895,0.00024167041,0.00055512757,0.00002078817,0.00011832667,0.0005231838,0.034055587],"genre_scores_gemma":[0.06035918,0.012200765,0.7605757,0.00024145836,0.00052292214,0.00020870367,0.0007092269,0.0010871312,0.16409492],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998105,0.00003162461,0.000005717999,0.00003670132,0.00010629039,0.000009097921],"domain_scores_gemma":[0.99990046,0.000045729987,0.000005496232,0.000016965678,0.000027832506,0.0000035780322],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023580172,0.0011559222,0.0006204247,0.00034932452,0.00021803926,0.0008134445,0.0011478318,0.0006952404,0.012287363],"category_scores_gemma":[0.00049663655,0.0005231697,0.0005659445,0.00070987333,0.0004238684,0.0009919839,0.00043257562,0.0013564923,0.00391197],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003300744,0.00004164942,0.00008353371,0.00035605006,0.00004018321,0.00005705687,0.000049573195,0.37284347,0.0040177256,0.15159135,0.055507917,0.41537845],"study_design_scores_gemma":[0.000012219202,0.00003318348,0.00018761215,0.000086141365,0.000022094824,0.0001293172,0.000020349447,0.7149467,0.002698368,0.12809864,0.15373705,0.000028299872],"about_ca_topic_score_codex":0.0020056579,"about_ca_topic_score_gemma":0.0031698626,"teacher_disagreement_score":0.012287363,"about_ca_system_score_codex":0.0005621936,"about_ca_system_score_gemma":0.0005485344,"threshold_uncertainty_score":0.04110527},"labels":[],"label_agreement":null},{"id":"W1696028247","doi":"10.5267/j.ijiec.2015.8.003","title":"A multi-objective Pareto ant colony algorithm for the Multi-Depot Vehicle Routing problem with Backhauls","year":2015,"lang":"en","type":"article","venue":"International Journal of Industrial Engineering Computations","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":41,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Vehicle routing problem; Pareto principle; Ant colony optimization algorithms; Mathematical optimization; Depot; ANT; Computer science; Algorithm; Multi-objective optimization; Mathematics; Engineering; Routing (electronic design automation); Computer network","score_opus":0.05743903590744473,"score_gpt":0.3045569092903155,"score_spread":0.24711787338287075,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1696028247","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010196057,0.00030770182,0.9841414,0.0001266831,0.00006131149,0.00010100249,0.00003740458,0.000225264,0.004803096],"genre_scores_gemma":[0.17328247,0.00049798295,0.81969935,0.00012247797,0.000034845754,0.00030831742,0.00014871753,0.00010595371,0.005799902],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999666,0.00009271621,0.000011733618,0.000042413285,0.00015509831,0.00003221495],"domain_scores_gemma":[0.9997756,0.000090287554,0.000023129742,0.000017046104,0.00007092708,0.000023077788],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005054939,0.00092079863,0.0007609501,0.00074869336,0.00063585356,0.0007364931,0.0011304957,0.0008792878,0.0018401281],"category_scores_gemma":[0.0010067616,0.0003405597,0.0006330386,0.00071762514,0.00039634143,0.00062800484,0.00084536464,0.0008415567,0.00050868694],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000046644334,0.000099296405,0.00025164423,0.00011521705,0.00004031475,0.000101954356,0.000056073586,0.8597669,0.0037195378,0.010123561,0.0018803001,0.123798445],"study_design_scores_gemma":[0.000015851114,0.000041701154,0.000055274773,0.0000076183833,0.0000065691615,0.000031114338,0.0000110598685,0.995659,0.00057621975,0.001815278,0.0017745434,0.000005791462],"about_ca_topic_score_codex":0.0032678533,"about_ca_topic_score_gemma":0.004025926,"teacher_disagreement_score":0.0032678533,"about_ca_system_score_codex":0.000457239,"about_ca_system_score_gemma":0.0011655067,"threshold_uncertainty_score":0.006497681},"labels":[],"label_agreement":null},{"id":"W1699895247","doi":"10.1007/s10696-015-9222-6","title":"The patient assignment problem in home health care: using a data-driven method to estimate the travel times of care givers","year":2015,"lang":"en","type":"article","venue":"Flexible Services and Manufacturing Journal","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":61,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"","keywords":"Workload; Set (abstract data type); Computer science; Health care; Service (business); Value (mathematics); Operations research; Mathematics; Machine learning; Business; Marketing","score_opus":0.029536877569350705,"score_gpt":0.32930822289301415,"score_spread":0.29977134532366345,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1699895247","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3570506,0.00036230043,0.6392143,0.00073500694,0.00011845249,0.00020993252,0.001212858,0.0003056674,0.00079085375],"genre_scores_gemma":[0.87471604,0.00018386338,0.12179126,0.00010401942,0.0000681232,0.0002925923,0.0013734532,0.000056377045,0.0014143814],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999288,0.00038480043,0.00003485215,0.00014015875,0.000074336866,0.0000778166],"domain_scores_gemma":[0.99447423,0.004383133,0.0003281184,0.00014384098,0.00036908445,0.00030158434],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025588095,0.0010324086,0.0014200738,0.0014751815,0.00058873784,0.0010929389,0.0020512217,0.002010367,0.0017337144],"category_scores_gemma":[0.008764482,0.0012604467,0.001193829,0.001162282,0.00060112565,0.0010858492,0.0009805593,0.0014338188,0.0001925937],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001719795,0.00018238985,0.0064339535,0.000055967594,0.0000880687,0.000075601696,0.000056777488,0.97978187,0.00021690068,0.0013451888,0.0006055512,0.010985737],"study_design_scores_gemma":[0.000008823857,0.00001259219,0.0003555301,0.0000021177075,0.0000048415486,0.000006711218,0.000015090911,0.9989303,0.000046902343,0.00056238874,0.00005079809,0.0000038507465],"about_ca_topic_score_codex":0.03139375,"about_ca_topic_score_gemma":0.01789084,"teacher_disagreement_score":0.03139375,"about_ca_system_score_codex":0.001289141,"about_ca_system_score_gemma":0.0023036979,"threshold_uncertainty_score":0.062422097},"labels":[],"label_agreement":null},{"id":"W1783225223","doi":"10.1023/a:1013613701606","title":"Constraint Programming Based Column Generation for Crew Assignment","year":2002,"lang":"en","type":"article","venue":"Journal of Heuristics","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":100,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Mathematical optimization; Column generation; Constraint (computer-aided design); Shortest path problem; Domain (mathematical analysis); Limit (mathematics); Path (computing); Computer science; Space (punctuation); Mathematics; Theoretical computer science","score_opus":0.0538748117578501,"score_gpt":0.2743114601091357,"score_spread":0.22043664835128562,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1783225223","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017296527,0.00017828634,0.9738461,0.00022293213,0.00009542907,0.00026742366,0.00047971823,0.0012487102,0.0063649],"genre_scores_gemma":[0.18481709,0.00014850851,0.8090634,0.00020057801,0.00004270927,0.00038256767,0.0010021228,0.00026963296,0.0040733363],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993697,0.00028528503,0.000020244335,0.000083615676,0.00014534667,0.00009582188],"domain_scores_gemma":[0.99834204,0.0010388619,0.00008599336,0.00016832573,0.00030163387,0.000063124135],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007051935,0.0008959963,0.0010167859,0.0012716408,0.0009317531,0.0010659315,0.0017338333,0.0009880493,0.009749372],"category_scores_gemma":[0.002993758,0.00075223803,0.0008646293,0.0023084613,0.00058092107,0.0010967348,0.00077512243,0.0012409053,0.000940334],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019137886,0.0002432869,0.00047184076,0.00016903513,0.000051762618,0.0001504458,0.00008498883,0.7527699,0.004033598,0.013613154,0.013087865,0.21513277],"study_design_scores_gemma":[0.00002952497,0.000029824241,0.00007378326,0.000007671802,0.0000106947045,0.000018798204,0.000019073555,0.9927981,0.0012162704,0.004491285,0.0012961554,0.000008774015],"about_ca_topic_score_codex":0.017202448,"about_ca_topic_score_gemma":0.01980662,"teacher_disagreement_score":0.017202448,"about_ca_system_score_codex":0.00083610154,"about_ca_system_score_gemma":0.001737533,"threshold_uncertainty_score":0.034204602},"labels":[],"label_agreement":null},{"id":"W1823652951","doi":"10.1007/3-540-45724-0_5","title":"An Experimental Study of a Simple Ant Colony System for the Vehicle Routing Problem with Time Windows","year":2002,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Vehicle routing problem; Metaheuristic; Computer science; Heuristics; Ant colony optimization algorithms; Ant colony; Mathematical optimization; Scheduling (production processes); Parallel metaheuristic; Job shop scheduling; Set (abstract data type); Ranging; Routing (electronic design automation); Operations research; Artificial intelligence; Mathematics; Computer network; Meta-optimization","score_opus":0.018252130103274436,"score_gpt":0.25866336348018026,"score_spread":0.24041123337690584,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1823652951","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9753948,0.0002048761,0.020078521,0.00011164455,0.00011642147,0.00020834728,0.00021399854,0.00028908753,0.003382326],"genre_scores_gemma":[0.9818595,0.00008722034,0.016525025,0.000019825053,0.000015004163,0.00008825747,0.00014382054,0.000053932243,0.0012074531],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993973,0.00023200076,0.00003843257,0.00010306199,0.00015567344,0.00007348165],"domain_scores_gemma":[0.9947864,0.003412616,0.0002397564,0.0007020644,0.0005510241,0.00030808322],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00093073584,0.00065930915,0.00080580445,0.00044144414,0.00049966195,0.0006710392,0.0013803501,0.001018895,0.0032692312],"category_scores_gemma":[0.00437595,0.00032191098,0.00034768737,0.00057935016,0.0006320788,0.0009528719,0.00049090345,0.00084857986,0.00042787896],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0070112576,0.012992357,0.0046183392,0.001280005,0.00026402558,0.0006017252,0.00036514387,0.6755682,0.19911286,0.0055002817,0.0024299785,0.090255864],"study_design_scores_gemma":[0.000357659,0.0047254693,0.0022958945,0.000011674446,0.000053146443,0.00010543237,0.00012633365,0.96192485,0.028315935,0.0013858479,0.0006707421,0.000027017839],"about_ca_topic_score_codex":0.0029089763,"about_ca_topic_score_gemma":0.0016253918,"teacher_disagreement_score":0.0032692312,"about_ca_system_score_codex":0.00042589213,"about_ca_system_score_gemma":0.0005972531,"threshold_uncertainty_score":0.010936677},"labels":[],"label_agreement":null},{"id":"W1823906467","doi":"10.1016/j.ejor.2015.10.058","title":"A two-stage solution method for the annual dairy transportation problem","year":2015,"lang":"en","type":"article","venue":"European Journal of Operational Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":37,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"Mitacs","keywords":"Computer science; Transportation theory; Operations research; Cluster analysis; Stage (stratigraphy); Order (exchange); Phase (matter); Operations management; Mathematical optimization; Mathematics; Business; Economics; Artificial intelligence","score_opus":0.13481051807414288,"score_gpt":0.41495660487977276,"score_spread":0.2801460868056299,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1823906467","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005089017,0.00007804279,0.9915451,0.000093791044,0.00006491638,0.00008652607,0.00005057308,0.00011836563,0.002873728],"genre_scores_gemma":[0.16783611,0.00023018487,0.814062,0.00014543242,0.00008888927,0.0006310925,0.0002838476,0.00019063843,0.016531756],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995203,0.00017679758,0.000023972747,0.00007805813,0.00012866226,0.00007221986],"domain_scores_gemma":[0.9990011,0.0005894505,0.000046294834,0.000054705346,0.0002426659,0.000065731496],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015535598,0.00075457786,0.0009768258,0.0006363919,0.0005120753,0.0008540925,0.0020352914,0.002082632,0.010498182],"category_scores_gemma":[0.0027815835,0.0007220811,0.0012537194,0.0006066021,0.0005083125,0.0011417054,0.0015968592,0.0016050938,0.0010380499],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021981026,0.00019916445,0.0005542078,0.00026317278,0.000061681145,0.00012018447,0.00012093664,0.8296483,0.006150089,0.026245799,0.0045317346,0.13188496],"study_design_scores_gemma":[0.000019758703,0.000040639312,0.000058826343,0.0000063439516,0.000006935937,0.000010854572,0.000006965494,0.9967051,0.0003549913,0.0017056718,0.0010769583,0.000006940728],"about_ca_topic_score_codex":0.007236579,"about_ca_topic_score_gemma":0.0068573905,"teacher_disagreement_score":0.010498182,"about_ca_system_score_codex":0.0006692905,"about_ca_system_score_gemma":0.0020647654,"threshold_uncertainty_score":0.03511995},"labels":[],"label_agreement":null},{"id":"W1832376799","doi":"10.1287/trsc.2015.0635","title":"A Branch-Price-and-Cut Algorithm for the Inventory-Routing Problem","year":2015,"lang":"en","type":"article","venue":"Transportation Science","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":134,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval; Polytechnique Montréal; Group for Research in Decision Analysis","funders":"","keywords":"Column generation; Branch and cut; Benchmark (surveying); Routing (electronic design automation); Mathematical optimization; Branch and price; Computer science; Vehicle routing problem; Set (abstract data type); State (computer science); Algorithm; Operations research; Integer programming; Mathematics","score_opus":0.0419161086913205,"score_gpt":0.2980385804930498,"score_spread":0.2561224718017293,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1832376799","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009425824,0.00034989053,0.9821776,0.0004187344,0.00010477982,0.00032359766,0.00031883243,0.00071958476,0.006161251],"genre_scores_gemma":[0.06616209,0.0003178904,0.9282212,0.00019690058,0.000061314,0.0005361506,0.00097375165,0.00021631528,0.0033143726],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999173,0.00025568603,0.000042208983,0.0001866074,0.00021789542,0.00012462378],"domain_scores_gemma":[0.99871945,0.00081721804,0.00009471821,0.00009659759,0.00020026676,0.000071787355],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012471023,0.0017044053,0.001388446,0.0011550654,0.0009543174,0.0014233759,0.0019565243,0.0021129015,0.0078057046],"category_scores_gemma":[0.0038031498,0.0007452484,0.0009831566,0.0021892095,0.00052275276,0.0017357839,0.0010800662,0.002839974,0.0013010628],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018669818,0.0003602666,0.0005529306,0.00022486491,0.0000758735,0.00013036952,0.00007362893,0.6852326,0.0016154624,0.028450122,0.017077575,0.2660196],"study_design_scores_gemma":[0.000080081845,0.00007414402,0.00009213523,0.000015437248,0.000020336727,0.00005541434,0.000018119541,0.98196983,0.0008062693,0.013686841,0.0031713548,0.00001007739],"about_ca_topic_score_codex":0.005548859,"about_ca_topic_score_gemma":0.006791743,"teacher_disagreement_score":0.0078057046,"about_ca_system_score_codex":0.0017533537,"about_ca_system_score_gemma":0.003156814,"threshold_uncertainty_score":0.026112676},"labels":[],"label_agreement":null},{"id":"W1839817983","doi":"10.1002/atr.1237","title":"A differential evolution approach for the vehicle routing problem with backhauls and time windows","year":2013,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Vehicle routing problem; Backhaul (telecommunications); Mathematical optimization; Benchmark (surveying); Computer science; Extension (predicate logic); Integer programming; Operations research; Routing (electronic design automation); Mathematics; Computer network","score_opus":0.0069416682432794745,"score_gpt":0.21626474240693105,"score_spread":0.2093230741636516,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1839817983","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00879433,0.00020436481,0.987931,0.0001287765,0.000036944944,0.00003523318,0.000020625494,0.000036493915,0.0028122014],"genre_scores_gemma":[0.42898548,0.0007488238,0.56078196,0.00015172259,0.00006719519,0.00040531965,0.00012856687,0.00006962901,0.008661386],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99969876,0.000102788945,0.00001539449,0.000055377655,0.00009168965,0.000036138343],"domain_scores_gemma":[0.9995586,0.00028971492,0.000040392897,0.000015488366,0.000072845956,0.000022972463],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008762447,0.0006174269,0.00071650406,0.0005850897,0.00037394676,0.0007338564,0.0008886513,0.0010305346,0.0016292918],"category_scores_gemma":[0.0015341815,0.00046949647,0.0009271879,0.00063770945,0.00051912345,0.0006655973,0.00077970076,0.0011323343,0.00019414336],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013127683,0.000017210637,0.00016919227,0.000027631813,0.000020740994,0.000038034712,0.000023394572,0.9760754,0.00095938094,0.009427522,0.00022772064,0.013000642],"study_design_scores_gemma":[0.0000031736881,0.000007828936,0.000022829163,0.0000021092706,0.0000024830963,0.000005517823,0.000002407099,0.9981818,0.000110670226,0.0012701569,0.00038943626,0.0000017320776],"about_ca_topic_score_codex":0.0046460098,"about_ca_topic_score_gemma":0.0031010704,"teacher_disagreement_score":0.0046460098,"about_ca_system_score_codex":0.0009548431,"about_ca_system_score_gemma":0.0007865611,"threshold_uncertainty_score":0.009237945},"labels":[],"label_agreement":null},{"id":"W1843716693","doi":"10.1016/j.ejor.2016.04.065","title":"Large neighborhood search for multi-trip vehicle routing","year":2016,"lang":"en","type":"article","venue":"European Journal of Operational Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":60,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"Belgian Federal Science Policy Office; Fonds De La Recherche Scientifique - FNRS; National Natural Science Foundation of China","keywords":"Vehicle routing problem; Heuristics; Computer science; Benchmark (surveying); Routing (electronic design automation); Mathematical optimization; Heuristic; Set (abstract data type); Local search (optimization); Artificial intelligence; Mathematics; Computer network","score_opus":0.13418820472179713,"score_gpt":0.3940342656961502,"score_spread":0.25984606097435303,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1843716693","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018835193,0.0010385526,0.9768708,0.00030805112,0.00009545199,0.000043127147,0.00007966084,0.00014853127,0.0025807025],"genre_scores_gemma":[0.5875333,0.0009853302,0.39519456,0.00015426442,0.0001614722,0.00042117902,0.00040875556,0.00018616326,0.014954979],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99949026,0.00029612216,0.000017381242,0.000063075415,0.00009994023,0.000033135988],"domain_scores_gemma":[0.9975647,0.0019221436,0.0001242811,0.00009818683,0.0002032572,0.000087494554],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015291832,0.0006120361,0.0016132202,0.0009881274,0.0006331155,0.0007695774,0.0017553446,0.0013948751,0.0025012898],"category_scores_gemma":[0.0057039005,0.00073227653,0.0006176567,0.0010381226,0.00074384565,0.0016544737,0.0014248244,0.0012516374,0.00033880124],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008613921,0.00004061407,0.00022733348,0.00005546005,0.000035306988,0.000028630835,0.000030808613,0.9632593,0.00035586196,0.013878017,0.0017344014,0.020268105],"study_design_scores_gemma":[0.0000037367772,0.0000062629633,0.00001807395,0.0000017241983,0.000001805518,0.0000026394084,0.0000025555398,0.9970041,0.000022094608,0.0027883602,0.00014718891,0.0000013539558],"about_ca_topic_score_codex":0.007693546,"about_ca_topic_score_gemma":0.0063432506,"teacher_disagreement_score":0.007693546,"about_ca_system_score_codex":0.0008975501,"about_ca_system_score_gemma":0.00087613653,"threshold_uncertainty_score":0.015297532},"labels":[],"label_agreement":null},{"id":"W1847516220","doi":"10.1007/978-0-387-77778-8_15","title":"Recent Models and Algorithms for One-to-One Pickup and Delivery Problems","year":2008,"lang":"en","type":"book-chapter","venue":"Operations research, computer science. Interface series","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":89,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"","keywords":"Heuristics; Pickup; Set (abstract data type); Heuristic; Computer science; Algorithm; Mathematical optimization; Order (exchange); Operations research; Engineering; Mathematics; Artificial intelligence; Business; Programming language","score_opus":0.14587001360493643,"score_gpt":0.3417906010138803,"score_spread":0.19592058740894389,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1847516220","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0025667013,0.013841714,0.96164155,0.0010778955,0.0005254343,0.00006468949,0.00018242274,0.00029378978,0.019805806],"genre_scores_gemma":[0.17851822,0.07389207,0.6630845,0.00103304,0.0024775788,0.00084248517,0.0018223244,0.0005787285,0.07775102],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.998806,0.00032843035,0.000056195313,0.00022793248,0.00044855673,0.00013296769],"domain_scores_gemma":[0.9984372,0.00094429526,0.00012858731,0.0001894827,0.00022361187,0.00007684276],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001658365,0.0026538684,0.0024477292,0.0012006087,0.0009920936,0.0031475571,0.0063840784,0.0029330754,0.010521876],"category_scores_gemma":[0.004100342,0.001102858,0.0024526396,0.003930401,0.0017453773,0.0055030077,0.0019609223,0.0050797816,0.0042262264],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006311722,0.00014226725,0.00019527749,0.0005092372,0.00006579465,0.00006942425,0.0000902307,0.31173164,0.00045951875,0.539259,0.023019612,0.12439488],"study_design_scores_gemma":[0.000024843806,0.00003972464,0.000088217894,0.00006366228,0.000037446152,0.0001355379,0.000032364445,0.56494206,0.0002730327,0.40946752,0.024872629,0.000022968308],"about_ca_topic_score_codex":0.0031451762,"about_ca_topic_score_gemma":0.003536743,"teacher_disagreement_score":0.010521876,"about_ca_system_score_codex":0.0023116078,"about_ca_system_score_gemma":0.0018168949,"threshold_uncertainty_score":0.035199225},"labels":[],"label_agreement":null},{"id":"W1848719272","doi":"10.1016/j.ejor.2015.10.046","title":"Branch-price-and-cut algorithms for the pickup and delivery problem with time windows and multiple stacks","year":2015,"lang":"en","type":"article","venue":"European Journal of Operational Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":50,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal; Polytechnique Montréal; Group for Research in Decision Analysis","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Pickup; FIFO and LIFO accounting; Benchmark (surveying); Computer science; Stack (abstract data type); Vehicle routing problem; Mathematical optimization; Travelling salesman problem; Position (finance); Shortest path problem; Path (computing); Algorithm; Routing (electronic design automation); Mathematics; Economics; FIFO (computing and electronics); Theoretical computer science; Artificial intelligence; Operating system","score_opus":0.08679891004603106,"score_gpt":0.3286413364171116,"score_spread":0.2418424263710805,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1848719272","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01812007,0.0015191599,0.9752085,0.0005531031,0.0001244556,0.00016709014,0.00016923575,0.00041971618,0.0037186407],"genre_scores_gemma":[0.22281556,0.0018991746,0.76352304,0.00022369093,0.00024148426,0.00060567865,0.00056819123,0.0004610309,0.009662084],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988286,0.00047920312,0.00005761292,0.0001573258,0.00025179272,0.00022538974],"domain_scores_gemma":[0.99409735,0.0049278866,0.0002546498,0.00014542618,0.0002776851,0.0002969482],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004263479,0.0025622428,0.004041099,0.0021031348,0.001413501,0.0024624283,0.003645467,0.00398895,0.008838704],"category_scores_gemma":[0.008075433,0.0024908052,0.0019583334,0.0034656443,0.0014348448,0.004285883,0.0023585304,0.0047890954,0.00092270615],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00027729184,0.00022737752,0.000344434,0.00017844973,0.00008371559,0.000046779827,0.00005790558,0.91339266,0.0003599267,0.020110492,0.0030197313,0.06190134],"study_design_scores_gemma":[0.00004019931,0.000038377548,0.00005048123,0.000010170696,0.000016778722,0.0000075142284,0.000009953815,0.9890442,0.000115956944,0.010287723,0.00037266267,0.00000592408],"about_ca_topic_score_codex":0.01322281,"about_ca_topic_score_gemma":0.011381447,"teacher_disagreement_score":0.01322281,"about_ca_system_score_codex":0.0030249215,"about_ca_system_score_gemma":0.003752325,"threshold_uncertainty_score":0.029568434},"labels":[],"label_agreement":null},{"id":"W1853087932","doi":"10.1016/j.ejor.2015.08.040","title":"Branch-and-price algorithms for the solution of the multi-trip vehicle routing problem with time windows","year":2015,"lang":"en","type":"article","venue":"European Journal of Operational Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":97,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"TRIPS architecture; Vehicle routing problem; Computer science; Mutual exclusion; Discretization; Set (abstract data type); Branch and price; Contrast (vision); Duration (music); Mathematical optimization; Dynamic programming; Routing (electronic design automation); Sequence (biology); Operations research; Algorithm; Integer programming; Mathematics; Theoretical computer science; Artificial intelligence; Parallel computing","score_opus":0.1111965887103439,"score_gpt":0.34816726863885056,"score_spread":0.23697067992850668,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1853087932","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008917463,0.0014543504,0.98390007,0.00046883873,0.0001554061,0.00013422896,0.00008540952,0.00030767976,0.0045765154],"genre_scores_gemma":[0.21537094,0.0024047475,0.77130836,0.00022642742,0.0002501523,0.00081387605,0.0003307807,0.00039754863,0.008897147],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989477,0.00050933904,0.00005287514,0.00009285427,0.00022549144,0.00017178747],"domain_scores_gemma":[0.995455,0.0038595325,0.00015174907,0.000113892864,0.00025149187,0.00016831761],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003436897,0.0019458993,0.002524542,0.0017276298,0.00125078,0.0019180897,0.002561704,0.0028654428,0.008195674],"category_scores_gemma":[0.009089155,0.0013857128,0.0013076295,0.0028919065,0.0013720467,0.003235677,0.0019799673,0.004230776,0.0010703711],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00030763965,0.00022800284,0.00032968674,0.00019902983,0.00009306934,0.00005371369,0.00010480546,0.8253332,0.00050439785,0.054395463,0.005701542,0.1127495],"study_design_scores_gemma":[0.000058423735,0.00003894407,0.000052385283,0.000014228698,0.000014547034,0.000007461674,0.000012160705,0.9786012,0.00013967196,0.02033127,0.00072331826,0.000006457984],"about_ca_topic_score_codex":0.012123288,"about_ca_topic_score_gemma":0.0108907735,"teacher_disagreement_score":0.012123288,"about_ca_system_score_codex":0.0019207598,"about_ca_system_score_gemma":0.0035061266,"threshold_uncertainty_score":0.027417243},"labels":[],"label_agreement":null},{"id":"W1876351940","doi":"10.1287/trsc.2014.0581","title":"Models and Algorithms for Stochastic and Robust Vehicle Routing with Deadlines","year":2015,"lang":"en","type":"article","venue":"Transportation Science","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":107,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"","keywords":"Vehicle routing problem; Computer science; Mathematical optimization; Routing (electronic design automation); Probability distribution; Set (abstract data type); Mathematics","score_opus":0.08101912272087147,"score_gpt":0.2989574792719695,"score_spread":0.217938356551098,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1876351940","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.001950141,0.0004337328,0.99541986,0.0002312364,0.000049732243,0.00002934054,0.00009634624,0.00014594637,0.0016436686],"genre_scores_gemma":[0.36759627,0.0032353897,0.6159497,0.00035949462,0.00046265725,0.0007722634,0.0011253771,0.00035308173,0.010145747],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980882,0.0006267382,0.00011527606,0.00041543914,0.00047942228,0.00027491417],"domain_scores_gemma":[0.99612504,0.0024771567,0.0005427502,0.00025503663,0.00046982555,0.00013014971],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024590467,0.0018720165,0.0021853163,0.0013748974,0.00075706386,0.0027008783,0.0031677373,0.002249848,0.004614563],"category_scores_gemma":[0.008632091,0.0011127557,0.0021689672,0.0022454178,0.0012661507,0.003440709,0.002357688,0.0035382695,0.00098937],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000020713762,0.000021848216,0.00011410674,0.00006486118,0.000026179456,0.00003057885,0.000029260718,0.9195033,0.00018600246,0.06904769,0.0008740341,0.010081484],"study_design_scores_gemma":[0.000008827979,0.000010355728,0.000022046359,0.000007082753,0.000004949404,0.000011533077,0.000007688725,0.9613123,0.000071825714,0.037548307,0.0009894518,0.0000056909516],"about_ca_topic_score_codex":0.007449032,"about_ca_topic_score_gemma":0.0052439035,"teacher_disagreement_score":0.007449032,"about_ca_system_score_codex":0.0024247689,"about_ca_system_score_gemma":0.0025480157,"threshold_uncertainty_score":0.017592967},"labels":[],"label_agreement":null},{"id":"W1894418030","doi":"10.1002/atr.1235","title":"Multi‐objective airport gate assignment problem in planning and operations","year":2013,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":50,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Maximization; Towing; Mathematical optimization; Computer science; Robustness (evolution); Assignment problem; Minification; Operations research; Function (biology); Engineering; Mathematics","score_opus":0.012207055737945564,"score_gpt":0.27083250160535066,"score_spread":0.2586254458674051,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1894418030","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11876176,0.0017432728,0.8426104,0.0017972573,0.00019603012,0.00026662738,0.0010356699,0.00026201375,0.033326875],"genre_scores_gemma":[0.8813405,0.0009658103,0.10603133,0.00012936517,0.00009551179,0.00020115255,0.0005359006,0.000067129964,0.010633266],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993395,0.00025953318,0.000023046327,0.00013606485,0.00010493701,0.0001368924],"domain_scores_gemma":[0.9995042,0.00028917578,0.00006911982,0.000027812192,0.000044619617,0.00006507867],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00076397887,0.0012352677,0.001087246,0.0007760644,0.0005164694,0.0014928707,0.0008944077,0.0012594284,0.0040832446],"category_scores_gemma":[0.001281709,0.00043795476,0.00089019065,0.0011668558,0.00077788584,0.001330408,0.0008525947,0.0011728437,0.00027524002],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004433101,0.00004445234,0.00032219998,0.00010619325,0.000032309155,0.00013467953,0.00003515886,0.9667007,0.0006917531,0.020698853,0.0011728856,0.010016435],"study_design_scores_gemma":[0.000011325444,0.000050673352,0.00020204646,0.000014557754,0.00001279799,0.000035415193,0.000036766174,0.98166096,0.0003464434,0.016086966,0.0015331826,0.000008867749],"about_ca_topic_score_codex":0.006053567,"about_ca_topic_score_gemma":0.0048334016,"teacher_disagreement_score":0.006053567,"about_ca_system_score_codex":0.0016976877,"about_ca_system_score_gemma":0.00120949,"threshold_uncertainty_score":0.013659835},"labels":[],"label_agreement":null},{"id":"W1895919608","doi":"10.1002/atr.1333","title":"A case study of Beijing bus crew scheduling: a variable neighborhood‐based approach","year":2015,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Beijing; Crew scheduling; Crew; Scheduling (production processes); Computer science; Variable (mathematics); Transport engineering; Operations research; Engineering; Aeronautics; Operations management; Mathematics; Geography; China","score_opus":0.03597201242362363,"score_gpt":0.2867550210488242,"score_spread":0.2507830086252006,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1895919608","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.931969,0.00047854087,0.049105596,0.00043811917,0.000032430267,0.0001907049,0.00030689006,0.00012388806,0.01735493],"genre_scores_gemma":[0.9753897,0.00019314831,0.020843668,0.000014651599,0.0000070470037,0.000070333845,0.00012746907,0.000014306423,0.003339693],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996568,0.00018991435,0.0000095260575,0.000045402136,0.000048327372,0.00004995064],"domain_scores_gemma":[0.99965835,0.00020177402,0.000029830775,0.00002648512,0.000041036335,0.000042609106],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00054741016,0.0005163117,0.00043520078,0.0007260688,0.0008015743,0.00062495406,0.0011339264,0.0010341962,0.0024760785],"category_scores_gemma":[0.0007877789,0.0002637933,0.00048220163,0.0010465966,0.00041544484,0.0004187773,0.0004628135,0.0003717806,0.000112534595],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000072095325,0.000097967575,0.0028405066,0.000065687695,0.00002990984,0.00076986285,0.00009885711,0.9812309,0.0012353534,0.0029140871,0.00054859795,0.010096083],"study_design_scores_gemma":[0.00002423212,0.000058636448,0.0014502389,0.0000047227886,0.000015413762,0.000064094595,0.0002230932,0.995131,0.0007343736,0.0008264173,0.0014589415,0.000008781659],"about_ca_topic_score_codex":0.0517974,"about_ca_topic_score_gemma":0.05609441,"teacher_disagreement_score":0.0517974,"about_ca_system_score_codex":0.0022090697,"about_ca_system_score_gemma":0.0012508282,"threshold_uncertainty_score":0.10299182},"labels":[],"label_agreement":null},{"id":"W1902275675","doi":"10.1002/mcda.1516","title":"Multicriteria Optimization of A Long‐Haul Routing and Scheduling Problem","year":2014,"lang":"en","type":"article","venue":"Journal of Multi-Criteria Decision Analysis","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal; Université du Québec à Montréal","funders":"HEC Montréal","keywords":"Tabu search; Scheduling (production processes); Truck; Vehicle routing problem; Computer science; Operations research; Economic shortage; Routing (electronic design automation); Job shop scheduling; Transport engineering; Engineering; Operations management; Computer network; Automotive engineering; Artificial intelligence","score_opus":0.020336022246136443,"score_gpt":0.3098093942348946,"score_spread":0.28947337198875817,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1902275675","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4111441,0.0019651055,0.5638556,0.0018445499,0.00021310999,0.00038706054,0.00080600363,0.00024176558,0.019542664],"genre_scores_gemma":[0.9062351,0.00042510525,0.08638477,0.0001293184,0.00007873663,0.00027219413,0.00031036933,0.00005803001,0.006106303],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988777,0.00065522327,0.000038967595,0.00017669842,0.00009689045,0.0001544727],"domain_scores_gemma":[0.99704367,0.002295051,0.0002913506,0.0000603767,0.00015954359,0.00015001635],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00248465,0.0013874137,0.0013943007,0.001054913,0.0006915478,0.0019504757,0.0013944183,0.0022493182,0.0038768784],"category_scores_gemma":[0.004606574,0.00073920225,0.0010493151,0.0013998789,0.00095457205,0.0011986397,0.0010966298,0.001148337,0.00026058513],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000058008955,0.000048591544,0.00035433273,0.000047575915,0.000032022148,0.00009038246,0.000020277055,0.9925835,0.00018820426,0.003216881,0.00037205513,0.002988209],"study_design_scores_gemma":[0.000018392075,0.000039584495,0.00015944809,0.0000062698155,0.000008732598,0.000019397614,0.00002375424,0.99752873,0.00010857198,0.0018620458,0.00022057748,0.0000043940254],"about_ca_topic_score_codex":0.0092227375,"about_ca_topic_score_gemma":0.0053602518,"teacher_disagreement_score":0.0092227375,"about_ca_system_score_codex":0.0018330825,"about_ca_system_score_gemma":0.0012893068,"threshold_uncertainty_score":0.018338144},"labels":[],"label_agreement":null},{"id":"W1925566585","doi":"10.1007/978-3-642-34413-8_19","title":"Vehicle Routing and Adaptive Iterated Local Search within the HyFlex Hyper-heuristic Framework","year":2012,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":46,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"Engineering and Physical Sciences Research Council","keywords":"Heuristics; Computer science; Iterated local search; Vehicle routing problem; Domain (mathematical analysis); Heuristic; Routing (electronic design automation); Incremental heuristic search; Local search (optimization); Mathematical optimization; Search algorithm; Artificial intelligence; Beam search; Algorithm; Mathematics","score_opus":0.024266817398528522,"score_gpt":0.26166402899121416,"score_spread":0.23739721159268565,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1925566585","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013300212,0.00076970475,0.97435385,0.00018448205,0.000078132645,0.000057808153,0.000039121107,0.0001802355,0.011036526],"genre_scores_gemma":[0.53685373,0.0010492,0.4454906,0.0001717286,0.00016177658,0.00047056758,0.00012363982,0.00025125244,0.01542745],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995741,0.00020619103,0.000013237272,0.000043797525,0.000116427735,0.00004629193],"domain_scores_gemma":[0.99954575,0.00027328415,0.000047725935,0.000043277538,0.000059980255,0.000029897645],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00089804287,0.0007556594,0.0012629373,0.00070253416,0.00049074955,0.0012760066,0.0021257498,0.00136851,0.003495638],"category_scores_gemma":[0.0018373479,0.0006066256,0.00076956203,0.00093985297,0.0011108007,0.0012669486,0.0014359683,0.0013160731,0.00048102415],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000027363436,0.000021151864,0.000076352095,0.00003463839,0.000025645577,0.000027323955,0.000022192153,0.9439725,0.0003397325,0.04029692,0.00060835155,0.014547858],"study_design_scores_gemma":[0.0000068588124,0.00001325066,0.000016710721,0.0000048320267,0.00000373037,0.0000057380585,0.000003981576,0.992382,0.00006874939,0.007157104,0.0003336729,0.0000033094598],"about_ca_topic_score_codex":0.0050970246,"about_ca_topic_score_gemma":0.004276306,"teacher_disagreement_score":0.0050970246,"about_ca_system_score_codex":0.0011776054,"about_ca_system_score_gemma":0.0011933822,"threshold_uncertainty_score":0.011694074},"labels":[],"label_agreement":null},{"id":"W1931616235","doi":"10.1287/opre.2015.1401","title":"Benders Decomposition for Production Routing Under Demand Uncertainty","year":2015,"lang":"en","type":"article","venue":"Operations Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":214,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"HEC Montréal; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Mathematical optimization; Production (economics); Routing (electronic design automation); Generalization; Branch and cut; Stochastic programming; Computer science; Pareto principle; Benders' decomposition; Upper and lower bounds; Exploit; Decomposition method (queueing theory); Decomposition; Time horizon; Mathematics; Linear programming; Economics","score_opus":0.16131367666491017,"score_gpt":0.438498401075115,"score_spread":0.2771847244102048,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1931616235","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004332566,0.00019244934,0.99190944,0.00013693896,0.000024644394,0.00005166758,0.0001333462,0.000115186675,0.003103741],"genre_scores_gemma":[0.21345407,0.0014176664,0.76965624,0.00020861233,0.00014994519,0.00066927006,0.0012082764,0.00033192415,0.012904024],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989624,0.00042941872,0.00004976242,0.00016400166,0.00026320302,0.00013123223],"domain_scores_gemma":[0.99909055,0.00058188406,0.0001016009,0.00006427872,0.000116789015,0.000044814293],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002265793,0.0019309305,0.0013567182,0.0011750925,0.0006082637,0.0013761406,0.00089322944,0.001395614,0.006392753],"category_scores_gemma":[0.0030098453,0.0011693035,0.0018616369,0.0014792377,0.00081382476,0.0016965889,0.0010270013,0.0025498462,0.000892357],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000027961049,0.000019428473,0.00010089308,0.00004176153,0.00001923682,0.000030705225,0.000023554458,0.9556358,0.0006164527,0.033056363,0.00065751286,0.009770336],"study_design_scores_gemma":[0.000008468112,0.000011034278,0.000037100315,0.00000743698,0.0000057112193,0.0000069394623,0.0000071821005,0.96709687,0.00018259448,0.03168423,0.0009482674,0.000004232855],"about_ca_topic_score_codex":0.0060313065,"about_ca_topic_score_gemma":0.004931083,"teacher_disagreement_score":0.006392753,"about_ca_system_score_codex":0.0020259966,"about_ca_system_score_gemma":0.0019753783,"threshold_uncertainty_score":0.021385849},"labels":[],"label_agreement":null},{"id":"W1942124306","doi":"","title":"Moving freight inside cross docking terminals","year":2010,"lang":"en","type":"article","venue":"PolyPublie (École Polytechnique de Montréal)","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Transshipment (information security); Consolidation (business); Computer science; Transport engineering; Business; Engineering; Computer security","score_opus":0.010702323404576953,"score_gpt":0.2574174238216123,"score_spread":0.24671510041703537,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1942124306","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.29074755,0.00044372797,0.6539019,0.0015056322,0.00028078823,0.00012870178,0.00038966298,0.00048743674,0.05211462],"genre_scores_gemma":[0.94865435,0.000318631,0.016908858,0.000106846055,0.000031674514,0.00006494778,0.0002010171,0.0000656397,0.033648048],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99955577,0.000091674614,0.000008252831,0.00014016473,0.0000777436,0.00012641797],"domain_scores_gemma":[0.99973875,0.000069460926,0.00006083611,0.000028435323,0.00004357084,0.000058938524],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040447726,0.0010011944,0.00087383634,0.00043341517,0.0013732342,0.0027577365,0.0013886621,0.003051027,0.009726703],"category_scores_gemma":[0.0011829552,0.00074371387,0.0009571001,0.0005940888,0.0012593877,0.0032541116,0.0018326595,0.0013459374,0.00116164],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006544387,0.000027926682,0.00066631264,0.000018939114,0.000009208171,0.0002467716,0.000057181187,0.9686083,0.0019240765,0.02382166,0.0010655457,0.0034886142],"study_design_scores_gemma":[0.000013675267,0.000053691012,0.0003475765,0.000011419371,0.000009028961,0.00005711412,0.00006352998,0.9923495,0.00079124345,0.004346016,0.0019412237,0.000016039749],"about_ca_topic_score_codex":0.026378632,"about_ca_topic_score_gemma":0.012030616,"teacher_disagreement_score":0.026378632,"about_ca_system_score_codex":0.0023014676,"about_ca_system_score_gemma":0.0013374276,"threshold_uncertainty_score":0.05245018},"labels":[],"label_agreement":null},{"id":"W1963745567","doi":"10.1002/atr.5670430205","title":"A multiobjective model for maximizing fleet availability under the presence of flight and maintenance requirements","year":2009,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"State Scholarships Foundation","keywords":"Heuristics; Aircraft maintenance; Operations research; Heuristic; Unit (ring theory); Computer science; Engineering; Reliability engineering; Aeronautics; Artificial intelligence; Mathematics","score_opus":0.02348607832181985,"score_gpt":0.2932145094591439,"score_spread":0.26972843113732403,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1963745567","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.23326209,0.0008579919,0.74277776,0.0016635715,0.00010181256,0.00034651364,0.0014686529,0.00035014213,0.019171573],"genre_scores_gemma":[0.89827764,0.00047938223,0.09208507,0.00013150628,0.000057365156,0.00039410256,0.0004109544,0.00007494561,0.008089101],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99922156,0.00037946156,0.000026725853,0.0001341579,0.00008848762,0.00014960002],"domain_scores_gemma":[0.9986966,0.000851705,0.00016971881,0.000049465372,0.000094502386,0.00013795699],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016746424,0.0013406147,0.0014705607,0.0011150802,0.0005759356,0.0020736519,0.0019048995,0.002383347,0.0043998538],"category_scores_gemma":[0.002735464,0.0009328933,0.0010470254,0.0014896678,0.0008728793,0.0015850253,0.0009251569,0.0012751058,0.00034830594],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026457019,0.000017157072,0.000089772904,0.000022603659,0.000015214302,0.00004747132,0.000014740921,0.99475765,0.00023066993,0.0035607247,0.00018345224,0.0010340448],"study_design_scores_gemma":[0.000017697183,0.000026260715,0.00007183468,0.0000055446712,0.0000083558825,0.000010629227,0.000014548355,0.9971244,0.00007375178,0.0024110763,0.000231512,0.0000044020653],"about_ca_topic_score_codex":0.01124207,"about_ca_topic_score_gemma":0.008359497,"teacher_disagreement_score":0.01124207,"about_ca_system_score_codex":0.0024331815,"about_ca_system_score_gemma":0.001556019,"threshold_uncertainty_score":0.022353232},"labels":[],"label_agreement":null},{"id":"W1964030714","doi":"10.3166/jesa.41.515-539","title":"Plateforme de simulation pour la gestion dynamique de tournées de véhicules","year":2007,"lang":"fr","type":"article","venue":"Journal Européen des Systèmes Automatisés","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Political science","score_opus":0.018759304805718056,"score_gpt":0.2875165309613185,"score_spread":0.26875722615560044,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1964030714","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.19280916,0.0005351797,0.7842173,0.00061484973,0.00020684475,0.00019000992,0.0010394179,0.0020757285,0.018311476],"genre_scores_gemma":[0.7990596,0.00046790548,0.18859549,0.00012416884,0.0000414916,0.000694179,0.0011047884,0.0003972505,0.009514942],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99938035,0.00026197737,0.000027218233,0.00008277831,0.00016503759,0.00008266684],"domain_scores_gemma":[0.99058163,0.008224773,0.00024420983,0.0001547875,0.0005987094,0.00019592275],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015322391,0.0018095829,0.0010907209,0.0010155356,0.00084968674,0.001831213,0.0008813664,0.0024956223,0.008904967],"category_scores_gemma":[0.005948751,0.00057810964,0.0016201471,0.0006860943,0.0012953478,0.0007259685,0.00078351685,0.0020360122,0.0008497876],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000046433885,0.000019268908,0.0004463862,0.00003219023,0.000013125137,0.000040057264,0.000033913908,0.9951271,0.00044951538,0.0020436756,0.00013478301,0.0016135514],"study_design_scores_gemma":[0.000009377679,0.000009625119,0.0000782097,0.0000040112027,0.000002004954,0.0000033287163,0.000009574847,0.99897516,0.0002636307,0.0004147683,0.00022683642,0.0000034778614],"about_ca_topic_score_codex":0.057757426,"about_ca_topic_score_gemma":0.02374174,"teacher_disagreement_score":0.057757426,"about_ca_system_score_codex":0.0017357442,"about_ca_system_score_gemma":0.0017548058,"threshold_uncertainty_score":0.114842474},"labels":[],"label_agreement":null},{"id":"W1965407067","doi":"10.1139/x04-043","title":"An optimization model for annual harvest planning","year":2004,"lang":"en","type":"article","venue":"Canadian Journal of Forest Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":70,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Production (economics); Integer programming; Operations research; Variable (mathematics); Computer science; Operations management; Mathematics; Engineering; Economics","score_opus":0.0747447326903002,"score_gpt":0.36674385567754997,"score_spread":0.2919991229872498,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1965407067","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02774084,0.0018540991,0.9205058,0.0016226615,0.00025295842,0.00028443674,0.0033800034,0.0006828133,0.043676462],"genre_scores_gemma":[0.6233619,0.0034332932,0.28971246,0.0005184785,0.00029058033,0.001485699,0.0040255995,0.00044484425,0.07672714],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99880314,0.00043991779,0.00005438275,0.00024972012,0.00020895644,0.00024379212],"domain_scores_gemma":[0.9990402,0.0005813874,0.00012923138,0.00003087226,0.00012936183,0.0000889823],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014393198,0.0017707708,0.0016994817,0.001024625,0.0008166618,0.0026457466,0.0022081628,0.002419819,0.014444797],"category_scores_gemma":[0.002220822,0.001040656,0.0014613542,0.0022544253,0.00090636924,0.001958503,0.0012192432,0.0022755684,0.0017806811],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000033767727,0.00002788659,0.0001421418,0.00006350563,0.000022147251,0.000088532586,0.000028000673,0.9724638,0.0002057722,0.020368978,0.001912023,0.0046434235],"study_design_scores_gemma":[0.000017569288,0.000025286627,0.000091882896,0.000011150478,0.000011826447,0.000024993695,0.000017439213,0.98429155,0.00006464911,0.012112615,0.003322416,0.000008696382],"about_ca_topic_score_codex":0.01887895,"about_ca_topic_score_gemma":0.017128654,"teacher_disagreement_score":0.01887895,"about_ca_system_score_codex":0.0028917224,"about_ca_system_score_gemma":0.0028900579,"threshold_uncertainty_score":0.048322678},"labels":[],"label_agreement":null},{"id":"W1965882376","doi":"10.1016/j.cor.2013.08.016","title":"An adaptive large neighborhood search for a vehicle routing problem with multiple routes","year":2013,"lang":"en","type":"article","venue":"Computers & Operations Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":183,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Université du Québec à Montréal; Université de Montréal","funders":"","keywords":"Vehicle routing problem; Computer science; Benchmark (surveying); Routing (electronic design automation); Mathematical optimization; Operations research; Computer network; Mathematics","score_opus":0.04793811169959512,"score_gpt":0.336181081294404,"score_spread":0.2882429695948089,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1965882376","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.028904596,0.00044054008,0.9669146,0.00030191953,0.000095398864,0.00009452349,0.00004747336,0.000092795104,0.0031081873],"genre_scores_gemma":[0.510777,0.0004685224,0.48043808,0.00016221887,0.00013820585,0.00048328703,0.0001938671,0.00009386108,0.0072449343],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995436,0.00021691762,0.000018176877,0.00008347516,0.00010265535,0.000035031437],"domain_scores_gemma":[0.99850214,0.0011294053,0.00009536699,0.000049029895,0.00015762319,0.0000665369],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015428587,0.00062027836,0.0013220898,0.0009204272,0.00057298166,0.00073565036,0.0018439244,0.0017680512,0.0024414768],"category_scores_gemma":[0.0044092447,0.0006789303,0.00076019234,0.0008738068,0.0007858142,0.0015403265,0.0014335649,0.0009025391,0.0002098757],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008573439,0.000057792564,0.00028009052,0.000055702094,0.000025570955,0.00004172272,0.000032232336,0.969842,0.0005952917,0.009063426,0.0010249873,0.018895393],"study_design_scores_gemma":[0.0000082797005,0.000014199638,0.000020010268,0.0000020649409,0.0000026448552,0.0000043874843,0.000003215449,0.9987852,0.000028623883,0.0010030748,0.000126546,0.0000017179113],"about_ca_topic_score_codex":0.006644996,"about_ca_topic_score_gemma":0.005961586,"teacher_disagreement_score":0.006644996,"about_ca_system_score_codex":0.0007595519,"about_ca_system_score_gemma":0.0009325391,"threshold_uncertainty_score":0.013212621},"labels":[],"label_agreement":null},{"id":"W1965979685","doi":"10.1002/net.21519","title":"The split delivery capacitated team orienteering problem","year":2013,"lang":"en","type":"article","venue":"Networks","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":33,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Group for Research in Decision Analysis; HEC Montréal","funders":"","keywords":"Orienteering; Computer science; Operations research; Profit (economics); Mathematical optimization; Set (abstract data type); Benchmark (surveying); Mathematics; Economics; Microeconomics","score_opus":0.006827192193179859,"score_gpt":0.197415121976764,"score_spread":0.19058792978358413,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1965979685","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.38088378,0.0014508558,0.5872883,0.0013228088,0.00019091957,0.00047858432,0.0013003495,0.00037570368,0.02670869],"genre_scores_gemma":[0.89498246,0.00064467365,0.09283089,0.00015203311,0.00008295693,0.00030297713,0.00094776344,0.00012332358,0.009932817],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99884295,0.00044239534,0.000047974154,0.0002232258,0.00019049627,0.0002529224],"domain_scores_gemma":[0.99880004,0.000662986,0.0001345017,0.00008213882,0.000112873044,0.00020743096],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011608254,0.0015766579,0.0013923628,0.00078588235,0.0007205489,0.0017047678,0.0019802472,0.0018066801,0.0074985055],"category_scores_gemma":[0.0025853713,0.00052155205,0.000844341,0.0017366542,0.00076408044,0.0018781286,0.0014503795,0.001110193,0.00055612944],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00036458395,0.0002730501,0.0012012555,0.00036479195,0.00015420887,0.0008814152,0.0002147655,0.90916646,0.00206389,0.031141374,0.0064401585,0.047734138],"study_design_scores_gemma":[0.000089623514,0.00021460826,0.00042643168,0.000037451464,0.000046256446,0.00032726367,0.00027397685,0.9586525,0.0012995545,0.034466416,0.0041412953,0.000024588104],"about_ca_topic_score_codex":0.0034910087,"about_ca_topic_score_gemma":0.0018257822,"teacher_disagreement_score":0.0074985055,"about_ca_system_score_codex":0.0013055034,"about_ca_system_score_gemma":0.0009208566,"threshold_uncertainty_score":0.025084972},"labels":[],"label_agreement":null},{"id":"W1966716113","doi":"10.1016/j.ejor.2006.02.019","title":"An exact algorithm for a single-vehicle routing problem with time windows and multiple routes","year":2006,"lang":"en","type":"article","venue":"European Journal of Operational Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":203,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Vehicle routing problem; Benchmark (surveying); Computer science; Set (abstract data type); Shortest path problem; Routing (electronic design automation); Mathematical optimization; Path (computing); Algorithm; Euclidean geometry; Mathematics; Theoretical computer science; Graph","score_opus":0.04058463769978288,"score_gpt":0.30744635570674017,"score_spread":0.2668617180069573,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1966716113","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011692188,0.00033414015,0.98222715,0.00021240821,0.00011782161,0.000117705255,0.00011879258,0.00087938964,0.004300313],"genre_scores_gemma":[0.120709516,0.00026520438,0.87459147,0.00010817285,0.000077287514,0.0002700959,0.00022469513,0.00016928108,0.0035843626],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99930096,0.00011505501,0.000036895053,0.00018900186,0.00021241805,0.00014564708],"domain_scores_gemma":[0.998852,0.00068858854,0.00007975671,0.0001354485,0.00017053183,0.00007362712],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013237101,0.0012895555,0.0019510569,0.0009746955,0.0009848352,0.0017105198,0.0027084437,0.002278733,0.008048504],"category_scores_gemma":[0.0036595073,0.0011473983,0.0010458969,0.0018250297,0.00089152507,0.0027291987,0.0018087237,0.0015511416,0.0011517885],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021037634,0.00013848425,0.0002853409,0.00014941166,0.00004617949,0.00007480589,0.00007722002,0.8304458,0.001340003,0.014542107,0.0037385954,0.1489516],"study_design_scores_gemma":[0.00006707618,0.000026263328,0.000059372836,0.0000074324025,0.000012905712,0.000024455985,0.000015337082,0.9908285,0.00023852434,0.007896526,0.00081645994,0.000007215675],"about_ca_topic_score_codex":0.013236823,"about_ca_topic_score_gemma":0.01418545,"teacher_disagreement_score":0.013236823,"about_ca_system_score_codex":0.0020113736,"about_ca_system_score_gemma":0.0038786524,"threshold_uncertainty_score":0.026924908},"labels":[],"label_agreement":null},{"id":"W1967987311","doi":"10.1016/j.cor.2010.01.004","title":"The quadratic minimum spanning tree problem: A lower bounding procedure and an efficient search algorithm","year":2010,"lang":"en","type":"article","venue":"Computers & Operations Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":43,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada; Galatasaray Üniversitesi","keywords":"Bounding overwatch; Minimum spanning tree; Tabu search; Spanning tree; Mathematics; Quadratic equation; k-minimum spanning tree; Heuristic; Mathematical optimization; Euclidean minimum spanning tree; Relaxation (psychology); Distributed minimum spanning tree; Local search (optimization); Algorithm; Kruskal's algorithm; Combinatorics; Computer science; Tree structure; Binary tree; Artificial intelligence","score_opus":0.0356672783289809,"score_gpt":0.36152426615279576,"score_spread":0.32585698782381484,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1967987311","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0019426614,0.00033861474,0.99458015,0.0002130422,0.000051593997,0.000048470753,0.000047733258,0.000118166296,0.002659584],"genre_scores_gemma":[0.078083165,0.00096751854,0.9139772,0.00016665256,0.0002099996,0.0003598607,0.00035536545,0.000306414,0.0055737305],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99861705,0.0005530927,0.000055094242,0.00017783299,0.0004944396,0.00010245129],"domain_scores_gemma":[0.9971317,0.0021413811,0.00012841918,0.00020342431,0.00032393352,0.00007118217],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0030597565,0.0014586952,0.001784766,0.0017043751,0.00095332734,0.0017293387,0.0020141744,0.0021735788,0.0059828307],"category_scores_gemma":[0.010554064,0.0008642652,0.0013521673,0.0030570521,0.0012242019,0.00384171,0.002556513,0.0032931687,0.0013618247],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017095315,0.00023666768,0.00033781602,0.0004034239,0.000057588702,0.00014576926,0.0001974843,0.54048795,0.00510691,0.2195599,0.016387943,0.21690759],"study_design_scores_gemma":[0.000017361865,0.000029957413,0.00008529045,0.000021030228,0.000014791406,0.00006815697,0.000014419471,0.95652056,0.0005201585,0.040085625,0.0026129847,0.000009653992],"about_ca_topic_score_codex":0.0028551817,"about_ca_topic_score_gemma":0.0031960132,"teacher_disagreement_score":0.0059828307,"about_ca_system_score_codex":0.0011313241,"about_ca_system_score_gemma":0.0016490717,"threshold_uncertainty_score":0.020014524},"labels":[],"label_agreement":null},{"id":"W1969119890","doi":"10.1016/j.orl.2014.10.001","title":"Decomposition theorems for linear programs","year":2014,"lang":"en","type":"article","venue":"Operations Research Letters","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Group for Research in Decision Analysis; HEC Montréal","funders":"","keywords":"Mathematics; Linear programming; Bounded function; Decomposition; Residual; Class (philosophy); Applied mathematics; Combinatorics; Discrete mathematics; Mathematical optimization; Algorithm; Computer science; Mathematical analysis","score_opus":0.05506757424898105,"score_gpt":0.39056686560416604,"score_spread":0.33549929135518497,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1969119890","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0071083866,0.0029527985,0.93253493,0.0018282381,0.0003357929,0.00006606516,0.00038926827,0.00021934023,0.05456517],"genre_scores_gemma":[0.38725087,0.014801949,0.4829281,0.002738054,0.002436882,0.0015492409,0.0025660924,0.001365496,0.1043632],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9988686,0.00044221978,0.00004491771,0.00018811143,0.00030885052,0.00014731771],"domain_scores_gemma":[0.99688524,0.0021124363,0.00016019391,0.00023514016,0.00044890578,0.00015813386],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027603447,0.0026970739,0.0014370983,0.0025464564,0.00091149355,0.0028474445,0.0014841909,0.0011269078,0.012692221],"category_scores_gemma":[0.007072828,0.0011697481,0.002844005,0.0028170992,0.002055175,0.005354489,0.002571296,0.0074247806,0.0023263022],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000030687675,0.0000546887,0.00013439743,0.00014592432,0.00004536374,0.000044434768,0.00012449187,0.013286182,0.0006594613,0.94717467,0.010866278,0.027433338],"study_design_scores_gemma":[0.000015693546,0.000015859288,0.00010723458,0.000043521402,0.000021168787,0.00003744691,0.000028016393,0.049344458,0.00028782702,0.9435565,0.00653344,0.000008786122],"about_ca_topic_score_codex":0.0018602269,"about_ca_topic_score_gemma":0.0015044665,"teacher_disagreement_score":0.012692221,"about_ca_system_score_codex":0.0019026082,"about_ca_system_score_gemma":0.0014218314,"threshold_uncertainty_score":0.042459667},"labels":[],"label_agreement":null},{"id":"W1969420428","doi":"10.4236/ti.2013.41b013","title":"Heterogeneous Fleet Vehicle Routing Problem with Selec-tion of Inter-Depots","year":2013,"lang":"en","type":"article","venue":"Technology and Investment","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Vedecká Grantová Agentúra MŠVVaŠ SR a SAV","keywords":"Computer science; Routing (electronic design automation); Vehicle routing problem; Operations research; Transport engineering; Computer network; Mathematics; Engineering","score_opus":0.007930304799182148,"score_gpt":0.21131050886032393,"score_spread":0.2033802040611418,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1969420428","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15479115,0.00048666948,0.8296204,0.00044797294,0.0000966566,0.00032980743,0.0011892929,0.0002316956,0.01280639],"genre_scores_gemma":[0.83448493,0.00049952645,0.15100048,0.00010004816,0.000053581396,0.0004073054,0.0015789616,0.00010327907,0.011771832],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999213,0.00026477978,0.000031292442,0.00018796446,0.00010632041,0.00019652235],"domain_scores_gemma":[0.9995635,0.00018765636,0.000078272926,0.000040611525,0.00003532043,0.00009460075],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009396972,0.0013515713,0.0012844091,0.00076027313,0.000811953,0.0012943385,0.0014700035,0.0011232718,0.0031263044],"category_scores_gemma":[0.00094106887,0.00052306673,0.0009618528,0.0018117875,0.0005273897,0.0014285232,0.00096990075,0.00082775886,0.00030028808],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012957813,0.00011157577,0.0005520355,0.00008127295,0.00006602959,0.00050908787,0.00003411815,0.9604722,0.0018422335,0.018959362,0.0017277775,0.015514655],"study_design_scores_gemma":[0.00006672075,0.00011928895,0.00059311884,0.000016557105,0.000038303835,0.00017639519,0.00008403101,0.97011596,0.0013324788,0.023563575,0.0038730653,0.000020584263],"about_ca_topic_score_codex":0.004510456,"about_ca_topic_score_gemma":0.0048864377,"teacher_disagreement_score":0.004510456,"about_ca_system_score_codex":0.0013689774,"about_ca_system_score_gemma":0.0011340275,"threshold_uncertainty_score":0.010458469},"labels":[],"label_agreement":null},{"id":"W1969471641","doi":"10.1016/j.orl.2006.11.004","title":"Interior point stabilization for column generation","year":2006,"lang":"en","type":"article","venue":"Operations Research Letters","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":112,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; Polytechnique Montréal","funders":"","keywords":"Column generation; Degeneracy (biology); Convergence (economics); Interior point method; Acceleration; Point (geometry); Mathematical optimization; Column (typography); Vehicle routing problem; Computer science; Dual (grammatical number); Space (punctuation); Extreme point; Mathematics; Routing (electronic design automation); Algorithm; Control theory (sociology); Combinatorics; Physics; Geometry; Artificial intelligence; Telecommunications; Classical mechanics","score_opus":0.06313064214832748,"score_gpt":0.35427776294184643,"score_spread":0.29114712079351895,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1969471641","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0045176,0.0002233877,0.98916745,0.00013977618,0.00010809671,0.00003629211,0.000073419695,0.00019404262,0.005539851],"genre_scores_gemma":[0.55377036,0.0009320096,0.41054913,0.00036334567,0.00028382495,0.00068887766,0.00062256603,0.0006614499,0.032128435],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997354,0.00012120932,0.0000067298347,0.000042802592,0.00006570012,0.000028234992],"domain_scores_gemma":[0.9994017,0.0003345152,0.000050671068,0.000062488594,0.00011638967,0.000034306042],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005756716,0.0011379025,0.001025479,0.0005905314,0.00052839826,0.00081611995,0.0008715006,0.00087929826,0.009472372],"category_scores_gemma":[0.0020915142,0.00049975124,0.00062358147,0.0007635953,0.0010441407,0.0007461164,0.0012407055,0.0017965629,0.0012360187],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000104301726,0.00007137521,0.0001306951,0.00016500484,0.000033011853,0.000038715094,0.00006483892,0.8358117,0.0035785022,0.08130731,0.008459978,0.07023459],"study_design_scores_gemma":[0.000009534352,0.000017951998,0.000022977032,0.000006544751,0.000003450638,0.000003879975,0.000005151094,0.9744221,0.00054265663,0.023895714,0.0010655543,0.000004467331],"about_ca_topic_score_codex":0.0036371201,"about_ca_topic_score_gemma":0.002507292,"teacher_disagreement_score":0.009472372,"about_ca_system_score_codex":0.0005767876,"about_ca_system_score_gemma":0.0007452804,"threshold_uncertainty_score":0.031688213},"labels":[],"label_agreement":null},{"id":"W1969506670","doi":"10.1016/j.dam.2015.01.035","title":"The Minimum Flow Cost Hamiltonian Cycle Problem: A comparison of formulations","year":2015,"lang":"en","type":"article","venue":"Discrete Applied Mathematics","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal; Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Hamiltonian path; Mathematics; Minimum-cost flow problem; Hamiltonian (control theory); Hamiltonian path problem; Combinatorics; Integer programming; Shortest path problem; Graph; Mathematical optimization; Flow network; Discrete mathematics","score_opus":0.036546015957150124,"score_gpt":0.3037352396843029,"score_spread":0.26718922372715276,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1969506670","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020039916,0.0098775495,0.91771066,0.0013929391,0.00025390348,0.0002626276,0.00041916492,0.00017927025,0.049863886],"genre_scores_gemma":[0.375893,0.025015429,0.5789116,0.00064386526,0.00049641985,0.0008861663,0.0012673436,0.0007709245,0.016115295],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988092,0.00054304535,0.00006148275,0.00011028071,0.00041569248,0.000060391856],"domain_scores_gemma":[0.9967483,0.0022090597,0.0002094819,0.00024634702,0.00049180415,0.00009498709],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003145164,0.001021255,0.0016002716,0.0019332174,0.0005277201,0.0029928077,0.0022557655,0.0019120341,0.007784425],"category_scores_gemma":[0.007845364,0.00052241475,0.0011832899,0.0030660785,0.0011086029,0.0041682688,0.0014563175,0.002145745,0.0007016951],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015852963,0.00024230249,0.0005188237,0.0010017407,0.00010809682,0.00005344087,0.00018075788,0.3763584,0.00064779964,0.42070624,0.008218672,0.1918052],"study_design_scores_gemma":[0.000057870977,0.00016180582,0.00060271897,0.00028905921,0.00007198355,0.00012486846,0.0002813361,0.80658066,0.00058099994,0.17858522,0.012624764,0.00003875807],"about_ca_topic_score_codex":0.002444805,"about_ca_topic_score_gemma":0.004115523,"teacher_disagreement_score":0.007784425,"about_ca_system_score_codex":0.0020061696,"about_ca_system_score_gemma":0.0028687418,"threshold_uncertainty_score":0.026041508},"labels":[],"label_agreement":null},{"id":"W1969579058","doi":"10.1287/trsc.1040.0106","title":"The Profitable Arc Tour Problem: Solution with a Branch-and-Price Algorithm","year":2005,"lang":"en","type":"article","venue":"Transportation Science","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":61,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Arc routing; Column generation; Mathematical optimization; Minimum-cost flow problem; Profit (economics); Integer programming; Limiting; Directed graph; Arc (geometry); Branch and price; Mathematics; Graph; Computer science; Flow network; Algorithm; Routing (electronic design automation); Economics; Engineering; Combinatorics","score_opus":0.011446536960808825,"score_gpt":0.249086160234552,"score_spread":0.23763962327374316,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1969579058","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010304189,0.00020240928,0.9820338,0.00035059208,0.00003770809,0.00012890922,0.0000843296,0.0003363008,0.0065217586],"genre_scores_gemma":[0.10880764,0.0003618483,0.8848064,0.000101940655,0.00006531037,0.0003443565,0.00022686707,0.00015895723,0.0051267077],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996165,0.0001414336,0.000013380088,0.000060097125,0.000103379556,0.000065105596],"domain_scores_gemma":[0.99945337,0.00039469168,0.000029495075,0.000030442496,0.000053770415,0.000038252623],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008541331,0.00078659895,0.00093860866,0.0007130023,0.00054015726,0.0010962569,0.0011527191,0.0016975073,0.008733722],"category_scores_gemma":[0.0022809554,0.0005011136,0.000527952,0.001406067,0.00054595794,0.0015963323,0.0010186465,0.0014053168,0.0009599531],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013850877,0.00016476536,0.0005004652,0.00013941695,0.000035682722,0.00015523375,0.00011505197,0.762706,0.0013433484,0.06698706,0.008985316,0.15872923],"study_design_scores_gemma":[0.00003913943,0.000027736125,0.000050687828,0.0000078215735,0.0000061596197,0.000031600943,0.000016788033,0.9734687,0.0002623579,0.024110306,0.0019739547,0.000004748532],"about_ca_topic_score_codex":0.0034090558,"about_ca_topic_score_gemma":0.0038613456,"teacher_disagreement_score":0.008733722,"about_ca_system_score_codex":0.00073139503,"about_ca_system_score_gemma":0.0018213288,"threshold_uncertainty_score":0.029217184},"labels":[],"label_agreement":null},{"id":"W1969909704","doi":"10.1016/j.ejor.2012.02.010","title":"A new exact discrete linear reformulation of the quadratic assignment problem","year":2012,"lang":"en","type":"article","venue":"European Journal of Operational Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":24,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Academy of Finland; Polytechnique Montréal; University of Pennsylvania","keywords":"Quadratic assignment problem; Linear programming; Mathematical optimization; Integer programming; Mathematics; Quadratic equation; Quadratic programming; Combinatorial optimization; Assignment problem; Computer science","score_opus":0.08035879040295515,"score_gpt":0.3671476523560229,"score_spread":0.28678886195306774,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1969909704","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0027243963,0.00013673985,0.9890863,0.00027812715,0.00017606808,0.000031883006,0.00012694401,0.00006482534,0.007374688],"genre_scores_gemma":[0.19000068,0.00079684047,0.7758136,0.00067378837,0.0007121685,0.00029485615,0.0010291997,0.0003773736,0.03030154],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9991387,0.00030799882,0.000030386886,0.00016180944,0.00029201235,0.00006899873],"domain_scores_gemma":[0.9993309,0.00030476312,0.000060701088,0.000102734266,0.00015244528,0.000048468104],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011584879,0.0008647796,0.0010181167,0.0005619397,0.00034270322,0.0013848421,0.0016772968,0.0009883543,0.011164868],"category_scores_gemma":[0.0031569463,0.0004395682,0.0007445244,0.0010804232,0.0008086482,0.0020679887,0.0016458522,0.00255212,0.0017128893],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011759109,0.00023267188,0.0002520968,0.00033078238,0.0000349669,0.000112136026,0.00011298868,0.47604808,0.0035942402,0.37360638,0.020585828,0.12497226],"study_design_scores_gemma":[0.000025097128,0.000057634858,0.00007001427,0.000015366662,0.0000070378273,0.00004720927,0.00001903449,0.93411463,0.00032223738,0.05780264,0.007509293,0.000009795238],"about_ca_topic_score_codex":0.0020826457,"about_ca_topic_score_gemma":0.0024910532,"teacher_disagreement_score":0.011164868,"about_ca_system_score_codex":0.0007154342,"about_ca_system_score_gemma":0.0010638691,"threshold_uncertainty_score":0.037350237},"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,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_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","score_opus":0.08886315424571074,"score_gpt":0.393188984849525,"score_spread":0.3043258306038143,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1970355999","genre_codex":"review","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003893927,0.4954194,0.2618449,0.028544134,0.04001889,0.00021398635,0.0007592695,0.0006445776,0.16866094],"genre_scores_gemma":[0.07265001,0.44515884,0.14353256,0.016411858,0.12837213,0.0006394152,0.0024971622,0.0012439617,0.18949406],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9988445,0.00031618122,0.00006350773,0.0003020097,0.00038912884,0.000084670864],"domain_scores_gemma":[0.997733,0.0012228944,0.000099299126,0.00020939691,0.00055359624,0.00018180767],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001509967,0.00142912,0.0011266099,0.002352679,0.00107036,0.0027948644,0.0010916751,0.0015617581,0.026897386],"category_scores_gemma":[0.0048066746,0.00055405515,0.0012202587,0.005919798,0.0015417741,0.004201859,0.0015158952,0.0036165835,0.0110715395],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000064409236,0.00011583229,0.00040987195,0.0012029884,0.000056746758,0.00012306201,0.00014369353,0.004471062,0.0009091105,0.28368744,0.34765497,0.36116084],"study_design_scores_gemma":[0.000023177261,0.000107678294,0.0005784791,0.0006156043,0.000036149253,0.00047420632,0.000092205824,0.007838759,0.0010069025,0.25066108,0.7385133,0.00005244889],"about_ca_topic_score_codex":0.0009160536,"about_ca_topic_score_gemma":0.0008245014,"teacher_disagreement_score":0.026897386,"about_ca_system_score_codex":0.0016229477,"about_ca_system_score_gemma":0.00095359754,"threshold_uncertainty_score":0.08998078},"labels":[],"label_agreement":null},{"id":"W1970402125","doi":"10.1016/j.disopt.2013.07.005","title":"A computational comparison of flow formulations for the capacitated location-routing problem","year":2013,"lang":"en","type":"article","venue":"Discrete Optimization","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":58,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; HEC Montréal; Transport Canada; École de Technologie Supérieure; Group for Research in Decision Analysis; Université du Québec à Montréal","funders":"","keywords":"Flow (mathematics); Mathematical optimization; Index (typography); Routing (electronic design automation); Mathematics; Flow routing; Scale (ratio); Computer science; Algorithm; Engineering","score_opus":0.023063067758610504,"score_gpt":0.2875200500293179,"score_spread":0.2644569822707074,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1970402125","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12607312,0.0018582313,0.8194142,0.0020007775,0.00044232473,0.00026620764,0.00060745404,0.0007179122,0.04861972],"genre_scores_gemma":[0.54245734,0.0013572661,0.4471776,0.00032055823,0.0001367316,0.00040648616,0.000680742,0.0004917387,0.0069716494],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99920624,0.00045293407,0.000033711774,0.00006466598,0.00018362865,0.000058812493],"domain_scores_gemma":[0.9941943,0.004652399,0.00015942058,0.00033933442,0.00053205405,0.00012246454],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024881647,0.0008602795,0.001063016,0.0010994326,0.00067904836,0.0018837568,0.0016816844,0.0018062957,0.007859287],"category_scores_gemma":[0.008986499,0.00045542637,0.00082449435,0.0015403405,0.0006825461,0.0021433043,0.0012002662,0.0020852012,0.00059956533],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025949127,0.0003209259,0.00047198302,0.00019019056,0.000033868404,0.000028032784,0.00007168417,0.8914173,0.00034333076,0.050757587,0.0031260324,0.052979477],"study_design_scores_gemma":[0.000027916478,0.000031608663,0.00006370486,0.000013563818,0.0000057703155,0.000007471888,0.000027177703,0.9942521,0.00012956563,0.004710167,0.00072650274,0.000004428007],"about_ca_topic_score_codex":0.007678181,"about_ca_topic_score_gemma":0.00921828,"teacher_disagreement_score":0.007859287,"about_ca_system_score_codex":0.0018056098,"about_ca_system_score_gemma":0.002070485,"threshold_uncertainty_score":0.026291966},"labels":[],"label_agreement":null},{"id":"W1970712172","doi":"10.1016/j.ejor.2015.04.017","title":"Exact and heuristic algorithms for the design of hub networks with multiple lines","year":2015,"lang":"en","type":"article","venue":"European Journal of Operational Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":56,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal; Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada; Conselho Nacional de Desenvolvimento Científico e Tecnológico","keywords":"Benchmark (surveying); Mathematical optimization; Heuristic; Variable neighborhood search; Set (abstract data type); Computer science; Algorithm; Greedy algorithm; Network planning and design; Greedy randomized adaptive search procedure; Descent (aeronautics); Constraint (computer-aided design); Mathematics; Metaheuristic","score_opus":0.18330858125461627,"score_gpt":0.37627837588089164,"score_spread":0.19296979462627536,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1970712172","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014578203,0.000527227,0.978224,0.00021665888,0.000064425825,0.00010683884,0.00012969783,0.00031878852,0.0058342707],"genre_scores_gemma":[0.3508437,0.00064217113,0.6409717,0.00015514834,0.00010499524,0.00051140616,0.00027836883,0.00022695416,0.006265533],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993025,0.00031487667,0.000024678553,0.00009340309,0.00015782371,0.000106729305],"domain_scores_gemma":[0.997184,0.0021838816,0.00018939002,0.000119149845,0.0002254919,0.00009805225],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017825806,0.0015642741,0.0014689344,0.0015083597,0.0007901464,0.0014125515,0.0020028928,0.0019622087,0.005881802],"category_scores_gemma":[0.005941154,0.0014109637,0.0009763444,0.0016169233,0.001307424,0.0014516894,0.0012266752,0.0016305005,0.0006139349],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026936761,0.000021068312,0.00006748402,0.000031465544,0.000009249928,0.000010537266,0.000017838784,0.9841452,0.00010735123,0.004503455,0.0005493335,0.010509972],"study_design_scores_gemma":[0.000015204388,0.000014010639,0.000021216369,0.000005542829,0.0000035178982,0.0000035427654,0.000008957347,0.9949839,0.000057744444,0.0046019056,0.0002816214,0.0000029216624],"about_ca_topic_score_codex":0.011616633,"about_ca_topic_score_gemma":0.012421064,"teacher_disagreement_score":0.011616633,"about_ca_system_score_codex":0.0020610148,"about_ca_system_score_gemma":0.0025232371,"threshold_uncertainty_score":0.023098052},"labels":[],"label_agreement":null},{"id":"W1970974711","doi":"10.1287/opre.1050.0218","title":"The Black and White Traveling Salesman Problem","year":2006,"lang":"en","type":"article","venue":"Operations Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal; Université de Montréal","funders":"Ministero dell'Università e della Ricerca","keywords":"Travelling salesman problem; Bottleneck traveling salesman problem; Combinatorics; Mathematics; Vertex (graph theory); Traveling purchaser problem; 2-opt; Bounded function; Vehicle routing problem; Branch and cut; Mathematical optimization; Hamiltonian path; Integer programming; Discrete mathematics; Graph; Computer science; Routing (electronic design automation)","score_opus":0.03776718920947337,"score_gpt":0.3386011763200925,"score_spread":0.30083398711061915,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1970974711","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06921074,0.0021151786,0.82486874,0.002956018,0.00048056585,0.00047444916,0.0016991002,0.0005831446,0.09761205],"genre_scores_gemma":[0.6153796,0.0039059946,0.30558008,0.0010283312,0.00033768622,0.0005611684,0.0025399951,0.00028182927,0.07038533],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9993325,0.0002179219,0.000025229729,0.00015180849,0.00013207021,0.00014039133],"domain_scores_gemma":[0.9996112,0.00019975744,0.000049969945,0.00002763342,0.000054693464,0.000056807912],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00073204,0.0008155361,0.0008066007,0.00043669285,0.0009542315,0.0020921952,0.0010951082,0.0013675543,0.0080903275],"category_scores_gemma":[0.0016262756,0.00040610286,0.000511111,0.0010418498,0.0008142526,0.0022018335,0.0009466729,0.0010689379,0.0011395823],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003193396,0.00023280001,0.0008779889,0.00042258506,0.00009652114,0.0006523772,0.0002623839,0.34214756,0.0032621913,0.4484654,0.037194446,0.16606636],"study_design_scores_gemma":[0.00009783702,0.00012743098,0.0003671498,0.00006232859,0.000050306066,0.00029487812,0.0002948552,0.6764389,0.0021598057,0.2630191,0.057048928,0.000038422677],"about_ca_topic_score_codex":0.0050527467,"about_ca_topic_score_gemma":0.003636536,"teacher_disagreement_score":0.0080903275,"about_ca_system_score_codex":0.00086194224,"about_ca_system_score_gemma":0.0023684562,"threshold_uncertainty_score":0.0270648},"labels":[],"label_agreement":null},{"id":"W1971036791","doi":"10.1002/net.21580","title":"Branch‐and‐cut and Branch‐and‐cut‐and‐price algorithms for the adjacent only quadratic minimum spanning tree problem","year":2015,"lang":"en","type":"article","venue":"Networks","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"Fundação de Amparo à Pesquisa do Estado de Minas Gerais; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior","keywords":"Spanning tree; Branch and cut; Maximum cut; Mathematics; Minimum spanning tree; Linear programming relaxation; Integer programming; Linear programming; Linearization; Branch and bound; Cutting-plane method; Quadratic equation; Combinatorics; Relaxation (psychology); Branch and price; Mathematical optimization; Algorithm; Graph; Nonlinear system","score_opus":0.02871880323920883,"score_gpt":0.266627552590149,"score_spread":0.23790874935094014,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1971036791","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01817067,0.0009757949,0.96804494,0.00070506457,0.00012186547,0.0002071555,0.00019444579,0.00049867755,0.011081417],"genre_scores_gemma":[0.22427557,0.0010661939,0.76426154,0.00042394327,0.00019599323,0.0006756249,0.0010936613,0.00039189318,0.007615593],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99841654,0.0005567747,0.00006842189,0.00028491672,0.00039391316,0.00027942137],"domain_scores_gemma":[0.99824995,0.0010751773,0.0001721787,0.00016795007,0.00018879697,0.00014586108],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018733534,0.0018301272,0.0018367505,0.0011074097,0.0008880317,0.0020045615,0.0023728576,0.0018028506,0.008194085],"category_scores_gemma":[0.006192899,0.00076908036,0.0016107186,0.002366938,0.000978626,0.0033083982,0.0021146708,0.0044515184,0.0012167437],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024337514,0.00047952397,0.0006163049,0.00036697768,0.00008313767,0.00013601036,0.00019335691,0.6924917,0.0012473732,0.12281862,0.013568055,0.16775556],"study_design_scores_gemma":[0.000045674187,0.000064840184,0.000115546376,0.000018350407,0.000015205712,0.00003642756,0.000028445367,0.95798004,0.00033681036,0.03946965,0.0018788498,0.000010131932],"about_ca_topic_score_codex":0.0048453477,"about_ca_topic_score_gemma":0.005360178,"teacher_disagreement_score":0.008194085,"about_ca_system_score_codex":0.0018112952,"about_ca_system_score_gemma":0.0033628806,"threshold_uncertainty_score":0.027411938},"labels":[],"label_agreement":null},{"id":"W1971063635","doi":"10.1007/s10479-011-0991-3","title":"A dynamic vehicle routing problem with multiple delivery routes","year":2011,"lang":"en","type":"article","venue":"Annals of Operations Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":182,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; Polytechnique Montréal; Université du Québec à Montréal","funders":"","keywords":"Vehicle routing problem; Computer science; Heuristic; Theory of computation; Routing (electronic design automation); Operations research; Service (business); Mathematical optimization; Computer network; Artificial intelligence","score_opus":0.1498618087874064,"score_gpt":0.3781981942429519,"score_spread":0.22833638545554552,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1971063635","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.071075,0.0013060436,0.9098629,0.0020620397,0.00038890415,0.00024006692,0.00081621314,0.00016513583,0.01408386],"genre_scores_gemma":[0.7230122,0.0018233771,0.23833337,0.00032746955,0.0005039374,0.0004345654,0.0009704259,0.00021960726,0.034374982],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998645,0.00045240778,0.00005653304,0.00042949847,0.00023391056,0.00018257635],"domain_scores_gemma":[0.9984145,0.0010522187,0.00020527632,0.000072536575,0.00010995574,0.00014539261],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017729746,0.0016426023,0.002402156,0.0013519672,0.0010067751,0.002634127,0.003129699,0.0035351822,0.006943937],"category_scores_gemma":[0.0044030617,0.0015908828,0.0015742216,0.0024034933,0.0011434256,0.0037213883,0.0018794762,0.0019415711,0.0005478943],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019050574,0.00012638954,0.0003883138,0.00021452233,0.00009131051,0.0004374219,0.00004741407,0.93189555,0.0012463836,0.041067336,0.0033536574,0.020941196],"study_design_scores_gemma":[0.000060491242,0.000093662435,0.00018064481,0.00001911757,0.000049359693,0.00017425705,0.00005573272,0.9748321,0.00041737242,0.021187399,0.0029077542,0.000021969212],"about_ca_topic_score_codex":0.0032311988,"about_ca_topic_score_gemma":0.0023918913,"teacher_disagreement_score":0.006943937,"about_ca_system_score_codex":0.0020269675,"about_ca_system_score_gemma":0.001276553,"threshold_uncertainty_score":0.023229778},"labels":[],"label_agreement":null},{"id":"W1971150951","doi":"10.1109/icsmc.2011.6083801","title":"An interactive heuristic approach for the P-forest problem","year":2011,"lang":"en","type":"article","venue":"","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Heuristics; GRASP; Greedy randomized adaptive search procedure; Computer science; Exploit; Heuristic; Domain (mathematical analysis); Greedy algorithm; Process (computing); Artificial intelligence; Mathematical optimization; Machine learning; Algorithm; Mathematics; Software engineering","score_opus":0.040203528840614004,"score_gpt":0.27576353239718027,"score_spread":0.23556000355656626,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1971150951","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0059383637,0.0001299098,0.9905363,0.00005851943,0.000014562096,0.00010584764,0.000023018916,0.00032789458,0.0028656318],"genre_scores_gemma":[0.17692085,0.00022437423,0.8204793,0.00012241249,0.000032360906,0.00049127825,0.00015452859,0.00014452687,0.001430355],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99843985,0.0007627381,0.0000495794,0.00018710642,0.00041122606,0.0001494462],"domain_scores_gemma":[0.9979304,0.001548645,0.00012339727,0.00021175238,0.00012524994,0.000060581806],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015804335,0.0010123097,0.0008541112,0.0009372441,0.00066587556,0.0008758006,0.0022424818,0.0014505936,0.005145905],"category_scores_gemma":[0.0041916356,0.00036106992,0.0009497034,0.0008669251,0.0009474665,0.0013906717,0.0012003159,0.001185238,0.0006616401],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001930575,0.00029416147,0.0007001478,0.0002965093,0.00008581315,0.00021235496,0.00017527281,0.76446855,0.0034865192,0.049923535,0.0027597635,0.1774044],"study_design_scores_gemma":[0.00007191886,0.00012916514,0.00013120445,0.000024584007,0.000023131699,0.00014377115,0.000054244665,0.9757833,0.00140786,0.018412976,0.0037942682,0.00002358015],"about_ca_topic_score_codex":0.0023494265,"about_ca_topic_score_gemma":0.0035927063,"teacher_disagreement_score":0.005145905,"about_ca_system_score_codex":0.00062330125,"about_ca_system_score_gemma":0.0015676881,"threshold_uncertainty_score":0.017214775},"labels":[],"label_agreement":null},{"id":"W1971529341","doi":"10.1142/s0217984900001105","title":"FINITE SIZE SCALING AND CRITICAL TRANSITION IN CONSTRAINED TRAVELING SALESMAN PROBLEM","year":2000,"lang":"en","type":"article","venue":"Modern Physics Letters B","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Lethbridge","funders":"","keywords":"Travelling salesman problem; Scaling; Function (biology); Mathematical optimization; Bottleneck traveling salesman problem; Statistical physics; Mathematics; Computer science; Physics; Geometry; Biology","score_opus":0.011900910847733787,"score_gpt":0.23593291920144047,"score_spread":0.22403200835370668,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1971529341","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.92351365,0.0012948831,0.06534448,0.001463326,0.000090542824,0.00003693358,0.000115033545,0.00022755085,0.007913744],"genre_scores_gemma":[0.99258476,0.0003321419,0.005935866,0.00010493775,0.000058803755,0.00004446731,0.000065098575,0.000046094054,0.0008278049],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999757,0.000089401736,0.000009842114,0.00003822354,0.000043519474,0.000061971376],"domain_scores_gemma":[0.99579597,0.0027454258,0.0006742407,0.00013745952,0.00024210753,0.00040478754],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00093954674,0.00046591004,0.00053184567,0.0008636753,0.0005917216,0.0011122121,0.0008694532,0.0010046522,0.0015517422],"category_scores_gemma":[0.008061793,0.0003699714,0.0004774175,0.0004192602,0.0018971674,0.0015677245,0.0007394017,0.00092354394,0.000088522036],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021694394,0.00018794864,0.0043723984,0.00027703357,0.00009982981,0.0007325295,0.00039144373,0.57520986,0.0133089665,0.39514443,0.0038849365,0.0061736577],"study_design_scores_gemma":[0.00002219526,0.00004003656,0.0011298417,0.000017961656,0.000010999517,0.00005590402,0.00005917697,0.93225694,0.00054093654,0.065617986,0.00023022274,0.000017857232],"about_ca_topic_score_codex":0.0029927136,"about_ca_topic_score_gemma":0.0014522424,"teacher_disagreement_score":0.0029927136,"about_ca_system_score_codex":0.00097440346,"about_ca_system_score_gemma":0.0005630074,"threshold_uncertainty_score":0.007069826},"labels":[],"label_agreement":null},{"id":"W1972280695","doi":"10.1007/s10479-011-0876-5","title":"The Robust Set Covering Problem with interval data","year":2011,"lang":"en","type":"article","venue":"Annals of Operations Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":43,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"The Scarborough Hospital; University of Toronto","funders":"","keywords":"Regret; Benders' decomposition; Mathematical optimization; Interval (graph theory); Theory of computation; Mathematics; Heuristic; Set (abstract data type); Genetic algorithm; Minimax; Context (archaeology); Algorithm; Heuristics; Computer science; Statistics; Combinatorics","score_opus":0.5469561555470586,"score_gpt":0.4569992869582814,"score_spread":0.08995686858877716,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1972280695","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.039259493,0.0017656133,0.953702,0.00075222633,0.00009445363,0.00005632306,0.00047584967,0.00011555871,0.0037786302],"genre_scores_gemma":[0.67183083,0.003411815,0.31704757,0.00025122464,0.000662316,0.00031094303,0.0015409446,0.00024094278,0.004703404],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99656856,0.0019868836,0.00016929289,0.00049756287,0.0005786728,0.00019899923],"domain_scores_gemma":[0.9864759,0.010870587,0.0010139025,0.0010073514,0.00037082503,0.000261455],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004867117,0.0010887884,0.002584133,0.0012818334,0.0004299601,0.0023763257,0.0022611818,0.0020968106,0.0029795452],"category_scores_gemma":[0.018448047,0.0012456635,0.0017954909,0.0028704896,0.0014477989,0.004129193,0.0018921006,0.0019279646,0.000290952],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015956002,0.00007737563,0.0008029463,0.0003080499,0.0002198873,0.0002171045,0.000101593476,0.84105337,0.0009522554,0.10902394,0.0030035216,0.04408043],"study_design_scores_gemma":[0.000018620583,0.000043393153,0.00028289334,0.00003215787,0.00003034376,0.000084847445,0.0000293027,0.89794725,0.00035063867,0.09971602,0.0014470014,0.000017582031],"about_ca_topic_score_codex":0.0016586226,"about_ca_topic_score_gemma":0.0007125156,"teacher_disagreement_score":0.004867117,"about_ca_system_score_codex":0.0011806407,"about_ca_system_score_gemma":0.0008022873,"threshold_uncertainty_score":0.025740087},"labels":[],"label_agreement":null},{"id":"W1973979653","doi":"10.1109/scis.2007.367696","title":"Structured Neighborhood Tabu Search for Assigning Judges to Competitions","year":2007,"lang":"en","type":"article","venue":"","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Tabu search; Metaheuristic; Guided Local Search; Diversification (marketing strategy); Computer science; Mathematical optimization; Contiguity; Competition (biology); Hill climbing; Simulated annealing; Operations research; Algorithm; Mathematics","score_opus":0.023868962353607005,"score_gpt":0.3069418959008773,"score_spread":0.28307293354727026,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1973979653","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.032652695,0.00034579076,0.95604974,0.00021396967,0.00008027874,0.00032867104,0.000066183085,0.0003814784,0.009881256],"genre_scores_gemma":[0.33714244,0.00021026596,0.6571985,0.00012902057,0.0000517344,0.00066102424,0.0001920865,0.00013632422,0.004278591],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984092,0.0009889248,0.00003656892,0.00010032845,0.0003225631,0.000142459],"domain_scores_gemma":[0.9989089,0.0006292649,0.00009123227,0.00007631986,0.00019170095,0.00010260005],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002748308,0.000703675,0.0011141718,0.0021251491,0.001098144,0.0013271892,0.00212644,0.0017235155,0.004643825],"category_scores_gemma":[0.0058877748,0.0007000276,0.00060882635,0.001962798,0.0010235211,0.00097190804,0.0010737834,0.0012515421,0.00070763193],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012669203,0.00010340184,0.0005464738,0.000074046104,0.000039357517,0.00006441089,0.00017943776,0.87204397,0.0012383709,0.045016035,0.0035186273,0.07704916],"study_design_scores_gemma":[0.000025360543,0.000037751975,0.00006767102,0.000010664298,0.000005447601,0.000010797499,0.000026588827,0.99388427,0.00027460398,0.004829563,0.00082003494,0.000007251795],"about_ca_topic_score_codex":0.005214122,"about_ca_topic_score_gemma":0.008381207,"teacher_disagreement_score":0.005214122,"about_ca_system_score_codex":0.0016152806,"about_ca_system_score_gemma":0.0022136844,"threshold_uncertainty_score":0.015535176},"labels":[],"label_agreement":null},{"id":"W1974436345","doi":"10.1016/j.jom.2004.10.017","title":"Analysis and improvement of delivery operations at the San Francisco Public Library","year":2005,"lang":"en","type":"article","venue":"Journal of Operations Management","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Tellabs (Canada)","funders":"","keywords":"Workload; Computer science; Heuristics; Service delivery framework; Operations research; Delivery Performance; Library management; Delivery system; Operations management; Service (business); World Wide Web; Business; Process management; Operating system; Engineering; Marketing","score_opus":0.01055823925243723,"score_gpt":0.236586939338591,"score_spread":0.22602870008615378,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1974436345","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9393148,0.00033067373,0.044990547,0.00044102027,0.000027463206,0.00017631627,0.00075428374,0.0006733827,0.013291578],"genre_scores_gemma":[0.97680753,0.00019728926,0.017115897,0.000022985992,0.000005204839,0.000045469536,0.00039369334,0.000048706104,0.005363352],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995209,0.00015001235,0.000015537624,0.00008972198,0.000110296474,0.000113528615],"domain_scores_gemma":[0.99895144,0.0004976769,0.00017372328,0.000057427344,0.00025502447,0.00006456656],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001169057,0.00072627224,0.00035025738,0.0013364111,0.0007024318,0.0011990508,0.0007951073,0.00047461424,0.004687728],"category_scores_gemma":[0.0019789396,0.0004746504,0.0005226058,0.0013623084,0.00033978184,0.00077955215,0.00041152234,0.0005219138,0.00038745155],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003100448,0.0002697495,0.022774478,0.00013130064,0.000051398747,0.00021475971,0.00017420288,0.91813815,0.0029980214,0.003166498,0.0038719026,0.04789944],"study_design_scores_gemma":[0.000026113461,0.0001929334,0.008722824,0.000011976428,0.0000307,0.00002164079,0.0002609174,0.9861268,0.0023408893,0.00027120666,0.0019788796,0.000015143407],"about_ca_topic_score_codex":0.15890862,"about_ca_topic_score_gemma":0.11680633,"teacher_disagreement_score":0.15890862,"about_ca_system_score_codex":0.007176923,"about_ca_system_score_gemma":0.0026459605,"threshold_uncertainty_score":0.31596732},"labels":[],"label_agreement":null},{"id":"W1974861471","doi":"10.1007/s13676-012-0005-x","title":"Districting for routing with stochastic customers","year":2012,"lang":"en","type":"article","venue":"EURO Journal on Transportation and Logistics","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":72,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"National University of Defense Technology; National Natural Science Foundation of China","keywords":"Heuristic; Mathematical optimization; Routing (electronic design automation); Vehicle routing problem; Computer science; Compact space; Operations research; Mathematics","score_opus":0.028451525478535144,"score_gpt":0.2688076632308992,"score_spread":0.24035613775236409,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1974861471","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02758525,0.0003096003,0.96674114,0.00018453645,0.000035247424,0.000071122886,0.00014397228,0.0001324574,0.004796752],"genre_scores_gemma":[0.70894897,0.00091120694,0.2799093,0.000115281575,0.000070040805,0.00028730097,0.00044829212,0.00015884388,0.009150773],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993641,0.00027211162,0.00002139395,0.00010251824,0.00014122632,0.00009871691],"domain_scores_gemma":[0.99941325,0.0003194141,0.00008921814,0.00005542613,0.00006560302,0.000057028912],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006841808,0.0005250866,0.0009836593,0.00046038683,0.00050919264,0.00085501425,0.0011534777,0.00086005783,0.0063157533],"category_scores_gemma":[0.0020257044,0.0004241117,0.0009046506,0.0012030125,0.00072660693,0.0010019266,0.0010256814,0.0011342949,0.0004852125],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000023802866,0.000015879157,0.00021142913,0.00004852433,0.000009086115,0.000054301356,0.000031206564,0.94523257,0.0006708185,0.042660035,0.0005648524,0.010477483],"study_design_scores_gemma":[0.000010962559,0.000054837947,0.00010521212,0.00000730884,0.0000050460653,0.00004961643,0.000020990894,0.9603302,0.00039117355,0.035986416,0.0030284335,0.000009856528],"about_ca_topic_score_codex":0.0029376517,"about_ca_topic_score_gemma":0.0035967405,"teacher_disagreement_score":0.0063157533,"about_ca_system_score_codex":0.0010341894,"about_ca_system_score_gemma":0.0010605995,"threshold_uncertainty_score":0.021128356},"labels":[],"label_agreement":null},{"id":"W1975388745","doi":"10.1016/j.cor.2014.09.006","title":"Model-based automatic neighborhood design by unsupervised learning","year":2014,"lang":"en","type":"article","venue":"Computers & Operations Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"","keywords":"Heuristic; Computer science; Integer programming; Space (punctuation); Key (lock); Domain (mathematical analysis); Unsupervised learning; Combinatorial optimization; Mathematical optimization; Artificial intelligence; Machine learning; Mathematics; Algorithm","score_opus":0.06769736086838636,"score_gpt":0.33860306439854077,"score_spread":0.2709057035301544,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1975388745","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0060918913,0.0000363114,0.99298334,0.00002447556,0.000013195958,0.000019647317,0.000021276865,0.00041143407,0.00039838935],"genre_scores_gemma":[0.49923822,0.00009270144,0.4973246,0.00007781189,0.000039480634,0.00027803273,0.00034872172,0.00039516023,0.0022052894],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99934644,0.0002228224,0.000032636173,0.00018336634,0.00015846531,0.000056312976],"domain_scores_gemma":[0.9985454,0.00065629464,0.00011924358,0.00025574554,0.0003673476,0.000056013094],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009197964,0.0007133061,0.0016202435,0.00084427703,0.00071263814,0.0007619925,0.0018587019,0.0010425086,0.002123455],"category_scores_gemma":[0.003503297,0.00091925845,0.0012406192,0.0005666537,0.00071554567,0.0014660493,0.0014075408,0.0010573021,0.0007673476],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000109305365,0.000084928935,0.00065426243,0.000061031697,0.00006417519,0.000029797,0.000054777684,0.8725647,0.004395699,0.0075353244,0.0017738579,0.11267212],"study_design_scores_gemma":[0.0000034340808,0.000009259682,0.000027404127,0.0000011649004,0.000002885018,0.000005486609,0.0000020349514,0.998206,0.00035635734,0.0012763735,0.00010774881,0.0000018524893],"about_ca_topic_score_codex":0.004318436,"about_ca_topic_score_gemma":0.0073106224,"teacher_disagreement_score":0.004318436,"about_ca_system_score_codex":0.00067974115,"about_ca_system_score_gemma":0.0011969096,"threshold_uncertainty_score":0.008586645},"labels":[],"label_agreement":null},{"id":"W1976305426","doi":"10.1111/itor.12050","title":"Searching for optimal integer solutions to set partitioning problems using column generation","year":2013,"lang":"en","type":"article","venue":"International Transactions in Operational Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Column generation; Subgradient method; Integer programming; Mathematical optimization; Linear programming relaxation; Integer (computer science); Heuristics; Lagrangian relaxation; Mathematics; Relaxation (psychology); Branch and price; Column (typography); Linear programming; Algorithm; Computer science","score_opus":0.23892640465991966,"score_gpt":0.4353722400366696,"score_spread":0.19644583537674992,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1976305426","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0259319,0.00020580905,0.9701376,0.00013841107,0.000035155354,0.0001701868,0.00010346854,0.0003033931,0.0029741235],"genre_scores_gemma":[0.20794019,0.00020274291,0.7893979,0.00014305682,0.00003139935,0.0003431773,0.0003649381,0.00014317995,0.0014333583],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9992536,0.0003691539,0.000026950102,0.00009178519,0.0001801188,0.00007841898],"domain_scores_gemma":[0.99819654,0.001250436,0.00017813484,0.000144676,0.0001830422,0.000047187983],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00092791877,0.0010804366,0.0007807483,0.0010572361,0.00048574028,0.0009496128,0.00065896334,0.0005958632,0.0039729113],"category_scores_gemma":[0.002652893,0.0005254328,0.0007838906,0.0011099185,0.00059004955,0.00085186015,0.0009028268,0.00092235766,0.0004955782],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018741349,0.00023982828,0.00095275586,0.00033047228,0.00008360502,0.00017723709,0.00013222439,0.82148886,0.017192636,0.025567062,0.0041541294,0.12949371],"study_design_scores_gemma":[0.000040285784,0.00013376815,0.00013901174,0.000027709646,0.000017644696,0.00005152989,0.000043201708,0.9822651,0.0057059885,0.008979983,0.0025828283,0.000012952853],"about_ca_topic_score_codex":0.0012122991,"about_ca_topic_score_gemma":0.0018408534,"teacher_disagreement_score":0.0039729113,"about_ca_system_score_codex":0.00051639066,"about_ca_system_score_gemma":0.0007954195,"threshold_uncertainty_score":0.013290763},"labels":[],"label_agreement":null},{"id":"W1976370345","doi":"10.1109/mis.2005.71","title":"Guest Editors' Introduction: Advanced Heuristics in Transportation and Logistics","year":2005,"lang":"en","type":"article","venue":"IEEE Intelligent Systems","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Heuristics; Computer science; Metaheuristic; Heuristic; Constructive; Quality (philosophy); Exploit; Hyper-heuristic; Artificial intelligence; Mathematical optimization; Operations research; Management science; Robot; Mathematics; Engineering","score_opus":0.014684641173514134,"score_gpt":0.2577644374988316,"score_spread":0.24307979632531745,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1976370345","genre_codex":"editorial","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00045368724,0.041656796,0.009727742,0.030463284,0.89547104,0.00005154128,0.00017375336,0.00026307156,0.021739056],"genre_scores_gemma":[0.007762161,0.040569242,0.007203792,0.012161259,0.86740625,0.00006692638,0.0002584361,0.0003247338,0.064247064],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.998382,0.00033043942,0.00016744179,0.00032696116,0.00065297727,0.00014030449],"domain_scores_gemma":[0.9918969,0.0032578001,0.00038274718,0.00030246252,0.0031525346,0.0010075702],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023541052,0.0018694485,0.0015765001,0.0024098896,0.00090848975,0.004248757,0.0020631116,0.0025387616,0.026463171],"category_scores_gemma":[0.008995485,0.0004344497,0.001159898,0.001994294,0.001010052,0.0027109706,0.0010499906,0.004951861,0.017872134],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000056871308,0.000028269784,0.00008547016,0.00036136454,0.000022146629,0.00007641072,0.000023747089,0.0007017611,0.00015402788,0.0048426394,0.9430644,0.050582886],"study_design_scores_gemma":[0.000027243063,0.00006128497,0.0002054984,0.00021927097,0.000029930612,0.00019924561,0.000030447532,0.0015755262,0.000190296,0.0046621175,0.99278027,0.000018968549],"about_ca_topic_score_codex":0.000607402,"about_ca_topic_score_gemma":0.0011380326,"teacher_disagreement_score":0.026463171,"about_ca_system_score_codex":0.0010352712,"about_ca_system_score_gemma":0.0009718015,"threshold_uncertainty_score":0.08852816},"labels":[],"label_agreement":null},{"id":"W1976836184","doi":"10.1007/s12532-014-0076-9","title":"The strength of multi-row models","year":2014,"lang":"en","type":"article","venue":"Mathematical Programming Computation","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Row; Cutting-plane method; Intersection (aeronautics); Theory of computation; Integer programming; Facet (psychology); Mathematics; Integer (computer science); Row and column spaces; Set (abstract data type); Relaxation (psychology); Mathematical optimization; Combinatorics; Algorithm; Computer science","score_opus":0.030477581354009544,"score_gpt":0.28848244266485273,"score_spread":0.2580048613108432,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1976836184","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006493467,0.0006601142,0.97275454,0.0019975686,0.0003747712,0.000026122567,0.00018765626,0.00030862665,0.017197266],"genre_scores_gemma":[0.5680534,0.0031745578,0.391562,0.0017092691,0.0012598302,0.00022545093,0.0005598081,0.00076105876,0.032694645],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99855953,0.00068981986,0.000052263287,0.00022643089,0.000392644,0.00007942993],"domain_scores_gemma":[0.9949875,0.002238823,0.00031718545,0.0016290952,0.00066410913,0.0001633399],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015907609,0.0009046074,0.0014205985,0.0004106324,0.00076784904,0.00261417,0.0032750336,0.0015166209,0.010825114],"category_scores_gemma":[0.0091684265,0.0008645131,0.0012440557,0.0007608315,0.0011902042,0.0051667066,0.0017723764,0.003314509,0.003991931],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008588774,0.0000842184,0.0007463006,0.00020854833,0.00012028472,0.00006944386,0.00008544225,0.48783225,0.0010215224,0.4577625,0.00868133,0.04330238],"study_design_scores_gemma":[0.0000098770015,0.000027533673,0.00007204503,0.0000241331,0.000019886287,0.000031857544,0.000022438086,0.851375,0.00039738166,0.14125617,0.006747231,0.00001643388],"about_ca_topic_score_codex":0.0031983184,"about_ca_topic_score_gemma":0.0033141058,"teacher_disagreement_score":0.010825114,"about_ca_system_score_codex":0.0005583349,"about_ca_system_score_gemma":0.0012434277,"threshold_uncertainty_score":0.036213636},"labels":[],"label_agreement":null},{"id":"W1977973577","doi":"10.3138/infor.51.1.41","title":"Hybridation de l’algorithme de colonie de Fourmis avec l’algorithme de recherche à grand Voisinage pour la résolution du VRPTW statique et dynamique","year":2013,"lang":"fr","type":"article","venue":"INFOR Information Systems and Operational Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Humanities; Physics; Vehicle routing problem; Philosophy; Computer science; Routing (electronic design automation)","score_opus":0.09913689856119007,"score_gpt":0.39497984875318554,"score_spread":0.29584295019199547,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1977973577","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.031001601,0.00041175124,0.9593873,0.0004254241,0.00015522748,0.00017054843,0.00007180846,0.0028451344,0.0055312794],"genre_scores_gemma":[0.1936926,0.0002772194,0.7928084,0.00020516125,0.000064288855,0.00028123823,0.00035046588,0.00054272136,0.011777987],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989262,0.00026770303,0.000081409926,0.00032453786,0.00024548479,0.00015458366],"domain_scores_gemma":[0.99807113,0.00089682156,0.00013137962,0.0003625972,0.00044349887,0.00009458308],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018002138,0.0012754465,0.00125614,0.0014032832,0.00088239566,0.002016814,0.0024103092,0.0019936524,0.009055581],"category_scores_gemma":[0.004293615,0.000732814,0.0017044035,0.0011741656,0.0011290535,0.0026509885,0.001877815,0.0021389246,0.0021860069],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00047209556,0.00036899763,0.0033220812,0.00042983072,0.0002562535,0.00031580616,0.0005700471,0.5174265,0.02231102,0.04755393,0.0049963053,0.40197715],"study_design_scores_gemma":[0.0000747703,0.00018240113,0.00031054238,0.00003761602,0.000054531287,0.00011658618,0.000110054585,0.9733426,0.006707668,0.009972274,0.009064674,0.000026340653],"about_ca_topic_score_codex":0.007392139,"about_ca_topic_score_gemma":0.0077325557,"teacher_disagreement_score":0.009055581,"about_ca_system_score_codex":0.001269951,"about_ca_system_score_gemma":0.002284123,"threshold_uncertainty_score":0.030293941},"labels":[],"label_agreement":null},{"id":"W1978433263","doi":"10.1016/j.comcom.2004.07.006","title":"Assigning cells to switches in mobile networks using an ant colony optimization heuristic","year":2004,"lang":"en","type":"article","venue":"Computer Communications","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":32,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Computer science; Ant colony optimization algorithms; Heuristic; Mathematical optimization; Optimization problem; Metaheuristic; Algorithm; Artificial intelligence; Mathematics","score_opus":0.04141503808203103,"score_gpt":0.29967456980762996,"score_spread":0.25825953172559896,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1978433263","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.25527343,0.0005395638,0.7293118,0.00047394776,0.00023885813,0.00034971192,0.000094556526,0.00065031176,0.013067743],"genre_scores_gemma":[0.79641694,0.00018696573,0.19909671,0.00012758639,0.000039458064,0.00016447777,0.000072528266,0.000082383594,0.003812921],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995894,0.00014877807,0.000014693617,0.00006482652,0.00007634688,0.000106022715],"domain_scores_gemma":[0.99918014,0.00049996463,0.00007762631,0.00005050335,0.00010268008,0.00008908257],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006772329,0.00089273124,0.0011971223,0.0010375412,0.0011196326,0.0014163029,0.0015545439,0.0013413037,0.0029244716],"category_scores_gemma":[0.0016429253,0.0007576134,0.0006117486,0.0013185291,0.00087751384,0.001049526,0.0008110451,0.0006880984,0.00029629166],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012228894,0.00009038363,0.0003426899,0.000029334735,0.000024610667,0.00005363343,0.000054651788,0.9687849,0.0015346904,0.0041496693,0.0008108646,0.024002207],"study_design_scores_gemma":[0.000018706876,0.00003372264,0.000045831745,0.0000026993412,0.00000956214,0.0000079684805,0.000025464555,0.9976884,0.00034120216,0.0016250965,0.00019833642,0.0000030079802],"about_ca_topic_score_codex":0.008579115,"about_ca_topic_score_gemma":0.012066534,"teacher_disagreement_score":0.008579115,"about_ca_system_score_codex":0.0010085674,"about_ca_system_score_gemma":0.0011430554,"threshold_uncertainty_score":0.017058313},"labels":[],"label_agreement":null},{"id":"W1979700544","doi":"10.1023/b:anor.0000032576.73681.29","title":"Solving VRPTWs with Constraint Programming Based Column Generation","year":2004,"lang":"en","type":"article","venue":"Annals of Operations Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":84,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Column generation; Constraint programming; Theory of computation; Mathematical optimization; Computer science; Scheduling (production processes); Concurrent constraint logic programming; Constraint logic programming; Constraint satisfaction; Constraint (computer-aided design); Directed graph; Directed acyclic graph; Mathematics; Stochastic programming; Algorithm; Artificial intelligence","score_opus":0.20972328302259866,"score_gpt":0.42493952686567527,"score_spread":0.2152162438430766,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1979700544","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020581994,0.00018798336,0.9709057,0.00020918326,0.00009103416,0.0002529817,0.0005194943,0.0012165956,0.006034958],"genre_scores_gemma":[0.19889842,0.00015832022,0.7941733,0.0001863235,0.000060606446,0.00046850386,0.0010974986,0.0004191743,0.0045377784],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99931526,0.0002473456,0.00003341848,0.00013094774,0.00015393038,0.00011912061],"domain_scores_gemma":[0.9978289,0.0015985462,0.00012381544,0.00013898606,0.00025736782,0.000052373583],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00083034125,0.0014235278,0.0015074271,0.0010511556,0.0006428671,0.0013855881,0.0018589203,0.0014399079,0.013145671],"category_scores_gemma":[0.0031409883,0.0013774728,0.0014400498,0.002116656,0.00062796025,0.0013677907,0.0010066048,0.0019275693,0.0010564659],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007999709,0.000116841606,0.00023594215,0.00017254884,0.000040923875,0.00009612934,0.000032236447,0.92685235,0.0016005661,0.004524904,0.0032506466,0.06299689],"study_design_scores_gemma":[0.000024354316,0.00003335065,0.000041394993,0.00000556753,0.000008283468,0.000014166365,0.000015807052,0.99633455,0.000705174,0.0023319565,0.00048015785,0.00000525548],"about_ca_topic_score_codex":0.015585183,"about_ca_topic_score_gemma":0.017622905,"teacher_disagreement_score":0.015585183,"about_ca_system_score_codex":0.0006724345,"about_ca_system_score_gemma":0.0020345002,"threshold_uncertainty_score":0.043976724},"labels":[],"label_agreement":null},{"id":"W1979906780","doi":"10.1287/inte.1110.0544","title":"Designing New Electoral Districts for the City of Edmonton","year":2011,"lang":"en","type":"article","venue":"INFORMS Journal on Applied Analytics","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":35,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"HEC Montréal; University of Alberta","funders":"","keywords":"Redistricting; Contiguity; Plan (archaeology); Heuristic; Operations research; Population; Computer science; Process (computing); Transport engineering; Tabu search; Engineering; Geography; Legislature; Sociology; Artificial intelligence","score_opus":0.062465748116388636,"score_gpt":0.27094747060065544,"score_spread":0.2084817224842668,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1979906780","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.74032736,0.00072626874,0.15832724,0.0017121327,0.0002893662,0.0035404675,0.0015087114,0.0020391722,0.09152929],"genre_scores_gemma":[0.7569837,0.00027633316,0.20992854,0.00012235243,0.00002998178,0.0012084012,0.0015915575,0.00016847673,0.029690687],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984983,0.00058080995,0.000058838174,0.00020674667,0.00020768178,0.00044757564],"domain_scores_gemma":[0.9987595,0.00022335132,0.000107042346,0.00015049739,0.000345069,0.00041457475],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019121106,0.00030453317,0.00059931906,0.0016196157,0.0045160996,0.0044052918,0.0014007251,0.0007303098,0.011106348],"category_scores_gemma":[0.0035460198,0.00063567696,0.0004945625,0.0019311238,0.00095751975,0.000979822,0.0020951428,0.0007197864,0.0015379786],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009940853,0.0009183455,0.060592584,0.00065115414,0.000109994086,0.0014023297,0.0071095387,0.29033202,0.007536403,0.123572424,0.049076136,0.45770493],"study_design_scores_gemma":[0.0008734969,0.0013719706,0.07639279,0.00023426098,0.00019603268,0.0006862179,0.034289032,0.41412443,0.0097710155,0.021561416,0.44016176,0.0003376266],"about_ca_topic_score_codex":0.075256556,"about_ca_topic_score_gemma":0.31519347,"teacher_disagreement_score":0.9247434,"about_ca_system_score_codex":0.0046094847,"about_ca_system_score_gemma":0.0106889615,"threshold_uncertainty_score":0.14963704},"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,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal; Simon Fraser University","funders":"","keywords":"Pickup; Computer science; Dynamic problem; Algorithm; Artificial intelligence","score_opus":0.19768793239961463,"score_gpt":0.3989842575124488,"score_spread":0.20129632511283418,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1980175144","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.063039124,0.0016133622,0.9253428,0.001065678,0.0001965901,0.00015154466,0.00028256324,0.00022394884,0.008084352],"genre_scores_gemma":[0.7876517,0.0029766976,0.15513448,0.00036023214,0.00032903836,0.00044672636,0.00062288134,0.0005221351,0.05195621],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99879515,0.0003521874,0.00006827904,0.00022494371,0.00020417022,0.00035532506],"domain_scores_gemma":[0.9955383,0.0033487338,0.00037695968,0.00011084888,0.00026585456,0.0003593765],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0035574432,0.0017499462,0.001977903,0.0012049748,0.00088135205,0.002609198,0.004552763,0.0021202648,0.011025045],"category_scores_gemma":[0.008368527,0.0017945383,0.0013889153,0.0015395164,0.0013928098,0.004777981,0.0015868541,0.002877125,0.000925993],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00037252225,0.0001868558,0.00050698884,0.0003558989,0.00010064841,0.00019260199,0.0002623492,0.74320304,0.0023091342,0.22652623,0.0040881443,0.02189555],"study_design_scores_gemma":[0.000043931163,0.000064407766,0.0001386761,0.000020706893,0.00004020028,0.00002621959,0.00006790604,0.96105796,0.00031503485,0.037064865,0.0011380856,0.000022082266],"about_ca_topic_score_codex":0.010494735,"about_ca_topic_score_gemma":0.0048562535,"teacher_disagreement_score":0.011025045,"about_ca_system_score_codex":0.002522469,"about_ca_system_score_gemma":0.0024858294,"threshold_uncertainty_score":0.03688246},"labels":[],"label_agreement":null},{"id":"W1980495840","doi":"10.1109/cec.2010.5586036","title":"The one-commodity traveling salesman problem with selective pickup and delivery: An ant colony approach","year":2010,"lang":"en","type":"article","venue":"","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":54,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Travelling salesman problem; Pickup; Computer science; Mathematical optimization; Ant colony optimization algorithms; Heuristic; Spare part; Extremal optimization; Traveling purchaser problem; Convergence (economics); 2-opt; Optimization problem; Artificial intelligence; Engineering; Mathematics; Operations management; Multi-swarm optimization; Economics","score_opus":0.015808267299622446,"score_gpt":0.2290344006878308,"score_spread":0.21322613338820834,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1980495840","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.027407058,0.00077049754,0.96270114,0.0005199108,0.00008192558,0.00008771044,0.00004270318,0.00012855594,0.008260457],"genre_scores_gemma":[0.52960104,0.0017356643,0.45980415,0.00015863038,0.00009397383,0.00020185247,0.00009633217,0.00009489396,0.008213541],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997603,0.00008879399,0.000009804656,0.0000396067,0.00007025462,0.000031320156],"domain_scores_gemma":[0.99975246,0.00013816,0.000036129397,0.000018406514,0.000028386889,0.000026532463],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037096647,0.00057941617,0.000687999,0.00042028486,0.00045059767,0.000939314,0.0011699114,0.00092713186,0.0013854945],"category_scores_gemma":[0.000953272,0.00037660933,0.0006302098,0.00084147364,0.00062799733,0.000979907,0.00073188514,0.0009150786,0.00023626079],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000027063295,0.000046772107,0.00025530095,0.00006559904,0.000021765722,0.0001777696,0.00005894763,0.93852836,0.0015671089,0.03285747,0.0012877225,0.025105996],"study_design_scores_gemma":[0.0000063060374,0.00001562539,0.000035242025,0.0000030948204,0.00000599449,0.0000395269,0.000013988822,0.99284256,0.00023282396,0.005751819,0.0010489005,0.0000041525827],"about_ca_topic_score_codex":0.0037121065,"about_ca_topic_score_gemma":0.0035687895,"teacher_disagreement_score":0.0037121065,"about_ca_system_score_codex":0.00057445455,"about_ca_system_score_gemma":0.00088035036,"threshold_uncertainty_score":0.007381022},"labels":[],"label_agreement":null},{"id":"W1981172686","doi":"10.4018/ijoris.2014010106","title":"Modeling and Simulation Analyses of Healthcare Delivery Operations for Inter-Hospital Patient Transfers","year":2014,"lang":"en","type":"article","venue":"International Journal of Operations Research and Information Systems","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Queen's University","funders":"","keywords":"Sizing; Health care; Operations research; Transfer (computing); Computer science; Operations management; Quality (philosophy); Engineering; Economics","score_opus":0.07347885653805221,"score_gpt":0.4028758624374857,"score_spread":0.3293970058994335,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1981172686","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7573634,0.00053403026,0.20834073,0.0017675209,0.000104414205,0.00020372776,0.000880093,0.00025983216,0.030546308],"genre_scores_gemma":[0.9819049,0.00031356173,0.01194172,0.00004742515,0.000013733153,0.00007561595,0.0002702813,0.00002630179,0.00540641],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994899,0.00021548127,0.000015848073,0.00006589864,0.00006628013,0.0001466418],"domain_scores_gemma":[0.99900156,0.0006479758,0.000111507456,0.00003433252,0.00013628398,0.00006847732],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00096651714,0.00085820653,0.00063933094,0.0007176816,0.0006597338,0.0011747187,0.0011740095,0.0016074444,0.0030173596],"category_scores_gemma":[0.0017437704,0.00051973964,0.0010606969,0.0009551279,0.00079825247,0.0008205328,0.0005460373,0.0007748621,0.00018762758],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008802784,0.000009878904,0.00025972386,0.000004130994,0.000003974717,0.000019138155,0.00000804503,0.9982652,0.0000857442,0.0009144709,0.00006903045,0.0003519079],"study_design_scores_gemma":[0.0000037763036,0.000009087444,0.00017494395,0.0000012850676,0.0000033239521,0.0000032543899,0.000025394633,0.99928313,0.000052482534,0.00033289677,0.00010823853,0.000002186151],"about_ca_topic_score_codex":0.13837177,"about_ca_topic_score_gemma":0.07732927,"teacher_disagreement_score":0.13837177,"about_ca_system_score_codex":0.004571963,"about_ca_system_score_gemma":0.0027963796,"threshold_uncertainty_score":0.27513272},"labels":[],"label_agreement":null},{"id":"W1981626956","doi":"10.4018/jsds.2010040104","title":"The Traveling Salesman Problem, the Vehicle Routing Problem, and Their Impact on Combinatorial Optimization","year":2010,"lang":"en","type":"article","venue":"International Journal of Strategic Decision Sciences","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"","keywords":"Travelling salesman problem; Vehicle routing problem; Combinatorial optimization; Metaheuristic; Heuristics; Mathematical optimization; Extremal optimization; 2-opt; Lin–Kernighan heuristic; Quadratic assignment problem; Computer science; Traveling purchaser problem; Routing (electronic design automation); Heuristic; Optimization problem; Bottleneck traveling salesman problem; Mathematics; Meta-optimization","score_opus":0.03393817514350536,"score_gpt":0.3286185773359895,"score_spread":0.29468040219248415,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1981626956","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.032095354,0.35107356,0.34518683,0.028267857,0.0040288046,0.00012976416,0.00048024053,0.0001954761,0.23854205],"genre_scores_gemma":[0.42477074,0.31984827,0.20601118,0.0035271966,0.0062348023,0.0002536545,0.0004925192,0.00017890181,0.038682785],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99882454,0.00046528588,0.00004287704,0.00015351476,0.00043346707,0.00008031537],"domain_scores_gemma":[0.99791914,0.0015874519,0.0001742303,0.000060395956,0.00018718869,0.00007151154],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011679811,0.0010145458,0.0006958794,0.0010961848,0.0008922639,0.003475997,0.0009976941,0.0023572596,0.0038783962],"category_scores_gemma":[0.0043155523,0.00048017525,0.00061965117,0.0030255402,0.0031968318,0.005242384,0.0014728984,0.0030622226,0.00070873933],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000037876096,0.00006352938,0.0005270115,0.00044471136,0.000031613723,0.0001539149,0.00012816826,0.049888693,0.00034564533,0.84703726,0.011436185,0.08990545],"study_design_scores_gemma":[0.000017872666,0.00006390098,0.00076290936,0.00024565557,0.000034728284,0.00031088857,0.00036186472,0.07085156,0.00039215275,0.8107107,0.116207875,0.00003978721],"about_ca_topic_score_codex":0.0049042185,"about_ca_topic_score_gemma":0.0040235366,"teacher_disagreement_score":0.0049042185,"about_ca_system_score_codex":0.0014158855,"about_ca_system_score_gemma":0.0016127479,"threshold_uncertainty_score":0.012974501},"labels":[],"label_agreement":null},{"id":"W1983050889","doi":"10.1016/j.ejor.2013.08.034","title":"Improvements to a large neighborhood search heuristic for an integrated aircraft and passenger recovery problem","year":2013,"lang":"en","type":"article","venue":"European Journal of Operational Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":93,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"","keywords":"Schedule; Computer science; Heuristic; Operations research; Context (archaeology); Mathematical optimization; Incremental heuristic search; Order (exchange); Search algorithm; Beam search; Engineering; Artificial intelligence; Algorithm; Mathematics","score_opus":0.05616401818405493,"score_gpt":0.3486139602267212,"score_spread":0.2924499420426663,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1983050889","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08060577,0.00063469855,0.90779364,0.00033524568,0.00026084468,0.00022632514,0.0001389876,0.0006712824,0.009333252],"genre_scores_gemma":[0.46748662,0.00024857456,0.52510226,0.00017037925,0.000100347905,0.0003194718,0.00032959826,0.00024955676,0.0059932647],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99942553,0.0002447356,0.000027036402,0.0000967991,0.00013583398,0.00007003826],"domain_scores_gemma":[0.9984377,0.0010008804,0.000090228845,0.00013376505,0.000248492,0.000088888446],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013273573,0.0008162561,0.0013523701,0.0008444134,0.00071904424,0.00085979444,0.002058194,0.0015288185,0.0046657696],"category_scores_gemma":[0.0040224516,0.00057206134,0.0009868433,0.00081783184,0.0005580899,0.0013260355,0.0011277325,0.0012097014,0.00053899724],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016093571,0.00020918634,0.00030218734,0.00006112772,0.000034095265,0.000059048805,0.000040457635,0.9451988,0.0011132368,0.0050685634,0.001603765,0.046148617],"study_design_scores_gemma":[0.000020679408,0.000033111908,0.0000447817,0.0000028298289,0.0000067784767,0.000007111537,0.000006788905,0.99856585,0.000120607714,0.00090517965,0.00028313568,0.0000031569202],"about_ca_topic_score_codex":0.010182545,"about_ca_topic_score_gemma":0.010915123,"teacher_disagreement_score":0.010182545,"about_ca_system_score_codex":0.0008524286,"about_ca_system_score_gemma":0.0015148044,"threshold_uncertainty_score":0.020246565},"labels":[],"label_agreement":null},{"id":"W1983755208","doi":"10.1504/ijbpscm.2010.036202","title":"Checking the feasibility of a vehicle route in integrated supply chain transportation model","year":2010,"lang":"en","type":"article","venue":"International Journal of Business Performance and Supply Chain Modelling","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Pickup; Vehicle routing problem; Supply chain; Computer science; Routing (electronic design automation); Service (business); Flow network; Operations research; Business; Mathematical optimization; Computer network; Engineering; Marketing","score_opus":0.024609862953461868,"score_gpt":0.26169077053549994,"score_spread":0.23708090758203806,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1983755208","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20578021,0.00012825368,0.7912295,0.00012502834,0.0000109190105,0.00008897778,0.00022576125,0.0002841003,0.0021271517],"genre_scores_gemma":[0.8862191,0.00011735623,0.111443505,0.000022348073,0.0000088606885,0.00011243056,0.00031823217,0.00004070521,0.0017174118],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99847275,0.0005060382,0.000075822776,0.0003725533,0.00037333273,0.00019948407],"domain_scores_gemma":[0.99649835,0.0022437712,0.00055785605,0.00020507,0.00035700027,0.00013797112],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016014149,0.0008578022,0.0011945309,0.001295868,0.00069213065,0.0016331241,0.0019176863,0.0016712404,0.0022233275],"category_scores_gemma":[0.00757694,0.0008899947,0.0010454367,0.0014905263,0.0016008004,0.0028688607,0.0015599382,0.00083786325,0.00020673941],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000082029226,0.00001669842,0.00078265776,0.000028475784,0.000024216708,0.00009555432,0.000040979758,0.98947376,0.0006033175,0.0054944186,0.00008165427,0.0032762755],"study_design_scores_gemma":[0.000010502414,0.00004476466,0.000103888866,0.000005584438,0.000011406494,0.000019482608,0.000019054614,0.9947391,0.00040435407,0.0045104357,0.0001252415,0.000006152039],"about_ca_topic_score_codex":0.014051323,"about_ca_topic_score_gemma":0.008543737,"teacher_disagreement_score":0.014051323,"about_ca_system_score_codex":0.0012378626,"about_ca_system_score_gemma":0.0016881508,"threshold_uncertainty_score":0.027939081},"labels":[],"label_agreement":null},{"id":"W1983758874","doi":"10.1007/s10479-011-0895-2","title":"The orienteering problem with stochastic travel and service times","year":2011,"lang":"en","type":"article","venue":"Annals of Operations Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":133,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Orienteering; Heuristics; Theory of computation; Computer science; Variety (cybernetics); Service (business); Mathematical optimization; Operations research; Stochastic programming; Mathematics; Marketing; Artificial intelligence; Algorithm; Business","score_opus":0.17549393651751152,"score_gpt":0.38041133706969343,"score_spread":0.2049174005521819,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1983758874","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16173792,0.0030648902,0.8142257,0.0025104189,0.00032953985,0.0001532626,0.001005922,0.00016870743,0.016803592],"genre_scores_gemma":[0.8773285,0.003968256,0.08359415,0.00031844288,0.00049988664,0.00026564798,0.0010309209,0.00023257875,0.032761622],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99800175,0.0009889746,0.000084624124,0.0004146733,0.0002216185,0.00028832394],"domain_scores_gemma":[0.9957106,0.0029857685,0.00051274116,0.00018159825,0.00020360161,0.0004057126],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0034242317,0.0021181027,0.0034777068,0.001400557,0.00077535294,0.003553169,0.0033928747,0.0038298285,0.0054566604],"category_scores_gemma":[0.010527407,0.001974616,0.001848882,0.0030655246,0.0026325905,0.0056070895,0.0021172352,0.002786243,0.00044571908],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021053568,0.000111370886,0.0009691207,0.00016528352,0.00013381583,0.0003026266,0.00009461267,0.79861903,0.00051230507,0.1856818,0.0025307075,0.010668754],"study_design_scores_gemma":[0.00005686886,0.00005543284,0.00036372003,0.000016583117,0.000050481663,0.00007206696,0.00006492485,0.9073625,0.00013161439,0.09019136,0.0016012315,0.00003321632],"about_ca_topic_score_codex":0.017741999,"about_ca_topic_score_gemma":0.008087217,"teacher_disagreement_score":0.017741999,"about_ca_system_score_codex":0.002939046,"about_ca_system_score_gemma":0.0020336485,"threshold_uncertainty_score":0.035277486},"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,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_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","score_opus":0.4602205926685543,"score_gpt":0.47685575810679187,"score_spread":0.016635165438237565,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1985290529","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08664816,0.0023831527,0.88037443,0.00079903495,0.00051120383,0.0005195145,0.0007284829,0.0028430636,0.025193078],"genre_scores_gemma":[0.3429472,0.0006119843,0.64561236,0.00036048217,0.00010363655,0.0005121038,0.0007611768,0.00048935774,0.008601672],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992237,0.00039915877,0.00002348406,0.00008703853,0.00013456438,0.00013206288],"domain_scores_gemma":[0.99833244,0.0010965952,0.00009467753,0.00012939984,0.0002650111,0.00008183841],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015155323,0.00087700854,0.0015458371,0.002021271,0.0012240806,0.001453579,0.0026238433,0.0026142262,0.012416327],"category_scores_gemma":[0.004762924,0.0008201653,0.0009290967,0.0029467049,0.0010831368,0.0014735359,0.0010599176,0.0011375769,0.0016285031],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021059669,0.00017839289,0.00041278274,0.00011706736,0.00005886747,0.00007715707,0.000092022005,0.8460909,0.00070397276,0.009674259,0.007961655,0.13442229],"study_design_scores_gemma":[0.000093501905,0.00008216241,0.00016512339,0.000025999454,0.000027544724,0.000026963266,0.00004452118,0.9920922,0.00026646836,0.005081326,0.0020784868,0.000015640273],"about_ca_topic_score_codex":0.013978814,"about_ca_topic_score_gemma":0.01202085,"teacher_disagreement_score":0.013978814,"about_ca_system_score_codex":0.0015352885,"about_ca_system_score_gemma":0.0023791012,"threshold_uncertainty_score":0.04153675},"labels":[],"label_agreement":null},{"id":"W1985833421","doi":"10.1287/mnsc.47.9.1290.9780","title":"An Optimization Model for the Simultaneous Operational Flight and Pilot Scheduling Problem","year":2001,"lang":"en","type":"article","venue":"Management Science","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":73,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Université de Montréal; Group for Research in Decision Analysis","funders":"","keywords":"Mathematical optimization; Computer science; Scheduling (production processes); Schedule; Integer programming; Branch and bound; Column generation; Operations research; Job shop scheduling; Optimization problem; Branch and cut; Flow network; Linear programming; Mathematics","score_opus":0.024481551444291677,"score_gpt":0.2811633431568842,"score_spread":0.25668179171259253,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1985833421","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017426103,0.0006080394,0.96726334,0.0007168425,0.00012424358,0.00018331638,0.00094531354,0.0003275603,0.012405203],"genre_scores_gemma":[0.5212582,0.0018861442,0.44212034,0.00032275784,0.00032157803,0.0015362405,0.0029584994,0.00027191977,0.029324364],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989329,0.000402614,0.00003894924,0.00022870595,0.00021218577,0.00018463732],"domain_scores_gemma":[0.999337,0.00041725652,0.00008498977,0.00003233727,0.00008643205,0.00004190061],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014091657,0.001675711,0.0014275556,0.0006695486,0.00060499675,0.0017973533,0.0014279602,0.00209477,0.008045086],"category_scores_gemma":[0.001986926,0.00071623066,0.0010432906,0.0014199702,0.0007850451,0.0019427208,0.00093775714,0.0022215005,0.0014543849],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000039581042,0.00005180431,0.00015920524,0.00006617108,0.000019964953,0.0000835858,0.000034965076,0.9618311,0.00037426525,0.025424844,0.0022867,0.00962784],"study_design_scores_gemma":[0.000025856203,0.000030677897,0.00010659659,0.0000061508,0.000009161275,0.000031239793,0.000018488608,0.98484737,0.000092711874,0.011816645,0.00300833,0.0000067135793],"about_ca_topic_score_codex":0.008777403,"about_ca_topic_score_gemma":0.008287549,"teacher_disagreement_score":0.008777403,"about_ca_system_score_codex":0.0015762944,"about_ca_system_score_gemma":0.0025822707,"threshold_uncertainty_score":0.026913464},"labels":[],"label_agreement":null},{"id":"W1985971056","doi":"10.1287/trsc.2015.0593","title":"A Branch-Cut-and-Price Algorithm for the Energy Minimization Vehicle Routing Problem","year":2015,"lang":"en","type":"article","venue":"Transportation Science","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":71,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Branch and cut; Mathematical optimization; Branch and price; Integer programming; Linear programming; Arc (geometry); Branch and bound; Column generation; Mathematics; Minification; Integer (computer science); Set (abstract data type); Vehicle routing problem; Routing (electronic design automation); Algorithm; Computer science","score_opus":0.027579331617463003,"score_gpt":0.2762793489469292,"score_spread":0.24870001732946617,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1985971056","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0069660456,0.00020799981,0.9878414,0.00028756444,0.000049939812,0.00014430085,0.0000789674,0.00030130317,0.0041224696],"genre_scores_gemma":[0.08738266,0.00032465355,0.9072774,0.00014572353,0.00006632505,0.00040030165,0.00033610183,0.00020900067,0.003857745],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99936026,0.00020376526,0.00003110531,0.00012824843,0.00018387758,0.000092822134],"domain_scores_gemma":[0.99911696,0.00058964227,0.000062859304,0.000055235643,0.00012298772,0.000052224088],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010769243,0.0014448252,0.0015866364,0.0009642977,0.0007371444,0.0013599596,0.0019363132,0.001992738,0.008521611],"category_scores_gemma":[0.0033532113,0.00076689105,0.000917327,0.0018023538,0.0006531806,0.0021167174,0.001402684,0.0022274144,0.0011837741],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000101931684,0.00020079245,0.00037498891,0.00017171199,0.000056857018,0.00014745464,0.00007946832,0.77710193,0.0015647807,0.044450007,0.00739611,0.16835399],"study_design_scores_gemma":[0.00003630479,0.000042265827,0.00004311223,0.000007087471,0.000009485125,0.000032847922,0.000014025848,0.98234093,0.00035209837,0.015586195,0.0015298774,0.000005755966],"about_ca_topic_score_codex":0.0041326415,"about_ca_topic_score_gemma":0.0044099586,"teacher_disagreement_score":0.008521611,"about_ca_system_score_codex":0.0012557805,"about_ca_system_score_gemma":0.0020741285,"threshold_uncertainty_score":0.02850765},"labels":[],"label_agreement":null},{"id":"W1986520459","doi":"10.1007/s10479-005-3445-y","title":"Depth-Optimized Convexity Cuts","year":2005,"lang":"en","type":"article","venue":"Annals of Operations Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"National Science Foundation","keywords":"Convexity; Theory of computation; Intersection (aeronautics); Mathematics; Set (abstract data type); Computation; Mathematical optimization; Integer programming; Regular polygon; Function (biology); Order (exchange); Convex function; Integer (computer science); Linear programming; Algorithm; Computer science; Geometry","score_opus":0.24676143754847643,"score_gpt":0.4702200630803455,"score_spread":0.22345862553186904,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1986520459","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010841921,0.00076236547,0.96625996,0.0005633207,0.00015994716,0.00014812371,0.00042290892,0.0003883887,0.020453097],"genre_scores_gemma":[0.19718765,0.0014500787,0.7669081,0.00042937236,0.00025528026,0.00053280586,0.0013675916,0.0010065171,0.030862654],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9986835,0.00045450663,0.000047465986,0.00023664422,0.00040191785,0.00017593823],"domain_scores_gemma":[0.99737215,0.0016507157,0.00017110808,0.00028843404,0.0003665309,0.00015101791],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022525694,0.0023907276,0.0025935005,0.0023410467,0.0010859573,0.0028716007,0.0024757443,0.0026824,0.016137565],"category_scores_gemma":[0.008727759,0.0021888379,0.0019304107,0.0024649582,0.0016897002,0.0041872794,0.0033026722,0.0056516705,0.0019530747],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00041518174,0.0002052023,0.0006236802,0.000336756,0.00008910687,0.00009670864,0.00015047609,0.4604624,0.003080212,0.29765308,0.029024048,0.20786314],"study_design_scores_gemma":[0.00006177187,0.0000885878,0.00027100707,0.00007913027,0.000038281065,0.00006702704,0.00004263916,0.7873433,0.0015265034,0.20237152,0.008089173,0.000020980475],"about_ca_topic_score_codex":0.0034455145,"about_ca_topic_score_gemma":0.0043944176,"teacher_disagreement_score":0.016137565,"about_ca_system_score_codex":0.0024882464,"about_ca_system_score_gemma":0.0022524323,"threshold_uncertainty_score":0.053985536},"labels":[],"label_agreement":null},{"id":"W1987114813","doi":"10.1080/18756891.2012.670526","title":"Commercial Territory Design for a Distribution Firm with New Constructive and Destructive Heuristics","year":2012,"lang":"en","type":"article","venue":"International Journal of Computational Intelligence Systems","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"Universidad Autónoma de Nuevo León; Consejo Nacional de Ciencia y Tecnología","keywords":"Heuristics; Constructive; Distribution (mathematics); Computer science; Operations research; Mathematics; Programming language","score_opus":0.035840868423751795,"score_gpt":0.30499818292496284,"score_spread":0.26915731450121105,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1987114813","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07998461,0.00038080596,0.902854,0.00024186821,0.00002609669,0.000300986,0.00009756891,0.00032041507,0.015793636],"genre_scores_gemma":[0.56103563,0.00031922272,0.43278846,0.00008039031,0.000021184134,0.00034423682,0.00019436383,0.00009636106,0.005120114],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993813,0.00031366307,0.000019862815,0.00008819617,0.00010213806,0.0000948336],"domain_scores_gemma":[0.9988381,0.0008030754,0.00010159896,0.00010696097,0.000065502085,0.00008474877],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00096481363,0.00070257304,0.0007602017,0.0010677675,0.0005268433,0.0013969304,0.0013863966,0.001178512,0.004173747],"category_scores_gemma":[0.0022183235,0.00057706726,0.0007728574,0.0013041631,0.00092565786,0.0014528711,0.00094638026,0.0007583184,0.00033293263],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000091420254,0.0001162955,0.0007747793,0.00017769785,0.000034659824,0.00019602168,0.000111635796,0.88152593,0.002168209,0.04941515,0.0019382195,0.063449934],"study_design_scores_gemma":[0.000030910327,0.000060033395,0.00012628808,0.000013525115,0.000016215039,0.00007198657,0.000064765154,0.98829705,0.0008604884,0.008057341,0.0023922557,0.000009124431],"about_ca_topic_score_codex":0.0035731003,"about_ca_topic_score_gemma":0.0048387004,"teacher_disagreement_score":0.004173747,"about_ca_system_score_codex":0.0013538162,"about_ca_system_score_gemma":0.001528203,"threshold_uncertainty_score":0.013962626},"labels":[],"label_agreement":null},{"id":"W1988046523","doi":"10.1016/j.trb.2005.05.005","title":"An extended branch-and-bound method for locomotive assignment","year":2005,"lang":"en","type":"article","venue":"Transportation Research Part B Methodological","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":49,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Group for Research in Decision Analysis","funders":"","keywords":"Backtracking; Branch and bound; Mathematical optimization; Computer science; Heuristic; Node (physics); Integer programming; Branch and cut; Set (abstract data type); Branch and price; Operations research; Mathematics; Engineering","score_opus":0.29082360243951133,"score_gpt":0.49626654877510373,"score_spread":0.2054429463355924,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1988046523","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0016406311,0.00012610896,0.9960318,0.000046467234,0.00005046598,0.000038169906,0.000040771898,0.00018234277,0.0018431931],"genre_scores_gemma":[0.0669376,0.0002923852,0.92483604,0.00010427225,0.0001241674,0.00033801454,0.0002303817,0.00027006626,0.0068670902],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9991886,0.000336399,0.000028054472,0.000094539384,0.0002764547,0.000075902324],"domain_scores_gemma":[0.99867415,0.00081930665,0.00005104538,0.00009351784,0.0003002556,0.00006170539],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001916035,0.0012963624,0.0021989662,0.0012130805,0.000756004,0.0012341553,0.0024731995,0.0017929861,0.009635057],"category_scores_gemma":[0.0033623835,0.0008724083,0.001110736,0.0019856612,0.0006977288,0.0014719471,0.0014601814,0.0020309202,0.0019034866],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014264359,0.00012050277,0.00022237361,0.00018142718,0.00006715957,0.000051445168,0.000058826117,0.77628136,0.0012944622,0.016539145,0.004307511,0.2007332],"study_design_scores_gemma":[0.00001759566,0.000015855974,0.000041624724,0.00000904969,0.000009536599,0.000007224489,0.0000037910784,0.9942181,0.00014227287,0.0043456084,0.0011855192,0.0000038243884],"about_ca_topic_score_codex":0.008564551,"about_ca_topic_score_gemma":0.007437737,"teacher_disagreement_score":0.009635057,"about_ca_system_score_codex":0.0007467751,"about_ca_system_score_gemma":0.001870278,"threshold_uncertainty_score":0.032232463},"labels":[],"label_agreement":null},{"id":"W1988125100","doi":"10.3138/infor.50.4.195","title":"A Large Neighbourhood Search Heuristic for a Periodic Supply Vessel Planning Problem Arising in Offshore Oil and Gas Operations","year":2012,"lang":"en","type":"article","venue":"INFOR Information Systems and Operational Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":42,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"Norges Forskningsråd","keywords":"Heuristic; Neighbourhood (mathematics); Submarine pipeline; Mathematical optimization; Time horizon; Computer science; Offshore oil and gas; Operations research; Engineering; Mathematics","score_opus":0.04873724817383932,"score_gpt":0.34939307179331064,"score_spread":0.3006558236194713,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1988125100","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0768694,0.001171726,0.9087925,0.0004968389,0.00010079898,0.00020668568,0.00016938172,0.00024551927,0.011947183],"genre_scores_gemma":[0.60203946,0.00059928006,0.39018628,0.000116064715,0.000057127174,0.00042730145,0.00037168735,0.00009143552,0.0061113965],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996439,0.00018791395,0.000013999908,0.00006436986,0.00005297011,0.00003679557],"domain_scores_gemma":[0.99864584,0.0011338504,0.00007575366,0.000042616153,0.000044434946,0.00005755779],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00079317944,0.0005091902,0.00093784166,0.00072793284,0.0005104447,0.0005711649,0.00088624476,0.0013118471,0.0025499554],"category_scores_gemma":[0.0032712219,0.0004413345,0.00063146645,0.00088222674,0.0007774101,0.0011737903,0.0008580695,0.00071554567,0.00025213358],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008716738,0.00006816251,0.0002788707,0.00007606347,0.000028223423,0.0001063316,0.00006696542,0.9596409,0.0005524785,0.011422129,0.0017501542,0.025922555],"study_design_scores_gemma":[0.000041687257,0.000053497235,0.000100741316,0.000013939533,0.00000908207,0.000031810046,0.000022725442,0.98928773,0.00015935783,0.009342834,0.00092892256,0.000007752823],"about_ca_topic_score_codex":0.0033635327,"about_ca_topic_score_gemma":0.0056583174,"teacher_disagreement_score":0.0033635327,"about_ca_system_score_codex":0.00074725994,"about_ca_system_score_gemma":0.0007908707,"threshold_uncertainty_score":0.008530438},"labels":[],"label_agreement":null},{"id":"W1988297777","doi":"10.1287/opre.51.2.228.12786","title":"An Oil Pipeline Design Problem","year":2003,"lang":"en","type":"article","venue":"Operations Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":64,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal; Royal Military College of Canada","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Tabu search; Port (circuit theory); Integer programming; Pipeline (software); Mathematical optimization; Set (abstract data type); Integer (computer science); Computer science; Connection (principal bundle); Variable (mathematics); Submarine pipeline; Branch and cut; Mathematics; Engineering; Geometry","score_opus":0.11329738327680722,"score_gpt":0.4025412408418159,"score_spread":0.2892438575650087,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1988297777","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12841018,0.003242018,0.7618955,0.0048332284,0.000402824,0.001164853,0.0059010913,0.0005430337,0.093607344],"genre_scores_gemma":[0.5945313,0.0038578822,0.33809376,0.000570217,0.00030771963,0.0011802956,0.004407149,0.0002092542,0.05684237],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989267,0.00045187384,0.00005233808,0.00028134006,0.0001437468,0.00014399653],"domain_scores_gemma":[0.9990526,0.00067964586,0.000078796445,0.00002797078,0.000083876934,0.00007711395],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012830715,0.0022047637,0.0012212442,0.0009311639,0.0008572568,0.002053553,0.0012447383,0.003414445,0.014645078],"category_scores_gemma":[0.0028398903,0.0009251138,0.001243904,0.0014210065,0.0010548512,0.0026093943,0.0015373146,0.0016216045,0.001296744],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026197205,0.00032463032,0.0014838683,0.0010509962,0.00014893712,0.0009488464,0.00020588949,0.80260944,0.0016705605,0.12100463,0.013717205,0.05657309],"study_design_scores_gemma":[0.00029599393,0.0004599881,0.000567912,0.000157991,0.00013420101,0.000436967,0.00041040775,0.80129206,0.0017331135,0.14206527,0.052394696,0.000051356776],"about_ca_topic_score_codex":0.0045245816,"about_ca_topic_score_gemma":0.004577324,"teacher_disagreement_score":0.014645078,"about_ca_system_score_codex":0.0013893183,"about_ca_system_score_gemma":0.0019123182,"threshold_uncertainty_score":0.048992634},"labels":[],"label_agreement":null},{"id":"W1988416901","doi":"10.1016/j.cor.2008.08.010","title":"Dynamic window reduction for the multiple depot vehicle scheduling problem with time windows","year":2008,"lang":"en","type":"article","venue":"Computers & Operations Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":36,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Group for Research in Decision Analysis; Polytechnique Montréal; Kronos (Canada)","funders":"","keywords":"Computer science; Depot; Scheduling (production processes); Reduction (mathematics); Mathematical optimization; Window (computing); Dynamic programming; Real-time computing; Algorithm; Mathematics; Operating system","score_opus":0.04590354069195404,"score_gpt":0.32038094198709677,"score_spread":0.27447740129514275,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1988416901","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03251944,0.0006690056,0.9638925,0.00021301047,0.000119726785,0.000049340506,0.00010075201,0.00018946662,0.0022467244],"genre_scores_gemma":[0.7222114,0.001420099,0.26733518,0.00009464018,0.00027011486,0.00024993074,0.00037805745,0.00023331925,0.0078071975],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995944,0.00010388625,0.000016433993,0.00007883045,0.00011363326,0.00009279147],"domain_scores_gemma":[0.9994062,0.00040427974,0.00005002468,0.00004429976,0.00005273777,0.000042409305],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007198519,0.000758896,0.0011075969,0.00042172923,0.00036474442,0.00077801745,0.0011482434,0.00048296942,0.002076803],"category_scores_gemma":[0.0017684594,0.0004391531,0.00073525676,0.00065625773,0.00033565928,0.0014329897,0.00073946017,0.0013323468,0.00024787747],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00038965154,0.00017270222,0.000297207,0.00020826519,0.0000746376,0.00009881145,0.00007970814,0.84001243,0.009824369,0.037038602,0.0039853137,0.107818194],"study_design_scores_gemma":[0.00001628713,0.000033366094,0.00010581276,0.000003965033,0.000014054449,0.000012769476,0.000010682584,0.98964536,0.0010295983,0.008380503,0.00074241473,0.000005207328],"about_ca_topic_score_codex":0.0036478112,"about_ca_topic_score_gemma":0.0022863843,"teacher_disagreement_score":0.0036478112,"about_ca_system_score_codex":0.00048789545,"about_ca_system_score_gemma":0.00091214647,"threshold_uncertainty_score":0.00725317},"labels":[],"label_agreement":null},{"id":"W1989080137","doi":"10.1057/palgrave.jors.2602305","title":"Solving school bus routing problems through integer programming","year":2006,"lang":"en","type":"article","venue":"Journal of the Operational Research Society","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":101,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; HEC Montréal","funders":"","keywords":"Vehicle routing problem; Integer programming; Routing (electronic design automation); Mathematical optimization; Purchasing; Computer science; Integer (computer science); Linear programming; Scheduling (production processes); Operations research; Mathematics; Engineering; Computer network; Operations management","score_opus":0.05082829012856907,"score_gpt":0.35152154272370767,"score_spread":0.30069325259513857,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1989080137","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0061623207,0.00019427882,0.98770124,0.00011785449,0.00002484955,0.00004604921,0.000059774236,0.00019301802,0.005500565],"genre_scores_gemma":[0.14045927,0.000946708,0.85406303,0.00008534278,0.00006590353,0.00027309608,0.0003076238,0.00012717528,0.0036719458],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99934715,0.0002668391,0.00003255103,0.00009502808,0.00016814414,0.000090346344],"domain_scores_gemma":[0.9994155,0.0003970576,0.000068111185,0.000040135466,0.00006343856,0.000015729736],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009196334,0.001096059,0.0007960545,0.0006952927,0.0005253234,0.0014186794,0.00095159403,0.0009488265,0.0050952043],"category_scores_gemma":[0.0019754178,0.0005443497,0.00070729817,0.0011205944,0.0005000112,0.0014325414,0.00075368746,0.001202763,0.0007903727],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000032968877,0.00007501661,0.00028702748,0.00018127939,0.000027895969,0.0000634731,0.00007347331,0.90599746,0.000888486,0.02931623,0.0013113127,0.06174548],"study_design_scores_gemma":[0.000016063646,0.000031621563,0.0000558335,0.000023533328,0.000009431127,0.000029635026,0.00006918281,0.98078567,0.00052680547,0.014858069,0.003587694,0.000006531642],"about_ca_topic_score_codex":0.0030735005,"about_ca_topic_score_gemma":0.0039029578,"teacher_disagreement_score":0.0050952043,"about_ca_system_score_codex":0.00051374,"about_ca_system_score_gemma":0.0012263522,"threshold_uncertainty_score":0.0170452},"labels":[],"label_agreement":null},{"id":"W1989456306","doi":"10.1016/j.trc.2010.02.003","title":"A dynamic capacitated arc routing problem with time-dependent service costs","year":2010,"lang":"en","type":"article","venue":"Transportation Research Part C Emerging Technologies","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":79,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Université de Montréal","funders":"","keywords":"Piecewise linear function; Service (business); Computer science; Arc routing; Mathematical optimization; Interval (graph theory); Heuristic; Routing (electronic design automation); Variable (mathematics); Function (biology); Piecewise; Arc (geometry); Operations research; Mathematics; Computer network; Economics","score_opus":0.023770545874777185,"score_gpt":0.3093435218559584,"score_spread":0.28557297598118125,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1989456306","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08456077,0.001023341,0.89035976,0.0019492769,0.00032156077,0.00018651092,0.0013613449,0.0003284996,0.019909006],"genre_scores_gemma":[0.8098505,0.0012340437,0.1525688,0.0002587214,0.000256468,0.00030045974,0.0011826975,0.0002582304,0.034090023],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990206,0.00021988712,0.000032595926,0.0003404764,0.00018772048,0.00019879075],"domain_scores_gemma":[0.99902725,0.0005316003,0.00012927236,0.000059101276,0.000113386755,0.00013946797],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010128804,0.0014394913,0.0018301064,0.001108876,0.00077221874,0.002825469,0.0030345242,0.0031370183,0.007597707],"category_scores_gemma":[0.0029771917,0.0012152026,0.0010837963,0.002585461,0.001081656,0.002588888,0.0013382497,0.001711767,0.0006297502],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000114722956,0.0000927533,0.00026426156,0.00014405987,0.000043064414,0.00023392994,0.00003311649,0.9426808,0.0014686737,0.037893873,0.002785634,0.014245147],"study_design_scores_gemma":[0.000026782853,0.0000398513,0.00014596908,0.000010368516,0.000023833389,0.000094863666,0.000030021356,0.98605007,0.00038337253,0.011043851,0.0021354812,0.000015635578],"about_ca_topic_score_codex":0.0077118753,"about_ca_topic_score_gemma":0.005329447,"teacher_disagreement_score":0.0077118753,"about_ca_system_score_codex":0.002767909,"about_ca_system_score_gemma":0.002079168,"threshold_uncertainty_score":0.02541691},"labels":[],"label_agreement":null},{"id":"W1989850675","doi":"10.1002/net.3","title":"The capacitated arc routing problem with intermediate facilities","year":2001,"lang":"en","type":"article","venue":"Networks","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":83,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal; Université de Montréal","funders":"","keywords":"Arc routing; Benchmark (surveying); Arc (geometry); Mathematical optimization; Routing (electronic design automation); Computer science; Relaxation (psychology); Set (abstract data type); Integer programming; Integer (computer science); Upper and lower bounds; Mathematics; Computer network; Geography","score_opus":0.011514392974517382,"score_gpt":0.2159405566209453,"score_spread":0.2044261636464279,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1989850675","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.081032746,0.0018751107,0.8567891,0.0013065686,0.00027332918,0.00028040458,0.0011957017,0.0007505998,0.056496445],"genre_scores_gemma":[0.8166336,0.0010989439,0.16149321,0.00018702428,0.00020926718,0.0002421999,0.0014025846,0.00016936327,0.018563874],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99866724,0.00048548827,0.000052822154,0.0002588283,0.00024633727,0.00028928462],"domain_scores_gemma":[0.998701,0.0007412077,0.00014507622,0.00010952526,0.00015130307,0.00015180546],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009860566,0.0010364465,0.00072451157,0.0007913173,0.00065121584,0.0021224136,0.0026286845,0.001577718,0.0099625625],"category_scores_gemma":[0.0025242069,0.0004420657,0.0006746845,0.001560572,0.00088764296,0.0020743941,0.0012402566,0.0013805703,0.0007806211],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017777471,0.000121040976,0.0004379846,0.00036172292,0.000055845274,0.00037758035,0.00006239896,0.81954974,0.001798255,0.12766255,0.008857795,0.040537395],"study_design_scores_gemma":[0.00004675151,0.00010215355,0.00023067162,0.000060046565,0.000039213104,0.000301516,0.00010724914,0.8938522,0.0018050151,0.085973255,0.017457059,0.00002491365],"about_ca_topic_score_codex":0.003299978,"about_ca_topic_score_gemma":0.0029140136,"teacher_disagreement_score":0.0099625625,"about_ca_system_score_codex":0.001269428,"about_ca_system_score_gemma":0.0013307579,"threshold_uncertainty_score":0.033328056},"labels":[],"label_agreement":null},{"id":"W1990619572","doi":"10.1080/00207543.2014.986299","title":"An optimised target-level inventory replenishment policy for vendor-managed inventory systems","year":2014,"lang":"en","type":"article","venue":"International Journal of Production Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal; Université Laval","funders":"","keywords":"Vendor; Benchmark (surveying); Context (archaeology); Vendor-managed inventory; Operations research; Inventory theory; Perpetual inventory; Order (exchange); Inventory management; Business; Inventory control; Computer science; Economic order quantity; Operations management; Supply chain; Marketing; Economics; Supply chain management; Engineering","score_opus":0.12116858708444392,"score_gpt":0.41696922488686344,"score_spread":0.2958006378024195,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1990619572","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.27701756,0.0012108135,0.7040439,0.0006265984,0.00008703386,0.00023133676,0.00056107796,0.0010846833,0.015136903],"genre_scores_gemma":[0.9132168,0.00019180591,0.084912665,0.000058773174,0.000011628464,0.0000658568,0.00019222453,0.000059799633,0.0012903345],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993241,0.00025104894,0.000032766486,0.00010659357,0.00013351253,0.00015195017],"domain_scores_gemma":[0.9991357,0.00045850934,0.000124803,0.00008043123,0.00012943504,0.00007113441],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013240069,0.0007194706,0.00092769915,0.00056657376,0.0003595126,0.0013674365,0.001298614,0.0009997436,0.0024932506],"category_scores_gemma":[0.002800413,0.00048704984,0.00047533138,0.0008862173,0.0005448852,0.0012308253,0.00073938904,0.0008212137,0.00029122675],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010473839,0.000040557705,0.0005004852,0.00006216194,0.000014899059,0.000042096708,0.000032237935,0.981911,0.0010909179,0.0040047825,0.0007278423,0.0114682065],"study_design_scores_gemma":[0.000020584383,0.00006633822,0.0002206709,0.000009058765,0.000008092702,0.000026768632,0.000023352448,0.9962425,0.0005525029,0.0023739785,0.00044903046,0.0000071573986],"about_ca_topic_score_codex":0.003906753,"about_ca_topic_score_gemma":0.003145417,"teacher_disagreement_score":0.003906753,"about_ca_system_score_codex":0.0013162124,"about_ca_system_score_gemma":0.0015017528,"threshold_uncertainty_score":0.009549916},"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,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_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","score_opus":0.01684428022553878,"score_gpt":0.25274875513127243,"score_spread":0.23590447490573366,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1991149726","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023694824,0.007795152,0.84270996,0.0035654756,0.00080986635,0.00045633552,0.0010674751,0.00042307383,0.11947786],"genre_scores_gemma":[0.49290204,0.021029446,0.4049446,0.0011069875,0.0017569113,0.0008506504,0.002084245,0.00031980447,0.075005315],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99859864,0.00047464285,0.000093166906,0.00026307406,0.0003764267,0.00019399474],"domain_scores_gemma":[0.9990829,0.00054010755,0.0001102247,0.00006775945,0.00013743535,0.000061625935],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009350173,0.0012643334,0.0010613115,0.00073567,0.0011669458,0.0034069747,0.0012565762,0.0015757912,0.008671001],"category_scores_gemma":[0.0039020106,0.00045594893,0.0009996531,0.002246797,0.0013680044,0.003956824,0.0015542175,0.0022730809,0.002083281],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000040024355,0.00007855439,0.0003116656,0.00028399433,0.00004731483,0.00027485355,0.00021345967,0.12443794,0.0005798984,0.7858513,0.02030717,0.06757376],"study_design_scores_gemma":[0.000032091753,0.000044135966,0.00021774971,0.00006427198,0.00002817771,0.00036132295,0.00021939371,0.23012461,0.00039186503,0.70720375,0.06128485,0.000027819831],"about_ca_topic_score_codex":0.0031872024,"about_ca_topic_score_gemma":0.0022283017,"teacher_disagreement_score":0.008671001,"about_ca_system_score_codex":0.0013380272,"about_ca_system_score_gemma":0.0016896904,"threshold_uncertainty_score":0.029007435},"labels":[],"label_agreement":null},{"id":"W1992769367","doi":"10.1016/j.disopt.2014.03.001","title":"A new exact algorithm for the multi-depot vehicle routing problem under capacity and route length constraints","year":2014,"lang":"en","type":"article","venue":"Discrete Optimization","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":212,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Group for Research in Decision Analysis; Université du Québec à Montréal","funders":"","keywords":"Vehicle routing problem; Mathematical optimization; Set (abstract data type); Routing (electronic design automation); Mathematics; Upper and lower bounds; Algorithm; Branch and cut; Computer science; Linear programming","score_opus":0.02244022931863883,"score_gpt":0.25673848990929843,"score_spread":0.2342982605906596,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1992769367","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002527856,0.00012761343,0.99428976,0.00010182617,0.00010309015,0.000042855227,0.000050384104,0.0003457993,0.0024108437],"genre_scores_gemma":[0.058380738,0.00021078702,0.93588305,0.00013953527,0.00009040764,0.00020442496,0.00020658351,0.00016771592,0.0047167297],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99936396,0.00011177239,0.000029786064,0.00012880393,0.0002892607,0.000076452634],"domain_scores_gemma":[0.99915254,0.00044359828,0.000056900022,0.000110676694,0.00018586351,0.00005041596],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00096126104,0.0009078022,0.001520271,0.0009199791,0.00060531055,0.0013849133,0.0021081364,0.0016162882,0.0070868325],"category_scores_gemma":[0.0028747031,0.0007413279,0.00075433595,0.0014953193,0.00062048377,0.0020193078,0.0014721742,0.0017601573,0.0012342805],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001461773,0.00013997495,0.0002779543,0.00016045145,0.00004634264,0.00006232237,0.000066125845,0.68552136,0.0027071086,0.031027293,0.0066196523,0.27322525],"study_design_scores_gemma":[0.000050315037,0.00002278929,0.00004596514,0.000008924653,0.000006706189,0.000023864597,0.000008537439,0.98947924,0.00034356437,0.007781419,0.002221734,0.0000068538916],"about_ca_topic_score_codex":0.0056064785,"about_ca_topic_score_gemma":0.0076849083,"teacher_disagreement_score":0.0070868325,"about_ca_system_score_codex":0.0012759962,"about_ca_system_score_gemma":0.0023234487,"threshold_uncertainty_score":0.023707867},"labels":[],"label_agreement":null},{"id":"W1995340813","doi":"10.1057/jors.2013.170","title":"A column generation algorithm for tactical timber transportation planning","year":2014,"lang":"en","type":"article","venue":"Journal of the Operational Research Society","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":28,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Column generation; Operations research; Truck; Computer science; Time horizon; Scheduling (production processes); Flow network; Heuristics; Engineering; Operations management; Economics; Finance; Mathematical optimization","score_opus":0.08738142139463055,"score_gpt":0.39749695608852875,"score_spread":0.3101155346938982,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1995340813","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0049612685,0.00014396186,0.9913482,0.00009325731,0.000030339717,0.00009482658,0.00016270661,0.00044650416,0.002718888],"genre_scores_gemma":[0.14350581,0.00020306192,0.85165757,0.0001348966,0.000034833218,0.0005649158,0.0006689841,0.00021777058,0.0030122178],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996382,0.00013984197,0.000015737758,0.00005492577,0.000090172325,0.00006110396],"domain_scores_gemma":[0.9987783,0.00089794706,0.000075887874,0.000054162567,0.00014519876,0.000048547212],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007968415,0.0009855701,0.0008224992,0.0008333057,0.00060117664,0.0009002078,0.0011659508,0.0009403707,0.006897708],"category_scores_gemma":[0.0018538003,0.0006640146,0.0007649193,0.0015288739,0.000605389,0.0008270075,0.0009443194,0.0013594552,0.0010485417],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000048924638,0.00007248308,0.00028092926,0.000076590906,0.000022754564,0.000055524964,0.00004656194,0.9142559,0.0007060022,0.010123562,0.0028598437,0.07145092],"study_design_scores_gemma":[0.000013366127,0.000017634575,0.000026890337,0.0000048524043,0.000003514341,0.000008007109,0.000006943606,0.99581844,0.00014935122,0.0032802543,0.00066712266,0.0000037024265],"about_ca_topic_score_codex":0.013395831,"about_ca_topic_score_gemma":0.015328448,"teacher_disagreement_score":0.013395831,"about_ca_system_score_codex":0.0009655926,"about_ca_system_score_gemma":0.0014766699,"threshold_uncertainty_score":0.026635766},"labels":[],"label_agreement":null},{"id":"W1995945236","doi":"10.1007/s10732-012-9194-6","title":"Experimental analysis of heuristics for the bottleneck traveling salesman problem","year":2012,"lang":"en","type":"article","venue":"Journal of Heuristics","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":18,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick; Simon Fraser University","funders":"","keywords":"Travelling salesman problem; Heuristics; Bottleneck; Mathematical optimization; Heuristic; Computer science; Lin–Kernighan heuristic; Bottleneck traveling salesman problem; Graph; Mathematics; Algorithm; Theoretical computer science","score_opus":0.028921182188774822,"score_gpt":0.30402834232860165,"score_spread":0.27510716013982683,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1995945236","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9459353,0.0019478892,0.03614484,0.0004589701,0.00027205763,0.00047813996,0.0014918614,0.00065998983,0.012610983],"genre_scores_gemma":[0.960378,0.00047980392,0.034699872,0.000078660596,0.000059917427,0.00026212202,0.00193407,0.00022664484,0.0018810458],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9933883,0.0039779698,0.00045908135,0.0006017196,0.0009861817,0.00058662886],"domain_scores_gemma":[0.8691915,0.11176915,0.0034577674,0.0073487917,0.007384013,0.0008488056],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0075546377,0.0014018407,0.0010570689,0.0018745414,0.0011557577,0.0013631858,0.0020941813,0.001215139,0.0066607334],"category_scores_gemma":[0.04198158,0.00072894426,0.0007630545,0.002254419,0.0013427474,0.002241332,0.0007691596,0.001627952,0.0006464038],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.011296825,0.017610788,0.0068305167,0.0020762144,0.0005601058,0.00013894231,0.00037032136,0.79410213,0.015537832,0.016049014,0.009652739,0.12577455],"study_design_scores_gemma":[0.0010026069,0.0046068444,0.0037163068,0.00007275769,0.00024272484,0.00009042432,0.00034502422,0.9630487,0.016558517,0.007825445,0.0024259388,0.00006462667],"about_ca_topic_score_codex":0.0075005856,"about_ca_topic_score_gemma":0.0071709687,"teacher_disagreement_score":0.0075546377,"about_ca_system_score_codex":0.0036153276,"about_ca_system_score_gemma":0.0026762392,"threshold_uncertainty_score":0.03995323},"labels":[],"label_agreement":null},{"id":"W1997194016","doi":"10.1016/j.cor.2005.05.024","title":"Arcs-states models for the vehicle routing problem with time windows and related problems","year":2005,"lang":"en","type":"article","venue":"Computers & Operations Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Column generation; Benchmark (surveying); Vehicle routing problem; Upper and lower bounds; Branch and bound; Relaxation (psychology); Routing (electronic design automation); Arc routing; Linear programming relaxation; Computer science; Arc (geometry); Mathematical optimization; Time complexity; Mathematics; Algorithm; Linear programming","score_opus":0.039670149775145915,"score_gpt":0.30868088785053627,"score_spread":0.26901073807539033,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1997194016","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.035919227,0.002129498,0.94060206,0.00163245,0.00021268857,0.000087039836,0.0009565192,0.00024204332,0.018218469],"genre_scores_gemma":[0.82202774,0.005012632,0.09438891,0.00038190102,0.0004857376,0.0005153321,0.0015944862,0.00032085337,0.07527239],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990878,0.000322273,0.000048878534,0.00020046008,0.00017445846,0.00016618501],"domain_scores_gemma":[0.9968526,0.002262392,0.0003949488,0.00011491221,0.00020440044,0.0001707416],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017974721,0.0017438642,0.0016624501,0.0011473513,0.0007020503,0.0031758496,0.0032967818,0.0024055247,0.008009383],"category_scores_gemma":[0.005580219,0.001362542,0.0020146077,0.0017237697,0.0018124465,0.004559125,0.0015269142,0.003748687,0.0008758858],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006179406,0.00004817715,0.00023086581,0.000064187305,0.000033966044,0.000051273553,0.00007129668,0.7814498,0.00029501616,0.21261626,0.0013657421,0.003711615],"study_design_scores_gemma":[0.0000133156145,0.000011822411,0.00008615909,0.000011301322,0.000017445202,0.000009902631,0.000020630296,0.89532274,0.00008801894,0.103478804,0.000928668,0.000011165546],"about_ca_topic_score_codex":0.013923568,"about_ca_topic_score_gemma":0.012943767,"teacher_disagreement_score":0.013923568,"about_ca_system_score_codex":0.0025042398,"about_ca_system_score_gemma":0.0019965265,"threshold_uncertainty_score":0.027685046},"labels":[],"label_agreement":null},{"id":"W1998193115","doi":"10.1007/s10479-010-0715-0","title":"Using local search to speed up filtering algorithms for some NP-hard constraints","year":2010,"lang":"en","type":"article","venue":"Annals of Operations Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Theory of computation; Local search (optimization); Algorithm; Computer science; Local consistency; Filter (signal processing); Consistency (knowledge bases); Computational complexity theory; Graph; Search tree; Search algorithm; Substructure; Constraint (computer-aided design); Guided Local Search; Mathematical optimization; Mathematics; Constraint satisfaction problem; Theoretical computer science; Artificial intelligence","score_opus":0.40089166799307197,"score_gpt":0.5075783819447462,"score_spread":0.10668671395167428,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1998193115","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013016383,0.00035517104,0.98303115,0.00027654285,0.0001160257,0.000078401725,0.000058370606,0.0008400739,0.002227874],"genre_scores_gemma":[0.23769702,0.00032022368,0.75383025,0.00047711597,0.0002045971,0.00047978133,0.00038890986,0.00045909747,0.0061429436],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99882287,0.00040273962,0.000066347995,0.00023377492,0.00029679865,0.00017748703],"domain_scores_gemma":[0.9866812,0.011651039,0.000361948,0.00049045176,0.00064987375,0.0001654682],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0039036514,0.0017916859,0.0025537421,0.0018821231,0.001059999,0.0017900901,0.0020231332,0.0031001994,0.008754753],"category_scores_gemma":[0.015334178,0.0014506979,0.0015309724,0.0017837536,0.0012533977,0.003718213,0.001500597,0.0031960253,0.0012458699],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00028192071,0.00027914383,0.0006054217,0.0002592654,0.00009278052,0.00012836195,0.000114650626,0.8211501,0.0019861874,0.017084291,0.0076628844,0.15035482],"study_design_scores_gemma":[0.000034527333,0.000028852819,0.00003897949,0.000008396808,0.000011283194,0.0000118642765,0.000008383487,0.99411184,0.00032507748,0.0051065134,0.0003102003,0.0000040620894],"about_ca_topic_score_codex":0.0085495515,"about_ca_topic_score_gemma":0.010717054,"teacher_disagreement_score":0.008754753,"about_ca_system_score_codex":0.0012664299,"about_ca_system_score_gemma":0.0019980967,"threshold_uncertainty_score":0.029287577},"labels":[],"label_agreement":null},{"id":"W1998243034","doi":"10.1016/j.dam.2003.09.012","title":"Variable neighborhood search for the maximum clique","year":2004,"lang":"en","type":"article","venue":"Discrete Applied Mathematics","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":83,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Group for Research in Decision Analysis; HEC Montréal","funders":"","keywords":"Mathematics; Clique; Heuristic; Variable neighborhood search; Clique problem; Vertex (graph theory); Greedy algorithm; Mathematical optimization; Simplicity; Combinatorics; Variable (mathematics); Algorithm; Metaheuristic; Graph; Chordal graph","score_opus":0.019684030115138522,"score_gpt":0.26877238570278233,"score_spread":0.2490883555876438,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1998243034","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0319482,0.00087229226,0.9558251,0.00093581685,0.000091847345,0.00007830068,0.00017280044,0.00015439795,0.00992109],"genre_scores_gemma":[0.5493489,0.00091873494,0.4299784,0.0003074392,0.00022988698,0.0004986591,0.00066159514,0.00029399962,0.017762456],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993006,0.0004329047,0.00001190132,0.0001107178,0.0000863582,0.000057453883],"domain_scores_gemma":[0.9967614,0.0027009526,0.000121398356,0.0001266665,0.0001787477,0.00011082197],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001559339,0.00055922725,0.0016180228,0.0011443229,0.0008814512,0.0010571047,0.0017821608,0.0014055428,0.005633037],"category_scores_gemma":[0.008554674,0.00068170205,0.0007455539,0.0014020656,0.0012020315,0.0021569743,0.0015808986,0.0014809424,0.00046121835],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020520097,0.000099504454,0.0005608365,0.0001828752,0.00007729354,0.000049544342,0.00013139338,0.7015879,0.00064075453,0.23852973,0.008691085,0.04924399],"study_design_scores_gemma":[0.000028492417,0.000014314295,0.00008134488,0.000011468748,0.000006614657,0.000008704983,0.0000139603735,0.9223863,0.00007994944,0.0763284,0.0010361517,0.0000043241407],"about_ca_topic_score_codex":0.004571136,"about_ca_topic_score_gemma":0.005538494,"teacher_disagreement_score":0.005633037,"about_ca_system_score_codex":0.0012433414,"about_ca_system_score_gemma":0.0011448,"threshold_uncertainty_score":0.018844366},"labels":[],"label_agreement":null},{"id":"W1999614211","doi":"10.1057/palgrave.jors.2601590","title":"Exact solution of the generalized routing problem through graph transformations","year":2003,"lang":"en","type":"article","venue":"Journal of the Operational Research Society","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":29,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"","keywords":"Travelling salesman problem; Graph traversal; Vehicle routing problem; Mathematical optimization; Computer science; Graph; Tree traversal; Scheduling (production processes); Routing (electronic design automation); Mathematics; Combinatorics; Theoretical computer science; Algorithm","score_opus":0.06518996431258268,"score_gpt":0.35665408507138946,"score_spread":0.2914641207588068,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1999614211","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.044322144,0.0002476201,0.9429332,0.0002807496,0.00005504613,0.000050255814,0.000092308794,0.00049875013,0.01151998],"genre_scores_gemma":[0.5762164,0.0005155811,0.41353214,0.00011730242,0.00005846672,0.00015356552,0.00030056294,0.00027358346,0.008832385],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996909,0.00010114535,0.000008322208,0.00006710351,0.00008553711,0.00004692092],"domain_scores_gemma":[0.99973685,0.0001324936,0.000026461346,0.00006755763,0.000026360998,0.0000103209895],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036138398,0.0006988998,0.0007405144,0.00037310107,0.00033212613,0.00072362385,0.0007053256,0.0007254349,0.004129132],"category_scores_gemma":[0.0016234183,0.00030557957,0.0005780058,0.0006636704,0.00085603073,0.0014689127,0.0011268895,0.0010331539,0.0005257219],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000052629402,0.00004027178,0.00016577539,0.00006957384,0.000015232344,0.000098829216,0.00006990559,0.8443411,0.0023800812,0.08112227,0.0023000264,0.069344394],"study_design_scores_gemma":[0.00002394017,0.000020352862,0.00006956341,0.000005424964,0.0000048199386,0.000035765548,0.000030510111,0.8983857,0.00065891474,0.098791696,0.0019686865,0.0000046195796],"about_ca_topic_score_codex":0.0034145974,"about_ca_topic_score_gemma":0.0036673518,"teacher_disagreement_score":0.004129132,"about_ca_system_score_codex":0.0005866253,"about_ca_system_score_gemma":0.0009912966,"threshold_uncertainty_score":0.013813376},"labels":[],"label_agreement":null},{"id":"W1999715008","doi":"10.1016/j.cor.2013.01.022","title":"An adaptive evolutionary approach for real-time vehicle routing and dispatching","year":2013,"lang":"en","type":"article","venue":"Computers & Operations Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":49,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Université Laval","funders":"","keywords":"Robustness (evolution); Computer science; Mathematical optimization; Vehicle routing problem; Evolutionary algorithm; Genetic algorithm; Convergence (economics); Adaptation (eye); Routing (electronic design automation); Artificial intelligence; Mathematics; Machine learning","score_opus":0.046476274989404244,"score_gpt":0.3361941171643727,"score_spread":0.28971784217496843,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1999715008","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008154862,0.00015459329,0.987703,0.00011561179,0.00009683569,0.000025620637,0.000010147669,0.000058479287,0.0036808583],"genre_scores_gemma":[0.44935137,0.0004739152,0.5372753,0.00020055869,0.00018116999,0.00024458053,0.00005340273,0.00010382624,0.012115954],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99976116,0.000081243255,0.000010871398,0.000031359818,0.00009312676,0.00002232358],"domain_scores_gemma":[0.99963,0.00020811117,0.00002845203,0.000021709084,0.00009562916,0.000016099622],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00066133024,0.000571028,0.0005781281,0.00064310886,0.0003465941,0.00060415285,0.0013425485,0.0010984291,0.0027261283],"category_scores_gemma":[0.0019963612,0.00040725328,0.0007010231,0.0007725643,0.0005569997,0.00069399754,0.0006581493,0.000836415,0.00025151437],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019387759,0.00003602127,0.00015894814,0.000029328909,0.00003196333,0.000048150512,0.000031872136,0.94004804,0.0014680435,0.01425986,0.0004217227,0.043446664],"study_design_scores_gemma":[0.0000038315366,0.000009542038,0.00003724305,0.0000020030368,0.0000034813947,0.000008375711,0.0000023948578,0.9979552,0.00007683417,0.0016118202,0.00028729087,0.0000020680038],"about_ca_topic_score_codex":0.003113546,"about_ca_topic_score_gemma":0.0028776845,"teacher_disagreement_score":0.003113546,"about_ca_system_score_codex":0.000464646,"about_ca_system_score_gemma":0.0004609204,"threshold_uncertainty_score":0.009119868},"labels":[],"label_agreement":null},{"id":"W1999987607","doi":"10.1007/s10479-014-1527-4","title":"Flow-based integer linear programs to solve the weekly log-truck scheduling problem","year":2014,"lang":"en","type":"article","venue":"Annals of Operations Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":27,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Truck; Theory of computation; Integer programming; Computer science; Scheduling (production processes); Linear programming; Integer (computer science); Mathematical optimization; Operations research; Mathematics; Algorithm; Engineering","score_opus":0.16754097586295918,"score_gpt":0.4287448416465164,"score_spread":0.2612038657835572,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1999987607","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02143287,0.00031684968,0.9695052,0.0006047474,0.00014895324,0.00017566989,0.000297335,0.00033789838,0.007180368],"genre_scores_gemma":[0.5002563,0.000728399,0.4810631,0.00033276627,0.00021938678,0.0010731403,0.0008983858,0.00044559166,0.014982961],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99928313,0.00039525534,0.000024169874,0.00007830068,0.00011428506,0.00010490358],"domain_scores_gemma":[0.9975339,0.0020376265,0.000117381926,0.000051391697,0.00017770162,0.00008201639],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002305441,0.0013858937,0.0013370037,0.0013196196,0.0005713985,0.0015156759,0.0016535178,0.001920801,0.0075533255],"category_scores_gemma":[0.005896961,0.0011818621,0.0011538629,0.001641107,0.00068401423,0.0019886484,0.0010494741,0.0026575425,0.00056199834],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000046912497,0.00012694456,0.00016202132,0.00003820031,0.000016022346,0.000017492546,0.000019534753,0.9794794,0.00017482415,0.008178113,0.0012793147,0.010461252],"study_design_scores_gemma":[0.000009078443,0.000010769511,0.000017694527,0.0000029278572,0.0000023003404,0.0000016771105,0.000005305231,0.9969015,0.000033161046,0.0028186054,0.00019529123,0.0000016566502],"about_ca_topic_score_codex":0.011482379,"about_ca_topic_score_gemma":0.011693539,"teacher_disagreement_score":0.011482379,"about_ca_system_score_codex":0.001473847,"about_ca_system_score_gemma":0.0025532977,"threshold_uncertainty_score":0.025268435},"labels":[],"label_agreement":null},{"id":"W2000702178","doi":"10.1016/j.cor.2007.01.007","title":"The single vehicle routing problem with deliveries and selective pickups","year":2007,"lang":"en","type":"article","venue":"Computers & Operations Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":115,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Norges Forskningsråd","keywords":"Heuristics; Pickup; Mathematical optimization; Computer science; Vehicle routing problem; Tabu search; Vertex (graph theory); Routing (electronic design automation); Integer programming; Mathematics; Graph; Theoretical computer science; Artificial intelligence; Computer network","score_opus":0.03535149278004031,"score_gpt":0.3189383663021784,"score_spread":0.2835868735221381,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2000702178","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20406887,0.0026373542,0.7736144,0.0019552964,0.0004106474,0.00023636973,0.0010005023,0.00026790512,0.01580865],"genre_scores_gemma":[0.825076,0.0021150534,0.13968995,0.00023147327,0.00046529266,0.00024704885,0.0006768051,0.00020447286,0.031293835],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99902046,0.00032606168,0.000043476546,0.00025944735,0.0001485416,0.00020210752],"domain_scores_gemma":[0.99801195,0.0013242537,0.00026081898,0.00012951677,0.000113383605,0.00016012051],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014581312,0.0013039986,0.002543735,0.0009422276,0.00089886686,0.0018486662,0.0028771071,0.002154846,0.0059298743],"category_scores_gemma":[0.004511182,0.0014071388,0.001618458,0.0020915263,0.0011711183,0.0033777198,0.0012549631,0.0016204764,0.00060057984],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00040242096,0.00013901475,0.0005939016,0.00031681385,0.00011107516,0.00034184568,0.000057725032,0.90920144,0.0012484016,0.05189371,0.004073551,0.031620078],"study_design_scores_gemma":[0.00006949949,0.00014509453,0.00044426453,0.000020847961,0.00006342925,0.00021890135,0.000076194185,0.92716974,0.0009375241,0.06856068,0.0022667074,0.000027155182],"about_ca_topic_score_codex":0.0028387818,"about_ca_topic_score_gemma":0.0026900545,"teacher_disagreement_score":0.0059298743,"about_ca_system_score_codex":0.0011351672,"about_ca_system_score_gemma":0.0014083111,"threshold_uncertainty_score":0.019837439},"labels":[],"label_agreement":null},{"id":"W2001465411","doi":"10.1016/j.cor.2004.07.006","title":"A survey of models and algorithms for winter road maintenance. Part I: system design for spreading and plowing","year":2004,"lang":"en","type":"article","venue":"Computers & Operations Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":104,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Group for Research in Decision Analysis; Polytechnique Montréal","funders":"","keywords":"Snow removal; Truck; Snow; Plough; Sizing; Computer science; Routing (electronic design automation); Transport engineering; Operations research; Algorithm; Meteorology; Engineering","score_opus":0.19652052242196252,"score_gpt":0.38302933976129844,"score_spread":0.18650881733933591,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2001465411","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0037704206,0.023143582,0.96589714,0.0003890954,0.00013001618,0.00003894984,0.00030400616,0.00041080208,0.0059160492],"genre_scores_gemma":[0.20313694,0.11069059,0.66615576,0.00036416418,0.00076423824,0.0004940876,0.0017096678,0.00047053368,0.016214103],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996909,0.0000788898,0.000027015636,0.00007498241,0.00009966226,0.000028546337],"domain_scores_gemma":[0.99959916,0.00021979865,0.000038365622,0.000052533185,0.00007500428,0.000015181459],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000623241,0.0014263367,0.0014120269,0.0008432106,0.00041354037,0.0013544519,0.0019787145,0.0011842467,0.0038394984],"category_scores_gemma":[0.0014759459,0.0008761587,0.0010216993,0.0020722975,0.00045902203,0.001802358,0.0005092601,0.0011412966,0.0017420356],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004551668,0.00009015975,0.0006751723,0.0007937157,0.00007379567,0.000032585944,0.00006397333,0.73386806,0.0014528086,0.04295915,0.01189778,0.20804732],"study_design_scores_gemma":[0.000018550825,0.00006138782,0.00032776894,0.00013596915,0.000045332235,0.00009572658,0.000028364402,0.904809,0.00085050537,0.056712065,0.036894813,0.000020585361],"about_ca_topic_score_codex":0.0040940186,"about_ca_topic_score_gemma":0.0048433063,"teacher_disagreement_score":0.0040940186,"about_ca_system_score_codex":0.0011582981,"about_ca_system_score_gemma":0.0011137644,"threshold_uncertainty_score":0.012844384},"labels":[],"label_agreement":null},{"id":"W2002282904","doi":"10.1023/b:jmma.0000038618.37710.f8","title":"Tabu Search Heuristics for the Arc Routing Problem with Intermediate Facilities under Capacity and Length Restrictions","year":2004,"lang":"en","type":"article","venue":"Journal of Mathematical Modelling and Algorithms","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":48,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"Ministero dell'Università e della Ricerca","keywords":"Arc routing; Tabu search; Heuristics; Benchmark (surveying); Mathematical optimization; Routing (electronic design automation); Constructive; Set (abstract data type); Computer science; Upper and lower bounds; Arc (geometry); Vehicle routing problem; Mathematics; Algorithm; Process (computing)","score_opus":0.05607874453509868,"score_gpt":0.2693621043498414,"score_spread":0.21328335981474275,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2002282904","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10761439,0.0035641815,0.85842985,0.0011365976,0.00032870745,0.00067957555,0.0014692879,0.0029001513,0.023877311],"genre_scores_gemma":[0.38830563,0.0013058903,0.5991306,0.0004307363,0.0001458421,0.0008529165,0.0013253562,0.0006675563,0.007835451],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99838364,0.0010016724,0.000049771024,0.00012451773,0.00015582079,0.00028460397],"domain_scores_gemma":[0.9926806,0.006186879,0.00033832295,0.00028030042,0.0003265697,0.00018729531],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0031721941,0.0012319783,0.0020588269,0.0027262308,0.001357629,0.0021730359,0.0029839254,0.0031145287,0.010319149],"category_scores_gemma":[0.007970089,0.0015285312,0.0014597977,0.005027626,0.0016452953,0.0028533116,0.0011249064,0.0021989127,0.0012080071],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022240987,0.00013447631,0.00026936017,0.00012264239,0.000055717814,0.000048426486,0.00006980779,0.94419074,0.00025038526,0.011850541,0.0050915447,0.037694022],"study_design_scores_gemma":[0.00012537639,0.00007611936,0.00012677738,0.000029759543,0.000029872715,0.000019880874,0.000045780438,0.98382896,0.00020614768,0.014265237,0.0012323419,0.000013710927],"about_ca_topic_score_codex":0.014942256,"about_ca_topic_score_gemma":0.016251963,"teacher_disagreement_score":0.014942256,"about_ca_system_score_codex":0.0024773062,"about_ca_system_score_gemma":0.0030814868,"threshold_uncertainty_score":0.034520984},"labels":[],"label_agreement":null},{"id":"W2002507421","doi":"10.1287/inte.1110.0611","title":"A Strategic Empty Container Logistics Optimization in a Major Shipping Company","year":2012,"lang":"en","type":"article","venue":"INFORMS Journal on Applied Analytics","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":60,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Booth University College","funders":"","keywords":"Operations research; Container (type theory); Stock (firearms); Safety stock; Business; Profit (economics); Service (business); Computer science; Operations management; Transport engineering; Marketing; Supply chain; Engineering; Economics","score_opus":0.04407673720646854,"score_gpt":0.28347552139311916,"score_spread":0.23939878418665061,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2002507421","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.595367,0.00040771204,0.36908048,0.0015452484,0.00014164615,0.0003429269,0.00064743456,0.0016605105,0.03080703],"genre_scores_gemma":[0.9051588,0.00012917788,0.08434646,0.00012274238,0.000021791117,0.00012553179,0.00038776672,0.00005756952,0.009650165],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99960345,0.00014644841,0.000013083037,0.00011453733,0.000044419987,0.000078054356],"domain_scores_gemma":[0.99976104,0.000073512456,0.000024080315,0.000017636969,0.00005634934,0.00006741979],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00069180556,0.00081801275,0.0005881035,0.00045448227,0.0010158495,0.0014048831,0.0005201656,0.000768851,0.004960018],"category_scores_gemma":[0.0006523811,0.00039235118,0.00063285785,0.00055830006,0.0004935323,0.00084122596,0.0008646544,0.00057659304,0.00033907496],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00036040603,0.00018426833,0.002288941,0.00004658553,0.000038981023,0.00023862834,0.000072243274,0.9662317,0.0020761741,0.0053911246,0.0022737938,0.020797191],"study_design_scores_gemma":[0.00003614454,0.00011691438,0.00040086007,0.0000036793354,0.000014257587,0.00001619612,0.00005941381,0.99645954,0.00067188917,0.0009223243,0.0012905896,0.000008162496],"about_ca_topic_score_codex":0.027790578,"about_ca_topic_score_gemma":0.019715367,"teacher_disagreement_score":0.027790578,"about_ca_system_score_codex":0.0015692097,"about_ca_system_score_gemma":0.0028849533,"threshold_uncertainty_score":0.05525762},"labels":[],"label_agreement":null},{"id":"W2002602643","doi":"10.1016/j.tre.2014.09.012","title":"A matheuristic for the liner shipping network design problem","year":2014,"lang":"en","type":"article","venue":"Transportation Research Part E Logistics and Transportation Review","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":75,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Group for Research in Decision Analysis","funders":"","keywords":"Network planning and design; Transport engineering; Flow network; Computer science; Operations research; Business; Marine engineering; Engineering; Computer network; Mathematical optimization; Mathematics","score_opus":0.14312901136925074,"score_gpt":0.37189955162197136,"score_spread":0.22877054025272062,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2002602643","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0019600096,0.0010583525,0.9760534,0.0012366783,0.00019504827,0.00008197234,0.00012922789,0.000044886605,0.019240316],"genre_scores_gemma":[0.16853724,0.009583865,0.7724041,0.0016634936,0.0016226546,0.0010625018,0.00062071177,0.00026383973,0.044241693],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991918,0.00033104006,0.000037225353,0.0001793901,0.00022501267,0.000035422174],"domain_scores_gemma":[0.9988242,0.0008019399,0.00009454655,0.000075420205,0.00016295722,0.000040883668],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018305978,0.0019026397,0.0009733254,0.0012338944,0.00067930215,0.0015752563,0.0019479981,0.002313368,0.007719169],"category_scores_gemma":[0.0039866166,0.00070272,0.0016909514,0.001370999,0.0014750797,0.002800208,0.0016873507,0.0030984844,0.0014848631],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000031406642,0.00008795386,0.00030098594,0.0005544379,0.00006696424,0.00012622667,0.00009242571,0.48889318,0.0013884539,0.4345091,0.010531689,0.06341723],"study_design_scores_gemma":[0.000020148338,0.000069914284,0.0001483876,0.00009203278,0.000028480557,0.00011834023,0.00003098188,0.68116254,0.00033982037,0.2972851,0.020683337,0.000020953032],"about_ca_topic_score_codex":0.002015235,"about_ca_topic_score_gemma":0.0019011237,"teacher_disagreement_score":0.007719169,"about_ca_system_score_codex":0.0015627198,"about_ca_system_score_gemma":0.0013030147,"threshold_uncertainty_score":0.025823176},"labels":[],"label_agreement":null},{"id":"W2003793882","doi":"10.1287/trsc.1090.0276","title":"<b>Guest Editorial</b>—Focused Issue on Freight Transportation","year":2009,"lang":"en","type":"article","venue":"Transportation Science","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; HEC Montréal; Université du Québec à Montréal","funders":"","keywords":"Traffic management; Transport engineering; Business; Transportation planning; Humanitarian Logistics; Transportation industry; Engineering; Industrial organization","score_opus":0.013395566103160445,"score_gpt":0.272174827976024,"score_spread":0.25877926187286354,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2003793882","genre_codex":"editorial","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00005972926,0.004652813,0.0001729321,0.021675536,0.968525,0.000019642313,0.00007280626,0.000048125443,0.004773374],"genre_scores_gemma":[0.0006411891,0.0054338393,0.0001026775,0.013402646,0.9659137,0.00001915273,0.000072044015,0.000049343445,0.014365474],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.998466,0.00023020156,0.00018059683,0.0003141424,0.00065708667,0.00015197427],"domain_scores_gemma":[0.99180883,0.0026993125,0.0003622331,0.00023308495,0.0037732043,0.0011233741],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020922837,0.0026939514,0.0023909041,0.002792379,0.0020698218,0.0061035845,0.0025713819,0.008854898,0.03125896],"category_scores_gemma":[0.00672821,0.00067451893,0.0019428402,0.0015949042,0.0018937439,0.00352764,0.0008754502,0.011026833,0.02434399],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000053333366,0.00001343374,0.000035152803,0.00016391385,0.000009829963,0.00006722753,0.0000037647314,0.00002629416,0.00009248177,0.00032025785,0.9929183,0.006295904],"study_design_scores_gemma":[0.000030134881,0.000032669952,0.00041999746,0.00023906578,0.000025961222,0.00022121634,0.000022032247,0.00020425186,0.00017741918,0.00070767116,0.9979042,0.000015422791],"about_ca_topic_score_codex":0.002247768,"about_ca_topic_score_gemma":0.0032770701,"teacher_disagreement_score":0.03125896,"about_ca_system_score_codex":0.0020980053,"about_ca_system_score_gemma":0.0013604416,"threshold_uncertainty_score":0.1045717},"labels":[],"label_agreement":null},{"id":"W2004985180","doi":"10.1287/inte.30.2.41.11673","title":"Air Transat Uses ALTITUDE to Manage Its Aircraft Routing, Crew Pairing, and Work Assignment","year":2000,"lang":"en","type":"article","venue":"INFORMS Journal on Applied Analytics","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Polytechnique Montréal; Group for Research in Decision Analysis; HEC Montréal","funders":"","keywords":"Charter; Scheduling (production processes); Operations research; Crew scheduling; Crew; Flexibility (engineering); Navy; Work (physics); Computer science; Operations management; Engineering; Aeronautics","score_opus":0.01623516938582708,"score_gpt":0.24733184391631788,"score_spread":0.2310966745304908,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2004985180","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20281476,0.0005303419,0.68157554,0.0003571494,0.0001389817,0.0006255896,0.0038529437,0.026909845,0.08319486],"genre_scores_gemma":[0.6259959,0.00043127663,0.3279491,0.00010950375,0.00007788994,0.00022569636,0.0053020217,0.00091445615,0.038994156],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9996842,0.000045318116,0.000014764645,0.00007575386,0.00012596184,0.0000539643],"domain_scores_gemma":[0.99960977,0.0000718894,0.00006950027,0.00007034406,0.00012575442,0.000052782783],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040979162,0.00068782066,0.00037689737,0.0006204699,0.0006492457,0.0009190141,0.0005782138,0.0002490337,0.0060899546],"category_scores_gemma":[0.0006661783,0.00028027565,0.0003571366,0.00067998905,0.00023017947,0.00065808534,0.0007319368,0.00033250664,0.002146361],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00059274846,0.0003125302,0.022191606,0.0001735855,0.000112371046,0.00016935641,0.00049285364,0.29289925,0.019621905,0.010392756,0.043169394,0.6098716],"study_design_scores_gemma":[0.00012389803,0.00035618801,0.013724031,0.000019812253,0.00006217152,0.0001995716,0.0002530245,0.85673505,0.01664905,0.004226597,0.10757702,0.000073661446],"about_ca_topic_score_codex":0.04563597,"about_ca_topic_score_gemma":0.06377198,"teacher_disagreement_score":0.04563597,"about_ca_system_score_codex":0.0008692469,"about_ca_system_score_gemma":0.0016149277,"threshold_uncertainty_score":0.09074068},"labels":[],"label_agreement":null},{"id":"W2005518909","doi":"10.4271/2013-01-0337","title":"An Application of Ant Colony Optimization to Energy Efficient Routing for Electric Vehicles","year":2013,"lang":"en","type":"article","venue":"SAE technical papers on CD-ROM/SAE technical paper series","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Chrysler (Canada)","funders":"","keywords":"Ant colony optimization algorithms; ANT; Computer science; Routing (electronic design automation); Ant colony; Energy (signal processing); Mathematical optimization; Computer network; Artificial intelligence; Mathematics","score_opus":0.008300266221701944,"score_gpt":0.2499519173500796,"score_spread":0.24165165112837766,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2005518909","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.030917592,0.0004783949,0.9525311,0.00036994583,0.00012974053,0.00006581461,0.00002388248,0.00031385457,0.015169637],"genre_scores_gemma":[0.600646,0.0007715697,0.3862221,0.00015640663,0.000050535866,0.00012504766,0.000047357662,0.000110083296,0.011870875],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99983704,0.00006209087,0.0000055102055,0.000022459191,0.000060892016,0.00001193881],"domain_scores_gemma":[0.9998178,0.00009471528,0.000015345428,0.000014410744,0.000049303384,0.000008431035],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026021796,0.00041237197,0.0003516199,0.00028135566,0.0002694337,0.00040377298,0.00044873395,0.00047117574,0.0012492489],"category_scores_gemma":[0.00088340946,0.00023229602,0.00026389872,0.00043095293,0.00029271375,0.00029055338,0.00037992926,0.0003865794,0.00021929627],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019750376,0.0000263106,0.00019724711,0.000024668225,0.000013944129,0.00007773569,0.000026688867,0.9447339,0.0033387267,0.006849355,0.0011239011,0.04356768],"study_design_scores_gemma":[0.0000030565034,0.00001058271,0.000030582716,0.0000014769928,0.0000019884342,0.000010631377,0.0000030980173,0.9976847,0.00033681715,0.001107863,0.0008076364,0.0000015609215],"about_ca_topic_score_codex":0.0038448845,"about_ca_topic_score_gemma":0.0034963558,"teacher_disagreement_score":0.0038448845,"about_ca_system_score_codex":0.0003184534,"about_ca_system_score_gemma":0.0004512973,"threshold_uncertainty_score":0.007645011},"labels":[],"label_agreement":null},{"id":"W2007402232","doi":"10.1016/j.cam.2008.10.055","title":"A capacity scaling heuristic for the multicommodity capacitated network design problem","year":2008,"lang":"en","type":"article","venue":"Journal of Computational and Applied Mathematics","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":46,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Ministry of Education, Culture, Sports, Science and Technology; Mitchell Center for Neurodegenerative Diseases, University of Texas Medical Branch; Université du Québec à Montréal","keywords":"Heuristic; Mathematics; Mathematical optimization; Column generation; Network planning and design; Scaling; Flow network; Multi-commodity flow problem; Algorithm; Computer science","score_opus":0.05912931673790746,"score_gpt":0.25239884197222734,"score_spread":0.1932695252343199,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2007402232","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07887493,0.001282775,0.89813167,0.00075848354,0.00030764512,0.00031233698,0.00019078287,0.00078807026,0.019353379],"genre_scores_gemma":[0.7260433,0.0004291851,0.2692177,0.00022702843,0.000109286535,0.0002481292,0.00016719413,0.00016834032,0.0033898687],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995239,0.0001852946,0.000017215996,0.00006401882,0.0001191022,0.00009050282],"domain_scores_gemma":[0.9989083,0.0006316615,0.000086677785,0.00008088177,0.0001922871,0.00010019325],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001161464,0.0011110917,0.0011620584,0.0011203501,0.0007663768,0.00088438264,0.0014274884,0.0011827558,0.0051225577],"category_scores_gemma":[0.0029315185,0.0006052179,0.00064910465,0.0012597374,0.0005207133,0.0011736115,0.0010421919,0.000954192,0.00035911935],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000687872,0.00008305791,0.00024856604,0.00008239646,0.000021229807,0.000056191544,0.00003748797,0.93567646,0.0011525721,0.008673056,0.0021351646,0.05176498],"study_design_scores_gemma":[0.000012719923,0.000024433817,0.000038899027,0.000008458374,0.0000074533077,0.000010845119,0.000010783426,0.9963102,0.00025264773,0.0028063916,0.0005128667,0.0000041975545],"about_ca_topic_score_codex":0.0053617195,"about_ca_topic_score_gemma":0.0058549354,"teacher_disagreement_score":0.0053617195,"about_ca_system_score_codex":0.001279132,"about_ca_system_score_gemma":0.0016186422,"threshold_uncertainty_score":0.017136693},"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,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_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","score_opus":0.06723250682678367,"score_gpt":0.36649379506851854,"score_spread":0.29926128824173487,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2008806816","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0026951197,0.0022415945,0.9915507,0.00046644115,0.00010447123,0.000016352198,0.000023122793,0.0001309557,0.002771316],"genre_scores_gemma":[0.24047258,0.00939317,0.7371466,0.00029686614,0.0006543234,0.00023362234,0.0001727692,0.0003271434,0.011303015],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99950266,0.0001733819,0.000024047427,0.00010162652,0.00015116509,0.000047052054],"domain_scores_gemma":[0.9994654,0.00026379756,0.000064331485,0.00007034973,0.00010835093,0.000027789316],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011526633,0.00127267,0.0009910634,0.0007300333,0.0004562833,0.0022555217,0.00203316,0.001260512,0.0029783908],"category_scores_gemma":[0.0025663504,0.0006201495,0.00063496124,0.0020771173,0.0011986073,0.0029612754,0.00085033354,0.0017428178,0.0007712969],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000086764434,0.00007247207,0.00017970792,0.00026526226,0.000041725223,0.000044435717,0.00007751678,0.63628966,0.0021350381,0.23637655,0.005538374,0.11889248],"study_design_scores_gemma":[0.000015237917,0.000017218052,0.000041510288,0.000013734655,0.000009864872,0.000033875087,0.000017499307,0.89481825,0.0005795049,0.10060107,0.0038443664,0.00000783382],"about_ca_topic_score_codex":0.0022874898,"about_ca_topic_score_gemma":0.002134189,"teacher_disagreement_score":0.0029783908,"about_ca_system_score_codex":0.0011322384,"about_ca_system_score_gemma":0.00097282976,"threshold_uncertainty_score":0.009963691},"labels":[],"label_agreement":null},{"id":"W2009946293","doi":"10.3846/1648-4142.2008.23.230-235","title":"DECISION SUPPORT SYSTEM FOR SOLVING THE STREET ROUTING PROBLEM","year":2008,"lang":"en","type":"article","venue":"Transport","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":31,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Vehicle routing problem; Heuristics; Routing (electronic design automation); Computer science; Key (lock); Software; Operations research; Range (aeronautics); Geographic information system; Path (computing); Engineering; Computer network; Computer security; Geography","score_opus":0.023069283242303097,"score_gpt":0.25007384062112215,"score_spread":0.22700455737881906,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2009946293","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.024599606,0.00049846014,0.9534125,0.00042822145,0.00012433465,0.00050264853,0.0011972738,0.007505028,0.0117319655],"genre_scores_gemma":[0.26189017,0.00055506243,0.7276248,0.00022160939,0.0000708619,0.0008952134,0.0022869657,0.00017058778,0.0062847375],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99951386,0.00014518989,0.00006202169,0.000083177314,0.00014716467,0.000048582504],"domain_scores_gemma":[0.9990451,0.00058718445,0.000052942163,0.000046868878,0.00022464109,0.000043229797],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00076063006,0.0008278969,0.0007428335,0.00085855246,0.00046288213,0.0012247127,0.00091510266,0.00095025863,0.014896404],"category_scores_gemma":[0.0027882103,0.00026056677,0.00059249025,0.00077739265,0.00018157317,0.00080999586,0.00067093177,0.00092993455,0.0026089088],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00066544424,0.00037938403,0.0014418003,0.0009191349,0.00008499999,0.0005306631,0.0002164255,0.38260937,0.009031324,0.017213373,0.020102903,0.5668051],"study_design_scores_gemma":[0.00014659276,0.00009222936,0.00020338246,0.000037445076,0.000021682856,0.0000772206,0.00006234983,0.9832009,0.0021661834,0.005505272,0.008473449,0.000013286799],"about_ca_topic_score_codex":0.0025863748,"about_ca_topic_score_gemma":0.0022967828,"teacher_disagreement_score":0.014896404,"about_ca_system_score_codex":0.0004197011,"about_ca_system_score_gemma":0.0010640722,"threshold_uncertainty_score":0.049833417},"labels":[],"label_agreement":null},{"id":"W2010102206","doi":"10.1016/j.cor.2008.05.005","title":"The pickup and delivery traveling salesman problem with first-in-first-out loading","year":2008,"lang":"en","type":"article","venue":"Computers & Operations Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":45,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"","keywords":"Travelling salesman problem; Pickup; Tabu search; Heuristics; Iterated local search; Mathematical optimization; Computer science; 2-opt; Bottleneck traveling salesman problem; Integer programming; Iterated function; Probabilistic logic; Metaheuristic; Traveling purchaser problem; Mathematics; Artificial intelligence","score_opus":0.05876710399495647,"score_gpt":0.30697327359526316,"score_spread":0.2482061696003067,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2010102206","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15087906,0.0023535392,0.7977404,0.0037450427,0.0009949871,0.0010707045,0.0023057836,0.0011916606,0.039718848],"genre_scores_gemma":[0.7258175,0.0022466243,0.19003654,0.0005017146,0.00069262367,0.00061620737,0.0015001461,0.00062034605,0.07796832],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99769455,0.00075890485,0.000095936346,0.00050018006,0.0003812912,0.000569116],"domain_scores_gemma":[0.9978405,0.0011813275,0.0002800611,0.00018160112,0.00017984265,0.00033660335],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024918409,0.0025064263,0.00588031,0.0016350057,0.002466543,0.0052221813,0.004555837,0.0061616753,0.016757512],"category_scores_gemma":[0.0059073963,0.0028327897,0.0027616262,0.0035741515,0.0019700327,0.005028638,0.0025028905,0.003084186,0.0022132515],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00072378264,0.00045258534,0.0009965366,0.0005287433,0.0001508594,0.0008866039,0.00013404156,0.90483165,0.0016105011,0.03390605,0.011946117,0.04383261],"study_design_scores_gemma":[0.000092596994,0.00025559732,0.00036664636,0.00002256287,0.000070080496,0.00019893624,0.00013809971,0.97686166,0.00088843453,0.017855026,0.0031998637,0.00005041931],"about_ca_topic_score_codex":0.010714264,"about_ca_topic_score_gemma":0.0076085473,"teacher_disagreement_score":0.016757512,"about_ca_system_score_codex":0.0021623415,"about_ca_system_score_gemma":0.003524378,"threshold_uncertainty_score":0.05605948},"labels":[],"label_agreement":null},{"id":"W2010203453","doi":"10.1007/s10288-014-0260-9","title":"Approximating the length of Chinese postman tours","year":2014,"lang":"en","type":"article","venue":"4OR","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Unit square; Combinatorics; Square (algebra); Simple (philosophy); Mathematics; Function (biology); Planar; Unit (ring theory); Arc length; Computer science; Discrete mathematics; Geometry; Arc (geometry)","score_opus":0.007203520721262863,"score_gpt":0.23812299029064365,"score_spread":0.23091946956938078,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2010203453","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17260563,0.00089271757,0.80984324,0.00074896775,0.00021933223,0.00010227627,0.0007267501,0.00037574008,0.014485317],"genre_scores_gemma":[0.74886805,0.00079258974,0.2263874,0.00017487201,0.00013071464,0.00024523167,0.0011325608,0.00042611323,0.02184254],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99955887,0.0001399504,0.000014966215,0.000098753284,0.00009316912,0.000094179115],"domain_scores_gemma":[0.9972289,0.001876646,0.0002156972,0.00016075211,0.00035364708,0.00016431896],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014897444,0.0010755812,0.0007489599,0.0016867002,0.00065876206,0.0012033341,0.0019384099,0.0012958365,0.0068016597],"category_scores_gemma":[0.0074257366,0.0007496411,0.0007781205,0.0015396754,0.0010039413,0.0018145597,0.0013499935,0.0014556464,0.00049818226],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001494232,0.000031479605,0.0014745313,0.00009273643,0.000023558434,0.000046080066,0.00008334852,0.93596154,0.00068645255,0.03794826,0.0027434402,0.020759009],"study_design_scores_gemma":[0.0000038793482,0.000011454047,0.00016514267,0.000008283531,0.0000031599034,0.0000066430616,0.000014799418,0.99284405,0.00017471811,0.006318259,0.0004461762,0.0000034030902],"about_ca_topic_score_codex":0.019442324,"about_ca_topic_score_gemma":0.023456195,"teacher_disagreement_score":0.019442324,"about_ca_system_score_codex":0.0033470115,"about_ca_system_score_gemma":0.0023326387,"threshold_uncertainty_score":0.03865832},"labels":[],"label_agreement":null},{"id":"W2010359686","doi":"10.1287/trsc.1110.0387","title":"Robust Inventory Routing Under Demand Uncertainty","year":2011,"lang":"en","type":"article","venue":"Transportation Science","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":122,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"","keywords":"Robust optimization; Mathematical optimization; Time horizon; Interval (graph theory); Routing (electronic design automation); Operations research; Computer science; Inventory theory; Product (mathematics); Integer programming; Inventory control; Set (abstract data type); Holding cost; Mathematics","score_opus":0.0778347292160422,"score_gpt":0.2723011943071366,"score_spread":0.19446646509109441,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2010359686","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0402392,0.0005121139,0.95219547,0.00044626003,0.00005330177,0.000054815413,0.00037490236,0.00042156107,0.0057023014],"genre_scores_gemma":[0.84779984,0.0005908825,0.1474814,0.000104049876,0.00008250741,0.0001445783,0.0005945694,0.00017932453,0.003022825],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985904,0.0005103194,0.000081002676,0.0002635166,0.00033668257,0.00021818579],"domain_scores_gemma":[0.9984035,0.0009575079,0.0002707086,0.00014724924,0.00015493124,0.00006614794],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014570652,0.0011054708,0.0013311026,0.0005977081,0.00047091887,0.0015626134,0.0014080221,0.0015123412,0.0019523286],"category_scores_gemma":[0.004088538,0.0008373776,0.00089570903,0.0012682339,0.00070523785,0.0021706994,0.0010421888,0.0010943881,0.00033363767],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000029410945,0.00000878013,0.00008089109,0.000023886725,0.000013259839,0.000050047074,0.000013649592,0.98720366,0.00053899403,0.007013826,0.00033121824,0.0046924623],"study_design_scores_gemma":[0.0000056172953,0.000013138195,0.00004903178,0.0000025467948,0.0000049218543,0.00001735567,0.0000076178844,0.9914551,0.00023827195,0.007830912,0.00037024333,0.0000052039704],"about_ca_topic_score_codex":0.0038243204,"about_ca_topic_score_gemma":0.0020197183,"teacher_disagreement_score":0.0038243204,"about_ca_system_score_codex":0.0015464254,"about_ca_system_score_gemma":0.0009220769,"threshold_uncertainty_score":0.011220157},"labels":[],"label_agreement":null},{"id":"W2010740862","doi":"10.5539/jms.v2n2p219","title":"A Heuristic Based Approach to Solve a Capacitated Location-routing Problem","year":2012,"lang":"en","type":"article","venue":"Journal of Management and Sustainability","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Mathematical optimization; Simulated annealing; Computer science; Heuristic; Vehicle routing problem; Greedy algorithm; Set (abstract data type); Routing (electronic design automation); Algorithm; Mathematics","score_opus":0.013749781698769786,"score_gpt":0.25083379867266514,"score_spread":0.23708401697389536,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2010740862","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0021760527,0.00017018628,0.99339736,0.000093370705,0.000042407944,0.000044415607,0.000027213033,0.00009131952,0.003957628],"genre_scores_gemma":[0.12448278,0.00046427085,0.8694514,0.00012960099,0.00006470949,0.000322735,0.00015311531,0.000091844355,0.0048395437],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992865,0.0002906715,0.000030482,0.00012217,0.00020981237,0.000060405615],"domain_scores_gemma":[0.9996164,0.00021858582,0.00003741857,0.000045268134,0.00006598621,0.000016311782],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007280809,0.00082398654,0.0006506518,0.0010266223,0.00049900037,0.0008417255,0.0012788037,0.0011957678,0.004666941],"category_scores_gemma":[0.001418156,0.00044095356,0.0009676546,0.0010649967,0.0007349345,0.0008493909,0.0007532365,0.0011304794,0.0007639199],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000029105704,0.000077049546,0.00032562122,0.00020978162,0.00005920464,0.00008646695,0.00008605505,0.8468262,0.0028252217,0.08045858,0.0022752706,0.06674148],"study_design_scores_gemma":[0.000016338214,0.000066221284,0.000085886124,0.000023743753,0.000018559442,0.00011356911,0.000041161362,0.96797556,0.001451208,0.021950208,0.008241466,0.000016112563],"about_ca_topic_score_codex":0.0020928544,"about_ca_topic_score_gemma":0.0027652057,"teacher_disagreement_score":0.004666941,"about_ca_system_score_codex":0.00087359996,"about_ca_system_score_gemma":0.0012209604,"threshold_uncertainty_score":0.015612543},"labels":[],"label_agreement":null},{"id":"W2010898123","doi":"10.1007/s10951-006-8596-4","title":"A case study of mutual routing-scheduling reformulation","year":2006,"lang":"en","type":"article","venue":"Journal of Scheduling","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Japan Society for the Promotion of Science; University College Cork; University of Glasgow","keywords":"Computer science; Vehicle routing problem; Scheduling (production processes); Mathematical optimization; Software; Job shop scheduling; Key (lock); Combinatorial optimization; Operations research; Constraint programming; Industrial engineering; Routing (electronic design automation); Mathematics; Algorithm; Engineering","score_opus":0.0248211093843664,"score_gpt":0.28351555630582065,"score_spread":0.25869444692145427,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2010898123","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.49027097,0.0004433943,0.40577248,0.0028937461,0.00014620797,0.00034125696,0.00038615894,0.0005400615,0.09920571],"genre_scores_gemma":[0.9167921,0.000110448775,0.07083666,0.00008651434,0.000034500365,0.00008259689,0.00012867605,0.000062371546,0.011866121],"study_design_codex":"simulation_or_modeling","study_design_gemma":"case_report","domain_scores_codex":[0.999008,0.0005351701,0.000020834408,0.000088323366,0.00018362234,0.00016410724],"domain_scores_gemma":[0.99858886,0.0009278765,0.00009885118,0.0001949069,0.00012358309,0.000065911605],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016596967,0.00033966135,0.0004561613,0.00032022374,0.0011346678,0.0011537535,0.0010115515,0.0018766606,0.0069623673],"category_scores_gemma":[0.004155788,0.00019992919,0.00068563607,0.00065661466,0.0007173824,0.0011096442,0.0010012133,0.001023863,0.00039085324],"study_design_candidate":"case_report","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002648304,0.00039730014,0.0013925463,0.00013232538,0.000042664775,0.0021862474,0.00046429213,0.62417877,0.002231471,0.32234323,0.0062585007,0.04010781],"study_design_scores_gemma":[0.00010050184,0.00021605109,0.0005434607,0.000015299504,0.0000332731,0.00049784745,0.0004448484,0.93787813,0.0031088316,0.043329448,0.013809612,0.000022760329],"about_ca_topic_score_codex":0.0064238887,"about_ca_topic_score_gemma":0.006057002,"teacher_disagreement_score":0.0069623673,"about_ca_system_score_codex":0.0011031359,"about_ca_system_score_gemma":0.0011261184,"threshold_uncertainty_score":0.023291409},"labels":[],"label_agreement":null},{"id":"W2011987043","doi":"10.4271/2014-01-1815","title":"Energy Efficient Routing for Electric Vehicles using Particle Swarm Optimization","year":2014,"lang":"en","type":"article","venue":"SAE technical papers on CD-ROM/SAE technical paper series","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Chrysler (Canada)","funders":"","keywords":"Particle swarm optimization; Computer science; Routing (electronic design automation); Multi-swarm optimization; Energy (signal processing); Mathematical optimization; Computer network; Algorithm; Physics; Mathematics","score_opus":0.014984236407706821,"score_gpt":0.2554483754922992,"score_spread":0.24046413908459238,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2011987043","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015733816,0.00022281121,0.9751799,0.00023665307,0.00007599815,0.000058315112,0.00004420795,0.00025517406,0.008193184],"genre_scores_gemma":[0.59202003,0.0006357479,0.38722184,0.00012765515,0.00007012986,0.00035472887,0.00026346435,0.0001229275,0.019183455],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999871,0.000043233445,0.0000062325394,0.000024298899,0.000041672945,0.000013519331],"domain_scores_gemma":[0.9998242,0.00009347217,0.00002381844,0.000012259724,0.000036591842,0.000009604614],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035804338,0.00059109647,0.0005200766,0.00043689148,0.00041871288,0.0007817441,0.0004476876,0.0007398803,0.0022500148],"category_scores_gemma":[0.000773389,0.0003497509,0.0005094709,0.0004901918,0.00039565572,0.00053558574,0.00047409488,0.00046986967,0.00030359736],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011804925,0.0000102746635,0.000094059964,0.000012498082,0.000007998489,0.000015782221,0.000011411671,0.98742336,0.0005796386,0.0035146268,0.0005342059,0.0077842805],"study_design_scores_gemma":[0.0000039547836,0.0000055593755,0.000023182054,0.0000015607416,0.0000013593027,0.000002317064,0.000003215986,0.99825495,0.000093211434,0.0012292141,0.00038012842,0.0000012565837],"about_ca_topic_score_codex":0.006659313,"about_ca_topic_score_gemma":0.0050382335,"teacher_disagreement_score":0.006659313,"about_ca_system_score_codex":0.0007345302,"about_ca_system_score_gemma":0.0007239432,"threshold_uncertainty_score":0.013241053},"labels":[],"label_agreement":null},{"id":"W2012738417","doi":"10.1007/s10732-007-9033-3","title":"Worst case analysis of Max-Regret, Greedy and other heuristics for Multidimensional Assignment and Traveling Salesman Problems","year":2007,"lang":"en","type":"article","venue":"Journal of Heuristics","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"Deutsche Forschungsgemeinschaft","keywords":"Travelling salesman problem; Heuristics; Regret; Greedy algorithm; Mathematical optimization; Computer science; Mathematics; Combinatorics; Machine learning","score_opus":0.0272815811744743,"score_gpt":0.28991862336377305,"score_spread":0.26263704218929873,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2012738417","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16352856,0.011157809,0.7803921,0.0028798934,0.0005847751,0.00036986568,0.0010329916,0.0010164911,0.039037455],"genre_scores_gemma":[0.8187441,0.0024126784,0.16702965,0.00069102447,0.0005678729,0.0003278446,0.00082970475,0.0007218417,0.008675385],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9891566,0.006563739,0.00026639964,0.0006551163,0.001641237,0.0017168433],"domain_scores_gemma":[0.9433042,0.049630046,0.0018969557,0.0016703425,0.0023238584,0.0011745594],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.018390745,0.0033027786,0.0030468511,0.0036963176,0.0019654264,0.0046269963,0.004822563,0.002542764,0.0064137895],"category_scores_gemma":[0.040509887,0.0016600128,0.0032207984,0.0040419837,0.003315029,0.0049281986,0.0020894422,0.003406068,0.00052391674],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00059660577,0.0001846287,0.0007226073,0.0002251282,0.00013774444,0.00007773147,0.000058970596,0.9591969,0.00035085288,0.0249467,0.0036548222,0.009847184],"study_design_scores_gemma":[0.000026986036,0.000086161344,0.00022554981,0.000020002557,0.000057174242,0.000040011048,0.000042937954,0.9864563,0.0002275344,0.01246888,0.00033464486,0.000013848272],"about_ca_topic_score_codex":0.008566026,"about_ca_topic_score_gemma":0.008626375,"teacher_disagreement_score":0.018390745,"about_ca_system_score_codex":0.0066844053,"about_ca_system_score_gemma":0.003948153,"threshold_uncertainty_score":0.09726071},"labels":[],"label_agreement":null},{"id":"W2013782474","doi":"10.1016/j.cor.2014.05.012","title":"The impact of hub failure in hub-and-spoke networks: Mathematical formulations and solution techniques","year":2014,"lang":"en","type":"article","venue":"Computers & Operations Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":88,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Backup; Operations research; Spoke-hub distribution paradigm; Computer science; Linearization; Heuristic; Facility location problem; Function (biology); Mathematical optimization; Natural disaster; Flow network; Mathematical model; Transport engineering; Mathematics; Engineering","score_opus":0.03528771779630964,"score_gpt":0.3742071828086215,"score_spread":0.33891946501231185,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2013782474","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.046279285,0.0069276635,0.9064922,0.0044283676,0.0007250401,0.0001916218,0.0006964677,0.00019646199,0.0340629],"genre_scores_gemma":[0.87753445,0.011177145,0.059428323,0.00071749726,0.0010536228,0.00048205827,0.0004890637,0.00034710366,0.048770674],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989505,0.00042036004,0.000030868396,0.00016546687,0.00021882115,0.00021395613],"domain_scores_gemma":[0.9940147,0.004618644,0.0005278046,0.00010358416,0.0005563635,0.00017903953],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003182196,0.0020344134,0.0022649555,0.00218332,0.0013015164,0.0030122933,0.0029216406,0.004118651,0.0057340786],"category_scores_gemma":[0.013341088,0.0018170851,0.0017671578,0.00297979,0.002631679,0.004494666,0.0022973751,0.003442765,0.0005033001],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000151960985,0.000024015635,0.00021218204,0.000086743734,0.000020604031,0.00006639264,0.00003298046,0.96653074,0.00013874596,0.028279802,0.0015940266,0.0029985472],"study_design_scores_gemma":[0.0000033516505,0.0000068142053,0.000107654065,0.00001895447,0.00001499156,0.000018023788,0.000038966413,0.98512065,0.000062171785,0.014180365,0.0004199312,0.000008099899],"about_ca_topic_score_codex":0.021824729,"about_ca_topic_score_gemma":0.016607018,"teacher_disagreement_score":0.021824729,"about_ca_system_score_codex":0.0035342602,"about_ca_system_score_gemma":0.0022708792,"threshold_uncertainty_score":0.04339534},"labels":[],"label_agreement":null},{"id":"W2015067034","doi":"10.1007/s10479-010-0806-y","title":"An interior-point Benders based branch-and-cut algorithm for mixed integer programs","year":2010,"lang":"en","type":"article","venue":"Annals of Operations Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":73,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Benders' decomposition; Branch and cut; Mathematics; Theory of computation; Cutting-plane method; Mathematical optimization; Integer programming; Integer (computer science); Branch and bound; Interior point method; Steiner tree problem; Facility location problem; Algorithm; Point (geometry); Linear programming relaxation; Computer science","score_opus":0.13655247256445424,"score_gpt":0.44150555228973304,"score_spread":0.3049530797252788,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2015067034","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0040613217,0.00014602684,0.99220496,0.00012351155,0.00004970787,0.00010583461,0.000060546503,0.000425222,0.0028228217],"genre_scores_gemma":[0.0439724,0.00014524869,0.95214295,0.000101885664,0.000047986727,0.0003689947,0.00021924077,0.00021797509,0.0027832526],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99876875,0.00050632947,0.000052752708,0.00016532715,0.00038453573,0.00012223102],"domain_scores_gemma":[0.99806744,0.0013505027,0.0001133271,0.00008752463,0.00028749858,0.00009382647],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002552222,0.0019877297,0.0027040127,0.0017143306,0.00093260803,0.0016880631,0.0024562387,0.0028084726,0.00853279],"category_scores_gemma":[0.0046342537,0.0016966745,0.0016740718,0.0018700182,0.0010311769,0.0018885228,0.0019478756,0.0039855563,0.0015791489],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003452065,0.00037025346,0.00033315105,0.00019474003,0.000074452364,0.000057254227,0.000095680996,0.7337119,0.0022202234,0.021096723,0.0050296346,0.23647083],"study_design_scores_gemma":[0.000045818088,0.000048990805,0.000037635953,0.000014902317,0.000010778984,0.000008861251,0.000007013932,0.9931977,0.0003446625,0.0055330824,0.00074382336,0.000006713068],"about_ca_topic_score_codex":0.004266895,"about_ca_topic_score_gemma":0.0047124457,"teacher_disagreement_score":0.00853279,"about_ca_system_score_codex":0.0012629763,"about_ca_system_score_gemma":0.00252905,"threshold_uncertainty_score":0.028545022},"labels":[],"label_agreement":null},{"id":"W2015274422","doi":"10.1287/opre.1070.0387","title":"The Undirected <i>m</i>-Peripatetic Salesman Problem: Polyhedral Results and New Algorithms","year":2007,"lang":"en","type":"article","venue":"Operations Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"","keywords":"Travelling salesman problem; Combinatorics; Euclidean geometry; Mathematics; Undirected graph; Bottleneck traveling salesman problem; Disjoint sets; Algorithm; Graph; Computer science; Discrete mathematics","score_opus":0.08440112380429196,"score_gpt":0.384531815803507,"score_spread":0.30013069199921505,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2015274422","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003590093,0.00087059493,0.98349947,0.00045389755,0.00010315677,0.00009450069,0.00017276726,0.00015696052,0.011058476],"genre_scores_gemma":[0.08194564,0.0025766864,0.90744543,0.00043595606,0.00033044993,0.00042323433,0.0011443752,0.0003393049,0.005358898],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9980363,0.0005242898,0.00013462847,0.00038457086,0.00072380836,0.00019632801],"domain_scores_gemma":[0.997468,0.0015383138,0.00027115617,0.000320712,0.00029963857,0.00010220847],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018729201,0.0016260403,0.0015202289,0.0019307717,0.0009982279,0.0038463,0.0031338956,0.0015525963,0.005736401],"category_scores_gemma":[0.00556503,0.0010077487,0.0015203006,0.003583286,0.0019450012,0.005721294,0.00217376,0.004956875,0.001553108],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00027905995,0.00040041938,0.00071579556,0.00053604064,0.00006121715,0.00014937037,0.00019091011,0.35159296,0.00305961,0.36474553,0.016107338,0.2621618],"study_design_scores_gemma":[0.00007729314,0.00008961656,0.00022840001,0.000122772,0.00003030439,0.00013638621,0.00006501483,0.66361415,0.0021432422,0.32042757,0.013033392,0.000031808377],"about_ca_topic_score_codex":0.0030869613,"about_ca_topic_score_gemma":0.003643803,"teacher_disagreement_score":0.005736401,"about_ca_system_score_codex":0.0020909212,"about_ca_system_score_gemma":0.0015330374,"threshold_uncertainty_score":0.019190192},"labels":[],"label_agreement":null},{"id":"W2016268962","doi":"10.1016/j.cor.2003.11.016","title":"Hub-and-spoke network design with congestion","year":2004,"lang":"en","type":"article","venue":"Computers & Operations Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":168,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Spoke-hub distribution paradigm; Network planning and design; Computer science; Mathematical optimization; Heuristic; Term (time); Function (biology); Network model; Operations research; Mathematics; Computer network; Artificial intelligence; Transport engineering","score_opus":0.07669300078247837,"score_gpt":0.3428464463994623,"score_spread":0.26615344561698395,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2016268962","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023872353,0.00015974493,0.96858907,0.00014310692,0.00007391341,0.00007981997,0.00007409323,0.00015348388,0.006854494],"genre_scores_gemma":[0.8076747,0.0004625743,0.17564918,0.00008860441,0.000060520073,0.00025696278,0.000120470504,0.00013814236,0.015548832],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996332,0.00012949815,0.000011289445,0.000089684145,0.00006692013,0.00006936641],"domain_scores_gemma":[0.9994536,0.0002396293,0.000061378596,0.000048969458,0.00014492866,0.000051575622],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009188705,0.001139293,0.0011442046,0.0007631581,0.0007097397,0.0009169814,0.0013543308,0.0012396342,0.0037613753],"category_scores_gemma":[0.0024497805,0.0007872206,0.00060500397,0.00068900717,0.00065130147,0.0013832682,0.0012599773,0.0008130179,0.000369448],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000558607,0.000020280297,0.000110478,0.000042567888,0.000016184615,0.000024829558,0.000018364617,0.9770082,0.00093073386,0.010988915,0.00054697675,0.010236755],"study_design_scores_gemma":[0.000010002386,0.00004601953,0.000032174918,0.0000049107025,0.000012129707,0.000010819274,0.000011510568,0.9923759,0.00048372833,0.0063493787,0.0006589194,0.0000045944917],"about_ca_topic_score_codex":0.0027023584,"about_ca_topic_score_gemma":0.0037127042,"teacher_disagreement_score":0.0037613753,"about_ca_system_score_codex":0.0011185243,"about_ca_system_score_gemma":0.001223429,"threshold_uncertainty_score":0.012583077},"labels":[],"label_agreement":null},{"id":"W2016619874","doi":"10.1016/j.ejor.2004.06.016","title":"The single-node dynamic service scheduling and dispatching problem","year":2004,"lang":"en","type":"article","venue":"European Journal of Operational Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":30,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; Université du Québec à Montréal","funders":"Air Force Office of Scientific Research; Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Scheduling (production processes); Dynamic priority scheduling; Operations research; Mathematical optimization; Distributed computing; Computer network; Quality of service; Mathematics","score_opus":0.0517134440202458,"score_gpt":0.3366651362674725,"score_spread":0.2849516922472267,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2016619874","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0921265,0.0017869299,0.88636035,0.0024773234,0.00062230945,0.00019535504,0.0014090588,0.00023547534,0.0147867855],"genre_scores_gemma":[0.79074925,0.0024230068,0.17572558,0.00033868576,0.0007360424,0.00039091852,0.0014135297,0.00025902208,0.027963974],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989538,0.0003372697,0.00003381213,0.0003719731,0.00014687421,0.00015625717],"domain_scores_gemma":[0.9983656,0.0010994816,0.00016169563,0.000082289815,0.00012353659,0.00016742578],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017487521,0.0014851488,0.0022815382,0.00093895657,0.00089865533,0.0021850138,0.0028761758,0.0025989693,0.0064448104],"category_scores_gemma":[0.0044368897,0.0012019073,0.0010560427,0.0024911903,0.001304803,0.0027667563,0.0013974999,0.0017788707,0.00082506175],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016449438,0.000060275303,0.0002616872,0.0001708394,0.00005516318,0.00013889992,0.000047229303,0.94122905,0.0006086,0.03587458,0.0036684466,0.01772069],"study_design_scores_gemma":[0.000044851146,0.000032117867,0.00014705831,0.000008121233,0.000017409155,0.000048055204,0.00003405097,0.96399075,0.00021942635,0.03395927,0.0014879066,0.000010946966],"about_ca_topic_score_codex":0.006060417,"about_ca_topic_score_gemma":0.0041297753,"teacher_disagreement_score":0.0064448104,"about_ca_system_score_codex":0.0021359427,"about_ca_system_score_gemma":0.00212851,"threshold_uncertainty_score":0.021560073},"labels":[],"label_agreement":null},{"id":"W2016827346","doi":"10.1016/j.cor.2012.04.010","title":"A branch-price-and-cut algorithm for the workover rig routing problem","year":2012,"lang":"en","type":"article","venue":"Computers & Operations Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":31,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal; Polytechnique Montréal; Group for Research in Decision Analysis","funders":"Conselho Nacional de Desenvolvimento Científico e Tecnológico; National Science Council","keywords":"Workover; Vehicle routing problem; Computer science; Routing (electronic design automation); Mathematical optimization; Set (abstract data type); Algorithm; Mathematics; Engineering; Petroleum engineering; Computer network","score_opus":0.06427568574875933,"score_gpt":0.3626531758041617,"score_spread":0.2983774900554024,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2016827346","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016472556,0.0002735804,0.97329366,0.0003666913,0.00010772801,0.00024967664,0.00018235145,0.0007817296,0.008271976],"genre_scores_gemma":[0.13110417,0.00032578752,0.8577546,0.00014512181,0.00009129808,0.0003583135,0.0005576075,0.00028418648,0.009378986],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99948126,0.0001327082,0.000019074485,0.00008876032,0.00018303255,0.00009520758],"domain_scores_gemma":[0.999278,0.00039603078,0.000048598653,0.000062654624,0.00012338281,0.000091262234],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009840061,0.0011907036,0.0020157325,0.0013692719,0.0010850509,0.0015083359,0.002283873,0.0025667863,0.013329417],"category_scores_gemma":[0.0024038865,0.0008402718,0.0009958403,0.0021367113,0.0006827439,0.001805194,0.0015070566,0.0019242805,0.0016081203],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024960656,0.00044031587,0.0004871831,0.00014570756,0.000051935793,0.00010998384,0.0000668235,0.6355036,0.0022162928,0.018275559,0.010529941,0.33192307],"study_design_scores_gemma":[0.000060144004,0.00006290151,0.00009758389,0.000009631312,0.000013081756,0.000023094912,0.000014734495,0.99020106,0.0003263071,0.007735056,0.0014482319,0.000008135376],"about_ca_topic_score_codex":0.0086187655,"about_ca_topic_score_gemma":0.009623462,"teacher_disagreement_score":0.013329417,"about_ca_system_score_codex":0.0012192979,"about_ca_system_score_gemma":0.0023998467,"threshold_uncertainty_score":0.044591367},"labels":[],"label_agreement":null},{"id":"W2017602820","doi":"10.1016/j.ejor.2007.08.021","title":"A tabu search heuristic for the generalized minimum spanning tree problem","year":2007,"lang":"en","type":"article","venue":"European Journal of Operational Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":39,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"","keywords":"Tabu search; Minimum spanning tree; Guided Local Search; Mathematical optimization; Heuristic; Spanning tree; Beam search; Mathematics; Vertex (graph theory); k-minimum spanning tree; Tree (set theory); Kruskal's algorithm; Set (abstract data type); Computer science; Graph; Algorithm; Search algorithm; Combinatorics; Tree structure; K-ary tree; Binary tree","score_opus":0.11878875091194746,"score_gpt":0.3928427993228669,"score_spread":0.27405404841091946,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2017602820","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09411089,0.0020923892,0.88583326,0.0006436766,0.00037168027,0.00032044327,0.00039102836,0.0019873043,0.014249246],"genre_scores_gemma":[0.2915547,0.00051322626,0.70225763,0.0002482497,0.00008279438,0.0003338371,0.00045990193,0.00036132243,0.0041883173],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994929,0.00028429812,0.0000151446075,0.00005065231,0.000084998974,0.00007212084],"domain_scores_gemma":[0.99889094,0.00074468757,0.00006889261,0.00009165357,0.00015727952,0.00004644919],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000965267,0.00064045185,0.0010119048,0.0011585386,0.0008596629,0.0009282221,0.0014789454,0.0016757526,0.007892832],"category_scores_gemma":[0.0037686261,0.00053177925,0.0007068043,0.0020259137,0.0007114209,0.0010722639,0.000827775,0.0008531038,0.0008974292],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019363526,0.000116701915,0.00041773717,0.00013915126,0.000056000008,0.00008122196,0.00011031436,0.79573256,0.0013461938,0.011229313,0.006805682,0.18377152],"study_design_scores_gemma":[0.000066786255,0.000060127782,0.00014352294,0.000022890747,0.000021026515,0.000031156513,0.000035520963,0.9909135,0.0003355146,0.006614619,0.0017452578,0.000010045324],"about_ca_topic_score_codex":0.006464592,"about_ca_topic_score_gemma":0.006208031,"teacher_disagreement_score":0.007892832,"about_ca_system_score_codex":0.00088859326,"about_ca_system_score_gemma":0.0013491554,"threshold_uncertainty_score":0.026404083},"labels":[],"label_agreement":null},{"id":"W2018660239","doi":"10.1016/j.orl.2008.09.006","title":"Certification of an optimal TSP tour through 85,900 cities","year":2008,"lang":"en","type":"article","venue":"Operations Research Letters","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":171,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Travelling salesman problem; Certification; Code (set theory); Traveling purchaser problem; Computer science; Mathematical optimization; Operations research; Bottleneck traveling salesman problem; Mathematics; Programming language; Economics","score_opus":0.12102799807608362,"score_gpt":0.374011808626763,"score_spread":0.25298381055067937,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2018660239","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.84021163,0.0001699864,0.09489586,0.0010001208,0.00022432535,0.0005094571,0.0018758919,0.0025653928,0.058547292],"genre_scores_gemma":[0.9322682,0.00007152638,0.060114335,0.000062383566,0.000007769296,0.000045974197,0.0021967874,0.00029933001,0.004933723],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99830097,0.0005468816,0.00007388606,0.00025340717,0.0005482293,0.0002766103],"domain_scores_gemma":[0.9969072,0.0007963285,0.00018089497,0.000592005,0.0012624898,0.0002612291],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019513394,0.00043927872,0.0004414069,0.00093468203,0.0013537607,0.0017758193,0.00091230206,0.001409369,0.01128619],"category_scores_gemma":[0.009598983,0.0004563953,0.0005710078,0.0009772624,0.0010167975,0.0010219101,0.00085190125,0.0007447468,0.0010575373],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001184893,0.0004933075,0.016399378,0.00045184637,0.000078166406,0.00080953666,0.00045866962,0.7445324,0.010923344,0.04299741,0.029280316,0.15239066],"study_design_scores_gemma":[0.0002291451,0.0004946132,0.012127476,0.00009202753,0.00003180483,0.000186965,0.0006737602,0.9509344,0.009582829,0.014109663,0.011496395,0.0000409653],"about_ca_topic_score_codex":0.05253322,"about_ca_topic_score_gemma":0.08927905,"teacher_disagreement_score":0.05253322,"about_ca_system_score_codex":0.0027115806,"about_ca_system_score_gemma":0.007812946,"threshold_uncertainty_score":0.104454875},"labels":[],"label_agreement":null},{"id":"W2019515431","doi":"10.1109/cipls.2013.6595205","title":"Discrete PSO for the uncapacitated single allocation hub location problem","year":2013,"lang":"en","type":"article","venue":"","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brock University","funders":"","keywords":"Heuristics; Mathematical optimization; Particle swarm optimization; Computer science; Benchmark (surveying); Node (physics); Metaheuristic; Operations research; Artificial intelligence; Engineering; Mathematics","score_opus":0.021096845887678906,"score_gpt":0.24619580082957948,"score_spread":0.22509895494190058,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2019515431","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.024378067,0.0004281496,0.96871287,0.00034004546,0.000085528714,0.00004893729,0.00004597961,0.00012302917,0.0058374903],"genre_scores_gemma":[0.65040845,0.0005847331,0.3439516,0.00012360864,0.00007222773,0.00016012874,0.00012284043,0.000046290552,0.00453002],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998155,0.00007356759,0.000007772728,0.00003132813,0.000053730786,0.00001809164],"domain_scores_gemma":[0.9996363,0.00022867456,0.00004673116,0.000023266974,0.000042281423,0.000022726825],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004123018,0.0004161861,0.00046852767,0.00030985332,0.00020849348,0.0005325312,0.0005436979,0.0005896082,0.0012277926],"category_scores_gemma":[0.0013685112,0.00020279254,0.00041767533,0.00037712022,0.00035796044,0.0004461114,0.0003694871,0.0006601841,0.00016927172],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000024460987,0.000032064803,0.0002885816,0.000065396474,0.00002144688,0.000039394366,0.000027981185,0.9651527,0.0011735716,0.011015946,0.0008531403,0.021305261],"study_design_scores_gemma":[0.000008401419,0.000016084143,0.00006309297,0.0000027636358,0.0000028130796,0.000008817519,0.000004891167,0.9978796,0.0001549371,0.0013958503,0.0004608349,0.000001986332],"about_ca_topic_score_codex":0.0026036631,"about_ca_topic_score_gemma":0.0021664544,"teacher_disagreement_score":0.0026036631,"about_ca_system_score_codex":0.00040382866,"about_ca_system_score_gemma":0.00067590177,"threshold_uncertainty_score":0.0051769614},"labels":[],"label_agreement":null},{"id":"W2020549453","doi":"10.1007/s10100-006-0169-2","title":"Synchronized routing of seasonal products through a production/distribution network","year":2006,"lang":"en","type":"article","venue":"Central European Journal of Operations Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Schedule; Time horizon; Production (economics); Product (mathematics); Routing (electronic design automation); Computer science; Heuristics; Operations research; Total cost; Vehicle routing problem; Scale (ratio); Business; Computer network; Mathematics; Economics; Microeconomics","score_opus":0.04278477667330224,"score_gpt":0.3139369132214654,"score_spread":0.27115213654816317,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2020549453","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2842775,0.00018805268,0.7098136,0.00015493552,0.000059074562,0.00007190412,0.0002995314,0.00032753448,0.0048078517],"genre_scores_gemma":[0.95128703,0.00017301511,0.044697605,0.000016471495,0.000018405115,0.000055669447,0.00018508799,0.000050521354,0.0035162475],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997739,0.00006779921,0.000010781709,0.00007237321,0.00003271609,0.00004246158],"domain_scores_gemma":[0.99963284,0.00015146317,0.00009475485,0.000039789877,0.00004803064,0.000033189106],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00057706365,0.00044423234,0.0006230373,0.00044147126,0.00042452873,0.00080323545,0.0007336592,0.000420123,0.0027391633],"category_scores_gemma":[0.0011216924,0.0005963234,0.00033148238,0.0009289648,0.00040009432,0.0007606583,0.00056092854,0.000285802,0.00022875825],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016584671,0.000020286881,0.00051478273,0.00002130366,0.000019910725,0.000053708543,0.000029247762,0.97454166,0.0032088612,0.005754332,0.00042785978,0.015242191],"study_design_scores_gemma":[0.000008958358,0.000023523708,0.00020455252,0.0000014217545,0.000007957598,0.000006837626,0.000010622682,0.99705315,0.00045277344,0.0019515672,0.00027581624,0.0000027908875],"about_ca_topic_score_codex":0.00521453,"about_ca_topic_score_gemma":0.005224979,"teacher_disagreement_score":0.00521453,"about_ca_system_score_codex":0.0008738155,"about_ca_system_score_gemma":0.00061674404,"threshold_uncertainty_score":0.010368407},"labels":[],"label_agreement":null},{"id":"W2020889483","doi":"10.1016/j.ejor.2014.09.015","title":"A column generation approach for a multi-attribute vehicle routing problem","year":2014,"lang":"en","type":"article","venue":"European Journal of Operational Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":53,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Université du Québec à Montréal; Université de Montréal","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada; Compute Canada","keywords":"Column generation; Vehicle routing problem; Column (typography); Computer science; Routing (electronic design automation); Mathematical optimization; Operations research; Mathematics; Computer network","score_opus":0.18071534324525806,"score_gpt":0.37511228400514873,"score_spread":0.19439694075989067,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2020889483","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0052240184,0.00022551061,0.9924206,0.00016935062,0.000076274155,0.000073006406,0.000121324476,0.00022753024,0.0014622756],"genre_scores_gemma":[0.18466811,0.00050283363,0.80876493,0.00026930173,0.00020143068,0.00032138135,0.00075173454,0.00018869393,0.0043315827],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993579,0.00026351147,0.000028332852,0.00009920308,0.00017577167,0.00007532103],"domain_scores_gemma":[0.998577,0.0008966487,0.00007560432,0.00009858198,0.00029209492,0.000059997543],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009063623,0.0009180807,0.0012126196,0.0010500291,0.00062251877,0.0011667773,0.0014173533,0.0011524805,0.005771867],"category_scores_gemma":[0.0019946499,0.00062897854,0.0012796457,0.0017222458,0.0004698405,0.0009905287,0.0009804968,0.0015670798,0.0008820523],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012528087,0.00021462885,0.00047298323,0.000259233,0.000079754434,0.00016827416,0.00008919067,0.7888876,0.0038696725,0.016992778,0.0075219446,0.18131869],"study_design_scores_gemma":[0.000011474751,0.000029643246,0.000051083418,0.0000073874194,0.000011640233,0.000022935903,0.000010620854,0.99400324,0.00032723503,0.0048807426,0.00063768285,0.0000063788084],"about_ca_topic_score_codex":0.0042758347,"about_ca_topic_score_gemma":0.0049946695,"teacher_disagreement_score":0.005771867,"about_ca_system_score_codex":0.0005730335,"about_ca_system_score_gemma":0.0010111203,"threshold_uncertainty_score":0.019308865},"labels":[],"label_agreement":null},{"id":"W2022879582","doi":"10.1016/j.cie.2012.07.006","title":"Relax-and-fix decomposition technique for solving large scale grid-based location problems","year":2012,"lang":"en","type":"article","venue":"Computers & Industrial Engineering","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Killam Trusts","keywords":"Decomposition; Mathematical optimization; Grid; Integer programming; Benders' decomposition; Computer science; Branch and bound; Scale (ratio); Linear programming; Branch and cut; Tree (set theory); Mathematics","score_opus":0.02363982486011327,"score_gpt":0.25562696010428,"score_spread":0.23198713524416673,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2022879582","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003823886,0.00013027064,0.99366367,0.00012474105,0.00004695829,0.000036366375,0.00008240557,0.00013748,0.0019542992],"genre_scores_gemma":[0.17372105,0.00035957346,0.82137316,0.00018196108,0.00007625972,0.00045394566,0.00033870962,0.00025616758,0.0032391965],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995709,0.00021412146,0.000016049065,0.00004849343,0.000094982934,0.000055461158],"domain_scores_gemma":[0.99908614,0.0005833035,0.000063984495,0.000104169856,0.00011412052,0.00004834555],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015527465,0.0010983103,0.0012834907,0.00064739486,0.00049892714,0.00062009244,0.0013193086,0.0009113025,0.004577137],"category_scores_gemma":[0.0032595806,0.0006885218,0.0012631183,0.00085314654,0.00073645735,0.00094220205,0.0018440869,0.0022705442,0.00078145025],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013687395,0.00007551469,0.0004916106,0.00018599968,0.00008934692,0.00010555629,0.0000722019,0.90620416,0.0020811677,0.032395087,0.006031024,0.052131422],"study_design_scores_gemma":[0.000021761954,0.000018741397,0.000045623605,0.000006760409,0.000008506693,0.000013099935,0.000014411468,0.9905743,0.00022668413,0.008065171,0.0010014281,0.0000035680823],"about_ca_topic_score_codex":0.005478674,"about_ca_topic_score_gemma":0.006019746,"teacher_disagreement_score":0.005478674,"about_ca_system_score_codex":0.00048516924,"about_ca_system_score_gemma":0.0011460547,"threshold_uncertainty_score":0.015312016},"labels":[],"label_agreement":null},{"id":"W2023445393","doi":"10.1287/trsc.1030.0086","title":"Heuristics for the One-Commodity Pickup-and-Delivery Traveling Salesman Problem","year":2004,"lang":"en","type":"article","venue":"Transportation Science","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":133,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Ministerio de Ciencia y Tecnología; Canada Research Chairs","keywords":"Travelling salesman problem; Pickup; Heuristics; Traveling purchaser problem; Mathematical optimization; Heuristic; Product (mathematics); Computer science; Greedy algorithm; 2-opt; Vehicle routing problem; Commodity; Mathematics; Operations research; Routing (electronic design automation); Economics; Artificial intelligence","score_opus":0.03870615525915687,"score_gpt":0.2777857777228835,"score_spread":0.2390796224637266,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2023445393","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.029933205,0.0029483098,0.9526279,0.00042397497,0.00018628738,0.00054914545,0.0002569969,0.0007802333,0.012293948],"genre_scores_gemma":[0.27353886,0.0030599618,0.71635604,0.0003030505,0.00016336488,0.00045717298,0.00046451567,0.00020508727,0.005451963],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992124,0.00035787735,0.000036512436,0.00011971613,0.00013229345,0.0001410637],"domain_scores_gemma":[0.9989532,0.00072475045,0.00013176275,0.000065254535,0.00006147003,0.000063587184],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010283701,0.0012718411,0.0012706001,0.0011583831,0.0006972944,0.0014166117,0.0017900108,0.0013882278,0.0036473556],"category_scores_gemma":[0.0023833355,0.00061129924,0.0009877764,0.0019894948,0.00079829013,0.0013282216,0.00085292954,0.0011914808,0.00067307346],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012196012,0.0002067229,0.00038098005,0.000472181,0.0000659303,0.00027920783,0.00015923263,0.8509044,0.0010801632,0.058331385,0.006666752,0.081331104],"study_design_scores_gemma":[0.00017023113,0.00016977036,0.00025711514,0.00007030518,0.000062005296,0.0002670323,0.0001356039,0.9447488,0.0012368443,0.041407723,0.0114358,0.000038707753],"about_ca_topic_score_codex":0.0040667877,"about_ca_topic_score_gemma":0.006131396,"teacher_disagreement_score":0.0040667877,"about_ca_system_score_codex":0.0014318861,"about_ca_system_score_gemma":0.0019239977,"threshold_uncertainty_score":0.012201607},"labels":[],"label_agreement":null},{"id":"W2023470442","doi":"10.1016/j.ejor.2015.01.020","title":"The discrete time window assignment vehicle routing problem","year":2015,"lang":"en","type":"article","venue":"European Journal of Operational Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":80,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Group for Research in Decision Analysis","funders":"","keywords":"Column generation; Vehicle routing problem; Heuristics; Mathematical optimization; Computer science; Set (abstract data type); Window (computing); Routing (electronic design automation); Assignment problem; Mathematics","score_opus":0.07976025754685145,"score_gpt":0.34897727786088706,"score_spread":0.2692170203140356,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2023470442","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.052995753,0.00089850544,0.9385726,0.00084708567,0.00033483788,0.00009453637,0.00043873256,0.00012980754,0.0056881686],"genre_scores_gemma":[0.7838448,0.0028840078,0.18449736,0.00019850068,0.00056037074,0.0003434401,0.0008488802,0.00017173005,0.026650872],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995596,0.00014458012,0.000017771577,0.0001323494,0.00007348167,0.00007217877],"domain_scores_gemma":[0.9992805,0.00046853244,0.000092659604,0.000037742142,0.00004514271,0.000075358745],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007953729,0.0008633709,0.0010194747,0.00042059264,0.00032818574,0.0013252074,0.0014380806,0.0013444684,0.004691841],"category_scores_gemma":[0.0026633677,0.0005867083,0.0005940401,0.0009173266,0.0004973197,0.0019265566,0.00075941253,0.0013290555,0.0004335015],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00041483392,0.0001636085,0.0007552688,0.00036071482,0.00009884152,0.00024258877,0.00009924995,0.81773734,0.0038971081,0.11157561,0.0055871727,0.05906762],"study_design_scores_gemma":[0.000039136652,0.000043496886,0.00019007396,0.000008667272,0.00002318728,0.00004332731,0.000023612947,0.97035587,0.0005490311,0.026264444,0.0024514343,0.000007787629],"about_ca_topic_score_codex":0.0026089735,"about_ca_topic_score_gemma":0.0014017519,"teacher_disagreement_score":0.004691841,"about_ca_system_score_codex":0.0006701764,"about_ca_system_score_gemma":0.0009944513,"threshold_uncertainty_score":0.01569575},"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,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_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","score_opus":0.07063186204221393,"score_gpt":0.3441733096093075,"score_spread":0.27354144756709353,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2023482164","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0061519453,0.00010502218,0.98893505,0.000215243,0.000042951207,0.00009754793,0.000066285,0.00058108545,0.0038048578],"genre_scores_gemma":[0.07859681,0.000114488095,0.9174724,0.0002364251,0.000025660727,0.00034240386,0.00029820795,0.00018124162,0.0027323884],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999275,0.0002462699,0.00003656128,0.00016030866,0.00015477765,0.00012704117],"domain_scores_gemma":[0.9992048,0.00039793193,0.00012004838,0.000088281144,0.00012885072,0.000060077706],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012280535,0.0012727203,0.0008162195,0.00095639005,0.0006975752,0.0011230417,0.0019312918,0.0015312968,0.00559072],"category_scores_gemma":[0.0032297678,0.00057340536,0.00095416966,0.001226386,0.00079197926,0.0012282109,0.0014059833,0.0015013096,0.0013031084],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004009783,0.00027145186,0.00077310565,0.00016901622,0.000053724125,0.00013405227,0.00022086671,0.5975805,0.0043251873,0.044512223,0.0103545105,0.3412044],"study_design_scores_gemma":[0.0000774475,0.00010163335,0.00007252665,0.000019919795,0.000010383252,0.00005507012,0.000036032885,0.98394704,0.0012203442,0.010909127,0.003536226,0.0000143653515],"about_ca_topic_score_codex":0.0034874785,"about_ca_topic_score_gemma":0.004505265,"teacher_disagreement_score":0.00559072,"about_ca_system_score_codex":0.0015108522,"about_ca_system_score_gemma":0.0022729435,"threshold_uncertainty_score":0.018702805},"labels":[],"label_agreement":null},{"id":"W2023863254","doi":"10.1057/palgrave.jors.2602469","title":"A tabu search heuristic for a routing problem arising in servicing of offshore oil and gas platforms","year":2007,"lang":"en","type":"article","venue":"Journal of the Operational Research Society","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":61,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Tabu search; Heuristics; Pickup; Computer science; Subsea; Vehicle routing problem; Operations research; Submarine pipeline; Heuristic; Routing (electronic design automation); Marine engineering; Engineering; Algorithm; Computer network; Operating system","score_opus":0.055558270644770216,"score_gpt":0.36890622218435587,"score_spread":0.31334795153958567,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2023863254","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15839358,0.0027621407,0.81236273,0.00085338694,0.00021017336,0.00078912673,0.0007882249,0.002134132,0.021706494],"genre_scores_gemma":[0.34695217,0.0010164498,0.6443686,0.00028848325,0.000047705398,0.0009031147,0.0008426729,0.0003063853,0.00527451],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99933773,0.00040562492,0.000017941773,0.00006240659,0.000073697905,0.00010249072],"domain_scores_gemma":[0.9982943,0.0013074767,0.0001251246,0.000068217865,0.00013866975,0.00006626446],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012852858,0.00080958114,0.0010049576,0.0014694576,0.0014558323,0.0015093065,0.0014461956,0.0021136869,0.0064756237],"category_scores_gemma":[0.0037964066,0.0007738604,0.00075916725,0.0027223974,0.0010997782,0.00102402,0.00076697144,0.0008601959,0.0009771897],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017717265,0.00018653258,0.0006552079,0.0002026662,0.000049363938,0.00015449132,0.00021172134,0.89147,0.0010165493,0.01161812,0.0045196926,0.08973843],"study_design_scores_gemma":[0.0001042839,0.00018073196,0.0002661378,0.0000536999,0.000035722787,0.00008502536,0.000195307,0.9878734,0.00080494443,0.0066642156,0.00371713,0.000019471474],"about_ca_topic_score_codex":0.008692102,"about_ca_topic_score_gemma":0.009605636,"teacher_disagreement_score":0.008692102,"about_ca_system_score_codex":0.0015862823,"about_ca_system_score_gemma":0.0020699587,"threshold_uncertainty_score":0.02166313},"labels":[],"label_agreement":null},{"id":"W2024936662","doi":"10.1109/cec.2010.5586382","title":"An efficient genetic algorithm for the uncapacitated single allocation hub location problem","year":2010,"lang":"en","type":"article","venue":"","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brock University","funders":"","keywords":"Crossover; Benchmark (surveying); Genetic algorithm; Encoding (memory); Computer science; Mathematical optimization; Flow network; Simple (philosophy); Chromosome; Facility location problem; Network planning and design; Representation (politics); Algorithm; Mathematics; Computer network; Artificial intelligence","score_opus":0.0141453342209531,"score_gpt":0.25239516910405196,"score_spread":0.23824983488309887,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2024936662","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.028239997,0.0003052653,0.9652886,0.00025434897,0.00006797043,0.00013835427,0.00008152641,0.00044256356,0.0051813484],"genre_scores_gemma":[0.25398073,0.0003708735,0.74078816,0.00018202809,0.000049460104,0.00045833422,0.0003081242,0.00009816889,0.0037641295],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999673,0.00009752478,0.000011282036,0.000069138674,0.000099870085,0.00004910387],"domain_scores_gemma":[0.9995778,0.000269494,0.00003839035,0.000028196586,0.00006799821,0.000018111088],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005875161,0.0010110375,0.0008019017,0.00078950817,0.0005095153,0.0007131361,0.0011670019,0.0014613138,0.0018655546],"category_scores_gemma":[0.0020229619,0.00035956313,0.00054541824,0.0010471761,0.0006193122,0.0006010887,0.00072578207,0.0010475127,0.0003625443],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000313036,0.00006871719,0.00048496472,0.000045993747,0.000029347726,0.000080020596,0.00006323,0.8964807,0.0017713789,0.01337626,0.0018512143,0.0857169],"study_design_scores_gemma":[0.000026952483,0.000025181602,0.00009313316,0.000006462904,0.000009768853,0.00002343046,0.000012571406,0.99440306,0.00035091987,0.003958771,0.0010847539,0.0000049712885],"about_ca_topic_score_codex":0.005966909,"about_ca_topic_score_gemma":0.0060060066,"teacher_disagreement_score":0.005966909,"about_ca_system_score_codex":0.0009576643,"about_ca_system_score_gemma":0.0016951285,"threshold_uncertainty_score":0.011864364},"labels":[],"label_agreement":null},{"id":"W2025149700","doi":"10.1287/trsc.1030.0064","title":"Designing Distribution Networks: Formulations and Solution Heuristic","year":2004,"lang":"en","type":"article","venue":"Transportation Science","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":75,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal; Université de Montréal; Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Transshipment (information security); Tabu search; Computer science; Truck; Metaheuristic; Heuristic; Mathematical optimization; Operations research; Consolidation (business); Context (archaeology); Engineering; Algorithm; Mathematics; Economics; Artificial intelligence","score_opus":0.017720834199634494,"score_gpt":0.2662849077816137,"score_spread":0.24856407358197918,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2025149700","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008868409,0.0011876549,0.9748616,0.00071191636,0.000069422815,0.0002887342,0.0002693096,0.00014599581,0.01359702],"genre_scores_gemma":[0.17474073,0.0034887944,0.80959874,0.00024343554,0.0002250421,0.0013407867,0.000469275,0.00020368258,0.009689595],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99920243,0.00046798875,0.000025565003,0.000093420545,0.0001262421,0.000084321684],"domain_scores_gemma":[0.99880266,0.0009085501,0.000116079995,0.000045315264,0.000091178386,0.000036198726],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020323445,0.0015039444,0.0011937085,0.0015345468,0.00062155427,0.002305745,0.0015909077,0.0025492157,0.005286617],"category_scores_gemma":[0.004342741,0.0011929885,0.0009517246,0.0024439364,0.0010904445,0.0015573336,0.001322868,0.0016143917,0.0006985682],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011967139,0.00003311874,0.00013120157,0.00008984458,0.000012638046,0.00005267691,0.000038920534,0.9564473,0.00015953979,0.028763698,0.00145539,0.012803658],"study_design_scores_gemma":[0.000021671187,0.00001771869,0.000039488925,0.000043100972,0.000009499276,0.000020019857,0.000058349327,0.9703846,0.0002203341,0.024693253,0.004485427,0.0000065576855],"about_ca_topic_score_codex":0.006604647,"about_ca_topic_score_gemma":0.006590661,"teacher_disagreement_score":0.006604647,"about_ca_system_score_codex":0.0028322171,"about_ca_system_score_gemma":0.0025013648,"threshold_uncertainty_score":0.020549297},"labels":[],"label_agreement":null},{"id":"W2025661878","doi":"10.1016/j.ejor.2003.09.024","title":"Branch-and-cut algorithms for the undirected m-Peripatetic Salesman Problem","year":2003,"lang":"en","type":"article","venue":"European Journal of Operational Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Group for Research in Decision Analysis; HEC Montréal","funders":"","keywords":"Travelling salesman problem; Undirected graph; Combinatorics; Bottleneck traveling salesman problem; Euclidean geometry; Disjoint sets; Lin–Kernighan heuristic; Mathematics; Algorithm; Branch and cut; Hamiltonian path; Discrete mathematics; Computer science; Graph; Integer programming","score_opus":0.10347229407901987,"score_gpt":0.36713532248867153,"score_spread":0.26366302840965167,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2025661878","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04752322,0.0011510247,0.93376505,0.0011227444,0.00012150572,0.0002237368,0.0004215406,0.00073296204,0.014938259],"genre_scores_gemma":[0.27386615,0.0010101978,0.71133363,0.00027167372,0.00017775531,0.00041482557,0.0011895731,0.00040234695,0.011333894],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99935395,0.0002737904,0.000027625492,0.0001218479,0.00011070914,0.00011213364],"domain_scores_gemma":[0.9973309,0.002011955,0.00020567731,0.00012438073,0.00016390097,0.00016314712],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015246004,0.001233009,0.0018955058,0.0013380246,0.0012029026,0.0018810923,0.0023411044,0.002370468,0.006960054],"category_scores_gemma":[0.0046128486,0.00097196316,0.0010298544,0.0025967848,0.0010183011,0.0025048198,0.001788191,0.0026643,0.0011166699],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00032389766,0.0003937194,0.0009542196,0.00032276972,0.00011805373,0.00013617906,0.00019245651,0.7191638,0.0008146786,0.065220684,0.014464745,0.19789487],"study_design_scores_gemma":[0.00007358578,0.000046025056,0.00014787294,0.000018933333,0.000023339559,0.000029106865,0.000042205447,0.92707473,0.0002576536,0.07053316,0.0017460905,0.000007306394],"about_ca_topic_score_codex":0.0070590614,"about_ca_topic_score_gemma":0.008380493,"teacher_disagreement_score":0.0070590614,"about_ca_system_score_codex":0.001371377,"about_ca_system_score_gemma":0.0020992104,"threshold_uncertainty_score":0.02328372},"labels":[],"label_agreement":null},{"id":"W2026522103","doi":"10.3758/bf03211819","title":"A model of human performance on the traveling salesperson problem","year":2000,"lang":"en","type":"article","venue":"Memory & Cognition","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":88,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Node (physics); Travelling salesman problem; Range (aeronautics); Heuristic; Variety (cybernetics); Mathematical optimization; Path (computing); Psychology; Algorithm; Mathematics; Computer science; Artificial intelligence; Engineering","score_opus":0.045619251886458365,"score_gpt":0.2545081547099788,"score_spread":0.2088889028235204,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2026522103","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7766214,0.0007182569,0.16813652,0.002519968,0.00008801606,0.0000884964,0.0006286635,0.0002832332,0.050915424],"genre_scores_gemma":[0.98746175,0.00022350518,0.007421202,0.000060292692,0.00001945551,0.00005148871,0.00011770091,0.000015506745,0.004629159],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997198,0.00012578396,0.000007321066,0.000060496335,0.00002280141,0.00006382828],"domain_scores_gemma":[0.9988569,0.00076499925,0.000089946196,0.00007982817,0.00008402702,0.00012426065],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006174166,0.0004990748,0.0005804606,0.00044525194,0.00033455723,0.0013186749,0.0010268824,0.0013577854,0.007761521],"category_scores_gemma":[0.0040172855,0.00021366618,0.00043590326,0.0006167176,0.00080487935,0.0013878775,0.00044372937,0.00066958496,0.0006470078],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003483511,0.00040145736,0.0049649877,0.00011561344,0.00013078336,0.00035734344,0.00090740446,0.7774307,0.0016022392,0.17860521,0.0047190576,0.030416805],"study_design_scores_gemma":[0.00005103205,0.00014070031,0.002741491,0.000014906642,0.000030214102,0.00007336074,0.0002517951,0.9235224,0.00016041852,0.07213831,0.00085330877,0.000021958871],"about_ca_topic_score_codex":0.015577043,"about_ca_topic_score_gemma":0.0068450933,"teacher_disagreement_score":0.015577043,"about_ca_system_score_codex":0.00078355224,"about_ca_system_score_gemma":0.00097243825,"threshold_uncertainty_score":0.030972779},"labels":[],"label_agreement":null},{"id":"W2026533796","doi":"10.1002/net.21529","title":"Incomplete service and split deliveries in a routing problem with profits","year":2013,"lang":"en","type":"article","venue":"Networks","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Group for Research in Decision Analysis; HEC Montréal","funders":"","keywords":"Orienteering; Heuristics; Computer science; Vehicle routing problem; Mathematical optimization; Operations research; Profit (economics); Constraint (computer-aided design); Service (business); Routing (electronic design automation); Mathematics; Business; Computer network; Economics; Microeconomics; Marketing","score_opus":0.008552179507714286,"score_gpt":0.19779935634048604,"score_spread":0.18924717683277176,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2026533796","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5012534,0.0010158506,0.48847437,0.0009619347,0.00007246865,0.00015598448,0.0003564334,0.00013304196,0.007576481],"genre_scores_gemma":[0.92908573,0.00037683413,0.06702576,0.00006684464,0.00005648392,0.00010325767,0.0002339725,0.000070063805,0.0029810986],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99856704,0.00061270234,0.000054584772,0.00024837846,0.0002644956,0.00025276525],"domain_scores_gemma":[0.9963744,0.0026871373,0.0003015152,0.00015962547,0.00017990118,0.00029735887],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019440758,0.0011642012,0.0013319199,0.00064791925,0.0007081513,0.001820385,0.0013507387,0.001210582,0.002766526],"category_scores_gemma":[0.0054695862,0.0006407804,0.00093564513,0.0011204131,0.0013786232,0.0026952059,0.0013460648,0.0014267884,0.00021293404],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000167992,0.0000722803,0.0006426077,0.00009039201,0.000050810315,0.00028387512,0.00009549639,0.9660256,0.0013332976,0.020773167,0.0006494491,0.009815072],"study_design_scores_gemma":[0.00003423874,0.00011987545,0.00037102052,0.000015764854,0.00003186958,0.00017993893,0.00011840036,0.96293354,0.001285146,0.034035295,0.0008614619,0.00001335893],"about_ca_topic_score_codex":0.0020391839,"about_ca_topic_score_gemma":0.0012735085,"teacher_disagreement_score":0.002766526,"about_ca_system_score_codex":0.0016524948,"about_ca_system_score_gemma":0.0010083757,"threshold_uncertainty_score":0.011989713},"labels":[],"label_agreement":null},{"id":"W2027505077","doi":"10.1016/j.dam.2014.10.001","title":"The Capacitated Orienteering Problem","year":2014,"lang":"en","type":"article","venue":"Discrete Applied Mathematics","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Orienteering; Mathematics; Mathematical optimization; Upper and lower bounds; Vehicle routing problem; Generalization; Subroutine; Facility location problem; Combinatorics; Euclidean geometry; Approximation algorithm; Adjacency list; Node (physics); Routing (electronic design automation); Computer science","score_opus":0.009095838749054999,"score_gpt":0.22291782385226225,"score_spread":0.21382198510320724,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2027505077","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.124432765,0.0019020935,0.781547,0.0019980636,0.00028653044,0.00012562069,0.000721488,0.00016107572,0.088825405],"genre_scores_gemma":[0.8157703,0.0026047025,0.117352545,0.00044977502,0.0002644719,0.00019744624,0.00082991767,0.00023898174,0.062291835],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996277,0.00012911661,0.000011219468,0.00010407396,0.000064394415,0.00006347381],"domain_scores_gemma":[0.99948907,0.00028038255,0.00005887213,0.000039563645,0.00005953906,0.00007252232],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005237347,0.0011084329,0.0008581124,0.00057596795,0.0005106447,0.0016134284,0.0015894118,0.0019436992,0.0074543995],"category_scores_gemma":[0.0020630448,0.000521602,0.0006152138,0.001282992,0.0009461445,0.0018290587,0.0013042961,0.0014389458,0.00055938103],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014060264,0.00010568358,0.0007521873,0.0002509682,0.00006384866,0.0002763949,0.00012060756,0.53560346,0.0017966182,0.39425835,0.011044121,0.055587124],"study_design_scores_gemma":[0.000033979537,0.00005340707,0.00035286642,0.000030018538,0.000026902057,0.00013115823,0.00010477819,0.78679836,0.0007387363,0.2010488,0.010658108,0.00002285749],"about_ca_topic_score_codex":0.0041519497,"about_ca_topic_score_gemma":0.0028714084,"teacher_disagreement_score":0.0074543995,"about_ca_system_score_codex":0.0011731632,"about_ca_system_score_gemma":0.00081365614,"threshold_uncertainty_score":0.02493745},"labels":[],"label_agreement":null},{"id":"W2028470441","doi":"10.1016/j.cor.2014.07.004","title":"Fleet-sizing for multi-depot and periodic vehicle routing problems using a modular heuristic algorithm","year":2014,"lang":"en","type":"article","venue":"Computers & Operations Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":66,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec; Polytechnique Montréal; Université du Québec à Montréal; Université de Montréal","funders":"Fonds de recherche du Québec – Nature et technologies","keywords":"Vehicle routing problem; Modular design; Heuristic; Computer science; Sizing; Mathematical optimization; Depot; Algorithm; Routing (electronic design automation); Mathematics","score_opus":0.11255804548950706,"score_gpt":0.3764900752146841,"score_spread":0.26393202972517704,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2028470441","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.063723505,0.00009321392,0.9301494,0.00006855599,0.000033922923,0.00014422653,0.00012734212,0.0003678555,0.00529207],"genre_scores_gemma":[0.48430952,0.0001314998,0.5108558,0.000045674264,0.00003090829,0.00041323592,0.00023760326,0.00017712334,0.0037986408],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998344,0.000049869974,0.00000821511,0.000031038,0.00004618073,0.000030283027],"domain_scores_gemma":[0.9996075,0.00025780863,0.00004944358,0.000032095286,0.000034960183,0.000018219971],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006118617,0.0008473009,0.0009332591,0.0008890917,0.0003882859,0.0005958723,0.0010741511,0.00065723824,0.0040111695],"category_scores_gemma":[0.0012154419,0.0005788741,0.0010556334,0.00089166866,0.00043042802,0.0008264061,0.00075105863,0.00059351075,0.0003722874],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004168605,0.000042605152,0.0001757125,0.000043299617,0.000024249077,0.000028357017,0.00001915713,0.960287,0.0024445641,0.004705933,0.00042703535,0.03176044],"study_design_scores_gemma":[0.000012835184,0.000034832996,0.00007568467,0.00000219872,0.000007382318,0.000007334885,0.0000037877066,0.9977144,0.0003207138,0.0016830207,0.00013530366,0.0000025964466],"about_ca_topic_score_codex":0.00300496,"about_ca_topic_score_gemma":0.005362734,"teacher_disagreement_score":0.0040111695,"about_ca_system_score_codex":0.00070484116,"about_ca_system_score_gemma":0.0009183423,"threshold_uncertainty_score":0.013418734},"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,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_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","score_opus":0.008733564536156346,"score_gpt":0.2388332489582275,"score_spread":0.23009968442207115,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2029341065","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025419181,0.00008671012,0.9714353,0.00015681001,0.000026672436,0.00009335057,0.00006803677,0.0006087402,0.0021051248],"genre_scores_gemma":[0.14613193,0.0000717707,0.8519684,0.000060751463,0.000019176152,0.00012909452,0.0001682304,0.00010097478,0.0013497251],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99949,0.00018538006,0.000022854114,0.000095398726,0.00013317517,0.0000730422],"domain_scores_gemma":[0.99835443,0.0010783563,0.00009562864,0.00023209708,0.00019490333,0.000044461012],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010118218,0.0006259591,0.0007390344,0.0005149218,0.0005305457,0.00056111335,0.0010019477,0.0008630053,0.0043133157],"category_scores_gemma":[0.0032225759,0.00035264666,0.00045658284,0.00085510866,0.000597324,0.0010227998,0.000792757,0.0010982424,0.00041064984],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012155989,0.00011177281,0.00045909395,0.00013324793,0.00002460926,0.00008006347,0.00010895176,0.79709077,0.0031571786,0.02831965,0.0028533142,0.16753973],"study_design_scores_gemma":[0.00005670058,0.00004036829,0.00008031318,0.0000059795148,0.0000059760546,0.000038854916,0.000020052823,0.98410416,0.0012087941,0.013206194,0.0012260642,0.0000065495437],"about_ca_topic_score_codex":0.004946777,"about_ca_topic_score_gemma":0.006324674,"teacher_disagreement_score":0.004946777,"about_ca_system_score_codex":0.0006947512,"about_ca_system_score_gemma":0.001624104,"threshold_uncertainty_score":0.01442951},"labels":[],"label_agreement":null},{"id":"W2029570945","doi":"10.1007/s11750-011-0188-6","title":"A generalized variable neighborhood search heuristic for the capacitated vehicle routing problem with stochastic service times","year":2011,"lang":"en","type":"article","venue":"Top","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":55,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"National University of Defense Technology","keywords":"Heuristics; Vehicle routing problem; Heuristic; Variable neighborhood search; Mathematical optimization; Variable (mathematics); Computer science; Routing (electronic design automation); Local search (optimization); Service (business); Mathematics; Metaheuristic","score_opus":0.03343292112571488,"score_gpt":0.24563083175669856,"score_spread":0.21219791063098367,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2029570945","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04190881,0.00060240785,0.9519478,0.00025743406,0.00016128537,0.00008751138,0.0000723378,0.00025695894,0.0047055306],"genre_scores_gemma":[0.5394343,0.0004328814,0.45305166,0.00019478475,0.00011819104,0.00029869887,0.00023253929,0.00018540444,0.0060515055],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99957484,0.00019116655,0.000011468832,0.000054760054,0.000113773305,0.000054000197],"domain_scores_gemma":[0.99938107,0.00037565723,0.000056500518,0.000039452596,0.00010148294,0.00004585747],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00083033065,0.0005297569,0.0012630275,0.0006580346,0.0004500015,0.0007214679,0.001459405,0.0011863591,0.002770436],"category_scores_gemma":[0.0020622115,0.00047105917,0.0006498205,0.0008458574,0.0005468425,0.00093832304,0.00085316517,0.0007500552,0.00026030355],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006492038,0.000043593252,0.0001631012,0.000033339962,0.000023032595,0.000028426546,0.000024302923,0.9621239,0.00048127802,0.009108674,0.0013080284,0.026597485],"study_design_scores_gemma":[0.000009797707,0.000013179228,0.000025372368,0.0000027432147,0.0000031656973,0.000004604512,0.0000037520124,0.99820614,0.00005054548,0.0014346953,0.00024406082,0.000002022847],"about_ca_topic_score_codex":0.0071134963,"about_ca_topic_score_gemma":0.00690274,"teacher_disagreement_score":0.0071134963,"about_ca_system_score_codex":0.000939865,"about_ca_system_score_gemma":0.0013146452,"threshold_uncertainty_score":0.014144182},"labels":[],"label_agreement":null},{"id":"W2030906319","doi":"10.1057/palgrave.jors.2602361","title":"Impact of the pheromone trail on the performance of ACO algorithms for solving the<i>car-sequencing</i>problem","year":2008,"lang":"en","type":"article","venue":"Journal of the Operational Research Society","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval; Université du Québec à Chicoutimi","funders":"","keywords":"Ant colony optimization algorithms; Computer science; Algorithm; Ant colony; Artificial intelligence; Purchasing; Machine learning; Mathematical optimization; Mathematics; Engineering; Operations management","score_opus":0.10655571609995261,"score_gpt":0.37194488080600396,"score_spread":0.26538916470605134,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2030906319","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.95147395,0.0013710784,0.03028176,0.0005480546,0.00011869977,0.00008294534,0.000059760496,0.0005440848,0.015519641],"genre_scores_gemma":[0.9894505,0.0002394085,0.009606509,0.000035330035,0.000013749339,0.00002081147,0.00004248845,0.000037663904,0.00055338995],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990476,0.00044418007,0.00007547616,0.00006484912,0.000221965,0.0001458432],"domain_scores_gemma":[0.986388,0.010197734,0.00074240664,0.0006994267,0.0016111387,0.00036118674],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028410354,0.0005537529,0.0005275518,0.00089431094,0.00057560304,0.0013001498,0.0006430265,0.0007896846,0.00079774373],"category_scores_gemma":[0.016993375,0.000193845,0.00025866283,0.0006791811,0.0007398813,0.0010321534,0.00042985624,0.00060115405,0.00019707],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010409801,0.00047232787,0.0061708363,0.00016006012,0.0000900724,0.000057282006,0.000087934946,0.90652895,0.0029780057,0.003099579,0.0008284854,0.07848551],"study_design_scores_gemma":[0.0001033791,0.0006631473,0.0015793277,0.000028736922,0.000044862834,0.00005156358,0.000091242,0.9907515,0.005408333,0.00090404955,0.0003588563,0.000014904779],"about_ca_topic_score_codex":0.0036152336,"about_ca_topic_score_gemma":0.002848303,"teacher_disagreement_score":0.0036152336,"about_ca_system_score_codex":0.0006829242,"about_ca_system_score_gemma":0.0012779648,"threshold_uncertainty_score":0.01502502},"labels":[],"label_agreement":null},{"id":"W2031202838","doi":"10.1287/trsc.2013.0493","title":"Cutting-Plane Matheuristic for Service Network Design with Design-Balanced Requirements","year":2014,"lang":"en","type":"article","venue":"Transportation Science","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":56,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal","funders":"Centre interuniversitaire de recherche sur les reseaux d'entreprise, la logistique et le transport; Université de Montréal; Université du Québec à Montréal","keywords":"Solver; Network planning and design; Cutting-plane method; Dimension (graph theory); Variable (mathematics); Computer science; Mathematical optimization; Service (business); Quality of service; Plane (geometry); Distributed computing; Integer programming; Mathematics; Computer network","score_opus":0.04324152256923113,"score_gpt":0.2835365767714388,"score_spread":0.24029505420220765,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2031202838","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0008443709,0.0000756233,0.9964206,0.00006801285,0.000013835517,0.000038940754,0.000029616485,0.00007993897,0.0024290823],"genre_scores_gemma":[0.0470403,0.00039781604,0.94891375,0.00013967494,0.000046988698,0.00038947773,0.00020828283,0.0001385918,0.0027251595],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991677,0.0003021904,0.00003899538,0.00011613771,0.00031058668,0.000064427244],"domain_scores_gemma":[0.9988913,0.00071904145,0.00009741024,0.00011421912,0.00014134042,0.000036740425],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002052452,0.0019883183,0.0009254541,0.0011191617,0.00061945274,0.0014609221,0.0015589183,0.0013306685,0.006924072],"category_scores_gemma":[0.0040627974,0.00078776275,0.0017119616,0.00127231,0.0013356742,0.0012435799,0.0018020262,0.0031284005,0.0014786124],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004850108,0.000068253095,0.00031423548,0.0002811023,0.00004374526,0.00009844468,0.00009466015,0.76590323,0.0023687843,0.15105882,0.003082261,0.07663795],"study_design_scores_gemma":[0.000020223131,0.000050193885,0.00006273159,0.00004462702,0.000014362272,0.00006368375,0.000021066144,0.9084287,0.0012372248,0.08073562,0.009309875,0.0000116541505],"about_ca_topic_score_codex":0.0018811148,"about_ca_topic_score_gemma":0.0022287725,"teacher_disagreement_score":0.006924072,"about_ca_system_score_codex":0.0012827959,"about_ca_system_score_gemma":0.0017524934,"threshold_uncertainty_score":0.023163319},"labels":[],"label_agreement":null},{"id":"W2031669620","doi":"10.1002/net.21605","title":"A railroad maintenance problem solved with a cut and column generation matheuristic","year":2015,"lang":"en","type":"article","venue":"Networks","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Column generation; Integer programming; Arc routing; Computer science; Scheduling (production processes); Mathematical optimization; Routing (electronic design automation); Schedule; Vehicle routing problem; Heuristic; Graph; Operations research; Artificial intelligence; Algorithm; Mathematics; Theoretical computer science","score_opus":0.021355889310019804,"score_gpt":0.2254491847852068,"score_spread":0.204093295475187,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2031669620","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07291273,0.00049319095,0.9147197,0.0006103299,0.00011849266,0.00023745393,0.0012509545,0.00064380164,0.009013389],"genre_scores_gemma":[0.30550927,0.00031203,0.6849216,0.00019643955,0.00008778076,0.00032500588,0.0018087326,0.00016730856,0.0066717505],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997389,0.00007493469,0.000011000687,0.00009199771,0.00004455444,0.000038701666],"domain_scores_gemma":[0.99924195,0.00053258066,0.0000689654,0.000053442982,0.00007217614,0.00003085303],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005055216,0.0008462399,0.0006031662,0.00078581227,0.0004239187,0.00081621174,0.00075094524,0.0010716006,0.006092293],"category_scores_gemma":[0.0015278778,0.00041584385,0.0007700792,0.0009748492,0.00041799323,0.00080626074,0.00051254925,0.000826164,0.00044474317],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014463718,0.00022339952,0.0010687,0.00026891238,0.00006567307,0.00015283827,0.00007146723,0.85975564,0.002285753,0.014307669,0.009052786,0.11260261],"study_design_scores_gemma":[0.00002290934,0.00004619335,0.00022559597,0.00000987742,0.000012343961,0.000053665255,0.000023813109,0.99090344,0.00065999164,0.006522052,0.0015145623,0.0000054549387],"about_ca_topic_score_codex":0.0059336675,"about_ca_topic_score_gemma":0.0069135777,"teacher_disagreement_score":0.006092293,"about_ca_system_score_codex":0.00084858725,"about_ca_system_score_gemma":0.0012564857,"threshold_uncertainty_score":0.020380735},"labels":[],"label_agreement":null},{"id":"W2031935785","doi":"10.1007/s00170-012-4708-9","title":"Solving reverse logistics vehicle routing problems with time windows","year":2013,"lang":"en","type":"article","venue":"The International Journal of Advanced Manufacturing Technology","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":48,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Vehicle routing problem; Simulated annealing; Mathematical optimization; Heuristic; Reverse logistics; Integer programming; Computer science; Routing (electronic design automation); Mathematics; Supply chain","score_opus":0.008513473753236417,"score_gpt":0.22839699537477223,"score_spread":0.21988352162153582,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2031935785","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.088313535,0.0008895673,0.9055287,0.00030394085,0.00014363913,0.00011950445,0.00014347251,0.00017330814,0.004384322],"genre_scores_gemma":[0.6197926,0.0012347406,0.36660782,0.0001339868,0.00018903386,0.00031638573,0.00032938423,0.00021215821,0.0111838775],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999433,0.00020312892,0.000032995875,0.00011846168,0.000094934425,0.000117409174],"domain_scores_gemma":[0.9981832,0.0014273454,0.00016186234,0.000061613435,0.0000953448,0.000070736285],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017785972,0.0014939999,0.0014862169,0.00070908666,0.0004060446,0.0012583636,0.0015772097,0.0016234221,0.0033652992],"category_scores_gemma":[0.0038988465,0.0013572101,0.0014093254,0.0009816146,0.0005119296,0.0022662815,0.0012242426,0.0016471572,0.0002556686],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011634949,0.00005591354,0.00023650177,0.000100383935,0.000044965942,0.0000647425,0.000026022926,0.97757155,0.0006984466,0.006667688,0.00042902856,0.013988474],"study_design_scores_gemma":[0.000019150148,0.00004758625,0.000056451467,0.000006046688,0.000017463815,0.000013056015,0.00001637588,0.9954348,0.00051268545,0.0035318276,0.00033978943,0.0000047758717],"about_ca_topic_score_codex":0.005242896,"about_ca_topic_score_gemma":0.003051912,"teacher_disagreement_score":0.005242896,"about_ca_system_score_codex":0.00062519836,"about_ca_system_score_gemma":0.0014178002,"threshold_uncertainty_score":0.011258066},"labels":[],"label_agreement":null},{"id":"W2032353485","doi":"10.1007/s10489-006-0033-z","title":"Dynamic vehicle routing using genetic algorithms","year":2007,"lang":"en","type":"article","venue":"Applied Intelligence","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":158,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brock University","funders":"Natural Sciences and Engineering Research Council of Canada; Brock University","keywords":"Computer science; Vehicle routing problem; Tabu search; Genetic algorithm; Ant colony optimization algorithms; Mathematical optimization; Routing (electronic design automation); Extension (predicate logic); Set (abstract data type); Algorithm; Machine learning; Mathematics","score_opus":0.022784954628706954,"score_gpt":0.291942552189336,"score_spread":0.26915759756062907,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2032353485","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008677265,0.0002725277,0.985106,0.00013517088,0.00005580895,0.000027150672,0.000015760714,0.00016891913,0.0055414448],"genre_scores_gemma":[0.4825264,0.0007858787,0.5063376,0.00012562821,0.000085944484,0.0002275165,0.000113098446,0.00013533633,0.009662593],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99973255,0.00010419203,0.000008616485,0.00003848354,0.00009457651,0.000021592909],"domain_scores_gemma":[0.99970144,0.00017273454,0.000027051397,0.000031212017,0.00005803411,0.000009442455],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00060105085,0.0007167325,0.00072501716,0.0009599485,0.0004296567,0.0010472739,0.0009189209,0.0010545062,0.0015808436],"category_scores_gemma":[0.0015849182,0.00051251013,0.0006345662,0.0011099228,0.0007406852,0.001063862,0.00066612946,0.00077495445,0.00031050097],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013092553,0.000017690772,0.000115453746,0.000015640277,0.000021629448,0.000015366286,0.000020018282,0.9444348,0.00049772125,0.018173102,0.00045192533,0.036223438],"study_design_scores_gemma":[0.000005508271,0.000006756074,0.00002909829,0.000003572731,0.0000046661676,0.0000061050127,0.0000047561944,0.99077016,0.00016078056,0.008444872,0.0005609763,0.0000026678813],"about_ca_topic_score_codex":0.005617617,"about_ca_topic_score_gemma":0.0035997631,"teacher_disagreement_score":0.005617617,"about_ca_system_score_codex":0.0009378582,"about_ca_system_score_gemma":0.00084750314,"threshold_uncertainty_score":0.011169851},"labels":[],"label_agreement":null},{"id":"W2033097589","doi":"10.1016/j.orl.2003.08.005","title":"A bilevel programming approach to the travelling salesman problem","year":2003,"lang":"en","type":"article","venue":"Operations Research Letters","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":32,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Group for Research in Decision Analysis; Université de Montréal","funders":"","keywords":"Travelling salesman problem; Bilevel optimization; Mathematical optimization; Extension (predicate logic); Linear programming relaxation; Computer science; Relaxation (psychology); Toll; Linear programming; Dual (grammatical number); Optimization problem; Mathematics","score_opus":0.07996579037548483,"score_gpt":0.3380938230751212,"score_spread":0.25812803269963636,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2033097589","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0020908662,0.0005093387,0.992547,0.00036468462,0.000055358832,0.000014155334,0.00002423352,0.000027993077,0.004366348],"genre_scores_gemma":[0.23906222,0.005014163,0.72990376,0.0002787389,0.00048098492,0.00029248564,0.0002333641,0.00020664444,0.024527693],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992219,0.00035655318,0.000033992786,0.00009990026,0.00021528322,0.000072406685],"domain_scores_gemma":[0.9993356,0.00039554213,0.00005443603,0.00003510822,0.00013539834,0.000043866152],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011337099,0.0008764376,0.0015244358,0.001021874,0.0007943401,0.0025105283,0.0017517371,0.0017609273,0.0038608797],"category_scores_gemma":[0.0034746726,0.0007685214,0.0011540205,0.0025681562,0.001309123,0.002412757,0.0021467071,0.0032310174,0.0009153857],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000027228294,0.000061538056,0.00015772352,0.00013244478,0.000046410667,0.00008186804,0.00012290926,0.528404,0.0005627473,0.420754,0.0021229528,0.04752617],"study_design_scores_gemma":[0.000010652904,0.000022263837,0.000036892514,0.000021306856,0.000011941432,0.000026961963,0.00003274573,0.76232755,0.00016517277,0.2325595,0.004772835,0.000012197528],"about_ca_topic_score_codex":0.0036552867,"about_ca_topic_score_gemma":0.0026990368,"teacher_disagreement_score":0.0038608797,"about_ca_system_score_codex":0.00089629344,"about_ca_system_score_gemma":0.0017875886,"threshold_uncertainty_score":0.012915969},"labels":[],"label_agreement":null},{"id":"W2033241398","doi":"10.3138/infor.52.1.20","title":"Planification des tournées dans le domaine de la messagerie rapide","year":2014,"lang":"fr","type":"article","venue":"INFOR Information Systems and Operational Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Humanities; Political science; Philosophy","score_opus":0.0370303201759169,"score_gpt":0.3365988316709486,"score_spread":0.2995685114950317,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2033241398","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21035312,0.000531834,0.7725705,0.00057379797,0.00021037481,0.0005422066,0.0003300029,0.005494774,0.009393397],"genre_scores_gemma":[0.6611978,0.00019171144,0.32609797,0.00014160325,0.00004971889,0.00039876596,0.0006252752,0.00054782315,0.010749353],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980597,0.00040202698,0.000110540874,0.00043479042,0.00061847735,0.0003745496],"domain_scores_gemma":[0.9968509,0.001629911,0.00024606683,0.00032604783,0.0006218543,0.00032518234],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021066342,0.0011581855,0.00080307404,0.0006514004,0.0010039915,0.002110934,0.001398591,0.0010335501,0.00837484],"category_scores_gemma":[0.005918092,0.00052319036,0.00096689607,0.00048958755,0.0007568211,0.0013270968,0.00146642,0.0017182882,0.0012036058],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0017887158,0.00045334682,0.011638158,0.00053226564,0.00016598773,0.0006958949,0.0010505203,0.5872838,0.058649745,0.019575521,0.0070995423,0.3110665],"study_design_scores_gemma":[0.000096730575,0.00041384576,0.002041566,0.00003083877,0.00004666829,0.00013303461,0.0002753402,0.96401834,0.016268438,0.005632939,0.011007203,0.00003499773],"about_ca_topic_score_codex":0.007517679,"about_ca_topic_score_gemma":0.007517526,"teacher_disagreement_score":0.00837484,"about_ca_system_score_codex":0.0012559623,"about_ca_system_score_gemma":0.002066998,"threshold_uncertainty_score":0.028016627},"labels":[],"label_agreement":null},{"id":"W2033271291","doi":"10.1016/j.cor.2009.08.003","title":"Branch-and-cut for the pickup and delivery traveling salesman problem with FIFO loading","year":2009,"lang":"en","type":"article","venue":"Computers & Operations Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":41,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"Ministero dell’Istruzione, dell’Università e della Ricerca","keywords":"Travelling salesman problem; Pickup; Branch and cut; FIFO (computing and electronics); Mathematical optimization; Computer science; 2-opt; Traveling purchaser problem; Branch and bound; Bottleneck traveling salesman problem; Algorithm; Combinatorial optimization; Mathematics; Integer programming; Artificial intelligence","score_opus":0.05253349140854373,"score_gpt":0.33150602625391756,"score_spread":0.2789725348453738,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2033271291","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.052960295,0.0019415013,0.9245587,0.0018726848,0.00024202408,0.00048806265,0.00082155585,0.0006820196,0.01643318],"genre_scores_gemma":[0.44573838,0.002886813,0.5213846,0.00033122822,0.00039254173,0.0009111584,0.0016166525,0.00045757365,0.02628111],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988212,0.00045342866,0.000047447276,0.0001501895,0.00026519393,0.0002625962],"domain_scores_gemma":[0.99642074,0.0028512068,0.00019869136,0.000108206645,0.00020981669,0.00021136733],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0037556386,0.0017089302,0.0030428374,0.0016600246,0.0014828482,0.002995643,0.0025556632,0.0028107974,0.011984749],"category_scores_gemma":[0.007475007,0.0016858196,0.0012754599,0.0027570939,0.0015041091,0.0036334395,0.0015149717,0.0029235485,0.0011198636],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00049711094,0.00030456253,0.00051613577,0.0002976666,0.00006614866,0.00009522818,0.00009726877,0.89198107,0.00050281966,0.039251544,0.006564532,0.059825886],"study_design_scores_gemma":[0.00006697714,0.00006521555,0.00013014427,0.00002122131,0.000022628908,0.000017360911,0.00002858187,0.9660137,0.00019357314,0.032543585,0.0008859424,0.000010966758],"about_ca_topic_score_codex":0.017761704,"about_ca_topic_score_gemma":0.010394984,"teacher_disagreement_score":0.017761704,"about_ca_system_score_codex":0.00313838,"about_ca_system_score_gemma":0.004009819,"threshold_uncertainty_score":0.040092945},"labels":[],"label_agreement":null},{"id":"W2033855616","doi":"10.1016/j.trb.2009.06.003","title":"Bidline scheduling with equity by heuristic dynamic constraint aggregation","year":2009,"lang":"en","type":"article","venue":"Transportation Research Part B Methodological","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Group for Research in Decision Analysis; Polytechnique Montréal","funders":"","keywords":"Heuristics; Rounding; Mathematical optimization; Heuristic; Computer science; Scheduling (production processes); Computation; Constraint (computer-aided design); Equity (law); Operations research; Mathematics; Algorithm","score_opus":0.23513138431625571,"score_gpt":0.46314257263806313,"score_spread":0.22801118832180742,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2033855616","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09547399,0.0004233593,0.8904419,0.00035101216,0.00017522111,0.0003102091,0.00032377566,0.00047201815,0.012028584],"genre_scores_gemma":[0.73792094,0.00015477848,0.25701007,0.00013483936,0.00010360581,0.00021599817,0.00029679475,0.0001536682,0.004009231],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988932,0.0004884853,0.000043315988,0.00015325396,0.00017643695,0.00024532198],"domain_scores_gemma":[0.99840975,0.0009718906,0.00012664872,0.00014907673,0.00021353218,0.0001290895],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018659098,0.0010032257,0.0023842063,0.0012274729,0.00092966703,0.0020055634,0.001674373,0.00095217593,0.0053547337],"category_scores_gemma":[0.0035049955,0.0010810688,0.00082997186,0.0026123072,0.00055300974,0.0017692626,0.0014778312,0.0011330552,0.00026513246],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010542721,0.000083581006,0.0002377371,0.000034331682,0.000028519406,0.000038970775,0.00002748728,0.96958643,0.00046996522,0.0067186328,0.001174175,0.021494731],"study_design_scores_gemma":[0.000016866792,0.000023245291,0.000050754472,0.0000024569617,0.000008290735,0.000004380793,0.0000106539865,0.99683785,0.0001326409,0.0026687204,0.00024047593,0.0000036902363],"about_ca_topic_score_codex":0.011709642,"about_ca_topic_score_gemma":0.012537639,"teacher_disagreement_score":0.011709642,"about_ca_system_score_codex":0.0016461132,"about_ca_system_score_gemma":0.0021437258,"threshold_uncertainty_score":0.023282945},"labels":[],"label_agreement":null},{"id":"W2034256646","doi":"10.1016/s0305-0548(03)00073-x","title":"Variable neighborhood decomposition search for the edge weighted k-cardinality tree problem","year":2003,"lang":"en","type":"article","venue":"Computers & Operations Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":32,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Group for Research in Decision Analysis; HEC Montréal; Royal Military College of Canada","funders":"","keywords":"Cardinality (data modeling); Mathematics; Decomposition; Heuristic; Combinatorics; Variable (mathematics); Decomposition method (queueing theory); Variable neighborhood search; Mathematical optimization; Tree (set theory); Graph; Enhanced Data Rates for GSM Evolution; Tree decomposition; Algorithm; Discrete mathematics; Computer science; Metaheuristic; Line graph; Pathwidth","score_opus":0.06097546952554979,"score_gpt":0.36983999250299276,"score_spread":0.308864522977443,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2034256646","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1509118,0.0009443058,0.8386717,0.0008524053,0.000076796736,0.00009575627,0.00030447915,0.0003077542,0.007835101],"genre_scores_gemma":[0.56021607,0.00033064594,0.43369418,0.00016546383,0.000060145518,0.00023240666,0.0006025434,0.00014662529,0.004551888],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99949753,0.00026869864,0.000015646792,0.00006724222,0.00008705508,0.00006384459],"domain_scores_gemma":[0.99859685,0.0010526708,0.00011508123,0.00005500278,0.00009700974,0.00008332622],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011880815,0.0005027158,0.0015688826,0.0009985688,0.0004996715,0.00096707954,0.0012801382,0.0012629908,0.0029741034],"category_scores_gemma":[0.0037129368,0.00051341474,0.0005979175,0.0011493799,0.0005897046,0.0014853835,0.0010378031,0.0011823587,0.00032402534],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023107068,0.00015239375,0.0009863956,0.00012615153,0.0000606125,0.00006300394,0.000070966795,0.9125973,0.0008674994,0.026827857,0.003901821,0.05411498],"study_design_scores_gemma":[0.000022800674,0.00001885,0.00007061302,0.000006402652,0.0000054511966,0.000009925677,0.000014563166,0.99225324,0.000084009356,0.0072798748,0.00023176271,0.0000024899246],"about_ca_topic_score_codex":0.0034964487,"about_ca_topic_score_gemma":0.0044099665,"teacher_disagreement_score":0.0034964487,"about_ca_system_score_codex":0.00078239315,"about_ca_system_score_gemma":0.00088086684,"threshold_uncertainty_score":0.0099493265},"labels":[],"label_agreement":null},{"id":"W2035201919","doi":"10.1057/palgrave.jors.2602603","title":"The capacitated team orienteering and profitable tour problems","year":2008,"lang":"en","type":"article","venue":"Journal of the Operational Research Society","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":162,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Group for Research in Decision Analysis; Polytechnique Montréal","funders":"","keywords":"Orienteering; Computer science; Operations research; Heuristic; Heuristics; Project management; Matching (statistics); The Internet; Purchasing; Operations management; Mathematical optimization; Engineering; Artificial intelligence; Mathematics; World Wide Web","score_opus":0.062302060283768806,"score_gpt":0.32529913499987456,"score_spread":0.2629970747161058,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2035201919","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13184996,0.0021319597,0.834808,0.0011683886,0.00022712021,0.0002308661,0.00039556943,0.00013283748,0.029055396],"genre_scores_gemma":[0.8015931,0.0029695595,0.17299592,0.00023827143,0.000289359,0.00045430334,0.00074055063,0.00013091705,0.02058802],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99919635,0.00033898925,0.000023558943,0.00012905576,0.00015515753,0.00015686749],"domain_scores_gemma":[0.99852246,0.0009657178,0.00017536113,0.000057691686,0.0001041089,0.00017464215],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008361601,0.001179815,0.0008008158,0.00054309395,0.0006564119,0.0012575118,0.0013392039,0.001533571,0.0052376213],"category_scores_gemma":[0.0034071542,0.00047507838,0.0008400204,0.0013925955,0.0010977648,0.0019013925,0.0011444237,0.0013339949,0.00038756238],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010777876,0.00009088935,0.0006861738,0.00019685342,0.000059554186,0.0003426053,0.00010920438,0.824893,0.0006689662,0.13632418,0.0048206793,0.031700093],"study_design_scores_gemma":[0.000047263035,0.00008950483,0.0003580784,0.000024950206,0.000019827303,0.00022243157,0.000121340265,0.84561944,0.0004449584,0.14833865,0.004689409,0.000024131426],"about_ca_topic_score_codex":0.0039401054,"about_ca_topic_score_gemma":0.0028343971,"teacher_disagreement_score":0.0052376213,"about_ca_system_score_codex":0.00088490563,"about_ca_system_score_gemma":0.0011475579,"threshold_uncertainty_score":0.01752156},"labels":[],"label_agreement":null},{"id":"W2035888716","doi":"10.1007/s13675-013-0012-1","title":"Separating valid odd-cycle and odd-set inequalities for the multiple depot vehicle scheduling problem","year":2013,"lang":"en","type":"article","venue":"EURO Journal on Computational Optimization","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kronos (Canada); Group for Research in Decision Analysis; Université du Québec à Montréal; Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Depot; Mathematical optimization; Scheduling (production processes); Multi-commodity flow problem; Computer science; Flow network; Column generation; Set (abstract data type); Mathematics; Operations research","score_opus":0.03596227856412682,"score_gpt":0.2892528394277084,"score_spread":0.2532905608635816,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2035888716","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05487125,0.00036197912,0.93849707,0.00042937143,0.00006128613,0.00023451674,0.0003271463,0.00013881901,0.0050786193],"genre_scores_gemma":[0.3910528,0.00090164586,0.60241354,0.00029982504,0.00009942903,0.00041610395,0.0009397449,0.00021975828,0.003657155],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99797434,0.00087714044,0.000111014306,0.00021602833,0.00049026817,0.00033117947],"domain_scores_gemma":[0.99142516,0.0065714875,0.0007176591,0.00040245484,0.00059080496,0.00029246786],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0043020444,0.0017913261,0.0012092389,0.0012131793,0.0008765031,0.0019946424,0.0019943025,0.0011308043,0.0035905784],"category_scores_gemma":[0.012169282,0.00067649403,0.0013913355,0.0016702812,0.0011572444,0.0029252449,0.0017375886,0.0021937003,0.0003574878],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00038776893,0.00030303086,0.0015538835,0.00030586257,0.00007366256,0.00024035858,0.00020325989,0.7753823,0.0031736854,0.12239426,0.0028022127,0.09317977],"study_design_scores_gemma":[0.00005805954,0.000074397656,0.0002136092,0.00002647698,0.00002230223,0.000039129955,0.000047357138,0.9224998,0.0019904405,0.073422775,0.0015881894,0.000017365253],"about_ca_topic_score_codex":0.0058281403,"about_ca_topic_score_gemma":0.0073963557,"teacher_disagreement_score":0.0058281403,"about_ca_system_score_codex":0.0015560655,"about_ca_system_score_gemma":0.0040481985,"threshold_uncertainty_score":0.02275163},"labels":[],"label_agreement":null},{"id":"W2035903817","doi":"10.1016/j.cor.2010.10.019","title":"Online vehicle routing and scheduling with dynamic travel times","year":2010,"lang":"en","type":"article","venue":"Computers & Operations Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":65,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Computer science; Vehicle routing problem; Scheduling (production processes); Operations research; Adaptive routing; Routing (electronic design automation); Dynamic priority scheduling; Distributed computing; Mathematical optimization; Static routing; Computer network; Routing protocol; Engineering; Mathematics","score_opus":0.02958572442583442,"score_gpt":0.343052482940414,"score_spread":0.3134667585145796,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2035903817","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019153161,0.00031692174,0.97684115,0.00033266764,0.0001478236,0.000058791305,0.00013814658,0.00022812262,0.002783276],"genre_scores_gemma":[0.7059035,0.000780239,0.27470702,0.00017312876,0.0005025572,0.00035852045,0.0004677179,0.0003638761,0.016743539],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982887,0.0007121031,0.000073659874,0.00038454577,0.00034060387,0.00020033431],"domain_scores_gemma":[0.9936539,0.004729457,0.0004931319,0.0004913427,0.0003825092,0.00024973162],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028782096,0.0013911374,0.0024974986,0.0010877195,0.00074488384,0.0024499123,0.0029360196,0.0019726257,0.0057588457],"category_scores_gemma":[0.010419548,0.0019501621,0.0011483555,0.0022995633,0.0012981626,0.0051777475,0.0019272034,0.002521513,0.0007602546],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020223521,0.00013099606,0.00026406595,0.000075168195,0.00003523009,0.000055948498,0.000037174672,0.94010735,0.00041063994,0.033190038,0.0014527978,0.024038425],"study_design_scores_gemma":[0.000009859484,0.000010537689,0.00003129387,0.0000020901657,0.000005339839,0.0000063944744,0.0000039955116,0.9900317,0.000087454086,0.009572873,0.00023555856,0.000002923268],"about_ca_topic_score_codex":0.006096152,"about_ca_topic_score_gemma":0.004219854,"teacher_disagreement_score":0.006096152,"about_ca_system_score_codex":0.002147079,"about_ca_system_score_gemma":0.0022809978,"threshold_uncertainty_score":0.019265294},"labels":[],"label_agreement":null},{"id":"W2037219660","doi":"10.1016/j.cor.2014.03.027","title":"Paired cooperative reoptimization strategy for the vehicle routing problem with stochastic demands","year":2014,"lang":"en","type":"article","venue":"Computers & Operations Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":34,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal; Polytechnique Montréal","funders":"Specialized Research Fund for the Doctoral Program of Higher Education of China; China Scholarship Council","keywords":"Computer science; Heuristic; Vehicle routing problem; Routing (electronic design automation); Mathematical optimization; Markov chain; Markov decision process; Embedding; Process (computing); Operations research; Sequence (biology); Markov process; Computer network; Mathematics; Artificial intelligence; Machine learning","score_opus":0.0653815366770034,"score_gpt":0.3409312984287469,"score_spread":0.2755497617517435,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2037219660","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.052631866,0.0002174513,0.93942386,0.00028494082,0.00007413713,0.0000875352,0.000055884208,0.00017450089,0.007049812],"genre_scores_gemma":[0.92846185,0.00013198164,0.063796975,0.00012652163,0.00005163283,0.00014679423,0.00009396973,0.00006745091,0.0071227388],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99929714,0.00030120724,0.000019650808,0.00013037802,0.000115252464,0.00013643304],"domain_scores_gemma":[0.9992823,0.00036766514,0.00008949045,0.00006004611,0.0001208124,0.000079734775],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012765779,0.0012700707,0.0013665596,0.00056385936,0.00046821605,0.0009672306,0.0017046161,0.0012839485,0.0040992536],"category_scores_gemma":[0.0017412199,0.0005843419,0.00077007664,0.0007355697,0.00061081705,0.0012156832,0.0013001511,0.0010265318,0.0004019287],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001446182,0.00007761405,0.00014432639,0.000035533925,0.000044399327,0.00009460806,0.000033712575,0.97033405,0.00158032,0.008170256,0.0013921306,0.017948449],"study_design_scores_gemma":[0.00001033002,0.000051722458,0.00003583291,0.0000017677974,0.0000059427443,0.000011682406,0.000009584277,0.99731904,0.0002044641,0.0021810438,0.00016497214,0.0000037308955],"about_ca_topic_score_codex":0.0028943412,"about_ca_topic_score_gemma":0.0021262546,"teacher_disagreement_score":0.0040992536,"about_ca_system_score_codex":0.0007471431,"about_ca_system_score_gemma":0.00092814566,"threshold_uncertainty_score":0.01371336},"labels":[],"label_agreement":null},{"id":"W2037425183","doi":"10.1057/palgrave.jors.2602356","title":"VLSN search algorithms for partitioning problems using matching neighbourhoods","year":2007,"lang":"en","type":"article","venue":"Journal of the Operational Research Society","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University; University of New Brunswick","funders":"","keywords":"Neighbourhood (mathematics); Matching (statistics); Computer science; Exponential function; Algorithm; Mathematical optimization; Class (philosophy); Theoretical computer science; Mathematics; Artificial intelligence; Statistics","score_opus":0.14358398482718118,"score_gpt":0.4266171361414274,"score_spread":0.2830331513142462,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2037425183","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015649548,0.00019690671,0.98169154,0.00006780369,0.000014992985,0.00006833991,0.000027013562,0.00023919284,0.0020445946],"genre_scores_gemma":[0.33502832,0.00035431096,0.66053796,0.00010744577,0.000036602632,0.00033623877,0.00039605482,0.00017677818,0.0030263178],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99933356,0.00023143043,0.000046454705,0.00014205802,0.0001754062,0.00007109539],"domain_scores_gemma":[0.998719,0.0008056635,0.00009280008,0.00020369665,0.00013633214,0.000042646472],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013704579,0.00047412587,0.0009139982,0.00087253464,0.0005950292,0.00080729544,0.0014790553,0.0008318242,0.0027430465],"category_scores_gemma":[0.0052516526,0.00034835926,0.00069816684,0.0012793349,0.0007123914,0.0024704083,0.002240883,0.00081680104,0.0005987153],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023234155,0.00012935289,0.0013018863,0.0002992029,0.00006412226,0.000109325054,0.0002870413,0.60907906,0.0062735905,0.11004998,0.003553526,0.26862058],"study_design_scores_gemma":[0.00004574965,0.000085414234,0.00019470404,0.000018838356,0.000014344784,0.000095571675,0.0000656239,0.9240645,0.0018680744,0.06958553,0.003950774,0.000010795357],"about_ca_topic_score_codex":0.0010031397,"about_ca_topic_score_gemma":0.0014239197,"teacher_disagreement_score":0.0027430465,"about_ca_system_score_codex":0.000628763,"about_ca_system_score_gemma":0.0005693864,"threshold_uncertainty_score":0.0091763735},"labels":[],"label_agreement":null},{"id":"W2038039225","doi":"10.1002/net.20383","title":"Modeling and solving a multimodal transportation problem with flexible‐time and scheduled services","year":2010,"lang":"en","type":"article","venue":"Networks","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":91,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"","keywords":"Column generation; Heuristics; Computer science; Flow network; Mathematical optimization; Integer programming; Digraph; Cutting-plane method; Mathematics; Algorithm","score_opus":0.00464616798209775,"score_gpt":0.20868945803766262,"score_spread":0.20404329005556487,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2038039225","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.30982953,0.0005952337,0.679442,0.00056856696,0.00007065286,0.00013569562,0.00034722476,0.0002544739,0.008756661],"genre_scores_gemma":[0.8898326,0.00026499698,0.10465037,0.000042708634,0.000037145488,0.00020045303,0.00023800776,0.000056583795,0.004677156],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996146,0.00017281591,0.000011312535,0.000071437724,0.000040991752,0.000088829656],"domain_scores_gemma":[0.99931526,0.00045383465,0.0000842109,0.000028435099,0.00005673794,0.00006149519],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010194324,0.0010207983,0.00091541314,0.000604948,0.00061308115,0.0014909251,0.0009107778,0.0015135919,0.0034826149],"category_scores_gemma":[0.0018246243,0.00059191283,0.001020215,0.0009492411,0.00076568656,0.0012497737,0.00083186774,0.0009443765,0.00024274981],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019170036,0.000014912166,0.00014551434,0.000011977794,0.00000827058,0.00003459721,0.000011464541,0.99553597,0.00016330935,0.0018962084,0.0001225337,0.0020360437],"study_design_scores_gemma":[0.000004599601,0.000011607625,0.00003808535,0.000001675506,0.0000034662808,0.000004310122,0.000012389078,0.99854136,0.00007592995,0.0011808232,0.00012383236,0.0000018663976],"about_ca_topic_score_codex":0.022173211,"about_ca_topic_score_gemma":0.014451139,"teacher_disagreement_score":0.022173211,"about_ca_system_score_codex":0.0015058509,"about_ca_system_score_gemma":0.001432289,"threshold_uncertainty_score":0.044088304},"labels":[],"label_agreement":null},{"id":"W2038160064","doi":"10.1109/ieom.2015.7093720","title":"Forest vehicle routing problem solved by New Insertion and meta-heuristics","year":2015,"lang":"en","type":"article","venue":"","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec en Abitibi-Témiscamingue; École de Technologie Supérieure","funders":"","keywords":"Tabu search; Vehicle routing problem; Heuristics; Mathematical optimization; Computer science; Routing (electronic design automation); Mathematics; Computer network","score_opus":0.04826331529118205,"score_gpt":0.2606783955150902,"score_spread":0.21241508022390815,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2038160064","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013745936,0.0005375058,0.9816212,0.0001284159,0.000047118414,0.00006567134,0.000032055144,0.00020442999,0.0036176972],"genre_scores_gemma":[0.25961715,0.0009412136,0.73511857,0.00010557475,0.000062209496,0.00024197712,0.00015481205,0.00008861109,0.0036698182],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996043,0.00014356036,0.00002126888,0.00005136088,0.00011846865,0.00006119018],"domain_scores_gemma":[0.99973327,0.00014510838,0.000043685603,0.000021819722,0.000041070067,0.000014942355],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00073609914,0.0008113852,0.00076901074,0.00089972455,0.0004461301,0.0008820918,0.0016011547,0.0009768895,0.0012562219],"category_scores_gemma":[0.0009558876,0.0004920944,0.0012008728,0.0010717675,0.0005145199,0.0012986152,0.0006112334,0.0010652443,0.00019716153],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007252969,0.00008159618,0.00046254596,0.00024833926,0.00006753384,0.00020990586,0.00012767313,0.8772113,0.0041532046,0.041257635,0.0017934863,0.07431418],"study_design_scores_gemma":[0.000016940654,0.000052212643,0.000090109854,0.000016356149,0.000020582698,0.000102676546,0.000028862842,0.98988897,0.0008521933,0.006741879,0.0021788138,0.00001037767],"about_ca_topic_score_codex":0.0031696635,"about_ca_topic_score_gemma":0.00416872,"teacher_disagreement_score":0.0031696635,"about_ca_system_score_codex":0.00077222753,"about_ca_system_score_gemma":0.0011549279,"threshold_uncertainty_score":0.0063024163},"labels":[],"label_agreement":null},{"id":"W2039059059","doi":"10.1016/j.ejor.2011.02.009","title":"Integrated airline crew scheduling: A bi-dynamic constraint aggregation method using neighborhoods","year":2011,"lang":"en","type":"article","venue":"European Journal of Operational Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":35,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Group for Research in Decision Analysis","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Crew; Column generation; Computer science; Crew scheduling; Schedule; Scheduling (production processes); Set cover problem; Constraint (computer-aided design); Mathematical optimization; Sequence (biology); Set (abstract data type); Constraint programming; Computational complexity theory; Operations research; Algorithm; Mathematics; Engineering","score_opus":0.15304093307016245,"score_gpt":0.3942011772132707,"score_spread":0.24116024414310824,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2039059059","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015196945,0.000119386365,0.9822245,0.00008299517,0.000051699495,0.00006581921,0.000086521264,0.0002081339,0.001963926],"genre_scores_gemma":[0.29894608,0.00015814086,0.6965599,0.00009234194,0.0000877563,0.0003411475,0.00034158654,0.00024034404,0.0032327909],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99928564,0.00029024994,0.000040284092,0.00014642964,0.00015810366,0.000079345984],"domain_scores_gemma":[0.9984938,0.0008504749,0.0001229321,0.00013078545,0.00029208226,0.000109915505],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015728478,0.0010223619,0.0021846052,0.0012953336,0.0010667396,0.0013152605,0.002322167,0.0013760155,0.0041124695],"category_scores_gemma":[0.002909723,0.0013003041,0.0012582929,0.0023626024,0.00048434973,0.0017228075,0.0016479031,0.0012134579,0.00044287933],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000089889705,0.000073099254,0.00031018085,0.00003852457,0.000057874768,0.00003377683,0.000042690255,0.957522,0.0005959336,0.0036965057,0.00088342495,0.036656193],"study_design_scores_gemma":[0.0000055772757,0.000007877501,0.00002087528,0.0000014167897,0.0000040205414,0.000002001394,0.000003670085,0.9992939,0.000049088572,0.0004995144,0.00011039233,0.0000017268009],"about_ca_topic_score_codex":0.016909134,"about_ca_topic_score_gemma":0.0150254,"teacher_disagreement_score":0.016909134,"about_ca_system_score_codex":0.00085797015,"about_ca_system_score_gemma":0.001580894,"threshold_uncertainty_score":0.03362143},"labels":[],"label_agreement":null},{"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,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"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","score_opus":0.1739552011743806,"score_gpt":0.4498479164768863,"score_spread":0.2758927153025057,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2039568841","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007526171,0.080774754,0.8728635,0.0044547967,0.0020422463,0.00006233963,0.00010623945,0.000203355,0.03196669],"genre_scores_gemma":[0.28325218,0.08244648,0.5958306,0.0010332034,0.0059095644,0.0004495399,0.00037517757,0.00042433757,0.030278927],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9985676,0.0009112698,0.00006450839,0.00009566748,0.00030329707,0.000057570956],"domain_scores_gemma":[0.99607867,0.0032050177,0.0001626656,0.00021001947,0.00026413408,0.000079495454],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003129226,0.0015170751,0.0018790505,0.0016056075,0.00061553286,0.0029829594,0.001737782,0.0025297177,0.0042100227],"category_scores_gemma":[0.0079434365,0.001061672,0.0009991446,0.0036227782,0.0028309033,0.003022393,0.0011198176,0.0057064374,0.0009714155],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007069456,0.00014851129,0.0003276097,0.00048467447,0.00020634792,0.000063203486,0.00012396625,0.43612143,0.00057926954,0.42228323,0.014119457,0.12547162],"study_design_scores_gemma":[0.000048005415,0.000038250375,0.00019008451,0.00014245852,0.000041364117,0.000036125388,0.000037672122,0.60327536,0.0002749183,0.37319708,0.022700155,0.000018554301],"about_ca_topic_score_codex":0.0033909513,"about_ca_topic_score_gemma":0.0034631456,"teacher_disagreement_score":0.0042100227,"about_ca_system_score_codex":0.0013981981,"about_ca_system_score_gemma":0.0015455399,"threshold_uncertainty_score":0.01654917},"labels":[],"label_agreement":null},{"id":"W2039638040","doi":"10.1016/j.cor.2014.07.001","title":"Heuristics for dynamic and stochastic inventory-routing","year":2014,"lang":"en","type":"article","venue":"Computers & Operations Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":99,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal; Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Heuristics; Mathematical optimization; Heuristic; Time horizon; Vendor; Routing (electronic design automation); Context (archaeology); Operations research; Inventory control; Inventory management; Stochastic programming; Dynamic programming; Operations management; Algorithm; Mathematics; Artificial intelligence; Economics","score_opus":0.050700185426448344,"score_gpt":0.36830883582356255,"score_spread":0.3176086503971142,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2039638040","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008397214,0.0010061163,0.983008,0.00025376765,0.00018041095,0.0001252222,0.00018374324,0.00037755165,0.006467932],"genre_scores_gemma":[0.26594356,0.0014002216,0.7224358,0.00022505701,0.00024232152,0.0004077449,0.00045635828,0.00028140203,0.008607606],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99861753,0.00067848363,0.00006300538,0.00017324141,0.0002837096,0.00018413701],"domain_scores_gemma":[0.9960729,0.0031008462,0.00019577553,0.00025271386,0.0002524821,0.00012528367],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002271295,0.0013155399,0.0018348125,0.0016324073,0.00093550346,0.0020671573,0.0023661645,0.0018332317,0.005872319],"category_scores_gemma":[0.008097594,0.0013842955,0.0013105209,0.002452055,0.0014859593,0.0022796146,0.001483782,0.0022001644,0.0006554855],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005640848,0.000053978587,0.0001242631,0.000089458554,0.00003588747,0.000028525561,0.000036998135,0.91461146,0.00024564614,0.056389555,0.0024310495,0.025896693],"study_design_scores_gemma":[0.000029403229,0.000015381049,0.000049343107,0.000015941423,0.000011282309,0.00001160301,0.000013588287,0.9572093,0.00011299577,0.04102985,0.0014929252,0.000008339087],"about_ca_topic_score_codex":0.011868618,"about_ca_topic_score_gemma":0.014861861,"teacher_disagreement_score":0.011868618,"about_ca_system_score_codex":0.0031230205,"about_ca_system_score_gemma":0.0030013896,"threshold_uncertainty_score":0.023599088},"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,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_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","score_opus":0.05395561392824383,"score_gpt":0.3389584209137308,"score_spread":0.28500280698548697,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2040075495","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05013501,0.0018954219,0.91989744,0.0020205632,0.0003503021,0.00018849307,0.0014745864,0.00025184796,0.02378626],"genre_scores_gemma":[0.6640889,0.0027123466,0.2841935,0.0007964784,0.00037617734,0.00063760823,0.002470524,0.00040132224,0.044323035],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99816847,0.0007624555,0.00006950404,0.0003578064,0.00040955428,0.00023223317],"domain_scores_gemma":[0.99865806,0.0007399572,0.00016906142,0.00008055095,0.00018756613,0.0001647003],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024207917,0.0024056481,0.0024778047,0.0017913994,0.0007872169,0.0028494773,0.0031787872,0.0040316526,0.006984025],"category_scores_gemma":[0.005601871,0.0011993153,0.0013989953,0.003383814,0.0012443069,0.0026788292,0.0031979398,0.0021389674,0.00079638726],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014151318,0.00012761689,0.00079901714,0.00032773794,0.00019762431,0.00025841896,0.00004820657,0.93962604,0.00083302543,0.03295912,0.003271437,0.02141039],"study_design_scores_gemma":[0.000037439888,0.0000614118,0.00032477244,0.000024064535,0.00004262643,0.000103760634,0.000035961868,0.97082627,0.00026178226,0.026139276,0.0021253463,0.000017236913],"about_ca_topic_score_codex":0.0058305473,"about_ca_topic_score_gemma":0.0040179035,"teacher_disagreement_score":0.006984025,"about_ca_system_score_codex":0.0013935813,"about_ca_system_score_gemma":0.001491334,"threshold_uncertainty_score":0.023363888},"labels":[],"label_agreement":null},{"id":"W2041088973","doi":"10.1016/j.cor.2014.06.007","title":"A decomposition-based heuristic for the multiple-product inventory-routing problem","year":2014,"lang":"en","type":"article","venue":"Computers & Operations Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":80,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Heuristic; Vendor; Computer science; Vehicle routing problem; Operations research; Routing (electronic design automation); Integer programming; Decomposition; Time horizon; Mathematical optimization; Product (mathematics); Linear programming; Mathematics; Algorithm; Business; Artificial intelligence; Marketing","score_opus":0.06450626381469744,"score_gpt":0.37307536225457844,"score_spread":0.308569098439881,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2041088973","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021370046,0.0005252888,0.9716331,0.00023614115,0.00016095457,0.0001186567,0.00013622479,0.00035972436,0.005459846],"genre_scores_gemma":[0.21856906,0.0004338551,0.7771335,0.00018951429,0.00007022074,0.00026716446,0.0004292587,0.00018454391,0.0027227758],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994962,0.00020405144,0.000019818359,0.000062229024,0.00010629256,0.00011145059],"domain_scores_gemma":[0.9990938,0.00053619465,0.00006265466,0.000065365624,0.00015238427,0.00008971374],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00105106,0.001104293,0.0014958291,0.0013043322,0.0006570028,0.00095764245,0.0012377051,0.0014289382,0.0046268404],"category_scores_gemma":[0.0023864736,0.0007326514,0.0013541649,0.001492288,0.0004843442,0.0011966896,0.0011871312,0.0012729429,0.00068120845],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015620382,0.00015008257,0.0002966643,0.00013673869,0.000049181537,0.00007125697,0.00004572731,0.88802695,0.0024877351,0.008800671,0.004139221,0.095639534],"study_design_scores_gemma":[0.000030815223,0.00003512523,0.00007229099,0.000014685462,0.000014442984,0.000020765967,0.00001381495,0.9955487,0.0003167838,0.0032349655,0.0006908223,0.0000067631536],"about_ca_topic_score_codex":0.00538217,"about_ca_topic_score_gemma":0.0052216537,"teacher_disagreement_score":0.00538217,"about_ca_system_score_codex":0.0011818386,"about_ca_system_score_gemma":0.0018240727,"threshold_uncertainty_score":0.015478313},"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,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_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","score_opus":0.09081247834877422,"score_gpt":0.38017269494339595,"score_spread":0.28936021659462174,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2041183433","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06774749,0.0057746423,0.8828891,0.0009682736,0.0005433288,0.0008681667,0.0018143065,0.0052070734,0.03418778],"genre_scores_gemma":[0.253492,0.0019713345,0.72833765,0.0005527455,0.00015337583,0.0012448496,0.0027536824,0.00076376187,0.0107306205],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99918646,0.00041542554,0.000024257613,0.000095830896,0.00015272723,0.00012526712],"domain_scores_gemma":[0.9988387,0.000716011,0.00010294861,0.000101008256,0.00018727472,0.00005403809],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000987519,0.0011793385,0.0013332347,0.0021299801,0.001307837,0.0015410152,0.0018223086,0.0019077674,0.009327731],"category_scores_gemma":[0.0038479099,0.0006399369,0.0009967126,0.004459372,0.0008457687,0.0013748201,0.0009246195,0.0012855203,0.0024091501],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016972715,0.00023541192,0.00054166716,0.00022599586,0.00008014071,0.00011862862,0.000104729734,0.718159,0.0013045435,0.013714249,0.019701617,0.24564418],"study_design_scores_gemma":[0.00007605346,0.00013148632,0.0002704266,0.000053265547,0.000036290083,0.00008471513,0.000056424964,0.9834442,0.00094381545,0.00830187,0.0065739215,0.000027475344],"about_ca_topic_score_codex":0.008599272,"about_ca_topic_score_gemma":0.007976663,"teacher_disagreement_score":0.009327731,"about_ca_system_score_codex":0.0014337949,"about_ca_system_score_gemma":0.0019521604,"threshold_uncertainty_score":0.031204402},"labels":[],"label_agreement":null},{"id":"W2041485345","doi":"10.1016/j.orl.2005.01.009","title":"The multiple TSP with time windows: vehicle bounds based on precedence graphs","year":2005,"lang":"en","type":"article","venue":"Operations Research Letters","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":33,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Travelling salesman problem; Vehicle routing problem; Combinatorics; Traveling purchaser problem; Mathematics; Computer science; Similarity (geometry); Mathematical optimization; 2-opt; Routing (electronic design automation); Artificial intelligence","score_opus":0.025797792919006197,"score_gpt":0.3082450610410842,"score_spread":0.282447268122078,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2041485345","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0118607655,0.001407142,0.9803329,0.0004398976,0.000135481,0.000030873733,0.0001232837,0.00012428251,0.00554527],"genre_scores_gemma":[0.6045204,0.0057172338,0.3739176,0.0003260957,0.0009672379,0.00022292601,0.00041808555,0.00057518244,0.013335255],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99826765,0.0005232333,0.00006884713,0.00032602702,0.0005310047,0.00028318077],"domain_scores_gemma":[0.9913168,0.006754579,0.00052567775,0.0004732709,0.0005378802,0.0003917489],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026757175,0.0016889813,0.0022722117,0.0018860067,0.0008791463,0.0028644144,0.0034709065,0.0014542976,0.0056929775],"category_scores_gemma":[0.014815925,0.0012043547,0.0013399132,0.0036764664,0.001190419,0.0073666135,0.0023315006,0.00409714,0.0006321474],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014566383,0.000056988345,0.0002780394,0.00015883251,0.00004630359,0.0000774392,0.00007452669,0.82586116,0.0011799216,0.12664956,0.002430091,0.043041427],"study_design_scores_gemma":[0.00000736507,0.00001860753,0.00008396823,0.000018210147,0.00001672031,0.00002028612,0.000011455068,0.9445199,0.00035314003,0.053947683,0.0009948778,0.000007755767],"about_ca_topic_score_codex":0.0069736554,"about_ca_topic_score_gemma":0.0068568806,"teacher_disagreement_score":0.0069736554,"about_ca_system_score_codex":0.0021019152,"about_ca_system_score_gemma":0.0021785633,"threshold_uncertainty_score":0.019044936},"labels":[],"label_agreement":null},{"id":"W2041825410","doi":"10.1016/j.ejor.2014.02.056","title":"Operational transportation planning of freight forwarding companies in horizontal coalitions","year":2014,"lang":"en","type":"article","venue":"European Journal of Operational Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":79,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; Computer Research Institute of Montréal","funders":"Deutsche Forschungsgemeinschaft","keywords":"Profitability index; Order (exchange); Computer science; Operational planning; Process (computing); Business; Traffic management; Routing (electronic design automation); Transportation planning; Operations research; Transport engineering; Industrial organization; Marketing; Finance; Computer network; Engineering","score_opus":0.08032545244081116,"score_gpt":0.3588431303229579,"score_spread":0.27851767788214676,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2041825410","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7225868,0.00016683259,0.25986218,0.0005798862,0.0000384876,0.00014867932,0.00027414726,0.00006239753,0.01628058],"genre_scores_gemma":[0.9745198,0.00008646438,0.02024895,0.000020103753,0.000008834569,0.000059683553,0.00017934384,0.000015351146,0.004861496],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99939966,0.00023200928,0.000015905009,0.000085857035,0.0000541791,0.00021227499],"domain_scores_gemma":[0.9988601,0.0006294368,0.00014090724,0.000059024023,0.00009804767,0.00021253605],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012845113,0.0005064698,0.0007083707,0.00066463224,0.00093446206,0.0019697226,0.0010491413,0.0011464292,0.0047481866],"category_scores_gemma":[0.0024186359,0.0005626415,0.0008878823,0.0011839664,0.00074654917,0.0018465988,0.0015908873,0.0007971579,0.00025801134],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007337457,0.00004945128,0.0014623989,0.000012420323,0.000024537238,0.00008242875,0.00005519531,0.97841966,0.00021632333,0.0141255595,0.00035893416,0.0051196944],"study_design_scores_gemma":[0.000006603274,0.000023876493,0.00027611674,0.0000044202006,0.000008514904,0.0000071679706,0.0001737999,0.99408025,0.000118026626,0.005030839,0.00026722343,0.0000031457798],"about_ca_topic_score_codex":0.023344403,"about_ca_topic_score_gemma":0.02298901,"teacher_disagreement_score":0.023344403,"about_ca_system_score_codex":0.001606749,"about_ca_system_score_gemma":0.0023915807,"threshold_uncertainty_score":0.046417058},"labels":[],"label_agreement":null},{"id":"W2043074264","doi":"10.1016/j.trc.2011.08.001","title":"Optimal shipment decisions for an airfreight forwarder: Formulation and solution methods","year":2011,"lang":"en","type":"article","venue":"Transportation Research Part C Emerging Technologies","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Mathematical optimization; Knapsack problem; Lagrangian relaxation; Integer programming; Heuristic; Heuristics; Computer science; Operations research; Set (abstract data type); Flow network; Set cover problem; Linear programming; Covering problems; Mathematics","score_opus":0.21478446497749762,"score_gpt":0.4287674817910912,"score_spread":0.21398301681359358,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2043074264","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012797696,0.00063330354,0.9771187,0.00088311173,0.0001461875,0.00018951602,0.00019298647,0.000084989435,0.00795351],"genre_scores_gemma":[0.4099367,0.0025844842,0.5374521,0.0005246158,0.00061206945,0.0014921501,0.00063002785,0.00034881316,0.046419],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989699,0.0004106181,0.000037694088,0.00025849452,0.0001822231,0.00014098846],"domain_scores_gemma":[0.9974855,0.0018950393,0.00018254202,0.000051453197,0.0002886669,0.00009674222],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0030164046,0.0020704651,0.0031518226,0.0014634207,0.0011497993,0.0036588816,0.0027977854,0.0063754637,0.01089787],"category_scores_gemma":[0.0059297034,0.0024318346,0.0024218985,0.0019663048,0.0015234215,0.0034288184,0.0023759627,0.0032569016,0.0010113727],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000021513402,0.000037814458,0.00009843463,0.0000734772,0.00001826496,0.000054439624,0.00002958468,0.9853816,0.00019439975,0.007706958,0.00079880084,0.0055846055],"study_design_scores_gemma":[0.000009106941,0.00001161171,0.00003202985,0.000007677683,0.0000088127545,0.0000062601835,0.000014890263,0.99699223,0.00008681095,0.002491829,0.00033356933,0.000005105414],"about_ca_topic_score_codex":0.024302354,"about_ca_topic_score_gemma":0.015387252,"teacher_disagreement_score":0.024302354,"about_ca_system_score_codex":0.0030617774,"about_ca_system_score_gemma":0.0044547427,"threshold_uncertainty_score":0.048321843},"labels":[],"label_agreement":null},{"id":"W2043131887","doi":"10.1007/s10898-014-0222-y","title":"The robust crew pairing problem: model and solution methodology","year":2014,"lang":"en","type":"article","venue":"Journal of Global Optimization","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Crew; Mathematical optimization; Nonlinear system; Mathematics; Pairing; Interval (graph theory); Relaxation (psychology); Engineering","score_opus":0.03915311288015694,"score_gpt":0.27883241837956735,"score_spread":0.2396793054994104,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2043131887","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0020789201,0.00032950687,0.99401987,0.000181543,0.000043755907,0.00004374581,0.0000654413,0.00003854411,0.0031987256],"genre_scores_gemma":[0.42080963,0.0035548685,0.54322237,0.00034416854,0.0004821933,0.0012693517,0.00080446026,0.00039854881,0.029114401],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991146,0.0003498399,0.000030660216,0.00021831614,0.00019484943,0.00009185586],"domain_scores_gemma":[0.9989254,0.00046880363,0.00020588496,0.00011193024,0.0002031935,0.00008480646],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019373066,0.0014233204,0.0019589402,0.001439252,0.0006255577,0.0020107306,0.0031097666,0.0034348397,0.0057772053],"category_scores_gemma":[0.0036054165,0.0010759112,0.0017035892,0.001634051,0.0016656306,0.0026002775,0.0036977879,0.0027249244,0.0012126063],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000028282813,0.00007304979,0.00026731618,0.00013044795,0.000039086342,0.00006811009,0.0000443205,0.8709961,0.00094066444,0.10977799,0.0023231609,0.0153115215],"study_design_scores_gemma":[0.000005391963,0.000023226765,0.000053960604,0.0000129931,0.0000102474605,0.000025901398,0.000018570818,0.98050576,0.00023202423,0.017333917,0.0017672344,0.000010679112],"about_ca_topic_score_codex":0.004278605,"about_ca_topic_score_gemma":0.0022830605,"teacher_disagreement_score":0.0057772053,"about_ca_system_score_codex":0.0009943377,"about_ca_system_score_gemma":0.0020596755,"threshold_uncertainty_score":0.019326687},"labels":[],"label_agreement":null},{"id":"W2043340090","doi":"10.1007/s10107-006-0011-6","title":"On the domino-parity inequalities for the STSP","year":2006,"lang":"en","type":"article","venue":"Mathematical Programming","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Southern Taiwan Science Park","keywords":"Domino; Travelling salesman problem; Separation (statistics); Polytope; Parity (physics); Mathematics; Mathematical optimization; Inequality; Class (philosophy); Algorithm; Computer science; Artificial intelligence; Combinatorics; Statistics","score_opus":0.036752343997007414,"score_gpt":0.28565320145190704,"score_spread":0.24890085745489962,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2043340090","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02952023,0.0023498596,0.8784534,0.003485943,0.00044906168,0.00006323865,0.00047169096,0.00006891173,0.08513766],"genre_scores_gemma":[0.76688397,0.0075040855,0.16848902,0.0022536456,0.0014311564,0.0005434902,0.0008461348,0.00030821798,0.051740274],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99931145,0.00030791815,0.000027265536,0.000080730126,0.00018135236,0.00009134993],"domain_scores_gemma":[0.99711144,0.0022357716,0.00015053389,0.000098219185,0.0002822675,0.00012176385],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020770193,0.0012127975,0.00092620566,0.0014194739,0.00093914807,0.0023252799,0.0015170744,0.0012278407,0.007680063],"category_scores_gemma":[0.00924136,0.00043831998,0.0010650504,0.0018402465,0.002290648,0.0034251837,0.0022759973,0.0036628002,0.00071114866],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000031744312,0.000018857452,0.00017050178,0.00008019621,0.000014900638,0.000053158932,0.00007517709,0.038628258,0.0006415733,0.9436735,0.0036254597,0.012986508],"study_design_scores_gemma":[0.000010912978,0.000015493837,0.000117116746,0.000032099873,0.000008918234,0.00003198043,0.000032588276,0.21022001,0.00019672146,0.78633875,0.0029839843,0.000011539556],"about_ca_topic_score_codex":0.0035198529,"about_ca_topic_score_gemma":0.0030264922,"teacher_disagreement_score":0.007680063,"about_ca_system_score_codex":0.0016155696,"about_ca_system_score_gemma":0.0015731773,"threshold_uncertainty_score":0.025692344},"labels":[],"label_agreement":null},{"id":"W2044126714","doi":"10.1007/s10696-012-9169-9","title":"Heuristics for an oil delivery vehicle routing problem","year":2012,"lang":"en","type":"article","venue":"Flexible Services and Manufacturing Journal","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Group for Research in Decision Analysis","funders":"","keywords":"Heuristics; Vehicle routing problem; Computer science; Routing (electronic design automation); Operations research; Computer network; Mathematics","score_opus":0.02221163774678293,"score_gpt":0.2610785118972925,"score_spread":0.23886687415050956,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2044126714","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.050407052,0.0014613711,0.92732644,0.0007128314,0.00025680376,0.0004288508,0.0003681043,0.0004330236,0.018605571],"genre_scores_gemma":[0.34112632,0.0018077521,0.6453432,0.00033091262,0.00017688396,0.0005753737,0.0004980402,0.00020008019,0.009941434],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99943,0.00025492872,0.000023754345,0.00007225915,0.00010287378,0.00011620088],"domain_scores_gemma":[0.9984596,0.0012213768,0.000099710815,0.000045032597,0.00009632044,0.00007793055],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014518388,0.0013640085,0.0013432237,0.0017261403,0.0007326643,0.0016612096,0.0016978525,0.0016794524,0.0036656864],"category_scores_gemma":[0.0036738075,0.0009614648,0.0011290057,0.0017047841,0.0009177037,0.001424536,0.0012003954,0.0014691316,0.00033901128],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000099559344,0.000089078174,0.00019547727,0.00012456163,0.000033260065,0.00007174698,0.000055778164,0.9490739,0.00055407465,0.020440452,0.0023648217,0.026897281],"study_design_scores_gemma":[0.000079905796,0.0000464388,0.000077616896,0.000021576983,0.000023002467,0.00001952116,0.000040887462,0.98442113,0.0002354945,0.013224251,0.0018000156,0.000010074002],"about_ca_topic_score_codex":0.012311601,"about_ca_topic_score_gemma":0.0124052055,"teacher_disagreement_score":0.012311601,"about_ca_system_score_codex":0.0021488348,"about_ca_system_score_gemma":0.002140307,"threshold_uncertainty_score":0.024479866},"labels":[],"label_agreement":null},{"id":"W2045466512","doi":"10.1287/trsc.1070.0197","title":"A Theoretical Comparison of Feasibility Cuts for the Integrated Aircraft-Routing and Crew-Pairing Problem","year":2008,"lang":"en","type":"article","venue":"Transportation Science","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Group for Research in Decision Analysis","funders":"","keywords":"Crew; Routing (electronic design automation); Pairing; Set (abstract data type); Decomposition; Computer science; Benders' decomposition; Mathematical optimization; Crew scheduling; Engineering; Operations research; Aeronautics; Computer network; Mathematics","score_opus":0.048540676101435594,"score_gpt":0.3307044574312802,"score_spread":0.2821637813298446,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2045466512","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.049235355,0.003957872,0.8794173,0.0020542883,0.00038106588,0.0006609658,0.00058297825,0.0005259592,0.06318418],"genre_scores_gemma":[0.33124855,0.005203341,0.65241504,0.0006191239,0.00068754976,0.0018039902,0.0019546854,0.0007269197,0.005340756],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99111253,0.0041135997,0.00032581788,0.000735012,0.0029452704,0.0007677868],"domain_scores_gemma":[0.94727385,0.046194185,0.0016949702,0.001820914,0.0023181397,0.0006979661],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010271748,0.0024214126,0.001244449,0.003688675,0.0015832946,0.004603391,0.0034601795,0.002597285,0.014595458],"category_scores_gemma":[0.05897715,0.0010902378,0.0020522517,0.004620768,0.002503705,0.0060953978,0.0020570338,0.0044370247,0.0010519018],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009183248,0.00051891734,0.0012348269,0.0008986912,0.000112012625,0.00017323361,0.00025181434,0.52979195,0.002048872,0.3517391,0.008695765,0.103616446],"study_design_scores_gemma":[0.00016823591,0.0006831688,0.0010626492,0.00026023312,0.00011062069,0.00034958633,0.00022040209,0.81490564,0.002181184,0.17184731,0.008163449,0.000047471854],"about_ca_topic_score_codex":0.0018586027,"about_ca_topic_score_gemma":0.0021763514,"teacher_disagreement_score":0.014595458,"about_ca_system_score_codex":0.0051241713,"about_ca_system_score_gemma":0.003543475,"threshold_uncertainty_score":0.05432284},"labels":[],"label_agreement":null},{"id":"W2045593082","doi":"10.5539/cis.v6n3p28","title":"Vehicle Routing in Multi-Echelon Distribution Systems with Cross-Docking: A Systematic Lexical-Metanarrative Analysis","year":2013,"lang":"en","type":"article","venue":"Computer and Information Science","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":29,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Vehicle routing problem; Unification; Notation; Heuristic; Metaheuristic; Operations research; Routing (electronic design automation); Artificial intelligence","score_opus":0.01749190093161343,"score_gpt":0.2714827123427632,"score_spread":0.25399081141114976,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2045593082","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0221454,0.022904575,0.9381135,0.0013212503,0.00014775437,0.0005243753,0.00033130555,0.000115502,0.014396283],"genre_scores_gemma":[0.2823782,0.024864376,0.6863879,0.00039746126,0.00015655273,0.0014935355,0.0005030158,0.00009472828,0.0037242314],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"systematic_review","domain_scores_codex":[0.9931711,0.004720545,0.0006341166,0.00045013655,0.0008565911,0.00016750117],"domain_scores_gemma":[0.994645,0.003738458,0.00041826617,0.00031718155,0.00083888153,0.00004211266],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0058020726,0.0009742354,0.0013374354,0.0064710975,0.0010359762,0.0045192493,0.0012612146,0.0012415722,0.0018104626],"category_scores_gemma":[0.0092573445,0.00073529244,0.0020888338,0.0064537474,0.0018340148,0.005449567,0.0014237352,0.0011008133,0.0002707955],"study_design_candidate":"systematic_review","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009049266,0.00016101828,0.0036193857,0.009079197,0.00044313044,0.00040476708,0.0023054744,0.10013893,0.0022277376,0.7000746,0.002335717,0.17911953],"study_design_scores_gemma":[0.000074779025,0.00046196228,0.0037417181,0.009165894,0.0010115007,0.0005965823,0.0071307374,0.3137996,0.004238232,0.5675007,0.09213001,0.00014819964],"about_ca_topic_score_codex":0.002997682,"about_ca_topic_score_gemma":0.004995399,"teacher_disagreement_score":0.0064710975,"about_ca_system_score_codex":0.003296,"about_ca_system_score_gemma":0.0037724015,"threshold_uncertainty_score":0.03068471},"labels":[],"label_agreement":null},{"id":"W2045902051","doi":"10.5267/j.ijiec.2011.08.015","title":"Modeling a four-layer location-routing problem","year":2011,"lang":"en","type":"article","venue":"International Journal of Industrial Engineering Computations","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Transshipment (information security); Integer programming; Routing (electronic design automation); Vehicle routing problem; Computer science; Mathematical optimization; Product (mathematics); Layer (electronics); Operations research; Distribution (mathematics); Supply chain; Computer network; Engineering; Mathematics; Business","score_opus":0.09533972141092655,"score_gpt":0.28932847398911915,"score_spread":0.1939887525781926,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2045902051","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.034269154,0.00072448654,0.9455637,0.0012452623,0.00010470886,0.00018178753,0.0008233736,0.00034935525,0.016738111],"genre_scores_gemma":[0.62106156,0.0021193984,0.34114844,0.00031558576,0.00014668544,0.00069290854,0.0014031285,0.00020604373,0.032906283],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99881566,0.0004777814,0.000055147102,0.0003038754,0.0001463845,0.00020119424],"domain_scores_gemma":[0.9990601,0.000540243,0.00017777111,0.00005462787,0.00009877916,0.00006854014],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011711234,0.001437244,0.0011265777,0.0007930483,0.00080290984,0.0032186252,0.002409651,0.0029629315,0.009388558],"category_scores_gemma":[0.0020279458,0.0011002136,0.0015547703,0.0017704193,0.0009870887,0.0037414196,0.002050122,0.0016163783,0.0012281378],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000021519027,0.000024472638,0.00025208952,0.000049948703,0.000017417,0.00010629547,0.000024858073,0.97359425,0.0002444706,0.021133898,0.000564327,0.0039664847],"study_design_scores_gemma":[0.0000116654755,0.000019733845,0.00006265592,0.000009484064,0.000012580122,0.00002973349,0.00002916348,0.98652595,0.0001257978,0.011188866,0.0019766034,0.000007671527],"about_ca_topic_score_codex":0.011297541,"about_ca_topic_score_gemma":0.008589029,"teacher_disagreement_score":0.011297541,"about_ca_system_score_codex":0.0023592587,"about_ca_system_score_gemma":0.0022130327,"threshold_uncertainty_score":0.031407893},"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,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_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","score_opus":0.15695137458492678,"score_gpt":0.3647560928907695,"score_spread":0.2078047183058427,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2046064436","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.080971666,0.0009272585,0.8910635,0.0003757457,0.0004047716,0.0004009917,0.0002423455,0.0027351298,0.0228786],"genre_scores_gemma":[0.33729932,0.00031427588,0.65433645,0.00020848974,0.00011372379,0.0006300909,0.000291134,0.00044084003,0.00636579],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99944705,0.00021051253,0.000022723432,0.000061349165,0.00017503607,0.00008335128],"domain_scores_gemma":[0.9992322,0.0003985355,0.000051608567,0.000102851955,0.00017661386,0.000038265243],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010610704,0.0007375948,0.0013087796,0.0010411643,0.00093049166,0.001027521,0.0022092273,0.0014600494,0.006515986],"category_scores_gemma":[0.002407031,0.00075942127,0.0010520016,0.001536332,0.0006667115,0.00097248005,0.00094222714,0.0010298501,0.0011617906],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021979416,0.00014449481,0.00029027858,0.000092378395,0.000060851387,0.00007263397,0.000054777294,0.87064666,0.0020356246,0.005246831,0.0031445462,0.117991135],"study_design_scores_gemma":[0.00006109828,0.00005080334,0.00006398272,0.000005574361,0.0000146237735,0.000014088942,0.000008145987,0.99699295,0.0003492407,0.0017384455,0.00069522933,0.0000058639666],"about_ca_topic_score_codex":0.009492086,"about_ca_topic_score_gemma":0.008440282,"teacher_disagreement_score":0.009492086,"about_ca_system_score_codex":0.00095230335,"about_ca_system_score_gemma":0.0017106117,"threshold_uncertainty_score":0.021798134},"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,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_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","score_opus":0.15094007849475852,"score_gpt":0.42007919479346856,"score_spread":0.26913911629871,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2046280257","genre_codex":"commentary","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0039889766,0.2159455,0.07165062,0.6243197,0.037081145,0.000052511543,0.0005708003,0.00038110296,0.04600963],"genre_scores_gemma":[0.15795587,0.38830906,0.08810495,0.2227243,0.08892414,0.00021660987,0.0014198214,0.0006699429,0.051675282],"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99745804,0.0010857738,0.00016182651,0.0004025258,0.00072355324,0.00016830055],"domain_scores_gemma":[0.9842726,0.009315971,0.00066474924,0.0010081382,0.0037735137,0.00096515147],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003842909,0.0010450572,0.0012311478,0.0012610027,0.0015504417,0.005553321,0.002030477,0.0051317737,0.018051185],"category_scores_gemma":[0.02356099,0.00039786645,0.00091279205,0.0017497428,0.0037685095,0.019117303,0.0014010713,0.0077823433,0.007928578],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002122225,0.00017697315,0.0017085358,0.0025968752,0.00015052519,0.0004004377,0.00053712376,0.006489118,0.00040330793,0.17655274,0.40741405,0.40335807],"study_design_scores_gemma":[0.00003613751,0.000084114894,0.0006910006,0.0028148105,0.000046490062,0.00072646653,0.0011904651,0.00411091,0.00040493513,0.47631964,0.51350105,0.00007397968],"about_ca_topic_score_codex":0.0026905918,"about_ca_topic_score_gemma":0.0016047193,"teacher_disagreement_score":0.018051185,"about_ca_system_score_codex":0.0014258465,"about_ca_system_score_gemma":0.0017363696,"threshold_uncertainty_score":0.060387194},"labels":[],"label_agreement":null},{"id":"W2046744595","doi":"10.1007/s10732-013-9229-7","title":"Less-Than-Truckload carrier collaboration problem: modeling framework and solution approach","year":2013,"lang":"en","type":"article","venue":"Journal of Heuristics","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":28,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Heuristics; Computer science; Vehicle routing problem; Local search (optimization); Transshipment (information security); Integer programming; Heuristic; Routing (electronic design automation); Mathematical optimization; Truck; Integer (computer science); Operations research; Mathematics; Computer network; Engineering; Artificial intelligence; Algorithm; Computer security","score_opus":0.01924323567560286,"score_gpt":0.2557735676930304,"score_spread":0.23653033201742754,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2046744595","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017776271,0.0005162943,0.96665025,0.0010989086,0.000106512154,0.00023295432,0.00034421525,0.00009847825,0.013176169],"genre_scores_gemma":[0.6163009,0.0022447365,0.35444793,0.0004198878,0.00031709514,0.00080696144,0.0007164052,0.00013363828,0.024612384],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987803,0.00046048986,0.000037875267,0.00027451932,0.00019683894,0.0002500011],"domain_scores_gemma":[0.99890625,0.0006286401,0.00012985965,0.000060035472,0.00015232529,0.00012287046],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019776376,0.0013964975,0.0021309676,0.0015142658,0.0011703438,0.0026488502,0.004897763,0.00396795,0.006833243],"category_scores_gemma":[0.003230858,0.0008867127,0.0018698002,0.0026460371,0.0012035376,0.0033790187,0.0025495843,0.002260628,0.0005003848],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000034466095,0.00011864021,0.0003359814,0.00010121738,0.000043262713,0.00014473303,0.00006118711,0.8897394,0.00042647246,0.09898248,0.0025312447,0.007480946],"study_design_scores_gemma":[0.000014901026,0.000025257757,0.000084468615,0.00001241899,0.000015976348,0.00004397656,0.000057658053,0.9725165,0.00012361204,0.025745125,0.001349248,0.000010870718],"about_ca_topic_score_codex":0.01215239,"about_ca_topic_score_gemma":0.00925035,"teacher_disagreement_score":0.01215239,"about_ca_system_score_codex":0.0021871491,"about_ca_system_score_gemma":0.0038867043,"threshold_uncertainty_score":0.024163365},"labels":[],"label_agreement":null},{"id":"W2046798010","doi":"10.1016/j.cor.2010.12.008","title":"An interior point cutting plane heuristic for mixed integer programming","year":2010,"lang":"en","type":"article","venue":"Computers & Operations Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Cutting-plane method; Integer programming; Integer (computer science); Mathematical optimization; Point (geometry); Heuristic; Plane (geometry); Interior point method; Mathematics; Branch and price; Computer science; Geometry","score_opus":0.046283844041415535,"score_gpt":0.38598582486289673,"score_spread":0.3397019808214812,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2046798010","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012398612,0.00018709307,0.9802131,0.00011304152,0.00015383589,0.000119239514,0.0000504772,0.0004478936,0.006316668],"genre_scores_gemma":[0.08239509,0.00013709451,0.9138991,0.00009226897,0.0000606772,0.00026329592,0.0001492819,0.00019532334,0.0028077986],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99928015,0.0002375716,0.000026254269,0.000080008525,0.0002793552,0.000096724856],"domain_scores_gemma":[0.99920815,0.00045025334,0.000058273166,0.00006883514,0.0001652925,0.00004917624],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011463456,0.00096322235,0.0015758248,0.0013609573,0.0007479503,0.0013294518,0.0019485265,0.0016761387,0.0057913885],"category_scores_gemma":[0.002577033,0.0011504994,0.001480346,0.0015135421,0.0007735474,0.001099175,0.0014383319,0.002024837,0.0010661031],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00027316963,0.000299015,0.00034599288,0.00017312082,0.0000596007,0.00013014404,0.0001147513,0.74160707,0.00466167,0.02226255,0.0043177553,0.22575514],"study_design_scores_gemma":[0.000043681757,0.000063648295,0.00006219677,0.000017180491,0.00001518652,0.00002321946,0.00001159315,0.9932035,0.00068185414,0.004665039,0.001203877,0.000009003612],"about_ca_topic_score_codex":0.0027562317,"about_ca_topic_score_gemma":0.002347306,"teacher_disagreement_score":0.0057913885,"about_ca_system_score_codex":0.0006656408,"about_ca_system_score_gemma":0.0011438472,"threshold_uncertainty_score":0.019374132},"labels":[],"label_agreement":null},{"id":"W2047226187","doi":"10.1002/net.21525","title":"Solving the close‐enough arc routing problem","year":2013,"lang":"en","type":"article","venue":"Networks","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":34,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Arc routing; Computer science; Routing (electronic design automation); Arc (geometry); Mathematical optimization; Mathematics; Computer network","score_opus":0.010267781984480634,"score_gpt":0.22271240807114842,"score_spread":0.21244462608666778,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2047226187","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09178353,0.00085335184,0.88447356,0.0017837834,0.00022261882,0.00014511442,0.00045608665,0.0003840975,0.019897768],"genre_scores_gemma":[0.69703996,0.00061654946,0.28974217,0.00034439485,0.0001694693,0.00020605669,0.0007470828,0.00025887406,0.010875451],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985078,0.0007101162,0.00005413633,0.00031827917,0.00024415646,0.00016553004],"domain_scores_gemma":[0.9965029,0.0026752874,0.00024909823,0.00018998873,0.00020818555,0.00017449731],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016361495,0.00093768706,0.0015119324,0.0009069403,0.0007518629,0.0018432199,0.0017324374,0.0023970031,0.012162841],"category_scores_gemma":[0.0071136383,0.00073589076,0.0010779795,0.0012830024,0.00095958274,0.003414325,0.0015967208,0.002131719,0.00061784306],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001488387,0.00016428737,0.00060067815,0.00021882776,0.00006879552,0.00021730919,0.00011478429,0.8846968,0.0009219858,0.07607539,0.0060600797,0.03071219],"study_design_scores_gemma":[0.00003502382,0.000045438665,0.00013339268,0.000023442342,0.000014317859,0.000093776434,0.000092615344,0.9130706,0.00041711997,0.083497465,0.0025649697,0.0000118050775],"about_ca_topic_score_codex":0.0024718929,"about_ca_topic_score_gemma":0.0019614217,"teacher_disagreement_score":0.012162841,"about_ca_system_score_codex":0.0009032987,"about_ca_system_score_gemma":0.0009449742,"threshold_uncertainty_score":0.040688753},"labels":[],"label_agreement":null},{"id":"W2048940061","doi":"10.1016/j.jmp.2006.01.001","title":"Facets of the linear ordering polytope: A unification for the fence family through weighted graphs","year":2006,"lang":"en","type":"article","venue":"Journal of Mathematical Psychology","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Group for Research in Decision Analysis; HEC Montréal","funders":"","keywords":"Polytope; Mathematics; Fence (mathematics); Combinatorics; Class (philosophy); Unification; Facet (psychology); Inequality; Binary number; Birkhoff polytope; Stability (learning theory); Discrete mathematics; Computer science; Geometry; Arithmetic; Artificial intelligence","score_opus":0.04045050360672561,"score_gpt":0.3367207538973854,"score_spread":0.2962702502906598,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2048940061","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.119184196,0.00065219303,0.82243586,0.000935591,0.00010231015,0.00015292685,0.0006983187,0.00018109026,0.05565751],"genre_scores_gemma":[0.7065093,0.0020769192,0.2524631,0.00025651298,0.00018466274,0.00031466206,0.0015555732,0.0005342765,0.03610501],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9992932,0.00015677622,0.00004089931,0.00021455516,0.0001721656,0.00012244411],"domain_scores_gemma":[0.99823904,0.00071746635,0.00018798086,0.00038598105,0.00025515145,0.00021436888],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008855378,0.00060279464,0.00093897316,0.0015381412,0.0020352178,0.0038803716,0.0014771073,0.0010844179,0.011692349],"category_scores_gemma":[0.0042424835,0.00066885084,0.0018436637,0.0019761042,0.0023767473,0.008602769,0.0018405478,0.0027385643,0.0007514793],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016080898,0.000015493913,0.0004942782,0.00003482456,0.0000095023715,0.000061009454,0.00025112738,0.006776861,0.00032506106,0.9798922,0.0012645234,0.010859095],"study_design_scores_gemma":[0.0000059700437,0.000009530328,0.00029947126,0.000031320375,0.000010611781,0.00010173907,0.00021558299,0.04433773,0.00034340503,0.948897,0.005734542,0.000013234792],"about_ca_topic_score_codex":0.008875473,"about_ca_topic_score_gemma":0.0074598384,"teacher_disagreement_score":0.011692349,"about_ca_system_score_codex":0.0013871448,"about_ca_system_score_gemma":0.0011460377,"threshold_uncertainty_score":0.039114773},"labels":[],"label_agreement":null},{"id":"W2050001280","doi":"10.1016/j.cor.2005.05.011","title":"Stabilized column generation for highly degenerate multiple-depot vehicle scheduling problems","year":2006,"lang":"en","type":"article","venue":"Computers & Operations Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":66,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Group for Research in Decision Analysis; HEC Montréal","funders":"","keywords":"Column generation; Scheduling (production processes); Computer science; Mathematical optimization; Linear programming; Relaxation (psychology); Preprocessor; Degenerate energy levels; Column (typography); Linear programming relaxation; Mathematics; Algorithm; Artificial intelligence","score_opus":0.09169753236726466,"score_gpt":0.34373809634236985,"score_spread":0.2520405639751052,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2050001280","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03812503,0.00035116225,0.9568992,0.00022492609,0.00011987697,0.000075236116,0.000116161485,0.00029044237,0.0037979712],"genre_scores_gemma":[0.77072495,0.00029021568,0.224137,0.00017937396,0.00010517112,0.00021865136,0.00031163642,0.00014866568,0.0038843153],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99972945,0.000113582406,0.000008484119,0.00003608356,0.000066888984,0.000045489734],"domain_scores_gemma":[0.99909437,0.0005191769,0.00008899494,0.00006427382,0.00016379244,0.00006942596],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007266783,0.0006868951,0.0009334939,0.00061687425,0.00049169915,0.00074659724,0.00083915837,0.0007597284,0.0029208616],"category_scores_gemma":[0.002199056,0.00054435956,0.0005110031,0.0007294161,0.0006658559,0.00058055134,0.00088792207,0.0008545968,0.0003369334],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010486973,0.00004820119,0.00016897771,0.000074098585,0.0000242951,0.000058067097,0.000030887593,0.9622371,0.002540995,0.009379074,0.0017970208,0.02353649],"study_design_scores_gemma":[0.000008953415,0.000015811609,0.000026859567,0.0000024635822,0.0000026658693,0.0000038591907,0.0000042188485,0.9962901,0.00033051433,0.0031766174,0.0001353132,0.0000026163443],"about_ca_topic_score_codex":0.0030849543,"about_ca_topic_score_gemma":0.0033284288,"teacher_disagreement_score":0.0030849543,"about_ca_system_score_codex":0.00059304846,"about_ca_system_score_gemma":0.00082256505,"threshold_uncertainty_score":0.009771287},"labels":[],"label_agreement":null},{"id":"W2050999462","doi":"10.1007/s10732-014-9244-3","title":"A hybrid generational genetic algorithm for the periodic vehicle routing problem with time windows","year":2014,"lang":"en","type":"article","venue":"Journal of Heuristics","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":31,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure; Université de Montréal; Transport Canada","funders":"Université de Montréal","keywords":"Crossover; Vehicle routing problem; Mathematical optimization; Population; Heuristics; Computer science; Local search (optimization); Genetic algorithm; Heuristic; Metaheuristic; Routing (electronic design automation); Algorithm; Mathematics; Artificial intelligence","score_opus":0.0087929748759454,"score_gpt":0.22447127078058876,"score_spread":0.21567829590464335,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2050999462","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05181366,0.00030329035,0.9413409,0.00017925182,0.0000914656,0.00008724147,0.000053949014,0.0004533668,0.005676824],"genre_scores_gemma":[0.52253634,0.00020846844,0.47256082,0.00013942701,0.00004536069,0.00025981953,0.00012757537,0.000100920915,0.0040211887],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997435,0.000095995674,0.000009239895,0.000037532656,0.00007702634,0.00003666734],"domain_scores_gemma":[0.9995678,0.00027822546,0.00003236967,0.00003536049,0.00006466958,0.000021433152],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00059970445,0.00047173994,0.0006238674,0.00050687965,0.00034420795,0.0005913723,0.0011283181,0.0010078378,0.0022314675],"category_scores_gemma":[0.0012843717,0.00033031707,0.00047237755,0.0005757164,0.00037674577,0.0005842129,0.0006525266,0.0005565857,0.00024393026],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006786318,0.00005648058,0.00029750392,0.0000278267,0.000030678162,0.000055088196,0.000038227427,0.9303714,0.0015235738,0.007127223,0.0007796131,0.059624575],"study_design_scores_gemma":[0.000016063545,0.00002202688,0.00004211943,0.0000024482783,0.000005746838,0.0000103533985,0.0000037582731,0.9985129,0.0001752284,0.00092639873,0.00028050167,0.0000024701856],"about_ca_topic_score_codex":0.0042110006,"about_ca_topic_score_gemma":0.0041807448,"teacher_disagreement_score":0.0042110006,"about_ca_system_score_codex":0.0005057879,"about_ca_system_score_gemma":0.0008489605,"threshold_uncertainty_score":0.008373022},"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,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_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","score_opus":0.044993797217322926,"score_gpt":0.3588639218862965,"score_spread":0.31387012466897357,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2051358678","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0049234712,0.0003663048,0.98946416,0.0003037144,0.00007065165,0.0002735541,0.00018501864,0.0005354437,0.0038775841],"genre_scores_gemma":[0.035713285,0.00035051245,0.9599584,0.00012918709,0.00004856103,0.0004847005,0.0006215429,0.00020642324,0.0024874182],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99874794,0.0004076315,0.00006786813,0.00026594845,0.0003327074,0.00017792245],"domain_scores_gemma":[0.99831855,0.0011477546,0.00011835295,0.000085878404,0.00023255534,0.00009701683],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017505093,0.001962798,0.002078095,0.0016015482,0.0014731628,0.0021342786,0.002151936,0.0021561075,0.009167028],"category_scores_gemma":[0.004261853,0.0010701785,0.0011478071,0.002727111,0.000716779,0.0023954362,0.0017155559,0.0032649485,0.0017297091],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002518666,0.00052340294,0.00065862236,0.00035352484,0.00010735459,0.0002298158,0.00016438392,0.5081094,0.0022038608,0.0439832,0.017905885,0.4255087],"study_design_scores_gemma":[0.00010861925,0.00009192489,0.00011544708,0.000029760622,0.000030014355,0.00008673776,0.000049447848,0.96541667,0.00088600005,0.027644899,0.005524298,0.000016145303],"about_ca_topic_score_codex":0.006243069,"about_ca_topic_score_gemma":0.0055550043,"teacher_disagreement_score":0.009167028,"about_ca_system_score_codex":0.0015542352,"about_ca_system_score_gemma":0.0031796498,"threshold_uncertainty_score":0.030666709},"labels":[],"label_agreement":null},{"id":"W2051767752","doi":"10.1016/j.ejor.2004.04.051","title":"Periodic airline fleet assignment with time windows, spacing constraints, and time dependent revenues","year":2005,"lang":"en","type":"article","venue":"European Journal of Operational Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":75,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal; Polytechnique Montréal; Group for Research in Decision Analysis","funders":"","keywords":"Schedule; Computer science; Flow network; Operations research; Assignment problem; Mathematical optimization; Integer (computer science); Revenue; Integer programming; Branch and bound; Mathematics; Economics; Algorithm; Finance","score_opus":0.03351273727517879,"score_gpt":0.3092219762336378,"score_spread":0.275709238958459,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2051767752","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.33892292,0.00042089308,0.65103066,0.00040126676,0.00013668464,0.00011850207,0.0004996942,0.00025306057,0.008216297],"genre_scores_gemma":[0.9515821,0.00029216288,0.04171367,0.00003134048,0.000131892,0.00008839976,0.0003128376,0.000073590214,0.0057739597],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991837,0.00030289753,0.00002791976,0.00016140766,0.0001298151,0.00019429658],"domain_scores_gemma":[0.9967039,0.0019223368,0.0007579106,0.00023333926,0.00017803715,0.0002045111],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017133362,0.00069068035,0.0011919218,0.00058244355,0.0003870508,0.00093566836,0.0016969411,0.0010177502,0.004210863],"category_scores_gemma":[0.0066357795,0.0009966162,0.0006739663,0.0013211563,0.00055012666,0.0014797766,0.0006184755,0.00093849684,0.000308259],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001528856,0.000038241815,0.00051799853,0.000032162505,0.000020286865,0.00014346489,0.000021155462,0.9798677,0.0004056479,0.011275636,0.00076435803,0.0067604478],"study_design_scores_gemma":[0.000021229336,0.000035732126,0.0003422634,0.0000029967284,0.000010967705,0.00003481702,0.000010887648,0.9914231,0.00012154003,0.0077663767,0.00022448698,0.0000056579365],"about_ca_topic_score_codex":0.0056827967,"about_ca_topic_score_gemma":0.0062549883,"teacher_disagreement_score":0.0056827967,"about_ca_system_score_codex":0.00078796543,"about_ca_system_score_gemma":0.0010223658,"threshold_uncertainty_score":0.014086723},"labels":[],"label_agreement":null},{"id":"W2053226773","doi":"","title":"The Single-Vehicle Routing Problem with Unrestricted Backhauls","year":2015,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Solver; Backhaul (telecommunications); Vehicle routing problem; Mathematical optimization; Computer science; Revenue; Set (abstract data type); Routing (electronic design automation); Operations research; Mathematics; Economics; Computer network; Finance","score_opus":0.015601314730342668,"score_gpt":0.23194090896135408,"score_spread":0.2163395942310114,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2053226773","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16548155,0.0015122694,0.79558766,0.001762659,0.00024077806,0.00041884216,0.0020244932,0.00038181024,0.03258991],"genre_scores_gemma":[0.7637605,0.0013607019,0.20007521,0.00025109362,0.00017590754,0.00055137154,0.0012296668,0.00017706062,0.032418516],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99946517,0.00017062301,0.000024963163,0.00015554215,0.00007341904,0.00011022923],"domain_scores_gemma":[0.99924916,0.00050898816,0.00007845154,0.000040308136,0.000047287504,0.00007571432],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007523391,0.0011870329,0.0011718983,0.00060111185,0.00069951214,0.0018292938,0.0019489402,0.0020637196,0.00761248],"category_scores_gemma":[0.002218507,0.00067451317,0.0010752359,0.0013877188,0.00076339155,0.0020676719,0.0010442521,0.0010857121,0.0006338584],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001290325,0.00006859671,0.00029762945,0.00021175752,0.000045805078,0.00026643733,0.000058444402,0.94621056,0.0008696551,0.032187816,0.0021482478,0.017505905],"study_design_scores_gemma":[0.00005712075,0.00007879169,0.00022067434,0.0000218714,0.000025923562,0.0001203245,0.00008517243,0.9620427,0.0006054742,0.033808906,0.0029151177,0.000017941733],"about_ca_topic_score_codex":0.0068204137,"about_ca_topic_score_gemma":0.006121951,"teacher_disagreement_score":0.00761248,"about_ca_system_score_codex":0.0011801685,"about_ca_system_score_gemma":0.001473854,"threshold_uncertainty_score":0.025466263},"labels":[],"label_agreement":null},{"id":"W2053944393","doi":"10.1007/s101070050007","title":"A branch-and-cut algorithm for the Undirected Rural Postman Problem","year":2000,"lang":"en","type":"article","venue":"Mathematical Programming","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":96,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Group for Research in Decision Analysis","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mathematics; Algorithm; Binary number; Undirected graph; Combinatorics; Mathematical optimization; Graph; Arithmetic","score_opus":0.012956686533581226,"score_gpt":0.26001142746504663,"score_spread":0.2470547409314654,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2053944393","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012224433,0.00020593143,0.9803004,0.0003599077,0.00006884655,0.00016376282,0.00018227227,0.00039050757,0.006103921],"genre_scores_gemma":[0.080508485,0.00023575619,0.9106948,0.00014948493,0.00006361561,0.0003164502,0.00048395692,0.00019701567,0.007350426],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995233,0.00015459405,0.000016666409,0.00010965287,0.00010609086,0.00008976447],"domain_scores_gemma":[0.9989311,0.0006951186,0.00008095292,0.000062459294,0.00013295164,0.000097428805],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011509708,0.0012299956,0.0018583066,0.0012089526,0.0012084295,0.001372921,0.0021823696,0.002274615,0.010236502],"category_scores_gemma":[0.002494635,0.0008951178,0.0008857057,0.0020336448,0.00079722505,0.0019420572,0.0015834703,0.002129437,0.0012753225],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020274511,0.00037404726,0.00055267836,0.00017476037,0.00006758276,0.00010394984,0.0001019518,0.6915604,0.0014972736,0.045276243,0.012158926,0.24792945],"study_design_scores_gemma":[0.000061913204,0.000054388514,0.00009651905,0.000014240443,0.000016665064,0.000030169827,0.000024561627,0.9734308,0.00033283213,0.023576615,0.002353496,0.000007752428],"about_ca_topic_score_codex":0.006260364,"about_ca_topic_score_gemma":0.008134841,"teacher_disagreement_score":0.010236502,"about_ca_system_score_codex":0.0012253725,"about_ca_system_score_gemma":0.0025612863,"threshold_uncertainty_score":0.034244537},"labels":[],"label_agreement":null},{"id":"W2054151210","doi":"10.1016/j.orl.2004.11.011","title":"Accelerated label setting algorithms for the elementary resource constrained shortest path problem","year":2005,"lang":"en","type":"article","venue":"Operations Research Letters","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":196,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"","keywords":"Shortest path problem; Path (computing); Constrained Shortest Path First; Mathematical optimization; Node (physics); Computer science; Algorithm; Longest path problem; K shortest path routing; Repetition (rhetorical device); Widest path problem; Mathematics; Theoretical computer science; Graph","score_opus":0.07945066765209823,"score_gpt":0.36972095527867616,"score_spread":0.2902702876265779,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2054151210","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020514015,0.00018190032,0.97309476,0.00032558283,0.00008586006,0.00007883246,0.00010087846,0.0005854598,0.005032774],"genre_scores_gemma":[0.26318616,0.00027885247,0.72486705,0.00023671785,0.00016310874,0.00037998962,0.00053903175,0.00044016377,0.009908952],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988985,0.00041915168,0.00003351722,0.00016286262,0.00030606103,0.00018000037],"domain_scores_gemma":[0.9977525,0.0013042988,0.00013397088,0.00037247545,0.00030627954,0.00013056603],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015922935,0.0009167232,0.0015989388,0.00076714944,0.0008520875,0.0016003242,0.0027765196,0.0014438151,0.008948724],"category_scores_gemma":[0.005616821,0.00057982,0.0007530284,0.0013064102,0.00077379844,0.0032860192,0.0026353488,0.0033600403,0.0014340209],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005028733,0.00039773306,0.0006090375,0.00018113058,0.000040938656,0.00006718425,0.00022597318,0.57880735,0.0030269902,0.117818385,0.01340721,0.28491518],"study_design_scores_gemma":[0.00006359758,0.00004771459,0.000089079185,0.0000063673892,0.000008279304,0.000014398674,0.000019330351,0.9481564,0.00049803976,0.049446974,0.0016418387,0.000007996494],"about_ca_topic_score_codex":0.0031325046,"about_ca_topic_score_gemma":0.0051963073,"teacher_disagreement_score":0.008948724,"about_ca_system_score_codex":0.0012437932,"about_ca_system_score_gemma":0.0018024157,"threshold_uncertainty_score":0.029936433},"labels":[],"label_agreement":null},{"id":"W2054669984","doi":"10.1057/palgrave.jors.2601420","title":"Some applications of the clustered travelling salesman problem","year":2002,"lang":"en","type":"article","venue":"Journal of the Operational Research Society","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":88,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"","keywords":"Travelling salesman problem; Computer science; Vehicle routing problem; Purchasing; Operations research; Scheduling (production processes); Traveling purchaser problem; Lin–Kernighan heuristic; Routing (electronic design automation); Mathematical optimization; Bottleneck traveling salesman problem; Engineering; Operations management; Algorithm; Computer network; Mathematics","score_opus":0.07207568656996226,"score_gpt":0.34042582006238425,"score_spread":0.268350133492422,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2054669984","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04179447,0.0022957388,0.91559356,0.0010916267,0.0003042478,0.00013055532,0.00028805382,0.00020147771,0.038300265],"genre_scores_gemma":[0.4529505,0.004462334,0.50324357,0.00072956586,0.0007909241,0.00037175792,0.001008623,0.00040576185,0.036036886],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989042,0.00042465306,0.00005168047,0.00021720347,0.00027116487,0.00013109675],"domain_scores_gemma":[0.9982443,0.0011125804,0.00014564382,0.00010881505,0.00029274705,0.000095929725],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013920247,0.0009808798,0.0009428747,0.0011370773,0.00125387,0.001179151,0.0026034962,0.0021894784,0.009470618],"category_scores_gemma":[0.004935294,0.00060098874,0.0019023718,0.0037691162,0.0009929532,0.0016275398,0.0013570911,0.0020787576,0.00080529397],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011721163,0.00019263492,0.00054307905,0.00023109006,0.0001322865,0.0003465969,0.0002258217,0.5747966,0.0009009304,0.35871464,0.01186507,0.051934086],"study_design_scores_gemma":[0.00004226606,0.00007072598,0.00032016338,0.000028032797,0.000025291009,0.00015910115,0.00011838501,0.7795323,0.0004177203,0.20610568,0.013150624,0.000029806228],"about_ca_topic_score_codex":0.0068280897,"about_ca_topic_score_gemma":0.0060935006,"teacher_disagreement_score":0.009470618,"about_ca_system_score_codex":0.0017075154,"about_ca_system_score_gemma":0.001099385,"threshold_uncertainty_score":0.031682372},"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,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_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","score_opus":0.006537182786802548,"score_gpt":0.2320161659181334,"score_spread":0.22547898313133086,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2055819286","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.27536526,0.0009324872,0.68784064,0.0045640855,0.00046322893,0.00021404367,0.0012823051,0.0003481369,0.028989848],"genre_scores_gemma":[0.97063905,0.00033428124,0.020738285,0.0001430477,0.00007578848,0.00007966117,0.00035298144,0.00005259275,0.007584411],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99898297,0.0005091749,0.000026746748,0.0001960597,0.00008713582,0.00019781255],"domain_scores_gemma":[0.999398,0.00031716426,0.00010080203,0.00002912013,0.00006423862,0.00009074757],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000932931,0.000828136,0.0008791319,0.0008369875,0.00067517074,0.0015925878,0.0014835768,0.0013556328,0.0062573864],"category_scores_gemma":[0.0029897813,0.0005997913,0.00071973255,0.001317941,0.0006338584,0.0016166914,0.0011979418,0.00086785527,0.00040363232],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010793661,0.000047217916,0.0012489591,0.000031847543,0.000035348363,0.00009162916,0.00005254103,0.9676051,0.0002267982,0.015762702,0.0023316543,0.012458239],"study_design_scores_gemma":[0.000015529864,0.000029868268,0.00039360882,0.0000088009565,0.000015043981,0.000031266205,0.00017756793,0.98705894,0.00010413256,0.011075613,0.0010820939,0.000007524735],"about_ca_topic_score_codex":0.016402248,"about_ca_topic_score_gemma":0.011463154,"teacher_disagreement_score":0.016402248,"about_ca_system_score_codex":0.0021107562,"about_ca_system_score_gemma":0.001568545,"threshold_uncertainty_score":0.032613575},"labels":[],"label_agreement":null},{"id":"W2056059863","doi":"10.1016/j.endm.2012.10.032","title":"MPI Parallelization of Variable Neighborhood Search","year":2012,"lang":"en","type":"article","venue":"Electronic Notes in Discrete Mathematics","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; Université du Québec à Montréal","funders":"","keywords":"Computer science; Benchmark (surveying); Generality; Parallel computing; Asynchronous communication; Variable neighborhood search; Scheduling (production processes); Variable (mathematics); Automatic parallelization; Multiprocessing; Heuristic; Metaheuristic; Algorithm; Mathematical optimization; Programming language; Mathematics; Artificial intelligence","score_opus":0.016105917460154974,"score_gpt":0.2769799389609048,"score_spread":0.2608740215007498,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2056059863","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12235802,0.00067654956,0.74154127,0.0011284137,0.00069730374,0.00021109033,0.0015870475,0.03718383,0.09461644],"genre_scores_gemma":[0.63484156,0.00020500888,0.33951724,0.00020642062,0.00012123692,0.0004894835,0.0019609763,0.0034971787,0.019160857],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993024,0.00017074823,0.00002760092,0.00010778059,0.00025476396,0.00013663988],"domain_scores_gemma":[0.99898034,0.0003316923,0.000034213503,0.00031289857,0.000277221,0.000063647814],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004555108,0.0008236168,0.00094017904,0.00078441773,0.0013397487,0.0012129396,0.0025614032,0.0006155109,0.020953253],"category_scores_gemma":[0.003220927,0.0003514036,0.00061098527,0.0016976803,0.0005294636,0.001017078,0.0011199606,0.0011835108,0.0032035888],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014838913,0.00036961344,0.0029077823,0.000466577,0.0002038707,0.00038925975,0.00061556516,0.3609246,0.013520839,0.097056866,0.08565379,0.4364073],"study_design_scores_gemma":[0.00016298093,0.000042403823,0.00050620444,0.000009796226,0.000025687255,0.000035371955,0.00006746328,0.9603944,0.00724005,0.021556493,0.009940544,0.00001873654],"about_ca_topic_score_codex":0.010263791,"about_ca_topic_score_gemma":0.013605975,"teacher_disagreement_score":0.020953253,"about_ca_system_score_codex":0.00093922636,"about_ca_system_score_gemma":0.0014147143,"threshold_uncertainty_score":0.07009566},"labels":[],"label_agreement":null},{"id":"W2057459863","doi":"10.1007/s10107-008-0254-5","title":"Multi-phase dynamic constraint aggregation for set partitioning type problems","year":2008,"lang":"en","type":"article","venue":"Mathematical Programming","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":49,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Group for Research in Decision Analysis","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mathematical optimization; Constraint (computer-aided design); Disjoint sets; Computation; Relaxation (psychology); Set (abstract data type); Computer science; Mathematics; Local consistency; Scheduling (production processes); Algorithm; Constraint satisfaction problem; Probabilistic logic","score_opus":0.06582903588106113,"score_gpt":0.33703000503881686,"score_spread":0.2712009691577557,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2057459863","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0085786125,0.00017387758,0.9885937,0.00013739614,0.000034767818,0.00005671322,0.0000623,0.000053379463,0.0023093184],"genre_scores_gemma":[0.3751153,0.00046884696,0.6179766,0.00017219382,0.00010829845,0.00044047035,0.00041768924,0.00016810969,0.005132487],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99892837,0.0005170565,0.000045316687,0.00014702306,0.0002566249,0.00010552253],"domain_scores_gemma":[0.99809784,0.001295831,0.0001686364,0.00016694248,0.000208662,0.00006208071],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023534915,0.0010151651,0.001433397,0.00092451856,0.0008500261,0.0017548775,0.0015594511,0.001013657,0.0036510986],"category_scores_gemma":[0.0048483354,0.0009834606,0.0010231141,0.002114562,0.0006306881,0.0026470982,0.0019066537,0.0015480326,0.0002855329],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007080337,0.00006268394,0.00021174784,0.00011358154,0.000048021215,0.000060661507,0.00006472829,0.92111063,0.0012860572,0.03794512,0.0017305998,0.037295368],"study_design_scores_gemma":[0.0000090728445,0.000018598786,0.000054595585,0.000007955251,0.0000072670578,0.000013939363,0.000012199918,0.9862293,0.0003799412,0.012547786,0.00071508595,0.00000432094],"about_ca_topic_score_codex":0.002742024,"about_ca_topic_score_gemma":0.0037966084,"teacher_disagreement_score":0.0036510986,"about_ca_system_score_codex":0.00095626933,"about_ca_system_score_gemma":0.0010068681,"threshold_uncertainty_score":0.012446582},"labels":[],"label_agreement":null},{"id":"W2057635030","doi":"10.1007/s10589-013-9544-9","title":"A cutting plane algorithm for the Capacitated Connected Facility Location Problem","year":2013,"lang":"en","type":"article","venue":"Computational Optimization and Applications","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":18,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; Center for Interuniversity Research and Analysis on Organizations","funders":"","keywords":"Facility location problem; 1-center problem; Integer programming; Mathematical optimization; Network planning and design; Branch and cut; Set (abstract data type); Set cover problem; Integer (computer science); Mathematics; Cutting-plane method; Theory of computation; Cover (algebra); Computer science; Algorithm; Computer network; Engineering","score_opus":0.014384514747038524,"score_gpt":0.2430500121766642,"score_spread":0.22866549742962566,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2057635030","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004674876,0.00008236492,0.99234796,0.000076974306,0.00004171178,0.0000496504,0.000060110917,0.00022785905,0.0024385136],"genre_scores_gemma":[0.057999745,0.0001702041,0.9379981,0.000085812346,0.000038205548,0.0002530767,0.0003042814,0.00013353054,0.0030170765],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996644,0.00007577244,0.000014853468,0.000060087805,0.0001441327,0.0000407763],"domain_scores_gemma":[0.9993364,0.00036925563,0.000044603406,0.000053764998,0.00015848337,0.000037508646],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00060953235,0.0010131607,0.0011306691,0.0010638575,0.00063614635,0.0011332451,0.0018844814,0.0019875653,0.0074984804],"category_scores_gemma":[0.002236864,0.00085539394,0.0010218638,0.0019030429,0.0005846262,0.0011036257,0.0013700208,0.0023380038,0.0012152386],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013899281,0.00020020654,0.00039610662,0.00010841249,0.000058039874,0.000087209635,0.00008518529,0.6817281,0.0030528493,0.032306876,0.006277038,0.275561],"study_design_scores_gemma":[0.000033634107,0.000035994257,0.00007146879,0.000011485139,0.000010414105,0.000026211535,0.000011478405,0.9875788,0.0004921706,0.009718505,0.0020024155,0.0000074203153],"about_ca_topic_score_codex":0.005991849,"about_ca_topic_score_gemma":0.0052285367,"teacher_disagreement_score":0.0074984804,"about_ca_system_score_codex":0.00073977065,"about_ca_system_score_gemma":0.001444165,"threshold_uncertainty_score":0.025084913},"labels":[],"label_agreement":null},{"id":"W2059252479","doi":"10.4171/owr/2012/19","title":"Mini-Workshop: Hypergraph Turán Problem","year":2013,"lang":"en","type":"article","venue":"Oberwolfach Reports","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Hypergraph; Computer science; Mathematics; Combinatorics","score_opus":0.012014865303501536,"score_gpt":0.23025278736930344,"score_spread":0.2182379220658019,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2059252479","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04355246,0.026718037,0.45003292,0.09556192,0.09723481,0.0006017449,0.0052891425,0.0018126942,0.2791963],"genre_scores_gemma":[0.2664641,0.018396696,0.14028089,0.021991933,0.074437775,0.0014403559,0.015070175,0.0030982476,0.4588198],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9988417,0.00041455985,0.00004892072,0.00031886296,0.00023124286,0.00014468048],"domain_scores_gemma":[0.9974368,0.0010244304,0.00007415353,0.00024950792,0.00062418287,0.0005909191],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002241384,0.0012724949,0.0010575952,0.0010202379,0.0012910062,0.0027135825,0.0021649513,0.00216822,0.045882795],"category_scores_gemma":[0.004371116,0.00053569576,0.0016992674,0.00095377164,0.0007253668,0.004515465,0.0032604064,0.005138729,0.013219301],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00032250668,0.00016266965,0.00043418902,0.00061364775,0.00010576902,0.00034552233,0.00019588722,0.006857062,0.002493759,0.184887,0.7070049,0.09657706],"study_design_scores_gemma":[0.00012248964,0.00020858237,0.0015145009,0.00046526108,0.000118070544,0.00068807276,0.00041023182,0.036345843,0.0035366872,0.36989844,0.586588,0.00010379008],"about_ca_topic_score_codex":0.0008259518,"about_ca_topic_score_gemma":0.0010996457,"teacher_disagreement_score":0.045882795,"about_ca_system_score_codex":0.0015876044,"about_ca_system_score_gemma":0.0012059514,"threshold_uncertainty_score":0.15349323},"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,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_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","score_opus":0.020126917279569764,"score_gpt":0.30792374553360674,"score_spread":0.28779682825403696,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2059559546","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014271082,0.00079877686,0.9747546,0.00026781406,0.00007856101,0.00021455898,0.0005844328,0.0005700661,0.008460123],"genre_scores_gemma":[0.16446698,0.0013893644,0.8183726,0.00013937164,0.00013409709,0.0007587943,0.0020639393,0.0003563692,0.01231851],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99903584,0.00041822778,0.000034472116,0.0001546332,0.00021133939,0.000145525],"domain_scores_gemma":[0.9993892,0.0003803383,0.000076802855,0.000042900214,0.00006556441,0.000045176344],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013446553,0.0020026613,0.0014718111,0.0012532927,0.00057505054,0.0012918949,0.0009872316,0.0011970452,0.007737903],"category_scores_gemma":[0.0021558192,0.0010882242,0.0015347888,0.0019271381,0.00050655217,0.0011656731,0.00081840565,0.0018998979,0.0012383125],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012994037,0.000094059345,0.0002362197,0.00016729016,0.000055655557,0.0000663189,0.0000697413,0.9251002,0.0016809104,0.027032562,0.0040381746,0.04132888],"study_design_scores_gemma":[0.000044305794,0.000049111826,0.00012937107,0.00001754956,0.000015216166,0.000023847337,0.000023914406,0.97185814,0.00052719,0.023612777,0.0036889433,0.000009636747],"about_ca_topic_score_codex":0.007838444,"about_ca_topic_score_gemma":0.00836853,"teacher_disagreement_score":0.007838444,"about_ca_system_score_codex":0.0013733696,"about_ca_system_score_gemma":0.001841119,"threshold_uncertainty_score":0.02588588},"labels":[],"label_agreement":null},{"id":"W2059760003","doi":"10.1007/s10732-014-9273-y","title":"Time-window relaxations in vehicle routing heuristics","year":2014,"lang":"fr","type":"article","venue":"Journal of Heuristics","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":58,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Université du Québec à Montréal","funders":"","keywords":"Iterated local search; Vehicle routing problem; Heuristics; Computer science; Mathematical optimization; Relaxation (psychology); Scalability; Heuristic; Local search (optimization); Metaheuristic; Routing (electronic design automation); Algorithm; Mathematics","score_opus":0.012152139364092714,"score_gpt":0.25583713383890005,"score_spread":0.24368499447480735,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2059760003","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017605076,0.002348667,0.9704865,0.0008495626,0.00039150266,0.000115792776,0.00013125972,0.00011966457,0.007951881],"genre_scores_gemma":[0.46309903,0.00469652,0.5094718,0.0005625235,0.0008972664,0.00096811797,0.000475754,0.0005813821,0.019247675],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984139,0.0009879397,0.000061206316,0.00014555483,0.00022224816,0.00016915923],"domain_scores_gemma":[0.9866888,0.012059356,0.00042129116,0.00023676337,0.0003461534,0.0002476044],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0056574745,0.0014017478,0.0019841252,0.0012046696,0.00065867766,0.0018438403,0.0023359358,0.0018681305,0.0071998797],"category_scores_gemma":[0.0193139,0.0018556595,0.0012859098,0.0021692365,0.0014267185,0.004329919,0.0016055417,0.0044510686,0.0005172626],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020764054,0.000111341426,0.00016766356,0.00018039744,0.000053751253,0.000039790706,0.00010398619,0.8856121,0.00038602704,0.08565122,0.0028347163,0.024651412],"study_design_scores_gemma":[0.00003064171,0.000028900062,0.000038043665,0.000026705588,0.000013523902,0.0000056941203,0.000018490538,0.9655624,0.00013171179,0.033145875,0.0009918999,0.0000061312126],"about_ca_topic_score_codex":0.0066913017,"about_ca_topic_score_gemma":0.004287294,"teacher_disagreement_score":0.0071998797,"about_ca_system_score_codex":0.0016524099,"about_ca_system_score_gemma":0.0017476819,"threshold_uncertainty_score":0.029919982},"labels":[],"label_agreement":null},{"id":"W2059816321","doi":"10.1287/ijoc.1040.0117","title":"The Shortest-Path Problem with Resource Constraints and <i>k</i>-Cycle Elimination for <i>k</i> ≥ 3","year":2006,"lang":"en","type":"article","venue":"INFORMS journal on computing","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":215,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kronos (Canada)","funders":"","keywords":"Shortest path problem; Mathematical optimization; Vehicle routing problem; Benchmark (surveying); Mathematics; Column generation; Relaxation (psychology); Integer (computer science); Path (computing); Scheduling (production processes); Routing (electronic design automation); Computer science; Combinatorics; Graph","score_opus":0.006429132118342962,"score_gpt":0.22400800837229531,"score_spread":0.21757887625395236,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2059816321","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.56865185,0.0027468875,0.35798565,0.0023697088,0.00025345507,0.00059585634,0.0063834284,0.00075350964,0.060259666],"genre_scores_gemma":[0.76884097,0.00128007,0.20902431,0.00030554374,0.00008486674,0.00041933855,0.007396282,0.00035792714,0.012290694],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991897,0.00021378418,0.000030775805,0.00017097517,0.00013331874,0.00026152798],"domain_scores_gemma":[0.9985279,0.0009359372,0.00018763523,0.00009451184,0.00014870486,0.00010533407],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00073316053,0.0013429896,0.0008703516,0.0005847059,0.0008312759,0.0013216453,0.0011518614,0.0009509392,0.0072938963],"category_scores_gemma":[0.0025244579,0.00046923832,0.0013082923,0.0017008819,0.0006182919,0.0016677711,0.0006933857,0.0022124525,0.00043631403],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002852612,0.00033259965,0.0010024564,0.0005283007,0.000056756584,0.0003366605,0.000115444585,0.9179599,0.0033349777,0.029613035,0.010061785,0.03637279],"study_design_scores_gemma":[0.0001076476,0.0001465414,0.0010680062,0.000036187885,0.000035263532,0.00020845931,0.00019499155,0.95642656,0.0024638383,0.03283006,0.0064545707,0.000027918504],"about_ca_topic_score_codex":0.016723324,"about_ca_topic_score_gemma":0.016459635,"teacher_disagreement_score":0.016723324,"about_ca_system_score_codex":0.0015690447,"about_ca_system_score_gemma":0.002510711,"threshold_uncertainty_score":0.033252},"labels":[],"label_agreement":null},{"id":"W2059958986","doi":"10.1007/s10732-008-9097-8","title":"Ant colony optimization for the arc routing problem with intermediate facilities under capacity and length restrictions","year":2008,"lang":"en","type":"article","venue":"Journal of Heuristics","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":24,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"","keywords":"Arc routing; Arc (geometry); Heuristics; Ant colony optimization algorithms; Mathematical optimization; Routing (electronic design automation); Computer science; Mathematics; Computer network","score_opus":0.03496000000940143,"score_gpt":0.24365280378072676,"score_spread":0.20869280377132532,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2059958986","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.042135928,0.000613264,0.94481575,0.00068774004,0.00011881014,0.00013845954,0.00019584,0.0002149692,0.01107921],"genre_scores_gemma":[0.576785,0.0009253428,0.40420422,0.00016005186,0.00015043141,0.00036074713,0.00032196692,0.00024200555,0.016850224],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999316,0.00035717114,0.000021296493,0.00007796035,0.0001245639,0.00010295636],"domain_scores_gemma":[0.9979285,0.0015422071,0.00015961516,0.00008516129,0.00017276415,0.00011173166],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011495261,0.0009818185,0.0014212359,0.00082863163,0.00055832585,0.0014674886,0.0017360979,0.0015390752,0.003812273],"category_scores_gemma":[0.0040899357,0.0007890114,0.0008367196,0.0013877783,0.0009975805,0.0016238233,0.0010413909,0.0017089067,0.00040619617],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000052249703,0.000034305103,0.00011845078,0.000046552177,0.000022766006,0.00003611504,0.000025108324,0.97929823,0.00033805022,0.01195523,0.001088766,0.0069841794],"study_design_scores_gemma":[0.000013729203,0.000012307974,0.000026385684,0.000003995765,0.000005364996,0.000006737881,0.000008621974,0.99325883,0.00006712839,0.006280261,0.00031414366,0.000002559424],"about_ca_topic_score_codex":0.0109719485,"about_ca_topic_score_gemma":0.0072315554,"teacher_disagreement_score":0.0109719485,"about_ca_system_score_codex":0.0011930753,"about_ca_system_score_gemma":0.0016219004,"threshold_uncertainty_score":0.021816194},"labels":[],"label_agreement":null},{"id":"W2060597711","doi":"10.1016/j.cor.2012.06.011","title":"Heuristics for dynamic and stochastic routing in industrial shipping","year":2012,"lang":"en","type":"article","venue":"Computers & Operations Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":55,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"","keywords":"Heuristics; Computer science; Operations research; Routing (electronic design automation); Vehicle routing problem; Metaheuristic; Key (lock); Mathematical optimization; Set (abstract data type); Engineering; Mathematics; Algorithm; Computer security; Computer network","score_opus":0.13374856411112754,"score_gpt":0.39890553355726316,"score_spread":0.26515696944613565,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2060597711","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.028068786,0.0011704089,0.9631614,0.0004155499,0.00017556894,0.00016087147,0.00016461685,0.00030153967,0.0063812756],"genre_scores_gemma":[0.4649036,0.0012737465,0.5256185,0.00025486606,0.00023202786,0.00040495332,0.0003722065,0.000239082,0.006701107],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987992,0.00059730123,0.000057188794,0.00015348817,0.0002070213,0.00018574037],"domain_scores_gemma":[0.9955315,0.003686316,0.00023222591,0.00016054769,0.00024425003,0.00014519872],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028399322,0.00096239086,0.0015552235,0.0017004295,0.0009911007,0.001680653,0.0022594107,0.0016388358,0.0039631566],"category_scores_gemma":[0.0074721985,0.0013366764,0.0012398816,0.0022228782,0.0016294399,0.001996897,0.0014061173,0.0016538815,0.00036397483],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000040616364,0.00003529087,0.00011933203,0.00004571684,0.000022387061,0.000017060942,0.000030065443,0.96194756,0.00013311942,0.0230902,0.0011140049,0.013404661],"study_design_scores_gemma":[0.000027224733,0.000016931632,0.00006217189,0.000010563088,0.000008994912,0.000006621877,0.000014389875,0.9828316,0.000081556,0.016223557,0.00070948334,0.000006966424],"about_ca_topic_score_codex":0.017214164,"about_ca_topic_score_gemma":0.020499645,"teacher_disagreement_score":0.017214164,"about_ca_system_score_codex":0.0030029668,"about_ca_system_score_gemma":0.002851966,"threshold_uncertainty_score":0.034227967},"labels":[],"label_agreement":null},{"id":"W2060706020","doi":"10.1109/wmnc.2011.6097256","title":"An ant colony optimization metaheuristic for solving bi-objective multi-sources multicommodity communication flow problem","year":2011,"lang":"en","type":"article","venue":"","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Computer science; Ant colony optimization algorithms; Metaheuristic; Mathematical optimization; Multicast; Node (physics); Relay; Path (computing); Optimization problem; Ant colony; Transmission (telecommunications); Computer network; Mathematics; Engineering; Algorithm; Telecommunications","score_opus":0.04895965192047832,"score_gpt":0.28591206624698196,"score_spread":0.23695241432650363,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2060706020","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03197045,0.0009133962,0.9578558,0.0004272009,0.00012367044,0.00016775368,0.0001081555,0.00041494338,0.008018585],"genre_scores_gemma":[0.320149,0.0006819075,0.67300457,0.0002285745,0.00006350221,0.0004994643,0.00021698428,0.000130913,0.005025075],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996178,0.00019399967,0.000015257203,0.00004284918,0.000093474184,0.000036609275],"domain_scores_gemma":[0.99919206,0.000556641,0.00009224443,0.000033362234,0.00008962918,0.000036209527],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000882328,0.0010610481,0.000909396,0.00092605344,0.00047155312,0.00087915675,0.0010598999,0.0014956654,0.0014548068],"category_scores_gemma":[0.0019098532,0.00043199415,0.00065281003,0.0012003628,0.00047754828,0.0006804691,0.00055666827,0.0010555806,0.00026388562],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000029935134,0.000049104037,0.0002051895,0.000063483254,0.000039303053,0.00003875801,0.000026050346,0.9724326,0.0006164535,0.005168965,0.0007580678,0.020572131],"study_design_scores_gemma":[0.000011934179,0.000022013102,0.000027529264,0.000005922328,0.000005719642,0.000008888976,0.000007048187,0.9978028,0.00013598733,0.0014299032,0.000539967,0.0000023012499],"about_ca_topic_score_codex":0.004406654,"about_ca_topic_score_gemma":0.0038225201,"teacher_disagreement_score":0.004406654,"about_ca_system_score_codex":0.0007105326,"about_ca_system_score_gemma":0.0011865286,"threshold_uncertainty_score":0.008762002},"labels":[],"label_agreement":null},{"id":"W2061091622","doi":"10.1016/j.ejor.2009.07.007","title":"Heuristics for the Stochastic Eulerian Tour Problem","year":2009,"lang":"en","type":"article","venue":"European Journal of Operational Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Heuristics; Eulerian path; Concatenation (mathematics); Mathematical optimization; Heuristic; Computer science; Grid; Probabilistic logic; Mathematics; Enhanced Data Rates for GSM Evolution; Algorithm; Combinatorics; Artificial intelligence; Applied mathematics; Lagrangian","score_opus":0.08847412350347472,"score_gpt":0.3704656567390892,"score_spread":0.2819915332356145,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2061091622","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05115039,0.0012861419,0.9328056,0.0006897608,0.00020560183,0.00023915393,0.00037922538,0.00072107505,0.012523149],"genre_scores_gemma":[0.40678126,0.0011361494,0.5812555,0.0003063864,0.0001534051,0.00032823882,0.0007762502,0.00035511798,0.008907667],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99924135,0.00032939206,0.000030815285,0.000105600906,0.00013699569,0.00015584046],"domain_scores_gemma":[0.99785465,0.0015585165,0.0001612751,0.0001300615,0.0001496806,0.00014577688],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001322858,0.0010355883,0.001149115,0.0012471367,0.00067639764,0.0012087655,0.0016069845,0.0012993452,0.0053880904],"category_scores_gemma":[0.0041640447,0.0008707345,0.0008809247,0.0018059209,0.0010104352,0.0015347712,0.001247953,0.0015690521,0.00054813037],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011173023,0.00007485306,0.00027143265,0.0000974547,0.000036431025,0.000044787517,0.000054617,0.9290968,0.00043233158,0.033757545,0.004138593,0.031883396],"study_design_scores_gemma":[0.000054573302,0.000031513595,0.00010255061,0.000014959289,0.000012429097,0.000016361735,0.000022361452,0.97757494,0.0001515256,0.02031929,0.0016910189,0.000008488713],"about_ca_topic_score_codex":0.0124201225,"about_ca_topic_score_gemma":0.018573968,"teacher_disagreement_score":0.0124201225,"about_ca_system_score_codex":0.002063306,"about_ca_system_score_gemma":0.0029983732,"threshold_uncertainty_score":0.024695635},"labels":[],"label_agreement":null},{"id":"W2061568192","doi":"10.1007/s10589-011-9432-0","title":"An exact method with variable fixing for solving the generalized assignment problem","year":2011,"lang":"en","type":"article","venue":"Computational Optimization and Applications","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":55,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Mathematics; Mathematical optimization; Simple (philosophy); Variable (mathematics); Weapon target assignment problem; Generalized assignment problem; Sequence (biology); Branch and cut; Branch and bound; Optimization problem; Augmented Lagrangian method; Lagrangian relaxation; Linear programming","score_opus":0.025524929425743054,"score_gpt":0.2797806794497151,"score_spread":0.25425575002397205,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2061568192","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0012993349,0.00008274531,0.99687105,0.000047589783,0.00007166619,0.00002179403,0.000021802989,0.00009105119,0.0014930428],"genre_scores_gemma":[0.052609745,0.00022296412,0.94211555,0.00013949971,0.00010176821,0.00024237845,0.000103231425,0.00018720323,0.004277726],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99936134,0.00025715856,0.000019427727,0.00008031647,0.0002283896,0.00005345115],"domain_scores_gemma":[0.99927884,0.00042725494,0.00003689241,0.000109641325,0.000121253135,0.000026064063],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011912431,0.0009988811,0.0010706556,0.00083655654,0.0006266721,0.0006226339,0.0018878902,0.0012946473,0.0057553165],"category_scores_gemma":[0.0037678005,0.0005821954,0.0010492802,0.0013242913,0.0009808864,0.0011344764,0.0014316835,0.0022111603,0.0010483623],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000841859,0.00010390109,0.00024651847,0.0002091044,0.00007271979,0.00009156498,0.00008857618,0.65190214,0.0031149944,0.1440837,0.0060701664,0.19393244],"study_design_scores_gemma":[0.00002571781,0.000028222146,0.00005482292,0.000013231794,0.000011086111,0.000024536763,0.0000096406575,0.9683087,0.00041991827,0.028082209,0.0030111203,0.00001077976],"about_ca_topic_score_codex":0.005086162,"about_ca_topic_score_gemma":0.00510211,"teacher_disagreement_score":0.0057553165,"about_ca_system_score_codex":0.0004775499,"about_ca_system_score_gemma":0.0013499202,"threshold_uncertainty_score":0.019253433},"labels":[],"label_agreement":null},{"id":"W2062485877","doi":"10.1080/0740817x.2012.705452","title":"Solving a stochastic facility location/fleet management problem with logic-based Benders' decomposition","year":2013,"lang":"en","type":"article","venue":"IIE Transactions","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":29,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Benders' decomposition; Decomposition; Facility location problem; Fleet management; Mathematical optimization; Computer science; Operations research; Engineering; Mathematics; Telecommunications","score_opus":0.013953868420819715,"score_gpt":0.2429219763353642,"score_spread":0.22896810791454447,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2062485877","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.033087615,0.000085127154,0.96401757,0.00021051658,0.000016420467,0.00006298884,0.00010860954,0.00011141526,0.002299741],"genre_scores_gemma":[0.5834979,0.00024369884,0.4116734,0.00014428905,0.000041940963,0.0002700414,0.00037363375,0.00007211436,0.003682999],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990264,0.0004181345,0.00004580038,0.00017477895,0.00016640317,0.00016844772],"domain_scores_gemma":[0.9986325,0.000989477,0.00015591254,0.00005476063,0.00011116192,0.0000560633],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020020609,0.0010348654,0.001119359,0.00068816193,0.00046270675,0.0013498714,0.0008817448,0.0014399792,0.0026599474],"category_scores_gemma":[0.0026495203,0.00083409104,0.0013951649,0.000808849,0.00084267964,0.0010622694,0.0009839545,0.0014116814,0.00022516587],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000028263068,0.000018466933,0.0001443058,0.000023133405,0.000013285271,0.000021802529,0.000014765475,0.9881948,0.00033595815,0.0068914182,0.00013822608,0.0041756136],"study_design_scores_gemma":[0.0000088442885,0.000016140433,0.00003160243,0.0000028459822,0.0000040646432,0.0000063654006,0.0000069256807,0.9946221,0.00014582454,0.00502745,0.00012518938,0.0000026110997],"about_ca_topic_score_codex":0.008472712,"about_ca_topic_score_gemma":0.0054116384,"teacher_disagreement_score":0.008472712,"about_ca_system_score_codex":0.0014972605,"about_ca_system_score_gemma":0.0023101335,"threshold_uncertainty_score":0.016846776},"labels":[],"label_agreement":null},{"id":"W2063718148","doi":"10.4067/s0718-33052009000300013","title":"MULTIPLE ANT COLONY SYSTEM FOR A VRP WITH TIME WINDOWS AND SCHEDULED LOADING","year":2009,"lang":"en","type":"article","venue":"Ingeniare. Revista chilena de ingeniería","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"Universidad Católica de la Santísima Concepción","keywords":"Computer science","score_opus":0.008246315451688555,"score_gpt":0.22502577299219015,"score_spread":0.2167794575405016,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2063718148","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03584052,0.0004437738,0.947007,0.00034465065,0.00012499013,0.00021282674,0.00023187368,0.004293267,0.011501022],"genre_scores_gemma":[0.38230625,0.00031291577,0.5991116,0.00012107684,0.000042841973,0.00034518528,0.0003846055,0.00052775216,0.016847828],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995783,0.00009678047,0.000028148077,0.00009778431,0.00013866152,0.00006026511],"domain_scores_gemma":[0.99957293,0.00017412804,0.000045652054,0.0000601682,0.00011037377,0.000036829748],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045958642,0.0007307742,0.0007222074,0.00039299895,0.00052131544,0.0012518532,0.0018438797,0.0009966376,0.0064129266],"category_scores_gemma":[0.0014632711,0.0004631476,0.00072310126,0.0004959882,0.0003332432,0.00084807957,0.00085181434,0.001078281,0.0012896421],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002906907,0.00011482822,0.00054424594,0.0003376143,0.000077291,0.00029678273,0.00021242857,0.82588977,0.018636981,0.012116029,0.004145203,0.13733807],"study_design_scores_gemma":[0.00003772497,0.000049786417,0.00009543084,0.0000074551645,0.000013607761,0.000041273168,0.000024189567,0.9929525,0.0016636857,0.001710334,0.0033950044,0.00000898475],"about_ca_topic_score_codex":0.008877856,"about_ca_topic_score_gemma":0.008419162,"teacher_disagreement_score":0.008877856,"about_ca_system_score_codex":0.00061547745,"about_ca_system_score_gemma":0.001352472,"threshold_uncertainty_score":0.02145338},"labels":[],"label_agreement":null},{"id":"W2064035628","doi":"10.1287/opre.51.4.655.16098","title":"Cycle-Based Neighbourhoods for Fixed-Charge Capacitated Multicommodity Network Design","year":2003,"lang":"en","type":"article","venue":"Operations Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":193,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Computer Research Institute of Montréal; Université du Québec à Montréal; Université de Montréal","funders":"","keywords":"Neighbourhood (mathematics); Tabu search; Mathematical optimization; Fixed charge; Network planning and design; Metaheuristic; Flow network; Computer science; Simple (philosophy); Flow (mathematics); Multi-commodity flow problem; Mathematics","score_opus":0.12441746632498082,"score_gpt":0.37505291087653597,"score_spread":0.2506354445515552,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2064035628","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01564002,0.00021488495,0.9805088,0.00007266363,0.000026776794,0.000059269172,0.000034491644,0.00016231385,0.0032807617],"genre_scores_gemma":[0.42225456,0.0003431719,0.57278407,0.00007701434,0.000037264082,0.00046247157,0.00016474206,0.0001326978,0.003744102],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996815,0.00012851853,0.000013679101,0.000037238835,0.00011245895,0.000026614462],"domain_scores_gemma":[0.99943966,0.0002923191,0.000059682894,0.00006785201,0.00010636613,0.000034039513],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00080129446,0.0005254483,0.0006896888,0.0009561581,0.0004807899,0.0006420001,0.0012301158,0.0008965625,0.0021940167],"category_scores_gemma":[0.0025313615,0.0003732809,0.00063393667,0.0008619164,0.00069539814,0.0011163603,0.00087120704,0.0006646386,0.00041913975],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000038998416,0.000027906259,0.000259016,0.00003931122,0.000015634627,0.000021817023,0.000051737825,0.9095024,0.000924183,0.034124915,0.0006414199,0.054352604],"study_design_scores_gemma":[0.000012873758,0.00004479486,0.000054915745,0.00000780238,0.000005114336,0.000013825969,0.00000861226,0.9833734,0.0004245159,0.014409053,0.0016373713,0.000007680105],"about_ca_topic_score_codex":0.002043109,"about_ca_topic_score_gemma":0.0024235246,"teacher_disagreement_score":0.0021940167,"about_ca_system_score_codex":0.0007765261,"about_ca_system_score_gemma":0.0006553324,"threshold_uncertainty_score":0.007339716},"labels":[],"label_agreement":null},{"id":"W2064073889","doi":"10.1007/bf02579017","title":"Tabu Search heuristics for the Vehicle Routing Problem with Time Windows","year":2002,"lang":"en","type":"article","venue":"Top","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":108,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; Université du Québec à Montréal","funders":"","keywords":"Vehicle routing problem; Tabu search; Heuristics; Benchmark (surveying); Mathematical optimization; Computer science; Set (abstract data type); Point (geometry); Routing (electronic design automation); Interval (graph theory); Metaheuristic; Mathematics","score_opus":0.024233844463323818,"score_gpt":0.2493872648064071,"score_spread":0.22515342034308328,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2064073889","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0286471,0.0040760813,0.95313,0.00043794574,0.00022211076,0.00016843158,0.00035253447,0.0013312515,0.0116344765],"genre_scores_gemma":[0.30184552,0.002391837,0.68482244,0.00027127008,0.00015409355,0.0005501301,0.0005877214,0.0006118024,0.00876527],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99930453,0.00039638588,0.000023310591,0.00006464965,0.000103784645,0.00010737414],"domain_scores_gemma":[0.9981686,0.0014883017,0.00010167242,0.00008538112,0.00010617819,0.000049990565],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013369024,0.00093815895,0.0010901401,0.0013348521,0.00073602545,0.0012742582,0.0014262357,0.0013248206,0.006596752],"category_scores_gemma":[0.0039672265,0.00093282486,0.0008989062,0.0026931597,0.0007723862,0.0017707446,0.0007444155,0.0017543364,0.0009817741],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012981358,0.0000823825,0.00019576341,0.00012399485,0.00005568095,0.000028760556,0.000058307127,0.885099,0.0004097166,0.020058528,0.0053494046,0.08840861],"study_design_scores_gemma":[0.000044958528,0.00003186493,0.00007731655,0.000019982921,0.00001767942,0.000011336939,0.000021830172,0.98231816,0.00022623962,0.0153671885,0.0018562895,0.000007079039],"about_ca_topic_score_codex":0.009562253,"about_ca_topic_score_gemma":0.009118099,"teacher_disagreement_score":0.009562253,"about_ca_system_score_codex":0.0013174703,"about_ca_system_score_gemma":0.0017514079,"threshold_uncertainty_score":0.022068322},"labels":[],"label_agreement":null},{"id":"W2064155562","doi":"10.1109/jproc.2011.2158181","title":"Dynamic Vehicle Routing for Robotic Systems","year":2011,"lang":"en","type":"article","venue":"Proceedings of the IEEE","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":222,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Robotics; Variety (cybernetics); Routing (electronic design automation); Queueing theory; Distributed computing; Process (computing); Service (business); Vehicle routing problem; Quality of service; Mathematical optimization; Robot; Operations research; Artificial intelligence; Computer network; Engineering","score_opus":0.02758031398550404,"score_gpt":0.24068281340754125,"score_spread":0.2131024994220372,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2064155562","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0030163147,0.005014517,0.976187,0.00090579456,0.00021141872,0.000046864763,0.000077560166,0.0001889111,0.01435172],"genre_scores_gemma":[0.4775108,0.02146061,0.46889907,0.00059856777,0.0011647703,0.0007231014,0.00052229495,0.00025762766,0.028863192],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994548,0.0001892956,0.000025051335,0.000096325464,0.00019269878,0.000041930332],"domain_scores_gemma":[0.9995308,0.00027155317,0.000054861608,0.000037848462,0.00008662343,0.000018419445],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006267339,0.0009566507,0.00062666077,0.00061488,0.00044279196,0.001184817,0.00091731735,0.0007661763,0.0032427069],"category_scores_gemma":[0.0021440643,0.00031176518,0.00039071185,0.0010347994,0.00076268194,0.0011012104,0.0010301743,0.0012116416,0.0007787481],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016768792,0.000019884836,0.00013806681,0.00020395906,0.000020998967,0.00004956796,0.00006634039,0.4755617,0.0011850693,0.44718662,0.005318645,0.07023232],"study_design_scores_gemma":[0.000009260583,0.00002164067,0.00008994085,0.000038603914,0.0000063117677,0.000052122316,0.000030590003,0.7067744,0.00027839627,0.26611617,0.026570054,0.000012474522],"about_ca_topic_score_codex":0.0025282418,"about_ca_topic_score_gemma":0.0023091105,"teacher_disagreement_score":0.0032427069,"about_ca_system_score_codex":0.0016289148,"about_ca_system_score_gemma":0.0012699795,"threshold_uncertainty_score":0.011818707},"labels":[],"label_agreement":null},{"id":"W2065109168","doi":"10.1007/s10288-006-0009-1","title":"An exact algorithm for team orienteering problems","year":2006,"lang":"en","type":"article","venue":"4OR","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":219,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; Université du Québec à Montréal","funders":"","keywords":"Orienteering; Vehicle routing problem; Computer science; Routing (electronic design automation); Algorithm; Mathematical optimization; Operations research; Work (physics); Artificial intelligence; Mathematics; Engineering; Computer network","score_opus":0.010941555900898292,"score_gpt":0.2544370093960222,"score_spread":0.24349545349512391,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2065109168","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0041669263,0.00010581077,0.9902148,0.000100421916,0.000073888885,0.000049378014,0.000049270107,0.00035239154,0.00488707],"genre_scores_gemma":[0.11392288,0.00018855296,0.87799424,0.0001391053,0.00006869551,0.00030712297,0.00019148973,0.00020308651,0.006984822],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994704,0.00012245344,0.000025200483,0.00010096096,0.00019092832,0.00009006378],"domain_scores_gemma":[0.99933463,0.00034641038,0.000042046173,0.00010605001,0.00012554848,0.00004528361],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009539129,0.00094104564,0.001118109,0.0008362254,0.0007414589,0.0009945784,0.0017571569,0.0017047274,0.009491019],"category_scores_gemma":[0.0031275856,0.0006103619,0.0008199707,0.0011135478,0.00069653994,0.0015217997,0.0019308295,0.0013560486,0.0014685876],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011816024,0.000097199845,0.00030581866,0.00011247779,0.000037602797,0.00005777985,0.0000785982,0.73276126,0.0013411565,0.0380314,0.005523747,0.22153479],"study_design_scores_gemma":[0.000039505318,0.000025885152,0.000058269266,0.0000092549235,0.00000829496,0.000017350369,0.0000123967575,0.981361,0.00025402472,0.016420508,0.0017877499,0.0000057185166],"about_ca_topic_score_codex":0.0071793627,"about_ca_topic_score_gemma":0.008318025,"teacher_disagreement_score":0.009491019,"about_ca_system_score_codex":0.0010734955,"about_ca_system_score_gemma":0.0018694878,"threshold_uncertainty_score":0.03175062},"labels":[],"label_agreement":null},{"id":"W2065275668","doi":"10.1109/ictai.2006.34","title":"Ant Colony with Stochastic Local Search for the Quadratic Assignment Problem","year":2006,"lang":"en","type":"article","venue":"Proceedings - International Conference on Tools with Artificial Intelligence, TAI","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"","keywords":"Quadratic assignment problem; Mathematical optimization; Ant colony optimization algorithms; Ant colony; Computer science; Quadratic equation; Local search (optimization); ANT; Quadratic programming; Mathematics; Combinatorial optimization; Computer network","score_opus":0.0709008042347709,"score_gpt":0.3054271221474704,"score_spread":0.2345263179126995,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2065275668","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013560029,0.00036381834,0.9819645,0.00015533889,0.000028621029,0.0000473708,0.000018930808,0.00039192586,0.0034693878],"genre_scores_gemma":[0.5601136,0.00065213395,0.4319293,0.000116434916,0.000063098494,0.00031209085,0.00013876858,0.00013551289,0.0065389858],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995221,0.00020984662,0.000015558266,0.000046015266,0.00017967835,0.00002672935],"domain_scores_gemma":[0.9995435,0.00027036716,0.000052796146,0.00003676595,0.00007669057,0.000019838813],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007490584,0.00052014575,0.00074317504,0.00043639616,0.00029498778,0.0004743607,0.00070500904,0.0006000861,0.0013506371],"category_scores_gemma":[0.0020515574,0.00024152,0.00055340544,0.00080949126,0.00043671473,0.00052813796,0.00057404296,0.00068795244,0.0002987062],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002913477,0.00003142358,0.0001516017,0.000063962005,0.000022783419,0.000054418353,0.00003260199,0.9561584,0.001522678,0.0076866844,0.0009496285,0.03329667],"study_design_scores_gemma":[0.0000088857,0.000017663608,0.000038784056,0.0000019234292,0.0000038106755,0.000015419466,0.0000027174258,0.9972582,0.00019094242,0.0018971225,0.0005618309,0.0000026537073],"about_ca_topic_score_codex":0.003646366,"about_ca_topic_score_gemma":0.0032277482,"teacher_disagreement_score":0.003646366,"about_ca_system_score_codex":0.00035802103,"about_ca_system_score_gemma":0.0007895183,"threshold_uncertainty_score":0.007250309},"labels":[],"label_agreement":null},{"id":"W2066066208","doi":"10.1007/s10732-013-9221-2","title":"A path relinking algorithm for a multi-depot periodic vehicle routing problem","year":2013,"lang":"en","type":"article","venue":"Journal of Heuristics","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":49,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec; Polytechnique Montréal; Université du Québec à Montréal; Université de Montréal","funders":"Fonds de recherche du Québec – Nature et technologies","keywords":"Benchmark (surveying); Vehicle routing problem; Mathematical optimization; Path (computing); Computer science; Homogeneous; Algorithm; Routing (electronic design automation); Mathematics","score_opus":0.02416431381556332,"score_gpt":0.2740220169103621,"score_spread":0.24985770309479877,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2066066208","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.054662637,0.00037682478,0.9380234,0.00026086086,0.00010292781,0.00021738095,0.00013913309,0.000836756,0.005380112],"genre_scores_gemma":[0.26107234,0.00028625588,0.7340605,0.00008630921,0.000036253336,0.0002111552,0.000294919,0.00015704686,0.0037952138],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996884,0.00008925811,0.000018738157,0.00007287251,0.00007168835,0.000059083053],"domain_scores_gemma":[0.99933726,0.00036342198,0.00006109279,0.000072155766,0.00011195129,0.000053993233],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009014618,0.0007253374,0.001225561,0.00080626184,0.00072548434,0.000861728,0.0018132715,0.001076872,0.004262661],"category_scores_gemma":[0.0018995907,0.00062639173,0.00066128455,0.0009952347,0.0005251494,0.0011306648,0.0010285989,0.0011536265,0.0004707138],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021626161,0.00015301819,0.0004075122,0.00012508911,0.000037659243,0.000055709264,0.00007804501,0.84588224,0.0026427768,0.008158084,0.0020874985,0.14015602],"study_design_scores_gemma":[0.000040474948,0.00005985992,0.00007397448,0.000006858306,0.000010354646,0.000017248769,0.000014204005,0.99658394,0.0003595463,0.0022159289,0.0006120324,0.000005586786],"about_ca_topic_score_codex":0.0063506514,"about_ca_topic_score_gemma":0.0061125,"teacher_disagreement_score":0.0063506514,"about_ca_system_score_codex":0.000782709,"about_ca_system_score_gemma":0.0017761261,"threshold_uncertainty_score":0.014259994},"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,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_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","score_opus":0.0132602200802881,"score_gpt":0.2520301703643327,"score_spread":0.23876995028404457,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2066086285","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013154468,0.013575763,0.9611138,0.0006160376,0.000115726754,0.0001162932,0.0001411804,0.00038268778,0.010784096],"genre_scores_gemma":[0.39185604,0.024297608,0.5623655,0.00029838784,0.0005227569,0.00065380667,0.0005331952,0.000318725,0.019153953],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995932,0.00017829242,0.000022044085,0.00007712287,0.00008407429,0.000045201],"domain_scores_gemma":[0.9995158,0.00032162544,0.000074827854,0.000029879819,0.000035949255,0.000021961112],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00069211214,0.0008593218,0.0011990588,0.00051124376,0.0004280464,0.001739366,0.0011049655,0.0010462686,0.0026869576],"category_scores_gemma":[0.0017572506,0.0004570096,0.00069362397,0.0018424051,0.0005772199,0.0022776534,0.0007035764,0.001134337,0.00069279486],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011253323,0.00011807909,0.00043493838,0.0006620647,0.000086502776,0.000104568,0.00012215658,0.7119612,0.002317845,0.06884886,0.008429431,0.20680176],"study_design_scores_gemma":[0.000048675654,0.00012475104,0.0002882833,0.000081904676,0.000047072986,0.00020104567,0.00008024577,0.9339967,0.0017499956,0.045032408,0.018324971,0.000023948063],"about_ca_topic_score_codex":0.0023179022,"about_ca_topic_score_gemma":0.0014958811,"teacher_disagreement_score":0.0026869576,"about_ca_system_score_codex":0.0008768331,"about_ca_system_score_gemma":0.00086839264,"threshold_uncertainty_score":0.008988738},"labels":[],"label_agreement":null},{"id":"W2066938616","doi":"10.1016/j.cor.2011.12.020","title":"The inventory-routing problem with transshipment","year":2012,"lang":"en","type":"article","venue":"Computers & Operations Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":223,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"","keywords":"Transshipment (information security); Heuristics; Computer science; Routing (electronic design automation); Heuristic; Mathematical optimization; Vehicle routing problem; Operations research; Vendor; Mathematics; Artificial intelligence; Computer network; Business","score_opus":0.06979017429723672,"score_gpt":0.3511868917928779,"score_spread":0.28139671749564116,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2066938616","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13076678,0.0026651751,0.8211335,0.0028951357,0.0005914528,0.00029358978,0.0015754037,0.00025815383,0.039820783],"genre_scores_gemma":[0.7686834,0.0027963223,0.16327861,0.00032334405,0.00034990982,0.00032352528,0.0012975169,0.00029145513,0.062655866],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989869,0.00052739755,0.00004415496,0.00021586799,0.00010012939,0.00012546638],"domain_scores_gemma":[0.99892765,0.0005890832,0.00012494766,0.00007927876,0.00008666541,0.00019234588],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018287706,0.001695072,0.0020058365,0.0008963596,0.0009995297,0.0030672944,0.0027408756,0.0027163117,0.013606186],"category_scores_gemma":[0.0042005144,0.0011782201,0.0015641042,0.0024503397,0.0012448024,0.005146767,0.002029534,0.0021354742,0.00104975],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00036036354,0.00018592307,0.00092309824,0.00031689362,0.00013669916,0.0006787326,0.00011714788,0.8489521,0.0007373941,0.11192912,0.0065267696,0.02913568],"study_design_scores_gemma":[0.000057466925,0.00012823491,0.00035439833,0.000043945805,0.000061179984,0.0002688451,0.000153271,0.90055454,0.0005186431,0.0925529,0.00527605,0.000030522693],"about_ca_topic_score_codex":0.0073203454,"about_ca_topic_score_gemma":0.0054707974,"teacher_disagreement_score":0.013606186,"about_ca_system_score_codex":0.0015740852,"about_ca_system_score_gemma":0.0016989408,"threshold_uncertainty_score":0.045517266},"labels":[],"label_agreement":null},{"id":"W2067136846","doi":"10.1002/net.20471","title":"Cutting planes for branch‐and‐price algorithms","year":2011,"lang":"en","type":"article","venue":"Networks","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":40,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal; Polytechnique Montréal; Group for Research in Decision Analysis","funders":"","keywords":"Algorithm; Computer science; Cutting-plane method; Mathematical optimization; Mathematics; Integer programming","score_opus":0.028313248287365478,"score_gpt":0.24163975919374486,"score_spread":0.21332651090637939,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2067136846","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0019412619,0.0006561755,0.9906883,0.00022542916,0.000065427426,0.00009492003,0.00011950094,0.00032420584,0.0058846883],"genre_scores_gemma":[0.09857567,0.0021061113,0.8913774,0.00024026688,0.00019247801,0.0007592478,0.00091665215,0.00038192162,0.0054503093],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.997413,0.001152266,0.00015657512,0.0002624862,0.0007788915,0.00023679076],"domain_scores_gemma":[0.9975841,0.0017098278,0.00016798665,0.00023107993,0.00025825066,0.000048622158],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0035159176,0.0019485478,0.0016080361,0.0022592,0.0007932418,0.0034815625,0.0017606156,0.0017300443,0.012563807],"category_scores_gemma":[0.008336409,0.0009744625,0.001676983,0.0034601977,0.00163773,0.002887444,0.0016807849,0.0037056715,0.002262752],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007818985,0.00007907878,0.00018621473,0.00021082176,0.00006849353,0.00007163084,0.000087450644,0.42054406,0.0008347961,0.42561963,0.0077827587,0.14443687],"study_design_scores_gemma":[0.00004240583,0.000033608278,0.0000519158,0.000074856485,0.000020598181,0.00002674765,0.000021300937,0.70317554,0.00066851906,0.2856241,0.010249517,0.000010876084],"about_ca_topic_score_codex":0.0042106826,"about_ca_topic_score_gemma":0.0029229245,"teacher_disagreement_score":0.012563807,"about_ca_system_score_codex":0.0024321575,"about_ca_system_score_gemma":0.0021264045,"threshold_uncertainty_score":0.042030096},"labels":[],"label_agreement":null},{"id":"W2068123526","doi":"10.1007/s10951-008-0072-x","title":"A comparison of five heuristics for the multiple depot vehicle scheduling problem","year":2008,"lang":"en","type":"article","venue":"Journal of Scheduling","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":124,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Group for Research in Decision Analysis; Polytechnique Montréal; Giro (Canada)","funders":"","keywords":"Heuristics; Column generation; Mathematical optimization; Tabu search; Scheduling (production processes); Computer science; Heuristic; Set (abstract data type); Job shop scheduling; Mathematics; Schedule","score_opus":0.05247880305225816,"score_gpt":0.31949700150722377,"score_spread":0.2670181984549656,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2068123526","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6485451,0.019611066,0.28749648,0.0012511796,0.0012157859,0.0012001687,0.0016355453,0.0028562034,0.03618838],"genre_scores_gemma":[0.75206417,0.003612192,0.23972614,0.0003477711,0.0001247252,0.0002909463,0.0011999299,0.00029397654,0.0023401536],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99754804,0.0011925527,0.00016594632,0.0002296456,0.0004630116,0.00040084263],"domain_scores_gemma":[0.98982936,0.007854287,0.0005250547,0.00056020176,0.00083087693,0.00040013448],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0036904924,0.0011793385,0.0016228767,0.0027746772,0.0009769668,0.0015491275,0.002450953,0.0018954771,0.0028544758],"category_scores_gemma":[0.00793305,0.00057109224,0.0013855861,0.0029710636,0.0004915705,0.0016348547,0.000794278,0.0011616473,0.00030404492],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0033187778,0.0016900718,0.004238342,0.0013393981,0.0007178038,0.000087439475,0.00016540423,0.68727547,0.0025665811,0.008456179,0.0047763223,0.28536817],"study_design_scores_gemma":[0.0009524008,0.0027617277,0.0051743,0.0001457055,0.0005387755,0.00012553307,0.00040532308,0.9743893,0.0031424973,0.007508919,0.0047834106,0.00007203912],"about_ca_topic_score_codex":0.009345695,"about_ca_topic_score_gemma":0.013746516,"teacher_disagreement_score":0.009345695,"about_ca_system_score_codex":0.002795751,"about_ca_system_score_gemma":0.0029887895,"threshold_uncertainty_score":0.020284712},"labels":[],"label_agreement":null},{"id":"W2068748054","doi":"10.1016/j.disopt.2011.05.002","title":"Finding low cost TSP and 2-matching solutions using certain half-integer subtour vertices","year":2011,"lang":"en","type":"article","venue":"Discrete Optimization","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":32,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Travelling salesman problem; Mathematics; Combinatorics; Matching (statistics); Integer (computer science); Graph; Constructive; Mathematical optimization; Discrete mathematics; Graph coloring; Computer science; Statistics","score_opus":0.05271715870122828,"score_gpt":0.27900776822092493,"score_spread":0.22629060951969665,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2068748054","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08711562,0.00018024085,0.8967322,0.00022565738,0.000053340915,0.0001441997,0.0002743212,0.0002330304,0.015041337],"genre_scores_gemma":[0.45200437,0.00025883433,0.536721,0.00011027437,0.000035618345,0.00024856318,0.00047088775,0.00018885739,0.009961507],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997036,0.00009080675,0.000011140798,0.000053186588,0.00007995713,0.00006134736],"domain_scores_gemma":[0.99952257,0.0003010497,0.000052801235,0.000050306036,0.000045430297,0.000027908885],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046030545,0.0009961687,0.0009609817,0.0010172316,0.00072758505,0.0012466338,0.0014308542,0.0012184845,0.007250597],"category_scores_gemma":[0.0026504153,0.0005205205,0.0009229603,0.0016862032,0.0005627021,0.0015870754,0.0013174332,0.0011302878,0.0006309656],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003435812,0.00020257397,0.0006400365,0.00033311005,0.00005813263,0.00016339097,0.00020928877,0.7735553,0.0068926862,0.111339,0.0037600517,0.10250294],"study_design_scores_gemma":[0.00003499975,0.000067022076,0.00016575772,0.000016451853,0.000012623183,0.00003741478,0.000049489914,0.9428978,0.0018043272,0.053549685,0.0013553675,0.000009009019],"about_ca_topic_score_codex":0.0036165528,"about_ca_topic_score_gemma":0.00401831,"teacher_disagreement_score":0.007250597,"about_ca_system_score_codex":0.00090054964,"about_ca_system_score_gemma":0.00090483675,"threshold_uncertainty_score":0.024255693},"labels":[],"label_agreement":null},{"id":"W2069090104","doi":"10.1007/s10732-014-9262-1","title":"Efficient heuristics for the workover rig routing problem with a heterogeneous fleet and a finite horizon","year":2014,"lang":"en","type":"article","venue":"Journal of Heuristics","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":25,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal; Polytechnique Montréal; Group for Research in Decision Analysis","funders":"","keywords":"Workover; Heuristics; Heuristic; Mathematical optimization; Vehicle routing problem; Time horizon; Genetic algorithm; Routing (electronic design automation); Computer science; Variable neighborhood search; Deck; Marine engineering; Operations research; Engineering; Metaheuristic; Mathematics; Petroleum engineering; Computer network; Structural engineering","score_opus":0.010517163735105747,"score_gpt":0.23276165718691072,"score_spread":0.22224449345180497,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2069090104","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11274908,0.00076318334,0.87761873,0.0003529779,0.00008443823,0.00031750926,0.00034907006,0.0004598318,0.0073050787],"genre_scores_gemma":[0.72524446,0.00036585855,0.27070388,0.000103252925,0.00004625858,0.00022905174,0.00042616154,0.000110079796,0.0027709806],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99931085,0.00024351326,0.00002813451,0.00012795672,0.0000766049,0.00021295421],"domain_scores_gemma":[0.99812394,0.0013931436,0.00017705963,0.00009415403,0.00008216624,0.00012943217],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015476348,0.0012205975,0.0017506094,0.0012300607,0.0008116571,0.0017284638,0.0025900516,0.0017403383,0.0030776367],"category_scores_gemma":[0.0031667193,0.0012811128,0.0011670275,0.0015136711,0.0010413046,0.0022549198,0.0012785479,0.0013889114,0.00023838233],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000847344,0.000055983113,0.00020719988,0.000040471677,0.000025760055,0.000038149905,0.000027051767,0.9841127,0.00030693613,0.004286484,0.0005949306,0.010219527],"study_design_scores_gemma":[0.000027406897,0.000030350242,0.00007797586,0.0000079489055,0.000011148408,0.000008630858,0.0000253041,0.9946577,0.000113488204,0.0047879643,0.00024598977,0.0000060667553],"about_ca_topic_score_codex":0.01619174,"about_ca_topic_score_gemma":0.020692455,"teacher_disagreement_score":0.01619174,"about_ca_system_score_codex":0.0021612234,"about_ca_system_score_gemma":0.0025673795,"threshold_uncertainty_score":0.03219503},"labels":[],"label_agreement":null},{"id":"W2069736946","doi":"10.1007/s10288-013-0238-z","title":"A new exact algorithm to solve the multi-trip vehicle routing problem with time windows and limited duration","year":2013,"lang":"en","type":"article","venue":"4OR","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":73,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"TRIPS architecture; Vehicle routing problem; Duration (music); Column generation; Computer science; Set (abstract data type); Routing (electronic design automation); Mathematical optimization; Time complexity; Variable (mathematics); Algorithm; Mathematics; Parallel computing; Computer network","score_opus":0.011790345112066511,"score_gpt":0.22661559542183235,"score_spread":0.21482525030976585,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2069736946","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0028851375,0.00016845275,0.99429435,0.00007139496,0.00010696262,0.000040636936,0.000040984294,0.00029873734,0.002093274],"genre_scores_gemma":[0.07213869,0.00023720988,0.92191464,0.0001266796,0.00009163186,0.00023855013,0.0001633234,0.00013255807,0.004956712],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99953055,0.00008664564,0.00002482491,0.000096520365,0.00019328471,0.00006826594],"domain_scores_gemma":[0.99942446,0.0002927074,0.000043381908,0.000062881954,0.00014180833,0.000034784054],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00081442326,0.0008267137,0.0011840425,0.0007597284,0.00052238884,0.0008895861,0.0018271236,0.0014426132,0.0059561427],"category_scores_gemma":[0.0023927414,0.00060527795,0.00072218536,0.0010855229,0.00044655858,0.0016464054,0.0011397284,0.0013139629,0.0010066278],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012431356,0.00012237331,0.00031888526,0.0001414764,0.000051444076,0.00006927761,0.000055768785,0.7321978,0.0024405087,0.021592222,0.005126282,0.2377597],"study_design_scores_gemma":[0.00003256025,0.000022827431,0.000042271688,0.0000065716986,0.0000060742464,0.000020063939,0.0000062488434,0.99422234,0.00026842,0.0038744728,0.0014927171,0.000005389398],"about_ca_topic_score_codex":0.0070835585,"about_ca_topic_score_gemma":0.008469872,"teacher_disagreement_score":0.0070835585,"about_ca_system_score_codex":0.0008126027,"about_ca_system_score_gemma":0.002206006,"threshold_uncertainty_score":0.019925296},"labels":[],"label_agreement":null},{"id":"W2069850304","doi":"10.1007/s10732-005-5431-6","title":"Evolutionary Algorithms for the Vehicle Routing Problem with Time Windows","year":2004,"lang":"en","type":"article","venue":"Journal of Heuristics","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":119,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; Université du Québec à Montréal","funders":"","keywords":"Vehicle routing problem; Benchmark (surveying); Evolutionary algorithm; Set (abstract data type); Computer science; Interval (graph theory); Point (geometry); Mathematical optimization; Routing (electronic design automation); Genetic algorithm; Algorithm; Mathematics; Combinatorics","score_opus":0.014545897641903893,"score_gpt":0.25012543441681245,"score_spread":0.23557953677490856,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2069850304","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.022877527,0.0013421322,0.97086674,0.00035934246,0.00008512062,0.000039774426,0.000033064785,0.000087619024,0.0043086424],"genre_scores_gemma":[0.44992915,0.0022466504,0.5367945,0.00018059711,0.00017597151,0.0002485632,0.00014086072,0.00013800537,0.010145678],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99961424,0.0001793756,0.000016831335,0.00004325958,0.0000920935,0.00005419611],"domain_scores_gemma":[0.9984049,0.0013274506,0.000077491335,0.000046747275,0.00009717024,0.000046268706],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011325377,0.0007053447,0.00087783247,0.00082019466,0.00041569307,0.0009493699,0.0011290712,0.0015341195,0.0023507266],"category_scores_gemma":[0.0046243686,0.0006454053,0.00070272066,0.0012055581,0.0006608095,0.0016506849,0.00087704824,0.0015203825,0.000245424],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000044205477,0.000035472298,0.00021427164,0.000034453602,0.000036460497,0.000024637466,0.000041916337,0.92815083,0.00032405125,0.035109762,0.0006024896,0.035381276],"study_design_scores_gemma":[0.000024212257,0.000015494914,0.00006285004,0.000008487678,0.000009242241,0.000010697145,0.000010491303,0.9842994,0.000095984426,0.014746701,0.0007126624,0.0000037695575],"about_ca_topic_score_codex":0.0045728353,"about_ca_topic_score_gemma":0.0033445787,"teacher_disagreement_score":0.0045728353,"about_ca_system_score_codex":0.00083317864,"about_ca_system_score_gemma":0.00081504905,"threshold_uncertainty_score":0.00909245},"labels":[],"label_agreement":null},{"id":"W206996077","doi":"10.1023/a:1012248319870","title":"High performing evolutionary techniques for solving complex location problems in industrial system design","year":2001,"lang":"en","type":"article","venue":"Journal of Intelligent Manufacturing","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":18,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Crossover; Genetic algorithm; Computer science; Workstation; Mathematical optimization; Class (philosophy); Convergence (economics); Scheme (mathematics); Mathematics; Artificial intelligence; Machine learning; Operating system","score_opus":0.06924656915657545,"score_gpt":0.2756523450357274,"score_spread":0.20640577587915193,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W206996077","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.052562065,0.00030563454,0.942893,0.00012067446,0.000038871895,0.000045939563,0.000020061165,0.00016247289,0.003851316],"genre_scores_gemma":[0.4504625,0.00022666469,0.54610264,0.000077067314,0.000046060617,0.00013465142,0.00005045202,0.00007649791,0.0028234904],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996038,0.00016385128,0.000019389938,0.000028699737,0.00014364494,0.000040666604],"domain_scores_gemma":[0.99870145,0.0009406617,0.00006628598,0.0000762238,0.00018595117,0.000029350313],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014038145,0.0007436224,0.0007691119,0.00092744466,0.00057872874,0.00059793657,0.00093760045,0.0011252016,0.0023311735],"category_scores_gemma":[0.0040773773,0.00055639027,0.00064739515,0.0009923248,0.0006704818,0.00068698416,0.0008204061,0.0009851519,0.00027712376],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000027397467,0.000042046624,0.00038610745,0.00003899346,0.000026985006,0.00003559637,0.000048732672,0.93980044,0.0014270768,0.004799412,0.00022194815,0.05314529],"study_design_scores_gemma":[0.000009100159,0.000024652887,0.00010181316,0.0000037738955,0.000009095483,0.000013907034,0.000008604378,0.99727553,0.00050868077,0.0018277228,0.00021495261,0.0000021860742],"about_ca_topic_score_codex":0.0032490012,"about_ca_topic_score_gemma":0.004799816,"teacher_disagreement_score":0.0032490012,"about_ca_system_score_codex":0.00040466548,"about_ca_system_score_gemma":0.0006492785,"threshold_uncertainty_score":0.0077985525},"labels":[],"label_agreement":null},{"id":"W2069990196","doi":"10.1016/j.tre.2014.02.002","title":"Optimizing road network daily maintenance operations with stochastic service and travel times","year":2014,"lang":"en","type":"article","venue":"Transportation Research Part E Logistics and Transportation Review","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":48,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Arc routing; Mathematical optimization; Stochastic programming; Computer science; Vehicle routing problem; Service (business); Routing (electronic design automation); Operations research; Flow network; Transport engineering; Engineering; Mathematics; Computer network","score_opus":0.05113434303327717,"score_gpt":0.3229365592133214,"score_spread":0.2718022161800442,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2069990196","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14749295,0.0036001215,0.8410139,0.00059680897,0.00013827663,0.00007716096,0.0002537768,0.0004438165,0.0063832398],"genre_scores_gemma":[0.9136418,0.0026059353,0.07635098,0.000060020735,0.000110742054,0.000086261796,0.0003035208,0.0001863356,0.006654356],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99955827,0.00017210816,0.000020226153,0.00008259967,0.00009565724,0.000071103714],"domain_scores_gemma":[0.99938715,0.00037227926,0.00008643778,0.000043544216,0.000082653525,0.000027851505],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000958395,0.0010093744,0.0010657669,0.0005335339,0.00019902556,0.000983902,0.0011866242,0.0009221372,0.0011320356],"category_scores_gemma":[0.001955695,0.0006406997,0.000914456,0.0014051588,0.00039744834,0.0012489192,0.0003789555,0.0007797345,0.00016413021],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000024077619,0.000017442957,0.00017645818,0.000040305313,0.00003515147,0.000008869038,0.000008807785,0.9795282,0.00045313156,0.0018875343,0.00033471864,0.017485304],"study_design_scores_gemma":[0.0000045790366,0.000031945357,0.000287261,0.0000033028336,0.000017613022,0.00000992552,0.000009011573,0.9971238,0.0002302302,0.0018194402,0.00045867596,0.000004187231],"about_ca_topic_score_codex":0.009257896,"about_ca_topic_score_gemma":0.006851171,"teacher_disagreement_score":0.009257896,"about_ca_system_score_codex":0.0013434573,"about_ca_system_score_gemma":0.0012250049,"threshold_uncertainty_score":0.018408},"labels":[],"label_agreement":null},{"id":"W2070134421","doi":"10.1016/j.disopt.2010.09.002","title":"Strengthening lattice-free cuts using non-negativity","year":2010,"lang":"en","type":"article","venue":"Discrete Optimization","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Mathematics; Negativity effect; Lattice (music); Algorithm; Cognitive psychology; Psychology; Physics","score_opus":0.013313346936846226,"score_gpt":0.2658952204674746,"score_spread":0.2525818735306284,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2070134421","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008136279,0.00014852732,0.98521173,0.00025104068,0.00013832867,0.000032573556,0.000045121586,0.0001052356,0.005931075],"genre_scores_gemma":[0.40222135,0.00059925136,0.58103746,0.0007902751,0.00034223896,0.00040819688,0.00034672552,0.00056840834,0.0136860525],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9962203,0.0018772859,0.000154834,0.0005740589,0.00094652956,0.00022696676],"domain_scores_gemma":[0.9873009,0.009248726,0.0005754013,0.0012628413,0.001129742,0.0004823175],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006871967,0.0019516876,0.0017707009,0.0016342984,0.0013782063,0.002310948,0.0029389479,0.0018770732,0.007937814],"category_scores_gemma":[0.023011582,0.001433954,0.0015944828,0.001117271,0.004352667,0.004893753,0.005665614,0.005873953,0.001007202],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00034885228,0.00011278173,0.00031893674,0.0002973835,0.00008372543,0.000106398504,0.00024370564,0.2113907,0.005116203,0.7381828,0.003065629,0.04073282],"study_design_scores_gemma":[0.000052349595,0.000070179754,0.00007342434,0.000042362914,0.000027557217,0.0000464236,0.000050768882,0.3830202,0.0015748298,0.61185735,0.003165027,0.000019591562],"about_ca_topic_score_codex":0.0014213232,"about_ca_topic_score_gemma":0.002025905,"teacher_disagreement_score":0.007937814,"about_ca_system_score_codex":0.0013751953,"about_ca_system_score_gemma":0.0011171572,"threshold_uncertainty_score":0.03634286},"labels":[],"label_agreement":null},{"id":"W2070349606","doi":"10.1016/j.dam.2011.05.014","title":"The periodic capacitated arc routing problem with irregular services","year":2011,"lang":"en","type":"article","venue":"Discrete Applied Mathematics","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":39,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Transport Canada","funders":"","keywords":"Arc routing; Heuristic; Arc (geometry); Routing (electronic design automation); Hierarchy; Time horizon; Mathematics; Cover (algebra); Set (abstract data type); Mathematical optimization; Vehicle routing problem; Computer science; Computer network; Engineering","score_opus":0.014794452863391894,"score_gpt":0.21091339920184088,"score_spread":0.19611894633844898,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2070349606","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.29706717,0.0008305605,0.67880934,0.0020549418,0.0002949718,0.000119588454,0.0008152623,0.00028482152,0.019723304],"genre_scores_gemma":[0.91941565,0.0005780314,0.064541765,0.00011290188,0.00017325651,0.00009521683,0.00040132308,0.00011695328,0.014564812],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994168,0.0001933477,0.000022712064,0.00013907652,0.00010193928,0.00012597763],"domain_scores_gemma":[0.99860185,0.0007834418,0.00022644199,0.00014775486,0.00009328608,0.00014715327],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00092634064,0.0005847234,0.001014747,0.0005923358,0.0005949975,0.0012982825,0.0020312588,0.0017033225,0.0044822185],"category_scores_gemma":[0.004064241,0.00067342765,0.00087529875,0.001349429,0.0010071545,0.0018665377,0.00095842785,0.0011840301,0.00029256198],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015704456,0.000044939687,0.0004605577,0.00008101147,0.000033773184,0.00022763434,0.000037984566,0.9198226,0.00065795105,0.06519589,0.0022532449,0.011027447],"study_design_scores_gemma":[0.000016916301,0.000023383063,0.00016166735,0.000005376443,0.00001105794,0.000061405204,0.000030243718,0.96271044,0.00018913017,0.035962477,0.00082039466,0.000007509398],"about_ca_topic_score_codex":0.0047388985,"about_ca_topic_score_gemma":0.003836303,"teacher_disagreement_score":0.0047388985,"about_ca_system_score_codex":0.0012422582,"about_ca_system_score_gemma":0.0010878987,"threshold_uncertainty_score":0.014994502},"labels":[],"label_agreement":null},{"id":"W2070595290","doi":"10.1016/j.cor.2009.09.014","title":"Solving a rich vehicle routing and inventory problem using column generation","year":2009,"lang":"en","type":"article","venue":"Computers & Operations Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":65,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Group for Research in Decision Analysis; HEC Montréal","funders":"","keywords":"Vehicle routing problem; Column generation; Computer science; Mathematical optimization; Routing (electronic design automation); Set (abstract data type); Column (typography); Production (economics); Work (physics); Sequence (biology); Construct (python library); Operations research; Mathematics; Economics; Engineering","score_opus":0.0986991988754276,"score_gpt":0.36455434726329644,"score_spread":0.26585514838786883,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2070595290","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09721532,0.00048887753,0.8932307,0.00046232683,0.00012856854,0.00016206106,0.00046287695,0.000548491,0.007300757],"genre_scores_gemma":[0.53278077,0.00034739342,0.45917413,0.00025751343,0.0001326557,0.00019693167,0.0005634124,0.0001376531,0.006409574],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997558,0.000102068705,0.000008439666,0.000039543946,0.00005277303,0.000041276708],"domain_scores_gemma":[0.9990528,0.0006834888,0.000059307007,0.00007729997,0.000083173436,0.000043959255],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00055886176,0.0006382463,0.0008233532,0.0006086459,0.0005282734,0.00091667345,0.00084814744,0.0009956468,0.0043525286],"category_scores_gemma":[0.0012571232,0.0006676437,0.0007586277,0.0013101739,0.0005555763,0.0009236293,0.00072499126,0.0009250393,0.00036427757],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009656356,0.00015439467,0.00046680236,0.00012381659,0.000047450514,0.00010765151,0.000037377555,0.9501447,0.0025280372,0.007499,0.0028327352,0.035961464],"study_design_scores_gemma":[0.000019408228,0.00003197655,0.00007979601,0.0000030797844,0.0000095678115,0.000013783563,0.000010591632,0.9952943,0.0006196026,0.0035331985,0.00038045953,0.0000042765996],"about_ca_topic_score_codex":0.00713124,"about_ca_topic_score_gemma":0.009387011,"teacher_disagreement_score":0.00713124,"about_ca_system_score_codex":0.00056096417,"about_ca_system_score_gemma":0.00085855165,"threshold_uncertainty_score":0.014560699},"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,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_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","score_opus":0.015973005086232974,"score_gpt":0.2513967951754557,"score_spread":0.23542379008922273,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2070637511","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023641234,0.0008109584,0.9725008,0.00021899719,0.00006182963,0.00004207285,0.000034868874,0.00011042157,0.002578751],"genre_scores_gemma":[0.54373884,0.0010355419,0.44911563,0.00013809805,0.000104541046,0.00030892153,0.00012274722,0.00009396898,0.005341719],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996213,0.00016425231,0.000015849324,0.00005376617,0.000099538214,0.000045288907],"domain_scores_gemma":[0.99918014,0.00059600174,0.000083531595,0.000026497331,0.00008545754,0.000028470487],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013566802,0.00090505625,0.0010000094,0.0008686719,0.0003910174,0.0008595379,0.0011693743,0.0014783089,0.0014617807],"category_scores_gemma":[0.002717255,0.00057281484,0.00072850357,0.0011948507,0.00052845845,0.0010959343,0.0006886353,0.0011839923,0.00018435708],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000027245116,0.000023014543,0.00009112348,0.00002262306,0.00002332644,0.000013337529,0.00001527527,0.97992545,0.0003079183,0.004330155,0.00022643534,0.014994136],"study_design_scores_gemma":[0.0000072219686,0.000010887524,0.000027032,0.0000030798128,0.000005179429,0.000002934247,0.0000025585173,0.9981552,0.00009255905,0.0015640298,0.00012785701,0.0000015351051],"about_ca_topic_score_codex":0.006558589,"about_ca_topic_score_gemma":0.004396129,"teacher_disagreement_score":0.006558589,"about_ca_system_score_codex":0.0010534051,"about_ca_system_score_gemma":0.0010341933,"threshold_uncertainty_score":0.013040841},"labels":[],"label_agreement":null},{"id":"W2070737448","doi":"10.1111/j.1475-3995.2010.00763.x","title":"Exact solution of emerging quadratic assignment problems","year":2010,"lang":"en","type":"article","venue":"International Transactions in Operational Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Université de Versailles Saint-Quentin-en-Yvelines; University of Waterloo; Naif Arab University for Security Sciences; U.S. Department of State; National Science Foundation","keywords":"Computer science; Class (philosophy); Quadratic assignment problem; Mathematical optimization; Taxonomy (biology); Management science; Operations research; Optimization problem; Artificial intelligence; Mathematics; Algorithm; Economics","score_opus":0.06427506739875978,"score_gpt":0.3943551422188767,"score_spread":0.3300800748201169,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2070737448","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01896088,0.0005913189,0.96991473,0.00043556714,0.000084259926,0.000035303656,0.000059434948,0.00009189502,0.009826685],"genre_scores_gemma":[0.49730173,0.0015355309,0.48746493,0.00029439834,0.00024867407,0.00019102829,0.00035390796,0.00010932683,0.012500409],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9991732,0.00030732702,0.00003213866,0.00011259746,0.0002861621,0.00008850359],"domain_scores_gemma":[0.99796367,0.0013637603,0.00015034048,0.0001567955,0.00029382732,0.00007163193],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015391676,0.0004951183,0.00064324896,0.00045252652,0.00038843014,0.0009072155,0.0012190557,0.0008466549,0.0055414597],"category_scores_gemma":[0.0063357507,0.00032648872,0.0004567402,0.000644129,0.0009417986,0.0014018981,0.0015235621,0.0015562294,0.0004730539],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005204581,0.00007756475,0.00051683874,0.00025139176,0.000023065826,0.000099573255,0.0001646923,0.73478734,0.0011817274,0.19012657,0.0047288323,0.067990385],"study_design_scores_gemma":[0.000016168746,0.000024663352,0.000096654534,0.000014773496,0.0000027828953,0.000028161223,0.000036606976,0.9286388,0.00023640684,0.06847417,0.0024269016,0.000003915767],"about_ca_topic_score_codex":0.002313609,"about_ca_topic_score_gemma":0.0021073993,"teacher_disagreement_score":0.0055414597,"about_ca_system_score_codex":0.0005769086,"about_ca_system_score_gemma":0.0007299651,"threshold_uncertainty_score":0.018538058},"labels":[],"label_agreement":null},{"id":"W2070782744","doi":"10.1287/trsc.35.2.192.10134","title":"Deliveries in an Inventory/Routing Problem Using Stochastic Dynamic Programming","year":2001,"lang":"en","type":"article","venue":"Transportation Science","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":81,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Dynamic programming; Product (mathematics); Operations research; Computer science; Routing (electronic design automation); Stochastic programming; Mathematical optimization; Point (geometry); Vehicle routing problem; Process (computing); Operations management; Engineering; Mathematics; Computer network","score_opus":0.03205989915677017,"score_gpt":0.31410429240534504,"score_spread":0.2820443932485749,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2070782744","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06282647,0.0020150344,0.92035943,0.002263613,0.0002070449,0.0002543931,0.000924691,0.00046563137,0.010683709],"genre_scores_gemma":[0.8346128,0.0022493242,0.13966633,0.00038522636,0.00037367627,0.00071257574,0.0010424667,0.00027550664,0.020682039],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99806577,0.0008441558,0.000091358736,0.000349619,0.00028630218,0.00036282215],"domain_scores_gemma":[0.99773335,0.0016535654,0.0002620887,0.00004288423,0.00014047713,0.00016757142],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025006176,0.0020104179,0.0031356912,0.001332963,0.0009467278,0.003372374,0.0018456744,0.0028002453,0.004345715],"category_scores_gemma":[0.0040782657,0.0020971338,0.0015231328,0.002339734,0.0013503688,0.0018702878,0.0015609686,0.0019043975,0.00048802156],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000044118337,0.000020205107,0.00013597247,0.000042845906,0.000028073182,0.000067760295,0.000020401656,0.9885844,0.00011640606,0.007774546,0.0004936519,0.002671677],"study_design_scores_gemma":[0.000023109116,0.00001973659,0.000045223325,0.000005625689,0.000012368759,0.000012793263,0.000011579637,0.99169403,0.000057010024,0.0076672514,0.00044405845,0.000007226139],"about_ca_topic_score_codex":0.023015158,"about_ca_topic_score_gemma":0.01317223,"teacher_disagreement_score":0.023015158,"about_ca_system_score_codex":0.0034386464,"about_ca_system_score_gemma":0.0027799534,"threshold_uncertainty_score":0.04576242},"labels":[],"label_agreement":null},{"id":"W2070890709","doi":"10.1007/s10878-007-9109-x","title":"An extension of the relaxation algorithm for solving a special case of capacitated arc routing problems","year":2007,"lang":"en","type":"article","venue":"Journal of Combinatorial Optimization","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"Iran National Science Foundation","keywords":"Arc routing; Subgradient method; Theory of computation; Mathematical optimization; Linear programming relaxation; Integer programming; Mathematics; Context (archaeology); Relaxation (psychology); Extension (predicate logic); Lagrangian relaxation; Algorithm; Linear programming; Branch and cut; Computer science; Routing (electronic design automation)","score_opus":0.015020409437286941,"score_gpt":0.2665742300886776,"score_spread":0.25155382065139065,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2070890709","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0063070534,0.00032292833,0.9833725,0.00029782765,0.00021122408,0.000085045926,0.00009219587,0.00036228137,0.008949038],"genre_scores_gemma":[0.08107869,0.0005467604,0.91136044,0.00023210295,0.00027567954,0.00019109662,0.00032003305,0.00029104738,0.005704096],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9990758,0.0003356509,0.00004740002,0.00014951744,0.0002567563,0.00013490884],"domain_scores_gemma":[0.9983241,0.00089619553,0.000106145024,0.00024841726,0.00033620457,0.000088955494],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016062526,0.0010611664,0.0014428879,0.0009851238,0.0007154295,0.0010734607,0.0022523915,0.0014901892,0.007663967],"category_scores_gemma":[0.005623534,0.00086865487,0.0016252224,0.001873043,0.0005869125,0.001927993,0.0014068742,0.0033681153,0.0017003898],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00032804278,0.0004779289,0.0007177861,0.00043176496,0.00015693664,0.00023576342,0.00017755358,0.57127804,0.0052165273,0.06315898,0.02113537,0.3366853],"study_design_scores_gemma":[0.00003551965,0.000046739686,0.00008975402,0.000020957976,0.00001850528,0.00008802835,0.000012713738,0.98152643,0.0005964848,0.0123350015,0.00521798,0.000011818297],"about_ca_topic_score_codex":0.0034363968,"about_ca_topic_score_gemma":0.0034366974,"teacher_disagreement_score":0.007663967,"about_ca_system_score_codex":0.0005141701,"about_ca_system_score_gemma":0.0015159141,"threshold_uncertainty_score":0.02563852},"labels":[],"label_agreement":null},{"id":"W2072209462","doi":"10.1103/physreve.63.047103","title":"Critical transition in the constrained traveling salesman problem","year":2001,"lang":"en","type":"article","venue":"Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Lethbridge","funders":"","keywords":"Travelling salesman problem; Scaling; Bottleneck traveling salesman problem; Function (biology); Combinatorics; Mathematical optimization; Physics; Mathematics; Statistical physics; Computer science; Biology; Geometry","score_opus":0.014523782319159488,"score_gpt":0.3232170729763605,"score_spread":0.308693290657201,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2072209462","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8304797,0.0019813534,0.14814538,0.0021426133,0.00015190149,0.000084867104,0.0002654368,0.00041022326,0.01633855],"genre_scores_gemma":[0.9853005,0.00056240126,0.011973992,0.00017999414,0.00008341036,0.00008531358,0.00013932565,0.00008719209,0.0015879489],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999608,0.00013766425,0.000014411597,0.00006845292,0.000062149506,0.000109259956],"domain_scores_gemma":[0.9935182,0.0046567074,0.0007629195,0.00013816605,0.0003580967,0.00056578696],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012664993,0.00067493046,0.0007419537,0.0011194448,0.000810692,0.0016069516,0.0009559869,0.0010709486,0.0023062988],"category_scores_gemma":[0.01208915,0.00050563435,0.0006060644,0.00062520424,0.0022742157,0.0021273831,0.0010426755,0.0013304612,0.0001548794],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00028198774,0.00015749829,0.003121467,0.00026828845,0.00011538712,0.0005391326,0.00042538496,0.6050575,0.005722406,0.37201843,0.0050415103,0.0072510145],"study_design_scores_gemma":[0.000028885064,0.000049906914,0.0008094371,0.000029940476,0.000017502141,0.00004696855,0.000081800856,0.9202595,0.00041172362,0.07775615,0.00048775092,0.000020384849],"about_ca_topic_score_codex":0.005823633,"about_ca_topic_score_gemma":0.0025236378,"teacher_disagreement_score":0.005823633,"about_ca_system_score_codex":0.0015263219,"about_ca_system_score_gemma":0.000978132,"threshold_uncertainty_score":0.011579514},"labels":[],"label_agreement":null},{"id":"W2074019423","doi":"10.1016/j.ejor.2010.02.022","title":"A general variable neighborhood search for solving the uncapacitated single allocation p-hub median problem","year":2010,"lang":"en","type":"article","venue":"European Journal of Operational Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":156,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Military College of Canada","funders":"Science and Engineering Research Board","keywords":"Variable neighborhood search; Heuristics; Variable (mathematics); Mathematical optimization; PlanetLab; Computer science; Descent (aeronautics); Local search (optimization); Mathematics; Metaheuristic","score_opus":0.0700874788475297,"score_gpt":0.33205587870339404,"score_spread":0.26196839985586434,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2074019423","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020813137,0.00024802302,0.97352296,0.00023040349,0.0000596605,0.000063124295,0.00007842104,0.00018136825,0.004802836],"genre_scores_gemma":[0.39034617,0.00027170178,0.599603,0.00016864587,0.0000890161,0.00043626447,0.00030851492,0.00016328035,0.008613482],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99971884,0.00012241416,0.000007752389,0.00005762469,0.000057672907,0.000035685724],"domain_scores_gemma":[0.99959344,0.0002769111,0.000027525355,0.000022280576,0.000055895696,0.000023971506],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008015652,0.00051456236,0.0012960833,0.00058419316,0.00045117922,0.00066978374,0.0014150228,0.0012308641,0.0048398348],"category_scores_gemma":[0.0019961863,0.00045253712,0.000680071,0.00085170806,0.00047354927,0.0009271491,0.0010761971,0.000853754,0.00038181336],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010372644,0.000060237395,0.0003129237,0.00005486289,0.000028274435,0.000033719327,0.000029205228,0.9431621,0.0005754609,0.016513448,0.002323933,0.036802083],"study_design_scores_gemma":[0.0000122174315,0.000016900638,0.000031993295,0.000002769955,0.000002670504,0.000005337599,0.0000053641547,0.9961385,0.0000576608,0.0034301972,0.00029467072,0.0000017639544],"about_ca_topic_score_codex":0.0045538545,"about_ca_topic_score_gemma":0.005584544,"teacher_disagreement_score":0.0048398348,"about_ca_system_score_codex":0.000576792,"about_ca_system_score_gemma":0.0010776345,"threshold_uncertainty_score":0.016190886},"labels":[],"label_agreement":null},{"id":"W2074030523","doi":"10.1007/s11590-014-0788-9","title":"A general variable neighborhood search variants for the travelling salesman problem with draft limits","year":2014,"lang":"en","type":"article","venue":"Optimization Letters","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":30,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Travelling salesman problem; Traveling purchaser problem; Variable neighborhood search; Descent (aeronautics); 2-opt; Mathematical optimization; Computational intelligence; Variable (mathematics); Set (abstract data type); Computer science; Limit (mathematics); Extension (predicate logic); Bottleneck traveling salesman problem; Context (archaeology); Mathematics; Metaheuristic; Artificial intelligence","score_opus":0.012782624187917832,"score_gpt":0.22573323936400003,"score_spread":0.2129506151760822,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2074030523","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018117988,0.00085949985,0.96656144,0.00059972436,0.00018615396,0.00005611125,0.000104370156,0.0000598033,0.013454858],"genre_scores_gemma":[0.46663994,0.0018358174,0.48429665,0.00035932267,0.00037653281,0.00039865117,0.0005028224,0.00033457516,0.045255795],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99923146,0.00039002165,0.000023910368,0.0001322507,0.00015819885,0.00006415085],"domain_scores_gemma":[0.9987697,0.0007178618,0.0001064131,0.00011941354,0.00020155698,0.00008499857],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019642445,0.0007054389,0.0011291642,0.00067035406,0.00053332595,0.0016161874,0.0024121671,0.0016719326,0.004967948],"category_scores_gemma":[0.0051230653,0.0005109186,0.0011471459,0.001140883,0.0010376188,0.0025071441,0.0016370985,0.0018685696,0.00053760066],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008122796,0.00007299422,0.00038055045,0.00010845718,0.00004464644,0.00012273902,0.00008022277,0.6408227,0.00091768254,0.32848525,0.004887786,0.023995822],"study_design_scores_gemma":[0.000009532207,0.000022955352,0.000055305438,0.000008139345,0.000006031769,0.000026228663,0.0000113002425,0.96484494,0.00009626526,0.03291005,0.0020032881,0.000006052622],"about_ca_topic_score_codex":0.0041373875,"about_ca_topic_score_gemma":0.0030886126,"teacher_disagreement_score":0.004967948,"about_ca_system_score_codex":0.000998771,"about_ca_system_score_gemma":0.001135204,"threshold_uncertainty_score":0.016619503},"labels":[],"label_agreement":null},{"id":"W2074193675","doi":"10.3138/infor.51.1.23","title":"Solution Methods for Fuel Supply of Trains","year":2013,"lang":"en","type":"article","venue":"INFOR Information Systems and Operational Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Truck; Tabu search; Computer science; Metaheuristic; Train; Mathematical optimization; Flexibility (engineering); Flow network; Linear programming; Assignment problem; Operations research; Engineering; Automotive engineering; Algorithm; Mathematics","score_opus":0.07749217862021154,"score_gpt":0.40279004517663813,"score_spread":0.32529786655642656,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2074193675","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0024325482,0.0007244028,0.98937535,0.0001809926,0.00008134002,0.00007644723,0.00008581064,0.00012629163,0.0069166613],"genre_scores_gemma":[0.12496258,0.0019652217,0.85453683,0.00013372104,0.00018756639,0.0006032435,0.0003406204,0.00025069254,0.017019415],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99959284,0.00014145872,0.000022547347,0.00007509041,0.00013097924,0.000037174577],"domain_scores_gemma":[0.9994223,0.00037331274,0.00004737994,0.000029381468,0.000112191796,0.00001537087],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009479116,0.0012040708,0.0008741669,0.0012295962,0.00055221014,0.0013196266,0.0014416717,0.0016016298,0.009416378],"category_scores_gemma":[0.0026712602,0.00046807123,0.0011265111,0.0014712148,0.000560927,0.0010404687,0.0010606656,0.0014507629,0.001097722],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000043217355,0.0000678165,0.0003156804,0.0004839318,0.00006387136,0.0000739021,0.000120424775,0.7316927,0.0014318347,0.096513055,0.0041566007,0.16503699],"study_design_scores_gemma":[0.00002042178,0.000018457902,0.000055986344,0.000040251758,0.000010048296,0.000025321568,0.000028050188,0.9678743,0.0004960622,0.024462132,0.0069624525,0.0000064464075],"about_ca_topic_score_codex":0.0046948125,"about_ca_topic_score_gemma":0.0048281727,"teacher_disagreement_score":0.009416378,"about_ca_system_score_codex":0.0010721429,"about_ca_system_score_gemma":0.0017635118,"threshold_uncertainty_score":0.031500876},"labels":[],"label_agreement":null},{"id":"W2074413296","doi":"10.1002/net.21587","title":"Timing problems and algorithms: Time decisions for sequences of activities","year":2015,"lang":"en","type":"article","venue":"Networks","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":50,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Université du Québec à Montréal","funders":"","keywords":"Computer science; Scheduling (production processes); Concatenation (mathematics); Vehicle routing problem; Inference; Combinatorial optimization; Algorithm; Mathematical optimization; Routing (electronic design automation); Artificial intelligence; Mathematics","score_opus":0.06351391128462434,"score_gpt":0.2892708729540661,"score_spread":0.22575696166944176,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2074413296","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0029701663,0.0013810206,0.989193,0.000595267,0.00012916008,0.00009746187,0.0001311903,0.000097876364,0.005404821],"genre_scores_gemma":[0.15588312,0.0047979215,0.82522047,0.0004135239,0.00082902116,0.0008145852,0.00076027354,0.00034089768,0.0109402],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9958002,0.0018168986,0.00031870033,0.001025176,0.0008081596,0.00023080822],"domain_scores_gemma":[0.992235,0.005984904,0.0006981214,0.00055544765,0.0003562474,0.00017028229],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0046368893,0.0021250308,0.0013707575,0.0012564205,0.0009887499,0.0040512024,0.0024276334,0.0024898814,0.0081112785],"category_scores_gemma":[0.018946923,0.0009033766,0.001729826,0.002909261,0.0027790836,0.0063635223,0.0022662254,0.0033038256,0.0014113777],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009089349,0.000060773156,0.00057810114,0.00030735048,0.00006760889,0.000071567454,0.0001840559,0.345994,0.0006110591,0.5639596,0.0044678464,0.08360721],"study_design_scores_gemma":[0.000034487555,0.000059908416,0.00013824378,0.000082235456,0.000026943804,0.00006946079,0.00006125247,0.4872042,0.0005639063,0.49946693,0.012270013,0.000022435746],"about_ca_topic_score_codex":0.0020984085,"about_ca_topic_score_gemma":0.0015339997,"teacher_disagreement_score":0.0081112785,"about_ca_system_score_codex":0.0023117883,"about_ca_system_score_gemma":0.002662452,"threshold_uncertainty_score":0.027134895},"labels":[],"label_agreement":null},{"id":"W2075642749","doi":"10.1504/ijenm.2009.032395","title":"Saving based algorithm for multi-depot version of vehicle routing problem with simultaneous pickup and delivery","year":2009,"lang":"en","type":"article","venue":"International Journal of Enterprise Network Management","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Pickup; Depot; Vehicle routing problem; Computer science; Algorithm; Routing (electronic design automation); Mathematical optimization; Simulation; Computer network; Mathematics; Artificial intelligence; Geography","score_opus":0.00911554741619326,"score_gpt":0.24856055401140728,"score_spread":0.23944500659521403,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2075642749","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.029875388,0.00017407972,0.9642713,0.00013976137,0.00004450772,0.00017483506,0.00011120889,0.00068586215,0.004523113],"genre_scores_gemma":[0.37885347,0.0003631186,0.61365783,0.00008731822,0.000047915568,0.00040673112,0.0006837361,0.00022006694,0.0056797406],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996145,0.000085209795,0.000020767724,0.0000740817,0.00012447046,0.00008091881],"domain_scores_gemma":[0.99956363,0.00022069759,0.000050876566,0.000061486724,0.00007156001,0.000031730193],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005744669,0.00078958296,0.0010444466,0.0007977537,0.0005660369,0.00082409097,0.0018768302,0.0007024274,0.00466142],"category_scores_gemma":[0.0009792882,0.00042103813,0.00065431825,0.0009924769,0.00045183237,0.0011990943,0.00089827395,0.00080091273,0.0005497131],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019290767,0.00011935741,0.0006781134,0.00020789063,0.000044884102,0.00014302824,0.00015361792,0.8016046,0.0051750937,0.022754762,0.0046306294,0.1642951],"study_design_scores_gemma":[0.00003112054,0.0000735297,0.00017828509,0.00000872097,0.000017695309,0.00007698331,0.0000356196,0.98769593,0.0020417739,0.0069673248,0.0028635322,0.000009453432],"about_ca_topic_score_codex":0.003305478,"about_ca_topic_score_gemma":0.0028925198,"teacher_disagreement_score":0.00466142,"about_ca_system_score_codex":0.00090990687,"about_ca_system_score_gemma":0.0011991896,"threshold_uncertainty_score":0.015593946},"labels":[],"label_agreement":null},{"id":"W2076332856","doi":"10.1016/j.tre.2013.06.001","title":"An heuristic search for the routing of heterogeneous trucks with single and double container loads","year":2013,"lang":"en","type":"article","venue":"Transportation Research Part E Logistics and Transportation Review","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":67,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; Université du Québec à Montréal","funders":"","keywords":"Truck; Container (type theory); Metaheuristic; Routing (electronic design automation); Computer science; Heuristic; Port (circuit theory); Vehicle routing problem; Mathematical optimization; Operations research; Engineering; Computer network; Algorithm; Mathematics; Automotive engineering; Artificial intelligence","score_opus":0.09280986466183076,"score_gpt":0.3560358834065543,"score_spread":0.2632260187447235,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2076332856","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05235191,0.0020038702,0.92767143,0.0007699739,0.00031561658,0.0003603681,0.00029014578,0.0003923947,0.015844256],"genre_scores_gemma":[0.39778116,0.00116895,0.59120965,0.0003505992,0.00021339934,0.00080008764,0.00044536087,0.00020583173,0.00782496],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99937844,0.00030082607,0.000023643779,0.00008620176,0.00010749775,0.00010343589],"domain_scores_gemma":[0.9983022,0.0013225196,0.00010561823,0.000043713815,0.00012845056,0.00009756117],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020019172,0.0014565762,0.0020297735,0.0021124803,0.00084052444,0.0016505521,0.0024248948,0.0029150639,0.0041559436],"category_scores_gemma":[0.0045403256,0.0015464915,0.0016295173,0.0024566196,0.0012956971,0.0015189174,0.0014939628,0.0014954453,0.0003987223],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000064448315,0.000039413673,0.00011874519,0.000063119245,0.00004161826,0.00004195152,0.000029029163,0.98119503,0.00019301691,0.0055115,0.00091774703,0.01178428],"study_design_scores_gemma":[0.000033972443,0.000025834745,0.000033437587,0.000009691559,0.000011788852,0.000008654322,0.000015733083,0.99724776,0.00004290843,0.0022464697,0.0003190495,0.0000047137532],"about_ca_topic_score_codex":0.011670439,"about_ca_topic_score_gemma":0.008224262,"teacher_disagreement_score":0.011670439,"about_ca_system_score_codex":0.0018395678,"about_ca_system_score_gemma":0.0027030932,"threshold_uncertainty_score":0.023205042},"labels":[],"label_agreement":null},{"id":"W2076697001","doi":"10.1007/s10732-010-9127-1","title":"On the asymptotic behavior of subtour-patching heuristics in solving the TSP on permuted Monge matrices","year":2010,"lang":"en","type":"article","venue":"Journal of Heuristics","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"Engineering and Physical Sciences Research Council","keywords":"Heuristics; Travelling salesman problem; Heuristic; Asymptotically optimal algorithm; Mathematics; Combinatorics; Mathematical optimization; Applied mathematics; Discrete mathematics; Computer science","score_opus":0.013308526026506036,"score_gpt":0.2593656246455304,"score_spread":0.2460570986190244,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2076697001","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.57174546,0.006202752,0.37113845,0.0041116895,0.00044164414,0.00027130486,0.00041637907,0.0013078355,0.044364523],"genre_scores_gemma":[0.8843464,0.0011076098,0.10899938,0.0005479546,0.00018543472,0.00020263322,0.0005253062,0.00048415843,0.0036009941],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9962108,0.0020990167,0.000113260816,0.00034963377,0.0005785986,0.0006486347],"domain_scores_gemma":[0.8945896,0.09450805,0.0022708483,0.003791725,0.0033981188,0.0014416244],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008362933,0.0011056345,0.0014712653,0.0017653812,0.0016317539,0.0017563676,0.0027060066,0.0020325736,0.0058629005],"category_scores_gemma":[0.08541317,0.0007956173,0.0008219312,0.002110755,0.0029386922,0.004384786,0.0021557081,0.0026432313,0.00060326647],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012369179,0.00046715536,0.0052082846,0.0005310852,0.0001530554,0.00012801496,0.00038665358,0.84872246,0.0032432,0.07843581,0.0077012707,0.053786077],"study_design_scores_gemma":[0.0000646683,0.0001793505,0.00062057684,0.00004611957,0.00004080831,0.000062001265,0.0000975964,0.9694485,0.000731993,0.028284525,0.0004094042,0.000014425815],"about_ca_topic_score_codex":0.007271808,"about_ca_topic_score_gemma":0.008008822,"teacher_disagreement_score":0.008362933,"about_ca_system_score_codex":0.0024201826,"about_ca_system_score_gemma":0.0033660976,"threshold_uncertainty_score":0.044227958},"labels":[],"label_agreement":null},{"id":"W2078190972","doi":"10.1002/net.20451","title":"The preemptive swapping problem on a tree","year":2011,"lang":"en","type":"article","venue":"Networks","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal; Group for Research in Decision Analysis; Université de Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Centre interuniversitaire de recherche sur les reseaux d'entreprise, la logistique et le transport; Canada Research Chairs","keywords":"Vertex (graph theory); Object (grammar); Computer science; Tree (set theory); Heuristic; Mathematical optimization; Combinatorics; Mathematics; Routing (electronic design automation); Type (biology); Graph; Computer network; Artificial intelligence","score_opus":0.025470249201273908,"score_gpt":0.22639831072444183,"score_spread":0.20092806152316792,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2078190972","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.30827448,0.0007941563,0.6703728,0.00082624756,0.00010729016,0.00022509141,0.00074683665,0.00045391513,0.018199246],"genre_scores_gemma":[0.784559,0.00064215425,0.20220701,0.00018479179,0.00007125561,0.00018575069,0.0011851192,0.0001613493,0.010803453],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99941254,0.00020361195,0.00002796421,0.00010237588,0.00009610906,0.0001574029],"domain_scores_gemma":[0.9991416,0.000502275,0.0000877021,0.00008908762,0.00007071243,0.0001086819],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00057579204,0.00056712807,0.00085180986,0.0005009338,0.0009226719,0.0009439416,0.0011115479,0.0009653654,0.006429598],"category_scores_gemma":[0.0016837368,0.00036267683,0.0006816481,0.0011350104,0.00065582787,0.0022026717,0.0010573358,0.0008842217,0.00073624187],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00043961906,0.00026787145,0.001236943,0.00038179892,0.000083027706,0.0010204478,0.00027988615,0.7544895,0.014724542,0.10784203,0.00975352,0.109480806],"study_design_scores_gemma":[0.00005456846,0.00016414898,0.00047034453,0.000026347501,0.000028974624,0.000368351,0.00016852826,0.8572374,0.0038885423,0.12771544,0.009860787,0.00001656948],"about_ca_topic_score_codex":0.0019185789,"about_ca_topic_score_gemma":0.0014421159,"teacher_disagreement_score":0.006429598,"about_ca_system_score_codex":0.00064599543,"about_ca_system_score_gemma":0.00067727204,"threshold_uncertainty_score":0.02150917},"labels":[],"label_agreement":null},{"id":"W2078227935","doi":"10.1287/opre.1090.0713","title":"Branch-and-Price-and-Cut for the Split-Delivery Vehicle Routing Problem with Time Windows","year":2009,"lang":"en","type":"article","venue":"Operations Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":198,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Group for Research in Decision Analysis","funders":"","keywords":"Column generation; Vehicle routing problem; Knapsack problem; Mathematical optimization; Computer science; Relaxation (psychology); Integer programming; Routing (electronic design automation); Branch and price; Shortest path problem; Set (abstract data type); Bounded function; Mathematics; Computer network","score_opus":0.03652744760081844,"score_gpt":0.32538523900211375,"score_spread":0.2888577914012953,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2078227935","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.022413533,0.0008657391,0.97188944,0.0002862863,0.000048641265,0.00017800042,0.00014976937,0.00018387966,0.003984689],"genre_scores_gemma":[0.2778203,0.0017389894,0.7134788,0.00010647105,0.000108313296,0.00063143735,0.00057681405,0.00019915537,0.0053397706],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99920136,0.00036029075,0.000029492478,0.000117582735,0.00016453756,0.00012680377],"domain_scores_gemma":[0.9988845,0.0008645213,0.00009080957,0.00003823581,0.00006337256,0.000058596124],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016299302,0.0013103512,0.0016004712,0.0008571257,0.0007167944,0.0015012065,0.0010530896,0.0010943224,0.0053845653],"category_scores_gemma":[0.00277793,0.00083878305,0.00085884694,0.0015599368,0.0009063286,0.001882659,0.0009345631,0.0016630786,0.0005877124],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018954434,0.0001064538,0.00038478442,0.000205244,0.00004946286,0.000116724106,0.00008110852,0.9014239,0.0013127669,0.03302133,0.002249912,0.0608588],"study_design_scores_gemma":[0.00003859464,0.000055791403,0.00008130959,0.000011887435,0.000013603054,0.000026842888,0.000021384287,0.9783447,0.00046673714,0.019599734,0.0013329457,0.000006571962],"about_ca_topic_score_codex":0.005377891,"about_ca_topic_score_gemma":0.0046243356,"teacher_disagreement_score":0.0053845653,"about_ca_system_score_codex":0.0013219896,"about_ca_system_score_gemma":0.001829508,"threshold_uncertainty_score":0.01801318},"labels":[],"label_agreement":null},{"id":"W2078415021","doi":"10.1287/trsc.1110.0398","title":"Exact Solution of Large-Scale Hub Location Problems with Multiple Capacity Levels","year":2012,"lang":"en","type":"article","venue":"Transportation Science","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":81,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal; Concordia University","funders":"","keywords":"Mathematical optimization; Robustness (evolution); Benders' decomposition; Benchmark (surveying); Enumeration; Reduction (mathematics); Decomposition; Linear programming; Scale (ratio); Integer programming; Mathematics; Computer science; Algorithm","score_opus":0.038280818502688885,"score_gpt":0.27657885353136896,"score_spread":0.23829803502868008,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2078415021","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07276998,0.00032611212,0.9200896,0.00024509127,0.00004436423,0.000088891466,0.00018816708,0.000463794,0.005783848],"genre_scores_gemma":[0.55171174,0.0002923485,0.44414255,0.000057851863,0.00004330484,0.00019817315,0.00034941384,0.000101081125,0.0031034981],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9995795,0.00016067804,0.000014196585,0.00006832419,0.000108413944,0.000068930945],"domain_scores_gemma":[0.99895144,0.0007033073,0.00011093537,0.00011071141,0.00008036618,0.00004320802],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00087119295,0.0009245291,0.00097623735,0.0005313116,0.00049662305,0.00085784437,0.0012739939,0.00095330324,0.0037701996],"category_scores_gemma":[0.0022669586,0.00056879973,0.00057680387,0.00097463094,0.0006050772,0.0012149614,0.0009354139,0.0011053834,0.00032472736],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026584503,0.000030304855,0.00016062302,0.00004571077,0.00001264396,0.00004016755,0.000013284375,0.97984767,0.000416195,0.0046309284,0.0004071492,0.014368802],"study_design_scores_gemma":[0.000013939514,0.000015419442,0.00006858037,0.0000033348663,0.000003264207,0.0000112293565,0.000014649083,0.9937372,0.0002939943,0.005580636,0.00025550733,0.0000021917815],"about_ca_topic_score_codex":0.0039428365,"about_ca_topic_score_gemma":0.0066407793,"teacher_disagreement_score":0.0039428365,"about_ca_system_score_codex":0.0008287454,"about_ca_system_score_gemma":0.0013162857,"threshold_uncertainty_score":0.012612581},"labels":[],"label_agreement":null},{"id":"W2078701526","doi":"10.1016/j.cor.2010.09.016","title":"The balanced traveling salesmanproblem","year":2010,"lang":"en","type":"article","venue":"Computers & Operations Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Travelling salesman problem; Mathematical optimization; Benchmark (surveying); Computer science; Heuristic; Mathematics","score_opus":0.04331335880890813,"score_gpt":0.3574347166550178,"score_spread":0.3141213578461097,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2078701526","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.039133206,0.0027271172,0.9047101,0.0028266497,0.00036940494,0.00014824221,0.00077915896,0.00022105388,0.049085017],"genre_scores_gemma":[0.5658685,0.0057188356,0.3630388,0.00067512743,0.0006056699,0.00055621756,0.001940722,0.00037254184,0.06122356],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99937904,0.0002464367,0.000018714734,0.00013667734,0.00011864193,0.00010041058],"domain_scores_gemma":[0.9994475,0.00031293326,0.000055161887,0.000040288385,0.00007763702,0.000066424196],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000992035,0.000978241,0.00110241,0.0007587333,0.00083029264,0.0021328288,0.0012123777,0.0018736637,0.014007334],"category_scores_gemma":[0.0034129266,0.00069430785,0.00050307583,0.0013443142,0.00081626466,0.0030597171,0.0013600761,0.0014034209,0.0016734694],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023276597,0.00013162885,0.0004673721,0.00036580747,0.000070212496,0.00015077772,0.00009819194,0.25248313,0.0018356962,0.5810284,0.026211774,0.13692422],"study_design_scores_gemma":[0.00008650024,0.000046206856,0.00021896909,0.000047409434,0.000030242772,0.00007397919,0.00006610935,0.54124904,0.0006135863,0.4362417,0.021311583,0.00001462841],"about_ca_topic_score_codex":0.0031909128,"about_ca_topic_score_gemma":0.0025064144,"teacher_disagreement_score":0.014007334,"about_ca_system_score_codex":0.0011432094,"about_ca_system_score_gemma":0.0014145378,"threshold_uncertainty_score":0.046859205},"labels":[],"label_agreement":null},{"id":"W2079586783","doi":"10.1002/atr.5670350104","title":"The planning of aircraft routes and flight frequencies in an airline network operations","year":2001,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Science Council","keywords":"Flow network; Operations research; Computer science; Profit (economics); Aviation; Network model; Flow (mathematics); Simulation; Engineering; Mathematical optimization; Aerospace engineering; Economics; Mathematics","score_opus":0.013267852951219591,"score_gpt":0.2755775030962472,"score_spread":0.2623096501450276,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2079586783","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.39633992,0.00025535547,0.5866275,0.0007275226,0.000039136736,0.00023758748,0.00059502665,0.00017476652,0.015003248],"genre_scores_gemma":[0.95387214,0.00015259061,0.04191276,0.000018949153,0.000008720255,0.00010236431,0.000105645646,0.000021594615,0.0038051805],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996427,0.00018572198,0.000007324328,0.00006294189,0.000047581045,0.000053771473],"domain_scores_gemma":[0.9996331,0.00022155036,0.000055118013,0.000011856932,0.000039550552,0.00003882392],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006090106,0.0005848397,0.00039962376,0.00056602375,0.000579205,0.0010983084,0.00048303892,0.0007793531,0.0021249794],"category_scores_gemma":[0.001313807,0.00048419973,0.00036515237,0.0005887033,0.0004920177,0.0011969558,0.0003862285,0.0005620361,0.00015759005],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019478714,0.000009427477,0.00019404465,0.0000061603723,0.0000033534466,0.000021307318,0.000012893074,0.9959573,0.00019877858,0.0018042186,0.00010073358,0.0016722529],"study_design_scores_gemma":[0.0000056748454,0.000013965651,0.000092215065,0.0000023950267,0.000002544644,0.0000048279608,0.000015586214,0.9984378,0.00014892135,0.0010657322,0.0002081945,0.0000021704989],"about_ca_topic_score_codex":0.03218088,"about_ca_topic_score_gemma":0.02541401,"teacher_disagreement_score":0.03218088,"about_ca_system_score_codex":0.0021517067,"about_ca_system_score_gemma":0.0018482212,"threshold_uncertainty_score":0.063987136},"labels":[],"label_agreement":null},{"id":"W2080484716","doi":"10.1007/s12532-012-0047-y","title":"The time dependent traveling salesman problem: polyhedra and algorithm","year":2012,"lang":"en","type":"article","venue":"Mathematical Programming Computation","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":87,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Travelling salesman problem; Polytope; Bottleneck traveling salesman problem; Polyhedron; Theory of computation; Mathematics; Combinatorics; Generalization; Dimension (graph theory); Heuristics; Traveling purchaser problem; Combinatorial optimization; Algorithm; Computer science; Mathematical optimization","score_opus":0.013270643753828533,"score_gpt":0.2620524660477406,"score_spread":0.24878182229391205,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2080484716","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0035467243,0.0006747264,0.9893828,0.00044398237,0.000097367214,0.000030761825,0.000071474635,0.00007748487,0.005674632],"genre_scores_gemma":[0.1447851,0.0031122637,0.83654934,0.00023772006,0.0002814255,0.00034987484,0.00048292355,0.0003082646,0.013893078],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99936086,0.00026020216,0.000025664236,0.00011810908,0.00018267657,0.000052602674],"domain_scores_gemma":[0.9986737,0.0008633873,0.00009581899,0.0001157109,0.00018614224,0.00006518205],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013388558,0.0010429427,0.0016227328,0.00111375,0.001113349,0.0028998014,0.002311798,0.0022444765,0.0054112715],"category_scores_gemma":[0.004457389,0.0010906538,0.001507058,0.0023219592,0.0016789376,0.0038853218,0.0019677035,0.003299382,0.0010733446],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000046046076,0.000083929466,0.0002624466,0.00014660021,0.000033507156,0.000035076206,0.00007904213,0.65691555,0.00042918252,0.27840886,0.0074324775,0.05612717],"study_design_scores_gemma":[0.000008134808,0.000009314256,0.00003719245,0.000016651364,0.0000061381857,0.000012702312,0.000021402133,0.8961737,0.0001457302,0.10035357,0.0032084289,0.0000069996395],"about_ca_topic_score_codex":0.00734591,"about_ca_topic_score_gemma":0.005695086,"teacher_disagreement_score":0.00734591,"about_ca_system_score_codex":0.0017988048,"about_ca_system_score_gemma":0.0022715004,"threshold_uncertainty_score":0.018102467},"labels":[],"label_agreement":null},{"id":"W2080943874","doi":"10.1007/s11590-015-0869-4","title":"Solving the clique partitioning problem as a maximally diverse grouping problem","year":2015,"lang":"en","type":"article","venue":"Optimization Letters","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":28,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Military College of Canada","funders":"Natural Sciences and Engineering Research Council of Canada; Agence Nationale de la Recherche","keywords":"Clique; Heuristic; Variable neighborhood search; Mathematical optimization; Local search (optimization); Computer science; Mathematics; Diversification (marketing strategy); Metaheuristic; Combinatorics","score_opus":0.02134978858238772,"score_gpt":0.2425588380450419,"score_spread":0.22120904946265418,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2080943874","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.026141547,0.0002082745,0.96554804,0.00053334923,0.000050713374,0.000087425185,0.00015510184,0.000116745316,0.007158713],"genre_scores_gemma":[0.29752824,0.00034663995,0.69000053,0.00033993743,0.00015754596,0.00037819057,0.00089190516,0.00028352055,0.010073446],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988889,0.0005767278,0.000025144349,0.00025208973,0.00015420066,0.00010300171],"domain_scores_gemma":[0.9985613,0.00097457774,0.000089248926,0.00015545143,0.00013245094,0.0000868922],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014390019,0.00095700903,0.0013671005,0.000776633,0.0009703688,0.0014178441,0.0020987343,0.002244577,0.0060154675],"category_scores_gemma":[0.0038368625,0.00075876655,0.001002057,0.0015020139,0.00084175955,0.0021838008,0.0018175261,0.0015728635,0.0006730555],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000113707974,0.00015256582,0.0006301986,0.00023861724,0.00010471884,0.00015482657,0.00018894837,0.8136861,0.0028208103,0.10688394,0.010142248,0.06488337],"study_design_scores_gemma":[0.000026748867,0.00003189888,0.00016851125,0.000014353348,0.000014324016,0.000047023423,0.00007649035,0.93210256,0.00047236762,0.0646105,0.002426038,0.000009202169],"about_ca_topic_score_codex":0.0027361547,"about_ca_topic_score_gemma":0.0039932663,"teacher_disagreement_score":0.0060154675,"about_ca_system_score_codex":0.0007884578,"about_ca_system_score_gemma":0.001023211,"threshold_uncertainty_score":0.02012378},"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,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_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","score_opus":0.2067050982487565,"score_gpt":0.4203752284752951,"score_spread":0.21367013022653863,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2081363577","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14886458,0.0012605896,0.8055262,0.004447536,0.00022427145,0.0002258878,0.001945885,0.00025597197,0.037249137],"genre_scores_gemma":[0.8146506,0.0011525917,0.13857412,0.00044523412,0.00030195827,0.000390854,0.0008748284,0.00028774535,0.043321993],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984572,0.0007314784,0.000046982495,0.0003623986,0.00025335042,0.00014856832],"domain_scores_gemma":[0.9970968,0.0021489616,0.0002874648,0.00011553899,0.0001553483,0.0001959095],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002315861,0.0012060583,0.0015784679,0.0015854805,0.0007399695,0.0020872313,0.0027988022,0.0029174348,0.007943418],"category_scores_gemma":[0.007394777,0.0012489728,0.0013922831,0.0023539225,0.001442513,0.004553952,0.0022645376,0.0015728949,0.00037112512],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001393509,0.000113698334,0.0016968094,0.00020758827,0.00016045784,0.0002986715,0.00007459805,0.8564366,0.001054799,0.1206947,0.0034127645,0.015709983],"study_design_scores_gemma":[0.00004953318,0.00007818398,0.001262874,0.000025052379,0.00007178329,0.00018324508,0.00009876021,0.86729336,0.0006002436,0.12653528,0.0037758674,0.000025827569],"about_ca_topic_score_codex":0.006024558,"about_ca_topic_score_gemma":0.0060500335,"teacher_disagreement_score":0.007943418,"about_ca_system_score_codex":0.0026930943,"about_ca_system_score_gemma":0.0012419304,"threshold_uncertainty_score":0.02657342},"labels":[],"label_agreement":null},{"id":"W2081655128","doi":"10.1080/00207543.2012.757668","title":"A branch-and-cut algorithm for the multi-product multi-vehicle inventory-routing problem","year":2013,"lang":"en","type":"article","venue":"International Journal of Production Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":158,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"","keywords":"Vendor-managed inventory; Vehicle routing problem; Routing (electronic design automation); Product (mathematics); Inventory theory; Computer science; Vendor; Mathematical optimization; Inventory control; Operations research; Consistency (knowledge bases); Quality (philosophy); Supply chain; Supply chain management; Engineering; Mathematics; Business; Artificial intelligence; Marketing","score_opus":0.10671239343453276,"score_gpt":0.3979318811228896,"score_spread":0.2912194876883568,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2081655128","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0035491514,0.00024297513,0.99266595,0.0002288617,0.000045587323,0.00015285423,0.00010304347,0.00026214137,0.0027494787],"genre_scores_gemma":[0.035197303,0.0002506436,0.96180874,0.000094307594,0.00003841143,0.00033329363,0.0003268707,0.000118172764,0.0018323145],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991393,0.00029364706,0.000045281064,0.00016975225,0.00023340448,0.00011855795],"domain_scores_gemma":[0.99870956,0.0008816446,0.0000885786,0.000059075264,0.00018341452,0.0000776586],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016535572,0.0017288547,0.0016479628,0.0012670176,0.0011982574,0.0016075983,0.0020260226,0.0026398003,0.0072787823],"category_scores_gemma":[0.0034141012,0.0009061819,0.0011116433,0.0021821859,0.00074661255,0.0020415152,0.0016881195,0.0027610022,0.0013989233],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014285617,0.0002465176,0.0006020506,0.00025208073,0.00006652418,0.00016478985,0.00011451109,0.74067456,0.0016755454,0.03133506,0.009590763,0.2151347],"study_design_scores_gemma":[0.000052254854,0.000052630097,0.00007492168,0.000021317213,0.000015328054,0.000049047663,0.000028577482,0.97652686,0.00042124905,0.020250585,0.0024978123,0.000009439143],"about_ca_topic_score_codex":0.005637216,"about_ca_topic_score_gemma":0.0059904694,"teacher_disagreement_score":0.0072787823,"about_ca_system_score_codex":0.0015229465,"about_ca_system_score_gemma":0.0026426355,"threshold_uncertainty_score":0.024349928},"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,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_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","score_opus":0.043283522705910266,"score_gpt":0.2781970216619149,"score_spread":0.23491349895600463,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2082772228","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.026025837,0.27562743,0.6511909,0.003877621,0.0009138039,0.0003262002,0.00061166444,0.00032282845,0.041103825],"genre_scores_gemma":[0.1977803,0.5082449,0.25487694,0.0013711568,0.004898686,0.00045704353,0.0024752659,0.00028723496,0.029608488],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.998473,0.00022390242,0.0001421542,0.0004126121,0.0006025203,0.00014582802],"domain_scores_gemma":[0.9981939,0.0009195559,0.00023323968,0.00013767915,0.00041574275,0.000099850884],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001720403,0.0017195564,0.002199367,0.004572636,0.00095483084,0.0030981943,0.0028647766,0.0020366856,0.0028117865],"category_scores_gemma":[0.0027479043,0.00083422614,0.0020162114,0.008736901,0.0012801357,0.0046634157,0.0014973376,0.002074168,0.0011623878],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000098383614,0.0006001611,0.0058601857,0.0031592732,0.00018101091,0.00018891554,0.00020648373,0.058908153,0.0014956804,0.089128904,0.026341092,0.8138318],"study_design_scores_gemma":[0.00006195868,0.0007916961,0.00902904,0.0015903005,0.0003849045,0.0033223352,0.0010476258,0.5006545,0.0031663645,0.20250612,0.27724916,0.00019595544],"about_ca_topic_score_codex":0.0018272932,"about_ca_topic_score_gemma":0.0018411005,"teacher_disagreement_score":0.004572636,"about_ca_system_score_codex":0.0009122725,"about_ca_system_score_gemma":0.0012556423,"threshold_uncertainty_score":0.009406388},"labels":[],"label_agreement":null},{"id":"W2083487387","doi":"10.1016/j.cor.2014.12.001","title":"A hybrid method for the Probabilistic Maximal Covering Location–Allocation Problem","year":2014,"lang":"en","type":"article","venue":"Computers & Operations Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":50,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"Centre interuniversitaire de recherche sur les reseaux d'entreprise, la logistique et le transport; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Université Laval","keywords":"Mathematical optimization; Benchmark (surveying); Solver; Probabilistic logic; Heuristic; Integer programming; Computer science; Metaheuristic; Linear programming; Mathematics; Algorithm; Artificial intelligence","score_opus":0.05287132260263117,"score_gpt":0.36430405751876127,"score_spread":0.3114327349161301,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2083487387","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002320099,0.00007764243,0.99601644,0.00005464015,0.000032074207,0.000023718634,0.00002126376,0.000081223196,0.0013728609],"genre_scores_gemma":[0.17236592,0.00023335783,0.8195805,0.00014088876,0.00013483067,0.0003176811,0.00016126009,0.00019441907,0.0068710535],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992592,0.00027466926,0.000026507034,0.00010140043,0.00027475494,0.00006337361],"domain_scores_gemma":[0.9989692,0.0006591039,0.000059367398,0.00008924248,0.00017080642,0.000052276955],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001391309,0.0006465338,0.00092924014,0.000872422,0.00046619834,0.0008922959,0.0018640956,0.0012625146,0.0050436864],"category_scores_gemma":[0.0024741678,0.00058313145,0.0011396826,0.000995643,0.0006244566,0.0011577902,0.0017035858,0.00105061,0.00068884826],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010994907,0.00008497267,0.00047088452,0.00017350054,0.00010413482,0.00006993782,0.000071613904,0.78324646,0.0042619607,0.06123041,0.002724977,0.14745124],"study_design_scores_gemma":[0.000010061282,0.000018277171,0.000045265173,0.0000052037203,0.000006971746,0.000022296848,0.000004486281,0.9922795,0.00024051676,0.006199567,0.0011624998,0.0000052835503],"about_ca_topic_score_codex":0.0028302183,"about_ca_topic_score_gemma":0.0028407357,"teacher_disagreement_score":0.0050436864,"about_ca_system_score_codex":0.00073184783,"about_ca_system_score_gemma":0.0010904627,"threshold_uncertainty_score":0.016872823},"labels":[],"label_agreement":null},{"id":"W2085138485","doi":"10.1016/j.mcm.2007.12.025","title":"Optimization of production allocation and transportation of customer orders for a leading forest products company","year":2008,"lang":"en","type":"article","venue":"Mathematical and Computer Modelling","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":34,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Production (economics); Order (exchange); Computer science; Process (computing); Operations research; Integer programming; Business; Operations management; Economics; Finance; Engineering","score_opus":0.036020724329612085,"score_gpt":0.23812427682272602,"score_spread":0.20210355249311393,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2085138485","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.75666195,0.000885734,0.20816256,0.0012316288,0.00016375113,0.00060778175,0.0011146126,0.00046387414,0.030708091],"genre_scores_gemma":[0.94873136,0.00027139398,0.040365975,0.000055232176,0.000025093219,0.00012914352,0.00034405856,0.000101677666,0.009975956],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993375,0.00025449245,0.000017979126,0.000089260226,0.00009767501,0.00020311125],"domain_scores_gemma":[0.9990458,0.0005661463,0.00009076586,0.000037961032,0.0001252053,0.00013415169],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013334213,0.0011175197,0.001324671,0.0014048731,0.0015333374,0.0025216953,0.0011186294,0.002138885,0.0071398276],"category_scores_gemma":[0.0021199216,0.0014844828,0.0011590972,0.0017381352,0.00090190076,0.0013886422,0.0006075737,0.0012870008,0.0007394796],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018871999,0.00008936377,0.0005944778,0.000042296473,0.000019537194,0.00007630338,0.00002415754,0.9895878,0.0013877683,0.0012710768,0.0007086678,0.006009862],"study_design_scores_gemma":[0.0000208845,0.0001249228,0.00091299345,0.000005651954,0.00002009922,0.000023870645,0.00006803526,0.9964276,0.0008746292,0.0010697438,0.00043905358,0.0000126258665],"about_ca_topic_score_codex":0.035354085,"about_ca_topic_score_gemma":0.03331574,"teacher_disagreement_score":0.035354085,"about_ca_system_score_codex":0.004019377,"about_ca_system_score_gemma":0.003333425,"threshold_uncertainty_score":0.070296586},"labels":[],"label_agreement":null},{"id":"W2085285568","doi":"10.1287/trsc.1070.0195","title":"Vehicle Routing for Urban Snow Plowing Operations","year":2008,"lang":"en","type":"article","venue":"Transportation Science","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":93,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Polytechnique Montréal; Group for Research in Decision Analysis","funders":"Fonds Québécois de la Recherche sur la Nature et les Technologies; Université de Moncton","keywords":"Routing (electronic design automation); Truck; Vehicle routing problem; Heuristic; Flow network; Transport engineering; Computer science; Snow removal; Set (abstract data type); Class (philosophy); Level of service; Operations research; Fleet management; Snow; Flow routing; Engineering; Mathematical optimization; Computer network; Mathematics; Geography","score_opus":0.03569784321385178,"score_gpt":0.28784008291687985,"score_spread":0.25214223970302807,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2085285568","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.19542308,0.0008139349,0.79036325,0.00053314696,0.000032957825,0.0001914994,0.00067714223,0.00045561485,0.011509316],"genre_scores_gemma":[0.84676725,0.0009364208,0.14270474,0.000038444345,0.00001590403,0.00014188554,0.00074105925,0.0000881505,0.008566105],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99982446,0.00006476328,0.0000048721845,0.000033543296,0.000031047817,0.00004139406],"domain_scores_gemma":[0.99989235,0.00005391545,0.000021690443,0.00000731611,0.0000147723395,0.000009926517],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003003027,0.00041285146,0.00034629638,0.00041608338,0.00058518536,0.00074659655,0.0006224743,0.00049533404,0.0030204195],"category_scores_gemma":[0.00046543064,0.0003442278,0.00039656158,0.0009459466,0.00035395316,0.0005359938,0.00027922983,0.0003285119,0.0002507905],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019124816,0.000012014845,0.00035270967,0.000025268122,0.0000073800197,0.000026832287,0.000028272652,0.9831277,0.00073548703,0.003949496,0.00048324888,0.011232451],"study_design_scores_gemma":[0.000010577744,0.000021365591,0.0003789783,0.000004210233,0.000007703704,0.000020798532,0.00006556172,0.99291295,0.0004969943,0.0038782994,0.0021968104,0.00000571981],"about_ca_topic_score_codex":0.0487942,"about_ca_topic_score_gemma":0.07563871,"teacher_disagreement_score":0.0487942,"about_ca_system_score_codex":0.00185867,"about_ca_system_score_gemma":0.0021446357,"threshold_uncertainty_score":0.09702039},"labels":[],"label_agreement":null},{"id":"W2085543268","doi":"10.1016/j.cor.2009.05.005","title":"The undirected capacitated arc routing problem with profits","year":2009,"lang":"en","type":"article","venue":"Computers & Operations Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":66,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Group for Research in Decision Analysis","funders":"","keywords":"Heuristics; Arc routing; Mathematical optimization; Profit (economics); Vehicle routing problem; Computer science; Undirected graph; Graph; Enhanced Data Rates for GSM Evolution; Mathematics; Routing (electronic design automation); Theoretical computer science; Economics; Artificial intelligence; Microeconomics","score_opus":0.045654646211437444,"score_gpt":0.3336104762566733,"score_spread":0.28795583004523584,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2085543268","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06863608,0.0016641294,0.8746058,0.0028881812,0.00023584643,0.00012413047,0.00093796634,0.00024381501,0.050664116],"genre_scores_gemma":[0.77207416,0.0024480787,0.1893062,0.00040802947,0.00030155576,0.00023066739,0.0010068121,0.00027449356,0.033950157],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990821,0.0003751761,0.000034509503,0.00021022506,0.00018477465,0.00011323996],"domain_scores_gemma":[0.99873716,0.0008873954,0.0000928031,0.000078427656,0.00012004049,0.00008425236],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009914463,0.00096023205,0.0008331366,0.0007837229,0.0005713467,0.0024674078,0.0017781359,0.0014879728,0.0045278664],"category_scores_gemma":[0.0043451437,0.00055452564,0.0006266527,0.0020533164,0.0010536269,0.0028947596,0.0011179046,0.0015038435,0.00059572974],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010284951,0.00010109384,0.00056416844,0.00022218376,0.00006674667,0.0002533414,0.00007821438,0.59932244,0.0009084297,0.32599545,0.009494534,0.06289059],"study_design_scores_gemma":[0.000025138426,0.000029055984,0.0002665985,0.000029716022,0.00003365934,0.00012344548,0.000056561355,0.6160629,0.0005435773,0.37662634,0.0061855363,0.000017421964],"about_ca_topic_score_codex":0.0024872487,"about_ca_topic_score_gemma":0.0021790795,"teacher_disagreement_score":0.0045278664,"about_ca_system_score_codex":0.001555182,"about_ca_system_score_gemma":0.0013569852,"threshold_uncertainty_score":0.015147209},"labels":[],"label_agreement":null},{"id":"W2086399386","doi":"10.1016/j.ejor.2012.02.007","title":"Heuristics for the multi-depot petrol station replenishment problem with time windows","year":2012,"lang":"en","type":"article","venue":"European Journal of Operational Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":104,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"TRIPS architecture; Truck; Heuristics; Computer science; Operations research; Heuristic; Set (abstract data type); Depot; Revenue; Vehicle routing problem; Mathematical optimization; Schedule; Gasoline; Transport engineering; Routing (electronic design automation); Engineering; Mathematics; Automotive engineering; Computer network","score_opus":0.08523717946583616,"score_gpt":0.3594690999722097,"score_spread":0.27423192050637357,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2086399386","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15816261,0.0035920467,0.82091033,0.00075997086,0.00031428513,0.0006152172,0.0005726733,0.000781488,0.014291363],"genre_scores_gemma":[0.6649878,0.0017156556,0.3266063,0.00019258652,0.00013492373,0.00032803148,0.00034398655,0.00014057236,0.0055502253],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993641,0.00025535034,0.000031276257,0.00008173748,0.00009069128,0.00017685378],"domain_scores_gemma":[0.9981445,0.0014448254,0.00013873729,0.00006394712,0.00007302928,0.00013488441],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013712595,0.0011882087,0.0016701921,0.0011867507,0.0006788595,0.0017502727,0.0020203209,0.001608749,0.003574775],"category_scores_gemma":[0.0027362707,0.0011255885,0.0010383951,0.0016999067,0.00064554985,0.0016849138,0.0010670552,0.0016383951,0.00028825967],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023380118,0.00016757514,0.00026736248,0.00016426735,0.000058495407,0.0000877104,0.00006582099,0.9536706,0.0007938475,0.011689864,0.0018503399,0.030950297],"study_design_scores_gemma":[0.000089375775,0.0000935668,0.00014360015,0.000017541894,0.000030019557,0.000024349001,0.000036867124,0.991268,0.00035574206,0.0069060693,0.0010224234,0.000012580412],"about_ca_topic_score_codex":0.010851534,"about_ca_topic_score_gemma":0.013520518,"teacher_disagreement_score":0.010851534,"about_ca_system_score_codex":0.0017290399,"about_ca_system_score_gemma":0.0023046285,"threshold_uncertainty_score":0.021576762},"labels":[],"label_agreement":null},{"id":"W2086411701","doi":"10.1287/trsc.1100.0336","title":"Checking the Feasibility of Dial-a-Ride Instances Using Constraint Programming","year":2010,"lang":"en","type":"article","venue":"Transportation Science","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":36,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; HEC Montréal","funders":"Canada Research Chairs","keywords":"Mathematical optimization; Computer science; Constraint (computer-aided design); Reduction (mathematics); Dynamic programming; Algorithm; Constraint programming; Stochastic programming; Mathematics","score_opus":0.056624502328832,"score_gpt":0.34097344120089756,"score_spread":0.2843489388720656,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2086411701","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.23328754,0.00043179552,0.74988973,0.0017758994,0.0001247673,0.0009589355,0.0024133374,0.0011719943,0.00994596],"genre_scores_gemma":[0.5252818,0.0003801838,0.46818444,0.00034868403,0.00010540813,0.00065055146,0.0029587771,0.00023066571,0.0018595124],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99247557,0.0031982958,0.0005377009,0.0014584764,0.001498621,0.0008312256],"domain_scores_gemma":[0.9422549,0.051393386,0.0019869814,0.0017673285,0.002021308,0.0005761896],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0068484917,0.0017165786,0.002188597,0.0022599073,0.0016511086,0.0048043323,0.0031581693,0.0032111928,0.007150842],"category_scores_gemma":[0.050074417,0.0018725744,0.0024982376,0.0030141054,0.0029136338,0.008061526,0.0026234665,0.003399508,0.0006853803],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006170681,0.00039912917,0.00489076,0.00041756497,0.00031329456,0.0008668595,0.00022865554,0.880499,0.002760074,0.061327025,0.0038639095,0.04381676],"study_design_scores_gemma":[0.00012946375,0.00013708186,0.00046541344,0.00005038581,0.000046931425,0.00012842451,0.00019078582,0.9334374,0.0029724413,0.061043076,0.0013578867,0.000040598337],"about_ca_topic_score_codex":0.00901198,"about_ca_topic_score_gemma":0.007636299,"teacher_disagreement_score":0.00901198,"about_ca_system_score_codex":0.0015812379,"about_ca_system_score_gemma":0.0035618572,"threshold_uncertainty_score":0.036218762},"labels":[],"label_agreement":null},{"id":"W2087852198","doi":"10.1016/j.ejor.2012.03.044","title":"A simple and effective metaheuristic for the Minimum Latency Problem","year":2012,"lang":"en","type":"article","venue":"European Journal of Operational Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":135,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Metaheuristic; Mathematical optimization; Computer science; Travelling salesman problem; Iterated local search; Scheduling (production processes); Benchmark (surveying); Iterated function; Job shop scheduling; Variable neighborhood search; Simple (philosophy); Algorithm; Mathematics","score_opus":0.07068969871385707,"score_gpt":0.37249692924056277,"score_spread":0.3018072305267057,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2087852198","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019020189,0.0005560958,0.9737269,0.00030601546,0.00027995594,0.00017536429,0.00009930776,0.00057315064,0.0052630035],"genre_scores_gemma":[0.1738993,0.0005443501,0.8192276,0.00026326065,0.00018888027,0.00040113446,0.00018085098,0.00022374859,0.0050709066],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996377,0.0001183054,0.000018437693,0.000043091484,0.00013106059,0.00005135481],"domain_scores_gemma":[0.99955803,0.0002363068,0.000048642043,0.000051575742,0.00006873406,0.00003672503],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00065284764,0.0011166655,0.00093225803,0.00096442166,0.00051174086,0.0009100877,0.0015775446,0.0019108506,0.0025067802],"category_scores_gemma":[0.001996021,0.00042233375,0.0010095417,0.0012137174,0.00047101697,0.0010210337,0.0010885592,0.0014766762,0.00052500976],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012360074,0.0002103131,0.00032828786,0.0001636607,0.00008946707,0.00009872116,0.000050250266,0.79516476,0.006953934,0.017516619,0.003534596,0.17576584],"study_design_scores_gemma":[0.00007943794,0.00009303023,0.00008627093,0.000012935106,0.000029999797,0.000042373726,0.000017111619,0.98776746,0.00080190995,0.00853884,0.0025179023,0.000012758562],"about_ca_topic_score_codex":0.0020880618,"about_ca_topic_score_gemma":0.0030232554,"teacher_disagreement_score":0.0025067802,"about_ca_system_score_codex":0.0006018262,"about_ca_system_score_gemma":0.0014763612,"threshold_uncertainty_score":0.0083860755},"labels":[],"label_agreement":null},{"id":"W2088165054","doi":"10.1016/j.disopt.2009.04.004","title":"Local search intensified: Very large-scale variable neighborhood search for the multi-resource generalized assignment problem","year":2009,"lang":"en","type":"article","venue":"Discrete Optimization","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":41,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Heuristics; Heuristic; Benchmark (surveying); Mathematical optimization; Variable neighborhood search; Mathematics; Local search (optimization); Variable (mathematics); Scale (ratio); Incremental heuristic search; Beam search; Algorithm; Search algorithm; Computer science; Metaheuristic","score_opus":0.022019800699498385,"score_gpt":0.2781955289469787,"score_spread":0.2561757282474803,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2088165054","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.031311303,0.0004066845,0.9636924,0.00022293186,0.00005531803,0.000046518788,0.00004892509,0.0002503719,0.003965503],"genre_scores_gemma":[0.6736957,0.0002778553,0.32032973,0.00020435332,0.000082124476,0.00026150103,0.0001490282,0.000148044,0.004851568],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99960095,0.00022895365,0.000010404353,0.00004095149,0.00008543257,0.000033411045],"domain_scores_gemma":[0.99930596,0.00044576748,0.000049050745,0.00007846679,0.00008571293,0.00003496264],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012681832,0.00048918265,0.0011008043,0.00039471465,0.00031812236,0.0006698811,0.0012986913,0.0009909996,0.0025473917],"category_scores_gemma":[0.003043305,0.00034520664,0.00047121482,0.0005862466,0.0006437862,0.0011342765,0.0012079668,0.0011229737,0.00026847594],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011954563,0.00007553633,0.0002640191,0.000076341574,0.000036408317,0.000050645125,0.000051924224,0.9273531,0.0011578626,0.02025425,0.0027181269,0.047842264],"study_design_scores_gemma":[0.00001587738,0.000018474657,0.0000363319,0.0000033155184,0.0000035763014,0.0000062993736,0.0000043412792,0.996494,0.00012256615,0.0030684613,0.00022500473,0.00000184437],"about_ca_topic_score_codex":0.0022822374,"about_ca_topic_score_gemma":0.0028971347,"teacher_disagreement_score":0.0025473917,"about_ca_system_score_codex":0.00054376863,"about_ca_system_score_gemma":0.00064342277,"threshold_uncertainty_score":0.008521855},"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,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_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","score_opus":0.03730997085608331,"score_gpt":0.31946399162222655,"score_spread":0.28215402076614327,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2088243692","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0026147913,0.0028258779,0.98958135,0.00020822274,0.00013438871,0.000023314158,0.000052569892,0.000105698615,0.0044538206],"genre_scores_gemma":[0.16395876,0.005924525,0.8004272,0.00020282723,0.00036923357,0.00039564222,0.00029560144,0.0002614584,0.02816475],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993861,0.00032983965,0.000020715033,0.00008203197,0.00015216102,0.000029149542],"domain_scores_gemma":[0.99853563,0.0010362514,0.00008548537,0.00010496385,0.00020505389,0.00003259682],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014756275,0.00069474994,0.001042906,0.0012021558,0.000482896,0.0010681236,0.0012245697,0.0016256805,0.004807254],"category_scores_gemma":[0.005246222,0.00045528464,0.0007011039,0.0018581703,0.0008759905,0.0013895035,0.0010064427,0.0012235652,0.0011351818],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000089001725,0.00007178227,0.00070544047,0.00042817128,0.00009937433,0.000059052007,0.00015453981,0.4518483,0.0011027266,0.24918802,0.008991936,0.2872617],"study_design_scores_gemma":[0.00002014391,0.000022677305,0.0001726921,0.000042665324,0.00001449791,0.0000292084,0.000018494015,0.91835225,0.00032886653,0.07247429,0.008510866,0.000013243395],"about_ca_topic_score_codex":0.0033492995,"about_ca_topic_score_gemma":0.003381332,"teacher_disagreement_score":0.004807254,"about_ca_system_score_codex":0.0005329819,"about_ca_system_score_gemma":0.0006317463,"threshold_uncertainty_score":0.01608187},"labels":[],"label_agreement":null},{"id":"W2089432311","doi":"10.1016/j.cor.2015.04.002","title":"A population-based metaheuristic for the pickup and delivery problem with time windows and LIFO loading","year":2015,"lang":"en","type":"article","venue":"Computers & Operations Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":79,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal; Polytechnique Montréal; Group for Research in Decision Analysis","funders":"","keywords":"Metaheuristic; FIFO and LIFO accounting; Crossover; Population; Mathematical optimization; Tabu search; Computer science; Local search (optimization); Vehicle routing problem; Mathematics; Algorithm; Artificial intelligence","score_opus":0.06616750905945257,"score_gpt":0.3289781835126778,"score_spread":0.26281067445322526,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2089432311","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.047991257,0.0007630822,0.941772,0.000459132,0.00020861467,0.0001908184,0.00009432728,0.00046357713,0.0080572525],"genre_scores_gemma":[0.4708063,0.0005424435,0.5199107,0.00034757378,0.00013018948,0.0006917685,0.00021681668,0.00014714744,0.007207046],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995509,0.00017555882,0.000017536178,0.000054677337,0.00012304794,0.00007822686],"domain_scores_gemma":[0.9993305,0.00040662277,0.00007135274,0.00003252138,0.0001039377,0.00005513418],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001102743,0.00077473995,0.0011549043,0.001098997,0.0006409559,0.0010546251,0.0019773552,0.0019340421,0.0027550403],"category_scores_gemma":[0.0024540375,0.000616479,0.0009804402,0.0010965588,0.00061272073,0.0010062774,0.0010570865,0.0013740531,0.00034115044],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007212178,0.00011719075,0.00043221225,0.00004559634,0.00004961582,0.000051043233,0.000049180155,0.9343834,0.0010396908,0.007755548,0.0013295102,0.054674864],"study_design_scores_gemma":[0.000032872133,0.000048498863,0.00008366532,0.000008238812,0.000013682251,0.000013502267,0.000010165585,0.9975248,0.00019701192,0.0012931421,0.0007688707,0.0000055294313],"about_ca_topic_score_codex":0.006028537,"about_ca_topic_score_gemma":0.005076303,"teacher_disagreement_score":0.006028537,"about_ca_system_score_codex":0.001239106,"about_ca_system_score_gemma":0.0015919821,"threshold_uncertainty_score":0.011986911},"labels":[],"label_agreement":null},{"id":"W2089448602","doi":"10.1007/s12469-012-0058-0","title":"A reduced integer programming model for the ferry scheduling problem","year":2012,"lang":"en","type":"article","venue":"Public Transport","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Integer programming; Crew scheduling; Scheduling (production processes); Solver; Mathematical optimization; Computer science; Job shop scheduling; Integer (computer science); Branch and price; Operations research; Mathematics; Schedule","score_opus":0.053920639627303556,"score_gpt":0.2819119838099116,"score_spread":0.22799134418260805,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2089448602","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023837678,0.0003614238,0.9461109,0.0008578704,0.00012104218,0.00009717761,0.00066332164,0.00025247692,0.027698133],"genre_scores_gemma":[0.6179276,0.0011642475,0.33227405,0.00033080464,0.00024666308,0.0005571769,0.0016682236,0.00034757316,0.04548367],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99940586,0.00026510583,0.000016030663,0.000090687805,0.00012890468,0.00009335211],"domain_scores_gemma":[0.999501,0.00031294464,0.00005165205,0.000033257948,0.00006440683,0.000036597143],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008069479,0.0008228155,0.0010868385,0.00065126695,0.0004708152,0.0016202172,0.0019825932,0.0013889868,0.007796536],"category_scores_gemma":[0.0020308285,0.0005845772,0.0009947404,0.00094638835,0.0006598322,0.0014001783,0.00081870286,0.0019894238,0.0009125968],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002725447,0.000057148936,0.00006920471,0.000044246513,0.000010570986,0.000055064465,0.000033023043,0.9379271,0.00033722425,0.05346196,0.0020765741,0.0059006526],"study_design_scores_gemma":[0.000010040761,0.000010205155,0.000027010727,0.0000043764635,0.0000044619983,0.0000065338286,0.000012256584,0.98579687,0.000059554335,0.012594855,0.0014704242,0.0000033681638],"about_ca_topic_score_codex":0.013415942,"about_ca_topic_score_gemma":0.00987109,"teacher_disagreement_score":0.013415942,"about_ca_system_score_codex":0.0016069907,"about_ca_system_score_gemma":0.001936667,"threshold_uncertainty_score":0.026675701},"labels":[],"label_agreement":null},{"id":"W2089725715","doi":"10.1057/jors.2009.83","title":"Saving-based algorithms for vehicle routing problem with simultaneous pickup and delivery","year":2009,"lang":"en","type":"article","venue":"Journal of the Operational Research Society","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":34,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Heuristics; Vehicle routing problem; Pickup; Computer science; Heuristic; Routing (electronic design automation); Extension (predicate logic); Mathematical optimization; Algorithm; Mathematics; Embedded system; Artificial intelligence","score_opus":0.04172816528484367,"score_gpt":0.33600109162972863,"score_spread":0.29427292634488494,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2089725715","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025297113,0.00039284214,0.9690248,0.0001644027,0.00004181769,0.00014390297,0.000050679766,0.00051365566,0.004370807],"genre_scores_gemma":[0.3925616,0.0007802929,0.60143924,0.0001182925,0.00006341688,0.00060447067,0.00033908655,0.00020332748,0.003890263],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995357,0.00015497032,0.000020290765,0.00006438707,0.00013889754,0.00008579006],"domain_scores_gemma":[0.9988293,0.0007873636,0.00013234229,0.00008735687,0.000109143606,0.000054526885],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012253649,0.0013254749,0.0013704168,0.0012363713,0.0006920377,0.0008576853,0.0020265414,0.00093872,0.0032855086],"category_scores_gemma":[0.0021127467,0.0006399351,0.0008543187,0.0014716057,0.00093593355,0.001408796,0.0013010864,0.0010576526,0.0004235053],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009333545,0.00006401645,0.0003100433,0.00011134565,0.00002908422,0.000051747364,0.00009483937,0.91451675,0.0009517974,0.023507642,0.0013791265,0.058890205],"study_design_scores_gemma":[0.00002434649,0.00004643364,0.00006625312,0.000008450276,0.000015409461,0.000025075506,0.000022297514,0.98771775,0.00044152467,0.010449971,0.0011754212,0.0000070887536],"about_ca_topic_score_codex":0.0041667153,"about_ca_topic_score_gemma":0.0036253643,"teacher_disagreement_score":0.0041667153,"about_ca_system_score_codex":0.0011623811,"about_ca_system_score_gemma":0.0013143515,"threshold_uncertainty_score":0.0109910965},"labels":[],"label_agreement":null},{"id":"W2090118375","doi":"10.1016/j.ejor.2006.05.009","title":"General solutions to the single vehicle routing problem with pickups and deliveries","year":2006,"lang":"en","type":"article","venue":"European Journal of Operational Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":106,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"Norges Forskningsråd","keywords":"Vehicle routing problem; Heuristics; Mathematical optimization; Tabu search; Computer science; Integer programming; Pickup; Routing (electronic design automation); Mathematics; Artificial intelligence","score_opus":0.06103351430024579,"score_gpt":0.3031094858809919,"score_spread":0.24207597158074612,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2090118375","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016187048,0.0008094072,0.96897817,0.00055553374,0.00015570867,0.000104722756,0.00025343982,0.00014160693,0.012814294],"genre_scores_gemma":[0.34947607,0.0048627853,0.58714074,0.00042586794,0.0005128744,0.00063243293,0.0012899626,0.00033703228,0.055322226],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999514,0.00014406539,0.000023064467,0.000101662576,0.00012853388,0.000088565015],"domain_scores_gemma":[0.9992446,0.00039975834,0.00010407932,0.0000676636,0.0001310268,0.00005283465],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012070133,0.0016457441,0.0012324114,0.0013200577,0.0005632406,0.0013682897,0.002641663,0.0023267914,0.009179891],"category_scores_gemma":[0.004888856,0.0008538553,0.00196253,0.0017618698,0.00086610357,0.0019813548,0.0016591027,0.0016288179,0.0010199599],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000053528533,0.00005739067,0.0001485954,0.00017808659,0.00003923319,0.00008235476,0.000066608445,0.8280439,0.000803154,0.14247593,0.0034917209,0.024559498],"study_design_scores_gemma":[0.000037237634,0.000031141142,0.000100578436,0.000021928612,0.000017653101,0.000053602358,0.000039204206,0.8670637,0.00022917138,0.1286883,0.0037031278,0.000014300007],"about_ca_topic_score_codex":0.002783971,"about_ca_topic_score_gemma":0.00404027,"teacher_disagreement_score":0.009179891,"about_ca_system_score_codex":0.0010905125,"about_ca_system_score_gemma":0.0018082045,"threshold_uncertainty_score":0.030709803},"labels":[],"label_agreement":null},{"id":"W2090707215","doi":"10.1002/net.21594","title":"Reaching the elementary lower bound in the vehicle routing problem with time windows","year":2015,"lang":"en","type":"article","venue":"Networks","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Group for Research in Decision Analysis; Polytechnique Montréal; Université du Québec à Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Column generation; Vehicle routing problem; Upper and lower bounds; Relaxation (psychology); Routing (electronic design automation); Mathematical optimization; Set (abstract data type); Shortest path problem; State space; Computer science; Mathematics; Tree (set theory); Branch and bound; State (computer science); Space (punctuation); Algorithm; Combinatorics","score_opus":0.015640875985707885,"score_gpt":0.23722605952330764,"score_spread":0.22158518353759976,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2090707215","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13611655,0.00129279,0.840011,0.00055844913,0.00007048268,0.00024971936,0.00019463972,0.00030728092,0.021199092],"genre_scores_gemma":[0.65061206,0.0016531842,0.3417238,0.00022003452,0.000082986684,0.00036823715,0.00042215758,0.00030292253,0.004614623],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99837863,0.00067785894,0.00005775108,0.00022911595,0.0002996834,0.00035702283],"domain_scores_gemma":[0.98855954,0.00984872,0.0004952559,0.0005425562,0.00029519742,0.00025863145],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0035471842,0.001771559,0.001437101,0.00081788213,0.0008258684,0.0020983955,0.0015303326,0.0012657613,0.0051966547],"category_scores_gemma":[0.011792526,0.00049664883,0.0011443654,0.0010108342,0.0012172603,0.0039579836,0.0017304972,0.0027447753,0.0005570586],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000306792,0.00022949999,0.0006274851,0.00032533528,0.00008346413,0.000087953136,0.00019037163,0.8540173,0.0039448547,0.09512674,0.0016292551,0.0434309],"study_design_scores_gemma":[0.000058089387,0.00023117286,0.00032790456,0.000045807054,0.000052300922,0.000059089758,0.000090318004,0.93677676,0.0038370406,0.056381468,0.0021232306,0.00001689226],"about_ca_topic_score_codex":0.002475676,"about_ca_topic_score_gemma":0.0031693322,"teacher_disagreement_score":0.0051966547,"about_ca_system_score_codex":0.001291607,"about_ca_system_score_gemma":0.0022053742,"threshold_uncertainty_score":0.018759489},"labels":[],"label_agreement":null},{"id":"W2090915089","doi":"10.1016/j.orl.2013.07.007","title":"Routing vehicles to minimize fuel consumption","year":2013,"lang":"en","type":"article","venue":"Operations Research Letters","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":66,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Lethbridge","funders":"","keywords":"Vehicle routing problem; Travelling salesman problem; Routing (electronic design automation); Generalization; Heuristic; Fuel efficiency; Mathematical optimization; Computer science; Mathematics; Engineering; Computer network","score_opus":0.06715273491549184,"score_gpt":0.35207197887978303,"score_spread":0.2849192439642912,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2090915089","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.040041003,0.00053202815,0.94613874,0.00042538633,0.00016576736,0.00014108828,0.00017163673,0.0002829323,0.012101354],"genre_scores_gemma":[0.61750585,0.0009674036,0.34930512,0.0001907312,0.000096924814,0.00039940007,0.00040823026,0.00038910942,0.030737087],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997278,0.000103650316,0.000008712535,0.000055572847,0.00006001773,0.000044195418],"domain_scores_gemma":[0.9997862,0.00011003217,0.00003188607,0.000016562433,0.000043106927,0.00001217646],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005778276,0.0013357054,0.0008570845,0.00079810794,0.00044351685,0.001022847,0.00092575786,0.0011142386,0.003662038],"category_scores_gemma":[0.0013715951,0.00070060894,0.0006828835,0.00096400606,0.0005074624,0.0009812817,0.00058161444,0.00070562144,0.00061929127],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026518313,0.000020703952,0.00010440335,0.000032779397,0.000021885027,0.000012036885,0.000022773089,0.97217983,0.0011861952,0.009078309,0.0009839054,0.016330754],"study_design_scores_gemma":[0.000009844322,0.000030062469,0.000051078325,0.0000073450406,0.000010485145,0.0000075328385,0.000023756164,0.9902502,0.0007262637,0.007501679,0.0013776746,0.0000040732875],"about_ca_topic_score_codex":0.00595428,"about_ca_topic_score_gemma":0.005618275,"teacher_disagreement_score":0.00595428,"about_ca_system_score_codex":0.0012037158,"about_ca_system_score_gemma":0.0012331124,"threshold_uncertainty_score":0.012250781},"labels":[],"label_agreement":null},{"id":"W2091219277","doi":"10.1016/j.dam.2014.05.040","title":"Partial-route inequalities for the multi-vehicle routing problem with stochastic demands","year":2014,"lang":"en","type":"article","venue":"Discrete Applied Mathematics","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":51,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Université du Québec à Montréal; HEC Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Vehicle routing problem; Bounding overwatch; Mathematical optimization; Stochastic programming; Integer (computer science); Routing (electronic design automation); Integer programming; Reduction (mathematics); Point (geometry); Mathematics; Computer science","score_opus":0.026159374279052298,"score_gpt":0.2626392306662744,"score_spread":0.23647985638722213,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2091219277","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01577791,0.0015121246,0.9630722,0.0015253933,0.00017697261,0.000054985085,0.0008115891,0.000086367174,0.01698247],"genre_scores_gemma":[0.7192736,0.005176799,0.23686348,0.0007119545,0.00060640945,0.00047734636,0.0018780136,0.00029128048,0.034721088],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99888295,0.00044924847,0.00005496997,0.00017184549,0.00032795675,0.000112994],"domain_scores_gemma":[0.99597555,0.0031342595,0.00025787114,0.00010987139,0.00040628604,0.00011614597],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020845681,0.0013984458,0.0011149043,0.0009927787,0.0004473796,0.0018311467,0.0017173723,0.0016191608,0.0063855606],"category_scores_gemma":[0.0071012615,0.00068863074,0.001474831,0.0015906678,0.0010498511,0.002607346,0.0014565422,0.002655728,0.0005367003],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007313719,0.00004910372,0.0004551869,0.00026764057,0.00007144665,0.00017569329,0.0000944808,0.6911745,0.0012011027,0.2833026,0.0049302774,0.018204832],"study_design_scores_gemma":[0.000011212699,0.000016455435,0.00016847263,0.000025558724,0.0000121039375,0.00003339022,0.000021310112,0.92605776,0.00015759697,0.07146164,0.0020244857,0.000009933219],"about_ca_topic_score_codex":0.0071170735,"about_ca_topic_score_gemma":0.006059641,"teacher_disagreement_score":0.0071170735,"about_ca_system_score_codex":0.0019980539,"about_ca_system_score_gemma":0.0015804063,"threshold_uncertainty_score":0.021361828},"labels":[],"label_agreement":null},{"id":"W2091480483","doi":"10.5539/cis.v8n2p1","title":"Comparing Algorithms for Minimizing Congestion and Cost in the Multi-Commodity k-Splittable Flow","year":2015,"lang":"en","type":"article","venue":"Computer and Information Science","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Natural Science Foundation of China; Chinese Academy of Sciences; National Science Foundation","keywords":"Computer science; Heuristic; Minimum-cost flow problem; Mathematical optimization; Flow network; Key (lock); Flow (mathematics); Algorithm; Point (geometry); Network congestion; Commodity; Mathematics; Computer network; Economics; Network packet; Artificial intelligence; Computer security","score_opus":0.09686624956856517,"score_gpt":0.3138079726063005,"score_spread":0.21694172303773535,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2091480483","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.47958046,0.0038212922,0.50106794,0.0007288655,0.00032173915,0.00060835376,0.00039123572,0.001141792,0.0123383375],"genre_scores_gemma":[0.68352795,0.001302297,0.31262016,0.0001583291,0.000056711964,0.00035840177,0.00046817196,0.00020596037,0.001301899],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982687,0.00072400173,0.000121636556,0.00022967567,0.0003368366,0.0003191432],"domain_scores_gemma":[0.99275136,0.005444084,0.00045671975,0.00048081361,0.0006085968,0.00025843224],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0038498312,0.0016204788,0.0017308005,0.002290757,0.0010185364,0.0014598492,0.002222325,0.0020685468,0.0023707433],"category_scores_gemma":[0.008592229,0.00054473925,0.0014041569,0.0026633837,0.0011505964,0.0031750982,0.0009504044,0.0011323052,0.00024116426],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006024483,0.0002666631,0.0010759028,0.00024304623,0.00010098982,0.00002341662,0.0000766943,0.93296707,0.0007224644,0.008005896,0.0011454215,0.05477001],"study_design_scores_gemma":[0.00011730034,0.00021986832,0.0005665333,0.00002214243,0.000037532223,0.000028560635,0.00008221091,0.99253297,0.00094855565,0.005022295,0.0004046414,0.00001741274],"about_ca_topic_score_codex":0.0058498,"about_ca_topic_score_gemma":0.0047298907,"teacher_disagreement_score":0.0058498,"about_ca_system_score_codex":0.0031260357,"about_ca_system_score_gemma":0.0027995235,"threshold_uncertainty_score":0.022681117},"labels":[],"label_agreement":null},{"id":"W2091672956","doi":"10.1002/atr.5670430401","title":"A two‐leveled multi‐objective symbiotic evolutionary algorithm for the hub and spoke location problem","year":2009,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Evolutionary algorithm; Mathematical optimization; Convergence (economics); Computer science; Variety (cybernetics); Set (abstract data type); Pareto principle; Multi-objective optimization; Mathematics; Artificial intelligence; Economics","score_opus":0.010033118997782538,"score_gpt":0.26890895472630255,"score_spread":0.25887583572852,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2091672956","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09265826,0.00021925732,0.9024773,0.00028011735,0.000030363724,0.0000945045,0.000036775757,0.0001139757,0.004089369],"genre_scores_gemma":[0.6169616,0.00014041837,0.37940916,0.000115843,0.000017150285,0.00030454103,0.00008981463,0.000030918098,0.0029304917],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996444,0.00016050713,0.000016013093,0.000049909497,0.0000792313,0.000049988],"domain_scores_gemma":[0.99945813,0.0003358889,0.00004347602,0.000025568741,0.00009257864,0.00004439172],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012144505,0.00069338665,0.00078778004,0.0005996796,0.00039491223,0.0007595786,0.0010638078,0.0014693765,0.0018896735],"category_scores_gemma":[0.0022984366,0.0003610828,0.00060715456,0.00056924805,0.000431519,0.00074612995,0.0010651641,0.00072337396,0.00014884984],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002891962,0.00003416939,0.00038925864,0.00002384721,0.000023787381,0.000041358493,0.00003468531,0.97747374,0.000739304,0.0032671038,0.00019608407,0.017747832],"study_design_scores_gemma":[0.000011626286,0.000017806964,0.00003914815,0.0000019956638,0.0000029029454,0.000006275555,0.000004861138,0.9990324,0.00009839367,0.0006603118,0.00012275866,0.000001560888],"about_ca_topic_score_codex":0.0027001323,"about_ca_topic_score_gemma":0.0018205391,"teacher_disagreement_score":0.0027001323,"about_ca_system_score_codex":0.00064143806,"about_ca_system_score_gemma":0.0009815819,"threshold_uncertainty_score":0.0064226985},"labels":[],"label_agreement":null},{"id":"W2093962234","doi":"10.1007/s10732-015-9281-6","title":"A hybrid metaheuristic for the vehicle routing problem with stochastic demand and duration constraints","year":2015,"lang":"en","type":"article","venue":"Journal of Heuristics","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":65,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"Centre interuniversitaire de recherche sur les reseaux d'entreprise, la logistique et le transport","keywords":"Duration (music); Vehicle routing problem; Mathematical optimization; GRASP; Heuristics; Metaheuristic; Computer science; Set (abstract data type); Heuristic; Local search (optimization); Constraint (computer-aided design); Greedy randomized adaptive search procedure; Routing (electronic design automation); Mathematics; Greedy algorithm","score_opus":0.024286659983326922,"score_gpt":0.25864225172967015,"score_spread":0.23435559174634324,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2093962234","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.045040276,0.0006058533,0.9465634,0.00030662946,0.00016367597,0.00012980476,0.00019226318,0.00043613263,0.00656191],"genre_scores_gemma":[0.4092031,0.00039297223,0.58349407,0.00033263944,0.00013431314,0.00047510504,0.00034385407,0.00017998264,0.0054439483],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999428,0.000245699,0.000028641185,0.00007495827,0.00013840987,0.00008428041],"domain_scores_gemma":[0.99901557,0.0006674428,0.00007582877,0.00006492977,0.00011949774,0.000056713565],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012295704,0.00096110615,0.0010844448,0.0013159836,0.00041756048,0.0011378103,0.0021560725,0.0018821313,0.0027355915],"category_scores_gemma":[0.0017221787,0.00077019085,0.0012682448,0.0014702757,0.0004689812,0.0010918805,0.0009381183,0.0011173993,0.00031084038],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008342852,0.00009311492,0.00029044284,0.000052986514,0.0000780499,0.000044418724,0.000022571774,0.9585127,0.001211049,0.0075609977,0.00090699486,0.03114332],"study_design_scores_gemma":[0.000028307524,0.000035348687,0.000047687678,0.0000059153076,0.000012775479,0.000012453285,0.00000600366,0.9975879,0.00015690606,0.0016682863,0.0004340431,0.0000043788787],"about_ca_topic_score_codex":0.005439724,"about_ca_topic_score_gemma":0.0062413597,"teacher_disagreement_score":0.005439724,"about_ca_system_score_codex":0.0011409954,"about_ca_system_score_gemma":0.0015229472,"threshold_uncertainty_score":0.010816157},"labels":[],"label_agreement":null},{"id":"W2094626628","doi":"10.1002/net.20485","title":"Branch‐and‐cut and hybrid local search for the multi‐level capacitated minimum spanning tree problem","year":2011,"lang":"en","type":"article","venue":"Networks","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Minimum spanning tree; Spanning tree; GRASP; Benchmark (surveying); Mathematical optimization; Branch and cut; Branch and bound; Polyhedron; Mathematics; Tree (set theory); Computer science; Cutting-plane method; Combinatorics; Linear programming; Integer programming","score_opus":0.06862574685694278,"score_gpt":0.26602709596399476,"score_spread":0.19740134910705198,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2094626628","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03583719,0.0004271654,0.95868313,0.00024484444,0.000023121142,0.00007332434,0.0000729468,0.000495382,0.004142909],"genre_scores_gemma":[0.4598446,0.00024037824,0.5367218,0.00011879471,0.00003974619,0.00042451682,0.0002747537,0.00018713885,0.0021483882],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99909353,0.00040322446,0.00002965371,0.00011039535,0.0002435274,0.00011968614],"domain_scores_gemma":[0.9983937,0.0010957619,0.00015356479,0.00013302085,0.00014799,0.000075960575],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018937702,0.00085182366,0.0011497485,0.0010478416,0.00054588926,0.0011843144,0.0013473711,0.0012010965,0.004097934],"category_scores_gemma":[0.004442106,0.0005881453,0.0006339031,0.0014700888,0.00086277915,0.0016008324,0.0014666218,0.0015441006,0.0006106453],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008984581,0.000069866226,0.0003227518,0.00007903461,0.000031872172,0.000022569759,0.00005385972,0.9255297,0.0010670639,0.015313976,0.0014925072,0.055926863],"study_design_scores_gemma":[0.000019111265,0.000026553413,0.000060495866,0.000006923444,0.000004639717,0.0000074661716,0.000008441775,0.9940402,0.00045179238,0.0050062495,0.00036472734,0.0000033567221],"about_ca_topic_score_codex":0.0026981425,"about_ca_topic_score_gemma":0.0027982765,"teacher_disagreement_score":0.004097934,"about_ca_system_score_codex":0.0011188756,"about_ca_system_score_gemma":0.0011751838,"threshold_uncertainty_score":0.013708949},"labels":[],"label_agreement":null},{"id":"W2094687245","doi":"10.1139/x07-065","title":"Optimization based planning tools for routing of forwarders at harvest areas","year":2007,"lang":"en","type":"article","venue":"Canadian Journal of Forest Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":40,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Global Positioning System; Geographic information system; Forwarder; Computer science; Routing (electronic design automation); Software; Vehicle routing problem; Matching (statistics); Real-time computing; Operations research; Computer network; Geography; Engineering; Remote sensing; Telecommunications; Mathematics","score_opus":0.08491294856966414,"score_gpt":0.3542125831139992,"score_spread":0.2692996345443351,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2094687245","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0007362599,0.00003753196,0.9978284,0.000026750435,0.000008191266,0.000026777616,0.0000526259,0.00033744232,0.0009461282],"genre_scores_gemma":[0.040705036,0.00023993404,0.95520407,0.000036211633,0.000017492608,0.00042490344,0.00023206463,0.00025690816,0.0028833286],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995547,0.00016761734,0.00003263893,0.000059291546,0.00014961707,0.00003605634],"domain_scores_gemma":[0.99907696,0.00063560356,0.00007329372,0.00004674904,0.00014734703,0.000020133642],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012260292,0.001278336,0.00089873886,0.0010548221,0.00057956506,0.000859095,0.0012823689,0.0010836434,0.008526971],"category_scores_gemma":[0.0026367013,0.0008252249,0.0009853574,0.0011341673,0.0005622108,0.0007986694,0.00080903893,0.0012637008,0.0014243075],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000023420098,0.000029479837,0.00017957299,0.00011703406,0.000022732767,0.000043100845,0.00009014835,0.91344726,0.0010168973,0.018676557,0.0021598034,0.06419405],"study_design_scores_gemma":[0.00001201525,0.000009087405,0.000033090517,0.000011318921,0.0000064059946,0.000013601669,0.000014776163,0.98995566,0.0005674198,0.0061946996,0.0031763294,0.0000056796794],"about_ca_topic_score_codex":0.008498384,"about_ca_topic_score_gemma":0.009518074,"teacher_disagreement_score":0.9915016,"about_ca_system_score_codex":0.00095095235,"about_ca_system_score_gemma":0.0018399863,"threshold_uncertainty_score":0.02852559},"labels":[],"label_agreement":null},{"id":"W2096788301","doi":"10.1287/ijoc.1110.0458","title":"Using Logic-Based Benders Decomposition to Solve the Capacity- and Distance-Constrained Plant Location Problem","year":2011,"lang":"en","type":"article","venue":"INFORMS journal on computing","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":65,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Research and Innovation Foundation","keywords":"Mathematical optimization; Benders' decomposition; Integer programming; Tabu search; Decomposition; Set (abstract data type); Branch and cut; Truck; Constraint programming; Computer science; Integer (computer science); Branch and price; Facility location problem; Constraint (computer-aided design); Mathematics; Stochastic programming; Engineering","score_opus":0.06676374751484022,"score_gpt":0.28343941804884426,"score_spread":0.21667567053400405,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2096788301","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008797229,0.0000652928,0.986384,0.00018710065,0.00002568066,0.00010795714,0.000095763826,0.00026549742,0.0040715565],"genre_scores_gemma":[0.13390605,0.00023591731,0.8616863,0.00022056322,0.00003777014,0.00035081952,0.00036597744,0.00012940845,0.003067227],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99920374,0.00034253008,0.000030616782,0.00010294302,0.00020901395,0.000111182235],"domain_scores_gemma":[0.9987941,0.0008709326,0.00010926379,0.000069063404,0.00012167469,0.000034964865],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015721964,0.0015681558,0.0009044144,0.0011923312,0.0007219789,0.0015583439,0.0010220226,0.0011767907,0.0062385034],"category_scores_gemma":[0.0030498526,0.000916401,0.0015128396,0.0012799723,0.0010628373,0.0015411063,0.0010104717,0.0017705804,0.000796232],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007353827,0.00008967262,0.0002650648,0.00011696909,0.000041685656,0.000080498394,0.000055892815,0.92468965,0.0026953185,0.03180425,0.0012759169,0.038811598],"study_design_scores_gemma":[0.000031389733,0.00004467448,0.0000368227,0.000014857158,0.00001597151,0.000021512133,0.000028715618,0.9779279,0.0013069239,0.019476801,0.0010859052,0.000008506691],"about_ca_topic_score_codex":0.0050514955,"about_ca_topic_score_gemma":0.0064082984,"teacher_disagreement_score":0.0062385034,"about_ca_system_score_codex":0.0014102478,"about_ca_system_score_gemma":0.0023631363,"threshold_uncertainty_score":0.020869851},"labels":[],"label_agreement":null},{"id":"W2096910555","doi":"10.1287/ijoc.2014.0600","title":"Districting for Arc Routing","year":2014,"lang":"en","type":"article","venue":"INFORMS journal on computing","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":57,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"Deutsche Forschungsgemeinschaft","keywords":"Arc routing; Roulette; Tabu search; Social connectedness; Compact space; Mathematical optimization; Subroutine; Routing (electronic design automation); Computer science; Heuristic; Context (archaeology); Vehicle routing problem; Mathematics","score_opus":0.017903501821924746,"score_gpt":0.2714472516494821,"score_spread":0.25354374982755734,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2096910555","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007121161,0.0006446631,0.98172045,0.000112102716,0.000056794313,0.00009260791,0.00007887351,0.00035951572,0.009813772],"genre_scores_gemma":[0.2104119,0.00091533165,0.7791955,0.00009857188,0.000074258365,0.00021236346,0.00028964164,0.00028262386,0.008519917],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993216,0.0002695138,0.00002657374,0.0001468032,0.00014824385,0.00008735293],"domain_scores_gemma":[0.99950135,0.00026006656,0.00005428413,0.000097015785,0.00005202368,0.000035194236],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000573811,0.00071451336,0.0008457977,0.0011542279,0.00093523617,0.0012473548,0.001277228,0.00074152957,0.010629008],"category_scores_gemma":[0.0017000628,0.00044881803,0.0008174178,0.0014960243,0.00094536546,0.0015304273,0.0012664028,0.0011333574,0.0013910494],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008471062,0.00006824967,0.0005892031,0.00037415093,0.000042585063,0.00009041975,0.00014872763,0.5287827,0.0049414425,0.18332393,0.00428257,0.27727124],"study_design_scores_gemma":[0.00003984266,0.00023300792,0.0005170517,0.000106417756,0.00005485115,0.00030604677,0.00016535188,0.7484349,0.0061983196,0.1806518,0.06324143,0.00005100743],"about_ca_topic_score_codex":0.0019850044,"about_ca_topic_score_gemma":0.0032168971,"teacher_disagreement_score":0.010629008,"about_ca_system_score_codex":0.00109787,"about_ca_system_score_gemma":0.0008258385,"threshold_uncertainty_score":0.035557568},"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,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_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","score_opus":0.01970802792664043,"score_gpt":0.2744546307225553,"score_spread":0.2547466027959149,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2098090443","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008983225,0.00098965,0.9779889,0.00022784612,0.00010131063,0.00022093272,0.00029625167,0.0017723762,0.009419561],"genre_scores_gemma":[0.06337054,0.0005983866,0.92974967,0.00014197551,0.000043992706,0.00043882648,0.0006018922,0.00034066814,0.004713966],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994844,0.00019893484,0.00002092926,0.000069831556,0.00016662893,0.0000591783],"domain_scores_gemma":[0.99963605,0.00017848771,0.000033543947,0.000042039286,0.00009229334,0.000017680633],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00065057,0.000990817,0.0010001303,0.001122452,0.00093657,0.0010249531,0.0013056981,0.001205682,0.00801649],"category_scores_gemma":[0.002062394,0.00051861384,0.0007391861,0.0021357578,0.00059786794,0.0010354745,0.0008725617,0.0012693928,0.0024853756],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010908632,0.00015445291,0.00046719934,0.0002491245,0.00007884741,0.000111798545,0.00015699233,0.5209718,0.0035498224,0.032342177,0.017148407,0.4246604],"study_design_scores_gemma":[0.00008952298,0.00010643241,0.00022787166,0.000038202008,0.00003192388,0.00015710187,0.000049842543,0.9586847,0.0014907905,0.020151105,0.01894943,0.000023088216],"about_ca_topic_score_codex":0.0045452532,"about_ca_topic_score_gemma":0.004394821,"teacher_disagreement_score":0.00801649,"about_ca_system_score_codex":0.0007121965,"about_ca_system_score_gemma":0.0013036137,"threshold_uncertainty_score":0.026817858},"labels":[],"label_agreement":null},{"id":"W2098400882","doi":"10.1287/opre.1050.0240","title":"A Branch-and-Cut Algorithm for the Multiple Depot Vehicle Scheduling Problem","year":2006,"lang":"en","type":"article","venue":"Operations Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":122,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal; Polytechnique Montréal","funders":"","keywords":"Column generation; Linear programming relaxation; Branch and cut; Polytope; Scheduling (production processes); Branch and bound; Mathematical optimization; Mathematics; Variable (mathematics); Computer science; Cutting-plane method; Lagrangian relaxation; Integer programming; Algorithm; Combinatorics","score_opus":0.050032287516005525,"score_gpt":0.34981777819464754,"score_spread":0.299785490678642,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2098400882","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010625103,0.00026991393,0.9842944,0.0002837365,0.00003068097,0.00013198798,0.0001601545,0.000389215,0.0038147892],"genre_scores_gemma":[0.08070508,0.00035177442,0.9162166,0.0000970578,0.00003305499,0.00029922347,0.0005147416,0.000114817405,0.001667625],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99951446,0.00014703134,0.000020221334,0.000085580024,0.00013719514,0.000095538904],"domain_scores_gemma":[0.99930286,0.0004921682,0.00004735982,0.000038080678,0.000072688475,0.00004680592],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00082378165,0.0011112251,0.001057956,0.0009686604,0.0007776579,0.0011210963,0.0011526956,0.001271358,0.0053719133],"category_scores_gemma":[0.0024828648,0.0006296166,0.00066956616,0.0015378842,0.0005002963,0.0012702438,0.0011725103,0.0017359374,0.0007645871],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016389106,0.00021388025,0.00054290425,0.00016856591,0.00005721852,0.00012461613,0.00011528383,0.69006485,0.0021213186,0.040962864,0.007370028,0.25809458],"study_design_scores_gemma":[0.000065131535,0.00005186933,0.0001022447,0.000014035961,0.000015958582,0.000034430224,0.000024888199,0.97692204,0.00064694544,0.019594232,0.0025215615,0.0000066556363],"about_ca_topic_score_codex":0.0058117164,"about_ca_topic_score_gemma":0.005082166,"teacher_disagreement_score":0.0058117164,"about_ca_system_score_codex":0.0010508962,"about_ca_system_score_gemma":0.0020828666,"threshold_uncertainty_score":0.0179708},"labels":[],"label_agreement":null},{"id":"W2098559645","doi":"10.1142/s0129626403001598","title":"PARALLEL ALGORITHMS FOR VEHICLE ROUTING PROBLEMS","year":2003,"lang":"en","type":"article","venue":"Parallel Processing Letters","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Time complexity; Routing (electronic design automation); Algorithm; Vehicle routing problem; Crew; Parallel algorithm; Routing table; Destination-Sequenced Distance Vector routing; Path (computing); Parallel computing; Computer network; Link-state routing protocol; Routing protocol","score_opus":0.03144977875051786,"score_gpt":0.2734180421681125,"score_spread":0.24196826341759464,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2098559645","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008982838,0.0016111056,0.9688093,0.0009908733,0.00020882238,0.00025806713,0.0002547613,0.0016202589,0.017263833],"genre_scores_gemma":[0.15678877,0.00236959,0.8254983,0.00034286827,0.00041354224,0.000994795,0.0011270703,0.000537344,0.011927776],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980925,0.0005518204,0.00012221538,0.00040581686,0.0004922009,0.00033551248],"domain_scores_gemma":[0.998168,0.0011223924,0.00013486907,0.00026487597,0.00023135482,0.00007860934],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018922483,0.0020855246,0.0016868407,0.0011067885,0.0015069239,0.0024732172,0.0023047633,0.001497441,0.00890703],"category_scores_gemma":[0.005984606,0.0007774464,0.001344288,0.0025140487,0.0012214391,0.0031598902,0.0022138918,0.0025576865,0.0025096496],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019792,0.00022338946,0.00052192865,0.0005486643,0.0001236842,0.00012838624,0.00022676618,0.5765955,0.0013744086,0.20432365,0.021972833,0.19376285],"study_design_scores_gemma":[0.00018267684,0.000043939926,0.00010480727,0.000030864896,0.00002756393,0.0000657402,0.00007139353,0.7022539,0.0006629582,0.28197214,0.01457087,0.000013201905],"about_ca_topic_score_codex":0.004136474,"about_ca_topic_score_gemma":0.0047139577,"teacher_disagreement_score":0.00890703,"about_ca_system_score_codex":0.0020560962,"about_ca_system_score_gemma":0.002143447,"threshold_uncertainty_score":0.029796958},"labels":[],"label_agreement":null},{"id":"W2098945664","doi":"10.1109/ccece.2003.1226035","title":"A constraint programming approach for the design problem of cellular wireless networks","year":2004,"lang":"en","type":"article","venue":"","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Constraint (computer-aided design); Computer science; Heuristic; Base station; Wireless; Transceiver; Computer network; Wireless network; Cellular network; Base transceiver station; Mathematical optimization; Constraint programming; Distributed computing; Wi-Fi array; Engineering; Mathematics; Stochastic programming; Telecommunications; Artificial intelligence","score_opus":0.027904475321195957,"score_gpt":0.23993760439169556,"score_spread":0.2120331290704996,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2098945664","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00046323496,0.000359747,0.9951728,0.00037362042,0.000053666645,0.00007132583,0.00007357044,0.000030568684,0.0034014555],"genre_scores_gemma":[0.054704573,0.0031401066,0.93376255,0.0005518471,0.00025891679,0.0013028773,0.00033952596,0.000091187256,0.0058484874],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980628,0.00090396684,0.00008776244,0.00027450087,0.0005480334,0.00012287952],"domain_scores_gemma":[0.99825734,0.0012944225,0.00010903171,0.000070488604,0.00021401637,0.000054832013],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022697505,0.002179795,0.0012300343,0.0009220207,0.0009843826,0.0019661926,0.0024063406,0.0022716108,0.004986531],"category_scores_gemma":[0.004430845,0.0010590284,0.0017843974,0.0028046628,0.0015255967,0.0015666068,0.0013569839,0.0038414344,0.0011165363],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002741803,0.00007162854,0.00014530096,0.00041969915,0.00006395099,0.00018409395,0.00010079226,0.7184913,0.0011497887,0.23669714,0.005068009,0.037580837],"study_design_scores_gemma":[0.000042619347,0.0000661542,0.000060843468,0.000119650955,0.00003495003,0.00011478749,0.000048128717,0.8737822,0.0006848198,0.097747326,0.027269104,0.000029373337],"about_ca_topic_score_codex":0.007964165,"about_ca_topic_score_gemma":0.0065086125,"teacher_disagreement_score":0.007964165,"about_ca_system_score_codex":0.0020699864,"about_ca_system_score_gemma":0.0031678893,"threshold_uncertainty_score":0.016681612},"labels":[],"label_agreement":null},{"id":"W2099343322","doi":"10.1287/trsc.1090.0295","title":"A Hybrid Monte Carlo Local Branching Algorithm for the Single Vehicle Routing Problem with Stochastic Demands","year":2010,"lang":"en","type":"article","venue":"Transportation Science","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":66,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal; Polytechnique Montréal; Université du Québec à Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Monte Carlo method; Vehicle routing problem; Mathematical optimization; Integer programming; Branching (polymer chemistry); Computer science; Routing (electronic design automation); Local search (optimization); Algorithm; Mathematics","score_opus":0.011128396429053071,"score_gpt":0.2433868474478958,"score_spread":0.2322584510188427,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2099343322","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005315169,0.00013696848,0.99268264,0.00007888914,0.0000212173,0.00003477751,0.00001893007,0.00024775855,0.0014636196],"genre_scores_gemma":[0.1537185,0.00024667208,0.8423845,0.00015655032,0.00005550855,0.00032338427,0.00015165549,0.00015152669,0.0028116712],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99930406,0.00025256083,0.000022915503,0.00009490543,0.00025793447,0.00006765989],"domain_scores_gemma":[0.99896455,0.000687767,0.000077763965,0.00005812699,0.00013033561,0.00008153605],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013896114,0.0006067745,0.0009978375,0.000638487,0.0005111171,0.00079970894,0.0015367036,0.0010417288,0.0028024416],"category_scores_gemma":[0.0027392402,0.00046779314,0.0005825981,0.00095120975,0.0007007201,0.0010036401,0.0011695275,0.0015733466,0.0005009097],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011125332,0.00011860306,0.0006232374,0.00006351247,0.000053410782,0.00007015936,0.00007661964,0.83128697,0.0029443866,0.03931674,0.0022835147,0.123051666],"study_design_scores_gemma":[0.00001926684,0.000017946171,0.00003644823,0.0000033552633,0.0000041448056,0.000016555854,0.000003012513,0.99564004,0.00026664964,0.003383039,0.00060534925,0.000004157795],"about_ca_topic_score_codex":0.003923069,"about_ca_topic_score_gemma":0.0047971536,"teacher_disagreement_score":0.003923069,"about_ca_system_score_codex":0.0009063412,"about_ca_system_score_gemma":0.0015208182,"threshold_uncertainty_score":0.009375095},"labels":[],"label_agreement":null},{"id":"W2099501018","doi":"10.1287/ijoc.1080.0312","title":"<b>State-of-the Art Review</b>—Evolutionary Algorithms for Vehicle Routing","year":2009,"lang":"en","type":"article","venue":"INFORMS journal on computing","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":103,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Vehicle routing problem; Benchmark (surveying); Evolutionary algorithm; Computer science; Particle swarm optimization; Mathematical optimization; Routing (electronic design automation); Metaheuristic; Genetic algorithm; Algorithm; Memetic algorithm; Mathematics","score_opus":0.02023764090783845,"score_gpt":0.28665828310789365,"score_spread":0.2664206422000552,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2099501018","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00794549,0.8290173,0.10166829,0.00498741,0.0039697797,0.00006525436,0.0003330365,0.0004665532,0.051546935],"genre_scores_gemma":[0.07860588,0.82363063,0.06903772,0.0017087306,0.0049372814,0.00007533705,0.0011611485,0.0001725392,0.020670677],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99962986,0.00007588654,0.000040143346,0.000071647184,0.00014961192,0.00003277339],"domain_scores_gemma":[0.9990872,0.00044017658,0.000064678934,0.000046708163,0.00033815444,0.000023103834],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005592518,0.00060215173,0.000732421,0.0013851877,0.00032620277,0.0015760801,0.0009993069,0.001141096,0.008729851],"category_scores_gemma":[0.0020222352,0.00025444734,0.00051360286,0.0040495256,0.00030464437,0.001792319,0.00036300064,0.0005598536,0.0038689922],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008048144,0.00007430379,0.0006558403,0.0030552668,0.000106010615,0.00011426659,0.000022866638,0.010996848,0.0028042027,0.01025651,0.049923558,0.92190987],"study_design_scores_gemma":[0.000031242755,0.00024550673,0.0025458261,0.002509544,0.00031466855,0.0015520654,0.00013749632,0.050103363,0.009048258,0.020015877,0.9134266,0.00006957828],"about_ca_topic_score_codex":0.0016606546,"about_ca_topic_score_gemma":0.001766009,"teacher_disagreement_score":0.008729851,"about_ca_system_score_codex":0.00035959863,"about_ca_system_score_gemma":0.00066713424,"threshold_uncertainty_score":0.02920425},"labels":[],"label_agreement":null},{"id":"W2099721629","doi":"10.5267/j.ijiec.2010.06.003","title":"A variable neighborhood descent based heuristic to solve the capacitated location-routing problem","year":2010,"lang":"en","type":"article","venue":"International Journal of Industrial Engineering Computations","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Variable neighborhood search; Descent (aeronautics); Heuristic; Variable (mathematics); Mathematical optimization; Computer science; Routing (electronic design automation); Facility location problem; Vehicle routing problem; Mathematics; Metaheuristic; Geography; Computer network","score_opus":0.022228937370306505,"score_gpt":0.25980138173700446,"score_spread":0.23757244436669794,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2099721629","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.026756868,0.0003444798,0.9677116,0.00017029082,0.00008128193,0.000116074436,0.00006333926,0.00023599874,0.004520118],"genre_scores_gemma":[0.3685984,0.00020359074,0.62666166,0.00012389441,0.00003999831,0.00032945158,0.00023641615,0.000086590284,0.003719889],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996594,0.00015615654,0.000012294498,0.000047794496,0.0000724598,0.000051866136],"domain_scores_gemma":[0.99958664,0.00024852282,0.000040733095,0.000027403166,0.0000651026,0.000031679043],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00056034024,0.00051622285,0.00095592544,0.00070237415,0.00047999562,0.00045556642,0.0010719738,0.00089374534,0.0019010975],"category_scores_gemma":[0.001255866,0.00041767614,0.0004471151,0.0008994893,0.00040863093,0.0005072987,0.0005996959,0.00052503805,0.0003083848],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000049837407,0.00007615722,0.00042299426,0.000054977594,0.000027585189,0.00006223589,0.000032230946,0.9422478,0.0010220942,0.0064650695,0.0019104432,0.047628526],"study_design_scores_gemma":[0.000013096662,0.000030746898,0.000056348323,0.0000032084622,0.0000034628645,0.000019683812,0.0000069720545,0.9979874,0.00017089667,0.0010871072,0.00061780296,0.0000031668967],"about_ca_topic_score_codex":0.006208611,"about_ca_topic_score_gemma":0.0071890587,"teacher_disagreement_score":0.006208611,"about_ca_system_score_codex":0.0007357548,"about_ca_system_score_gemma":0.0011379427,"threshold_uncertainty_score":0.012344897},"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,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_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","score_opus":0.06359111993787328,"score_gpt":0.3323522610403489,"score_spread":0.2687611411024756,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2099860183","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009460258,0.0002856512,0.9856397,0.0001988297,0.00012073048,0.000108065244,0.00010196374,0.0005628989,0.0035217989],"genre_scores_gemma":[0.1395732,0.00031112455,0.8550409,0.00014357497,0.000099207085,0.00035882843,0.00024075672,0.00015251763,0.0040798658],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99911135,0.00016768799,0.00004631195,0.0002125901,0.00028321907,0.00017875848],"domain_scores_gemma":[0.99856913,0.000911638,0.00009627333,0.00014310729,0.00020474635,0.00007501143],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014878843,0.001258173,0.0020361266,0.0010348398,0.0009845992,0.0016667752,0.0026062229,0.0023078178,0.0070298663],"category_scores_gemma":[0.0043154745,0.0011471059,0.0012219383,0.0018004536,0.00097365037,0.0025429586,0.0019320484,0.0015971183,0.0008752876],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001563863,0.00011570264,0.0002842391,0.0001227311,0.00004515421,0.00007079786,0.00007073577,0.8553041,0.0010145543,0.01712256,0.002945725,0.122747295],"study_design_scores_gemma":[0.000058345664,0.000027383694,0.00006027841,0.0000074118648,0.000012175638,0.000024944316,0.000014741786,0.98953617,0.00017796365,0.009289299,0.00078440004,0.000006894368],"about_ca_topic_score_codex":0.014196717,"about_ca_topic_score_gemma":0.013867751,"teacher_disagreement_score":0.014196717,"about_ca_system_score_codex":0.001955867,"about_ca_system_score_gemma":0.004140304,"threshold_uncertainty_score":0.028228164},"labels":[],"label_agreement":null},{"id":"W2100711246","doi":"10.1287/ijoc.1060.0202","title":"Variable Neighborhood Search for the Pickup and Delivery Traveling Salesman Problem with LIFO Loading","year":2007,"lang":"en","type":"article","venue":"INFORMS journal on computing","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":139,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"","keywords":"Travelling salesman problem; Pickup; FIFO and LIFO accounting; Variable (mathematics); Mathematical optimization; Variable neighborhood search; Heuristic; 2-opt; Traveling purchaser problem; Heuristics; Computer science; Mathematics; FIFO (computing and electronics); Metaheuristic; Artificial intelligence","score_opus":0.01899676715682416,"score_gpt":0.2579686718548559,"score_spread":0.23897190469803173,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2100711246","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3541658,0.00085247366,0.63589597,0.00034529413,0.00004693177,0.0001498599,0.00017307099,0.0007728917,0.0075977324],"genre_scores_gemma":[0.75909126,0.00019703113,0.23736355,0.00005139825,0.00002041983,0.00017029876,0.00025584974,0.00010082213,0.0027494137],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996575,0.00016119218,0.000010972969,0.000050282324,0.00006898283,0.00005101428],"domain_scores_gemma":[0.9993783,0.00044702285,0.00006898619,0.000030781768,0.00003931326,0.00003556107],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00092940306,0.00040279113,0.00076773233,0.00056643336,0.00044984493,0.00057902973,0.00087079505,0.0006147842,0.0018781385],"category_scores_gemma":[0.0022377912,0.00024153404,0.00030991714,0.0007162089,0.0004654262,0.0009907556,0.00051181985,0.0005421403,0.00020317556],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018465787,0.00013954188,0.000990119,0.000046770998,0.000019999654,0.00006155059,0.00006232527,0.9471568,0.0008603702,0.0076204664,0.0009960148,0.041861273],"study_design_scores_gemma":[0.000030307365,0.00006377478,0.000137493,0.000003818645,0.000005924369,0.000013166996,0.000021565544,0.99649256,0.00032048358,0.0025216474,0.00038532758,0.000003849689],"about_ca_topic_score_codex":0.00574484,"about_ca_topic_score_gemma":0.006432203,"teacher_disagreement_score":0.00574484,"about_ca_system_score_codex":0.0006796408,"about_ca_system_score_gemma":0.00072229374,"threshold_uncertainty_score":0.011422813},"labels":[],"label_agreement":null},{"id":"W2101137675","doi":"10.1109/hicss.2001.926326","title":"Recent trends in logistics and the need for real-time decision tools in the trucking industry","year":2005,"lang":"en","type":"article","venue":"","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Global Positioning System; Electronic data interchange; The Internet; Decision support system; Computer science; Order (exchange); Real-time data; Information technology; Operations research; Transport engineering; Telecommunications; Business; Engineering; World Wide Web","score_opus":0.0507097824258726,"score_gpt":0.32338946525950196,"score_spread":0.27267968283362937,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2101137675","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06153144,0.13330081,0.61838514,0.08481534,0.0021482867,0.000111898735,0.00023455104,0.0006993087,0.098773256],"genre_scores_gemma":[0.36798695,0.19196105,0.39299306,0.005405157,0.0047494574,0.0001749472,0.00048723965,0.00026300858,0.03597923],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9986135,0.000563299,0.000098133176,0.00017354812,0.0004966604,0.000054867895],"domain_scores_gemma":[0.9970799,0.0017980748,0.00034053507,0.0001403941,0.000511189,0.0001297766],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027659317,0.0005634701,0.00035021047,0.0008718511,0.00047091037,0.0039835563,0.001278886,0.0022406667,0.004667455],"category_scores_gemma":[0.0046304436,0.00043207654,0.00038736625,0.0031217707,0.0016728945,0.0056655225,0.00070299394,0.0017373502,0.0011437704],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016024339,0.0001884421,0.00334765,0.0015369093,0.00004273856,0.0002821399,0.0008015602,0.058407,0.003048638,0.2958456,0.018694628,0.6176444],"study_design_scores_gemma":[0.000056432797,0.00020923541,0.0030847117,0.0006975731,0.000071812545,0.000849897,0.0025606507,0.20417859,0.002795721,0.31569844,0.46966922,0.00012761758],"about_ca_topic_score_codex":0.0013599409,"about_ca_topic_score_gemma":0.0021039865,"teacher_disagreement_score":0.004667455,"about_ca_system_score_codex":0.0012189605,"about_ca_system_score_gemma":0.0011497965,"threshold_uncertainty_score":0.015614212},"labels":[],"label_agreement":null},{"id":"W2101182156","doi":"10.1002/j.2158-1592.2002.tb00025.x","title":"MEASURING AND MANAGING THE LEARNING REQUIREMENTS OF ROUTE REOPTIMIZATION ON DELIVERY VEHICLE DRIVERS","year":2002,"lang":"en","type":"article","venue":"Journal of Business Logistics","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Wilfrid Laurier University","funders":"","keywords":"Flexibility (engineering); Computer science; Traverse; Key (lock); Vehicle routing problem; Risk analysis (engineering); Fleet management; Routing (electronic design automation); Operations management; Transport engineering; Business; Operations research; Computer security; Engineering; Telecommunications; Economics; Computer network","score_opus":0.07466880390798403,"score_gpt":0.2464278950928287,"score_spread":0.17175909118484467,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2101182156","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9601233,0.00005489425,0.037962172,0.0002721414,0.000009609104,0.00006747982,0.00008783478,0.00005296451,0.0013695486],"genre_scores_gemma":[0.9942813,0.000032373486,0.005318678,0.0000104945275,0.000006498712,0.000033732933,0.000056365458,0.000007671037,0.00025294657],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99775237,0.0012697207,0.00010524804,0.00027955763,0.00028219554,0.00031085682],"domain_scores_gemma":[0.9483299,0.043204572,0.0040522274,0.0015920895,0.0019540873,0.0008670647],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0038743452,0.0007588638,0.0006835203,0.00059206603,0.0004324307,0.0009779591,0.0011270914,0.0012911016,0.0011578872],"category_scores_gemma":[0.04738412,0.00042014127,0.0002803502,0.000666831,0.0005882524,0.002798693,0.00091608765,0.0009742257,0.00009935461],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006921625,0.00038076346,0.017917912,0.000098472476,0.000057998834,0.00007688854,0.00015738062,0.9446438,0.0025550723,0.002379548,0.00024758835,0.030792372],"study_design_scores_gemma":[0.000029823605,0.00046766826,0.009826152,0.0000070125384,0.00004277705,0.00003392849,0.00024557428,0.98214877,0.0046841684,0.0023019933,0.00018572822,0.000026339954],"about_ca_topic_score_codex":0.0068143667,"about_ca_topic_score_gemma":0.00550349,"teacher_disagreement_score":0.0068143667,"about_ca_system_score_codex":0.0015758675,"about_ca_system_score_gemma":0.0015407373,"threshold_uncertainty_score":0.020489752},"labels":[],"label_agreement":null},{"id":"W2102346823","doi":"10.1002/net.21562","title":"Adaptive large neighborhood search for the periodic capacitated arc routing problem with inventory constraints","year":2014,"lang":"en","type":"article","venue":"Networks","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Group for Research in Decision Analysis; Polytechnique Montréal","funders":"","keywords":"Routing (electronic design automation); Truck; Traverse; Computer science; Mathematical optimization; Arc routing; Vehicle routing problem; Operations research; Mathematics; Engineering; Automotive engineering; Computer network; Geography","score_opus":0.01909616450260213,"score_gpt":0.23929610966925796,"score_spread":0.22019994516665584,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2102346823","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.037907016,0.00077379704,0.9562663,0.00033757713,0.000046754754,0.00009334748,0.00007240765,0.0001523284,0.0043505663],"genre_scores_gemma":[0.5641348,0.0005416382,0.42735094,0.00013687316,0.000075328404,0.0005424343,0.0002099941,0.00010315228,0.006904919],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99954754,0.00024318261,0.00001770978,0.00007940675,0.00006952245,0.000042626],"domain_scores_gemma":[0.9983494,0.0013291598,0.00014428531,0.00003720589,0.000083131046,0.00005680811],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013702889,0.00079048186,0.0010729638,0.0008602324,0.0005400927,0.00066177465,0.0013062995,0.0010477827,0.0019511544],"category_scores_gemma":[0.003195934,0.000607649,0.0005752948,0.0009080094,0.0007072651,0.0011479306,0.00084207964,0.00084026426,0.00019116208],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000029552195,0.000031622018,0.00020271189,0.000034094133,0.000020207117,0.000032394055,0.000025997351,0.9820877,0.00018765619,0.008323383,0.00047332732,0.008551375],"study_design_scores_gemma":[0.000007256474,0.000014125656,0.00002271381,0.0000024092533,0.0000027921615,0.000005879202,0.0000053187505,0.9967303,0.000038164304,0.0029729705,0.00019615675,0.0000018813347],"about_ca_topic_score_codex":0.0057819374,"about_ca_topic_score_gemma":0.0066649397,"teacher_disagreement_score":0.0057819374,"about_ca_system_score_codex":0.0011588136,"about_ca_system_score_gemma":0.0009738902,"threshold_uncertainty_score":0.0114966035},"labels":[],"label_agreement":null},{"id":"W2102515551","doi":"10.1007/s00291-010-0229-9","title":"Models and algorithms for the heterogeneous dial-a-ride problem with driver-related constraints","year":2010,"lang":"en","type":"article","venue":"OR Spectrum","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":107,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"Austrian Science Fund","keywords":"Column generation; Computer science; Variable neighborhood search; Mathematical optimization; Heuristic; Integer programming; Algorithm; Set (abstract data type); Variable (mathematics); Relaxation (psychology); Linear programming; Mathematics; Metaheuristic; Artificial intelligence","score_opus":0.01607248361109651,"score_gpt":0.2482842718808206,"score_spread":0.2322117882697241,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2102515551","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006083138,0.00040253947,0.98858273,0.00035855052,0.000041594987,0.00010745903,0.00018979216,0.00017569956,0.004058594],"genre_scores_gemma":[0.23702171,0.0017468018,0.74938506,0.00028621632,0.00017308486,0.0010123056,0.0010924381,0.00032625638,0.00895618],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986833,0.0005231199,0.000055918452,0.00027336334,0.00022558229,0.00023869952],"domain_scores_gemma":[0.99667823,0.002469199,0.00033896216,0.00016587973,0.00022290969,0.00012479463],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026669886,0.002108009,0.0020085233,0.001112697,0.0011903753,0.0029353858,0.003989541,0.0027609724,0.006688611],"category_scores_gemma":[0.0063978676,0.0013782083,0.00215321,0.00180777,0.0012923995,0.0034344804,0.0020449192,0.003210532,0.0011442597],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002054323,0.000049773942,0.00017006663,0.000058220387,0.000020044354,0.000032300486,0.00004899479,0.9572593,0.00009321976,0.032652985,0.0014649368,0.008129643],"study_design_scores_gemma":[0.000017282227,0.000011604542,0.00004096018,0.0000127050125,0.000008345217,0.000014214752,0.000023075176,0.975807,0.000062450024,0.022947017,0.0010486391,0.000006683741],"about_ca_topic_score_codex":0.014351201,"about_ca_topic_score_gemma":0.0132595375,"teacher_disagreement_score":0.014351201,"about_ca_system_score_codex":0.0029726063,"about_ca_system_score_gemma":0.0029288756,"threshold_uncertainty_score":0.028535306},"labels":[],"label_agreement":null},{"id":"W2103364890","doi":"10.1287/ijoc.1050.0168","title":"Discrepancy-Based Additive Bounding Procedures","year":2006,"lang":"en","type":"article","venue":"INFORMS journal on computing","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Bounding overwatch; Search tree; Constraint (computer-aided design); Constraint satisfaction problem; Relaxation (psychology); Mathematical optimization; Mathematics; Branch and bound; Key (lock); Upper and lower bounds; Constraint satisfaction; Local consistency; Computer science; Constraint programming; Node (physics); Simple (philosophy); Search algorithm; Artificial intelligence","score_opus":0.009396840119445738,"score_gpt":0.2564636089048323,"score_spread":0.24706676878538653,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2103364890","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0016679311,0.00011899953,0.99552333,0.00012346717,0.000020801035,0.000028984585,0.00004302272,0.0002268783,0.0022466106],"genre_scores_gemma":[0.16926615,0.0004610793,0.8234909,0.00037369176,0.00010673982,0.00034792296,0.0003204087,0.0005051637,0.005127852],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99440235,0.0021105115,0.00027322795,0.0008500573,0.0018951857,0.00046875552],"domain_scores_gemma":[0.98771244,0.006972579,0.0008614195,0.0030804374,0.0010060915,0.0003670209],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0055950405,0.0019051597,0.0014839217,0.0016999034,0.0008988365,0.002630718,0.0050724032,0.0020882655,0.0077034836],"category_scores_gemma":[0.023607982,0.0010093917,0.0018844743,0.0020906376,0.0027132109,0.0062970947,0.006658049,0.005442258,0.002168895],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012111958,0.00009202226,0.0007643788,0.00018379897,0.000054828863,0.00009894716,0.00023398768,0.38464582,0.003194238,0.50888443,0.004340018,0.09738634],"study_design_scores_gemma":[0.000021968266,0.000050550363,0.000092713366,0.000046942034,0.0000201375,0.000072611285,0.000022633061,0.83373207,0.0021603026,0.15678146,0.006976892,0.000021692313],"about_ca_topic_score_codex":0.001686754,"about_ca_topic_score_gemma":0.0020085482,"teacher_disagreement_score":0.0077034836,"about_ca_system_score_codex":0.001674291,"about_ca_system_score_gemma":0.0025327855,"threshold_uncertainty_score":0.029589772},"labels":[],"label_agreement":null},{"id":"W2103676410","doi":"10.1016/j.ejor.2013.05.024","title":"Vehicle routing with soft time windows and stochastic travel times: A column generation and branch-and-price solution approach","year":2013,"lang":"en","type":"article","venue":"European Journal of Operational Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":163,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Column generation; Vehicle routing problem; Branch and price; Computer science; Mathematical optimization; Column (typography); Operations research; Shortest path problem; Integer programming; Integer (computer science); Set (abstract data type); Routing (electronic design automation); Service (business); Mathematics; Economics","score_opus":0.0456891675178521,"score_gpt":0.28375451622333697,"score_spread":0.23806534870548487,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2103676410","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.022934642,0.00055525557,0.9698696,0.0004978057,0.00015775418,0.0001609586,0.00030056055,0.00029090687,0.005232517],"genre_scores_gemma":[0.5619608,0.0011621992,0.4219353,0.0003247141,0.0003844227,0.0005866552,0.0007192476,0.00041026363,0.012516411],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992379,0.00041500892,0.000024531735,0.00007869059,0.00013175994,0.00011211527],"domain_scores_gemma":[0.99657947,0.0026843091,0.00019993218,0.000097788725,0.00030185864,0.0001366735],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002227426,0.0017774192,0.0030328424,0.0016856694,0.0008603832,0.0023594473,0.002135268,0.0023271756,0.0078089773],"category_scores_gemma":[0.0048266836,0.0021392056,0.0016093288,0.0031611174,0.001068289,0.002124749,0.001035353,0.0023712441,0.0006829102],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000036966107,0.000042413103,0.000104780505,0.00003489635,0.000022873324,0.00002126586,0.0000104742085,0.98663205,0.00010085362,0.0050919536,0.00084380433,0.0070576863],"study_design_scores_gemma":[0.0000050657036,0.0000052793166,0.000017127919,0.0000020574594,0.0000045734755,0.0000015848268,0.0000023323169,0.9981806,0.00002572265,0.0016937758,0.000059918333,0.0000020191874],"about_ca_topic_score_codex":0.026026621,"about_ca_topic_score_gemma":0.020335931,"teacher_disagreement_score":0.026026621,"about_ca_system_score_codex":0.001675913,"about_ca_system_score_gemma":0.0026325814,"threshold_uncertainty_score":0.051750243},"labels":[],"label_agreement":null},{"id":"W2105075386","doi":"10.1016/s0166-218x(00)00283-3","title":"The vehicle routing problem with pickups and deliveries on some special graphs","year":2002,"lang":"en","type":"article","venue":"Discrete Applied Mathematics","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":23,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Vehicle routing problem; Pickup; Depot; Mathematics; Combinatorics; Routing (electronic design automation); Tree (set theory); Time complexity; Computer science; Graph; Mathematical optimization; Artificial intelligence; Geography","score_opus":0.012250185658381098,"score_gpt":0.20777666955318044,"score_spread":0.19552648389479935,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2105075386","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.31350273,0.0018362154,0.6532893,0.003428682,0.00036037085,0.00016105472,0.00085951126,0.00023672676,0.026325336],"genre_scores_gemma":[0.8800143,0.0026914207,0.09105133,0.000475342,0.0008619362,0.00021328487,0.0011136623,0.0002193818,0.02335941],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990753,0.00037753402,0.000043433512,0.0002280109,0.00012490808,0.000150751],"domain_scores_gemma":[0.99645376,0.0021934162,0.0005761102,0.00020228908,0.00022567106,0.0003486925],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015334331,0.0015422798,0.0024672293,0.0014867838,0.0014656557,0.0023294883,0.0022620654,0.0028376698,0.0038608573],"category_scores_gemma":[0.0068140924,0.0013191818,0.0023907004,0.0022666103,0.0019008406,0.00405885,0.001675837,0.0028365366,0.00035885122],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00036053735,0.0001959614,0.001366675,0.00041768336,0.00019129139,0.00095694873,0.00022524796,0.45557302,0.0017302244,0.50391585,0.011154378,0.0239122],"study_design_scores_gemma":[0.000059910333,0.000063422915,0.0005335586,0.00002258461,0.00007106004,0.00033504807,0.0001328112,0.6348732,0.0005800761,0.36022115,0.0030743764,0.000032815977],"about_ca_topic_score_codex":0.003308552,"about_ca_topic_score_gemma":0.0028141232,"teacher_disagreement_score":0.0038608573,"about_ca_system_score_codex":0.0015957109,"about_ca_system_score_gemma":0.0010690464,"threshold_uncertainty_score":0.01291585},"labels":[],"label_agreement":null},{"id":"W2105277320","doi":"10.3138/infor.45.1.41","title":"Un algorithme de minimisation du transport à vide appliqué à l'industrie forestière","year":2007,"lang":"fr","type":"article","venue":"INFOR Information Systems and Operational Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Université de Montréal; HEC Montréal","funders":"","keywords":"Minimisation (clinical trials); Humanities; Computer science; Philosophy; Mathematics","score_opus":0.04160118805698877,"score_gpt":0.32969989694323837,"score_spread":0.2880987088862496,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2105277320","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03410828,0.0004937845,0.9623444,0.00022459276,0.000071314156,0.000073000316,0.00007678084,0.00033698397,0.0022708196],"genre_scores_gemma":[0.43171188,0.00062797155,0.5559975,0.00012625312,0.00010010463,0.00027741483,0.00026774243,0.00019909893,0.010692039],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995042,0.00017228919,0.00002158316,0.0001303584,0.00009278647,0.0000787917],"domain_scores_gemma":[0.99915695,0.0005930524,0.000058942882,0.000036522804,0.000117706535,0.000036814636],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014099577,0.0012012841,0.0013852371,0.00080840715,0.0006308318,0.0015795142,0.0010488608,0.002008101,0.0028917498],"category_scores_gemma":[0.0024896455,0.0005295885,0.0008992686,0.00091528794,0.0008590832,0.0010535718,0.0008288469,0.0011762169,0.00037880073],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014892613,0.00005182127,0.00046657136,0.000080433834,0.000042944215,0.000032061875,0.000058547626,0.9289489,0.0018163708,0.006278719,0.0009625054,0.061112244],"study_design_scores_gemma":[0.000025331172,0.000079561,0.0001401942,0.000009649866,0.000008850217,0.000017826718,0.00001633723,0.9958111,0.00070677034,0.0022112888,0.0009678959,0.0000051821644],"about_ca_topic_score_codex":0.010794192,"about_ca_topic_score_gemma":0.0072060614,"teacher_disagreement_score":0.010794192,"about_ca_system_score_codex":0.0014693325,"about_ca_system_score_gemma":0.001625323,"threshold_uncertainty_score":0.021462739},"labels":[],"label_agreement":null},{"id":"W2105559690","doi":"10.1109/mis.2005.58","title":"A Guided Cooperative Search for the Vehicle Routing Problem with Time Windows","year":2005,"lang":"en","type":"article","venue":"IEEE Intelligent Systems","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":73,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec; Université du Québec à Montréal; Université de Montréal","funders":"","keywords":"Computer science; Metaheuristic; Mechanism (biology); Diversification (marketing strategy); Identification (biology); Search algorithm; Focus (optics); Guided Local Search; Search engine; Data mining; Artificial intelligence; Information retrieval; Algorithm","score_opus":0.03859701775495493,"score_gpt":0.29243788783210617,"score_spread":0.2538408700771512,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2105559690","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04787445,0.00023750296,0.94837314,0.00015934509,0.000024174435,0.00007491166,0.000030269239,0.00012155791,0.003104648],"genre_scores_gemma":[0.68748546,0.00025640047,0.30728954,0.000081409205,0.000028886288,0.00037089046,0.00009308446,0.000044222605,0.00435012],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99965703,0.000116996176,0.000015073142,0.000062698964,0.00009416987,0.000054110016],"domain_scores_gemma":[0.9995542,0.00025526792,0.00005768717,0.000031606614,0.00006098252,0.000040163475],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010074015,0.0004975719,0.00061059435,0.00047591687,0.00034683276,0.00066800014,0.0011401947,0.000935573,0.0014309672],"category_scores_gemma":[0.0020399778,0.00029129675,0.00044419445,0.0004938196,0.0006266055,0.00085847516,0.0011439715,0.00048203944,0.0001806484],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010997067,0.000052022468,0.000352837,0.00006105347,0.00004359347,0.00008806393,0.00012506598,0.930098,0.0035320548,0.027632538,0.0006414441,0.037263375],"study_design_scores_gemma":[0.000028303743,0.000051209925,0.00004138758,0.0000034929244,0.0000064747665,0.000011359972,0.000010208332,0.9949797,0.00030724783,0.004000755,0.0005564705,0.0000033688877],"about_ca_topic_score_codex":0.003284572,"about_ca_topic_score_gemma":0.0022499505,"teacher_disagreement_score":0.003284572,"about_ca_system_score_codex":0.0005579854,"about_ca_system_score_gemma":0.0009618766,"threshold_uncertainty_score":0.006530881},"labels":[],"label_agreement":null},{"id":"W2106541949","doi":"10.1287/trsc.37.1.1.12822","title":"Models and Methods for Merge-in-Transit Operations","year":2003,"lang":"en","type":"article","venue":"Transportation Science","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":99,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Heuristics; Mathematical optimization; Rounding; Integer programming; Merge (version control); Linear programming; Cutting-plane method; Computer science; Piecewise linear function; Branch and bound; Mathematics","score_opus":0.038218333922510804,"score_gpt":0.3652933841581758,"score_spread":0.327075050235665,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2106541949","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00052715966,0.0004800248,0.99429375,0.00023671934,0.00005421989,0.00005145945,0.000094994575,0.00008746813,0.0041742763],"genre_scores_gemma":[0.08324745,0.0042788223,0.89511484,0.00024914477,0.0003976984,0.0011959422,0.0007055147,0.00029511598,0.014515413],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998522,0.00056708226,0.00008238673,0.000211981,0.00048049638,0.00013603979],"domain_scores_gemma":[0.99835145,0.0010528798,0.00021034817,0.00014373654,0.00018211253,0.000059513277],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026962892,0.002222123,0.0011756737,0.0018795308,0.0009859632,0.0027224077,0.003575376,0.0020291351,0.009859669],"category_scores_gemma":[0.0057395287,0.0011059132,0.0024464699,0.0024180962,0.0012270556,0.004101082,0.0020668278,0.0037377472,0.002582379],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013441666,0.000052001087,0.00015459205,0.00014428473,0.000036486945,0.000048985417,0.00010492313,0.4946284,0.0002717295,0.47101274,0.004006824,0.029525595],"study_design_scores_gemma":[0.000011413713,0.000012022344,0.000034894067,0.000042588617,0.000013253771,0.00003450162,0.000028469742,0.79918134,0.00019391482,0.18729651,0.0131384,0.000012668612],"about_ca_topic_score_codex":0.005789107,"about_ca_topic_score_gemma":0.0051624365,"teacher_disagreement_score":0.009859669,"about_ca_system_score_codex":0.0019476551,"about_ca_system_score_gemma":0.0021207905,"threshold_uncertainty_score":0.0329839},"labels":[],"label_agreement":null},{"id":"W2106852598","doi":"10.1016/j.cor.2005.07.015","title":"A first multilevel cooperative algorithm for capacitated multicommodity network design","year":2005,"lang":"en","type":"article","venue":"Computers & Operations Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":69,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba; Université du Québec à Montréal; Université de Montréal","funders":"","keywords":"Tabu search; Benchmark (surveying); Computer science; Mathematical optimization; Set (abstract data type); Network planning and design; Algorithm; Mathematics; Computer network","score_opus":0.13078813271017706,"score_gpt":0.3751517516909518,"score_spread":0.24436361898077472,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2106852598","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008600321,0.000090619294,0.9846198,0.00009677195,0.00004200198,0.000059932267,0.000039019917,0.0002486183,0.0062029846],"genre_scores_gemma":[0.2122452,0.000121832505,0.7814418,0.00012198469,0.000038871734,0.00038145002,0.00011305272,0.00013105494,0.005404733],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996371,0.000092017035,0.00001056603,0.000049105947,0.00015406207,0.000057067915],"domain_scores_gemma":[0.99944824,0.00021383462,0.000042408134,0.000089806366,0.00015345571,0.000052296436],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00062956923,0.00064979296,0.00085124717,0.0007821133,0.00080014986,0.0007510104,0.0015714578,0.0010980703,0.0075514745],"category_scores_gemma":[0.0021258562,0.00047566247,0.00075645774,0.0009504366,0.00045174515,0.0010403869,0.0019721903,0.0011543037,0.0008935363],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017606394,0.00017436921,0.00056302483,0.00016938314,0.00005272391,0.000058668422,0.0002064644,0.6166968,0.006334885,0.08231261,0.006642747,0.28661227],"study_design_scores_gemma":[0.000019835894,0.00005924536,0.000067866415,0.000008468223,0.000007408485,0.00001189808,0.000009699046,0.9909175,0.00070306915,0.006323776,0.0018658887,0.000005330587],"about_ca_topic_score_codex":0.0034601598,"about_ca_topic_score_gemma":0.0063237273,"teacher_disagreement_score":0.0075514745,"about_ca_system_score_codex":0.0009384647,"about_ca_system_score_gemma":0.0012104714,"threshold_uncertainty_score":0.025262177},"labels":[],"label_agreement":null},{"id":"W2107306858","doi":"10.1007/s10732-006-4192-1","title":"Path relinking for the vehicle routing problem","year":2006,"lang":"en","type":"article","venue":"Journal of Heuristics","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":104,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; Computer Research Institute of Montréal","funders":"Norges Forskningsråd","keywords":"Tabu search; Guided Local Search; Mathematical optimization; Path (computing); Vehicle routing problem; Hill climbing; Mathematics; Local search (optimization); Heuristic; Local optimum; Travelling salesman problem; Routing (electronic design automation); Computer science","score_opus":0.012837469884270425,"score_gpt":0.24824476457955647,"score_spread":0.23540729469528604,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2107306858","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.051242147,0.0013530526,0.9378989,0.00067271467,0.0001575284,0.0001609178,0.00019641295,0.00025840016,0.00805993],"genre_scores_gemma":[0.43548563,0.0020485672,0.55192053,0.00019485882,0.00014836919,0.0002627659,0.0004865567,0.00029487637,0.0091578765],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990445,0.00049960025,0.000033013926,0.00012308768,0.00015805737,0.00014178916],"domain_scores_gemma":[0.99701595,0.0022994806,0.00018450775,0.00016633648,0.00020223805,0.00013145855],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017797982,0.0010246992,0.0013243557,0.0010572829,0.00081510615,0.001156192,0.0016770007,0.0011435814,0.005312195],"category_scores_gemma":[0.0066269436,0.0007721158,0.0008464669,0.0014351924,0.0011905556,0.0025217864,0.0014635071,0.0026718355,0.00054966175],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022033816,0.00012367716,0.0004099003,0.00022766071,0.000043661905,0.00006205672,0.00012003617,0.88714075,0.00088518875,0.04221092,0.0033016216,0.06525416],"study_design_scores_gemma":[0.000047344638,0.00008293196,0.00010853627,0.00002845175,0.000023900671,0.00003671761,0.00005220068,0.94967616,0.00040082133,0.047948536,0.0015826817,0.000011708355],"about_ca_topic_score_codex":0.0062449747,"about_ca_topic_score_gemma":0.0049955584,"teacher_disagreement_score":0.0062449747,"about_ca_system_score_codex":0.0010090119,"about_ca_system_score_gemma":0.001731702,"threshold_uncertainty_score":0.017771065},"labels":[],"label_agreement":null},{"id":"W2107357529","doi":"10.1287/opre.2014.1263","title":"Hub Location as the Minimization of a Supermodular Set Function","year":2014,"lang":"en","type":"article","venue":"Operations Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":44,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Solver; Mathematical optimization; Benchmark (surveying); Class (philosophy); Minification; Heuristic; Set (abstract data type); Mathematics; Integer programming; Greedy algorithm; Integer (computer science); Function (biology); Path (computing); Exploit; Computer science; Artificial intelligence","score_opus":0.056461730768427844,"score_gpt":0.36038887978756,"score_spread":0.30392714901913215,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2107357529","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01720115,0.00032551133,0.9743518,0.00024771556,0.00004719977,0.000056767447,0.00019339228,0.0000986246,0.0074778018],"genre_scores_gemma":[0.58755827,0.0015280244,0.39302024,0.0002839635,0.00017350611,0.0005060855,0.0004943734,0.00034227493,0.016093275],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9988938,0.0005345884,0.000026249625,0.0002002068,0.00023620967,0.00010899286],"domain_scores_gemma":[0.99881244,0.0007870282,0.00014242239,0.000086620385,0.00010060951,0.00007095288],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00229372,0.0014485512,0.0013240308,0.0007362605,0.00040815966,0.001529404,0.0020086064,0.0012703545,0.0055072415],"category_scores_gemma":[0.0024430428,0.0005692369,0.0009541684,0.0013712451,0.0011138985,0.002363223,0.0011613606,0.0015391419,0.00061573443],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000049893788,0.00004866958,0.00017578917,0.00014390358,0.00003107486,0.00009025986,0.000055441687,0.88995713,0.0022312778,0.08741738,0.0021524453,0.017646752],"study_design_scores_gemma":[0.000007926537,0.00005480385,0.00008768144,0.000011930105,0.0000084543,0.000041471052,0.000020743746,0.96196985,0.00056799175,0.03545422,0.0017667752,0.0000081172575],"about_ca_topic_score_codex":0.001212052,"about_ca_topic_score_gemma":0.0016123434,"teacher_disagreement_score":0.0055072415,"about_ca_system_score_codex":0.0019277489,"about_ca_system_score_gemma":0.0012686937,"threshold_uncertainty_score":0.018423498},"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,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_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","score_opus":0.011913991363816104,"score_gpt":0.27806692144220835,"score_spread":0.26615293007839225,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2108538642","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20096225,0.0054777744,0.7705454,0.0013595234,0.0001558945,0.000087074564,0.000044581448,0.00025496265,0.021112658],"genre_scores_gemma":[0.96319824,0.0010964609,0.03179807,0.00008866586,0.00007733899,0.000040073646,0.000027790677,0.000038655795,0.003634654],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99917454,0.00048072648,0.00002598079,0.00006076671,0.00017701965,0.00008099682],"domain_scores_gemma":[0.99852186,0.0010019668,0.00020693659,0.00007274958,0.00013002279,0.0000665956],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017528106,0.00053390244,0.00047565773,0.00049801485,0.00059706613,0.001193327,0.0006667341,0.00075494254,0.0021068607],"category_scores_gemma":[0.0035994586,0.00030729952,0.00025762073,0.00083916803,0.0012067413,0.0016674425,0.00082189986,0.00083717535,0.00018874831],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009761817,0.000027006256,0.00057160633,0.00006379022,0.000018009236,0.0001397136,0.00013679481,0.9244711,0.0012404239,0.025625573,0.0006962345,0.046912223],"study_design_scores_gemma":[0.000016200573,0.00008514462,0.00028887653,0.000020600086,0.000008750337,0.00011505211,0.00011920578,0.95731056,0.0012777073,0.038060952,0.0026826244,0.000014304742],"about_ca_topic_score_codex":0.0015281563,"about_ca_topic_score_gemma":0.0011212864,"teacher_disagreement_score":0.0021068607,"about_ca_system_score_codex":0.00078373484,"about_ca_system_score_gemma":0.00053468114,"threshold_uncertainty_score":0.009269834},"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,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_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","score_opus":0.02831864026295009,"score_gpt":0.2741303086561243,"score_spread":0.24581166839317423,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2108911946","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005575381,0.00070613984,0.98797464,0.0003066277,0.00006188166,0.00014839372,0.00018771038,0.0002799911,0.0047592223],"genre_scores_gemma":[0.09317124,0.0010863928,0.90005,0.00018153802,0.00012242813,0.0006261446,0.0007101484,0.00021297159,0.0038391103],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988397,0.0004122475,0.00006844925,0.0002062371,0.0003285257,0.00014489795],"domain_scores_gemma":[0.99803287,0.0013028865,0.0002067745,0.00009721113,0.00028519562,0.00007501413],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019858945,0.0020592522,0.0020409352,0.0015311912,0.00086257374,0.0024664104,0.002196633,0.0019550556,0.0069074226],"category_scores_gemma":[0.004341541,0.0010735594,0.0012816247,0.0029450844,0.00092606014,0.0020293244,0.0014930967,0.0030974373,0.0010574758],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005178081,0.00009166099,0.00020913269,0.00013039351,0.00003556495,0.000060558228,0.0000496114,0.9157146,0.00045076545,0.027830033,0.0026381884,0.05273772],"study_design_scores_gemma":[0.000027261427,0.000020767387,0.000035913465,0.000016719177,0.000009413842,0.000019314295,0.00002273975,0.97945887,0.00024893705,0.018550333,0.0015839111,0.000005807394],"about_ca_topic_score_codex":0.005980784,"about_ca_topic_score_gemma":0.005287342,"teacher_disagreement_score":0.0069074226,"about_ca_system_score_codex":0.0017927514,"about_ca_system_score_gemma":0.0023035873,"threshold_uncertainty_score":0.023107648},"labels":[],"label_agreement":null},{"id":"W2109346100","doi":"10.5267/j.ijiec.2012.10.001","title":"Differential evolution algorithm for multi-commodity and multi-level of service hub covering location problem","year":2012,"lang":"en","type":"article","venue":"International Journal of Industrial Engineering Computations","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Punctuality; Commodity; Service (business); Computer science; Reliability (semiconductor); Quality of service; Operations research; Mathematical optimization; Economics; Business; Mathematics; Computer network; Engineering; Marketing; Transport engineering; Finance","score_opus":0.11017214759722337,"score_gpt":0.3225071914205642,"score_spread":0.21233504382334084,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2109346100","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012060469,0.0003352115,0.9822305,0.0002157755,0.00004042036,0.000048515492,0.000054985478,0.000116598356,0.004897469],"genre_scores_gemma":[0.58392555,0.0008056434,0.405447,0.00020474686,0.000052456875,0.0005145603,0.00038589392,0.00010178413,0.008562452],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996413,0.00011881327,0.0000162578,0.000071726696,0.00008665152,0.00006532471],"domain_scores_gemma":[0.99952114,0.00031220782,0.000045710454,0.000016124575,0.00008107918,0.000023706065],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00071267993,0.0007080005,0.0010799582,0.0005775129,0.00037892244,0.00092249346,0.0010963859,0.0010656412,0.0024256075],"category_scores_gemma":[0.0014637902,0.0004035044,0.00079331547,0.00083554996,0.0004612797,0.00066836434,0.001012521,0.0011037256,0.00024672213],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000024332156,0.00002290495,0.00035498134,0.00004943946,0.000018351835,0.000054864904,0.000037012873,0.9741631,0.0006727656,0.0085134925,0.0007610292,0.015327679],"study_design_scores_gemma":[0.0000043584946,0.000009410694,0.00003410196,0.00000287471,0.0000025682007,0.0000068647328,0.000005253679,0.9980983,0.00009650558,0.0013783728,0.0003596211,0.0000017829252],"about_ca_topic_score_codex":0.0063332985,"about_ca_topic_score_gemma":0.0032769362,"teacher_disagreement_score":0.0063332985,"about_ca_system_score_codex":0.0009915011,"about_ca_system_score_gemma":0.0011005186,"threshold_uncertainty_score":0.012592852},"labels":[],"label_agreement":null},{"id":"W2110105948","doi":"10.1002/net.10009","title":"A comparative analysis of several formulations for the generalized minimum spanning tree problem","year":2001,"lang":"en","type":"article","venue":"Networks","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":55,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Minimum spanning tree; Polytope; Spanning tree; Heuristics; Combinatorics; Mathematics; Tree (set theory); Mathematical optimization; Computer science","score_opus":0.03965971433503258,"score_gpt":0.31007744587188296,"score_spread":0.2704177315368504,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2110105948","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3056823,0.013298571,0.6046217,0.001568566,0.00025642378,0.00054294337,0.00054493116,0.0008132865,0.07267134],"genre_scores_gemma":[0.6780815,0.006928303,0.3090059,0.00027426518,0.00013512344,0.00039154864,0.00087421155,0.00036156425,0.0039475868],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997692,0.001245341,0.000073280054,0.0001190256,0.0006581975,0.00021227109],"domain_scores_gemma":[0.9939752,0.0047168424,0.00040965085,0.00024068342,0.00056051585,0.000097143304],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004240834,0.0008150468,0.00068587,0.001432257,0.0006346385,0.001931007,0.0009380816,0.0009315583,0.0038147783],"category_scores_gemma":[0.010527223,0.00036164833,0.0010030197,0.002077304,0.0005589965,0.0019898375,0.0007534116,0.001109642,0.0003551255],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00042277083,0.00028137735,0.0010477478,0.0007285362,0.00012520928,0.000108400636,0.00018102741,0.77417797,0.0018559087,0.07043598,0.0052004624,0.14543462],"study_design_scores_gemma":[0.000087201544,0.00046793153,0.0009078441,0.00015411736,0.00008838128,0.00014883204,0.0002718174,0.96887153,0.0015974111,0.020367466,0.007012289,0.000025134672],"about_ca_topic_score_codex":0.0024846226,"about_ca_topic_score_gemma":0.0038734458,"teacher_disagreement_score":0.004240834,"about_ca_system_score_codex":0.0020491935,"about_ca_system_score_gemma":0.0016857103,"threshold_uncertainty_score":0.022427976},"labels":[],"label_agreement":null},{"id":"W2110620774","doi":"10.1287/opre.1120.1154","title":"An Exact Algorithm for the Capacitated Arc Routing Problem with Deadheading Demand","year":2013,"lang":"en","type":"article","venue":"Operations Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"","keywords":"Arc routing; Benchmark (surveying); Column generation; Mathematical optimization; Routing (electronic design automation); Computer science; Vehicle routing problem; Arc (geometry); Enhanced Data Rates for GSM Evolution; Mathematics; Artificial intelligence","score_opus":0.05729250841713738,"score_gpt":0.3586774357342097,"score_spread":0.3013849273170723,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2110620774","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014694402,0.00037725212,0.9712478,0.00034831802,0.00009813234,0.00024018581,0.00035897733,0.0017947343,0.010840317],"genre_scores_gemma":[0.13829431,0.00031993332,0.85546625,0.0001730048,0.00006816189,0.00039181896,0.0009878563,0.00037105713,0.0039276737],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9990772,0.00016443523,0.00004376156,0.00025956923,0.00026198704,0.00019310568],"domain_scores_gemma":[0.9983773,0.0009601015,0.00012201927,0.0002751271,0.0001963784,0.000069072776],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00097038044,0.0015825342,0.0014525024,0.0012913075,0.00082997745,0.0019165111,0.002353395,0.0016057113,0.012906785],"category_scores_gemma":[0.003898052,0.00076847966,0.0010792691,0.0023482733,0.0007182005,0.0024154799,0.0014130392,0.0016965759,0.0018171482],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013399287,0.0002446973,0.0004978155,0.00025701115,0.000042052456,0.00010231879,0.00009786473,0.71377945,0.0017010801,0.03400228,0.009115466,0.24002595],"study_design_scores_gemma":[0.00006761406,0.000046530557,0.00009263092,0.000013617751,0.000012380223,0.000051293653,0.000033445798,0.97077405,0.0005076939,0.026173852,0.0022176448,0.000009138638],"about_ca_topic_score_codex":0.008888902,"about_ca_topic_score_gemma":0.011267917,"teacher_disagreement_score":0.012906785,"about_ca_system_score_codex":0.0020971429,"about_ca_system_score_gemma":0.003813878,"threshold_uncertainty_score":0.043177485},"labels":[],"label_agreement":null},{"id":"W2111891958","doi":"10.1287/trsc.1110.0379","title":"Integrated Airline Crew Pairing and Crew Assignment by Dynamic Constraint Aggregation","year":2011,"lang":"en","type":"article","venue":"Transportation Science","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":46,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Group for Research in Decision Analysis","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Crew; Crew scheduling; Column generation; Set cover problem; Operations research; Computer science; Context (archaeology); Scheduling (production processes); Constraint (computer-aided design); Schedule; Set (abstract data type); Engineering; Mathematical optimization; Aeronautics; Operations management; Mathematics","score_opus":0.022086663682596548,"score_gpt":0.25354447872781116,"score_spread":0.23145781504521462,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2111891958","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.052445143,0.0002006192,0.9410711,0.0001716324,0.000055555814,0.00023040731,0.0003465354,0.00046848695,0.0050105606],"genre_scores_gemma":[0.53017384,0.0002327974,0.4644206,0.00008966443,0.00006416314,0.00040702254,0.00081998436,0.00011743341,0.0036744697],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987464,0.00046414594,0.00004702964,0.00023698638,0.0003223116,0.00018318424],"domain_scores_gemma":[0.9989219,0.00041940942,0.00017598642,0.00026653006,0.00014488777,0.00007133635],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001134731,0.0010091426,0.0013175957,0.00085019664,0.0006344688,0.0015383931,0.0013802266,0.0008703429,0.0031082397],"category_scores_gemma":[0.0024595815,0.0007357162,0.0012886102,0.0028131746,0.00044074928,0.0017017089,0.0012851511,0.0012483924,0.00037600216],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000053351552,0.00008305304,0.0007422393,0.00004172455,0.00004444072,0.000063607964,0.000036411373,0.9547495,0.001100863,0.0053105815,0.0010590344,0.036715224],"study_design_scores_gemma":[0.000012011552,0.000037670314,0.00026484655,0.0000035192027,0.000013513249,0.00002269724,0.000018836332,0.9947872,0.0005622222,0.0029838972,0.0012867873,0.0000068262584],"about_ca_topic_score_codex":0.01741805,"about_ca_topic_score_gemma":0.018093606,"teacher_disagreement_score":0.01741805,"about_ca_system_score_codex":0.0010729097,"about_ca_system_score_gemma":0.0023065722,"threshold_uncertainty_score":0.03463334},"labels":[],"label_agreement":null},{"id":"W2111991140","doi":"10.1016/j.cor.2013.07.002","title":"A column-and-cut generation algorithm for planning of Canadian armed forces tactical logistics distribution","year":2013,"lang":"en","type":"article","venue":"Computers & Operations Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Defence Research and Development Canada","funders":"","keywords":"Column generation; Column (typography); Computer science; Operations research; Distribution (mathematics); Algorithm; Mathematical optimization; Mathematics; Telecommunications","score_opus":0.11643665802848882,"score_gpt":0.3754091867212523,"score_spread":0.25897252869276344,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2111991140","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07079524,0.0005279798,0.907614,0.00045247332,0.00014549344,0.00042084226,0.001318853,0.0020321533,0.016692946],"genre_scores_gemma":[0.37565815,0.00026157626,0.6121584,0.00013373788,0.000032179018,0.00027243866,0.0015244527,0.00029647033,0.009662654],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99982065,0.000029989893,0.0000064749293,0.000032976677,0.00006077035,0.00004916647],"domain_scores_gemma":[0.99973327,0.00010404923,0.000013118247,0.000013563557,0.00011264027,0.00002335821],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043143696,0.0008350437,0.00070369645,0.0012267827,0.001153347,0.0010409025,0.0012831423,0.0008680194,0.0073048384],"category_scores_gemma":[0.0010036778,0.00062112435,0.000599031,0.0015799053,0.00044847885,0.0005142458,0.0005362181,0.00069484295,0.00043135794],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005612115,0.000032455842,0.00037821545,0.000036270598,0.000011297632,0.00004150473,0.000029941784,0.9248475,0.0006750082,0.0033164101,0.0040991586,0.066476084],"study_design_scores_gemma":[0.000007825172,0.0000073008573,0.000085111285,0.0000027796737,0.0000041511526,0.0000040393943,0.000006810432,0.9984817,0.00022375835,0.00063384685,0.0005396054,0.0000031377517],"about_ca_topic_score_codex":0.40095755,"about_ca_topic_score_gemma":0.45364204,"teacher_disagreement_score":0.5990424,"about_ca_system_score_codex":0.0033567061,"about_ca_system_score_gemma":0.006325603,"threshold_uncertainty_score":0.7972474},"labels":[],"label_agreement":null},{"id":"W2112496246","doi":"10.1016/j.cor.2005.02.033","title":"Variable neighborhood search and local branching","year":2005,"lang":"en","type":"article","venue":"Computers & Operations Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":188,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Group for Research in Decision Analysis","funders":"","keywords":"Variable neighborhood search; Solver; Mathematical optimization; Integer programming; Local search (optimization); Branching (polymer chemistry); Heuristic; Variable (mathematics); Mathematics; Limit (mathematics); Set (abstract data type); Black box; Computer science; Integer (computer science); Metaheuristic; Algorithm; Artificial intelligence","score_opus":0.03926352547568592,"score_gpt":0.3419667686314819,"score_spread":0.302703243155796,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2112496246","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09124337,0.0030272198,0.87984496,0.0009961105,0.00015235766,0.000039529215,0.000056724057,0.000145164,0.02449453],"genre_scores_gemma":[0.8181966,0.0017448858,0.15438722,0.00019616097,0.00021084312,0.00014980634,0.00014341866,0.00014930258,0.024821784],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993894,0.00033714095,0.000011612232,0.00008867094,0.00011519377,0.00005801478],"domain_scores_gemma":[0.996416,0.0029554786,0.00019211326,0.00016951478,0.00015749906,0.000109435496],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014830317,0.00040015896,0.0010930108,0.00088364055,0.0009523691,0.0013107742,0.0013123475,0.001187501,0.0041319034],"category_scores_gemma":[0.0099598635,0.0005100038,0.0005494716,0.0015279629,0.0020890168,0.0022868207,0.0012502159,0.0019294398,0.00031642153],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000901185,0.00006126348,0.00066082017,0.00005254155,0.00002711019,0.00006075555,0.00012851208,0.32072204,0.0003525144,0.6450715,0.0023788132,0.03039399],"study_design_scores_gemma":[0.000020800637,0.000015809705,0.00013676216,0.000011945918,0.000009466686,0.000025974205,0.000020859756,0.6273617,0.00014329232,0.37083274,0.0014134011,0.000007182341],"about_ca_topic_score_codex":0.0029605085,"about_ca_topic_score_gemma":0.003348397,"teacher_disagreement_score":0.0041319034,"about_ca_system_score_codex":0.00103223,"about_ca_system_score_gemma":0.00061355927,"threshold_uncertainty_score":0.013822615},"labels":[],"label_agreement":null},{"id":"W2113574878","doi":"10.1287/opre.2013.1160","title":"Robust Partitioning for Stochastic Multivehicle Routing","year":2013,"lang":"en","type":"article","venue":"Operations Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":74,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"Boeing","keywords":"Computer science; Partition (number theory); Independent and identically distributed random variables; Vehicle routing problem; Heuristic; Mathematical optimization; Regular polygon; Workload; Order (exchange); Product (mathematics); Service (business); Routing (electronic design automation); Operations research; Random variable; Mathematics; Economics; Artificial intelligence","score_opus":0.1431070788548276,"score_gpt":0.3784082997950221,"score_spread":0.2353012209401945,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2113574878","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01078951,0.0002390252,0.98692,0.0001414334,0.00002085655,0.000033830514,0.000095242285,0.0001409172,0.0016191335],"genre_scores_gemma":[0.8023133,0.0008025572,0.1884688,0.00014809455,0.00012445658,0.00035114237,0.0006243885,0.0003250509,0.0068421434],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99867177,0.00054011313,0.000050465813,0.00024619047,0.0002931734,0.00019829917],"domain_scores_gemma":[0.9964263,0.0024919386,0.00044346187,0.00019476021,0.00028790726,0.00015568208],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028209584,0.0013397607,0.0014414961,0.0011563634,0.0005068185,0.0012102218,0.0014794568,0.0010449487,0.002567696],"category_scores_gemma":[0.008312853,0.00084156985,0.0010832223,0.0009369981,0.0013072224,0.0013436383,0.001711093,0.0013481888,0.00036037853],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000030100811,0.00000754013,0.00017714762,0.0000235881,0.00001859843,0.000019624718,0.000021958342,0.97868675,0.0003936657,0.0166745,0.00029550816,0.003651014],"study_design_scores_gemma":[0.0000030619399,0.0000070068327,0.000058892387,0.0000035033343,0.0000028252523,0.0000055308956,0.0000057474654,0.98872197,0.00010427836,0.010845614,0.00023835365,0.0000032337755],"about_ca_topic_score_codex":0.006016161,"about_ca_topic_score_gemma":0.0034474651,"teacher_disagreement_score":0.006016161,"about_ca_system_score_codex":0.0024360558,"about_ca_system_score_gemma":0.001162153,"threshold_uncertainty_score":0.017674923},"labels":[],"label_agreement":null},{"id":"W2113781679","doi":"10.1287/trsc.1100.0322","title":"Column Generation with Dynamic Duty Selection for Railway Crew Rescheduling","year":2010,"lang":"en","type":"article","venue":"EUR Research Repository (Erasmus University Rotterdam)","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":95,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Group for Research in Decision Analysis","funders":"","keywords":"Column generation; Heuristics; Computer science; Selection (genetic algorithm); Crew; Computation; Core (optical fiber); Mathematical optimization; Column (typography); Operations research; Crew scheduling; Lagrangian; Engineering; Algorithm; Mathematics; Artificial intelligence; Computer network","score_opus":0.02692870416305316,"score_gpt":0.28469859632522826,"score_spread":0.2577698921621751,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2113781679","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07439403,0.000463784,0.91888916,0.00021796416,0.00006325443,0.00016696901,0.00019772188,0.0006760865,0.0049310196],"genre_scores_gemma":[0.6707897,0.00028163756,0.3249058,0.00012329534,0.00003624731,0.00022508088,0.00032861138,0.00013147107,0.0031780764],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997968,0.00009054316,0.000005467501,0.000029169252,0.0000348443,0.000043157037],"domain_scores_gemma":[0.9995691,0.00025911568,0.000053196476,0.000037180747,0.000048812573,0.000032629723],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004664366,0.00049723295,0.0005673073,0.0005263978,0.0003909192,0.00045201197,0.0006677124,0.00037992184,0.0023609272],"category_scores_gemma":[0.0010538081,0.00032258718,0.0004082408,0.0008296932,0.00040641654,0.0003672209,0.00054672605,0.00047200333,0.00029228485],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000072509814,0.00006380535,0.00044392957,0.000057584268,0.00001896649,0.00005681074,0.0000453168,0.9336187,0.0016714095,0.0041856766,0.0019847266,0.057780575],"study_design_scores_gemma":[0.0000148922945,0.00002502627,0.000077228346,0.000003165761,0.0000044484373,0.000010656958,0.000010569952,0.9971796,0.00043667405,0.0016516795,0.0005821052,0.0000038374487],"about_ca_topic_score_codex":0.007586472,"about_ca_topic_score_gemma":0.009120451,"teacher_disagreement_score":0.007586472,"about_ca_system_score_codex":0.000625406,"about_ca_system_score_gemma":0.00088185654,"threshold_uncertainty_score":0.015084624},"labels":[],"label_agreement":null},{"id":"W2113900306","doi":"10.1287/trsc.2014.0576","title":"The Hub Line Location Problem","year":2015,"lang":"en","type":"article","venue":"Transportation Science","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":84,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal; Concordia University; Université de Montréal","funders":"","keywords":"Mathematical optimization; Solver; Benders' decomposition; Benchmark (surveying); Routing (electronic design automation); Path (computing); Computer science; Line (geometry); Minification; Decomposition; Network planning and design; Scheme (mathematics); Set (abstract data type); Mathematics; Computer network","score_opus":0.03745478217311991,"score_gpt":0.3004402146947597,"score_spread":0.26298543252163975,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2113900306","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03203605,0.0016296513,0.87186843,0.0025428045,0.0005363247,0.000410297,0.0037447487,0.00090013345,0.08633152],"genre_scores_gemma":[0.60445774,0.003459233,0.28888795,0.0007410501,0.0007534124,0.00066613697,0.007379524,0.00059257785,0.09306235],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99879646,0.0004097537,0.000051585033,0.00034468673,0.00018250449,0.00021500775],"domain_scores_gemma":[0.99915755,0.00042734406,0.000101190155,0.00009263054,0.00012899376,0.000092224975],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00079735124,0.0013136675,0.0011708596,0.0008134417,0.0009726648,0.002520224,0.0021281505,0.002960791,0.032574467],"category_scores_gemma":[0.0023428581,0.00052597345,0.0008481155,0.0017155707,0.0008174735,0.002778275,0.0017097831,0.0016945687,0.0043214923],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003192325,0.00027180513,0.0012386584,0.000859058,0.00012761891,0.0010285345,0.00019899897,0.42241117,0.0024622192,0.3609698,0.072865985,0.13724682],"study_design_scores_gemma":[0.0001848634,0.00023655484,0.00071047887,0.00017094331,0.00009694277,0.0010131345,0.00056733383,0.6177192,0.0032232555,0.23988014,0.1361297,0.00006741105],"about_ca_topic_score_codex":0.002129671,"about_ca_topic_score_gemma":0.0020226412,"teacher_disagreement_score":0.032574467,"about_ca_system_score_codex":0.001002951,"about_ca_system_score_gemma":0.0015641073,"threshold_uncertainty_score":0.10897249},"labels":[],"label_agreement":null},{"id":"W2114217004","doi":"10.1287/trsc.1090.0290","title":"An Adaptive Large Neighbourhood Search Heuristic for the Capacitated Arc-Routing Problem with Stochastic Demands","year":2009,"lang":"en","type":"article","venue":"Transportation Science","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":127,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"","keywords":"Arc routing; Vehicle routing problem; Mathematical optimization; Computer science; Neighbourhood (mathematics); Heuristic; Routing (electronic design automation); Arc (geometry); Context (archaeology); Mathematics; Geography","score_opus":0.023895904420274276,"score_gpt":0.2914730769878286,"score_spread":0.2675771725675543,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2114217004","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04615073,0.00053319463,0.948655,0.00019959144,0.00006177278,0.00009004119,0.000051594343,0.00021809689,0.0040399274],"genre_scores_gemma":[0.614056,0.00031609533,0.38148603,0.000092355804,0.000042245523,0.0002910362,0.00018641802,0.000105414016,0.00342438],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99960166,0.00018432393,0.000016908025,0.000055921642,0.00009231843,0.00004883982],"domain_scores_gemma":[0.9990231,0.0007176493,0.00006985907,0.00003881492,0.00008655871,0.00006392368],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00087612827,0.0005082912,0.0008887457,0.00074974715,0.00043335918,0.00050258695,0.0012525873,0.001207348,0.0017962056],"category_scores_gemma":[0.0027298203,0.0004211192,0.0006124907,0.0008951584,0.000697334,0.0010856352,0.0008700146,0.0006845956,0.0002153833],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006324143,0.00004169131,0.0001949263,0.000035979865,0.000019779713,0.000042613778,0.00004721785,0.9664003,0.000573229,0.0077223,0.0007059531,0.024152754],"study_design_scores_gemma":[0.000017909526,0.000026481392,0.000042928685,0.0000045956444,0.0000038005023,0.000012533092,0.000010923981,0.9960995,0.00013739629,0.0032625333,0.00037653424,0.000004933389],"about_ca_topic_score_codex":0.0043842886,"about_ca_topic_score_gemma":0.006156559,"teacher_disagreement_score":0.0043842886,"about_ca_system_score_codex":0.00090359664,"about_ca_system_score_gemma":0.00092007115,"threshold_uncertainty_score":0.008717537},"labels":[],"label_agreement":null},{"id":"W2114675299","doi":"10.1016/j.disopt.2010.06.002","title":"The Delivery Man Problem with time windows","year":2010,"lang":"en","type":"article","venue":"Discrete Optimization","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":31,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"","keywords":"Mathematical optimization; Travelling salesman problem; Heuristic; Integer programming; Window (computing); Computer science; Mathematics; Algorithm","score_opus":0.004443702601807077,"score_gpt":0.20733811642787947,"score_spread":0.2028944138260724,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2114675299","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020170989,0.0014569093,0.9707619,0.0011531627,0.00023658642,0.00007806124,0.00027407418,0.000102642254,0.005765632],"genre_scores_gemma":[0.7091216,0.0055606524,0.21258052,0.00048724073,0.00082468474,0.00058592367,0.000672271,0.00036045143,0.06980665],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987502,0.0006271725,0.000041109033,0.00023544508,0.00016490962,0.00018112971],"domain_scores_gemma":[0.99746096,0.0019024104,0.00025639532,0.00009354794,0.000108026834,0.00017856006],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00296941,0.0017106703,0.002143072,0.0008306982,0.0005942607,0.0021122121,0.0028595089,0.0022429896,0.0052883253],"category_scores_gemma":[0.006970747,0.0014099792,0.0010700235,0.0016603036,0.0014368307,0.0037439917,0.0017263448,0.0026067856,0.0005324325],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019938967,0.00008096669,0.00034399805,0.00019619604,0.00008054873,0.00012906622,0.00006618009,0.8242376,0.0005645948,0.15512232,0.003375468,0.015603726],"study_design_scores_gemma":[0.00003405898,0.000035464982,0.0000995929,0.000011568187,0.000025336254,0.00003138235,0.000028772369,0.9554594,0.00023372693,0.04227832,0.0017517229,0.000010592296],"about_ca_topic_score_codex":0.00531926,"about_ca_topic_score_gemma":0.002808124,"teacher_disagreement_score":0.00531926,"about_ca_system_score_codex":0.0020033629,"about_ca_system_score_gemma":0.0016348097,"threshold_uncertainty_score":0.017691255},"labels":[],"label_agreement":null},{"id":"W2114778474","doi":"10.1287/trsc.1120.0443","title":"Optimization-Based Adaptive Large Neighborhood Search for the Production Routing Problem","year":2012,"lang":"en","type":"article","venue":"Transportation Science","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":201,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Group for Research in Decision Analysis; HEC Montréal","funders":"","keywords":"Heuristics; Mathematical optimization; Benchmark (surveying); Routing (electronic design automation); Heuristic; Computer science; Production (economics); Set (abstract data type); Vehicle routing problem; Operations research; Mathematics; Economics","score_opus":0.03499057401878422,"score_gpt":0.29746752266634596,"score_spread":0.26247694864756177,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2114778474","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.026029369,0.0007032398,0.96863425,0.00023894559,0.00004536858,0.000109502056,0.000048877308,0.00021703048,0.003973462],"genre_scores_gemma":[0.45720956,0.00059328857,0.53796864,0.00016473127,0.00006297237,0.0005060084,0.0002607591,0.00011759426,0.0031165802],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995074,0.00027786315,0.000016234337,0.00007316025,0.00008769606,0.000037641392],"domain_scores_gemma":[0.9988651,0.0008857664,0.00009511603,0.00003681434,0.000081673614,0.000035460635],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010886245,0.00064977905,0.0010791755,0.0006971592,0.00051231746,0.0005268445,0.00094480003,0.0008741715,0.0017180624],"category_scores_gemma":[0.0027973547,0.00040469517,0.0004571054,0.0009032553,0.0005832293,0.0009222179,0.00065780943,0.00085880817,0.00022841454],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000032047534,0.000034885612,0.0001646341,0.000029092116,0.000013925854,0.000028742052,0.000016662741,0.9795958,0.0003175355,0.004339445,0.00063237705,0.01479486],"study_design_scores_gemma":[0.000009629697,0.000013902263,0.00002799729,0.0000027171109,0.0000024694505,0.0000068515888,0.0000045054435,0.9977399,0.0000812255,0.0018574317,0.00025144583,0.000001889726],"about_ca_topic_score_codex":0.0053616785,"about_ca_topic_score_gemma":0.00610383,"teacher_disagreement_score":0.0053616785,"about_ca_system_score_codex":0.00080242223,"about_ca_system_score_gemma":0.001029518,"threshold_uncertainty_score":0.010660946},"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,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_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","score_opus":0.07150300697132775,"score_gpt":0.38650730740420897,"score_spread":0.3150043004328812,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2114939271","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016678013,0.056978837,0.88021976,0.0014131005,0.0006986001,0.00018920314,0.00037702537,0.0006777706,0.04276767],"genre_scores_gemma":[0.23409086,0.03815598,0.70397115,0.00090846285,0.0009411952,0.00027035348,0.00069359486,0.00021778543,0.020750636],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987643,0.00048578187,0.00006175034,0.00015143593,0.00042440917,0.00011228464],"domain_scores_gemma":[0.9993759,0.00038402734,0.00005568021,0.000073702555,0.000086175554,0.000024659968],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012034235,0.0007044419,0.0005356197,0.0011629249,0.00043211522,0.0016456,0.0010026991,0.00089734234,0.006544775],"category_scores_gemma":[0.0021681562,0.00034151148,0.0005919613,0.0020392963,0.00094233535,0.0014889785,0.0006809572,0.0010353492,0.0011495579],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010270079,0.00010984518,0.00049279176,0.001004653,0.00010565523,0.00011091278,0.00012156748,0.25515422,0.0025873918,0.33304864,0.023059063,0.3841025],"study_design_scores_gemma":[0.000110777815,0.00019885067,0.0012739175,0.00039535508,0.00007237733,0.00046451786,0.00021220399,0.43805605,0.0029745565,0.35307625,0.203098,0.000067104585],"about_ca_topic_score_codex":0.0019619137,"about_ca_topic_score_gemma":0.002832228,"teacher_disagreement_score":0.006544775,"about_ca_system_score_codex":0.0011959027,"about_ca_system_score_gemma":0.0012880451,"threshold_uncertainty_score":0.021894455},"labels":[],"label_agreement":null},{"id":"W2115690509","doi":"10.1139/x07-106","title":"Backhauling in forest transportation: models, methods, and practical usage","year":2007,"lang":"en","type":"article","venue":"Canadian Journal of Forest Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":81,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Backhaul (telecommunications); Column generation; Computer science; Transportation theory; Flow network; Operations research; Mathematical optimization; Linear programming; Flow (mathematics); Mathematics; Computer network","score_opus":0.11301015791540846,"score_gpt":0.42411496718166264,"score_spread":0.31110480926625417,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2115690509","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011879341,0.0045894575,0.97517157,0.0007541597,0.00006444923,0.00007631918,0.00010704342,0.00013396094,0.0072236606],"genre_scores_gemma":[0.45628506,0.014344911,0.51258075,0.00023796703,0.00035746003,0.00069663214,0.00033816855,0.00014303937,0.015015932],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991223,0.0005341982,0.00002857029,0.00008732518,0.00015832072,0.000069260575],"domain_scores_gemma":[0.99790907,0.001721484,0.0001391573,0.00006534031,0.00012241241,0.000042549393],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019457808,0.0012015858,0.0012108018,0.0009465845,0.00072004367,0.0015005856,0.0016234554,0.0019171222,0.0031816654],"category_scores_gemma":[0.0032098773,0.00068377436,0.00093407347,0.0022552127,0.0012160016,0.002065361,0.00085078215,0.0016209502,0.00044414637],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017728595,0.00006356021,0.00040854473,0.00012797913,0.000017862938,0.000048051206,0.00007915568,0.9153413,0.00016167927,0.051113725,0.0010246198,0.031595826],"study_design_scores_gemma":[0.000007504091,0.00001385938,0.0000666221,0.000019236266,0.000006142027,0.000018355757,0.000028802273,0.9684636,0.000094032264,0.02942932,0.0018457449,0.0000067829574],"about_ca_topic_score_codex":0.023257881,"about_ca_topic_score_gemma":0.02064113,"teacher_disagreement_score":0.023257881,"about_ca_system_score_codex":0.0017016473,"about_ca_system_score_gemma":0.0019603334,"threshold_uncertainty_score":0.04624504},"labels":[],"label_agreement":null},{"id":"W2115781168","doi":"10.1057/jors.2011.25","title":"An incremental tabu search heuristic for the generalized vehicle routing problem with time windows","year":2011,"lang":"en","type":"article","venue":"Journal of the Operational Research Society","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":73,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"","keywords":"Tabu search; Vehicle routing problem; Mathematical optimization; Guided Local Search; Computer science; Neighbourhood (mathematics); Incremental heuristic search; Heuristic; Heuristics; Beam search; Routing (electronic design automation); Algorithm; Search algorithm; Mathematics","score_opus":0.07959455024081594,"score_gpt":0.3510110840672343,"score_spread":0.2714165338264184,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2115781168","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.028733337,0.0005491984,0.9618491,0.000113376525,0.00006970113,0.00023414035,0.000096469856,0.00074038375,0.0076143215],"genre_scores_gemma":[0.24714401,0.00040377263,0.7480956,0.00009319978,0.00004008492,0.00042567734,0.00029980575,0.00025024638,0.0032475735],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996921,0.00010590355,0.000010658215,0.000036116155,0.000111520814,0.000043731816],"domain_scores_gemma":[0.99961984,0.00022836875,0.000036941303,0.000034979956,0.000060273826,0.000019514784],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00057267374,0.00047790393,0.0007849801,0.000743548,0.0004942107,0.0005505783,0.0015706276,0.0007130967,0.003186978],"category_scores_gemma":[0.0021784937,0.0003107977,0.0006058583,0.00093282794,0.00048068515,0.0009991927,0.00064820144,0.0007164092,0.00047216128],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001170469,0.00010116512,0.0005190209,0.0001591094,0.000044131666,0.00010748574,0.000108600085,0.7320433,0.003255403,0.027126053,0.0034474658,0.23297128],"study_design_scores_gemma":[0.00003491342,0.00009991247,0.00020386593,0.000015935322,0.00002048346,0.00006662336,0.000029156434,0.98688406,0.001092722,0.008166801,0.0033709689,0.000014606126],"about_ca_topic_score_codex":0.0041377586,"about_ca_topic_score_gemma":0.0062996135,"teacher_disagreement_score":0.0041377586,"about_ca_system_score_codex":0.00077482814,"about_ca_system_score_gemma":0.0010636036,"threshold_uncertainty_score":0.010661483},"labels":[],"label_agreement":null},{"id":"W2116517820","doi":"10.1007/s13676-013-0020-6","title":"Vehicle routing: historical perspective and recent contributions","year":2013,"lang":"en","type":"article","venue":"EURO Journal on Transportation and Logistics","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":59,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"","keywords":"Perspective (graphical); Vehicle routing problem; Set (abstract data type); Routing (electronic design automation); Computer science; Operations research; Engineering; Management science; Artificial intelligence; Computer network","score_opus":0.023715562606487994,"score_gpt":0.26785541019620596,"score_spread":0.24413984758971796,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2116517820","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0028796378,0.95790964,0.00834158,0.0028549538,0.00195736,0.000016130443,0.00024647047,0.000053318126,0.025741003],"genre_scores_gemma":[0.02736761,0.9588977,0.0048505524,0.00078201643,0.003816709,0.000022987348,0.00037538275,0.000034655553,0.0038523767],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9994795,0.00011664877,0.000051166455,0.00019822121,0.00011214194,0.000042467538],"domain_scores_gemma":[0.9988984,0.0004954808,0.00013584146,0.00006248227,0.0003363725,0.00007142317],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013898959,0.0009747638,0.00062169257,0.003184658,0.0006816224,0.0026444823,0.001103814,0.0011834183,0.0039964896],"category_scores_gemma":[0.002363187,0.0005298907,0.0003975427,0.0076604215,0.0010747659,0.0041158884,0.0011349217,0.0022572319,0.0020379866],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016826032,0.000120114535,0.0021963154,0.0074135303,0.0000941399,0.00042583913,0.0003516991,0.00918895,0.00079863,0.17356756,0.045906562,0.7597684],"study_design_scores_gemma":[0.000006469481,0.00009351627,0.0017388419,0.0020447748,0.00007619703,0.0009879821,0.00025558236,0.0018445179,0.00043186263,0.03399914,0.95847976,0.00004135726],"about_ca_topic_score_codex":0.0015157011,"about_ca_topic_score_gemma":0.0011156279,"teacher_disagreement_score":0.0039964896,"about_ca_system_score_codex":0.0012902127,"about_ca_system_score_gemma":0.00088255206,"threshold_uncertainty_score":0.01336962},"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,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_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","score_opus":0.014021572184757499,"score_gpt":0.24713416884135248,"score_spread":0.23311259665659498,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2116675171","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01474734,0.016558584,0.9463648,0.0010954798,0.00047307898,0.0002264256,0.00030025138,0.0003987324,0.019835288],"genre_scores_gemma":[0.3238566,0.030479688,0.6104713,0.0007431524,0.0012202894,0.00078888476,0.0007587948,0.0005366235,0.03114469],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99954754,0.0001615892,0.00003091797,0.00007643548,0.00011389732,0.000069582085],"domain_scores_gemma":[0.9996296,0.00021000794,0.000067630375,0.0000384315,0.000031745854,0.00002255835],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006777043,0.0012401728,0.0011215237,0.0007549576,0.00044855927,0.002201799,0.0010946253,0.0014582375,0.0029799133],"category_scores_gemma":[0.0014979857,0.00046312818,0.0011194923,0.002394651,0.00061522,0.0020454885,0.00067674037,0.0014758362,0.00072971435],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000095949035,0.00011662012,0.00035965574,0.0004931364,0.00013606242,0.00012115638,0.000077027034,0.74262005,0.0032333352,0.08553803,0.007893762,0.15931521],"study_design_scores_gemma":[0.000066880835,0.00017828106,0.00042278072,0.00013919463,0.00006183013,0.00026826042,0.00008670207,0.85443014,0.0041632894,0.09145794,0.048691858,0.00003280327],"about_ca_topic_score_codex":0.0025111064,"about_ca_topic_score_gemma":0.0017955763,"teacher_disagreement_score":0.0029799133,"about_ca_system_score_codex":0.0010569517,"about_ca_system_score_gemma":0.0011949001,"threshold_uncertainty_score":0.009968758},"labels":[],"label_agreement":null},{"id":"W2116691041","doi":"10.1109/hipc.1997.634485","title":"Parallel algorithms for vehicle routing problems","year":2002,"lang":"en","type":"article","venue":"","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Time complexity; Routing (electronic design automation); Vehicle routing problem; Crew; Algorithm; Routing table; Destination-Sequenced Distance Vector routing; Static routing; Parallel algorithm; Computer network; Link-state routing protocol; Routing protocol; Engineering","score_opus":0.0487229125440266,"score_gpt":0.2695773464828055,"score_spread":0.2208544339387789,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2116691041","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010389608,0.0015851327,0.9630087,0.0011240783,0.0002768561,0.00035167218,0.00036113441,0.0021306707,0.02077215],"genre_scores_gemma":[0.1502761,0.0023618261,0.8317357,0.00039437058,0.00035642687,0.0011053215,0.001371546,0.0005467949,0.011851965],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99796945,0.000580931,0.00012740397,0.00046767274,0.00051502936,0.0003394754],"domain_scores_gemma":[0.99816173,0.0010858318,0.00014195993,0.00027079228,0.00026047006,0.0000792068],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016901321,0.002122749,0.0017726936,0.0010298,0.0016479071,0.0025631408,0.0024885505,0.001536847,0.009040775],"category_scores_gemma":[0.0058803996,0.00081161875,0.0014198769,0.0025479137,0.0012317231,0.0029485913,0.0021400412,0.0027095901,0.002608084],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020019175,0.00025451367,0.0005665539,0.0005416938,0.00013335353,0.000146364,0.00019332433,0.6070321,0.0015330852,0.1751965,0.024297155,0.18990515],"study_design_scores_gemma":[0.00018381946,0.000051393577,0.00012826719,0.000030841104,0.00003081829,0.00007747613,0.00007561085,0.7169679,0.00078942196,0.26427197,0.017378774,0.000013668465],"about_ca_topic_score_codex":0.004318055,"about_ca_topic_score_gemma":0.004748888,"teacher_disagreement_score":0.009040775,"about_ca_system_score_codex":0.0021956917,"about_ca_system_score_gemma":0.0021395201,"threshold_uncertainty_score":0.03024441},"labels":[],"label_agreement":null},{"id":"W2117319367","doi":"10.1007/s10107-011-0497-4","title":"Improved lower bounds and exact algorithm for the capacitated arc routing problem","year":2011,"lang":"en","type":"article","venue":"Mathematical Programming","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":52,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"","keywords":"Arc routing; Column generation; Bounding overwatch; Benchmark (surveying); Mathematical optimization; Mathematics; Undirected graph; Set (abstract data type); Upper and lower bounds; Algorithm; Vehicle routing problem; Arc (geometry); Routing (electronic design automation); Graph; Computer science; Combinatorics; Artificial intelligence","score_opus":0.03169759203907864,"score_gpt":0.25875518175346096,"score_spread":0.22705758971438234,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2117319367","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005524793,0.0007915018,0.9807755,0.0004609822,0.00022746042,0.00007509244,0.00019176221,0.000870471,0.011082481],"genre_scores_gemma":[0.121090785,0.0009563486,0.86458874,0.00040549936,0.00038247867,0.00048300065,0.00076528464,0.00076890946,0.010558929],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9968816,0.0008705944,0.00012108436,0.0004295064,0.0013134305,0.00038382344],"domain_scores_gemma":[0.9940124,0.0036298481,0.00025785813,0.00095738145,0.0009732467,0.00016929983],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003196622,0.0022762008,0.0020728498,0.0025488713,0.001041078,0.0039170687,0.0045808256,0.0025575298,0.016461581],"category_scores_gemma":[0.014377233,0.0013021915,0.0016748188,0.0037261506,0.0014406211,0.0051939883,0.003117798,0.005986896,0.0034152449],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00035861807,0.00039069937,0.00038983312,0.0003214222,0.00008047476,0.00007153097,0.00014466228,0.6220709,0.0026034333,0.13721047,0.019899303,0.21645868],"study_design_scores_gemma":[0.000049329657,0.000033466793,0.000108506785,0.000029598516,0.000018796225,0.00003096123,0.000017010587,0.94233966,0.00079828815,0.052978616,0.0035785087,0.000017168773],"about_ca_topic_score_codex":0.0054645925,"about_ca_topic_score_gemma":0.0070440536,"teacher_disagreement_score":0.016461581,"about_ca_system_score_codex":0.003306512,"about_ca_system_score_gemma":0.003636626,"threshold_uncertainty_score":0.055069447},"labels":[],"label_agreement":null},{"id":"W2117833774","doi":"10.1287/ijoc.1080.0291","title":"Scenario Tree-Based Heuristics for Stochastic Inventory-Routing Problems","year":2008,"lang":"en","type":"article","venue":"INFORMS journal on computing","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":70,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"","keywords":"Heuristics; Mathematical optimization; Computer science; Routing (electronic design automation); Operations research; Vendor; Inventory theory; Vehicle routing problem; Time horizon; Markov decision process; Column generation; Markov chain; Inventory management; Inventory control; Markov process; Mathematics; Operations management; Engineering","score_opus":0.03873982677101141,"score_gpt":0.26712172372284937,"score_spread":0.22838189695183797,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2117833774","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.030442826,0.0011253248,0.96320224,0.00033173745,0.0000635975,0.0001874472,0.00024796775,0.00033684765,0.0040619476],"genre_scores_gemma":[0.6280134,0.001473841,0.36771277,0.0001692077,0.0000601788,0.0004371007,0.0006531236,0.000102389466,0.001378042],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987489,0.00082215766,0.000054076223,0.000099214136,0.00013609664,0.00013962605],"domain_scores_gemma":[0.996971,0.0024849498,0.00019299891,0.00009058324,0.00012633434,0.0001340944],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017712285,0.0010627183,0.0013568883,0.0009692993,0.0006499379,0.0010882268,0.0012378473,0.0010083512,0.0028372477],"category_scores_gemma":[0.0033931856,0.00070383394,0.0009509049,0.0016948292,0.0008656497,0.0016846139,0.0009908302,0.0013111919,0.00034195065],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000045311786,0.000031815278,0.0001539497,0.00004721085,0.00002776365,0.000043723616,0.000034394907,0.9708562,0.00015668276,0.018547818,0.00072815764,0.009326985],"study_design_scores_gemma":[0.000026013557,0.000028068438,0.000042616586,0.0000132488585,0.000008969081,0.00001714098,0.00002265613,0.9720675,0.00010681111,0.02694617,0.00071376533,0.0000070921774],"about_ca_topic_score_codex":0.0046873004,"about_ca_topic_score_gemma":0.007053175,"teacher_disagreement_score":0.0046873004,"about_ca_system_score_codex":0.0015695916,"about_ca_system_score_gemma":0.0017459673,"threshold_uncertainty_score":0.011388302},"labels":[],"label_agreement":null},{"id":"W2118037331","doi":"","title":"Tuning the Parameters of a Memetic Algorithm to Solve Vehicle Routing Problem with Backhauls Using Design of Experiments","year":2007,"lang":"en","type":"article","venue":"","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Vehicle routing problem; Heuristics; Mathematical optimization; Memetic algorithm; Computer science; Metaheuristic; Heuristic; Tabu search; Algorithm; Routing (electronic design automation); Mathematics; Local search (optimization)","score_opus":0.04755783648365442,"score_gpt":0.2944235111409095,"score_spread":0.24686567465725506,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2118037331","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.66479397,0.0005785435,0.3284352,0.00014875879,0.00014544811,0.0012351981,0.00008965106,0.00033793907,0.0042352993],"genre_scores_gemma":[0.88026935,0.00016345007,0.11786737,0.000027823544,0.000008147773,0.0011263943,0.000034307788,0.000014097784,0.00048896356],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986798,0.0007121698,0.000102286,0.000120352364,0.00027558417,0.00010981789],"domain_scores_gemma":[0.99589765,0.0027838452,0.00049084204,0.0002651394,0.00048300833,0.0000796124],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0033245885,0.0009770637,0.0007035838,0.0009142582,0.00042051086,0.00073128246,0.00095717807,0.0009535444,0.0010548905],"category_scores_gemma":[0.006067958,0.00039685448,0.0005798532,0.00037748585,0.00053717295,0.00043876545,0.000445902,0.00062454614,0.00009693776],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013535465,0.0021108866,0.0040718117,0.00089453685,0.0003549828,0.00012069725,0.00026244528,0.8364523,0.06512554,0.0035896502,0.0003133465,0.08535026],"study_design_scores_gemma":[0.00038355633,0.0052543264,0.0027892918,0.000062947,0.00019601025,0.00006991487,0.000087389766,0.88777995,0.09984926,0.0015637297,0.001915792,0.00004782257],"about_ca_topic_score_codex":0.00057446794,"about_ca_topic_score_gemma":0.00038252122,"teacher_disagreement_score":0.0033245885,"about_ca_system_score_codex":0.00061267253,"about_ca_system_score_gemma":0.0007882471,"threshold_uncertainty_score":0.017582297},"labels":[],"label_agreement":null},{"id":"W2118128890","doi":"10.1109/icsssm.2006.320760","title":"Availability Optimization of Series-Parallel Multi-State Systems Using a Tabu Search Meta-heuristic","year":2006,"lang":"en","type":"article","venue":"","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval; Université de Montréal; Queen's University","funders":"","keywords":"Tabu search; Computer science; Redundancy (engineering); Mathematical optimization; Disjoint sets; Meta heuristic; Series (stratigraphy); Heuristic; Incremental heuristic search; Guided Local Search; Best-first search; Beam search; Algorithm; Set (abstract data type); Search algorithm; Mathematics","score_opus":0.0671351408694801,"score_gpt":0.293350252545344,"score_spread":0.22621511167586394,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2118128890","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15209892,0.0006237923,0.8377254,0.00016120219,0.00004030789,0.00007108251,0.000096029464,0.00067497767,0.008508231],"genre_scores_gemma":[0.91314167,0.00017588209,0.08498274,0.000032607848,0.0000141624805,0.00011138507,0.00006988331,0.000059634745,0.0014120531],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997464,0.0001145382,0.000012004708,0.000032890497,0.00006550114,0.000028548033],"domain_scores_gemma":[0.9995529,0.000263152,0.00007454167,0.00003406598,0.00005978081,0.000015493257],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000714512,0.0005785239,0.00072929414,0.0008978007,0.00042972,0.00067554886,0.00063483085,0.0005321606,0.0014573233],"category_scores_gemma":[0.0011848498,0.00045908746,0.0005989344,0.00068026903,0.0004806437,0.00057559944,0.0004117299,0.00041219077,0.00015434514],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014631926,0.000008085196,0.00009418399,0.000013348095,0.000013701579,0.0000123822765,0.000007995742,0.9917653,0.00042199326,0.0010293423,0.000088542416,0.006530446],"study_design_scores_gemma":[0.000006207303,0.000015670272,0.000045996207,0.000002521383,0.0000054580373,0.0000052056425,0.000004067977,0.9986318,0.0002819475,0.00086212595,0.00013724704,0.0000017916988],"about_ca_topic_score_codex":0.0038372795,"about_ca_topic_score_gemma":0.003168371,"teacher_disagreement_score":0.0038372795,"about_ca_system_score_codex":0.0008414981,"about_ca_system_score_gemma":0.00067160674,"threshold_uncertainty_score":0.007629931},"labels":[],"label_agreement":null},{"id":"W2118493070","doi":"10.1287/trsc.1100.0333","title":"Branch and Price for Service Network Design with Asset Management Constraints","year":2010,"lang":"en","type":"article","venue":"Transportation Science","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":102,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; Université du Québec à Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Norges Forskningsråd; Université de Montréal","keywords":"Column generation; Transshipment (information security); Network planning and design; Integer programming; Computer science; Mathematical optimization; Flow network; Service (business); Operations research; Path (computing); Integer (computer science); Branch and price; Shortest path problem; Engineering; Computer network; Economics; Mathematics; Graph","score_opus":0.018662796349693887,"score_gpt":0.2654168289073473,"score_spread":0.2467540325576534,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2118493070","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008364656,0.000649143,0.98081505,0.0003611163,0.000096364,0.00015895725,0.0001088732,0.00015284278,0.00929307],"genre_scores_gemma":[0.23622207,0.0022680496,0.74387634,0.00023067527,0.00023078024,0.0012269353,0.00052673643,0.00029357223,0.015124859],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99916255,0.00044033007,0.000021502083,0.000083374376,0.00018249756,0.00010975651],"domain_scores_gemma":[0.9981933,0.0014228057,0.00009697256,0.00004481275,0.00015630345,0.00008566977],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023310636,0.0016745398,0.001652719,0.0016195317,0.0008014579,0.0017516612,0.0011288659,0.0015812011,0.015736466],"category_scores_gemma":[0.005442774,0.0008959472,0.00092024874,0.0028738885,0.0009610568,0.0019921218,0.0011448719,0.0021210124,0.0013913765],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000098920835,0.0000788204,0.00036900633,0.0001690198,0.00003663462,0.00009174264,0.000064196756,0.8434949,0.0004915308,0.08225295,0.0047012926,0.068151005],"study_design_scores_gemma":[0.000020575573,0.000028253697,0.000028980647,0.00001134694,0.00000763179,0.000013614249,0.000007476902,0.97498655,0.00013361199,0.022640714,0.00211759,0.0000036729405],"about_ca_topic_score_codex":0.0047636363,"about_ca_topic_score_gemma":0.005437328,"teacher_disagreement_score":0.015736466,"about_ca_system_score_codex":0.0017843082,"about_ca_system_score_gemma":0.002525994,"threshold_uncertainty_score":0.052643716},"labels":[],"label_agreement":null},{"id":"W2118555996","doi":"10.1002/net.21496","title":"The orienteering problem with variable profits","year":2013,"lang":"en","type":"article","venue":"Networks","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":79,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"","keywords":"Orienteering; Vertex (graph theory); Profit (economics); Operations research; Mathematical optimization; Mathematics; Graph; Combinatorics; Computer science; Travel time; Time limit; Mathematical economics; Economics; Microeconomics; Transport engineering; Engineering","score_opus":0.005110104516139893,"score_gpt":0.19108211177042117,"score_spread":0.18597200725428129,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2118555996","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.26237115,0.0011853096,0.7002815,0.0012322601,0.00012100146,0.00014757867,0.00042286213,0.00020952427,0.0340287],"genre_scores_gemma":[0.93149406,0.00071134313,0.058321204,0.00013732178,0.00007438311,0.000092931994,0.00031050594,0.00007971517,0.008778573],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988176,0.00049487886,0.000038531794,0.0002318713,0.00017237556,0.000244559],"domain_scores_gemma":[0.9990175,0.0005598157,0.0001261759,0.000090851114,0.00008893819,0.00011674617],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012094796,0.0009835426,0.0010979061,0.0005096508,0.00056584197,0.0018312093,0.0012410701,0.0012773502,0.003430166],"category_scores_gemma":[0.0029739381,0.00040198484,0.00073511177,0.0011159499,0.0013700619,0.0025312088,0.0015163053,0.0011115305,0.0002520293],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015960363,0.00010167293,0.0012227606,0.00013121215,0.00006715993,0.00055701117,0.00015452389,0.7217768,0.0014025946,0.23000626,0.0043150377,0.04010535],"study_design_scores_gemma":[0.000042177515,0.00009018324,0.0004179768,0.000022094431,0.0000288679,0.00021903591,0.00016449319,0.8126063,0.0008483931,0.17992961,0.0056057503,0.000025047904],"about_ca_topic_score_codex":0.0053612487,"about_ca_topic_score_gemma":0.0029369364,"teacher_disagreement_score":0.0053612487,"about_ca_system_score_codex":0.0015726209,"about_ca_system_score_gemma":0.0007967656,"threshold_uncertainty_score":0.011475027},"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,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_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","score_opus":0.01091285829767171,"score_gpt":0.23968207380273326,"score_spread":0.22876921550506155,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2119097848","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014884392,0.0008208994,0.978522,0.000441834,0.000072226576,0.00016087896,0.00010709615,0.00020532626,0.0047853654],"genre_scores_gemma":[0.1844822,0.0018714991,0.8043836,0.00018817495,0.00015480164,0.0005467353,0.0005857067,0.00029517076,0.0074922247],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988907,0.00047928732,0.000042202046,0.0001386231,0.00029334647,0.00015586513],"domain_scores_gemma":[0.99732816,0.002217482,0.00013807113,0.000083191546,0.00014788691,0.00008519931],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027616262,0.0015109486,0.0014540926,0.0010724815,0.00096325454,0.002171499,0.0012219307,0.001568153,0.008339729],"category_scores_gemma":[0.0057711476,0.0010079963,0.0009848014,0.0021212343,0.0011836591,0.0032664263,0.0011768271,0.003376141,0.0009445466],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003486796,0.00016547905,0.0005905412,0.0002982766,0.000071961585,0.0001956714,0.00014577871,0.6558555,0.0021240646,0.18400696,0.006503978,0.14969315],"study_design_scores_gemma":[0.000045611712,0.00007508999,0.00011951483,0.00002154668,0.000020381027,0.000047158872,0.000024353845,0.93026525,0.0007401511,0.06610714,0.002522007,0.000011861172],"about_ca_topic_score_codex":0.005807045,"about_ca_topic_score_gemma":0.0056617414,"teacher_disagreement_score":0.008339729,"about_ca_system_score_codex":0.0019867094,"about_ca_system_score_gemma":0.0023685102,"threshold_uncertainty_score":0.027899206},"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,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_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","score_opus":0.06420246300526486,"score_gpt":0.3580434042946693,"score_spread":0.2938409412894044,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2119601963","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017545557,0.00029588747,0.97751063,0.000117430754,0.00004588867,0.000059559326,0.000035907757,0.0003245699,0.0040646233],"genre_scores_gemma":[0.2815878,0.00034466223,0.7133792,0.00014367606,0.000053801195,0.00032233464,0.00018077594,0.000087746695,0.0039000676],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99971205,0.000081620405,0.000009580685,0.000045935874,0.000115661875,0.0000351042],"domain_scores_gemma":[0.9998093,0.00009963055,0.000022041619,0.000021375148,0.000034399985,0.000013211427],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005106041,0.00067302916,0.00068555964,0.000745024,0.0003546001,0.0006270249,0.0013903639,0.0011693259,0.0015978668],"category_scores_gemma":[0.0008821351,0.0002939891,0.0005936496,0.0009120122,0.00044065205,0.0006613195,0.00071956415,0.0007064245,0.00030442458],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026619458,0.000048739617,0.000356686,0.000038924227,0.000044465964,0.000060687176,0.00003701315,0.8929232,0.0017279919,0.016466228,0.0010799522,0.087189406],"study_design_scores_gemma":[0.000017493943,0.000029908653,0.000066825494,0.0000048988986,0.000008230686,0.000025461915,0.0000065615745,0.99490756,0.000289667,0.0032015133,0.0014378768,0.000003859448],"about_ca_topic_score_codex":0.003076952,"about_ca_topic_score_gemma":0.0035507847,"teacher_disagreement_score":0.003076952,"about_ca_system_score_codex":0.00053995365,"about_ca_system_score_gemma":0.0009768668,"threshold_uncertainty_score":0.006118059},"labels":[],"label_agreement":null},{"id":"W2119673940","doi":"10.1007/s10107-008-0234-9","title":"The traveling salesman problem with pickup and delivery: polyhedral results and a branch-and-cut algorithm","year":2008,"lang":"en","type":"article","venue":"Mathematical Programming","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":84,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"","keywords":"Travelling salesman problem; Pickup; Vertex (graph theory); Mathematics; Branch and cut; Bottleneck traveling salesman problem; Algorithm; Dimension (graph theory); Graph; Polytope; Combinatorics; Integer programming; Mathematical optimization; Computer science","score_opus":0.016277189734309045,"score_gpt":0.22943170088497788,"score_spread":0.21315451115066883,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2119673940","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0036174068,0.0010314959,0.9851953,0.0005012313,0.00012023401,0.000098871715,0.00012634916,0.00008429473,0.009224836],"genre_scores_gemma":[0.120365106,0.004571186,0.85632056,0.0003572946,0.00041938055,0.00064324745,0.0005683586,0.0003642094,0.016390597],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981949,0.0006413171,0.000061124185,0.00027651186,0.00062940526,0.00019678098],"domain_scores_gemma":[0.9963606,0.0027869327,0.00024073265,0.0001328533,0.00033017254,0.0001486654],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003522883,0.0029719213,0.004061852,0.0029936186,0.0020089545,0.004467258,0.0043294257,0.004368691,0.009658637],"category_scores_gemma":[0.009763055,0.0028136778,0.003014998,0.0059616086,0.003232838,0.006441248,0.002433333,0.0057731834,0.0017555518],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011352236,0.00019304137,0.00020624755,0.00023216364,0.000058503883,0.00005636349,0.00008366455,0.79524404,0.0004667885,0.15515432,0.0061204256,0.04207089],"study_design_scores_gemma":[0.000023896802,0.000027993026,0.000050078455,0.000038879407,0.00002293524,0.00002552781,0.00002017041,0.91909385,0.00021961662,0.0786225,0.0018416705,0.0000129812815],"about_ca_topic_score_codex":0.011458303,"about_ca_topic_score_gemma":0.0071257693,"teacher_disagreement_score":0.011458303,"about_ca_system_score_codex":0.0032782245,"about_ca_system_score_gemma":0.0034190202,"threshold_uncertainty_score":0.03231138},"labels":[],"label_agreement":null},{"id":"W2119760912","doi":"10.1109/iccie.2009.5223922","title":"A rolling horizon solution approach for the airline crew pairing problem","year":2009,"lang":"en","type":"article","venue":"","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Group for Research in Decision Analysis","funders":"","keywords":"Crew scheduling; Crew; Column generation; Computer science; Pairing; Schedule; Scheduling (production processes); Mathematical optimization; Heuristic; Job shop scheduling; Set (abstract data type); Process (computing); Time horizon; Operations research; Mathematics; Engineering; Artificial intelligence; Aeronautics","score_opus":0.025674868270813198,"score_gpt":0.2587993696184967,"score_spread":0.2331245013476835,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2119760912","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004495072,0.00017711062,0.99198115,0.00010002055,0.000031231422,0.000053494274,0.000058654838,0.00016948352,0.0029338482],"genre_scores_gemma":[0.2935222,0.00052781793,0.70068413,0.0001216126,0.000072147006,0.00030923766,0.00031804835,0.00009916989,0.0043456266],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99959105,0.00017448909,0.000014728548,0.0000679783,0.000098118006,0.00005364966],"domain_scores_gemma":[0.9997075,0.00016057608,0.00003017552,0.00001724675,0.00006149252,0.00002293965],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007655093,0.00073219487,0.0007567068,0.0004944192,0.0004076351,0.00056801445,0.00076594885,0.0006931158,0.004861914],"category_scores_gemma":[0.0011571633,0.00035686293,0.0005938791,0.0007014995,0.0002762838,0.00055237784,0.00045986054,0.0009862666,0.00042113068],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004305636,0.00004325246,0.00012730598,0.00008106326,0.000022539112,0.0000539972,0.000039748684,0.929038,0.0014509975,0.009371025,0.0020162016,0.05771295],"study_design_scores_gemma":[0.000011600537,0.00004116852,0.00005729332,0.0000046871473,0.000006624117,0.000013712393,0.000009922435,0.9952277,0.00033523006,0.0029760883,0.0013109175,0.000005050669],"about_ca_topic_score_codex":0.007981008,"about_ca_topic_score_gemma":0.0068423906,"teacher_disagreement_score":0.007981008,"about_ca_system_score_codex":0.00058747106,"about_ca_system_score_gemma":0.0012458515,"threshold_uncertainty_score":0.016264737},"labels":[],"label_agreement":null},{"id":"W2119849184","doi":"10.1287/inte.30.2.54.11677","title":"TransAlta Redesigns Its Service-Delivery Network","year":2000,"lang":"en","type":"article","venue":"INFORMS Journal on Applied Analytics","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"TransAlta (Canada); University of Alberta","funders":"","keywords":"Staffing; Service (business); Heuristics; Service delivery framework; Operations management; Operations research; Business; Engineering; Transport engineering; Computer science; Marketing","score_opus":0.022148449344022318,"score_gpt":0.24065371943035052,"score_spread":0.2185052700863282,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2119849184","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.29337755,0.0011941727,0.4647179,0.015254365,0.002282738,0.0029917005,0.0055993637,0.032690905,0.18189126],"genre_scores_gemma":[0.5754766,0.0007993102,0.33471385,0.001769585,0.0001923285,0.0006087284,0.006332624,0.0011598805,0.07894701],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982016,0.00041751127,0.00006286793,0.000262302,0.00069028256,0.00036542647],"domain_scores_gemma":[0.99637765,0.0004516174,0.00015453594,0.0004418871,0.0021281792,0.00044620995],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019901472,0.00065794267,0.00038212794,0.0011067985,0.0013649844,0.0023023828,0.0013285999,0.00087194785,0.007502102],"category_scores_gemma":[0.005363469,0.00029666675,0.0003848844,0.0015888588,0.0004304824,0.0019693288,0.0010509903,0.0015508854,0.0030004936],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00073710416,0.0007720897,0.015687358,0.00017047998,0.00006188158,0.00029647027,0.00054926856,0.11127807,0.013267905,0.0339164,0.17303638,0.65022653],"study_design_scores_gemma":[0.00024273373,0.00045177262,0.0084901,0.000072128445,0.00008023528,0.00034141404,0.0007893619,0.5580032,0.011101456,0.005716464,0.41458932,0.00012177881],"about_ca_topic_score_codex":0.37727866,"about_ca_topic_score_gemma":0.3865153,"teacher_disagreement_score":0.37727866,"about_ca_system_score_codex":0.007667551,"about_ca_system_score_gemma":0.013796675,"threshold_uncertainty_score":0.7501653},"labels":[],"label_agreement":null},{"id":"W2120018445","doi":"10.1007/s10479-005-3455-9","title":"On Compact Formulations for Integer Programs Solved by Column Generation","year":2005,"lang":"en","type":"article","venue":"Annals of Operations Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":46,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Group for Research in Decision Analysis; Kronos (Canada); HEC Montréal; St Mary's Hospital Centre","funders":"","keywords":"Column generation; Theory of computation; Diagonal; Mathematical optimization; Mathematics; Integer programming; Integer (computer science); Compatibility (geochemistry); Branching (polymer chemistry); Computer science; Algorithm","score_opus":0.3155450072934694,"score_gpt":0.48209199191541985,"score_spread":0.16654698462195044,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2120018445","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0054100044,0.0013223799,0.9823942,0.00058352394,0.00017277632,0.00014106435,0.0002642032,0.00022721721,0.009484708],"genre_scores_gemma":[0.16410005,0.0034961936,0.81794256,0.0008355984,0.0005959767,0.0009649282,0.0011905719,0.0007310682,0.01014306],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99828005,0.00090426015,0.00007288903,0.00014261484,0.00044083153,0.00015944634],"domain_scores_gemma":[0.98845285,0.009803331,0.00043868407,0.00058264483,0.0005920742,0.00013034666],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0037312384,0.0030176095,0.002002854,0.0015425766,0.000784762,0.0026601534,0.0017582816,0.0018124614,0.011292486],"category_scores_gemma":[0.015021181,0.0014569486,0.0017406647,0.0036667313,0.0017533329,0.004237378,0.0022086715,0.005287422,0.0017788945],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002152929,0.0002614533,0.0002868942,0.0003753736,0.0000577495,0.0001255366,0.00018080375,0.6713701,0.0013409478,0.20668468,0.012221958,0.106879205],"study_design_scores_gemma":[0.000043424072,0.00005004495,0.00006710275,0.00008043557,0.000017938362,0.00002674333,0.000035610043,0.86583483,0.00038116294,0.12995104,0.0034985037,0.000013111089],"about_ca_topic_score_codex":0.003640365,"about_ca_topic_score_gemma":0.0057678623,"teacher_disagreement_score":0.011292486,"about_ca_system_score_codex":0.0015175742,"about_ca_system_score_gemma":0.0012191795,"threshold_uncertainty_score":0.037777126},"labels":[],"label_agreement":null},{"id":"W2120035164","doi":"10.1504/ijise.2012.048861","title":"A heuristic method for solving reverse logistics vehicle routing problems with time windows","year":2012,"lang":"en","type":"article","venue":"International Journal of Industrial and Systems Engineering","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Vehicle routing problem; Reverse logistics; Heuristic; Computer science; City logistics; Routing (electronic design automation); Transport engineering; Business; Engineering; Supply chain; Embedded system; Artificial intelligence; Marketing","score_opus":0.04019467145360096,"score_gpt":0.2700177521787467,"score_spread":0.22982308072514573,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2120035164","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005352585,0.0004652884,0.9915643,0.000053364256,0.00007732258,0.00009727926,0.000033035074,0.00019749306,0.0021593096],"genre_scores_gemma":[0.121916935,0.00079041417,0.87375945,0.000088001114,0.00007463268,0.00049736095,0.0001405948,0.00008388745,0.0026486532],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99959236,0.00015152537,0.000020805857,0.000057497957,0.00012489171,0.00005284946],"domain_scores_gemma":[0.99947804,0.0003315874,0.00006912598,0.000029372826,0.00006546003,0.000026476002],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007519277,0.0010860163,0.0012347137,0.0010156536,0.00050025526,0.0006507294,0.0012347032,0.0010585927,0.0025811754],"category_scores_gemma":[0.0011517243,0.00057765795,0.0012484163,0.0011055466,0.00047491337,0.0007911707,0.0006126153,0.0012012847,0.000518535],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009529501,0.00012469439,0.0002204189,0.00021773817,0.000079629855,0.00011542545,0.00006273443,0.868123,0.004959088,0.014268136,0.0015073174,0.11022651],"study_design_scores_gemma":[0.00003571602,0.0000779589,0.000062664505,0.000016307042,0.000019843521,0.000051451574,0.000014644511,0.9932319,0.0010364063,0.003378845,0.0020620807,0.000012180655],"about_ca_topic_score_codex":0.002991439,"about_ca_topic_score_gemma":0.002675934,"teacher_disagreement_score":0.002991439,"about_ca_system_score_codex":0.0005410108,"about_ca_system_score_gemma":0.0011341453,"threshold_uncertainty_score":0.008634925},"labels":[],"label_agreement":null},{"id":"W2120159299","doi":"10.1023/a:1020325926188","title":"Cooperative Parallel Tabu Search for Capacitated Network Design","year":2002,"lang":"en","type":"article","venue":"Journal of Heuristics","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":117,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; Université du Québec à Montréal","funders":"","keywords":"Tabu search; Computer science; Implementation; Mathematical optimization; Class (philosophy); Algorithm; Mathematics; Artificial intelligence","score_opus":0.07304272904768266,"score_gpt":0.2879784058940713,"score_spread":0.21493567684638865,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2120159299","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.059508532,0.00045338346,0.92920136,0.00020777213,0.00008906857,0.00020406557,0.00010006058,0.0008795768,0.009356115],"genre_scores_gemma":[0.5175674,0.00019600822,0.4766559,0.00014805132,0.000058490095,0.0007262537,0.00020704369,0.00021063314,0.0042301835],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991721,0.00045407808,0.000025254736,0.00009090781,0.00014775312,0.00011001745],"domain_scores_gemma":[0.9977912,0.0014573906,0.00016264655,0.00022315646,0.00028601574,0.000079603575],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021907184,0.0010664292,0.0013086486,0.0015640863,0.0012031811,0.0008882809,0.0022306098,0.0013024204,0.005226444],"category_scores_gemma":[0.004455176,0.0010661996,0.0008465346,0.002011971,0.0009557947,0.001341139,0.0012123447,0.0010784499,0.00069964415],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011662403,0.00008449503,0.00024341865,0.000044928922,0.000030485786,0.000021661617,0.000046894293,0.95552224,0.0005885883,0.0034664473,0.0010547444,0.03877952],"study_design_scores_gemma":[0.000032271153,0.000031488347,0.00003360979,0.000002596055,0.0000071903332,0.0000045697866,0.000009215812,0.99699044,0.0002128284,0.0024381834,0.00023527422,0.0000022546205],"about_ca_topic_score_codex":0.008684323,"about_ca_topic_score_gemma":0.009071293,"teacher_disagreement_score":0.008684323,"about_ca_system_score_codex":0.0009958096,"about_ca_system_score_gemma":0.0017061983,"threshold_uncertainty_score":0.017484188},"labels":[],"label_agreement":null},{"id":"W2120324231","doi":"10.1287/trsc.1100.0317","title":"A Branch-and-Price Method for a Liquefied Natural Gas Inventory Routing Problem","year":2010,"lang":"en","type":"article","venue":"Transportation Science","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":137,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal; Polytechnique Montréal; Group for Research in Decision Analysis","funders":"","keywords":"Liquefied natural gas; Column generation; Operations research; Variable (mathematics); Routing (electronic design automation); Branch and price; Inventory theory; Natural gas; Computer science; Engineering; Integer programming; Waste management; Inventory control; Mathematical optimization; Mathematics; Computer network","score_opus":0.014639070204195309,"score_gpt":0.30146552957011996,"score_spread":0.28682645936592466,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2120324231","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013580336,0.00041246595,0.97894645,0.00037474558,0.00007399452,0.00019095847,0.00016611704,0.00029933677,0.0059556323],"genre_scores_gemma":[0.18492311,0.0006612069,0.80473036,0.00015026827,0.00010412215,0.00054883066,0.00042820984,0.00020075985,0.00825306],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99952304,0.00021444289,0.000016695994,0.00007588886,0.000102042715,0.00006790072],"domain_scores_gemma":[0.99914134,0.00063502975,0.00004715294,0.000025966452,0.000097297016,0.00005316048],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014079082,0.0010741164,0.0013341481,0.00088297355,0.0006083081,0.001260125,0.0013479571,0.001582043,0.010199721],"category_scores_gemma":[0.0020545784,0.0006567943,0.0007811949,0.0014305012,0.0005307624,0.0011983481,0.0007163501,0.0014793384,0.0009374396],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000087795124,0.00008366067,0.00025677233,0.00013375812,0.000033793425,0.00011868013,0.00005511296,0.914592,0.00085868436,0.0155706685,0.0031409236,0.06506807],"study_design_scores_gemma":[0.000028082683,0.00002536108,0.000028919903,0.0000063572356,0.0000059376216,0.000012741923,0.000008846002,0.9941168,0.00014960964,0.0047280625,0.0008857512,0.0000036296783],"about_ca_topic_score_codex":0.0064431923,"about_ca_topic_score_gemma":0.0068980544,"teacher_disagreement_score":0.010199721,"about_ca_system_score_codex":0.0011709717,"about_ca_system_score_gemma":0.0019415905,"threshold_uncertainty_score":0.034121454},"labels":[],"label_agreement":null},{"id":"W2120930954","doi":"10.1287/ijoc.2013.0549","title":"An Exact Algorithm Based on Cut-and-Column Generation for the Capacitated Location-Routing Problem","year":2013,"lang":"en","type":"article","venue":"INFORMS journal on computing","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":115,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; Computer Research Institute of Montréal; HEC Montréal; Université du Québec à Montréal","funders":"","keywords":"Column generation; Mathematical optimization; Column (typography); Computer science; Routing (electronic design automation); Set (abstract data type); Upper and lower bounds; Path (computing); Shortest path problem; Enumeration; Vehicle routing problem; Mathematics; Algorithm; Theoretical computer science; Combinatorics","score_opus":0.023142711761351704,"score_gpt":0.26970206619011905,"score_spread":0.24655935442876734,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2120930954","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0048725926,0.00013912657,0.9902126,0.00012780262,0.000052825228,0.00014181866,0.00017153089,0.0011539413,0.003127825],"genre_scores_gemma":[0.06460728,0.000153267,0.93240756,0.00011442542,0.000029795563,0.00023866328,0.0007871323,0.00022090053,0.0014408962],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99920243,0.00019664288,0.00003586745,0.00016552581,0.00024737098,0.00015218051],"domain_scores_gemma":[0.99878806,0.0007014324,0.000082656305,0.00022380504,0.0001642778,0.00003976952],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007503183,0.0015498542,0.0010894202,0.0011546,0.0008848226,0.0012144077,0.0015842099,0.0011733706,0.010682932],"category_scores_gemma":[0.0025993106,0.0006992479,0.0009669454,0.0017913133,0.0006598061,0.0018238202,0.0010856056,0.0017792841,0.0018826105],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025158632,0.0003268795,0.000648673,0.0003311379,0.00007631815,0.00016990313,0.00011647509,0.51915824,0.0059378506,0.040629618,0.015388116,0.41696522],"study_design_scores_gemma":[0.000086137,0.00006848846,0.00013477851,0.000018803781,0.000021920057,0.00014730144,0.000036236215,0.969543,0.002273621,0.023674088,0.003975893,0.000019802856],"about_ca_topic_score_codex":0.0053256466,"about_ca_topic_score_gemma":0.0065474887,"teacher_disagreement_score":0.010682932,"about_ca_system_score_codex":0.0013710088,"about_ca_system_score_gemma":0.002287284,"threshold_uncertainty_score":0.03573799},"labels":[],"label_agreement":null},{"id":"W2121266360","doi":"10.1287/ijoc.1100.0439","title":"A Hybrid Heuristic for an Inventory Routing Problem","year":2011,"lang":"en","type":"article","venue":"INFORMS journal on computing","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":199,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Group for Research in Decision Analysis; HEC Montréal","funders":"","keywords":"Stockout; Mathematical optimization; Heuristic; Benchmark (surveying); Computer science; Integer programming; Service level; Inventory control; Operations research; Inventory theory; Routing (electronic design automation); Set (abstract data type); Time horizon; Vehicle routing problem; Tabu search; Mathematics","score_opus":0.05024524965669892,"score_gpt":0.28063752694019994,"score_spread":0.23039227728350103,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2121266360","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.079882436,0.0008672458,0.90089613,0.00036312582,0.000110768255,0.0003500613,0.00031459265,0.0012500534,0.015965631],"genre_scores_gemma":[0.47933617,0.00045580624,0.5138586,0.00023928327,0.00005284755,0.0006938986,0.0003632022,0.0001507531,0.004849407],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99941266,0.0002849832,0.000018851233,0.00006886341,0.00012052479,0.00009414271],"domain_scores_gemma":[0.99917525,0.00059147587,0.000062089486,0.00005367134,0.000070637565,0.000046906196],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00094399555,0.00092373224,0.0009478773,0.001147782,0.0005331877,0.0013731577,0.0016229902,0.0014824993,0.0043793623],"category_scores_gemma":[0.0016624588,0.0006228449,0.0006211874,0.0013220343,0.0006709321,0.0009927517,0.00081584003,0.00071112864,0.0005448065],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010089806,0.00011959771,0.00027785244,0.00009433208,0.00004112332,0.00010235003,0.00006184674,0.95047843,0.0010710455,0.011299202,0.001487832,0.03486556],"study_design_scores_gemma":[0.00004719007,0.00006353238,0.000050135506,0.000012116524,0.000011998225,0.00003236998,0.0000241512,0.9952028,0.0003134052,0.0030383738,0.0011966795,0.0000071655604],"about_ca_topic_score_codex":0.003859259,"about_ca_topic_score_gemma":0.0045954753,"teacher_disagreement_score":0.0043793623,"about_ca_system_score_codex":0.001161686,"about_ca_system_score_gemma":0.0012864941,"threshold_uncertainty_score":0.014650404},"labels":[],"label_agreement":null},{"id":"W2121490839","doi":"10.1016/j.cor.2005.07.002","title":"Managing large fixed costs in vehicle routing and crew scheduling problems solved by column generation","year":2005,"lang":"en","type":"article","venue":"Computers & Operations Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Group for Research in Decision Analysis; Polytechnique Montréal","funders":"","keywords":"Column generation; Mathematical optimization; Fixed cost; Computer science; Minification; Vehicle routing problem; Scheduling (production processes); Shortest path problem; Lexicographical order; Routing (electronic design automation); Mathematics","score_opus":0.04871979780575466,"score_gpt":0.33819297534956044,"score_spread":0.2894731775438058,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2121490839","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.24323972,0.0011873975,0.7424142,0.00107348,0.00028079643,0.00030265035,0.00047577004,0.00055596797,0.010470136],"genre_scores_gemma":[0.8633485,0.0004928595,0.1297835,0.00022374293,0.000118672484,0.0002198305,0.00033913204,0.00018451712,0.0052892347],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995034,0.00022369272,0.000013112629,0.000057183013,0.00007497465,0.00012768598],"domain_scores_gemma":[0.997359,0.0020421017,0.00018237933,0.00010552649,0.0001766296,0.00013426051],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013954391,0.0010394908,0.0012994522,0.0007477333,0.0007237032,0.00157926,0.0014445886,0.0015009143,0.0041354485],"category_scores_gemma":[0.003722575,0.001012683,0.0006073256,0.0016565054,0.0008643492,0.0016478105,0.0008325886,0.0013507593,0.00028712934],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000967738,0.00007646544,0.00027257012,0.000060842165,0.000023427952,0.00004293611,0.000020860873,0.9804248,0.0005345471,0.004114615,0.001736884,0.012595111],"study_design_scores_gemma":[0.000014320529,0.000027095493,0.00006721021,0.0000028752386,0.000008057864,0.000005936942,0.000010129057,0.9973578,0.00020394426,0.0021336079,0.00016589377,0.0000032381759],"about_ca_topic_score_codex":0.017062591,"about_ca_topic_score_gemma":0.015151832,"teacher_disagreement_score":0.017062591,"about_ca_system_score_codex":0.0014008319,"about_ca_system_score_gemma":0.0013914164,"threshold_uncertainty_score":0.033926606},"labels":[],"label_agreement":null},{"id":"W2122102418","doi":"10.1109/case.2011.6042475","title":"Maximizing the throughput of multimodal logistic platforms by simulation-optimization: The Duferco case study","year":2011,"lang":"en","type":"article","venue":"","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Ministero dell'Università e della Ricerca; Canada Research Chairs","keywords":"Schedule; Computer science; Scheduling (production processes); Factory (object-oriented programming); Throughput; Mathematical optimization; Real-time computing; Mathematics","score_opus":0.08324695455072333,"score_gpt":0.3044429500353009,"score_spread":0.22119599548457758,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2122102418","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9275138,0.0002619299,0.060975328,0.00031176786,0.000025674017,0.00015073118,0.00028291653,0.00015653696,0.010321381],"genre_scores_gemma":[0.99209917,0.00007492956,0.006419875,0.000008559358,0.000003730701,0.000053265787,0.00005926625,0.000009521174,0.0012716317],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99946696,0.00025128436,0.000013417286,0.00006889645,0.00005730627,0.0001420782],"domain_scores_gemma":[0.99883336,0.00077548216,0.00013213271,0.00005546708,0.00010950114,0.0000940449],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00084995,0.0013252591,0.00088874483,0.0009506688,0.0007511993,0.0013665823,0.0008564799,0.0017778947,0.0016789008],"category_scores_gemma":[0.0018195854,0.0004428639,0.00078520895,0.0010302637,0.0007767995,0.0006700267,0.00075007515,0.0005985501,0.0001306879],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004596037,0.000018457113,0.0004697425,0.00000833785,0.0000067327264,0.00008354355,0.000009193072,0.99781466,0.0002560192,0.00044872562,0.000036987894,0.00080160785],"study_design_scores_gemma":[0.0000140116645,0.00007201862,0.0002972277,0.000003894645,0.000007972353,0.000025581281,0.000038734594,0.9981647,0.0007095317,0.00042694286,0.00023186296,0.000007558539],"about_ca_topic_score_codex":0.025849378,"about_ca_topic_score_gemma":0.013033642,"teacher_disagreement_score":0.025849378,"about_ca_system_score_codex":0.0025611303,"about_ca_system_score_gemma":0.0017123839,"threshold_uncertainty_score":0.0513978},"labels":[],"label_agreement":null},{"id":"W2122999407","doi":"10.1287/mnsc.1050.0392","title":"Efficient Production-Distribution System Design","year":2005,"lang":"en","type":"article","venue":"Management Science","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":56,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; University of Waterloo","funders":"","keywords":"Column generation; Relaxation (psychology); Mathematical optimization; Context (archaeology); Cutting-plane method; Upper and lower bounds; Branch and bound; Point (geometry); Distribution (mathematics); Production (economics); Plane (geometry); Hierarchy; Interior point method; Mathematics; Linear programming relaxation; Computer science; Supply chain; Applied mathematics; Geometry; Mathematical analysis; Linear programming; Integer programming","score_opus":0.01627386220479309,"score_gpt":0.24489262091747838,"score_spread":0.22861875871268528,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2122999407","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0048851813,0.00023593316,0.9875103,0.00009618987,0.000023056657,0.000072453535,0.000063524596,0.00020580026,0.0069074463],"genre_scores_gemma":[0.5822035,0.00087260624,0.40407166,0.00007971897,0.000057238944,0.0003297751,0.0003304121,0.00013628446,0.011918731],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99935657,0.00020328858,0.000020128427,0.00009244704,0.0002593654,0.00006815935],"domain_scores_gemma":[0.99978226,0.00008270891,0.000027071523,0.000028704997,0.000063832755,0.000015420108],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007054214,0.0006838226,0.0007618186,0.00048042863,0.00043032772,0.0011431783,0.0007121399,0.0005992936,0.0071597714],"category_scores_gemma":[0.0009655258,0.0003465287,0.000350529,0.000838662,0.00036602002,0.0007087664,0.0007133397,0.0006558732,0.0011194411],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000042755215,0.00003143748,0.00023584701,0.00020269543,0.000019672247,0.000061090366,0.00003771152,0.8632025,0.0048400355,0.030680181,0.0021012325,0.09854497],"study_design_scores_gemma":[0.000018705668,0.000050528815,0.00014562442,0.000014976698,0.000014866229,0.0000351959,0.000016724598,0.9711157,0.0023303637,0.015567047,0.010684467,0.0000058837327],"about_ca_topic_score_codex":0.0015372084,"about_ca_topic_score_gemma":0.0023585912,"teacher_disagreement_score":0.0071597714,"about_ca_system_score_codex":0.00088365015,"about_ca_system_score_gemma":0.0017754339,"threshold_uncertainty_score":0.023951828},"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,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_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","score_opus":0.026683949259711164,"score_gpt":0.2532070197623499,"score_spread":0.22652307050263873,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2123789618","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13744342,0.004962476,0.72964835,0.0030321658,0.0004113352,0.0011532381,0.0006182728,0.009091,0.11363978],"genre_scores_gemma":[0.5388985,0.0035244501,0.4197343,0.00035962433,0.00007880529,0.0007100226,0.0011201693,0.00060190115,0.034972247],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99833775,0.00052218186,0.000142273,0.00025700446,0.00056761067,0.00017330358],"domain_scores_gemma":[0.99730104,0.00078199164,0.00020505169,0.0004402245,0.0009989289,0.0002728481],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018077681,0.0010761305,0.0005246671,0.0015899476,0.0014439346,0.005611002,0.0012527854,0.0011934084,0.006151654],"category_scores_gemma":[0.0054548206,0.00063740474,0.000981091,0.0010014294,0.0013460468,0.003761273,0.002653278,0.0015484949,0.0017506458],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002991007,0.0005544462,0.02059121,0.002693835,0.00023101803,0.0021456734,0.011231574,0.041344263,0.035176806,0.1522515,0.019819608,0.71366096],"study_design_scores_gemma":[0.00011324271,0.000619335,0.025194323,0.0017059781,0.00051089417,0.0017802095,0.011384309,0.32889172,0.025398523,0.07721126,0.52690697,0.00028321872],"about_ca_topic_score_codex":0.02050495,"about_ca_topic_score_gemma":0.020104656,"teacher_disagreement_score":0.02050495,"about_ca_system_score_codex":0.0019014076,"about_ca_system_score_gemma":0.0045333225,"threshold_uncertainty_score":0.040771186},"labels":[],"label_agreement":null},{"id":"W2124521189","doi":"10.1287/trsc.1060.0166","title":"Solving a Dynamic and Stochastic Vehicle Routing Problem with a Sample Scenario Hedging Heuristic","year":2006,"lang":"en","type":"article","venue":"Transportation Science","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":198,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"Norges Forskningsråd","keywords":"Vehicle routing problem; Heuristic; Mathematical optimization; Stochastic programming; Routing (electronic design automation); Sample (material); Dynamic programming; Problem statement; Computer science; Statement (logic); Operations research; Mathematics; Engineering; Management science","score_opus":0.007078761078080346,"score_gpt":0.23236681254588554,"score_spread":0.2252880514678052,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2124521189","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09429652,0.00030018485,0.90057707,0.00035001326,0.000037220503,0.00015643451,0.00012944064,0.00018768721,0.0039653303],"genre_scores_gemma":[0.7317626,0.00018911886,0.26520115,0.00010510622,0.000038134967,0.00031588555,0.00021380286,0.0000525949,0.0021215838],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99942386,0.00030211196,0.00002342157,0.00009917879,0.0000688387,0.00008254391],"domain_scores_gemma":[0.9981592,0.0014273473,0.00013606068,0.000074662756,0.00011053925,0.00009218142],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018790691,0.0008365083,0.0013632092,0.0007185582,0.00034937463,0.00094974943,0.0013132015,0.0016509041,0.0025352754],"category_scores_gemma":[0.0028556488,0.0008664763,0.0007306865,0.0009938793,0.00081127585,0.0011201756,0.00077767164,0.0010252559,0.00016131092],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000025626197,0.000021874435,0.00011653316,0.000014175242,0.000013927316,0.000020968098,0.00001016225,0.9932066,0.000106400636,0.0026400143,0.00015253271,0.0036712037],"study_design_scores_gemma":[0.000011609759,0.000016662963,0.000033603275,0.0000019427516,0.000003924466,0.000004306407,0.000006428269,0.9978472,0.00006182734,0.0019238839,0.000086250446,0.000002355687],"about_ca_topic_score_codex":0.005138972,"about_ca_topic_score_gemma":0.00437988,"teacher_disagreement_score":0.005138972,"about_ca_system_score_codex":0.0010217831,"about_ca_system_score_gemma":0.0015712455,"threshold_uncertainty_score":0.010218084},"labels":[],"label_agreement":null},{"id":"W2125126715","doi":"10.1287/ijoc.1110.0489","title":"A Branch-and-Cut Algorithm for the Double Traveling Salesman Problem with Multiple Stacks","year":2011,"lang":"en","type":"article","venue":"INFORMS journal on computing","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":44,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"Ministero dell'Università e della Ricerca","keywords":"Travelling salesman problem; Bottleneck traveling salesman problem; 2-opt; Traveling purchaser problem; Pickup; Branch and cut; Mathematical optimization; Algorithm; Computer science; Path (computing); Mathematics; Branch and bound; Shortest path problem; Nearest neighbour algorithm; Combinatorial optimization; Hamiltonian path; Stack (abstract data type); Integer programming; Theoretical computer science; Graph; Artificial intelligence","score_opus":0.03182662460064117,"score_gpt":0.25576659836804566,"score_spread":0.2239399737674045,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2125126715","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014791531,0.00044224484,0.976014,0.0003569026,0.00008548023,0.0003519455,0.00032966695,0.0012880962,0.006340229],"genre_scores_gemma":[0.06487407,0.0002665427,0.92999417,0.00009518611,0.000044993103,0.00040765258,0.000887387,0.00027139802,0.003158619],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992612,0.00016989217,0.00004216499,0.00019568163,0.0001756275,0.00015546591],"domain_scores_gemma":[0.99896,0.0006558377,0.00008246423,0.00007959368,0.00013836128,0.000083808874],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011145009,0.0017157249,0.0016855034,0.0012058383,0.0014263476,0.0017172929,0.0019865865,0.0019629186,0.011928862],"category_scores_gemma":[0.0026262626,0.0009768426,0.0011023391,0.002477041,0.0005210926,0.002312678,0.0016309081,0.0024236687,0.001901169],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026208165,0.00048677129,0.00060220365,0.0002889971,0.00008560285,0.00021287304,0.00016615813,0.5627932,0.0025370605,0.023711564,0.016350918,0.39250252],"study_design_scores_gemma":[0.00011203178,0.000100164,0.00013797266,0.00002015575,0.000024361078,0.00007051909,0.00005813568,0.9787714,0.000806287,0.016094238,0.003790536,0.000014217336],"about_ca_topic_score_codex":0.008820892,"about_ca_topic_score_gemma":0.008912121,"teacher_disagreement_score":0.011928862,"about_ca_system_score_codex":0.001414026,"about_ca_system_score_gemma":0.003013035,"threshold_uncertainty_score":0.039906025},"labels":[],"label_agreement":null},{"id":"W2125771650","doi":"10.1287/opre.50.3.538.7737","title":"Hybrid Fiber Coaxial Network Design","year":2002,"lang":"en","type":"article","venue":"Operations Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Heuristics; Network planning and design; Mathematical optimization; Heuristic; Context (archaeology); Tabu search; Exploit; The Internet; Distributed computing; Algorithm; Computer network; Artificial intelligence; Mathematics","score_opus":0.1388816461261699,"score_gpt":0.3622179414103387,"score_spread":0.2233362952841688,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2125771650","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.034058116,0.0004273881,0.9310339,0.00024170418,0.00005354723,0.00015866398,0.00014000351,0.00028611993,0.033600662],"genre_scores_gemma":[0.569508,0.00070781197,0.41035235,0.00013347143,0.0000433625,0.00027280595,0.00024198258,0.000057877653,0.018682262],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997029,0.00009247185,0.0000080571235,0.000059767088,0.000097065786,0.000039727973],"domain_scores_gemma":[0.99983716,0.000050254108,0.000029901572,0.000021066591,0.00004992574,0.000011585813],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031112056,0.00054193026,0.00019379953,0.00040908926,0.00034382046,0.0005745312,0.0006267984,0.00052119966,0.0051860902],"category_scores_gemma":[0.00044966917,0.00013993065,0.00022598122,0.00053201045,0.00031676076,0.0006219918,0.00043312978,0.000310713,0.00055126776],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011408687,0.0000572659,0.00087408524,0.00015820387,0.000032216187,0.00014211684,0.00007013816,0.7524039,0.013735444,0.061577383,0.0033738185,0.16746125],"study_design_scores_gemma":[0.000018366365,0.00011364021,0.00020100042,0.000012195297,0.00001626712,0.00014450455,0.000036284033,0.96556574,0.0047049453,0.013060016,0.016117873,0.000009255018],"about_ca_topic_score_codex":0.0028105353,"about_ca_topic_score_gemma":0.0054942463,"teacher_disagreement_score":0.0051860902,"about_ca_system_score_codex":0.0007193359,"about_ca_system_score_gemma":0.0005973442,"threshold_uncertainty_score":0.017349184},"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,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_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","score_opus":0.012473771387788539,"score_gpt":0.25472848918032187,"score_spread":0.24225471779253333,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2126043710","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8195189,0.00056952157,0.170147,0.0005134571,0.000044031316,0.0000714123,0.00070246286,0.00021971884,0.008213497],"genre_scores_gemma":[0.98545027,0.0002008507,0.011545563,0.000027727589,0.0000062778504,0.000031782653,0.00025658836,0.00004988477,0.002431177],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995647,0.00015667023,0.000014270081,0.000070718095,0.00009069531,0.0001029702],"domain_scores_gemma":[0.9975823,0.0016807731,0.00033985704,0.00008464803,0.00023013825,0.00008222902],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00078706414,0.0007357004,0.0003630034,0.0007447947,0.0002796473,0.000819004,0.00078951113,0.0007316529,0.001994291],"category_scores_gemma":[0.0032811547,0.00047054267,0.0006793743,0.0011010986,0.0004552575,0.0008684104,0.0002976288,0.0006584555,0.00012080171],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003081501,0.0000126394725,0.00051224465,0.000013826543,0.000013107898,0.000017959293,0.000009531334,0.9945681,0.00070685445,0.0027518654,0.000118246266,0.0012447842],"study_design_scores_gemma":[0.0000017888897,0.000008632847,0.00045107558,0.000001446732,0.0000051737575,0.00000803204,0.000012109319,0.9982829,0.00027378928,0.00084181485,0.00011003544,0.0000031973846],"about_ca_topic_score_codex":0.02077314,"about_ca_topic_score_gemma":0.012724786,"teacher_disagreement_score":0.02077314,"about_ca_system_score_codex":0.0025116378,"about_ca_system_score_gemma":0.0009415218,"threshold_uncertainty_score":0.04130447},"labels":[],"label_agreement":null},{"id":"W2126208553","doi":"10.1287/mnsc.48.11.1446.267","title":"Effective Zero-Inventory-Ordering Policies for the Single-Warehouse Multiretailer Problem with Piecewise Linear Cost Structures","year":2002,"lang":"en","type":"article","venue":"Management Science","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":147,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Office of Naval Research","keywords":"Computer science; Holding cost; Warehouse; Mathematical optimization; Heuristic; Time horizon; Piecewise linear function; Inventory control; Supply chain; Total cost; Zero (linguistics); Dynamic programming; Operations research; Inventory theory; Simple (philosophy); Piecewise; Mathematics; Economics; Microeconomics; Business","score_opus":0.027819486697966744,"score_gpt":0.258638289461894,"score_spread":0.23081880276392724,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2126208553","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20422114,0.0005192939,0.7890265,0.00050244504,0.000031743693,0.00016811129,0.00024239186,0.0003199925,0.004968342],"genre_scores_gemma":[0.859569,0.0004904806,0.13709547,0.00006690167,0.00001842393,0.00011906012,0.00022495085,0.00007010954,0.0023455543],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992901,0.0002658501,0.00003239968,0.00010334541,0.00010044589,0.00020787898],"domain_scores_gemma":[0.99780756,0.0014310478,0.0003719497,0.00012440767,0.000108846,0.00015616693],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016008187,0.0010473296,0.0011662857,0.0006625847,0.00049686263,0.0014054559,0.0013375226,0.00092737883,0.002772385],"category_scores_gemma":[0.0033322165,0.00064649683,0.0005659366,0.0009951262,0.0008885696,0.002169251,0.0007640211,0.001132734,0.00025422886],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012375036,0.000062444655,0.00030045354,0.00007456447,0.000013225568,0.00003991609,0.000045158315,0.9724578,0.00090117543,0.015078792,0.0003646944,0.010537924],"study_design_scores_gemma":[0.000035436777,0.00006830161,0.00013528003,0.000010149941,0.000010717744,0.000014640718,0.00004784688,0.98858577,0.00091786886,0.009712775,0.0004521963,0.000009087405],"about_ca_topic_score_codex":0.0034404297,"about_ca_topic_score_gemma":0.0026213848,"teacher_disagreement_score":0.0034404297,"about_ca_system_score_codex":0.0018470329,"about_ca_system_score_gemma":0.0018486432,"threshold_uncertainty_score":0.01340121},"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,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_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","score_opus":0.019102653731363887,"score_gpt":0.26441347945848326,"score_spread":0.24531082572711937,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2127654349","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006220654,0.46308333,0.17178531,0.046194393,0.016233344,0.00021673093,0.0013102836,0.00054821605,0.29440767],"genre_scores_gemma":[0.15579163,0.5691098,0.11103665,0.01305533,0.013672732,0.0003773102,0.0022873404,0.0004316627,0.13423754],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99703777,0.0007950347,0.00023509676,0.00058149814,0.0011287065,0.00022192573],"domain_scores_gemma":[0.9974942,0.0010006325,0.00018265734,0.00037784764,0.00074027677,0.00020443196],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0032493297,0.0012175987,0.0010725367,0.0018514637,0.0016483042,0.0057666595,0.0019391277,0.0023442195,0.01530325],"category_scores_gemma":[0.008189115,0.0007156514,0.00089297665,0.0041943653,0.0032209696,0.006879554,0.0033030643,0.0036976545,0.0064468714],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010298618,0.000058514066,0.0008135488,0.00065260637,0.00004902975,0.00013244814,0.00035244878,0.010977281,0.00033647454,0.6141763,0.056361858,0.31598657],"study_design_scores_gemma":[0.0000098379005,0.000041065363,0.0002557306,0.00061812665,0.000012723009,0.00015514232,0.00015853238,0.0027363128,0.00017905748,0.18953209,0.8062677,0.000033639444],"about_ca_topic_score_codex":0.004946911,"about_ca_topic_score_gemma":0.004037347,"teacher_disagreement_score":0.01530325,"about_ca_system_score_codex":0.004417795,"about_ca_system_score_gemma":0.0036598588,"threshold_uncertainty_score":0.05119449},"labels":[],"label_agreement":null},{"id":"W2128811073","doi":"10.1287/ijoc.1110.0454","title":"A Hybrid Tabu Search and Constraint Programming Algorithm for the Dynamic Dial-a-Ride Problem","year":2011,"lang":"en","type":"article","venue":"INFORMS journal on computing","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":108,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"","keywords":"Tabu search; Computer science; Hybrid algorithm (constraint satisfaction); Algorithm; Mathematical optimization; Constraint programming; Guided Local Search; Scheduling (production processes); Constraint (computer-aided design); Heuristic; Dynamic programming; Constraint logic programming; Mathematics; Stochastic programming","score_opus":0.025654230530110424,"score_gpt":0.2752020308302267,"score_spread":0.24954780030011628,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2128811073","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006377932,0.000267619,0.9893401,0.000097584256,0.00003533054,0.0000981873,0.00009140741,0.0009244128,0.0027674804],"genre_scores_gemma":[0.071930654,0.000186044,0.9246969,0.00012865965,0.0000284139,0.00032658508,0.00027751923,0.00026170086,0.0021633895],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99908316,0.00037120716,0.000040138653,0.00013006535,0.0002769995,0.000098402896],"domain_scores_gemma":[0.9989623,0.00063691573,0.00006759032,0.000088200315,0.00020334193,0.000041595784],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012646116,0.000995183,0.0011089261,0.0013592843,0.00083617517,0.0012012831,0.002184279,0.0013229097,0.0048587685],"category_scores_gemma":[0.0029396177,0.0006999269,0.0007680448,0.0027816247,0.0006232615,0.0013199747,0.0009665797,0.0012961921,0.0011285681],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016307588,0.00014311234,0.0005258476,0.00013067985,0.00010018636,0.00006540255,0.00008288277,0.67260283,0.002259991,0.017459864,0.00548922,0.30097687],"study_design_scores_gemma":[0.00004336234,0.000032995627,0.000084187646,0.000008651229,0.000011324565,0.000035342673,0.000014340761,0.99269897,0.00058128516,0.0040018894,0.0024767711,0.000010921597],"about_ca_topic_score_codex":0.01249953,"about_ca_topic_score_gemma":0.010067717,"teacher_disagreement_score":0.01249953,"about_ca_system_score_codex":0.00096517225,"about_ca_system_score_gemma":0.00205233,"threshold_uncertainty_score":0.024853587},"labels":[],"label_agreement":null},{"id":"W2129462568","doi":"10.1287/trsc.1090.0307","title":"The Stochastic Multiperiod Location Transportation Problem","year":2010,"lang":"en","type":"article","venue":"Transportation Science","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":70,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Tabu search; Mathematical optimization; Scheduling (production processes); Computer science; Heuristic; Operations research; Neighbourhood (mathematics); Mathematics","score_opus":0.01011121697135362,"score_gpt":0.26157433246239836,"score_spread":0.2514631154910447,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2129462568","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06741732,0.00093449705,0.9174399,0.00137025,0.00010850652,0.00011914882,0.0006712849,0.00018791994,0.01175112],"genre_scores_gemma":[0.8525875,0.001262436,0.13803753,0.00018360262,0.0001439166,0.00025993903,0.0009032502,0.00008368531,0.0065381182],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99878937,0.0006701816,0.000042478343,0.00018316033,0.00016899579,0.00014589814],"domain_scores_gemma":[0.998938,0.00064616976,0.00017635823,0.00005569245,0.00008100754,0.000102777754],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011622875,0.00060392317,0.0007583726,0.00043823794,0.00058774464,0.0010169322,0.0008515546,0.0012294416,0.0022990347],"category_scores_gemma":[0.0020301763,0.00042791935,0.00079629273,0.00091052783,0.00069511816,0.0012929289,0.0008454742,0.0007832144,0.00030024286],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000058094116,0.000046658926,0.0006214851,0.00011128617,0.00004944788,0.00026537263,0.00005459832,0.9127121,0.00084635697,0.06952144,0.0020102232,0.013702907],"study_design_scores_gemma":[0.000031106403,0.00005165949,0.0003570748,0.000015715546,0.000018226528,0.00021682997,0.00005621238,0.9474509,0.00035273944,0.045749098,0.0056847613,0.000015686095],"about_ca_topic_score_codex":0.0039148964,"about_ca_topic_score_gemma":0.0036689253,"teacher_disagreement_score":0.0039148964,"about_ca_system_score_codex":0.0011960189,"about_ca_system_score_gemma":0.0018703617,"threshold_uncertainty_score":0.008677721},"labels":[],"label_agreement":null},{"id":"W2129828643","doi":"10.1287/trsc.1120.0417","title":"Long-Haul Vehicle Routing and Scheduling with Working Hour Rules","year":2012,"lang":"en","type":"article","venue":"Transportation Science","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":89,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"","keywords":"Tabu search; Vehicle routing problem; Scheduling (production processes); TRIPS architecture; Operations research; Computer science; Transport engineering; Truck; Job shop scheduling; Engineering; Routing (electronic design automation); Operations management; Computer network; Automotive engineering; Algorithm","score_opus":0.0235828219791595,"score_gpt":0.26791987820707636,"score_spread":0.24433705622791685,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2129828643","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.24435998,0.00033898192,0.743993,0.00023539535,0.000083924075,0.00016184288,0.00024482707,0.0002059717,0.010376176],"genre_scores_gemma":[0.85733134,0.00029289717,0.137374,0.00005929448,0.000043798966,0.00014397468,0.00027471225,0.000075707205,0.004404182],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994068,0.00027201168,0.000024492258,0.00007353359,0.000099778,0.0001234312],"domain_scores_gemma":[0.99913764,0.00040182823,0.00019406318,0.00011448177,0.00009103707,0.00006097071],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000932759,0.00046116803,0.00044501454,0.00027873163,0.0006114411,0.0010668326,0.0010721383,0.0006472687,0.0015576895],"category_scores_gemma":[0.002247384,0.0003768115,0.00052678527,0.000822908,0.00067777955,0.0011050554,0.00045520088,0.0006749192,0.00019192055],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000046166624,0.00003701899,0.00066967774,0.000028055976,0.000020265976,0.00006011268,0.00005218777,0.9739933,0.0009725762,0.010307903,0.00041648667,0.013396264],"study_design_scores_gemma":[0.00001503314,0.000054613593,0.00051592314,0.000005546493,0.000010246393,0.000033057284,0.00009224648,0.9812044,0.0011816302,0.015329503,0.0015476787,0.000010090164],"about_ca_topic_score_codex":0.00720658,"about_ca_topic_score_gemma":0.008441958,"teacher_disagreement_score":0.00720658,"about_ca_system_score_codex":0.00076387945,"about_ca_system_score_gemma":0.0014003469,"threshold_uncertainty_score":0.014329255},"labels":[],"label_agreement":null},{"id":"W2130189990","doi":"10.1007/978-3-642-02882-3_23","title":"Approximation Algorithms for a Network Design Problem","year":2009,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Monotone polygon; Computer science; Path (computing); Tree (set theory); Mathematical optimization; Constraint (computer-aided design); Routing (electronic design automation); Integer (computer science); Class (philosophy); Algorithm; Mathematics; Combinatorics","score_opus":0.03091854809030773,"score_gpt":0.26336024853421175,"score_spread":0.23244170044390403,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2130189990","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0024199062,0.0008145245,0.98715186,0.0003820535,0.00011400577,0.000055714583,0.00006677746,0.00015597284,0.008839329],"genre_scores_gemma":[0.1288357,0.0032307738,0.8457327,0.00032750636,0.00037512678,0.00058076944,0.00046561917,0.0003398163,0.020111943],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989801,0.00044336505,0.000036542053,0.00015313755,0.00029868633,0.00008817446],"domain_scores_gemma":[0.9972869,0.0021520325,0.000109773864,0.00020344977,0.00019206747,0.000055747252],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022864584,0.0022505994,0.0017538305,0.0011694155,0.0006896901,0.0019598475,0.0023533292,0.002717326,0.008580183],"category_scores_gemma":[0.0076579927,0.0011256658,0.0017029649,0.002392317,0.001140432,0.0027530799,0.001653917,0.004053795,0.0015123831],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010397004,0.00011464672,0.00024017316,0.00026351638,0.000053922846,0.0000530409,0.00009215971,0.6937091,0.0005170217,0.13724378,0.011492092,0.15611672],"study_design_scores_gemma":[0.000036262813,0.000025568981,0.0000525156,0.000048964022,0.00002073892,0.00004598994,0.00002103616,0.9063834,0.00022250175,0.08773188,0.005403971,0.0000072436183],"about_ca_topic_score_codex":0.0035015412,"about_ca_topic_score_gemma":0.0030792332,"teacher_disagreement_score":0.008580183,"about_ca_system_score_codex":0.0020494012,"about_ca_system_score_gemma":0.0012195371,"threshold_uncertainty_score":0.02870357},"labels":[],"label_agreement":null},{"id":"W2130798138","doi":"10.1016/j.cor.2007.10.017","title":"The capacity and distance constrained plant location problem","year":2007,"lang":"en","type":"article","venue":"Computers & Operations Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":36,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Universidad Carlos III de Madrid","keywords":"Tabu search; Mathematical optimization; Computer science; Extension (predicate logic); Heuristic; TRIPS architecture; Operations research; Mathematics","score_opus":0.053392370844684185,"score_gpt":0.33606661152574513,"score_spread":0.28267424068106095,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2130798138","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.072064966,0.0022254854,0.8769458,0.004083901,0.00028380376,0.00015658402,0.0029395467,0.00027302463,0.041026905],"genre_scores_gemma":[0.8014657,0.0024019904,0.149136,0.00047485152,0.00041933122,0.00036049463,0.0018045835,0.00032956558,0.043607585],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988563,0.00045849878,0.000036127487,0.00032072526,0.00018222946,0.00014624896],"domain_scores_gemma":[0.9968664,0.002363093,0.0002256672,0.00013729741,0.00023720758,0.00017037278],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001301775,0.0013143021,0.001994371,0.001268574,0.00076816883,0.0023355058,0.0032332481,0.003636144,0.011690089],"category_scores_gemma":[0.0072675752,0.0013584049,0.00084130134,0.0031734174,0.0017006132,0.003955672,0.0020757786,0.0017979463,0.00087154174],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000108867265,0.00005651968,0.0004119774,0.00020984955,0.00004669961,0.0001978562,0.000063479914,0.9052966,0.00048548818,0.07167587,0.0049874354,0.016459292],"study_design_scores_gemma":[0.000041168485,0.000028922972,0.00025327047,0.000027118791,0.000022684024,0.00009251821,0.000056224355,0.9242228,0.00035542884,0.07136847,0.0035058667,0.00002557092],"about_ca_topic_score_codex":0.013889775,"about_ca_topic_score_gemma":0.008307736,"teacher_disagreement_score":0.013889775,"about_ca_system_score_codex":0.0021888472,"about_ca_system_score_gemma":0.0017440402,"threshold_uncertainty_score":0.039107203},"labels":[],"label_agreement":null},{"id":"W2130863730","doi":"10.1016/j.dam.2006.04.043","title":"Solution techniques for the Large Set Covering Problem","year":2006,"lang":"en","type":"article","venue":"Discrete Applied Mathematics","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":23,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Group for Research in Decision Analysis; Polytechnique Montréal","funders":"","keywords":"Mathematics; Set (abstract data type); Set cover problem; Combinatorics; Mathematical optimization; Computer science; Programming language","score_opus":0.014788920217505755,"score_gpt":0.26203942753925386,"score_spread":0.2472505073217481,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2130863730","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0045368094,0.00073646737,0.988581,0.0003349174,0.000081069025,0.000030740208,0.000043682772,0.00008093067,0.005574368],"genre_scores_gemma":[0.22434431,0.0028193304,0.75298,0.00034896669,0.00045692542,0.00043459618,0.00044286685,0.0003895723,0.017783524],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993893,0.00024997228,0.000019530462,0.00007363778,0.00020822544,0.0000593155],"domain_scores_gemma":[0.9985139,0.0010972073,0.00008197804,0.00011600539,0.0001360082,0.000054779295],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012532538,0.0009009389,0.000965376,0.0010732204,0.0005465457,0.0010359802,0.0013275899,0.0011117841,0.005111013],"category_scores_gemma":[0.005321723,0.00058282196,0.001196097,0.0015524974,0.0007144133,0.0016314152,0.0019025519,0.0025479004,0.0007512029],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007444869,0.00010282564,0.00038879964,0.00038026684,0.000099431716,0.00012089736,0.00025720743,0.43433082,0.0035147993,0.34149188,0.014132349,0.2051063],"study_design_scores_gemma":[0.00002029615,0.000026999625,0.00012143155,0.000030506715,0.000018156466,0.00006621312,0.000038564798,0.8263542,0.00049933715,0.16484919,0.007966783,0.000008427998],"about_ca_topic_score_codex":0.0021973148,"about_ca_topic_score_gemma":0.0018566409,"teacher_disagreement_score":0.005111013,"about_ca_system_score_codex":0.0008821279,"about_ca_system_score_gemma":0.00075661554,"threshold_uncertainty_score":0.01709801},"labels":[],"label_agreement":null},{"id":"W2130923090","doi":"10.1287/trsc.1110.0394","title":"Feasibility of the Pickup and Delivery Problem with Fixed Partial Routes: A Complexity Analysis","year":2012,"lang":"en","type":"article","venue":"Transportation Science","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Pickup; Set (abstract data type); Computer science; Mathematical optimization; Mathematics; Artificial intelligence","score_opus":0.053287495377932294,"score_gpt":0.29179182450612057,"score_spread":0.23850432912818828,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2130923090","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22956032,0.0020661938,0.74038297,0.0045193806,0.0001565496,0.000797863,0.0014780582,0.00037676428,0.020661851],"genre_scores_gemma":[0.62585336,0.0023211085,0.362059,0.00044192985,0.00033351648,0.00084002345,0.0021456028,0.0002257189,0.0057797874],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99655414,0.0012902203,0.0001566245,0.0005806875,0.00081747153,0.00060094614],"domain_scores_gemma":[0.97088575,0.026654014,0.00087540597,0.0005909346,0.00071849785,0.00027540303],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0036709565,0.0014400306,0.0014990745,0.0012346208,0.0010430212,0.003256041,0.0018252408,0.0019477672,0.0058930237],"category_scores_gemma":[0.02275747,0.0010058015,0.0030977938,0.0016593892,0.001761028,0.0056879967,0.0018562963,0.0037730825,0.000324434],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009121014,0.0005039973,0.0034117359,0.0011690835,0.00021583613,0.0005859586,0.00051419396,0.7998813,0.0050468324,0.117788404,0.0064365095,0.06353402],"study_design_scores_gemma":[0.00013524058,0.00015785493,0.0010099078,0.00006610085,0.00009007084,0.0002990057,0.00022486152,0.90728563,0.0019109685,0.08574419,0.0030307812,0.000045414723],"about_ca_topic_score_codex":0.0057858806,"about_ca_topic_score_gemma":0.0039161607,"teacher_disagreement_score":0.0058930237,"about_ca_system_score_codex":0.0026019723,"about_ca_system_score_gemma":0.002872498,"threshold_uncertainty_score":0.019714117},"labels":[],"label_agreement":null},{"id":"W2132103269","doi":"10.1155/2013/203032","title":"A New Formulation of the Set Covering Problem for Metaheuristic Approaches","year":2013,"lang":"en","type":"article","venue":"ISRN Operations Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Metaheuristic; Heuristic; Redundancy (engineering); Mathematical optimization; Computer science; Set (abstract data type); Computation; Greedy algorithm; Algorithm; Mathematics","score_opus":0.17930486376530602,"score_gpt":0.37034667105220825,"score_spread":0.19104180728690223,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2132103269","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002183133,0.0012504485,0.98933315,0.000506658,0.00019199435,0.0001120189,0.00007639098,0.00006959519,0.006276516],"genre_scores_gemma":[0.07255021,0.0024900753,0.91843575,0.00040371143,0.00039024057,0.00053060235,0.00021346346,0.00014055983,0.00484536],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982875,0.0008692452,0.00008601803,0.00017773735,0.00049132266,0.0000880505],"domain_scores_gemma":[0.9989944,0.00059179845,0.0001116533,0.00012713746,0.00014196933,0.000032968066],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001696584,0.001469012,0.00092736067,0.0009784438,0.00043561813,0.0016873435,0.0018419666,0.0018256849,0.0035562753],"category_scores_gemma":[0.003810872,0.0005749355,0.0019439296,0.001965345,0.00094191753,0.001871441,0.0013360719,0.0029024512,0.0007015891],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003746983,0.00009729488,0.00029876234,0.00065061625,0.00012265502,0.0002570728,0.00019754206,0.5321197,0.004525474,0.33935204,0.008380842,0.1139606],"study_design_scores_gemma":[0.000036887177,0.00012483129,0.00019429144,0.00017511335,0.00005986695,0.00041565608,0.00008584824,0.83458275,0.0017126688,0.11973596,0.042839464,0.0000366751],"about_ca_topic_score_codex":0.0010052789,"about_ca_topic_score_gemma":0.0013574472,"teacher_disagreement_score":0.0035562753,"about_ca_system_score_codex":0.001247018,"about_ca_system_score_gemma":0.0011124474,"threshold_uncertainty_score":0.011896968},"labels":[],"label_agreement":null},{"id":"W2132499281","doi":"10.1287/ijoc.1060.0208","title":"The Integrated Production and Transportation Scheduling Problem for a Product with a Short Lifespan","year":2007,"lang":"en","type":"article","venue":"INFORMS journal on computing","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":173,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; University of Texas at Dallas","keywords":"Mathematical optimization; Job shop scheduling; Scheduling (production processes); Computer science; Production schedule; Heuristic; Operations research; Schedule; Memetic algorithm; Heuristics; Genetic algorithm; Mathematics","score_opus":0.015318365491527371,"score_gpt":0.2665560813810715,"score_spread":0.25123771588954413,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2132499281","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21397914,0.0010187523,0.77044016,0.0011555316,0.00014572551,0.0006844255,0.0014309179,0.00046900095,0.010676393],"genre_scores_gemma":[0.5922015,0.0010802865,0.39179292,0.00016386999,0.00015524859,0.00092168344,0.0019736006,0.00021204089,0.011498849],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993654,0.00014858182,0.000038510443,0.00021857013,0.00011854611,0.000110507346],"domain_scores_gemma":[0.9990025,0.0005465122,0.00018624133,0.00006726233,0.00007832783,0.0001191006],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011632683,0.0015033102,0.0016400629,0.00090857066,0.0009919283,0.0014639029,0.0015137431,0.0017607505,0.0074627344],"category_scores_gemma":[0.0024095846,0.00068760506,0.0012570028,0.0018077131,0.0008476538,0.002083148,0.0009309718,0.001032932,0.0007465535],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00056278566,0.0003888003,0.0018317777,0.00071183563,0.00017961724,0.0008380613,0.00025619092,0.8662512,0.011756282,0.027125986,0.004220911,0.08587669],"study_design_scores_gemma":[0.00018261069,0.0008515997,0.0017027715,0.00004652304,0.000118410746,0.0005905338,0.00025582893,0.951303,0.004350911,0.03163519,0.00891633,0.00004632302],"about_ca_topic_score_codex":0.0035858387,"about_ca_topic_score_gemma":0.0030780917,"teacher_disagreement_score":0.0074627344,"about_ca_system_score_codex":0.001390256,"about_ca_system_score_gemma":0.0023854407,"threshold_uncertainty_score":0.024965346},"labels":[],"label_agreement":null},{"id":"W2132824134","doi":"10.1002/atr.111","title":"Multiple objective optimization of the fleet sizing problem for road freight transportation","year":2011,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":69,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Sizing; Operations research; Computer science; Transportation theory; Set (abstract data type); Decision maker; Queueing theory; Sample (material); Pareto principle; Transportation planning; Mathematical optimization; Fleet management; Transport engineering; Engineering; Mathematics","score_opus":0.016292504477764987,"score_gpt":0.2413963853853628,"score_spread":0.2251038809075978,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2132824134","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1807454,0.0008114017,0.8054134,0.0004592793,0.000043726086,0.00018936828,0.00031301568,0.00014030002,0.011884164],"genre_scores_gemma":[0.8647194,0.00041930398,0.12809525,0.0000540806,0.000029727957,0.00021580297,0.00022447642,0.00006580834,0.0061762673],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994362,0.00030704518,0.000017653345,0.00007037358,0.00009795202,0.00007078605],"domain_scores_gemma":[0.999348,0.00046320382,0.00007556137,0.000018294313,0.000053419964,0.00004162819],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001362956,0.0009810695,0.000965622,0.00094970287,0.00037282638,0.0011191005,0.0005675672,0.00080549385,0.0031148617],"category_scores_gemma":[0.0015786979,0.00048585673,0.0008653302,0.0010618991,0.0004901584,0.0007671845,0.0006497397,0.00071269524,0.00019332227],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000021708669,0.000018094164,0.00013294976,0.000033723463,0.000023683471,0.00003490893,0.000014417824,0.9910348,0.00068571855,0.002944149,0.0002185788,0.0048373705],"study_design_scores_gemma":[0.00000735964,0.000021724383,0.000103239436,0.000004294482,0.000007793966,0.000008642137,0.000013079401,0.99701047,0.00026582173,0.0022752297,0.000278914,0.0000033721246],"about_ca_topic_score_codex":0.0059665693,"about_ca_topic_score_gemma":0.0055488013,"teacher_disagreement_score":0.0059665693,"about_ca_system_score_codex":0.0014459946,"about_ca_system_score_gemma":0.0010602034,"threshold_uncertainty_score":0.0118637085},"labels":[],"label_agreement":null},{"id":"W2134125472","doi":"10.1007/s10479-015-1805-9","title":"Multi-period hub network design problems with modular capacities","year":2015,"lang":"en","type":"article","venue":"Annals of Operations Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":69,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Integer programming; Theory of computation; Modular design; Mathematical optimization; Set (abstract data type); Integer (computer science); Computer science; Relevance (law); Linear programming; Measure (data warehouse); Network planning and design; Facility location problem; Mathematics; Algorithm; Data mining; Telecommunications","score_opus":0.42360263638416223,"score_gpt":0.4270872876495254,"score_spread":0.003484651265363181,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2134125472","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06757225,0.00060138496,0.9227475,0.0004141047,0.00009875625,0.00014979982,0.00041760676,0.000113538284,0.007885036],"genre_scores_gemma":[0.807083,0.0011947555,0.17644492,0.000114441464,0.00015535906,0.00048036533,0.000341854,0.00016364864,0.014021615],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993063,0.00034897248,0.000024192204,0.000145733,0.000058718393,0.00011611073],"domain_scores_gemma":[0.9977254,0.0015784921,0.0003177575,0.00008626888,0.000119130666,0.00017283593],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024407895,0.0018049148,0.0018096503,0.0011732851,0.00053473824,0.0013903102,0.0019478515,0.002126603,0.006157557],"category_scores_gemma":[0.004832968,0.0013287598,0.0015015848,0.0014149386,0.0008676143,0.0021957157,0.0013406588,0.0011830623,0.00037295718],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007223887,0.000027018368,0.0001810035,0.00009157838,0.000049454306,0.00006338624,0.000018753239,0.97989523,0.0005505537,0.013317006,0.0005910724,0.0051426603],"study_design_scores_gemma":[0.00002241659,0.000074115174,0.00014590086,0.000014564291,0.00003282843,0.000023995675,0.000021136746,0.98422825,0.0002680394,0.01462149,0.0005386967,0.000008582948],"about_ca_topic_score_codex":0.0013898595,"about_ca_topic_score_gemma":0.0020306527,"teacher_disagreement_score":0.006157557,"about_ca_system_score_codex":0.0014845526,"about_ca_system_score_gemma":0.0010069789,"threshold_uncertainty_score":0.020599067},"labels":[],"label_agreement":null},{"id":"W2134381195","doi":"10.1287/trsc.2013.0489","title":"A Branch-and-Price Algorithm for the Multidepot Vehicle Routing Problem with Interdepot Routes","year":2014,"lang":"en","type":"article","venue":"Transportation Science","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":62,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"","keywords":"Column generation; Vehicle routing problem; Mathematical optimization; Integer programming; Routing (electronic design automation); Branch and price; Shortest path problem; Computer science; Linear programming relaxation; Set (abstract data type); Relaxation (psychology); Branch and cut; Extension (predicate logic); Linear programming; Path (computing); Integer (computer science); Mathematics; Graph; Theoretical computer science","score_opus":0.012188179354596644,"score_gpt":0.25526921868208735,"score_spread":0.2430810393274907,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2134381195","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007567392,0.00020355039,0.987153,0.00027461554,0.000047413097,0.00016463635,0.0001321478,0.0004946046,0.003962667],"genre_scores_gemma":[0.0937363,0.00026780515,0.90181184,0.00014911301,0.000047731977,0.00032920242,0.0005147049,0.00022051034,0.0029226334],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993625,0.00021417158,0.000027629812,0.00012105323,0.00014797758,0.00012671179],"domain_scores_gemma":[0.99891233,0.0007450871,0.00007726374,0.00007767821,0.00011758864,0.00007011769],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010830087,0.0012169216,0.0012482491,0.0009313245,0.0008756016,0.001338474,0.0015819097,0.00160118,0.009493888],"category_scores_gemma":[0.002512362,0.00072976167,0.00091923046,0.0015017443,0.00058907736,0.0017924509,0.001159348,0.002231617,0.0015794331],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015358464,0.00025054233,0.00063416903,0.00017696411,0.00005198668,0.00012048883,0.000110070396,0.75699437,0.0020143753,0.0380303,0.010667906,0.19079518],"study_design_scores_gemma":[0.00005348944,0.000052428855,0.000084817744,0.000010084664,0.00000996151,0.000036339894,0.000023970691,0.98336756,0.0005331321,0.013647491,0.0021725749,0.000008205187],"about_ca_topic_score_codex":0.0060261404,"about_ca_topic_score_gemma":0.0078067123,"teacher_disagreement_score":0.009493888,"about_ca_system_score_codex":0.0014178029,"about_ca_system_score_gemma":0.002127115,"threshold_uncertainty_score":0.031760216},"labels":[],"label_agreement":null},{"id":"W2134465458","doi":"10.1002/net.20447","title":"A branch‐and‐cut algorithm for the preemptive swapping problem","year":2011,"lang":"en","type":"article","venue":"Networks","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; HEC Montréal","funders":"","keywords":"Vertex (graph theory); Algorithm; Computer science; Graph; Integer programming; Branch and cut; Object (grammar); Combinatorics; Integer (computer science); Mathematics; Mathematical optimization; Artificial intelligence","score_opus":0.02521659239295936,"score_gpt":0.23731270554829037,"score_spread":0.212096113155331,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2134465458","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.028542345,0.0004929569,0.9619425,0.00048063783,0.00008547683,0.00027903213,0.0002535744,0.0009050329,0.007018424],"genre_scores_gemma":[0.18192302,0.00027215446,0.8115914,0.00017396918,0.00006820649,0.0005220374,0.00075862074,0.00023985798,0.0044507408],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99932563,0.0002689839,0.000027381595,0.00013049928,0.00013366014,0.00011384912],"domain_scores_gemma":[0.9986594,0.0009714519,0.000090476984,0.0000632764,0.0001277976,0.00008771865],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013163553,0.001300187,0.0014570784,0.0011510289,0.00088235614,0.0011161662,0.0017517597,0.0017400135,0.008412174],"category_scores_gemma":[0.0026034652,0.00074064214,0.0007452062,0.0016955868,0.0006481675,0.001434359,0.001177649,0.0019837362,0.00097071833],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000269896,0.00034844095,0.00048751073,0.0001662039,0.00005348777,0.00012683666,0.000090953836,0.78009135,0.0010666666,0.024381002,0.009426748,0.18349092],"study_design_scores_gemma":[0.000063538435,0.00004177317,0.0000513213,0.000007963682,0.000007357661,0.000019832616,0.000013155184,0.9880562,0.00026217144,0.010495087,0.0009776374,0.000004038115],"about_ca_topic_score_codex":0.0057662493,"about_ca_topic_score_gemma":0.0050410046,"teacher_disagreement_score":0.008412174,"about_ca_system_score_codex":0.001382451,"about_ca_system_score_gemma":0.0021678715,"threshold_uncertainty_score":0.028141558},"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,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Group for Research in Decision Analysis","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","score_opus":0.035265094387614375,"score_gpt":0.27635904149410156,"score_spread":0.24109394710648718,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2135680984","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0908509,0.002650295,0.8796239,0.0011040026,0.00013114607,0.00024411958,0.00068754464,0.00035270044,0.024355324],"genre_scores_gemma":[0.55584306,0.0026708632,0.43312472,0.0004038009,0.0002250433,0.00057834695,0.001314114,0.00024591712,0.005594268],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990096,0.00048584375,0.00003105744,0.0000975019,0.00021503231,0.00016088305],"domain_scores_gemma":[0.99741745,0.001969033,0.00030673313,0.00012491245,0.00012064675,0.00006119259],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015089931,0.0014215668,0.00096184225,0.0011200992,0.000595175,0.0014117676,0.001522246,0.0010375081,0.0045161615],"category_scores_gemma":[0.006394053,0.0005059646,0.0010740064,0.0026814535,0.0015153432,0.0018873124,0.0010662738,0.0024618683,0.0003799711],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013838014,0.00009248943,0.00062293396,0.00028422763,0.00007658741,0.0000995233,0.00009799285,0.825759,0.0008936346,0.12414849,0.003517582,0.044269294],"study_design_scores_gemma":[0.00003882135,0.00007792945,0.00030675952,0.00004095955,0.000022806684,0.00004063568,0.000032654178,0.9305932,0.00075241574,0.06581043,0.0022669868,0.000016350456],"about_ca_topic_score_codex":0.007191292,"about_ca_topic_score_gemma":0.008210295,"teacher_disagreement_score":0.007191292,"about_ca_system_score_codex":0.0015348654,"about_ca_system_score_gemma":0.0019726935,"threshold_uncertainty_score":0.015108049},"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,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_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","score_opus":0.01782215380448123,"score_gpt":0.27707571884409293,"score_spread":0.2592535650396117,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2136611127","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10397761,0.0004751849,0.88405216,0.00026292203,0.00003843413,0.00018951185,0.00015929349,0.00094195065,0.009902953],"genre_scores_gemma":[0.5816013,0.00026076747,0.41492495,0.00011786464,0.000026959333,0.00041284852,0.0003113197,0.00012222321,0.0022216733],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99932075,0.00031880083,0.00002171214,0.0000792129,0.00014395858,0.00011562179],"domain_scores_gemma":[0.9993686,0.00037604503,0.000085941036,0.00008097409,0.00005411592,0.000034400633],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011334518,0.0008051686,0.0010213709,0.0010233783,0.0005338847,0.0012155581,0.0016721674,0.0012643954,0.002647427],"category_scores_gemma":[0.0019772414,0.0006608,0.0007428936,0.0018903221,0.0006208204,0.0009570121,0.0008012983,0.0007616233,0.0004102292],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000067998284,0.00006222504,0.00029124945,0.000045438843,0.000026486478,0.000045009278,0.000053210348,0.96831435,0.0006685,0.00574737,0.0008142503,0.023863876],"study_design_scores_gemma":[0.000026558797,0.000024283238,0.000057494566,0.000006423945,0.000009067979,0.000014159006,0.000023351513,0.9968432,0.00024077577,0.0023103377,0.00044072274,0.0000036387025],"about_ca_topic_score_codex":0.007916004,"about_ca_topic_score_gemma":0.0072450903,"teacher_disagreement_score":0.007916004,"about_ca_system_score_codex":0.00096985634,"about_ca_system_score_gemma":0.0016631711,"threshold_uncertainty_score":0.015739858},"labels":[],"label_agreement":null},{"id":"W2136708107","doi":"10.1287/trsc.1100.0316","title":"The Traveling Salesman Problem with Pickups, Deliveries, and Handling Costs","year":2010,"lang":"en","type":"article","venue":"Transportation Science","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":45,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"","keywords":"Travelling salesman problem; Vehicle routing problem; Heuristic; Routing (electronic design automation); Mathematical optimization; Computer science; Pickup; Integer programming; Operations research; Linear programming; Material handling; Fixed cost; Integer (computer science); Engineering; Industrial engineering; Mathematics; Economics; Artificial intelligence","score_opus":0.006388144429665132,"score_gpt":0.22850926522083284,"score_spread":0.22212112079116772,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2136708107","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.039135415,0.0014688617,0.9465962,0.0006137228,0.0002351618,0.00032970734,0.0007107115,0.0002445403,0.010665743],"genre_scores_gemma":[0.37077668,0.0042872145,0.6031479,0.00027258758,0.0005606945,0.00082748244,0.0015480592,0.00023560721,0.018343816],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99833566,0.00069945713,0.00009597859,0.00035374897,0.00030991223,0.00020529964],"domain_scores_gemma":[0.9988446,0.0007915874,0.00015668746,0.00007802695,0.000075050666,0.00005403032],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001366149,0.0022500043,0.001987272,0.0008166303,0.0008375411,0.0023916252,0.002466794,0.0021134024,0.0044666096],"category_scores_gemma":[0.0035931587,0.0011555733,0.0014866416,0.0028127625,0.0009219092,0.00335759,0.0010086534,0.0022080524,0.0005709006],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009731771,0.00013349103,0.00038444487,0.0003239367,0.000090427464,0.00023245155,0.00005294989,0.9039403,0.00078890746,0.053459477,0.002338624,0.038157642],"study_design_scores_gemma":[0.000042091167,0.00009785986,0.00028386715,0.000020912208,0.00005535904,0.00014759328,0.000051073457,0.9563344,0.00062170625,0.03733673,0.004981972,0.00002639631],"about_ca_topic_score_codex":0.006641736,"about_ca_topic_score_gemma":0.00540027,"teacher_disagreement_score":0.006641736,"about_ca_system_score_codex":0.0015266496,"about_ca_system_score_gemma":0.0027934771,"threshold_uncertainty_score":0.014942348},"labels":[],"label_agreement":null},{"id":"W2136863244","doi":"10.1057/jors.2009.86","title":"An ant colony optimization metaheuristic for single-path multicommodity network flow problems","year":2009,"lang":"en","type":"article","venue":"Journal of the Operational Research Society","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Metaheuristic; Ant colony optimization algorithms; Mathematical optimization; Multi-commodity flow problem; Computer science; Path (computing); Flow network; Ant colony; Node (physics); Minimum-cost flow problem; Parallel metaheuristic; Optimization problem; Flow (mathematics); Mathematics; Computer network; Engineering","score_opus":0.07605495017992003,"score_gpt":0.36306116388250936,"score_spread":0.28700621370258933,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2136863244","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02902289,0.0008804595,0.96088666,0.00040801472,0.00012388278,0.00015553324,0.00009678947,0.00036246327,0.00806324],"genre_scores_gemma":[0.27143833,0.0007069644,0.7235169,0.00023195343,0.00008850065,0.00031112725,0.00018767956,0.00011267816,0.003405902],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995473,0.00019727547,0.00002081125,0.000061202154,0.0001274829,0.000045892928],"domain_scores_gemma":[0.9991584,0.00055112597,0.00009204343,0.00005878276,0.00010209998,0.000037514626],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008413273,0.0009161827,0.0009726745,0.00073594623,0.00050502527,0.0008796955,0.0011814439,0.0013532399,0.0012816662],"category_scores_gemma":[0.0022244556,0.00039992368,0.0006454324,0.0011650244,0.00054585224,0.0008707237,0.0006389874,0.0010864742,0.00023548627],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002985034,0.000050115304,0.00021485786,0.000057140158,0.000032562297,0.00004463162,0.00002900938,0.9568658,0.00076680107,0.007933005,0.0008820164,0.033094224],"study_design_scores_gemma":[0.000013325475,0.000018515666,0.0000301144,0.000005556135,0.0000056836,0.0000134546535,0.0000061610817,0.9967315,0.00019116254,0.0020969997,0.00088482315,0.0000026711102],"about_ca_topic_score_codex":0.0036511421,"about_ca_topic_score_gemma":0.0037212488,"teacher_disagreement_score":0.0036511421,"about_ca_system_score_codex":0.0006411606,"about_ca_system_score_gemma":0.0011585883,"threshold_uncertainty_score":0.007259786},"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,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_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","score_opus":0.02032142200990912,"score_gpt":0.2890078836574357,"score_spread":0.2686864616475266,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2136871443","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01940219,0.00039857475,0.9725762,0.0005686253,0.000052888987,0.0002767355,0.00035807796,0.00055731926,0.005809417],"genre_scores_gemma":[0.115865074,0.00036087085,0.8791915,0.00014822869,0.000046972018,0.000544472,0.00078749086,0.00015821295,0.0028971084],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99912196,0.00038779783,0.000036796846,0.00015676087,0.00015430401,0.00014230656],"domain_scores_gemma":[0.99831426,0.0012788739,0.00010487274,0.00005510407,0.00015512956,0.00009177773],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018915172,0.0016834403,0.0017472793,0.0011275617,0.000991624,0.0017356316,0.001971448,0.0025996554,0.008339256],"category_scores_gemma":[0.0032504515,0.0010637272,0.00092675426,0.0019651074,0.00069852854,0.0016581188,0.0012302638,0.0020687475,0.0009955794],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012315801,0.00017799667,0.0003482532,0.00012330046,0.000037975533,0.00009222465,0.000065671244,0.9254643,0.0005134252,0.01371935,0.0035014565,0.055833034],"study_design_scores_gemma":[0.00005681417,0.000032842534,0.00004194819,0.000010195541,0.000008926593,0.000015176816,0.000017554816,0.9910942,0.0001617004,0.0077117723,0.00084428495,0.000004549277],"about_ca_topic_score_codex":0.007885685,"about_ca_topic_score_gemma":0.007389176,"teacher_disagreement_score":0.008339256,"about_ca_system_score_codex":0.0017624157,"about_ca_system_score_gemma":0.0025737223,"threshold_uncertainty_score":0.027897596},"labels":[],"label_agreement":null},{"id":"W2137507954","doi":"10.1139/x08-017","title":"RuttOpt — a decision support system for routing of logging trucks","year":2008,"lang":"en","type":"article","venue":"Canadian Journal of Forest Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":51,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Truck; Gantt chart; Computer science; Scheduling (production processes); Logging; Decision support system; Schedule; Database; Range (aeronautics); Vehicle routing problem; Time horizon; Information system; Operations research; Routing (electronic design automation); Data mining; Engineering; Mathematical optimization; Operations management; Systems engineering; Geography; Mathematics","score_opus":0.06825843467581155,"score_gpt":0.33914817182682394,"score_spread":0.2708897371510124,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2137507954","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.036594722,0.0004802001,0.74406934,0.0003524915,0.00014537122,0.0006963097,0.011058696,0.1907087,0.015894214],"genre_scores_gemma":[0.23491253,0.00065601355,0.7252398,0.00027321713,0.00009368094,0.0010043192,0.021438986,0.003670292,0.012711139],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99945444,0.00012756666,0.00005784403,0.00013611178,0.00018016016,0.000043991444],"domain_scores_gemma":[0.99884963,0.0005178446,0.00011857039,0.00017338828,0.0002538467,0.00008669246],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008720131,0.000820437,0.00087864755,0.0009024694,0.00042993206,0.0011648978,0.0018606669,0.000597787,0.025967807],"category_scores_gemma":[0.0023292887,0.000550178,0.00057876296,0.0010337875,0.00018980933,0.0010958894,0.0006973084,0.00069178443,0.0050174133],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008932442,0.00037980295,0.0026882552,0.0006914223,0.00014482526,0.0004797681,0.00022944152,0.24388474,0.013533059,0.0072740954,0.12416512,0.6056362],"study_design_scores_gemma":[0.00037361184,0.00018338767,0.0015804943,0.00007196757,0.00005434398,0.00031425842,0.0000764739,0.8939888,0.009258343,0.0052129566,0.088798,0.000087336266],"about_ca_topic_score_codex":0.0075450307,"about_ca_topic_score_gemma":0.0070058582,"teacher_disagreement_score":0.99245495,"about_ca_system_score_codex":0.00063110836,"about_ca_system_score_gemma":0.0013507631,"threshold_uncertainty_score":0.08687097},"labels":[],"label_agreement":null},{"id":"W2137944694","doi":"10.1023/a:1009679511137","title":"Tabu Search for a Network Loading Problem with Multiple Facilities","year":2000,"lang":"en","type":"article","venue":"Journal of Heuristics","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":49,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal; Université de Montréal; Université du Québec à Montréal","funders":"","keywords":"Tabu search; Computer science; Vertex (graph theory); Mathematical optimization; Network planning and design; Heuristic; Computer network; Mathematics; Algorithm; Theoretical computer science; Graph","score_opus":0.019457902165281662,"score_gpt":0.25524968761737876,"score_spread":0.2357917854520971,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2137944694","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18428165,0.0017577613,0.7850865,0.0013390753,0.00025103506,0.00058806,0.0010740939,0.001856643,0.023765162],"genre_scores_gemma":[0.4300352,0.00053307565,0.5595274,0.00025622695,0.00008783262,0.0006024766,0.0007706987,0.00043222652,0.007754844],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991092,0.0005202016,0.00002826126,0.000090032176,0.00010608838,0.00014620104],"domain_scores_gemma":[0.9959674,0.0033465847,0.00018869483,0.00012577148,0.00025895383,0.00011264883],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002096614,0.0010079829,0.0015336104,0.0022905837,0.0015431291,0.0017366341,0.002142448,0.0027576238,0.01190671],"category_scores_gemma":[0.007817013,0.0014660927,0.0011724774,0.0031324942,0.0011941174,0.002160703,0.0010480992,0.0015323908,0.0009509138],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018065677,0.00015689115,0.000448733,0.000145938,0.000045506655,0.000060344737,0.000069763526,0.9503018,0.0003738805,0.008125047,0.003412394,0.036678936],"study_design_scores_gemma":[0.0000582469,0.000046387973,0.00009888393,0.000015653903,0.000017136483,0.000015652962,0.000039959312,0.99286216,0.00014880054,0.006125012,0.0005641482,0.000007886222],"about_ca_topic_score_codex":0.018050665,"about_ca_topic_score_gemma":0.0146903135,"teacher_disagreement_score":0.018050665,"about_ca_system_score_codex":0.0017777628,"about_ca_system_score_gemma":0.002238264,"threshold_uncertainty_score":0.039831936},"labels":[],"label_agreement":null},{"id":"W2138023714","doi":"10.1057/jors.2010.53","title":"Cost allocation in the establishment of a collaborative transportation agreement—an application in the furniture industry","year":2010,"lang":"en","type":"article","venue":"Journal of the Operational Research Society","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":123,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Polytechnique Montréal; Université Laval","funders":"","keywords":"Purchasing; Supply chain; Cost reduction; Business; Computer science; Key (lock); Operations research; Operations management; Marketing; Economics; Engineering; Computer security","score_opus":0.049066076679056714,"score_gpt":0.3867948432864896,"score_spread":0.3377287666074329,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2138023714","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.107823774,0.0001626467,0.88393915,0.0004791295,0.000048569953,0.00043614418,0.0000451502,0.000119885844,0.0069456175],"genre_scores_gemma":[0.74193984,0.00017175218,0.2548778,0.000039024275,0.000032066193,0.00043891073,0.000036538833,0.000058745736,0.0024052204],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9953235,0.0032399697,0.00014962947,0.00037254262,0.00054899213,0.000365368],"domain_scores_gemma":[0.9886673,0.008715269,0.0006753898,0.00052018097,0.0009176535,0.00050412765],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008463899,0.0009985688,0.0014287859,0.0014012235,0.001754536,0.0020111315,0.0022163705,0.0022722732,0.0040727714],"category_scores_gemma":[0.017698068,0.0006532908,0.0009940687,0.0015028414,0.0013714901,0.0031102193,0.0033592116,0.0015137093,0.00027505663],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00034134203,0.00032031978,0.0013911946,0.00014717752,0.00007098419,0.00023588128,0.00038506027,0.87145686,0.0014705034,0.06410443,0.00076644216,0.059309755],"study_design_scores_gemma":[0.00004176134,0.00011048399,0.00025104333,0.000010556092,0.000027150898,0.000038826307,0.00016288363,0.9872961,0.0006114017,0.010684906,0.0007484752,0.000016362512],"about_ca_topic_score_codex":0.007447146,"about_ca_topic_score_gemma":0.004433024,"teacher_disagreement_score":0.008463899,"about_ca_system_score_codex":0.0034467687,"about_ca_system_score_gemma":0.0026526167,"threshold_uncertainty_score":0.044761956},"labels":[],"label_agreement":null},{"id":"W2138596841","doi":"10.1287/trsc.1100.0328","title":"European Driver Rules in Vehicle Routing with Time Windows","year":2010,"lang":"en","type":"article","venue":"Transportation Science","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":98,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Group for Research in Decision Analysis","funders":"","keywords":"Tabu search; Vehicle routing problem; Heuristics; Column generation; Computer science; Task (project management); Routing (electronic design automation); Heuristic; Mathematical optimization; Operations research; Algorithm; Engineering; Artificial intelligence; Mathematics; Embedded system","score_opus":0.009295564871308826,"score_gpt":0.23761108514239387,"score_spread":0.22831552027108504,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2138596841","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11733418,0.0020606327,0.8250027,0.0009351343,0.0002412033,0.00022859569,0.00063284405,0.00033932205,0.05322549],"genre_scores_gemma":[0.66960514,0.001639905,0.3059762,0.00016555634,0.00009429742,0.00026993948,0.000654458,0.00020009786,0.021394465],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985077,0.00074923853,0.00007388396,0.00022169024,0.0002879979,0.0001593565],"domain_scores_gemma":[0.998976,0.0006289361,0.00014157071,0.00011185299,0.00009853977,0.000043146658],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016982154,0.0005183319,0.00045240813,0.0004982974,0.00057500263,0.0016752478,0.0011562962,0.0010468579,0.0033045954],"category_scores_gemma":[0.0039024707,0.0004786585,0.00083808164,0.0009305616,0.000754767,0.0016478804,0.00092026143,0.00093525613,0.00047843883],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017038181,0.000063108164,0.001183562,0.00013335368,0.00004811838,0.00029605292,0.00019515565,0.6316243,0.001613168,0.28966358,0.0046934653,0.07031573],"study_design_scores_gemma":[0.000067699104,0.00007932298,0.0008529849,0.00005912901,0.00004476268,0.00021001157,0.00019031389,0.7914799,0.0035694954,0.16493943,0.038457382,0.000049538132],"about_ca_topic_score_codex":0.010452856,"about_ca_topic_score_gemma":0.00980784,"teacher_disagreement_score":0.010452856,"about_ca_system_score_codex":0.0009281045,"about_ca_system_score_gemma":0.0015700894,"threshold_uncertainty_score":0.02078402},"labels":[],"label_agreement":null},{"id":"W2139434864","doi":"10.1287/trsc.1060.0172","title":"An Exact Solution Approach for the Preferential Bidding System Problem in the Airline Industry","year":2007,"lang":"en","type":"article","venue":"Transportation Science","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Group for Research in Decision Analysis","funders":"","keywords":"Bidding; Schedule; Crew; Column generation; Operations research; Order (exchange); Computer science; Aircrew; Seniority; Quality (philosophy); Work (physics); Mathematical optimization; Engineering; Business; Mathematics; Finance; Marketing; Aeronautics","score_opus":0.041561768691632965,"score_gpt":0.31415356704378317,"score_spread":0.2725917983521502,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2139434864","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004553008,0.0001313509,0.9915257,0.00009039746,0.000026155585,0.000045637822,0.000063119034,0.00011523537,0.003449449],"genre_scores_gemma":[0.11535299,0.00030588565,0.879892,0.0000965893,0.000048656326,0.00020328007,0.00022473796,0.000093453964,0.0037824064],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991328,0.00031459812,0.000034546756,0.00012087084,0.0002939696,0.00010326304],"domain_scores_gemma":[0.9993197,0.00036618274,0.000057716385,0.00011217385,0.00011397924,0.000030316545],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001049489,0.0007751915,0.0008190821,0.0006643998,0.0005854649,0.00089592114,0.001269639,0.00074058183,0.0074470188],"category_scores_gemma":[0.0030717975,0.0005238503,0.0006952553,0.0013408598,0.0005347915,0.0014531424,0.0010751161,0.0011430569,0.0008703567],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000043317785,0.00007959188,0.0004225106,0.00015533139,0.000023872646,0.000070681745,0.00007604306,0.8203013,0.0010418531,0.05649786,0.003434956,0.117852665],"study_design_scores_gemma":[0.000019348327,0.00003831645,0.00008624441,0.000012559544,0.000008259476,0.000051232993,0.0000311941,0.958761,0.00034870527,0.036981903,0.0036535952,0.000007702856],"about_ca_topic_score_codex":0.0062694717,"about_ca_topic_score_gemma":0.0092163645,"teacher_disagreement_score":0.0074470188,"about_ca_system_score_codex":0.0008902988,"about_ca_system_score_gemma":0.0025121504,"threshold_uncertainty_score":0.024912715},"labels":[],"label_agreement":null},{"id":"W2141089842","doi":"10.1016/j.trc.2014.08.007","title":"The time-dependent vehicle routing problem with soft time windows and stochastic travel times","year":2014,"lang":"en","type":"article","venue":"Transportation Research Part C Emerging Technologies","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":117,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"","keywords":"Vehicle routing problem; Tabu search; Metaheuristic; Computer science; Mathematical optimization; Reliability (semiconductor); Service (business); Routing (electronic design automation); Service quality; Algorithm; Mathematics; Computer network","score_opus":0.019140656369509616,"score_gpt":0.28264868442206814,"score_spread":0.2635080280525585,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2141089842","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11204467,0.0020879386,0.87666947,0.0022736222,0.00040024103,0.00014079089,0.0010202195,0.00016928697,0.005193765],"genre_scores_gemma":[0.9122635,0.0025121898,0.06193135,0.0003166376,0.00046455886,0.00027805628,0.00094007386,0.00018972302,0.02110389],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99772483,0.00091615884,0.00010776859,0.00060015847,0.00031390323,0.00033718455],"domain_scores_gemma":[0.99235183,0.005505956,0.0010582713,0.00022511189,0.00038079763,0.00047799564],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003663348,0.0022470024,0.0029747398,0.0013106937,0.0006214846,0.0029730885,0.0040028,0.0038468335,0.00350528],"category_scores_gemma":[0.0119817965,0.0026869718,0.0020042134,0.0022856644,0.002000005,0.006151773,0.0017426676,0.0031781194,0.00036854413],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016100377,0.000057729,0.0003921449,0.00013155078,0.000094399635,0.00017670315,0.000036676243,0.95036656,0.000545744,0.04320039,0.0008401688,0.003996918],"study_design_scores_gemma":[0.000024655084,0.000031358235,0.00019674106,0.000007781141,0.000031121297,0.00003077671,0.000018215895,0.98362154,0.0001721103,0.015520561,0.00032836382,0.000016758526],"about_ca_topic_score_codex":0.009633066,"about_ca_topic_score_gemma":0.005826679,"teacher_disagreement_score":0.009633066,"about_ca_system_score_codex":0.0027616092,"about_ca_system_score_gemma":0.0024542008,"threshold_uncertainty_score":0.020036936},"labels":[],"label_agreement":null},{"id":"W2141557529","doi":"10.1007/0-387-25036-0_9","title":"Recent Trends in Arc Routing","year":2005,"lang":"en","type":"book-chapter","venue":"","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":28,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Group for Research in Decision Analysis","funders":"","keywords":"Arc routing; Arc (geometry); Traverse; Heuristic; Routing (electronic design automation); Vehicle routing problem; Computer science; Mathematical optimization; Enhanced Data Rates for GSM Evolution; Operations research; Mathematics; Geography; Computer network; Artificial intelligence; Cartography; Geometry","score_opus":0.03149969619143034,"score_gpt":0.27114872171483473,"score_spread":0.2396490255234044,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2141557529","genre_codex":"other","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0018178974,0.14257254,0.38868853,0.0055470746,0.004450399,0.000101782374,0.00034250703,0.0016976759,0.45478162],"genre_scores_gemma":[0.020501548,0.22774982,0.24013814,0.0020036176,0.0032960104,0.00018825397,0.00090138376,0.0013382015,0.50388294],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99933416,0.00011932719,0.000024062228,0.00012015624,0.0003650538,0.000037307178],"domain_scores_gemma":[0.9993611,0.00027782802,0.000022569462,0.000107417596,0.00019593799,0.00003516504],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00070035364,0.0011432531,0.00078620104,0.0018558644,0.00047289906,0.002452433,0.0018908448,0.0010723487,0.03812961],"category_scores_gemma":[0.0016273693,0.00072677835,0.0006069457,0.0054306095,0.0009908736,0.004453066,0.0013121889,0.00249368,0.01562459],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016320351,0.000054050808,0.00008794095,0.00065515784,0.000016898837,0.000028961651,0.000085071006,0.012561757,0.000680013,0.24610034,0.10965117,0.6300623],"study_design_scores_gemma":[0.0000039786396,0.000015691887,0.0000927938,0.00018632719,0.000011159913,0.00010120941,0.00004265582,0.010505494,0.0003989164,0.0906129,0.89801717,0.00001176653],"about_ca_topic_score_codex":0.00236937,"about_ca_topic_score_gemma":0.0038943954,"teacher_disagreement_score":0.03812961,"about_ca_system_score_codex":0.0014573081,"about_ca_system_score_gemma":0.0015016333,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W2141809992","doi":"10.1007/s10732-007-9046-y","title":"Local and variable neighborhood search for the k-cardinality subgraph problem","year":2007,"lang":"en","type":"article","venue":"Journal of Heuristics","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Military College of Canada","funders":"","keywords":"Constructive; Heuristics; Variable neighborhood search; Combinatorics; Mathematics; Induced subgraph isomorphism problem; Cardinality (data modeling); Local search (optimization); Metaheuristic; Subgraph isomorphism problem; Heuristic; Graph; Combinatorial optimization; Variable (mathematics); Discrete mathematics; Mathematical optimization; Computer science; Line graph; Data mining","score_opus":0.018834917495229954,"score_gpt":0.2812296731848295,"score_spread":0.2623947556895995,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2141809992","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.19018432,0.0010381277,0.79804176,0.00086036645,0.000075242606,0.00011773544,0.00020549961,0.00034827934,0.009128587],"genre_scores_gemma":[0.76840574,0.0003132109,0.22603396,0.000113747905,0.000060143688,0.0001729993,0.00026997674,0.00011377698,0.0045163813],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99941754,0.00035616144,0.00001355299,0.000065043496,0.00007763957,0.00006997214],"domain_scores_gemma":[0.9971686,0.0023296678,0.00016450658,0.000108004024,0.00012192931,0.00010723019],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012801003,0.0004184441,0.0012535488,0.00091126905,0.0006423909,0.0008005618,0.0014887303,0.0008269315,0.0025408114],"category_scores_gemma":[0.005196433,0.00042794013,0.00060294,0.0011286255,0.0008156003,0.0014917075,0.0009417991,0.0008010799,0.0001758552],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002015694,0.000109957065,0.0009861381,0.000077055935,0.000049414375,0.000042813055,0.00006759719,0.9402904,0.00042289804,0.029261447,0.0019288877,0.02656184],"study_design_scores_gemma":[0.000027887423,0.00002254822,0.000118096,0.00000571842,0.000009070979,0.000009843798,0.00001949031,0.9873487,0.00009736833,0.012103721,0.00023389835,0.0000036884048],"about_ca_topic_score_codex":0.0067885523,"about_ca_topic_score_gemma":0.009799905,"teacher_disagreement_score":0.0067885523,"about_ca_system_score_codex":0.0009508483,"about_ca_system_score_gemma":0.0010470634,"threshold_uncertainty_score":0.013498068},"labels":[],"label_agreement":null},{"id":"W2142169698","doi":"10.1287/trsc.2014.0525","title":"Service Network Design with Resource Constraints","year":2014,"lang":"en","type":"article","venue":"Transportation Science","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":116,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; Université du Québec à Montréal","funders":"Infrastructure Canada; Compute Canada","keywords":"Column generation; Solver; Benchmark (surveying); Heuristic; Network planning and design; Computer science; Service (business); Consolidation (business); Quality of service; Operations research; Mathematical optimization; Computer network; Engineering; Business; Artificial intelligence; Mathematics","score_opus":0.021099234204862673,"score_gpt":0.2463486574531667,"score_spread":0.22524942324830402,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2142169698","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018878262,0.0002971157,0.9667331,0.00054785545,0.000084515734,0.00012985163,0.0003055624,0.00021772826,0.012806056],"genre_scores_gemma":[0.5397736,0.0011480282,0.44326246,0.0003859528,0.00010763585,0.00050091685,0.0006778426,0.00022529051,0.013918269],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992569,0.00031786307,0.000020075164,0.000089680485,0.00019136492,0.0001240849],"domain_scores_gemma":[0.99943024,0.00028386578,0.000052645973,0.000042298478,0.00013851147,0.000052303923],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007930986,0.001038639,0.0006767197,0.0005192836,0.00047725,0.0013256182,0.0009377023,0.0010862905,0.0050620707],"category_scores_gemma":[0.0019078909,0.00052755786,0.00066232425,0.0011551705,0.00073622784,0.0011236238,0.0008689661,0.0010446477,0.00058588246],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000021776817,0.000017929187,0.00014021732,0.000054316413,0.000010779854,0.000058420293,0.000019685389,0.96213865,0.0007932327,0.026125599,0.0015427364,0.0090766],"study_design_scores_gemma":[0.000009044805,0.000015792437,0.000026298134,0.0000062784798,0.000004723128,0.000018723227,0.00001448582,0.98767823,0.00033893672,0.009280297,0.0026038534,0.0000033697293],"about_ca_topic_score_codex":0.01036085,"about_ca_topic_score_gemma":0.0095495125,"teacher_disagreement_score":0.01036085,"about_ca_system_score_codex":0.0017017826,"about_ca_system_score_gemma":0.0024698137,"threshold_uncertainty_score":0.020601034},"labels":[],"label_agreement":null},{"id":"W2143046423","doi":"10.1007/s00291-013-0348-1","title":"Self-imposed time windows in vehicle routing problems","year":2013,"lang":"en","type":"article","venue":"OR Spectrum","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":75,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"","keywords":"Vehicle routing problem; Benchmark (surveying); Heuristic; Computer science; Tabu search; Mathematical optimization; Routing (electronic design automation); Order (exchange); Operations research; Mathematics; Economics","score_opus":0.009466649504264044,"score_gpt":0.22174500512405423,"score_spread":0.2122783556197902,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2143046423","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.030569982,0.0008213651,0.9606667,0.00040227652,0.00024708585,0.000026215264,0.000055795168,0.0000512904,0.0071592964],"genre_scores_gemma":[0.87747663,0.0021802273,0.09413267,0.00021820245,0.00045989497,0.00018911548,0.00014954888,0.0002292194,0.024964394],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994692,0.00028166824,0.000018238312,0.000073852985,0.000092925606,0.00006413589],"domain_scores_gemma":[0.99761,0.001723153,0.0002380145,0.00012871354,0.00017818202,0.0001220785],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019200944,0.00080465,0.00070890825,0.00040973868,0.00032428713,0.001055653,0.0012589289,0.0011693515,0.002227656],"category_scores_gemma":[0.0064691813,0.00064831146,0.0005853491,0.0005563036,0.0010458521,0.0023130267,0.0012076647,0.0018481269,0.00022833985],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009570432,0.000053362863,0.00033094658,0.00011343274,0.0000359073,0.00009086215,0.00008105521,0.749406,0.0014308104,0.2359333,0.00138158,0.011046945],"study_design_scores_gemma":[0.000008877678,0.000013936292,0.00006397575,0.000010096143,0.0000073042434,0.000009721216,0.000015156453,0.96746564,0.0002540467,0.03140807,0.000737572,0.0000057465722],"about_ca_topic_score_codex":0.0019657454,"about_ca_topic_score_gemma":0.0013673231,"teacher_disagreement_score":0.002227656,"about_ca_system_score_codex":0.0007257527,"about_ca_system_score_gemma":0.00087226863,"threshold_uncertainty_score":0.010154545},"labels":[],"label_agreement":null},{"id":"W2143105265","doi":"10.1287/inte.2013.0683","title":"Mathematical Programming Guides Air-Ambulance Routing at Ornge","year":2013,"lang":"en","type":"article","venue":"INFORMS Journal on Applied Analytics","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":33,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Operations research; Work (physics); Computer science; Fixed wing; Routing (electronic design automation); Route planning; Air traffic control; Range (aeronautics); Vehicle routing problem; Operations management; Aeronautics; Transport engineering; Engineering; Computer network; Wing","score_opus":0.018716778310650772,"score_gpt":0.2584463641114855,"score_spread":0.23972958580083475,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2143105265","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0025632274,0.00044479963,0.95439583,0.002503466,0.00009184006,0.00009660842,0.00043041146,0.00062368036,0.038850043],"genre_scores_gemma":[0.08266805,0.001935067,0.8810272,0.00035245012,0.000101432575,0.00025083672,0.0005852234,0.00057912606,0.032500584],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998979,0.00044958142,0.000040131672,0.00013015057,0.0003190555,0.00008215259],"domain_scores_gemma":[0.99852955,0.00092832453,0.000113399255,0.000087766144,0.00029376356,0.00004713625],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017475762,0.0013099031,0.00068193336,0.0009632821,0.001220143,0.0026265238,0.0013348559,0.0008777839,0.018739086],"category_scores_gemma":[0.0049263844,0.00089559646,0.0005867727,0.0016780708,0.0010588035,0.0017488818,0.0009411447,0.001992523,0.0042769616],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003794558,0.000058879206,0.00077989296,0.00020962147,0.000017153448,0.00016287534,0.00031201012,0.50299233,0.002269236,0.28054842,0.045984473,0.1666271],"study_design_scores_gemma":[0.000020607342,0.000022026921,0.00027984308,0.00009925401,0.00000725994,0.00006150708,0.0002205617,0.77497375,0.00094939844,0.09329369,0.13004339,0.000028708748],"about_ca_topic_score_codex":0.12171789,"about_ca_topic_score_gemma":0.232816,"teacher_disagreement_score":0.8782821,"about_ca_system_score_codex":0.0052313623,"about_ca_system_score_gemma":0.008289226,"threshold_uncertainty_score":0.24201882},"labels":[],"label_agreement":null},{"id":"W2143519380","doi":"","title":"Evolutionary Algorithms for Vehicle Routing","year":2007,"lang":"en","type":"article","venue":"","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Vehicle routing problem; Benchmark (surveying); Evolutionary algorithm; Computer science; Particle swarm optimization; Routing (electronic design automation); Mathematical optimization; Evolutionary computation; Metaheuristic; Algorithm; Artificial intelligence; Mathematics; Geography","score_opus":0.025214133791604403,"score_gpt":0.29275019287515,"score_spread":0.26753605908354555,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2143519380","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0037601723,0.032882683,0.92432743,0.0015981555,0.0007154513,0.000080982274,0.00022342168,0.0003279823,0.036083646],"genre_scores_gemma":[0.29195824,0.06079083,0.59205246,0.00083630305,0.0014364402,0.00061073806,0.00085036986,0.0002714557,0.051193234],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99959666,0.00013245798,0.000021805841,0.000054948847,0.00016923176,0.000024979023],"domain_scores_gemma":[0.9996369,0.0002180914,0.000025112833,0.000032246808,0.00007746231,0.000010170567],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004799318,0.00086182647,0.00096033374,0.0007163669,0.00042365308,0.0010901039,0.00088958733,0.001320379,0.004780224],"category_scores_gemma":[0.002233272,0.0003049806,0.0005200794,0.0017214639,0.00073288707,0.001047192,0.0008294821,0.0014916877,0.0013456175],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016363449,0.0000277281,0.00033987776,0.00033852947,0.00007691372,0.000093643765,0.000068954614,0.46158782,0.00088492583,0.30518752,0.01124013,0.22013766],"study_design_scores_gemma":[0.000028638544,0.000029003442,0.00027408588,0.0001225547,0.00002144433,0.00014499537,0.000035801768,0.57027715,0.0004530602,0.3320237,0.09656645,0.000023037936],"about_ca_topic_score_codex":0.0028851484,"about_ca_topic_score_gemma":0.0018901817,"teacher_disagreement_score":0.004780224,"about_ca_system_score_codex":0.0007142155,"about_ca_system_score_gemma":0.00063960033,"threshold_uncertainty_score":0.01599145},"labels":[],"label_agreement":null},{"id":"W2144303296","doi":"10.1287/ijoc.1090.0341","title":"Path-Reduced Costs for Eliminating Arcs in Routing and Scheduling","year":2009,"lang":"en","type":"article","venue":"INFORMS journal on computing","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":69,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kronos (Canada); HEC Montréal; Polytechnique Montréal; Group for Research in Decision Analysis","funders":"","keywords":"Column generation; Mathematical optimization; Computer science; Speedup; Constrained Shortest Path First; Shortest path problem; Vehicle routing problem; Path (computing); Scheduling (production processes); Routing (electronic design automation); Context (archaeology); Longest path problem; K shortest path routing; Mathematics; Parallel computing; Theoretical computer science; Computer network","score_opus":0.017409394862990268,"score_gpt":0.28754947685326876,"score_spread":0.2701400819902785,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2144303296","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017996378,0.00035319454,0.9749463,0.00029440963,0.00005685243,0.00007764087,0.000111682435,0.00028923224,0.0058741737],"genre_scores_gemma":[0.3132844,0.000719815,0.6762821,0.00012279097,0.00006280886,0.0003550663,0.00030916577,0.0003204954,0.00854332],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9993641,0.0002458319,0.000015481543,0.00005293286,0.00025801983,0.00006360163],"domain_scores_gemma":[0.99911124,0.0005660652,0.00006736421,0.0001112047,0.000120147655,0.000023839348],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007815429,0.00077864784,0.0004929601,0.0009233327,0.00053051737,0.00072463276,0.0009816057,0.0007074633,0.0063925325],"category_scores_gemma":[0.004045456,0.00036346182,0.00052693894,0.0014422506,0.00074643537,0.0015349706,0.00075478136,0.0013465809,0.00077072304],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011839224,0.00009370592,0.0004219424,0.00014798285,0.00003555448,0.00008307785,0.00007155813,0.66738075,0.0028534005,0.18185475,0.0043249195,0.14261387],"study_design_scores_gemma":[0.000026565314,0.000058552618,0.00021664688,0.000019164156,0.000020974128,0.00005358057,0.000018473102,0.90117526,0.0024819463,0.09069688,0.005215892,0.0000159167],"about_ca_topic_score_codex":0.0048282505,"about_ca_topic_score_gemma":0.0061296043,"teacher_disagreement_score":0.0063925325,"about_ca_system_score_codex":0.0008774173,"about_ca_system_score_gemma":0.0013769174,"threshold_uncertainty_score":0.021385193},"labels":[],"label_agreement":null},{"id":"W2145366356","doi":"","title":"A multi-objective hub covering location problem under congestion using simulated annealing algorithm","year":2013,"lang":"en","type":"article","venue":"Uncertain Supply Chain Management","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Simulated annealing; Mathematical optimization; Computer science; Metric (unit); Integer programming; Algorithm; Operations research; Mathematics; Operations management; Engineering","score_opus":0.021455756965323494,"score_gpt":0.26936220426759827,"score_spread":0.2479064473022748,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2145366356","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11212773,0.00047941934,0.8789273,0.00030641956,0.000040748306,0.00012854108,0.00010399364,0.00021468787,0.007671043],"genre_scores_gemma":[0.85013324,0.00028391302,0.1457139,0.00005748833,0.000027440943,0.00020613766,0.00014074417,0.00004950867,0.0033876225],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995888,0.00020508836,0.000013396508,0.000059606078,0.00006901546,0.00006414864],"domain_scores_gemma":[0.99923825,0.00050624507,0.00009691146,0.000028627885,0.000083948864,0.00004589274],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00088509585,0.00077096734,0.0014551572,0.0007979217,0.00048800782,0.0009776829,0.0009002176,0.0013830392,0.0019219521],"category_scores_gemma":[0.0017768398,0.0005842636,0.0009919268,0.000992782,0.00057553913,0.0006484641,0.00079034816,0.0006212557,0.00013322422],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001341134,0.0000082763345,0.000124923,0.000014728689,0.000012115951,0.00001743802,0.000014054079,0.9959514,0.00017127318,0.0010208095,0.00010572584,0.0025457074],"study_design_scores_gemma":[0.000006160423,0.000016191207,0.00005773899,0.000002743588,0.0000055871433,0.0000051160323,0.000006774598,0.99904484,0.00007370274,0.0006501459,0.00012904221,0.0000019442673],"about_ca_topic_score_codex":0.0073761316,"about_ca_topic_score_gemma":0.0047225654,"teacher_disagreement_score":0.0073761316,"about_ca_system_score_codex":0.0010258782,"about_ca_system_score_gemma":0.0011085564,"threshold_uncertainty_score":0.0146663785},"labels":[],"label_agreement":null},{"id":"W2145935606","doi":"10.48550/arxiv.1406.0941","title":"Augmentative Message Passing for Traveling Salesman Problem and Graph Partitioning","year":2014,"lang":"en","type":"article","venue":"arXiv (Cornell University)","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Travelling salesman problem; Computer science; Message passing; 2-opt; Cutting-plane method; Mathematical optimization; Optimization problem; Graph; Combinatorial optimization; Graph partition; Context (archaeology); Theoretical computer science; Bottleneck traveling salesman problem; Mathematics; Algorithm; Integer programming; Parallel computing","score_opus":0.045906100470516735,"score_gpt":0.19448794099779942,"score_spread":0.14858184052728268,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2145935606","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007445083,0.000109405264,0.9890035,0.00020851934,0.000032443808,0.000064653526,0.000041987085,0.00021631751,0.0028781267],"genre_scores_gemma":[0.2601946,0.00030514965,0.73229504,0.00018425807,0.00011788073,0.00045916595,0.00020815391,0.00018018515,0.0060556005],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990965,0.00047979265,0.000029684299,0.00014456912,0.00016773221,0.000081814316],"domain_scores_gemma":[0.9976138,0.0016134876,0.00021194518,0.000320657,0.00016853001,0.00007158838],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017617473,0.0012654345,0.00086113403,0.00075578724,0.00058188254,0.0010533795,0.0015284693,0.0013345839,0.0055355793],"category_scores_gemma":[0.0065939496,0.00044308134,0.00094248395,0.0010387707,0.0012309989,0.0024079722,0.0016280891,0.0019446097,0.00080428936],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013533117,0.000116837226,0.00036602753,0.00021363498,0.000042069347,0.00014020521,0.00021630357,0.71897167,0.0034292983,0.1700648,0.00427509,0.10202869],"study_design_scores_gemma":[0.000015204675,0.000047453002,0.000048302296,0.000010367917,0.000008953315,0.000029861123,0.00001744026,0.95019186,0.0009681729,0.04586436,0.0027914864,0.000006599242],"about_ca_topic_score_codex":0.0020667412,"about_ca_topic_score_gemma":0.0020586888,"teacher_disagreement_score":0.0055355793,"about_ca_system_score_codex":0.0009175731,"about_ca_system_score_gemma":0.0007709416,"threshold_uncertainty_score":0.018518329},"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,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_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","score_opus":0.13790653681425669,"score_gpt":0.4106109157336956,"score_spread":0.27270437891943894,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2146004814","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.037021082,0.000681737,0.95802927,0.00019009523,0.000066360226,0.00013332945,0.000049061146,0.00019402707,0.0036349632],"genre_scores_gemma":[0.5061894,0.00042508784,0.4899481,0.00015676983,0.000057478297,0.0003276016,0.0002152426,0.00007913992,0.002601133],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995246,0.00023810996,0.000018817584,0.00006771265,0.00009680011,0.000054057407],"domain_scores_gemma":[0.9992448,0.00053889246,0.0000657227,0.00003591539,0.000071244365,0.000043465498],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000957475,0.00047447512,0.0010372407,0.0006161207,0.00048159028,0.0005373624,0.0012915923,0.00093523617,0.0016524736],"category_scores_gemma":[0.00185552,0.00032519113,0.0006078384,0.0006649304,0.00045425943,0.0011104293,0.00077494193,0.00070736854,0.00020746964],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006852511,0.00009249676,0.00042938863,0.00006178925,0.00003186747,0.00006101338,0.0000414385,0.94408596,0.0006772919,0.010005068,0.0012148387,0.04323033],"study_design_scores_gemma":[0.000014290343,0.000027916816,0.000047931266,0.0000037932718,0.0000044314233,0.000013260581,0.000011335034,0.99717367,0.00013777972,0.0020706803,0.000491287,0.0000035533265],"about_ca_topic_score_codex":0.00412501,"about_ca_topic_score_gemma":0.0055326303,"teacher_disagreement_score":0.00412501,"about_ca_system_score_codex":0.0006975832,"about_ca_system_score_gemma":0.0010233066,"threshold_uncertainty_score":0.008201957},"labels":[],"label_agreement":null},{"id":"W2147424049","doi":"10.1016/j.cor.2009.02.017","title":"An ant colony system (ACS) for vehicle routing problem with simultaneous delivery and pickup","year":2009,"lang":"en","type":"article","venue":"Computers & Operations Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":196,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Vehicle routing problem; Pickup; Computer science; Ant colony optimization algorithms; Benchmark (surveying); Mathematical optimization; Ant colony; Routing (electronic design automation); Backhaul (telecommunications); Combinatorial optimization; Algorithm; Artificial intelligence; Mathematics; Computer network","score_opus":0.03016327053796509,"score_gpt":0.3280864310040777,"score_spread":0.2979231604661126,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2147424049","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07308357,0.00055205513,0.9113413,0.0006070461,0.00032608272,0.00031717797,0.00013722337,0.00056068104,0.01307483],"genre_scores_gemma":[0.7056282,0.00037751615,0.28527823,0.00013968865,0.00011826076,0.0002843909,0.00014821868,0.000057126937,0.007968235],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99945945,0.0001698943,0.000029428833,0.00010015173,0.0001883414,0.000052667045],"domain_scores_gemma":[0.99938333,0.00020799198,0.00006252668,0.00005188649,0.00023663577,0.000057609635],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005907164,0.00056578807,0.0007650225,0.00046741022,0.000575642,0.00090736384,0.0011536288,0.0011352726,0.0019056784],"category_scores_gemma":[0.0015795061,0.00030306412,0.00042138746,0.00082366465,0.00048938993,0.0006956096,0.00088794495,0.0009030268,0.00031212892],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002070511,0.00014361842,0.0005636365,0.00016879255,0.000040770195,0.00015139891,0.00006925634,0.8919242,0.0068267854,0.011850408,0.0038091405,0.08424484],"study_design_scores_gemma":[0.000017697253,0.0000719116,0.00005154364,0.0000024907754,0.000009688051,0.0000260011,0.000008473455,0.9969886,0.0004531586,0.0012104743,0.0011557594,0.000004219846],"about_ca_topic_score_codex":0.0057266075,"about_ca_topic_score_gemma":0.0048410543,"teacher_disagreement_score":0.0057266075,"about_ca_system_score_codex":0.00046817967,"about_ca_system_score_gemma":0.0015181343,"threshold_uncertainty_score":0.011386573},"labels":[],"label_agreement":null},{"id":"W2148767145","doi":"10.1287/trsc.2014.0535","title":"Branch-Price-and-Cut Algorithms for the Pickup and Delivery Problem with Time Windows and Last-in-First-Out Loading","year":2014,"lang":"en","type":"article","venue":"Transportation Science","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":56,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal; Polytechnique Montréal; Group for Research in Decision Analysis","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"FIFO and LIFO accounting; Pickup; Vehicle routing problem; Mathematical optimization; Computation; Column generation; Computer science; Algorithm; Path (computing); Dynamic programming; Shortest path problem; Mathematics; Routing (electronic design automation); FIFO (computing and electronics); Theoretical computer science","score_opus":0.014906122694752602,"score_gpt":0.24406077677824442,"score_spread":0.2291546540834918,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2148767145","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005578133,0.00050937035,0.98995274,0.00022055158,0.000047067897,0.0001462296,0.00012360855,0.00035650714,0.0030659216],"genre_scores_gemma":[0.09561758,0.0010969148,0.89692914,0.00014374196,0.00009409367,0.0006593252,0.00065083767,0.00035411865,0.0044541475],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99864405,0.0005139497,0.000066154804,0.00023427088,0.00029902812,0.00024252482],"domain_scores_gemma":[0.99699116,0.0022917686,0.00022735217,0.00017342008,0.0001937244,0.00012253414],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025803114,0.0025827903,0.0025777258,0.0015208566,0.001384879,0.0024146284,0.00348727,0.0026540349,0.009694532],"category_scores_gemma":[0.0062053455,0.0017619987,0.0019706215,0.0027179567,0.0011856142,0.0039828997,0.0016026138,0.003972036,0.0014913105],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000091767964,0.00013554448,0.00025187468,0.00011859912,0.000040003604,0.00003981099,0.00006742974,0.9020604,0.0003429694,0.03457502,0.0025979334,0.059678722],"study_design_scores_gemma":[0.000028323459,0.000024980065,0.000035269524,0.000011376682,0.000009109308,0.000012136081,0.000012004213,0.98095644,0.00015747033,0.017915223,0.00083154085,0.000006025698],"about_ca_topic_score_codex":0.010581586,"about_ca_topic_score_gemma":0.011854897,"teacher_disagreement_score":0.010581586,"about_ca_system_score_codex":0.003564497,"about_ca_system_score_gemma":0.003582945,"threshold_uncertainty_score":0.032431483},"labels":[],"label_agreement":null},{"id":"W2149852331","doi":"10.1287/inte.1040.0113","title":"Bombardier Flexjet Significantly Improves Its Fractional Aircraft Ownership Operations","year":2005,"lang":"en","type":"article","venue":"INFORMS Journal on Applied Analytics","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Group for Research in Decision Analysis; HEC Montréal","funders":"","keywords":"Crew; Charter; Engineering; Aeronautics; Operations research; Service (business); Operations management; Business; Marketing","score_opus":0.022731258156740753,"score_gpt":0.2711719378261229,"score_spread":0.24844067966938216,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2149852331","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.82568556,0.0006992484,0.087058276,0.0010490882,0.00016070447,0.00012609869,0.0006559578,0.0056121415,0.07895289],"genre_scores_gemma":[0.9425383,0.00027095273,0.040152226,0.00012140392,0.00002068143,0.000020973364,0.00058762915,0.00027753031,0.016010294],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997204,0.00003274212,0.000009551208,0.00004174956,0.00013512225,0.000060458766],"domain_scores_gemma":[0.9997472,0.00005557896,0.000027342014,0.00005586912,0.00007617703,0.000037825437],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00050361606,0.0005520649,0.00029577827,0.00053847505,0.0003321768,0.0009940374,0.0003888686,0.0003286156,0.00546983],"category_scores_gemma":[0.0010724805,0.00011191402,0.00024606858,0.00051483436,0.00022230927,0.0010609952,0.00054814655,0.00046451407,0.00087536726],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00087309553,0.0005980143,0.008562855,0.00010094186,0.000041029503,0.00020128497,0.00012910414,0.17668973,0.056869414,0.012419165,0.0148429815,0.7286724],"study_design_scores_gemma":[0.0002275414,0.0024073387,0.026325108,0.000054919175,0.00009047554,0.0005316402,0.000292412,0.68686116,0.14800954,0.006628467,0.12849426,0.00007709921],"about_ca_topic_score_codex":0.007750405,"about_ca_topic_score_gemma":0.006618894,"teacher_disagreement_score":0.007750405,"about_ca_system_score_codex":0.00078216876,"about_ca_system_score_gemma":0.00091118354,"threshold_uncertainty_score":0.018298388},"labels":[],"label_agreement":null},{"id":"W2149985337","doi":"10.1287/trsc.1080.0232","title":"The Stochastic Eulerian Tour Problem","year":2008,"lang":"en","type":"article","venue":"Transportation Science","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Eulerian path; Mathematics; A priori and a posteriori; Combinatorics; Graph; Expected value; Probability distribution; Mathematical optimization; Discrete mathematics; Applied mathematics; Statistics","score_opus":0.02164490420805822,"score_gpt":0.2595778368169822,"score_spread":0.23793293260892398,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2149985337","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09170386,0.00066241564,0.8876516,0.0018410316,0.0001374331,0.00019848358,0.0010699612,0.00035577235,0.016379472],"genre_scores_gemma":[0.7527009,0.0014109279,0.22685853,0.00047132827,0.00020544311,0.00043288717,0.0021047997,0.00030789882,0.01550726],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99862635,0.0006004493,0.00006945388,0.00024995708,0.00024117035,0.00021262474],"domain_scores_gemma":[0.9965276,0.0024201614,0.00036441066,0.00021790375,0.00021229651,0.00025760668],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014880487,0.00074231543,0.0009706133,0.00054845464,0.0006997932,0.0011819206,0.0011044946,0.0012279599,0.005447735],"category_scores_gemma":[0.0070359465,0.00050618674,0.0007608645,0.0009733379,0.0013103724,0.002835485,0.0012864567,0.0016197283,0.00048115116],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017291389,0.000061841725,0.0009418571,0.00017637543,0.000068775145,0.00017317595,0.00017671435,0.6483518,0.0011839803,0.31857914,0.0072171725,0.022896284],"study_design_scores_gemma":[0.00003833679,0.000066705215,0.00033941414,0.00002805478,0.000014001388,0.00015591453,0.000069218,0.7448514,0.0006092151,0.24678238,0.007025026,0.000020302521],"about_ca_topic_score_codex":0.0023303463,"about_ca_topic_score_gemma":0.0023898317,"teacher_disagreement_score":0.005447735,"about_ca_system_score_codex":0.0016365048,"about_ca_system_score_gemma":0.001954955,"threshold_uncertainty_score":0.018224537},"labels":[],"label_agreement":null},{"id":"W2149994569","doi":"10.3138/infor.46.1.71","title":"Metaheuristics: A Canadian Perspective","year":2008,"lang":"en","type":"article","venue":"INFOR Information Systems and Operational Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Metaheuristic; Perspective (graphical); Computer science; Parallel metaheuristic; Simple (philosophy); Sociology; Management science; Artificial intelligence; Operations research; Mathematics; Epistemology; Engineering; Philosophy","score_opus":0.06716616501221806,"score_gpt":0.34087110619448663,"score_spread":0.27370494118226857,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2149994569","genre_codex":"other","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002910721,0.26067352,0.114173435,0.10020252,0.0035202403,0.00014549971,0.00085068285,0.00040307344,0.51712024],"genre_scores_gemma":[0.16954774,0.5387946,0.12435857,0.010680404,0.0029575508,0.00018835822,0.0007358262,0.00048351858,0.15225352],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9972383,0.000520383,0.00009770603,0.00025955195,0.0016246554,0.00025940966],"domain_scores_gemma":[0.9974068,0.00042237184,0.000120231285,0.00014218004,0.0016290665,0.0002794043],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0033869946,0.0015832696,0.0010063123,0.00512395,0.0029581601,0.0069147046,0.0031478012,0.0027882075,0.008465764],"category_scores_gemma":[0.006696888,0.0006786825,0.0007981384,0.007969248,0.0055172765,0.0038559057,0.002033004,0.0038614406,0.0015484934],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000027055301,0.000023605706,0.00027829426,0.00035569916,0.000028416784,0.00005975032,0.0001821374,0.011628969,0.00021588075,0.8574215,0.03696663,0.09281207],"study_design_scores_gemma":[0.000026095522,0.000029553044,0.0007075929,0.0008742794,0.00004114378,0.00011618274,0.0003878077,0.013269152,0.0004891847,0.13141844,0.8525833,0.00005727456],"about_ca_topic_score_codex":0.8439985,"about_ca_topic_score_gemma":0.8272395,"teacher_disagreement_score":0.15600151,"about_ca_system_score_codex":0.047223706,"about_ca_system_score_gemma":0.06339023,"threshold_uncertainty_score":0.34263355},"labels":[],"label_agreement":null},{"id":"W2150123606","doi":"10.1016/j.dam.2008.04.022","title":"0–1 reformulations of the multicommodity capacitated network design problem","year":2008,"lang":"en","type":"article","venue":"Discrete Applied Mathematics","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":112,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; Computer Research Institute of Montréal; Université du Québec à Montréal","funders":"","keywords":"Mathematics; Cutting-plane method; Integer (computer science); Residual; Variable (mathematics); Integer programming; Mathematical optimization; Network planning and design; Piecewise linear function; Branch and cut; Piecewise; Algorithm; Computer science","score_opus":0.03861793585691882,"score_gpt":0.24257225088065193,"score_spread":0.20395431502373312,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2150123606","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023245063,0.0008799695,0.89337844,0.0014533788,0.0003046408,0.00009570273,0.00033484836,0.000075099386,0.08023297],"genre_scores_gemma":[0.62309587,0.0016788598,0.3165219,0.0008604049,0.00071121537,0.00035464772,0.0006981347,0.00020655882,0.055872355],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992447,0.000372721,0.000024644294,0.000117892465,0.00015614356,0.00008388449],"domain_scores_gemma":[0.9989537,0.00059351226,0.00013223059,0.000070253256,0.00020040487,0.00004991708],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016388724,0.0009252602,0.00053526246,0.00069787714,0.00042195688,0.0012332881,0.0012470945,0.0013048036,0.009070038],"category_scores_gemma":[0.0047195205,0.00032586613,0.00059140194,0.00068949815,0.0007646155,0.0019052958,0.0011757128,0.0014441395,0.000740277],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000052200157,0.00007277166,0.00024048694,0.00014553049,0.000017003507,0.00007122991,0.00006970712,0.3267262,0.00064724986,0.6276638,0.008195543,0.036098193],"study_design_scores_gemma":[0.000014096297,0.00005280627,0.00015434735,0.000027545855,0.000009362313,0.00005274562,0.000027871913,0.7290061,0.00037308448,0.26108688,0.009182948,0.000012182169],"about_ca_topic_score_codex":0.0020920478,"about_ca_topic_score_gemma":0.0027061752,"teacher_disagreement_score":0.009070038,"about_ca_system_score_codex":0.0010955161,"about_ca_system_score_gemma":0.0008365911,"threshold_uncertainty_score":0.03034228},"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,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_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","score_opus":0.016014294489356155,"score_gpt":0.29095552040227646,"score_spread":0.2749412259129203,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2152304735","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02367569,0.001328686,0.93662065,0.00053547806,0.00040371442,0.00013517454,0.00019520568,0.00049456157,0.036610823],"genre_scores_gemma":[0.27674156,0.00071740313,0.6928264,0.00040361704,0.00019596604,0.00036766607,0.00070257357,0.00038239412,0.027662374],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995141,0.00021672015,0.000014653317,0.000099700344,0.00010247147,0.00005235987],"domain_scores_gemma":[0.9994209,0.00031773277,0.000035643734,0.00008463098,0.00010601354,0.000035177713],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008397948,0.00073085434,0.0013461636,0.0011894432,0.0008501987,0.0011510109,0.0010093037,0.0013139141,0.011766904],"category_scores_gemma":[0.0029766383,0.0007105193,0.0008246004,0.0012573262,0.0007583617,0.0012429961,0.0009921413,0.0013083422,0.0014490678],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002750498,0.00021657284,0.00081032317,0.00015218313,0.00011425689,0.00006522565,0.000107100466,0.56234795,0.0016911577,0.09347977,0.023943454,0.3167969],"study_design_scores_gemma":[0.00005320726,0.000059913426,0.00014683402,0.000029689236,0.000024116147,0.00003700827,0.000025346013,0.96950924,0.0005676379,0.023794018,0.005745017,0.000007869416],"about_ca_topic_score_codex":0.0034228603,"about_ca_topic_score_gemma":0.0043275123,"teacher_disagreement_score":0.011766904,"about_ca_system_score_codex":0.0006955572,"about_ca_system_score_gemma":0.0010373114,"threshold_uncertainty_score":0.03936422},"labels":[],"label_agreement":null},{"id":"W2154025454","doi":"10.1016/j.cor.2011.12.016","title":"The Canadian minimum duration truck driver scheduling problem","year":2012,"lang":"en","type":"article","venue":"Computers & Operations Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":32,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Truck; Duration (music); Integer programming; Schedule; Operations research; Scheduling (production processes); Computer science; Dynamic programming; Service (business); Transport engineering; Linear programming; Operations management; Automotive engineering; Business; Engineering; Marketing; Algorithm; Operating system","score_opus":0.05594305595633055,"score_gpt":0.3457641357038667,"score_spread":0.2898210797475362,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2154025454","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.29946136,0.005378003,0.24642883,0.018973462,0.001339346,0.0007260076,0.014037333,0.00071585906,0.41293973],"genre_scores_gemma":[0.8492143,0.002398432,0.0647278,0.000566368,0.00021662498,0.00019410659,0.0037532332,0.00022661891,0.078702554],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99921024,0.00012777507,0.000016888449,0.00014782726,0.00022043975,0.0002767031],"domain_scores_gemma":[0.9993079,0.00020980244,0.00003857152,0.00003290813,0.00022698815,0.00018384938],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010243786,0.000960719,0.00084011286,0.0012389447,0.0032676992,0.0027182035,0.0027453993,0.0016001764,0.012801939],"category_scores_gemma":[0.0030186193,0.00059071573,0.0006614005,0.0024418775,0.001123152,0.0012169674,0.00088593405,0.001485407,0.00052341283],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00072357355,0.00021351609,0.0031312304,0.00032246683,0.000069635,0.00032788608,0.00038015636,0.59005326,0.0014547277,0.22496106,0.099566616,0.07879595],"study_design_scores_gemma":[0.00026041712,0.00009445734,0.005631427,0.00008339211,0.00007757862,0.00014995999,0.0010242208,0.8030855,0.001099596,0.0849858,0.103381924,0.00012570427],"about_ca_topic_score_codex":0.9203824,"about_ca_topic_score_gemma":0.93208826,"teacher_disagreement_score":0.07961762,"about_ca_system_score_codex":0.027600342,"about_ca_system_score_gemma":0.039490152,"threshold_uncertainty_score":0.20025545},"labels":[],"label_agreement":null},{"id":"W2154441251","doi":"10.1007/s10696-012-9162-3","title":"Environmental and economic issues arising from the pooling of SMEs’ supply chains: case study of the food industry in western France","year":2012,"lang":"en","type":"article","venue":"Flexible Services and Manufacturing Journal","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":53,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"Conservatoire National des Arts et Métiers","keywords":"Pooling; Supply chain; Business; Order (exchange); Supply network; Industrial organization; Environmental economics; Computer science; Operations research; Marketing; Economics; Engineering; Finance","score_opus":0.013582670686573402,"score_gpt":0.2457775649177627,"score_spread":0.2321948942311893,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2154441251","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99579465,0.00013586205,0.0010397466,0.0003660065,0.0000026228608,0.000016986309,0.000029250432,0.00000819313,0.0026067484],"genre_scores_gemma":[0.9988036,0.000061155275,0.00035461772,0.000017896597,0.0000033416868,0.0000061589885,0.000023521898,0.0000023942207,0.0007272322],"study_design_codex":"observational","study_design_gemma":"qualitative","domain_scores_codex":[0.99896276,0.0005162746,0.000036296904,0.000098027354,0.00010159653,0.00028506035],"domain_scores_gemma":[0.99809843,0.0011508869,0.00028335775,0.000094509036,0.00019711412,0.00017566203],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017527404,0.00036328327,0.0005113639,0.0011510771,0.0028283037,0.002979898,0.0007870608,0.0023509066,0.0037728515],"category_scores_gemma":[0.002766197,0.00032954832,0.00088133366,0.0023899307,0.0015524167,0.001994836,0.002106962,0.0005104642,0.00022198269],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.003098019,0.003015733,0.37007022,0.0008154283,0.0012667109,0.044229202,0.035467815,0.3590918,0.016890783,0.03943345,0.0044454224,0.122175366],"study_design_scores_gemma":[0.00039471808,0.0026135368,0.36696726,0.0005419114,0.0013697309,0.0032114126,0.28182286,0.27201584,0.0082200635,0.037875663,0.024608226,0.0003587949],"about_ca_topic_score_codex":0.08530455,"about_ca_topic_score_gemma":0.09681353,"teacher_disagreement_score":0.08530455,"about_ca_system_score_codex":0.00402074,"about_ca_system_score_gemma":0.0017395911,"threshold_uncertainty_score":0.16961604},"labels":[],"label_agreement":null},{"id":"W2155032754","doi":"10.1016/j.ejor.2012.08.014","title":"Rich routing problems arising in supply chain management","year":2012,"lang":"en","type":"article","venue":"European Journal of Operational Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":105,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"","keywords":"Interdependence; Computer science; Supply chain; Vehicle routing problem; Scheduling (production processes); Supply chain management; Routing (electronic design automation); Operations research; Context (archaeology); Mathematical optimization; Focus (optics); Mathematics; Business","score_opus":0.07711657160685632,"score_gpt":0.35139712469357054,"score_spread":0.27428055308671423,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2155032754","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10908976,0.0036289184,0.86241794,0.0030915267,0.00024063737,0.00009606699,0.00045460725,0.0001340968,0.020846417],"genre_scores_gemma":[0.8025799,0.004770371,0.15929689,0.00073504273,0.0007488475,0.00026929678,0.00093565194,0.00023385431,0.030430207],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99879265,0.00066869246,0.00004179651,0.00015537211,0.00024601867,0.00009544488],"domain_scores_gemma":[0.9931605,0.00570457,0.0004835762,0.00016099795,0.00025135762,0.00023908891],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002266164,0.0011854559,0.0017245987,0.0016998888,0.0011002735,0.0025982624,0.0019025691,0.003926259,0.0049772607],"category_scores_gemma":[0.013242628,0.0013432943,0.001369784,0.0022118262,0.0020882078,0.0055532153,0.0029835268,0.0032838036,0.0003135755],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000075983946,0.00009973157,0.0005378727,0.00031682692,0.00010343873,0.0002547646,0.00016030762,0.5555337,0.00068145455,0.4301392,0.0034459739,0.008650794],"study_design_scores_gemma":[0.000025513502,0.000028167444,0.00020506338,0.000044588607,0.00003130138,0.000066304536,0.00007645293,0.65962774,0.00023613028,0.3380764,0.0015652601,0.000017041752],"about_ca_topic_score_codex":0.0015723506,"about_ca_topic_score_gemma":0.0017753476,"teacher_disagreement_score":0.0049772607,"about_ca_system_score_codex":0.0012248778,"about_ca_system_score_gemma":0.0006762594,"threshold_uncertainty_score":0.016650558},"labels":[],"label_agreement":null},{"id":"W2155052104","doi":"10.1109/ccece.2007.350","title":"A Swarm Optimizer Based on Multi-Criterion Decision Making, Part II: Case Study","year":2007,"lang":"en","type":"article","venue":"","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Generality; Benchmark (surveying); Travelling salesman problem; Mathematical optimization; Computer science; Class (philosophy); Fraction (chemistry); Swarm behaviour; Mathematics; Artificial intelligence","score_opus":0.038915302723277215,"score_gpt":0.3448650715935365,"score_spread":0.3059497688702593,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2155052104","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9443755,0.00039708946,0.045485068,0.00043764611,0.00007581802,0.0004563414,0.00043223627,0.00031547848,0.008024757],"genre_scores_gemma":[0.9562447,0.00013672683,0.04001799,0.000041702544,0.000011500191,0.00022568877,0.0002729876,0.000028080178,0.0030206041],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993268,0.0002913496,0.00004430993,0.00007143739,0.00017557337,0.00009047497],"domain_scores_gemma":[0.99867827,0.000757713,0.000080532765,0.00018549856,0.00019013582,0.000107943306],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014611733,0.00082594156,0.00083402265,0.00047762046,0.0005520526,0.0006616495,0.0008625774,0.001849231,0.0017634236],"category_scores_gemma":[0.0026818593,0.00027516522,0.00069451984,0.0005823347,0.0006309562,0.0006731373,0.000579541,0.0007139195,0.00033591568],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00092946837,0.0014828267,0.008607613,0.00047135138,0.00019192733,0.0020583642,0.00024065183,0.9182922,0.008287079,0.0045660995,0.0032589487,0.05161343],"study_design_scores_gemma":[0.00031266324,0.0017361608,0.0041176663,0.000024016714,0.00004011581,0.00030111976,0.00017366916,0.9787235,0.010963906,0.001250691,0.0023257707,0.000030760646],"about_ca_topic_score_codex":0.0056564556,"about_ca_topic_score_gemma":0.003848124,"teacher_disagreement_score":0.0056564556,"about_ca_system_score_codex":0.00059882517,"about_ca_system_score_gemma":0.0004997146,"threshold_uncertainty_score":0.011247039},"labels":[],"label_agreement":null},{"id":"W2155293443","doi":"10.1016/j.cor.2013.08.010","title":"A hybrid variable neighborhood tabu search heuristic for the vehicle routing problem with multiple time windows","year":2013,"lang":"en","type":"article","venue":"Computers & Operations Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":160,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Group for Research in Decision Analysis; HEC Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Tabu search; Benchmark (surveying); Vehicle routing problem; Heuristic; Computer science; Mathematical optimization; Variable (mathematics); Variable neighborhood search; Ant colony optimization algorithms; Algorithm; Routing (electronic design automation); Metaheuristic; Mathematics","score_opus":0.030289850259653747,"score_gpt":0.29436816292635276,"score_spread":0.264078312666699,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2155293443","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.096585386,0.0012168003,0.8902723,0.00025530683,0.00026267514,0.00018195355,0.00018872098,0.0008581491,0.010178744],"genre_scores_gemma":[0.49693283,0.00036386173,0.4957435,0.00019017523,0.00007664526,0.00036732157,0.00026285712,0.00021857055,0.005844247],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996321,0.0001475496,0.000014294908,0.00004223901,0.0001084609,0.000055289165],"domain_scores_gemma":[0.99943,0.00033194924,0.00004643226,0.000044047294,0.00011251346,0.000035051613],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000812923,0.0004896174,0.0010797225,0.00085264555,0.0005628625,0.00082189986,0.001936116,0.0013801476,0.00413086],"category_scores_gemma":[0.0016285131,0.00054718606,0.00065874826,0.0012036883,0.00036303245,0.000874974,0.0006756384,0.0006269248,0.0005163584],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020858645,0.0001316221,0.00036468718,0.0000750516,0.00006130734,0.000057120214,0.00004240092,0.9025411,0.0019033554,0.0050650025,0.0019835085,0.087566264],"study_design_scores_gemma":[0.000033200286,0.00005507213,0.00008442706,0.000005344225,0.000011146121,0.000013462687,0.000008408591,0.99824107,0.0002571799,0.0007564721,0.00052901957,0.0000051783677],"about_ca_topic_score_codex":0.0065446324,"about_ca_topic_score_gemma":0.006489536,"teacher_disagreement_score":0.0065446324,"about_ca_system_score_codex":0.0008169961,"about_ca_system_score_gemma":0.0010717466,"threshold_uncertainty_score":0.013819158},"labels":[],"label_agreement":null},{"id":"W2155694204","doi":"10.1287/trsc.1120.0449","title":"Analysis and Branch-and-Cut Algorithm for the Time-Dependent Travelling Salesman Problem","year":2012,"lang":"en","type":"article","venue":"Transportation Science","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":66,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Travelling salesman problem; Branch and cut; Bottleneck traveling salesman problem; Bounding overwatch; Mathematics; 2-opt; Traveling purchaser problem; Integer programming; Tree traversal; Branch and bound; Graph; Combinatorics; Hamiltonian path; Time complexity; Combinatorial optimization; Mathematical optimization; Algorithm; Computer science","score_opus":0.015636451574653952,"score_gpt":0.270342152026532,"score_spread":0.25470570045187807,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2155694204","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01861703,0.0005924116,0.9695013,0.0005558546,0.00006472685,0.00022421511,0.0002588667,0.0006797677,0.009505817],"genre_scores_gemma":[0.13852811,0.0007019668,0.8540403,0.00018006554,0.00011087403,0.0005185584,0.0010628098,0.00038378115,0.0044734855],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987293,0.00040821207,0.000058933165,0.00020133174,0.00035463966,0.00024758818],"domain_scores_gemma":[0.99762636,0.0016976659,0.00016387402,0.00012771216,0.00028821,0.000096224736],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020691433,0.0017775317,0.0016907622,0.0017398926,0.0011506316,0.002431711,0.0023090257,0.0016077,0.008574202],"category_scores_gemma":[0.0055800313,0.00087901513,0.0014067963,0.0025426126,0.0009120643,0.0024633855,0.0013760339,0.0031302094,0.0013012859],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022575115,0.0003228787,0.0007943417,0.0002551347,0.000078700454,0.00012920021,0.00015125517,0.7663187,0.0021368423,0.08008197,0.00892004,0.1405852],"study_design_scores_gemma":[0.000027938784,0.000025955847,0.0000744937,0.000012980913,0.00001409866,0.000016083404,0.000017532651,0.9749634,0.00032716966,0.023481127,0.0010332596,0.000005882826],"about_ca_topic_score_codex":0.012346975,"about_ca_topic_score_gemma":0.010129451,"teacher_disagreement_score":0.012346975,"about_ca_system_score_codex":0.002989517,"about_ca_system_score_gemma":0.0045880848,"threshold_uncertainty_score":0.028683543},"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,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_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","score_opus":0.12692481900359817,"score_gpt":0.40012140670801616,"score_spread":0.273196587704418,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2157158960","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00460643,0.00013713186,0.99183196,0.00009065852,0.000024880002,0.000055955134,0.00007258963,0.00028632322,0.002894117],"genre_scores_gemma":[0.117578074,0.00034079453,0.87641484,0.00006270266,0.000051312378,0.0003186561,0.00046029536,0.00025202028,0.00452132],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99947006,0.00019822577,0.00001889613,0.00008277058,0.0001719627,0.00005808302],"domain_scores_gemma":[0.9994697,0.000326948,0.000053761705,0.00006431102,0.00006097306,0.00002436766],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00097441784,0.0013033665,0.0009531085,0.0007526155,0.0005448179,0.0008629271,0.0010307339,0.00085280795,0.0054547405],"category_scores_gemma":[0.0021737786,0.0007350396,0.00085545424,0.0009974655,0.00063637714,0.0010530936,0.0009800782,0.001675806,0.0010909998],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000050945826,0.000040432584,0.00013529362,0.00010915081,0.000023458344,0.000059075708,0.000036403297,0.9081748,0.001414805,0.030641617,0.0022303243,0.057083767],"study_design_scores_gemma":[0.000015565629,0.000016191729,0.000046372545,0.0000095174655,0.0000046852883,0.000017771996,0.000011923603,0.9762891,0.0005731061,0.021206815,0.0018038065,0.000005201997],"about_ca_topic_score_codex":0.00260539,"about_ca_topic_score_gemma":0.0035635503,"teacher_disagreement_score":0.0054547405,"about_ca_system_score_codex":0.0009059009,"about_ca_system_score_gemma":0.0013638345,"threshold_uncertainty_score":0.018247962},"labels":[],"label_agreement":null},{"id":"W2157589720","doi":"10.1287/trsc.2013.0503","title":"A New Formulation Based on Customer Delivery Patterns for a Maritime Inventory Routing Problem","year":2014,"lang":"en","type":"article","venue":"Transportation Science","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":29,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Group for Research in Decision Analysis","funders":"","keywords":"Column generation; Solver; Integer programming; Revenue; Operations research; Mathematical optimization; Liquefied natural gas; Scheduling (production processes); Benchmark (surveying); Computer science; Routing (electronic design automation); Lagrangian relaxation; Branch and price; Term (time); Engineering; Mathematics; Economics; Natural gas; Computer network","score_opus":0.01901026960619377,"score_gpt":0.2648938226044016,"score_spread":0.24588355299820783,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2157589720","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005914673,0.0002079285,0.984783,0.0006240643,0.00014072945,0.00014374437,0.00036344046,0.00011607196,0.007706383],"genre_scores_gemma":[0.12226743,0.0008387876,0.86458176,0.00038180593,0.00028433502,0.0006223479,0.0008926463,0.000199949,0.009930976],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99874216,0.0005046728,0.00006237094,0.0002287491,0.00033855028,0.00012342301],"domain_scores_gemma":[0.9989613,0.0005566829,0.00014014872,0.00008765568,0.00018147475,0.000072728035],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015444984,0.0013602497,0.00086793175,0.00078946527,0.00041172167,0.0017196343,0.0017389761,0.001493182,0.006788337],"category_scores_gemma":[0.0030045072,0.0007294863,0.00092906837,0.0019026877,0.0005067446,0.0022520272,0.0010135922,0.0024147239,0.0009151302],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008390978,0.00016691239,0.0006366384,0.00030301188,0.000051194296,0.00021662451,0.00009814248,0.77045345,0.0023452735,0.13963275,0.013611882,0.07240016],"study_design_scores_gemma":[0.00002586681,0.00005102576,0.000108200715,0.000026565795,0.000013603903,0.00011200938,0.00003087749,0.964351,0.0004795946,0.026841866,0.007949347,0.000009935719],"about_ca_topic_score_codex":0.0027166826,"about_ca_topic_score_gemma":0.0035791784,"teacher_disagreement_score":0.006788337,"about_ca_system_score_codex":0.0012022852,"about_ca_system_score_gemma":0.001922533,"threshold_uncertainty_score":0.02270925},"labels":[],"label_agreement":null},{"id":"W2158175311","doi":"10.1287/opre.51.6.940.24921","title":"A Branch-and-Cut Algorithm for the Undirected Traveling Purchaser Problem","year":2003,"lang":"en","type":"article","venue":"Operations Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":87,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"","keywords":"Branch and cut; Linear programming relaxation; Mathematical optimization; Facet (psychology); Relaxation (psychology); Undirected graph; Integer programming; Mathematics; Integer (computer science); Linear programming; Algorithm; Polyhedron; Travelling salesman problem; Computer science; Combinatorics; Graph","score_opus":0.06787206902597621,"score_gpt":0.3684388767560152,"score_spread":0.300566807730039,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2158175311","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0062134494,0.00018106464,0.9888493,0.00015621183,0.000026814378,0.00017878835,0.00012109625,0.00044227252,0.0038310008],"genre_scores_gemma":[0.042646132,0.00023960153,0.95384943,0.00009280677,0.000025777481,0.0003378311,0.00058245804,0.00018025462,0.0020457446],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99922764,0.00024860442,0.000040771076,0.00015539014,0.00021378211,0.00011383818],"domain_scores_gemma":[0.9989938,0.000689729,0.0000707988,0.00006200468,0.00014280081,0.000040785606],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010273345,0.0012744095,0.001256006,0.0011516222,0.0010069382,0.0011986946,0.0014338932,0.0014129215,0.006909369],"category_scores_gemma":[0.002994783,0.0006457016,0.000764469,0.00201012,0.00053441414,0.0018072702,0.0012818463,0.0018906342,0.0011893994],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017854746,0.0003365034,0.00070039957,0.00030132572,0.00009033507,0.00023485596,0.00018442706,0.41287044,0.0034308487,0.04547686,0.0137105975,0.5224849],"study_design_scores_gemma":[0.00008575123,0.000094962495,0.00015164273,0.00002330523,0.000031793774,0.00010339075,0.000051093175,0.9675001,0.0013134071,0.024606178,0.006023452,0.000014949268],"about_ca_topic_score_codex":0.0055248905,"about_ca_topic_score_gemma":0.005769484,"teacher_disagreement_score":0.006909369,"about_ca_system_score_codex":0.0010233681,"about_ca_system_score_gemma":0.0018559607,"threshold_uncertainty_score":0.023114145},"labels":[],"label_agreement":null},{"id":"W2158354221","doi":"10.1287/trsc.1100.0363","title":"Enhanced Branch and Price and Cut for Vehicle Routing with Split Deliveries and Time Windows","year":2011,"lang":"en","type":"article","venue":"Transportation Science","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":99,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Group for Research in Decision Analysis; Université Laval","funders":"","keywords":"Vehicle routing problem; Column generation; Tabu search; Mathematical optimization; Routing (electronic design automation); Path (computing); Computer science; Branch and cut; Mathematics; Integer programming; Computer network","score_opus":0.017258190346143072,"score_gpt":0.24024294705219787,"score_spread":0.2229847567060548,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2158354221","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014223624,0.00042843944,0.9824532,0.00011162078,0.000042487773,0.00007316968,0.000047480968,0.000103177525,0.0025168422],"genre_scores_gemma":[0.24421301,0.0008014123,0.7495729,0.000101971054,0.00009327543,0.00026894544,0.00024507093,0.00014309974,0.004560358],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990037,0.0004298167,0.000029690715,0.00009387114,0.00033494562,0.000108008964],"domain_scores_gemma":[0.99835485,0.0011838936,0.00012496584,0.000115389805,0.00015983084,0.00006098339],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019080552,0.0009346855,0.0011361522,0.0006481196,0.0005201307,0.0010163616,0.0012031627,0.0008722213,0.0036931653],"category_scores_gemma":[0.0048151556,0.00063898345,0.0008425223,0.0014167359,0.000822663,0.002311423,0.0010151515,0.0023383219,0.00053187076],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016870118,0.00010414353,0.0004289246,0.00015233601,0.00005678728,0.000097496806,0.00007085668,0.83180195,0.0019611232,0.06833604,0.0019327496,0.09488893],"study_design_scores_gemma":[0.000015834656,0.0000357383,0.000065656175,0.0000057420084,0.000007799113,0.000022078579,0.0000063951984,0.9813757,0.0004629701,0.017045612,0.00095266243,0.0000038133358],"about_ca_topic_score_codex":0.002666356,"about_ca_topic_score_gemma":0.0027821378,"teacher_disagreement_score":0.0036931653,"about_ca_system_score_codex":0.00087781035,"about_ca_system_score_gemma":0.0014827746,"threshold_uncertainty_score":0.012354851},"labels":[],"label_agreement":null},{"id":"W2158930858","doi":"10.3138/infor.47.3.223","title":"Locating Satellite Yards in Forestry Operations","year":2009,"lang":"en","type":"article","venue":"INFOR Information Systems and Operational Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"HEC Montréal","funders":"Natural Resources Canada; Social Sciences and Humanities Research Council of Canada; Natural Sciences and Engineering Research Council of Canada; FPInnovations","keywords":"Yard; Satellite; Integer programming; Operations research; Thunder; Linear programming; Computer science; Engineering; Geography; Meteorology","score_opus":0.04898157019413371,"score_gpt":0.3574831592876238,"score_spread":0.3085015890934901,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2158930858","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.887828,0.00033441006,0.07735048,0.0005675312,0.000033727305,0.00041391692,0.000720157,0.00016856787,0.03258318],"genre_scores_gemma":[0.97502863,0.00020078017,0.016665429,0.000018238377,0.000004507518,0.00006423565,0.00017944911,0.000011492844,0.007827215],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99941397,0.00019870106,0.000015666625,0.000100109166,0.00012017122,0.00015141787],"domain_scores_gemma":[0.9996723,0.00016965956,0.00004240668,0.000019282172,0.000053756452,0.000042587315],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005313,0.00046237675,0.00034937364,0.0005578708,0.0018180731,0.0015895322,0.0011291761,0.0015059592,0.0034767552],"category_scores_gemma":[0.0009555517,0.00042929157,0.0004463981,0.0015157495,0.00085700525,0.000910246,0.0005241208,0.00050075265,0.0002790893],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010473411,0.000087401946,0.00588912,0.000069079004,0.00001818552,0.0009951214,0.00041581262,0.97138906,0.001975223,0.0069396156,0.0007893511,0.01132725],"study_design_scores_gemma":[0.00005271039,0.0002059529,0.005574821,0.000040346138,0.000036008427,0.000280245,0.002952916,0.9796138,0.0024335494,0.002190538,0.0065754317,0.00004368478],"about_ca_topic_score_codex":0.5256366,"about_ca_topic_score_gemma":0.7092859,"teacher_disagreement_score":0.5256366,"about_ca_system_score_codex":0.007838911,"about_ca_system_score_gemma":0.0068883724,"threshold_uncertainty_score":0.954314},"labels":[],"label_agreement":null},{"id":"W2159784590","doi":"10.1287/opre.1050.0222","title":"Dynamic Aggregation of Set-Partitioning Constraints in Column Generation","year":2005,"lang":"en","type":"article","venue":"Operations Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":95,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kronos (Canada); Polytechnique Montréal; Group for Research in Decision Analysis","funders":"","keywords":"Column generation; Crew scheduling; Mathematical optimization; Scheduling (production processes); Computer science; Degeneracy (biology); Equivalence relation; Relaxation (psychology); Set (abstract data type); Equivalence (formal languages); Job shop scheduling; Relation (database); Routing (electronic design automation); Mathematics; Discrete mathematics","score_opus":0.07615527304630118,"score_gpt":0.3934507778213092,"score_spread":0.317295504775008,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2159784590","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.037408777,0.00025820817,0.9570859,0.00016127127,0.0000748904,0.00024185411,0.00020192908,0.0006824157,0.0038846864],"genre_scores_gemma":[0.28084108,0.00022949785,0.71542263,0.000135847,0.00006703469,0.00034823758,0.00066775776,0.00017692191,0.0021108608],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990694,0.00033092216,0.000051137362,0.00012700654,0.00029240202,0.00012918218],"domain_scores_gemma":[0.9974579,0.0014480442,0.00020323061,0.00038250012,0.00042624178,0.0000821194],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011270338,0.00089046557,0.0011781742,0.0011026232,0.0007911824,0.0009419431,0.0009579198,0.00054422725,0.0040829303],"category_scores_gemma":[0.0042246054,0.00053328834,0.0008168226,0.0022188586,0.00060498784,0.0012568556,0.00124717,0.0011700541,0.0005580947],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015634944,0.0002345328,0.0012714316,0.00021191091,0.00006025371,0.00017024647,0.00020985775,0.69884217,0.011844311,0.021108769,0.006829986,0.2590601],"study_design_scores_gemma":[0.000042951546,0.000090321024,0.00026639114,0.000017114853,0.00002160213,0.000059159564,0.00004709995,0.9765772,0.0057224967,0.012589129,0.004546012,0.000020504083],"about_ca_topic_score_codex":0.0045874724,"about_ca_topic_score_gemma":0.0063311406,"teacher_disagreement_score":0.0045874724,"about_ca_system_score_codex":0.0006667795,"about_ca_system_score_gemma":0.0012917651,"threshold_uncertainty_score":0.013658702},"labels":[],"label_agreement":null},{"id":"W2159913713","doi":"10.1007/s10288-010-0145-5","title":"A large neighbourhood search heuristic for the aircraft and passenger recovery problem","year":2010,"lang":"en","type":"article","venue":"4OR","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":73,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal; Université de Montréal","funders":"HEC Montréal","keywords":"Iterated local search; Operations research; Schedule; Computer science; Heuristic; Context (archaeology); Neighbourhood (mathematics); Set (abstract data type); Vehicle routing problem; Randomness; Mathematical optimization; Local search (optimization); Routing (electronic design automation); Engineering; Mathematics; Artificial intelligence","score_opus":0.012263228661227093,"score_gpt":0.2616284954726309,"score_spread":0.2493652668114038,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2159913713","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.049703524,0.0010271366,0.934738,0.00048952707,0.00021645518,0.00015045752,0.0001313636,0.0004013027,0.013142139],"genre_scores_gemma":[0.5154577,0.0004468369,0.4680098,0.00025407766,0.00012338551,0.00040078026,0.0003125414,0.00021372338,0.014781244],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99951863,0.00025757292,0.000016189466,0.000060607206,0.000092767725,0.000054152282],"domain_scores_gemma":[0.9985488,0.0011282034,0.00008389558,0.000065369604,0.000098811586,0.00007487462],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011348758,0.00053365546,0.0011470567,0.0011164364,0.00057837804,0.00071140827,0.0015398155,0.0018205158,0.0059087044],"category_scores_gemma":[0.003536076,0.0005133812,0.00077469676,0.00097315706,0.0008358104,0.0012895457,0.0011572543,0.0009446965,0.0005701643],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015173531,0.00009568384,0.00020572846,0.00007204399,0.00003517844,0.00007430292,0.000051694806,0.9415246,0.0008170345,0.012531572,0.0020461287,0.042394217],"study_design_scores_gemma":[0.00004304429,0.000036455654,0.000060566803,0.000008737783,0.000007139982,0.000013678617,0.000009883751,0.9947001,0.00011627169,0.004339546,0.00065876567,0.0000058139717],"about_ca_topic_score_codex":0.0057026353,"about_ca_topic_score_gemma":0.0067827296,"teacher_disagreement_score":0.0059087044,"about_ca_system_score_codex":0.0010353058,"about_ca_system_score_gemma":0.0011165784,"threshold_uncertainty_score":0.01976657},"labels":[],"label_agreement":null},{"id":"W2160968569","doi":"10.1287/trsc.1100.0326","title":"The Dynamic Uncapacitated Hub Location Problem","year":2010,"lang":"en","type":"article","venue":"Transportation Science","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":104,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"","keywords":"Lagrangian relaxation; Mathematical optimization; Lagrangian; Reduction (mathematics); Set (abstract data type); Exploit; Relaxation (psychology); Upper and lower bounds; Tree (set theory); Function (biology); Computer science; Branch and bound; Mathematics; Applied mathematics; Combinatorics","score_opus":0.008700321292497787,"score_gpt":0.26349886339977696,"score_spread":0.25479854210727915,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2160968569","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05969798,0.0013530633,0.9008796,0.0013838985,0.000252074,0.00015037348,0.0010953642,0.0003253164,0.03486222],"genre_scores_gemma":[0.8333916,0.001979332,0.13705646,0.00021445812,0.00022156011,0.00028346726,0.0015364982,0.0001698263,0.025146822],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99948585,0.00015202363,0.000018746212,0.00014728145,0.00009225624,0.00010394403],"domain_scores_gemma":[0.999567,0.00024307142,0.00006130214,0.000035474728,0.000043337805,0.00004986491],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048761192,0.0009039553,0.0008217205,0.00048370313,0.0005904594,0.0013489575,0.0017649612,0.001295976,0.0057267756],"category_scores_gemma":[0.0012239459,0.00050825387,0.0004879271,0.0012278815,0.0006764883,0.0016820064,0.0010182831,0.0010912659,0.000548537],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000104559185,0.00009163737,0.00040406283,0.00022907846,0.000048707705,0.0003575977,0.000052623316,0.88301295,0.001242585,0.06698503,0.006483343,0.0409878],"study_design_scores_gemma":[0.000041527794,0.000060260994,0.00031274994,0.000026441596,0.000026294436,0.00021007152,0.00007856795,0.9454519,0.001041618,0.041020926,0.011710052,0.000019691557],"about_ca_topic_score_codex":0.0033590407,"about_ca_topic_score_gemma":0.0033979558,"teacher_disagreement_score":0.0057267756,"about_ca_system_score_codex":0.001033202,"about_ca_system_score_gemma":0.0013231516,"threshold_uncertainty_score":0.019158006},"labels":[],"label_agreement":null},{"id":"W2161733364","doi":"10.1287/trsc.1050.0130","title":"Inbound Logistic Planning: Minimizing Transportation and Inventory Cost","year":2006,"lang":"en","type":"article","venue":"Transportation Science","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":65,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Lagrangian relaxation; Mathematical optimization; Heuristic; Supply chain; Operations research; Supply chain management; Computer science; Nonlinear programming; Set (abstract data type); Integer programming; Relaxation (psychology); Pipeline (software); Linear programming; Inventory theory; Nonlinear system; Inventory control; Mathematics","score_opus":0.04512122447174366,"score_gpt":0.305077298297317,"score_spread":0.25995607382557334,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2161733364","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.037064306,0.00088929886,0.9408734,0.00068813405,0.000089009176,0.0002098368,0.00042164678,0.0004541375,0.0193103],"genre_scores_gemma":[0.67329985,0.0022828823,0.29115307,0.00019641183,0.00012702629,0.00043464432,0.0010121157,0.00041719602,0.031076808],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993781,0.00025808005,0.000018270714,0.00007892336,0.00013310488,0.00013363757],"domain_scores_gemma":[0.99952507,0.00023914828,0.00006513305,0.000027973174,0.000069125876,0.0000734895],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00091495324,0.0023141163,0.0016554326,0.0011376739,0.0009566821,0.001922308,0.0021887566,0.001711502,0.0071561453],"category_scores_gemma":[0.0014993637,0.0012867018,0.0006549922,0.0024969012,0.0010948573,0.0020627426,0.0011588725,0.0012261044,0.001265607],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000087492,0.00005113062,0.00022725273,0.000121125755,0.000021478103,0.000080144484,0.00004521678,0.9699836,0.00042518147,0.008193127,0.0018706131,0.018893743],"study_design_scores_gemma":[0.000020555452,0.00004060818,0.00008410857,0.00001629678,0.00001416633,0.000026831616,0.000047785248,0.98741686,0.0006099823,0.009724077,0.001988682,0.0000100322395],"about_ca_topic_score_codex":0.019360682,"about_ca_topic_score_gemma":0.018213253,"teacher_disagreement_score":0.019360682,"about_ca_system_score_codex":0.002179993,"about_ca_system_score_gemma":0.0023638771,"threshold_uncertainty_score":0.038496017},"labels":[],"label_agreement":null},{"id":"W2163513576","doi":"10.1002/net.21520","title":"A branch‐price‐and‐cut algorithm for the min‐max <i>k</i>‐vehicle windy rural postman problem","year":2013,"lang":"en","type":"article","venue":"Networks","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Group for Research in Decision Analysis","funders":"","keywords":"Set (abstract data type); Branch and cut; Computer science; Mathematical optimization; Algorithm; Graph; Vehicle routing problem; Mathematics; Combinatorics; Integer programming; Routing (electronic design automation)","score_opus":0.007274112739817685,"score_gpt":0.21954171832004143,"score_spread":0.21226760558022376,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2163513576","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03805892,0.00054904475,0.9496461,0.00049585366,0.0000812015,0.0003547811,0.00035496792,0.00046196475,0.009996989],"genre_scores_gemma":[0.18763426,0.00034858758,0.8046831,0.00013866623,0.00005964927,0.00047884765,0.0006971882,0.00018332837,0.0057762805],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99960023,0.00014213198,0.000017659964,0.00008294534,0.0000694564,0.000087639],"domain_scores_gemma":[0.9988921,0.00078210025,0.000091030764,0.000045076,0.00009921739,0.000090495734],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010042551,0.0011567936,0.0013834211,0.000902915,0.00080544176,0.0010831429,0.0015372772,0.0015245401,0.009351803],"category_scores_gemma":[0.0021706459,0.0006641403,0.000707646,0.001461039,0.00055747235,0.0013528476,0.0011147169,0.0015855922,0.0008811308],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000254314,0.00029020951,0.000733569,0.00022041642,0.000058609865,0.00016115264,0.000113309696,0.81887895,0.0013131263,0.02069612,0.009441944,0.14783831],"study_design_scores_gemma":[0.00006142507,0.00006203496,0.00010596138,0.000012514835,0.000010175362,0.00003818821,0.000029438557,0.98582065,0.00037951206,0.012003711,0.0014693775,0.000006943555],"about_ca_topic_score_codex":0.0057224436,"about_ca_topic_score_gemma":0.0058414782,"teacher_disagreement_score":0.009351803,"about_ca_system_score_codex":0.0010621172,"about_ca_system_score_gemma":0.0019030619,"threshold_uncertainty_score":0.03128487},"labels":[],"label_agreement":null},{"id":"W2163715979","doi":"10.1007/978-0-387-77778-8_8","title":"Parallel Solution Methods for Vehicle Routing Problems","year":2008,"lang":"en","type":"book-chapter","venue":"Operations research, computer science. Interface series","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":45,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal; Université de Montréal","funders":"","keywords":"Vehicle routing problem; Heuristic; Pace; Computer science; Metaheuristic; Mathematical optimization; Routing (electronic design automation); Field (mathematics); Combinatorial optimization; Algorithm; Mathematics; Geography; Computer network","score_opus":0.09522652726484755,"score_gpt":0.3867614583324927,"score_spread":0.29153493106764516,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2163715979","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0015780979,0.0023295577,0.9783876,0.00033226583,0.00034182536,0.000042936477,0.00007069239,0.00030227206,0.016614828],"genre_scores_gemma":[0.11334703,0.0080197025,0.822478,0.00035479185,0.0008695091,0.0007399386,0.0005163245,0.0008155373,0.05285913],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99944,0.00016810541,0.000026371878,0.000064997956,0.0002623445,0.000038146285],"domain_scores_gemma":[0.9994741,0.0002423651,0.00003315717,0.00008194411,0.00014939457,0.00001909426],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007823626,0.0012984452,0.0010025832,0.0006939602,0.0005465952,0.0009903159,0.0013862216,0.0009808326,0.008478667],"category_scores_gemma":[0.0023919311,0.00057255337,0.00091317215,0.0013900322,0.0007183032,0.001172092,0.0013210241,0.0019496063,0.0029406687],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000078625846,0.00010541709,0.00023418985,0.0004383999,0.00008315306,0.00008136142,0.00010506211,0.42684835,0.0021555978,0.2200186,0.030676458,0.31917486],"study_design_scores_gemma":[0.000041396663,0.00002074138,0.000084368614,0.00004052001,0.000016716052,0.000051852632,0.00002133142,0.7839109,0.0007832094,0.18578771,0.029232083,0.000009235716],"about_ca_topic_score_codex":0.0030879357,"about_ca_topic_score_gemma":0.0031412796,"teacher_disagreement_score":0.008478667,"about_ca_system_score_codex":0.00068487297,"about_ca_system_score_gemma":0.0009660734,"threshold_uncertainty_score":0.028364003},"labels":[],"label_agreement":null},{"id":"W2163940244","doi":"10.1057/palgrave.jors.2601775","title":"Design of balanced MBA student teams","year":2004,"lang":"en","type":"article","venue":"Journal of the Operational Research Society","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":39,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Group for Research in Decision Analysis; HEC Montréal","funders":"","keywords":"Computer science; Metaheuristic; Project management; Population; Set (abstract data type); Operations research; Mathematical optimization; Norm (philosophy); Representation (politics); Management science; Mathematics; Engineering","score_opus":0.06223547916946872,"score_gpt":0.392809572784927,"score_spread":0.3305740936154583,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2163940244","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22032696,0.00023871833,0.76239556,0.00036030923,0.00020999942,0.0021139162,0.00022592691,0.0005370894,0.013591557],"genre_scores_gemma":[0.69903684,0.000113604736,0.28953567,0.00010983597,0.00006573596,0.0030371258,0.0001987693,0.00009461737,0.0078077656],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.996979,0.001756146,0.000112408496,0.00045594876,0.0003638099,0.00033268618],"domain_scores_gemma":[0.99617344,0.0009382998,0.00054213067,0.00034311714,0.0008365447,0.0011665937],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026917835,0.00061919395,0.00088349654,0.0007221719,0.00086709106,0.0014394651,0.0018245217,0.000845237,0.00828972],"category_scores_gemma":[0.0069180476,0.0005571664,0.0004395837,0.00038871774,0.00047832783,0.00081649807,0.001984675,0.00088471174,0.0022346207],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0048979307,0.002702004,0.016392643,0.00060034485,0.00028153113,0.00029844765,0.0017736745,0.2962355,0.027422719,0.060084235,0.009041255,0.58026975],"study_design_scores_gemma":[0.0009818429,0.006209517,0.004239978,0.00009852576,0.000104280414,0.00023696605,0.0012413812,0.9137076,0.013680488,0.025857985,0.033582374,0.000059113678],"about_ca_topic_score_codex":0.0006633613,"about_ca_topic_score_gemma":0.00086435047,"teacher_disagreement_score":0.00828972,"about_ca_system_score_codex":0.000766261,"about_ca_system_score_gemma":0.0012210725,"threshold_uncertainty_score":0.027731895},"labels":[],"label_agreement":null},{"id":"W2164283282","doi":"10.1109/lcomm.2004.827437","title":"A New Hybrid Constraint-Based Approach for 3G Network Planning","year":2004,"lang":"en","type":"article","venue":"IEEE Communications Letters","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":32,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Heuristics; Computer science; Constraint (computer-aided design); Mathematical optimization; Range (aeronautics); Hybrid algorithm (constraint satisfaction); Network planning and design; Constraint satisfaction; Artificial intelligence; Mathematics; Local consistency; Computer network","score_opus":0.04587738194204006,"score_gpt":0.2900443951567786,"score_spread":0.24416701321473855,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2164283282","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0013699379,0.00011273773,0.99527353,0.00013856402,0.000022621032,0.000028266237,0.00010022006,0.00012770122,0.0028263207],"genre_scores_gemma":[0.1372588,0.00059135054,0.85637563,0.00024351424,0.000059798786,0.00031488194,0.00034574524,0.00013294943,0.004677266],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99954045,0.0001435349,0.000017582464,0.00006604151,0.00019959096,0.000032826883],"domain_scores_gemma":[0.9997409,0.0001270345,0.000027742793,0.00003805387,0.000049832033,0.000016456195],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045148702,0.00073903607,0.00054605806,0.0006446271,0.00046210663,0.00091454794,0.0014537466,0.00079936115,0.00350596],"category_scores_gemma":[0.00073937816,0.00041826762,0.00081366155,0.001421845,0.00044621498,0.0011119907,0.0008508741,0.00093876023,0.0005798474],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000044362205,0.00004304972,0.00023725153,0.00011422352,0.00006159347,0.00013908632,0.00005042804,0.82419294,0.0035974963,0.083486386,0.00534843,0.08268471],"study_design_scores_gemma":[0.0000091094125,0.000014083795,0.00004473013,0.0000073260294,0.000008174406,0.0000418434,0.000006919595,0.98057395,0.00062858424,0.013045739,0.0056101023,0.000009550852],"about_ca_topic_score_codex":0.008220222,"about_ca_topic_score_gemma":0.013540752,"teacher_disagreement_score":0.008220222,"about_ca_system_score_codex":0.0011499384,"about_ca_system_score_gemma":0.0015832771,"threshold_uncertainty_score":0.016344786},"labels":[],"label_agreement":null},{"id":"W2165941447","doi":"10.1016/j.cor.2012.04.003","title":"Lower and upper bounds for the two-echelon capacitated location-routing problem","year":2012,"lang":"en","type":"article","venue":"Computers & Operations Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":166,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal; Transport Canada","funders":"Natural Sciences and Engineering Research Council of Canada; Austrian Science Fund","keywords":"Heuristics; Mathematical optimization; Computer science; Vehicle routing problem; Heuristic; Upper and lower bounds; Branch and bound; Routing (electronic design automation); Set (abstract data type); Neighbourhood (mathematics); Mathematics","score_opus":0.07168323218806766,"score_gpt":0.37346540804947786,"score_spread":0.3017821758614102,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2165941447","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012951242,0.007423044,0.90884763,0.0030573925,0.0005470305,0.00019752664,0.0011459205,0.00058055256,0.06524971],"genre_scores_gemma":[0.39882022,0.010676271,0.55970913,0.0013048272,0.0016575203,0.0013093053,0.0035313067,0.0011471885,0.021844082],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9948626,0.0013041258,0.00023058825,0.0007176233,0.0020371564,0.00084791594],"domain_scores_gemma":[0.98425436,0.01191739,0.0009355734,0.0009056041,0.001426975,0.000560069],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004913009,0.0027913034,0.0021672815,0.0038745978,0.0013921454,0.0064307344,0.004573725,0.003204289,0.019104766],"category_scores_gemma":[0.022289198,0.0009176754,0.0019745436,0.0046694507,0.0018846052,0.00782499,0.003681014,0.006105624,0.0034255872],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002305811,0.00028511265,0.00084105594,0.0008588482,0.00010586075,0.00019954247,0.00018332258,0.59859157,0.0018820037,0.2898446,0.018419141,0.08855828],"study_design_scores_gemma":[0.00003888922,0.00008149373,0.0004080408,0.00020447154,0.000043354525,0.00019665336,0.000089689274,0.81988907,0.0012982793,0.16548921,0.012218435,0.00004237551],"about_ca_topic_score_codex":0.0023950601,"about_ca_topic_score_gemma":0.0030599006,"teacher_disagreement_score":0.019104766,"about_ca_system_score_codex":0.0039031748,"about_ca_system_score_gemma":0.0024225402,"threshold_uncertainty_score":0.063911855},"labels":[],"label_agreement":null},{"id":"W2167459401","doi":"10.1002/net.10105","title":"The generalized minimum spanning tree problem: Polyhedral analysis and branch‐and‐cut algorithm","year":2004,"lang":"en","type":"article","venue":"Networks","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":31,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"Fonds pour la Formation à la Recherche dans l’Industrie et dans l’Agriculture; Natural Sciences and Engineering Research Council of Canada","keywords":"Minimum spanning tree; Steiner tree problem; Spanning tree; Branch and cut; k-minimum spanning tree; Tabu search; Vertex (graph theory); Mathematics; Integer programming; Combinatorics; Kruskal's algorithm; Euclidean geometry; Heuristic; Algorithm; Graph; Mathematical optimization; Tree structure; K-ary tree; Binary tree","score_opus":0.007846760493643152,"score_gpt":0.2338371477629094,"score_spread":0.22599038726926624,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2167459401","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011841389,0.0005074108,0.9808301,0.00028494943,0.000049311788,0.0000935694,0.00010112161,0.00016670863,0.0061254594],"genre_scores_gemma":[0.22253124,0.0009454387,0.7710676,0.00014250622,0.00010963531,0.0003437739,0.00057391135,0.00016708081,0.00411874],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999276,0.0003113823,0.000022836846,0.00009419881,0.0002049288,0.000090638845],"domain_scores_gemma":[0.9992605,0.000476835,0.00006881964,0.00005423845,0.00010099146,0.000038530354],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010090071,0.0008960795,0.0012392848,0.00080988166,0.00058774545,0.0014112997,0.001090056,0.0012255312,0.0045191552],"category_scores_gemma":[0.0030208244,0.00047012593,0.00077603373,0.0017683996,0.0008189178,0.0013923794,0.00094249885,0.0012722658,0.0005789682],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005241888,0.00005136869,0.00022184025,0.00011292056,0.000028440703,0.000053027114,0.000052526415,0.8524824,0.0007760806,0.06494136,0.0042671026,0.076960474],"study_design_scores_gemma":[0.000014784903,0.000014188445,0.000046193723,0.000014242917,0.000005742565,0.000024796846,0.000012849941,0.9639839,0.00022047295,0.033992115,0.0016674502,0.0000033796807],"about_ca_topic_score_codex":0.004785267,"about_ca_topic_score_gemma":0.0038152407,"teacher_disagreement_score":0.004785267,"about_ca_system_score_codex":0.0010666201,"about_ca_system_score_gemma":0.0012409082,"threshold_uncertainty_score":0.015118122},"labels":[],"label_agreement":null},{"id":"W2167917756","doi":"10.1287/trsc.1070.0217","title":"An Effective Multirestart Deterministic Annealing Metaheuristic for the Fleet Size and Mix Vehicle-Routing Problem with Time Windows","year":2008,"lang":"en","type":"article","venue":"Transportation Science","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":107,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; Computer Research Institute of Montréal","funders":"Norges Forskningsråd","keywords":"Metaheuristic; Vehicle routing problem; Simulated annealing; Mathematical optimization; Computer science; Benchmark (surveying); Integer programming; Local search (optimization); Heuristics; Heuristic; Integer (computer science); Routing (electronic design automation); Mathematics","score_opus":0.01505666634643012,"score_gpt":0.26844187007922954,"score_spread":0.25338520373279944,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2167917756","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020254342,0.0005892739,0.9745798,0.00013310384,0.000052643412,0.00011745208,0.000051819494,0.00026285707,0.003958751],"genre_scores_gemma":[0.27755865,0.000550257,0.7169963,0.000143451,0.000050116687,0.0004375019,0.00018078169,0.00013135589,0.0039515467],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99956685,0.00017248758,0.000020672773,0.0000701172,0.00012105362,0.000048796264],"domain_scores_gemma":[0.9997137,0.00015438275,0.00004959571,0.000029565907,0.000032405223,0.000020370002],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00073104026,0.0008326811,0.0009988144,0.00067769527,0.0004358528,0.0006926721,0.0011527045,0.0012249454,0.0016589842],"category_scores_gemma":[0.0012211336,0.0005358942,0.0011635548,0.0006022471,0.00042188464,0.0007711492,0.00063838385,0.0009592231,0.00029968977],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000062224884,0.000081920705,0.00034220918,0.00010223178,0.00006909766,0.00006375776,0.00006649127,0.9191781,0.0039589345,0.013488086,0.0010478813,0.06153892],"study_design_scores_gemma":[0.00002552717,0.00006544972,0.00012905187,0.000012470242,0.00002402075,0.000038391823,0.000010271888,0.993148,0.0012011589,0.0026555543,0.00268125,0.000008853788],"about_ca_topic_score_codex":0.0020906786,"about_ca_topic_score_gemma":0.0028828995,"teacher_disagreement_score":0.0020906786,"about_ca_system_score_codex":0.0007740038,"about_ca_system_score_gemma":0.0009754335,"threshold_uncertainty_score":0.00561589},"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,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_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","score_opus":0.016207461148893893,"score_gpt":0.27872062482300336,"score_spread":0.2625131636741095,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2168210873","genre_codex":"other","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007724381,0.32246187,0.123262614,0.0624905,0.021926232,0.00015065102,0.0012311765,0.00052233716,0.46023026],"genre_scores_gemma":[0.21199697,0.44240606,0.068257466,0.020738577,0.015373439,0.00036021706,0.0019814596,0.00056327175,0.23832269],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99827385,0.00049336214,0.00009077205,0.0003765583,0.00058776245,0.0001776407],"domain_scores_gemma":[0.99849653,0.000640352,0.00009089765,0.00024684286,0.0003934938,0.00013182365],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020395138,0.0008693169,0.00084519107,0.0012446062,0.0016467575,0.004226537,0.0015057492,0.0022561303,0.020331375],"category_scores_gemma":[0.006019369,0.00047947955,0.00060100394,0.002548692,0.0038093426,0.0058954153,0.0029920891,0.0032637399,0.007691697],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000057666955,0.000036474645,0.00042987207,0.0003187198,0.000022465847,0.00009079105,0.0003476184,0.004759351,0.00021436214,0.7631709,0.06584596,0.16470575],"study_design_scores_gemma":[0.0000064310925,0.000025773727,0.00018464685,0.00038299363,0.0000059006406,0.00009051956,0.00014201205,0.0010986303,0.000103883875,0.1577395,0.8402022,0.000017478937],"about_ca_topic_score_codex":0.0039217467,"about_ca_topic_score_gemma":0.0030042,"teacher_disagreement_score":0.020331375,"about_ca_system_score_codex":0.002986957,"about_ca_system_score_gemma":0.002220466,"threshold_uncertainty_score":0.06801528},"labels":[],"label_agreement":null},{"id":"W2168381469","doi":"10.1016/s0377-2217(99)00073-9","title":"An efficient transformation of the generalized vehicle routing problem","year":2000,"lang":"en","type":"article","venue":"European Journal of Operational Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":155,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Group for Research in Decision Analysis","funders":"","keywords":"Arc routing; Vehicle routing problem; Transformation (genetics); Computer science; Routing (electronic design automation); Mathematical optimization; Arc (geometry); Mathematics; Computer network","score_opus":0.05034688933492008,"score_gpt":0.33841100554714104,"score_spread":0.28806411621222094,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2168381469","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0115436,0.0000924649,0.977957,0.00018526369,0.0001096265,0.00007476847,0.0001308551,0.00022466133,0.009681741],"genre_scores_gemma":[0.3352959,0.0005052867,0.64170325,0.00019310974,0.00017283094,0.00030480183,0.00095887296,0.0006463806,0.020219555],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9995272,0.00016322643,0.00001540095,0.00008590648,0.00015697093,0.00005142995],"domain_scores_gemma":[0.99973017,0.0001050797,0.00002337485,0.00006757418,0.000054963886,0.000018914769],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044259636,0.00067268196,0.0007895409,0.00057104346,0.0004008546,0.0009751372,0.0009720969,0.00060321676,0.006784406],"category_scores_gemma":[0.0014431752,0.0002785555,0.0009456636,0.0008124398,0.0005744863,0.0011144811,0.0017799413,0.0014791143,0.0014547784],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001280366,0.0001607797,0.0003111708,0.00016305094,0.000035955138,0.00022018101,0.0001546044,0.40286133,0.008460804,0.3459192,0.011435161,0.23014987],"study_design_scores_gemma":[0.00002828399,0.000050629773,0.00010260281,0.000010353665,0.000012156314,0.000092247166,0.000045682427,0.8436435,0.0010707688,0.14456986,0.010364706,0.000009145508],"about_ca_topic_score_codex":0.002035799,"about_ca_topic_score_gemma":0.0019068868,"teacher_disagreement_score":0.006784406,"about_ca_system_score_codex":0.0004983805,"about_ca_system_score_gemma":0.0009009123,"threshold_uncertainty_score":0.022696137},"labels":[],"label_agreement":null},{"id":"W2170216278","doi":"10.1287/trsc.35.3.286.10153","title":"Simultaneous Vehicle and Crew Scheduling in Urban Mass Transit Systems","year":2001,"lang":"en","type":"article","venue":"Transportation Science","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":194,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal; Polytechnique Montréal; Group for Research in Decision Analysis","funders":"","keywords":"Crew scheduling; Scheduling (production processes); Column generation; Mathematical optimization; Homogeneous; Computer science; Crew; Heuristic; Branch and bound; Job shop scheduling; Operations research; Engineering; Mathematics; Schedule","score_opus":0.015211003904855951,"score_gpt":0.26064639540075274,"score_spread":0.2454353914958968,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2170216278","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22217132,0.00045807136,0.76867604,0.00022098895,0.000040424173,0.00016181532,0.00017920755,0.000424267,0.0076678037],"genre_scores_gemma":[0.90398264,0.00022777692,0.09225532,0.00002863606,0.000031387095,0.00009371449,0.00014076316,0.00004261429,0.0031972444],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99954766,0.00019580718,0.00001044677,0.000062009945,0.00010413492,0.00007983472],"domain_scores_gemma":[0.9995547,0.0002955034,0.000056382643,0.00003128112,0.000029393546,0.000032657892],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005190102,0.0005901457,0.0005920079,0.00048500465,0.0005336603,0.0007793598,0.0007632032,0.00065983593,0.0020331368],"category_scores_gemma":[0.0013030656,0.0005101677,0.00039207414,0.0008756696,0.00054469507,0.0007352746,0.000653114,0.00041900636,0.00019587648],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007251973,0.00003356105,0.0003617093,0.00004723379,0.000017456716,0.00009795913,0.00004948893,0.96973497,0.0015547306,0.006294996,0.0004484087,0.02128705],"study_design_scores_gemma":[0.000017328879,0.00004143418,0.00020441179,0.0000025962859,0.0000057524985,0.000025982838,0.00002809053,0.9950368,0.0007247835,0.0031864136,0.00072293007,0.0000034195136],"about_ca_topic_score_codex":0.0106496215,"about_ca_topic_score_gemma":0.0138134835,"teacher_disagreement_score":0.0106496215,"about_ca_system_score_codex":0.00062826887,"about_ca_system_score_gemma":0.001049465,"threshold_uncertainty_score":0.021175265},"labels":[],"label_agreement":null},{"id":"W2170296950","doi":"10.1287/ijoc.1040.0128","title":"A Memetic Heuristic for the Generalized Quadratic Assignment Problem","year":2006,"lang":"en","type":"article","venue":"INFORMS journal on computing","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":56,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"","keywords":"Quadratic assignment problem; Mathematical optimization; Heuristic; Memetic algorithm; Assignment problem; Generalized assignment problem; Generalization; Integer programming; Matrix (chemical analysis); Mathematics; Weapon target assignment problem; Computer science; Branch and bound; Linear bottleneck assignment problem; Container (type theory); Linear programming; Quadratic equation; Local search (optimization); Optimization problem; Engineering","score_opus":0.01745726383268033,"score_gpt":0.2652813078728558,"score_spread":0.24782404404017547,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2170296950","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03727227,0.00061619945,0.9496542,0.000648129,0.00021168489,0.00022639502,0.00011738512,0.0004999538,0.010753783],"genre_scores_gemma":[0.4883091,0.00048112308,0.50050676,0.0005517213,0.00015959858,0.00072280597,0.00025936775,0.00014074295,0.008868871],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994723,0.00025642398,0.000017956036,0.000077010365,0.00009837609,0.00007783748],"domain_scores_gemma":[0.99919516,0.00051833177,0.00008197016,0.000061306404,0.000093599294,0.00004959519],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010905566,0.0009165267,0.0009324334,0.0010032248,0.0007519786,0.00088519405,0.0016477581,0.0016280597,0.0032797984],"category_scores_gemma":[0.0026036,0.0005699432,0.0007385412,0.0009627963,0.00094980514,0.00091147755,0.00094167166,0.00091780175,0.0004120097],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008979223,0.00007130931,0.00015765567,0.00006831,0.000036988957,0.00010684122,0.000049699756,0.95348734,0.0007899151,0.010709982,0.0023974916,0.03203468],"study_design_scores_gemma":[0.00004654694,0.00004433778,0.00004144363,0.000010149934,0.000008050912,0.00003765846,0.000015363566,0.992301,0.00023259291,0.0058880537,0.00136861,0.000006140262],"about_ca_topic_score_codex":0.0027540186,"about_ca_topic_score_gemma":0.0027522552,"teacher_disagreement_score":0.0032797984,"about_ca_system_score_codex":0.0010792253,"about_ca_system_score_gemma":0.0013469998,"threshold_uncertainty_score":0.010972023},"labels":[],"label_agreement":null},{"id":"W2170951393","doi":"10.1287/trsc.1120.0454","title":"Designing Production-Inventory-Transportation Systems with Capacitated Cross-Docks","year":2013,"lang":"en","type":"article","venue":"Transportation Science","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; University of Waterloo","funders":"","keywords":"Column generation; Truck; Mathematical optimization; Integer programming; Cutting stock problem; Safety stock; Supply chain; Transportation theory; Facility location problem; Computer science; Operations research; Fixed charge; Nonlinear programming; Linear programming; Set (abstract data type); Optimization problem; Nonlinear system; Mathematics; Engineering","score_opus":0.01946998476016498,"score_gpt":0.2618670104765673,"score_spread":0.24239702571640231,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2170951393","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13176742,0.00043618362,0.8561762,0.00045120815,0.00006589261,0.00045738273,0.000434579,0.000395445,0.009815676],"genre_scores_gemma":[0.86456233,0.00040415814,0.12996821,0.00008253023,0.000038905295,0.00046643967,0.00033494591,0.00007213815,0.004070381],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989693,0.00037904788,0.000045627487,0.00023790101,0.00013971294,0.0002284407],"domain_scores_gemma":[0.9987356,0.00070996396,0.00022159773,0.00006953133,0.00013610549,0.0001272116],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015385066,0.0015084055,0.0016913401,0.0010206889,0.0009463022,0.0025084396,0.0018488748,0.0020539274,0.0049455776],"category_scores_gemma":[0.0034304825,0.0014754407,0.0009570235,0.0018770234,0.0011460934,0.0017696426,0.00228511,0.0011540118,0.00066700863],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000024855382,0.000025706595,0.00017607323,0.000034163735,0.00001146847,0.00004825925,0.000017478293,0.9951565,0.00039659135,0.0021369837,0.00011180374,0.0018601404],"study_design_scores_gemma":[0.000019081757,0.000041201034,0.00006819528,0.000005238349,0.0000075465537,0.0000092868195,0.000020553118,0.997276,0.00023257961,0.0020397725,0.00027504723,0.0000055709093],"about_ca_topic_score_codex":0.00851472,"about_ca_topic_score_gemma":0.008514044,"teacher_disagreement_score":0.00851472,"about_ca_system_score_codex":0.0023552768,"about_ca_system_score_gemma":0.0022570633,"threshold_uncertainty_score":0.01708883},"labels":[],"label_agreement":null},{"id":"W2171349556","doi":"10.1504/ijstl.2010.033508","title":"An airfreight forwarder's shipment planning: simultaneous decisions on job, route and agent selection","year":2010,"lang":"en","type":"article","venue":"International Journal of Shipping and Transport Logistics","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Selection (genetic algorithm); Profit (economics); Variety (cybernetics); Computer science; Plan (archaeology); Forwarder; Operations research; Business; Destinations; Function (biology); Operations management; Microeconomics; Tourism; Economics","score_opus":0.027137626681478507,"score_gpt":0.30183404629131744,"score_spread":0.2746964196098389,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2171349556","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5998675,0.00053322566,0.38587752,0.0011739767,0.000076865916,0.0004112626,0.00018489662,0.00019449592,0.011680222],"genre_scores_gemma":[0.92989117,0.00025754177,0.06509883,0.00007218913,0.000027470385,0.00017263382,0.0000709877,0.000032041917,0.0043771346],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99904734,0.00058469485,0.00002281088,0.00010755934,0.00006502077,0.00017245482],"domain_scores_gemma":[0.9979038,0.0015103165,0.00025256726,0.000033612843,0.000100801175,0.00019893132],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026093966,0.0011314211,0.0012586783,0.00057382387,0.0007464528,0.0016586209,0.0009843123,0.0016837937,0.0039406163],"category_scores_gemma":[0.004025211,0.00090535166,0.00079668476,0.0007566596,0.0010849837,0.0014684596,0.0008816049,0.00089016394,0.00033715813],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025403383,0.0000711529,0.0009038643,0.000057035828,0.000033769385,0.00018979372,0.00008158061,0.9874685,0.0011488292,0.0033595844,0.00026013993,0.006171633],"study_design_scores_gemma":[0.000059313265,0.00021257717,0.00060527,0.000008609916,0.000033143213,0.000029486084,0.000112866794,0.9959189,0.00056223763,0.002084266,0.00035761812,0.000015666963],"about_ca_topic_score_codex":0.012930096,"about_ca_topic_score_gemma":0.011629173,"teacher_disagreement_score":0.012930096,"about_ca_system_score_codex":0.0012279336,"about_ca_system_score_gemma":0.0018942144,"threshold_uncertainty_score":0.025709689},"labels":[],"label_agreement":null},{"id":"W2177426957","doi":"10.1179/1942787514y.0000000042","title":"A case study of snow plow routing using an adaptive large hood search metaheuristic","year":2015,"lang":"en","type":"article","venue":"Transportation Letters","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Polytechnique Montréal; Transport Canada","funders":"Natural Sciences and Engineering Research Council of Canada; Fonds Québécois de la Recherche sur la Nature et les Technologies","keywords":"Metaheuristic; Workload; Routing (electronic design automation); Computer science; Hierarchy; Local search (optimization); Work (physics); Transport engineering; Operations research; Artificial intelligence; Engineering; Computer network","score_opus":0.09827727275834967,"score_gpt":0.3182714321715683,"score_spread":0.2199941594132186,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2177426957","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.92038804,0.00024714784,0.056097303,0.00059201726,0.000059491103,0.000302132,0.00048531155,0.00022102847,0.02160747],"genre_scores_gemma":[0.96599203,0.00012047626,0.028373905,0.00003273677,0.000007323045,0.000075398944,0.00017905021,0.00002801225,0.0051909727],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996166,0.00015656604,0.000008902409,0.00004111708,0.00006056542,0.00011614369],"domain_scores_gemma":[0.9993432,0.00039549384,0.00003583264,0.000044094824,0.00007880133,0.00010261402],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00071227015,0.0007046591,0.0004654164,0.0006087402,0.0016248842,0.0008560438,0.0012340612,0.0016115898,0.003544315],"category_scores_gemma":[0.0009481126,0.0002576765,0.00060859194,0.001193493,0.0007503708,0.00062097807,0.00042522984,0.0005310175,0.00021785454],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017220646,0.00023802325,0.0033742136,0.00009516377,0.0000381913,0.0025534718,0.00016607923,0.9721996,0.0028251242,0.0048949686,0.00194557,0.011497305],"study_design_scores_gemma":[0.000086045125,0.0002884759,0.0020864008,0.000014292779,0.000028453444,0.00024118653,0.0008936737,0.987432,0.0028330907,0.002213325,0.0038659777,0.000017007123],"about_ca_topic_score_codex":0.07354763,"about_ca_topic_score_gemma":0.12780894,"teacher_disagreement_score":0.07354763,"about_ca_system_score_codex":0.0023026757,"about_ca_system_score_gemma":0.0016105268,"threshold_uncertainty_score":0.1462391},"labels":[],"label_agreement":null},{"id":"W2186075603","doi":"10.1007/978-3-319-09129-7_20","title":"A Multi-start Tabu Search Approach for Solving the Information Routing Problem","year":2014,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Tabu search; Computer science; Routing (electronic design automation); Guided Local Search; Vehicle routing problem; Mathematical optimization; Algorithm; Computer network; Mathematics","score_opus":0.030398284504125416,"score_gpt":0.26339126101884625,"score_spread":0.23299297651472084,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2186075603","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0080765635,0.0008074707,0.97602606,0.00019281737,0.00017450625,0.00016056318,0.00017675062,0.0010336111,0.013351613],"genre_scores_gemma":[0.07877069,0.0004767745,0.91196364,0.00017633679,0.000085555264,0.00035993019,0.00034398932,0.0003494035,0.007473783],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994654,0.00020235359,0.000018834853,0.00006412249,0.00018421974,0.00006507404],"domain_scores_gemma":[0.9994031,0.0003569142,0.0000325194,0.000054615994,0.00012523192,0.000027579757],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008927049,0.0011157608,0.0012223783,0.0013363467,0.00097535923,0.0012176093,0.0023902229,0.00201662,0.013050151],"category_scores_gemma":[0.0024581158,0.00075479975,0.001230809,0.0026119668,0.0006946579,0.00119651,0.0011237235,0.0019587444,0.002135669],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008355363,0.00010735877,0.00016932176,0.00015789962,0.000054417156,0.000063091305,0.00007472562,0.8198252,0.0013647641,0.01983034,0.0079465015,0.1503228],"study_design_scores_gemma":[0.000028196177,0.0000497336,0.00007239219,0.000019761013,0.0000134025295,0.000022997187,0.000020605405,0.98927003,0.00038055584,0.0076081203,0.0025038791,0.000010374239],"about_ca_topic_score_codex":0.006959464,"about_ca_topic_score_gemma":0.008316089,"teacher_disagreement_score":0.013050151,"about_ca_system_score_codex":0.00083782285,"about_ca_system_score_gemma":0.001482186,"threshold_uncertainty_score":0.043657064},"labels":[],"label_agreement":null},{"id":"W2186282916","doi":"","title":"Modeling and Solving a Multimodal Routing Problem with Timetables and Time Windows","year":2008,"lang":"en","type":"article","venue":"","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; HEC Montréal","funders":"","keywords":"Column generation; Heuristics; Vehicle routing problem; Computer science; Mathematical optimization; Flow network; Cutting-plane method; Pickup; Integer programming; Routing (electronic design automation); Scheduling (production processes); Mathematics; Artificial intelligence; Computer network","score_opus":0.013010784106900925,"score_gpt":0.2151223155670533,"score_spread":0.20211153146015237,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2186282916","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10148836,0.00047903048,0.89084727,0.00032483038,0.00005227589,0.00010800138,0.00032797965,0.0002849433,0.0060872925],"genre_scores_gemma":[0.6357549,0.0008060503,0.3544577,0.00007290202,0.000067962195,0.0004001604,0.0005784753,0.00015870748,0.007703106],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996642,0.00013104743,0.000013541011,0.00007673978,0.000043835276,0.00007055093],"domain_scores_gemma":[0.9992693,0.0005149227,0.00009465654,0.000034070315,0.000042370106,0.00004456087],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000820769,0.0011509303,0.0007569394,0.00051980803,0.0004910483,0.001449025,0.0009819935,0.0014299109,0.0038143583],"category_scores_gemma":[0.0018261168,0.00069168815,0.00096290355,0.0009716354,0.0005783893,0.0019339988,0.00079105457,0.0011762505,0.00030942293],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019415646,0.000017023054,0.00016866089,0.000020201867,0.000010223777,0.000031002543,0.000018405346,0.99024874,0.00025567831,0.0048634135,0.00018461504,0.0041626007],"study_design_scores_gemma":[0.0000062553,0.000014583773,0.000058171925,0.000002866057,0.000005757483,0.00000946015,0.000017954759,0.9956436,0.00015220282,0.0036904884,0.00039543386,0.00000323805],"about_ca_topic_score_codex":0.015340487,"about_ca_topic_score_gemma":0.015671378,"teacher_disagreement_score":0.015340487,"about_ca_system_score_codex":0.0009944069,"about_ca_system_score_gemma":0.0013395784,"threshold_uncertainty_score":0.030502379},"labels":[],"label_agreement":null},{"id":"W2186951678","doi":"","title":"AN ANALYSIS OF THE ASSIGNMENT OF DELIVERY ROUTES TO VEHICLE DRIVERS IN STOCHASTIC VEHICLE ROUTING OPERATIONS","year":2004,"lang":"en","type":"article","venue":"","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Wilfrid Laurier University","funders":"","keywords":"Vehicle routing problem; Markov decision process; Operations research; Routing (electronic design automation); Markov chain; Markov process; Computer science; Range (aeronautics); Transport engineering; Engineering; Computer network","score_opus":0.011922643264747722,"score_gpt":0.25698144603733136,"score_spread":0.24505880277258363,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2186951678","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9074312,0.000086162785,0.09005819,0.00024741216,0.000009887448,0.00007644557,0.0001870663,0.00006121107,0.0018425562],"genre_scores_gemma":[0.9908241,0.00006346699,0.00812177,0.000016322927,0.000007583074,0.000034770645,0.00017908715,0.000017015655,0.00073601346],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99758995,0.0011237506,0.0000899656,0.0003329634,0.0004637363,0.00039955226],"domain_scores_gemma":[0.9743945,0.01998565,0.0030400208,0.000884225,0.0012333519,0.00046226513],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0047308584,0.0004017272,0.0004785536,0.0010084781,0.0005148199,0.0009530953,0.00090535893,0.0006551529,0.002093666],"category_scores_gemma":[0.021352272,0.0005550338,0.00065357884,0.0011316189,0.00093444384,0.0013903143,0.00059585413,0.0010123961,0.00018732368],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002365637,0.00010623693,0.029729873,0.000029755396,0.00006134184,0.0001298676,0.00033560843,0.9266823,0.0014055958,0.028580898,0.0005289677,0.012173012],"study_design_scores_gemma":[0.000011430681,0.00008454439,0.010639663,0.0000037025184,0.000017148557,0.00004545396,0.00017470973,0.98147523,0.00052531646,0.0066748513,0.00033216868,0.000015710326],"about_ca_topic_score_codex":0.012198362,"about_ca_topic_score_gemma":0.01119302,"teacher_disagreement_score":0.012198362,"about_ca_system_score_codex":0.002288144,"about_ca_system_score_gemma":0.0009966936,"threshold_uncertainty_score":0.025019526},"labels":[],"label_agreement":null},{"id":"W2188576392","doi":"10.1287/ijoc.2015.0649","title":"Exact and Heuristic Algorithms for Capacitated Vehicle Routing Problems with Quadratic Costs Structure","year":2015,"lang":"en","type":"article","venue":"INFORMS journal on computing","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Group for Research in Decision Analysis; Université du Québec à Montréal","funders":"","keywords":"Vehicle routing problem; Quadratic equation; Algorithm; Branch and cut; Heuristic; Routing (electronic design automation); Mathematical optimization; Metaheuristic; Computer science; Mathematics; Integer programming","score_opus":0.028336016636880533,"score_gpt":0.2709757504084353,"score_spread":0.24263973377155476,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2188576392","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009510393,0.001141947,0.979506,0.00035911927,0.00008617358,0.00010859526,0.0001358662,0.00036410397,0.008787732],"genre_scores_gemma":[0.21880648,0.0012842367,0.77343035,0.00021455402,0.00012387511,0.00041373316,0.00053250615,0.00019312027,0.0050010616],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988675,0.00040956054,0.000051292493,0.00018763552,0.000318901,0.00016509659],"domain_scores_gemma":[0.9973157,0.0019494123,0.0001928667,0.00020642107,0.0002665363,0.00006911431],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016112078,0.0014678703,0.00092025223,0.0010799515,0.00078460685,0.0016427869,0.0019506011,0.0016789592,0.0059258537],"category_scores_gemma":[0.0058636065,0.00078638387,0.0008523831,0.002810637,0.0008972064,0.0017450368,0.0011917889,0.0020988379,0.00083799945],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000047497888,0.00009763144,0.00021047141,0.00013806502,0.000023353909,0.000030687657,0.000058451653,0.90492094,0.0003101008,0.03592358,0.002366579,0.05587261],"study_design_scores_gemma":[0.000023433719,0.000020301188,0.000054035612,0.00001354992,0.000006693836,0.00001585077,0.000025574931,0.97518426,0.00016724509,0.023068704,0.0014150238,0.000005309065],"about_ca_topic_score_codex":0.0067305723,"about_ca_topic_score_gemma":0.00846839,"teacher_disagreement_score":0.0067305723,"about_ca_system_score_codex":0.0019713168,"about_ca_system_score_gemma":0.0024330101,"threshold_uncertainty_score":0.019823968},"labels":[],"label_agreement":null},{"id":"W2189140217","doi":"10.4018/978-1-61350-086-6.ch002","title":"Vehicle Routing Models and Algorithms for Winter Road Spreading Operations","year":2011,"lang":"en","type":"book-chapter","venue":"IGI Global eBooks","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Routing (electronic design automation); Truck; Key (lock); Computer science; Vehicle routing problem; Limiting; Transport engineering; Operations research; Engineering; Computer network; Computer security","score_opus":0.04654899351961165,"score_gpt":0.27824678453282164,"score_spread":0.23169779101321,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2189140217","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007327097,0.0016686942,0.979688,0.00042366408,0.00009084615,0.00006763608,0.00025191426,0.00031733772,0.0101648355],"genre_scores_gemma":[0.3391968,0.007517628,0.614561,0.00029799403,0.00024668555,0.000645444,0.0017601212,0.00054572936,0.035228536],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99966955,0.00012328844,0.000014905878,0.00007232377,0.00006485436,0.000055103887],"domain_scores_gemma":[0.999511,0.0002807992,0.00006955539,0.000029458319,0.00008479138,0.000024287836],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00086267723,0.001507526,0.0012508568,0.000752792,0.0005903198,0.0022863606,0.0025172923,0.0019022061,0.006994738],"category_scores_gemma":[0.0020264739,0.000869049,0.0010815323,0.0017635898,0.0005502866,0.0023916445,0.0008217714,0.001733835,0.0015057314],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010102584,0.000012328688,0.00009300145,0.000036270463,0.000012258425,0.000010393813,0.00002297414,0.96544045,0.000094915005,0.018936893,0.0015158004,0.013814662],"study_design_scores_gemma":[0.000005090277,0.0000052659175,0.00003275761,0.000010261129,0.0000044469793,0.000009399686,0.0000149458965,0.9812558,0.00004370208,0.016885981,0.0017288984,0.000003436543],"about_ca_topic_score_codex":0.012353052,"about_ca_topic_score_gemma":0.009473902,"teacher_disagreement_score":0.012353052,"about_ca_system_score_codex":0.0022764283,"about_ca_system_score_gemma":0.0015559767,"threshold_uncertainty_score":0.0245623},"labels":[],"label_agreement":null},{"id":"W2204163060","doi":"10.1016/j.cor.2015.12.013","title":"The Steiner traveling salesman problem with online advanced edge blockages","year":2015,"lang":"en","type":"article","venue":"Computers & Operations Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":24,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Zhejiang Sci-Tech University; National Natural Science Foundation of China; China Postdoctoral Science Foundation; Alberta Innovates - Technology Futures; Genome Canada; Southern Taiwan Science Park; Georgia Southern University","keywords":"Travelling salesman problem; Enhanced Data Rates for GSM Evolution; Computer science; Competitive analysis; Mathematical optimization; Upper and lower bounds; Online algorithm; Service (business); Steiner tree problem; Mathematics; Algorithm; Telecommunications; Economics","score_opus":0.08018762853346349,"score_gpt":0.36395403354220485,"score_spread":0.28376640500874134,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2204163060","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.24123809,0.00097120175,0.7247474,0.0011179023,0.0001781244,0.0001478583,0.0008677866,0.00033070857,0.030400898],"genre_scores_gemma":[0.79721117,0.0011010176,0.17589799,0.00014111126,0.00019165415,0.00013966266,0.00089518074,0.00017594757,0.024246281],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992168,0.00031338123,0.000031560772,0.00015068351,0.00013547613,0.00015217936],"domain_scores_gemma":[0.9983619,0.0009811411,0.00022519486,0.00016284623,0.00011376775,0.00015521466],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00092188455,0.0007962913,0.0014181924,0.00053421146,0.0006258931,0.0018248317,0.0016453047,0.0019564256,0.008203189],"category_scores_gemma":[0.0037728194,0.0007756164,0.0009419731,0.0013507921,0.00074613385,0.00438877,0.0014445694,0.0016416915,0.00079296704],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005118571,0.0002039264,0.0008584878,0.0002512609,0.00007872523,0.0003913045,0.00009176689,0.78161186,0.00287605,0.16265613,0.007010981,0.043457635],"study_design_scores_gemma":[0.000038210645,0.00009972373,0.00025757123,0.000015237341,0.00002042703,0.00009637556,0.000047567977,0.92511284,0.00055415096,0.071686365,0.0020603673,0.000011099144],"about_ca_topic_score_codex":0.0024533768,"about_ca_topic_score_gemma":0.0022849676,"teacher_disagreement_score":0.008203189,"about_ca_system_score_codex":0.0008910915,"about_ca_system_score_gemma":0.0011705607,"threshold_uncertainty_score":0.027442396},"labels":[],"label_agreement":null},{"id":"W2214969595","doi":"10.1287/opre.2016.1535","title":"Exact Algorithms for Electric Vehicle-Routing Problems with Time Windows","year":2016,"lang":"en","type":"article","venue":"Operations Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; École de Technologie Supérieure; Group for Research in Decision Analysis","funders":"","keywords":"Vehicle routing problem; Routing (electronic design automation); Computer science; Battery (electricity); Groundwater recharge; Extension (predicate logic); Algorithm; Mathematical optimization; Real-time computing; Engineering; Mathematics; Computer network; Power (physics)","score_opus":0.05546575356943034,"score_gpt":0.34658538942623596,"score_spread":0.2911196358568056,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2214969595","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016438413,0.0008654132,0.97209644,0.00035486429,0.00006777762,0.00012551244,0.00024111522,0.00046300815,0.009347377],"genre_scores_gemma":[0.32579622,0.0013328437,0.6627383,0.00021466904,0.00012508643,0.0005674377,0.0008572017,0.00032656585,0.008041683],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990934,0.00033827554,0.000048188474,0.0002030661,0.00015677372,0.0001602957],"domain_scores_gemma":[0.99659663,0.0027780388,0.00023172489,0.000173592,0.00014189747,0.000078238816],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017165461,0.001702917,0.0012379327,0.0010045498,0.00063359266,0.001776187,0.0017059962,0.0014726992,0.0069318996],"category_scores_gemma":[0.005571,0.0007595844,0.00092650397,0.0017931423,0.0009268474,0.0021831922,0.0012534437,0.001901501,0.00088300765],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006431559,0.00008664617,0.0003172388,0.00014499671,0.000027385708,0.00003458423,0.000060963666,0.9129764,0.00030651398,0.03903162,0.0016850402,0.04526438],"study_design_scores_gemma":[0.000027706148,0.00002083133,0.000055962566,0.000015081786,0.000008108484,0.000013030033,0.000023740875,0.95857316,0.00018661254,0.040158097,0.0009128227,0.000004882634],"about_ca_topic_score_codex":0.00818225,"about_ca_topic_score_gemma":0.01037874,"teacher_disagreement_score":0.00818225,"about_ca_system_score_codex":0.0020753627,"about_ca_system_score_gemma":0.0025324672,"threshold_uncertainty_score":0.023189545},"labels":[],"label_agreement":null},{"id":"W2216088815","doi":"10.2139/ssrn.2724060","title":"Airfreight Forwarding Under System-Wide and Double Discounts","year":2015,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Business; Commerce; Computer science","score_opus":0.016906695179991806,"score_gpt":0.24994967460103837,"score_spread":0.23304297942104657,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2216088815","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.29018524,0.0018354973,0.68105364,0.0022986862,0.000396928,0.00014806126,0.000794704,0.00033454632,0.022952784],"genre_scores_gemma":[0.96842366,0.00061739906,0.017512636,0.000094796866,0.00015387197,0.000048896003,0.00018122095,0.00008239654,0.012885195],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982085,0.0006608954,0.000057805646,0.0003732918,0.00020132196,0.00049815926],"domain_scores_gemma":[0.9915685,0.006470393,0.00063834456,0.00037451743,0.00055525854,0.0003930419],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004989984,0.0014805003,0.0029125148,0.0010702952,0.00091166986,0.0038920748,0.002403421,0.002899181,0.0063838125],"category_scores_gemma":[0.016197985,0.0013374507,0.0010220936,0.0015438219,0.0016869515,0.0052931607,0.0021387485,0.0024475558,0.0004225254],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00031465528,0.000056980505,0.00066992955,0.00008017284,0.000051023784,0.00021255574,0.000063517204,0.92856437,0.0004470882,0.05900397,0.0018740232,0.008661768],"study_design_scores_gemma":[0.000021691281,0.00004044766,0.00015888605,0.000009481418,0.000029138804,0.000056992223,0.000044420733,0.97458017,0.0001257897,0.024411932,0.0005046051,0.000016380107],"about_ca_topic_score_codex":0.008085615,"about_ca_topic_score_gemma":0.0054961927,"teacher_disagreement_score":0.008085615,"about_ca_system_score_codex":0.0030859916,"about_ca_system_score_gemma":0.0015705611,"threshold_uncertainty_score":0.026389837},"labels":[],"label_agreement":null},{"id":"W2225208082","doi":"10.1007/s13676-016-0101-4","title":"The vehicle routing problem with hard time windows and stochastic service times","year":2016,"lang":"en","type":"article","venue":"EURO Journal on Transportation and Logistics","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":70,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal; Polytechnique Montréal; Group for Research in Decision Analysis; École de Technologie Supérieure","funders":"","keywords":"Vehicle routing problem; Mathematical optimization; Probabilistic logic; Set (abstract data type); Stochastic dominance; Computer science; Service (business); Dimension (graph theory); Resource constraints; Routing (electronic design automation); Mathematics; Distributed computing; Artificial intelligence; Economics","score_opus":0.014529178717328553,"score_gpt":0.22272130247434482,"score_spread":0.20819212375701626,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2225208082","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.056420777,0.0005139536,0.9365079,0.0007813402,0.0001122389,0.00010631197,0.0004076843,0.00016646151,0.004983294],"genre_scores_gemma":[0.7731043,0.0011706469,0.20956078,0.0003101253,0.00034753594,0.00040206095,0.00077818555,0.00023201366,0.014094327],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981166,0.0006897362,0.000074594805,0.000480777,0.00030636063,0.0003318651],"domain_scores_gemma":[0.99717855,0.0020232515,0.00036556757,0.00011696728,0.00013546902,0.00018019823],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001532825,0.001636607,0.001528579,0.00060831127,0.0006627214,0.0019232678,0.0018820532,0.002021326,0.0036443104],"category_scores_gemma":[0.005007684,0.00097133184,0.0013357907,0.0014249593,0.0011518258,0.0029020663,0.0012467982,0.0020157767,0.00036856014],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000074760566,0.000044018132,0.00031253163,0.00008829772,0.00004012723,0.0001381046,0.000038835384,0.9287358,0.0008640692,0.058765806,0.00089249643,0.010005185],"study_design_scores_gemma":[0.000025715952,0.000045831704,0.00016160539,0.0000071936456,0.00001530457,0.00007171928,0.000025616744,0.95195115,0.00049872097,0.045972727,0.0012097645,0.000014750327],"about_ca_topic_score_codex":0.0051622577,"about_ca_topic_score_gemma":0.003987554,"teacher_disagreement_score":0.0051622577,"about_ca_system_score_codex":0.0015357996,"about_ca_system_score_gemma":0.0017676365,"threshold_uncertainty_score":0.012191474},"labels":[],"label_agreement":null},{"id":"W2230461717","doi":"10.1007/s10479-015-2091-2","title":"Exact and heuristic approaches for the cycle hub location problem","year":2016,"lang":"en","type":"article","venue":"Annals of Operations Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":47,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Bounding overwatch; Benchmark (surveying); Mathematical optimization; Metaheuristic; Theory of computation; Heuristic; Tree (set theory); Steiner tree problem; Network topology; Tree network; Set (abstract data type); Flow network; Branch and bound; Network planning and design; Routing (electronic design automation); Algorithm; Mathematics","score_opus":0.2801187575592472,"score_gpt":0.42866052791683307,"score_spread":0.14854177035758587,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2230461717","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012063014,0.0019622673,0.97475284,0.0005951371,0.00018581502,0.00007567123,0.00012457246,0.00010703297,0.010133596],"genre_scores_gemma":[0.391465,0.003237769,0.5934335,0.0003023961,0.00051180355,0.00035947878,0.00029526354,0.00020899416,0.01018579],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99871063,0.0006445635,0.000051566924,0.00012743818,0.00033557942,0.00013023579],"domain_scores_gemma":[0.9949196,0.004088865,0.00023394558,0.00027303866,0.00036443069,0.00012007475],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026354727,0.0012880481,0.001378375,0.0019522401,0.0006348266,0.0019239883,0.0023653745,0.001988708,0.005541363],"category_scores_gemma":[0.01033393,0.0011082735,0.0010265533,0.0025899052,0.0015725138,0.0026422334,0.0015350394,0.0023526317,0.00046133093],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000052101164,0.00007332245,0.0001605602,0.00011406757,0.000027473536,0.000024632425,0.00004659956,0.91395164,0.00014597773,0.055428118,0.0017477096,0.028227802],"study_design_scores_gemma":[0.00001679308,0.000015597838,0.00005046091,0.000015482803,0.00000759925,0.000008918907,0.000019833331,0.96068907,0.000055059707,0.038234692,0.0008806521,0.000005902711],"about_ca_topic_score_codex":0.010199384,"about_ca_topic_score_gemma":0.011302489,"teacher_disagreement_score":0.010199384,"about_ca_system_score_codex":0.0020033228,"about_ca_system_score_gemma":0.0025944766,"threshold_uncertainty_score":0.020280063},"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,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_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","score_opus":0.1917023661751183,"score_gpt":0.44913487507889294,"score_spread":0.25743250890377467,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2233419640","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00020808588,0.9966454,0.001757949,0.00016153808,0.00022936078,0.00000893683,0.00004057201,0.000014272192,0.0009338382],"genre_scores_gemma":[0.0010456331,0.99660337,0.0015079218,0.00009380416,0.0002326729,0.000008369528,0.00005949338,0.0000043119385,0.00044440548],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9996295,0.000050338294,0.000060489543,0.000086984575,0.00014248032,0.000030096062],"domain_scores_gemma":[0.9990308,0.0005655774,0.000107367865,0.00003195762,0.00021768601,0.00004656339],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000969794,0.0016033357,0.002386082,0.0030508717,0.00034611335,0.0018758274,0.0016928246,0.0014604623,0.004582291],"category_scores_gemma":[0.0017567005,0.0007447791,0.0010388973,0.005805187,0.00057104457,0.0027762689,0.001047698,0.0014254508,0.0018234659],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006852335,0.000159393,0.00032760855,0.03108327,0.00021145564,0.000100987694,0.00004657067,0.0048676953,0.0009088966,0.008222591,0.030764064,0.92323893],"study_design_scores_gemma":[0.000051911724,0.00020862884,0.0011792096,0.010495972,0.00064539636,0.00089332566,0.00014848619,0.004063651,0.00093535834,0.01234947,0.9689311,0.000097514734],"about_ca_topic_score_codex":0.0017700318,"about_ca_topic_score_gemma":0.0025981574,"teacher_disagreement_score":0.004582291,"about_ca_system_score_codex":0.0005932989,"about_ca_system_score_gemma":0.0019328098,"threshold_uncertainty_score":0.015329301},"labels":[],"label_agreement":null},{"id":"W2250014226","doi":"10.5555/2722129.2722164","title":"Linear programming-based approximation algorithms for multi-vehicle minimum latency problems: extended abstract","year":2015,"lang":"en","type":"article","venue":"Symposium on Discrete Algorithms","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Rounding; Approximation algorithm; Linear programming; Latency (audio); Leverage (statistics); Computer science; Mathematical optimization; Vehicle routing problem; Algorithm; Mathematics; Routing (electronic design automation); Computer network; Artificial intelligence; Telecommunications","score_opus":0.05793689123546252,"score_gpt":0.31696333685336847,"score_spread":0.25902644561790594,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2250014226","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017276144,0.00086871476,0.96862674,0.0011366546,0.0001391721,0.00009778132,0.00021917197,0.00087031646,0.010765351],"genre_scores_gemma":[0.35046828,0.0008483324,0.6369488,0.0007223031,0.00023014574,0.00048013087,0.00083861133,0.00047176433,0.008991613],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987876,0.00045994017,0.000046040954,0.00021526887,0.00022078276,0.0002703573],"domain_scores_gemma":[0.9976922,0.0015658326,0.00017936256,0.00021444546,0.0002390859,0.00010905398],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021815982,0.0014132169,0.0011177426,0.0007160935,0.00072335114,0.002088961,0.0022114967,0.0016168369,0.007376805],"category_scores_gemma":[0.0054981294,0.0006252007,0.0011317779,0.0017138778,0.0007792821,0.0023862647,0.0017534096,0.0030745158,0.0017775496],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017661453,0.00020416258,0.00062186166,0.0001760067,0.00004820436,0.000060420138,0.00007950261,0.90944433,0.00059965695,0.031944696,0.006920706,0.04972378],"study_design_scores_gemma":[0.000019626017,0.000016972255,0.000035339363,0.000012231677,0.00000449054,0.000010108399,0.00002069588,0.98619926,0.00019542874,0.012704089,0.0007787101,0.000003000934],"about_ca_topic_score_codex":0.00767233,"about_ca_topic_score_gemma":0.008119266,"teacher_disagreement_score":0.00767233,"about_ca_system_score_codex":0.002068526,"about_ca_system_score_gemma":0.0021053255,"threshold_uncertainty_score":0.024677932},"labels":[],"label_agreement":null},{"id":"W2252993480","doi":"10.5539/mas.v10n4p128","title":"A Heuristic and Exact Method: Integrated Aircraft Routing and Crew Pairing Problem","year":2016,"lang":"en","type":"article","venue":"Modern Applied Science","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Crew; Computer science; Heuristic; Integer programming; Routing (electronic design automation); Crew scheduling; Mathematical optimization; Particle swarm optimization; Operations research; Pairing; Heuristics; Mathematics; Algorithm; Engineering; Aeronautics; Artificial intelligence; Computer network","score_opus":0.014067659575462457,"score_gpt":0.2548301235589542,"score_spread":0.24076246398349171,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2252993480","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004343563,0.00021466373,0.98812264,0.00014712846,0.000062689614,0.00010186083,0.000048301386,0.00016857186,0.006790564],"genre_scores_gemma":[0.14989588,0.0004395546,0.8427514,0.0001886256,0.00008781899,0.00036406695,0.00026702753,0.00007863115,0.005927059],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984818,0.0006088776,0.00005054136,0.00023447664,0.0004802851,0.0001439586],"domain_scores_gemma":[0.999092,0.0004887093,0.000086933826,0.000118272954,0.00016348742,0.00005058556],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015280312,0.0009140775,0.0010070009,0.0012208051,0.0005558033,0.001411707,0.001761708,0.0015417292,0.005811383],"category_scores_gemma":[0.0029418096,0.0006016552,0.0011201644,0.001684767,0.00084738765,0.0015109645,0.0011812173,0.0013871883,0.000850684],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007902851,0.00016401487,0.0005204434,0.00019595405,0.000048864822,0.0001170497,0.00010185692,0.7964107,0.0017816401,0.05340107,0.004177147,0.1430022],"study_design_scores_gemma":[0.000024956826,0.000060579565,0.00010076832,0.000019991354,0.000012272172,0.00006464184,0.00003173139,0.9865132,0.0006528747,0.0091061,0.0034019593,0.000010763863],"about_ca_topic_score_codex":0.004950285,"about_ca_topic_score_gemma":0.004531825,"teacher_disagreement_score":0.005811383,"about_ca_system_score_codex":0.0010560403,"about_ca_system_score_gemma":0.0024138063,"threshold_uncertainty_score":0.019441068},"labels":[],"label_agreement":null},{"id":"W2259887679","doi":"10.1007/s11067-015-9311-9","title":"Spatial Analysis of Single Allocation Hub Location Problems","year":2015,"lang":"en","type":"article","venue":"Networks and Spatial Economics","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Operations research; Transport engineering; Mathematical optimization; Engineering; Mathematics","score_opus":0.02169642552667028,"score_gpt":0.22219142139101855,"score_spread":0.20049499586434827,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2259887679","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1378123,0.0017203792,0.8285305,0.0025652284,0.00012650131,0.00006118149,0.0002868092,0.00010513132,0.02879198],"genre_scores_gemma":[0.9511026,0.0015757806,0.029288352,0.00012508624,0.00014572857,0.0000976363,0.00019689303,0.00009384094,0.01737416],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994795,0.00027065582,0.000013333674,0.00006205493,0.0000884373,0.00008608206],"domain_scores_gemma":[0.9972915,0.0019415014,0.00030094603,0.00007940559,0.00030038744,0.000086193206],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017088425,0.0007606068,0.00081589684,0.0015122808,0.0005231722,0.001416891,0.0014126888,0.0010143844,0.004676384],"category_scores_gemma":[0.0057033817,0.0005760226,0.0008732337,0.0016205605,0.0013122926,0.001968031,0.0013181544,0.0007641988,0.00019702358],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000025296766,0.000024703604,0.0006636209,0.000048544665,0.000027986738,0.000037511196,0.000039124392,0.8172426,0.0002223002,0.17580341,0.0012710736,0.0045938795],"study_design_scores_gemma":[0.0000041103376,0.000006730119,0.00021672912,0.0000071394097,0.000007233335,0.000008978264,0.00004035069,0.9442533,0.000075746786,0.054791495,0.00058409624,0.000004024522],"about_ca_topic_score_codex":0.0111500425,"about_ca_topic_score_gemma":0.0072081084,"teacher_disagreement_score":0.0111500425,"about_ca_system_score_codex":0.002115325,"about_ca_system_score_gemma":0.0014241332,"threshold_uncertainty_score":0.022170246},"labels":[],"label_agreement":null},{"id":"W2269669929","doi":"10.1007/s11590-015-0973-5","title":"A basic variable neighborhood search heuristic for the uncapacitated multiple allocation p-hub center problem","year":2015,"lang":"en","type":"article","venue":"Optimization Letters","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":33,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Military College of Canada; Royal Ottawa Mental Health Centre","funders":"Russian Science Foundation; National Research University Higher School of Economics; Agence Nationale de la Recherche","keywords":"Variable neighborhood search; Heuristics; Mathematical optimization; Heuristic; Benchmark (surveying); Solver; Pairwise comparison; Computational intelligence; Local search (optimization); Computer science; Set (abstract data type); Variable (mathematics); Metaheuristic; Mathematics; Artificial intelligence","score_opus":0.029756073684588987,"score_gpt":0.24712882116455606,"score_spread":0.21737274747996707,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2269669929","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01901876,0.0003123075,0.97098416,0.00018403944,0.000093217655,0.000091272144,0.00007663482,0.00022807824,0.0090115415],"genre_scores_gemma":[0.4202371,0.0002976932,0.5705297,0.0001547474,0.00007534366,0.00029756827,0.00022078418,0.0001905356,0.00799654],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997453,0.00010628842,0.000006315228,0.00003900509,0.00006418598,0.00003883761],"domain_scores_gemma":[0.9996493,0.00020886917,0.00002931995,0.000026223597,0.00006111204,0.000025226394],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005980141,0.0004890705,0.00095719687,0.00056930986,0.00047963523,0.0005934719,0.0014165823,0.00093969266,0.004524682],"category_scores_gemma":[0.0015242812,0.0003612302,0.00044870982,0.0009150159,0.000430338,0.00083902566,0.0007236487,0.00069483503,0.0004328982],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000087077424,0.00005891729,0.00014663972,0.0000454749,0.000017828363,0.000031651398,0.000024930952,0.93187153,0.0007136066,0.019174153,0.0028496722,0.044978518],"study_design_scores_gemma":[0.000012507559,0.000019190871,0.00003388991,0.0000041938447,0.0000033822575,0.000008033478,0.000005828616,0.9962483,0.00012985633,0.0029409775,0.0005910306,0.0000028621637],"about_ca_topic_score_codex":0.004383552,"about_ca_topic_score_gemma":0.005481663,"teacher_disagreement_score":0.004524682,"about_ca_system_score_codex":0.0007298545,"about_ca_system_score_gemma":0.0011113326,"threshold_uncertainty_score":0.01513654},"labels":[],"label_agreement":null},{"id":"W2269671001","doi":"10.1007/s11590-016-1081-x","title":"On a linearization technique for solving the quadratic set covering problem and variations","year":2016,"lang":"en","type":"preprint","venue":"Optimization Letters","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Linearization; Heuristic; Class (philosophy); Mathematical optimization; Set (abstract data type); Quadratic equation; Mathematics; Quadratic model; Computer science; Algorithm; Nonlinear system; Artificial intelligence; Statistics","score_opus":0.015207257924394528,"score_gpt":0.2564950073204825,"score_spread":0.241287749396088,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2269671001","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0019284582,0.00027193778,0.9922396,0.00015182371,0.00006578165,0.000025568186,0.000030393012,0.000100838595,0.0051855636],"genre_scores_gemma":[0.2513999,0.0020405368,0.7153126,0.00068567647,0.000497669,0.00037740235,0.00032350552,0.00056763017,0.028795138],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99969625,0.00012241221,0.000011182186,0.000046228626,0.00010202594,0.00002194617],"domain_scores_gemma":[0.9996495,0.00021949364,0.000024769923,0.0000343313,0.00005613351,0.000015713458],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00063810614,0.0010314363,0.000699066,0.00074138114,0.00037739426,0.00060518505,0.0007665842,0.0007716623,0.005110375],"category_scores_gemma":[0.0017637913,0.0004018172,0.0010408711,0.00089803943,0.00087864255,0.0011170758,0.0016220115,0.0020354495,0.0010921354],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007740371,0.00012368514,0.00030740647,0.0004534983,0.000090518195,0.00016919937,0.00037027185,0.4497974,0.017298255,0.32718143,0.013409135,0.19072187],"study_design_scores_gemma":[0.000009452879,0.00007858891,0.000110004774,0.000027858214,0.000017119792,0.0000644244,0.00003705221,0.9217087,0.0017985372,0.067542754,0.008586194,0.000019367211],"about_ca_topic_score_codex":0.0022952284,"about_ca_topic_score_gemma":0.0024185565,"teacher_disagreement_score":0.005110375,"about_ca_system_score_codex":0.000498711,"about_ca_system_score_gemma":0.0004503967,"threshold_uncertainty_score":0.017095923},"labels":[],"label_agreement":null},{"id":"W2277326438","doi":"","title":"The One-Commodity Traveling Salesman Problem with Selective Pickup and Delivery","year":2011,"lang":"en","type":"article","venue":"Document Server@UHasselt (UHasselt)","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Travelling salesman problem; Pickup; 2-opt; Bottleneck traveling salesman problem; Traveling purchaser problem; Computer science; Heuristic; Domain (mathematical analysis); Mathematical optimization; Combinatorial optimization; Operations research; Engineering; Mathematics; Artificial intelligence","score_opus":0.02399300994395222,"score_gpt":0.23255189581993152,"score_spread":0.2085588858759793,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2277326438","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06758884,0.0012536241,0.9089603,0.0011790952,0.00027250254,0.00033286642,0.0005136185,0.00024917375,0.01964992],"genre_scores_gemma":[0.5725162,0.0025910623,0.3816892,0.0004207195,0.0002955071,0.00052655884,0.000906981,0.0002614333,0.04079237],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99942255,0.00018404712,0.000028986684,0.00015138552,0.000102095335,0.00011093192],"domain_scores_gemma":[0.99937797,0.0003749753,0.00009643846,0.000040037852,0.00004494014,0.000065719425],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00076537434,0.0009472517,0.001210503,0.0005663532,0.0010657492,0.0016248712,0.0017727579,0.0020493106,0.0057236976],"category_scores_gemma":[0.0015608149,0.000606126,0.00115049,0.0013303339,0.0010150718,0.0020734682,0.00090373354,0.0011652296,0.0007670862],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00027265085,0.00030651307,0.0007527198,0.0005833445,0.00017072423,0.0012524019,0.00023142317,0.7504597,0.0029556276,0.15384567,0.011921532,0.07724775],"study_design_scores_gemma":[0.00009565831,0.00021735267,0.0003691266,0.000041794043,0.000073952644,0.00069997297,0.0002539606,0.8754367,0.0019655947,0.104289114,0.016514102,0.000042634863],"about_ca_topic_score_codex":0.0028620565,"about_ca_topic_score_gemma":0.002903355,"teacher_disagreement_score":0.0057236976,"about_ca_system_score_codex":0.001025041,"about_ca_system_score_gemma":0.0013500731,"threshold_uncertainty_score":0.019147635},"labels":[],"label_agreement":null},{"id":"W2281133739","doi":"10.1287/trsc.2015.0637","title":"An Inventory-Routing Problem with Pickups and Deliveries Arising in the Replenishment of Automated Teller Machines","year":2016,"lang":"en","type":"article","venue":"Transportation Science","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":80,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal; Université Laval","funders":"","keywords":"Mathematical optimization; Routing (electronic design automation); Computer science; Column generation; Branch and cut; Integer programming; Cluster analysis; Economic shortage; Time horizon; Linear programming; Branch and price; Operations research; Integer (computer science); Mathematics; Artificial intelligence","score_opus":0.013733779326738225,"score_gpt":0.2711333527092343,"score_spread":0.257399573382496,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2281133739","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17941748,0.0011354003,0.8070298,0.0009985545,0.00013111981,0.00045174884,0.0010694857,0.00023120805,0.009535187],"genre_scores_gemma":[0.6793312,0.0008192379,0.30974606,0.00015424029,0.00013952042,0.0004925941,0.000807451,0.00009654348,0.008413095],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99870765,0.0006099247,0.000053605007,0.00029887696,0.00015830995,0.00017160703],"domain_scores_gemma":[0.9986187,0.0010651448,0.00014699783,0.000048997383,0.00005320528,0.00006693113],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013994044,0.00151673,0.0017970719,0.0009445642,0.0010320427,0.002098765,0.0018739589,0.003612514,0.0038332755],"category_scores_gemma":[0.0030005435,0.0011913134,0.0016070909,0.0019531623,0.0010460333,0.001917021,0.001056936,0.0015439505,0.0002745664],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005611717,0.000056948506,0.0004096187,0.00009315567,0.000038966056,0.00024338462,0.000045946832,0.9817036,0.0004948681,0.009679151,0.00059839705,0.0065798797],"study_design_scores_gemma":[0.00003709135,0.00006597717,0.0003469567,0.000011926197,0.00003392617,0.00012903579,0.00008847052,0.9884919,0.0004205561,0.00894743,0.0014110699,0.000015818898],"about_ca_topic_score_codex":0.010951233,"about_ca_topic_score_gemma":0.011131121,"teacher_disagreement_score":0.010951233,"about_ca_system_score_codex":0.0020791246,"about_ca_system_score_gemma":0.0017519023,"threshold_uncertainty_score":0.021774948},"labels":[],"label_agreement":null},{"id":"W2282509245","doi":"","title":"Comparaison d'approches de modélisation de problèmes tests pour le pilotage du transport : application aux mines à ciel ouvert","year":2007,"lang":"fr","type":"article","venue":"PolyPublie (École Polytechnique de Montréal)","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Benchmark (surveying); Computer science; Operations research; Simulation; Engineering; Geology","score_opus":0.017932773643221187,"score_gpt":0.25918195043522496,"score_spread":0.24124917679200378,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2282509245","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12844421,0.0006246697,0.8602486,0.00049240934,0.00015600368,0.0003460867,0.00020363799,0.0010458695,0.008438432],"genre_scores_gemma":[0.7309213,0.000702657,0.26372567,0.00012090983,0.000059972976,0.0007048436,0.0004535972,0.00040088725,0.002910178],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9973379,0.0012945345,0.00018144163,0.00026421386,0.00074017164,0.00018171141],"domain_scores_gemma":[0.99084395,0.0070583373,0.0003036429,0.00064872694,0.0009929803,0.00015230894],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00425095,0.0013911978,0.0016178486,0.0010088705,0.0006833503,0.0022062769,0.0020100905,0.0022213345,0.003110009],"category_scores_gemma":[0.013508777,0.0006910428,0.0015923785,0.0008707096,0.000982364,0.0016354371,0.0013156252,0.0019658548,0.00036122813],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007702102,0.00008599448,0.0006056416,0.000070368944,0.000025973963,0.000015534632,0.000034530225,0.98450446,0.0005458424,0.002120818,0.00013480199,0.011778991],"study_design_scores_gemma":[0.000011460614,0.00003557608,0.00011107759,0.000007130826,0.00000678294,0.0000052404703,0.000012397268,0.998329,0.000390025,0.00078972353,0.00029762185,0.000003933797],"about_ca_topic_score_codex":0.015289733,"about_ca_topic_score_gemma":0.007197034,"teacher_disagreement_score":0.015289733,"about_ca_system_score_codex":0.0017008634,"about_ca_system_score_gemma":0.0017314358,"threshold_uncertainty_score":0.030401468},"labels":[],"label_agreement":null},{"id":"W2282607975","doi":"10.4230/lipics.fsttcs.2012.325","title":"k-delivery traveling salesman problem on tree networks","year":2012,"lang":"en","type":"article","venue":"DROPS (Schloss Dagstuhl – Leibniz Center for Informatics)","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Travelling salesman problem; Pickup; Routing (electronic design automation); Vehicle routing problem; Tree (set theory); Traveling purchaser problem; Computer science; Mathematical optimization; Mathematics; Combinatorics; 2-opt; Artificial intelligence; Computer network","score_opus":0.017013542441611736,"score_gpt":0.2445203401976485,"score_spread":0.22750679775603677,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2282607975","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10847253,0.0010808257,0.87774915,0.0007872557,0.00008699346,0.00015127313,0.000582863,0.0002569442,0.010832285],"genre_scores_gemma":[0.65412027,0.0026351516,0.3261667,0.00028032536,0.00014425442,0.00033465936,0.0014559221,0.00018943178,0.014673326],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999511,0.00015994819,0.000028368693,0.00012305594,0.000083966894,0.0000935764],"domain_scores_gemma":[0.99924004,0.000509071,0.00009596371,0.000039107737,0.00006132844,0.000054446296],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006404714,0.0005022546,0.00076628366,0.0003957038,0.0006079038,0.0010171884,0.00092967233,0.00088360644,0.0044895587],"category_scores_gemma":[0.0022769393,0.000307339,0.0005251449,0.0012008734,0.00046304235,0.0024884664,0.00091372046,0.0006800366,0.0006413039],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019394206,0.00011321714,0.0007769582,0.00042241954,0.00006836113,0.0003717425,0.00026148642,0.76353186,0.0031379703,0.16055854,0.008924072,0.061639484],"study_design_scores_gemma":[0.000040839426,0.00007461129,0.0002649482,0.000018326444,0.000017848708,0.0002125624,0.00013634542,0.8957713,0.0008393123,0.09624501,0.006366952,0.0000119224815],"about_ca_topic_score_codex":0.0023725722,"about_ca_topic_score_gemma":0.0015796751,"teacher_disagreement_score":0.0044895587,"about_ca_system_score_codex":0.00097485015,"about_ca_system_score_gemma":0.00072676246,"threshold_uncertainty_score":0.015019119},"labels":[],"label_agreement":null},{"id":"W2283042507","doi":"10.1007/0-306-48213-4_15","title":"The Bottleneck TSP","year":2006,"lang":"en","type":"book-chapter","venue":"Combinatorial optimization","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":18,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Bottleneck; Computer science; Embedded system","score_opus":0.009923679561488942,"score_gpt":0.2187868876056296,"score_spread":0.20886320804414066,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2283042507","genre_codex":"other","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0094526075,0.011861101,0.29803288,0.0033261534,0.0012120975,0.0001016741,0.0009659933,0.0015610582,0.6734865],"genre_scores_gemma":[0.20766811,0.019266952,0.20532376,0.0021344132,0.0013779177,0.0003944946,0.0031867938,0.0026494765,0.557998],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9995697,0.00008397247,0.000014597363,0.00012348733,0.00014475474,0.00006341676],"domain_scores_gemma":[0.99961036,0.00012703557,0.000024062798,0.00009849501,0.000094635325,0.00004544781],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044964647,0.0010859371,0.0010645726,0.0014480898,0.001120245,0.0028310244,0.0016504303,0.0012379233,0.03661698],"category_scores_gemma":[0.0019913777,0.0006814945,0.0005983245,0.0025139097,0.0011071686,0.0041034566,0.0018305107,0.0026679693,0.010158868],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000058635847,0.00007751388,0.0001448338,0.00032569215,0.000032640557,0.000102510356,0.00009447841,0.024365874,0.0013568157,0.61578447,0.14293274,0.21472381],"study_design_scores_gemma":[0.000016893953,0.000030370107,0.00021660294,0.00013911299,0.000019765088,0.00025634348,0.00007329265,0.053958345,0.0017602352,0.634424,0.3090871,0.000017969205],"about_ca_topic_score_codex":0.002625506,"about_ca_topic_score_gemma":0.0032327273,"teacher_disagreement_score":0.03661698,"about_ca_system_score_codex":0.0018940874,"about_ca_system_score_gemma":0.0018630906,"threshold_uncertainty_score":0.12249601},"labels":[],"label_agreement":null},{"id":"W2289752907","doi":"","title":"Separating Valid Odd-Cycle and Odd-Set Inequalities for the Multiple Depot Vehicle Scheduling Problem","year":2013,"lang":"en","type":"article","venue":"PolyPublie (École Polytechnique de Montréal)","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kronos (Canada); Group for Research in Decision Analysis; Université du Québec à Montréal; Polytechnique Montréal","funders":"","keywords":"Mathematical optimization; Scheduling (production processes); Depot; Multi-commodity flow problem; Computer science; Mathematics; Set (abstract data type); Flow network; Job shop scheduling; Column generation; Operations research; Schedule","score_opus":0.0217638503829815,"score_gpt":0.26136430973450403,"score_spread":0.23960045935152252,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2289752907","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.036012474,0.00032427645,0.9582396,0.00034530257,0.000050827108,0.00018361134,0.00022439747,0.000112034875,0.004507449],"genre_scores_gemma":[0.33137962,0.00090378325,0.6617902,0.0002816348,0.00009779281,0.00041066427,0.00074333616,0.00020475987,0.004188256],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9982224,0.00078326964,0.000091636015,0.00019500639,0.00043103177,0.00027672233],"domain_scores_gemma":[0.9928732,0.0055200183,0.0005913746,0.00029962935,0.00048988604,0.00022593536],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004044509,0.0016416007,0.0010246241,0.0011520211,0.00081225665,0.0017590399,0.0017646117,0.0010049533,0.0034675733],"category_scores_gemma":[0.011078438,0.0006374427,0.0012033558,0.0015216868,0.001078551,0.0024954265,0.0015760605,0.0020178622,0.00035285583],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00029047058,0.00027760759,0.001499919,0.00027282006,0.0000682164,0.00020450649,0.00019596632,0.7628322,0.0028525838,0.12515266,0.002925396,0.103427686],"study_design_scores_gemma":[0.00004717996,0.000057445148,0.00018642783,0.000026023017,0.00001811993,0.0000308092,0.000041937565,0.93320096,0.0016605281,0.06296748,0.0017489766,0.000014182782],"about_ca_topic_score_codex":0.0069104712,"about_ca_topic_score_gemma":0.00918433,"teacher_disagreement_score":0.0069104712,"about_ca_system_score_codex":0.0015383608,"about_ca_system_score_gemma":0.0039712302,"threshold_uncertainty_score":0.021389604},"labels":[],"label_agreement":null},{"id":"W2291592553","doi":"10.1057/jors.2015.90","title":"Solving the vehicle routing problem with lunch break arising in the furniture delivery industry","year":2015,"lang":"en","type":"article","venue":"Journal of the Operational Research Society","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":36,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Vehicle routing problem; Computer science; Heuristic; Mathematical optimization; Routing (electronic design automation); Integer programming; Heuristics; Linear programming; Process (computing); Last mile (transportation); Operations research; Algorithm; Mathematics; Mile; Artificial intelligence","score_opus":0.07099147204568494,"score_gpt":0.34300034658101936,"score_spread":0.2720088745353344,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2291592553","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.25779045,0.002241461,0.72399575,0.0014493383,0.000177214,0.0002914721,0.00041594385,0.0004673511,0.01317101],"genre_scores_gemma":[0.6989289,0.0010812215,0.2907136,0.00024876135,0.000101799975,0.0002939907,0.0007149614,0.00018450848,0.007732269],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993813,0.00024228437,0.000021282312,0.00016577836,0.000064632426,0.00012460661],"domain_scores_gemma":[0.9989525,0.0007733032,0.0001238556,0.000039245988,0.00004414519,0.000066911816],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009790974,0.0012681953,0.0013347721,0.00046289669,0.0007578947,0.0013077641,0.0012106731,0.00232241,0.004806143],"category_scores_gemma":[0.002486662,0.00060932385,0.001012823,0.0008653669,0.00062830775,0.0012364974,0.0011053997,0.0015958415,0.00034638256],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000089321664,0.0001209058,0.0006237388,0.00019721499,0.000046697347,0.00018116459,0.00006404111,0.9742483,0.00085385377,0.005691596,0.0013614148,0.016521776],"study_design_scores_gemma":[0.000053082877,0.00013056537,0.00033932456,0.000015174625,0.000025218922,0.00010440042,0.0001549624,0.9889951,0.00060306385,0.007860261,0.0017058023,0.000013148799],"about_ca_topic_score_codex":0.007877362,"about_ca_topic_score_gemma":0.0063516963,"teacher_disagreement_score":0.007877362,"about_ca_system_score_codex":0.0009098436,"about_ca_system_score_gemma":0.00131668,"threshold_uncertainty_score":0.016078115},"labels":[],"label_agreement":null},{"id":"W2292410759","doi":"","title":"Scheduling Issues in Vehicle Routing","year":2012,"lang":"en","type":"article","venue":"Les Cahiers du GERAD","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"","keywords":"Computer science; Equal-cost multi-path routing; Static routing; Policy-based routing; Multipath routing; Link-state routing protocol; Distributed computing; Computer network; Routing (electronic design automation); Routing protocol","score_opus":0.013045123559327482,"score_gpt":0.2537664492992031,"score_spread":0.24072132573987562,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2292410759","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00963357,0.02102034,0.8791645,0.005576894,0.003979109,0.00009186742,0.00020376916,0.00020330542,0.08012666],"genre_scores_gemma":[0.61688423,0.041066308,0.25113717,0.0017422998,0.008758858,0.00027150306,0.00057162985,0.0004473593,0.07912056],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985481,0.0006776514,0.000057149722,0.00023018953,0.00037608546,0.00011087704],"domain_scores_gemma":[0.9986743,0.0009042826,0.000120308214,0.000083504914,0.00015574103,0.00006185575],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001736659,0.0008727151,0.0007206172,0.00059928105,0.0012398799,0.00220977,0.0010871341,0.0015100432,0.008069225],"category_scores_gemma":[0.0054175523,0.00049756904,0.0006545862,0.0019942105,0.0012596541,0.0024783977,0.0009531762,0.0020088742,0.001559091],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003707039,0.000039676164,0.00024008456,0.00024770224,0.00003159672,0.00014807962,0.00014788698,0.16616817,0.0007327226,0.7312223,0.016715208,0.084269516],"study_design_scores_gemma":[0.000023604493,0.00006353302,0.0002122005,0.00008277817,0.000023107974,0.0001248374,0.00015410804,0.16547716,0.00061607163,0.72140884,0.11179276,0.00002085916],"about_ca_topic_score_codex":0.0032236853,"about_ca_topic_score_gemma":0.0019938631,"teacher_disagreement_score":0.008069225,"about_ca_system_score_codex":0.0015035243,"about_ca_system_score_gemma":0.0012348572,"threshold_uncertainty_score":0.026994228},"labels":[],"label_agreement":null},{"id":"W2292509389","doi":"10.48550/arxiv.1602.08508","title":"A joint routing and speed optimization problem","year":2016,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Joint (building); Computer science; Routing (electronic design automation); Mathematical optimization; Computer network; Engineering; Mathematics; Structural engineering","score_opus":0.059450403824680934,"score_gpt":0.18857151191658467,"score_spread":0.12912110809190375,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2292509389","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04598234,0.000807303,0.92293817,0.0015971572,0.00028031395,0.00036562167,0.0014750562,0.0006687315,0.025885398],"genre_scores_gemma":[0.530756,0.0008984164,0.4369092,0.00048990396,0.00032664996,0.0009219629,0.0023266752,0.0006066011,0.02676455],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983455,0.0004934472,0.00006954117,0.0004978843,0.0003072977,0.00028633815],"domain_scores_gemma":[0.9989177,0.0006276966,0.000099900324,0.00007889272,0.00015486078,0.000121027275],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017134276,0.0025682172,0.0024545896,0.0012334795,0.0009146668,0.0026391,0.0021846178,0.004057857,0.013391842],"category_scores_gemma":[0.0034951863,0.0012269457,0.0017594905,0.0022341306,0.00096289755,0.0033064587,0.0018502894,0.0025190988,0.0013552487],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007403746,0.000107277505,0.00046499225,0.00014938987,0.000057381054,0.0001735897,0.000039972223,0.93910795,0.00082042447,0.023598464,0.00595879,0.0294477],"study_design_scores_gemma":[0.000033509612,0.00006611299,0.00028348347,0.00001729511,0.000033830485,0.00011865163,0.00004800666,0.9743911,0.0005777909,0.019891089,0.0045219404,0.00001710004],"about_ca_topic_score_codex":0.004839545,"about_ca_topic_score_gemma":0.003174401,"teacher_disagreement_score":0.013391842,"about_ca_system_score_codex":0.0017631021,"about_ca_system_score_gemma":0.0022758273,"threshold_uncertainty_score":0.044800222},"labels":[],"label_agreement":null},{"id":"W2294468862","doi":"10.1016/j.eswa.2016.02.037","title":"Erratum to “A cooperative coevolutionary algorithm for the Multi-Depot Vehicle Routing Problem [Expert Systems with Applications 43 (2015) 117–130]”","year":2016,"lang":"en","type":"erratum","venue":"Expert Systems with Applications","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; Université du Québec à Montréal","funders":"","keywords":"Computer science; Vehicle routing problem; Depot; Routing (electronic design automation); Routing algorithm; Mathematical optimization; Artificial intelligence; Computer network; Mathematics; Routing protocol","score_opus":0.01891774664071865,"score_gpt":0.28406008841891456,"score_spread":0.2651423417781959,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2294468862","genre_codex":"editorial","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00062010996,0.0022329807,0.009315983,0.062650286,0.90310544,0.00009102526,0.0020020634,0.00069095095,0.019291159],"genre_scores_gemma":[0.02502592,0.010277603,0.03968872,0.10020228,0.14744118,0.0004644309,0.009732992,0.0024055832,0.66476125],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99702865,0.0005037618,0.00051341986,0.0003692323,0.0014172692,0.00016769965],"domain_scores_gemma":[0.98733056,0.0030335966,0.00058235606,0.0007231235,0.008013486,0.00031685937],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026947677,0.0017603441,0.0015730126,0.0021588616,0.0028334714,0.002473302,0.003172761,0.005868734,0.052011136],"category_scores_gemma":[0.03690613,0.00085255166,0.0013098913,0.0018932194,0.0017245698,0.0026254249,0.0018043435,0.005379529,0.02480793],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005164481,0.000012635806,0.00007082563,0.00009337959,0.000009426562,0.00013375792,0.000022059296,0.00033324317,0.000093534945,0.0025121756,0.98599094,0.010676273],"study_design_scores_gemma":[0.000058907925,0.00005671131,0.00051118125,0.00029111427,0.00003284873,0.00022473758,0.00007660932,0.0024841833,0.00085888297,0.004590529,0.99075,0.000064238375],"about_ca_topic_score_codex":0.020466737,"about_ca_topic_score_gemma":0.026683034,"teacher_disagreement_score":0.052011136,"about_ca_system_score_codex":0.0031318346,"about_ca_system_score_gemma":0.004460729,"threshold_uncertainty_score":0.1739946},"labels":[],"label_agreement":null},{"id":"W2295299369","doi":"","title":"Solution Techniques for the Large Set Covering Problem","year":2003,"lang":"en","type":"article","venue":"PolyPublie (École Polytechnique de Montréal)","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Group for Research in Decision Analysis; Polytechnique Montréal","funders":"","keywords":"Set cover problem; Mathematics; Cover (algebra); Constraint satisfaction problem; Heuristic; Set (abstract data type); Extension (predicate logic); Combinatorics; Constraint (computer-aided design); Family of sets; Computation; Discrete mathematics; Algorithm; Mathematical optimization; Computer science","score_opus":0.01391323732630745,"score_gpt":0.24987864402170837,"score_spread":0.23596540669540092,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2295299369","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003737887,0.0004267486,0.9916334,0.00022167798,0.000027283779,0.00010013665,0.000064905005,0.00017417237,0.0036136797],"genre_scores_gemma":[0.069504224,0.0008717776,0.9259159,0.00012177667,0.00009969252,0.0004438148,0.0003560803,0.00014406619,0.0025427316],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.99829644,0.0005807781,0.00007960113,0.00029251204,0.0005858998,0.00016478205],"domain_scores_gemma":[0.9977912,0.0015673386,0.00017940892,0.00022921052,0.00018349104,0.000049430706],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018520902,0.001266006,0.0010519866,0.0016184989,0.0009454875,0.0014982761,0.0016021354,0.0014106923,0.005952722],"category_scores_gemma":[0.0066109165,0.00078380096,0.0016936541,0.0028559677,0.0009842404,0.0019641828,0.0020107946,0.002145549,0.0008368435],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000117892945,0.0001555244,0.0007689235,0.00069288333,0.000110442605,0.00020485216,0.00045382624,0.4509455,0.0043123695,0.22604574,0.010105906,0.30608606],"study_design_scores_gemma":[0.00010647933,0.0000978356,0.0002591697,0.00012369137,0.000054006665,0.0003296914,0.00014941982,0.7532113,0.0025535212,0.21757524,0.02550963,0.000029911935],"about_ca_topic_score_codex":0.0020662437,"about_ca_topic_score_gemma":0.0025484276,"teacher_disagreement_score":0.005952722,"about_ca_system_score_codex":0.0013663722,"about_ca_system_score_gemma":0.0014354733,"threshold_uncertainty_score":0.019913793},"labels":[],"label_agreement":null},{"id":"W2298220966","doi":"10.1016/j.disopt.2017.09.003","title":"The quadratic minimum spanning tree problem and its variations","year":2017,"lang":"en","type":"preprint","venue":"Discrete Optimization","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Spanning tree; Minimum spanning tree; Distributed minimum spanning tree; Combinatorics; Euclidean minimum spanning tree; Matroid; Mathematics; Context (archaeology); Kruskal's algorithm; Bottleneck; k-minimum spanning tree; Quadratic equation; Tree (set theory); Simple (philosophy); Minimum degree spanning tree; Discrete mathematics; Binary tree; K-ary tree; Computer science; Tree structure","score_opus":0.02206177484825251,"score_gpt":0.28212960636901624,"score_spread":0.26006783152076374,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2298220966","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013364469,0.004794541,0.96660286,0.0015428725,0.0003101139,0.000022237013,0.00015681626,0.00006574594,0.013140401],"genre_scores_gemma":[0.54691434,0.013395399,0.39133948,0.0009803608,0.001967686,0.00025490185,0.00075094425,0.00043262352,0.04396438],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993525,0.00026414183,0.000022196977,0.00015661471,0.00017128147,0.000033303753],"domain_scores_gemma":[0.99859554,0.0008816175,0.00015990337,0.00008787614,0.00019093227,0.0000841293],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015170765,0.0008392276,0.00091076695,0.00077601115,0.00049317186,0.0014674608,0.0014595148,0.0015728342,0.0030674855],"category_scores_gemma":[0.0060268315,0.00041168812,0.00055662525,0.0017227132,0.0014808233,0.0030655318,0.0015253694,0.00272954,0.0005187792],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000068704285,0.00008210124,0.00031765844,0.00027781777,0.00003970346,0.00006828056,0.00009951907,0.26145014,0.0012654748,0.6656554,0.008661376,0.062013775],"study_design_scores_gemma":[0.000012043503,0.000020852354,0.00016917451,0.000019204523,0.000011115965,0.000063240324,0.000023680881,0.6408992,0.00015088984,0.35114974,0.0074691763,0.000011669902],"about_ca_topic_score_codex":0.0023410576,"about_ca_topic_score_gemma":0.002094812,"teacher_disagreement_score":0.0030674855,"about_ca_system_score_codex":0.0008440414,"about_ca_system_score_gemma":0.00075969304,"threshold_uncertainty_score":0.010261774},"labels":[],"label_agreement":null},{"id":"W2301536381","doi":"10.5539/jmr.v8n2p49","title":"Optimal Two Hubs Location and Network Construction for a Regional Company of WAEMU Zone","year":2016,"lang":"en","type":"article","venue":"Journal of Mathematics Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Laurentian University","funders":"","keywords":"Profit (economics); Integer programming; Operations research; Business; Mathematics; Operations management; Transport engineering; Mathematical optimization; Engineering; Economics; Microeconomics","score_opus":0.09717918017672626,"score_gpt":0.39004391466650534,"score_spread":0.29286473448977907,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2301536381","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.44581512,0.00075473974,0.53026396,0.000856588,0.00012099443,0.00030695042,0.0007811813,0.00032617568,0.020774253],"genre_scores_gemma":[0.9118089,0.00034583398,0.0756006,0.000039631555,0.000015589958,0.0001707712,0.0003390678,0.000065927765,0.011613777],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99951506,0.00016851867,0.000010536356,0.00011426875,0.000039825616,0.00015191342],"domain_scores_gemma":[0.9996356,0.00015065813,0.000055973163,0.000021280988,0.000054629847,0.00008185612],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00068109744,0.0010506595,0.0008566044,0.00085924356,0.0010782419,0.001971966,0.0010928113,0.0018245425,0.006808318],"category_scores_gemma":[0.0010475252,0.00078546867,0.0011750762,0.0010321165,0.00074288086,0.0015796368,0.001199497,0.0011613478,0.00046768322],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008674677,0.000045176843,0.00078772946,0.00006986011,0.000016177177,0.00021146103,0.00005154951,0.983405,0.0010568877,0.008617027,0.0006407354,0.00501164],"study_design_scores_gemma":[0.000018378825,0.00008322926,0.00040830596,0.000009997712,0.00001845118,0.000045284116,0.00013919323,0.9946555,0.0005599175,0.0029394177,0.0011087275,0.000013596564],"about_ca_topic_score_codex":0.02234792,"about_ca_topic_score_gemma":0.023523519,"teacher_disagreement_score":0.02234792,"about_ca_system_score_codex":0.0028755097,"about_ca_system_score_gemma":0.0024021103,"threshold_uncertainty_score":0.04443568},"labels":[],"label_agreement":null},{"id":"W2302298156","doi":"10.1007/s10479-015-2001-7","title":"Multi-trip pickup and delivery problem with time windows and synchronization","year":2016,"lang":"en","type":"article","venue":"Annals of Operations Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":42,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Computer Research Institute of Montréal; École de Technologie Supérieure; Université de Montréal; Transport Canada","funders":"","keywords":"Vehicle routing problem; Tabu search; Computer science; Pickup; Benchmark (surveying); Operations research; Scheduling (production processes); Routing (electronic design automation); Job shop scheduling; Synchronization (alternating current); Mathematical optimization; Theory of computation; Computer network; Algorithm; Mathematics; Artificial intelligence","score_opus":0.091053267242863,"score_gpt":0.36662861446233885,"score_spread":0.27557534721947585,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2302298156","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.056694355,0.0011151957,0.9358889,0.000822553,0.00019147633,0.00015867376,0.00053175923,0.0001567776,0.0044403486],"genre_scores_gemma":[0.87812674,0.0019572978,0.09778033,0.00016498432,0.0003720302,0.0005060991,0.00064808485,0.00018062237,0.020263797],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984837,0.00055621035,0.00009462869,0.00042393096,0.0001921997,0.0002493476],"domain_scores_gemma":[0.99445,0.004151291,0.00067488704,0.00016037909,0.00022332612,0.00034010634],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0035827751,0.002094235,0.004394226,0.0015400882,0.0008504843,0.0025207275,0.0035344032,0.0031404227,0.005086927],"category_scores_gemma":[0.008491487,0.00210561,0.0021922793,0.0026242626,0.0012865671,0.004312392,0.0026839133,0.0023397484,0.00044709377],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000262662,0.00006992996,0.00038938614,0.00016236241,0.00010856509,0.0001980515,0.00004352411,0.96799344,0.0005769919,0.02269019,0.00088663696,0.0066183303],"study_design_scores_gemma":[0.00003231294,0.0000612376,0.00012974291,0.000006254288,0.00003542241,0.000028038248,0.000024211917,0.9928439,0.00015842359,0.006409716,0.00025707623,0.000013675024],"about_ca_topic_score_codex":0.006602079,"about_ca_topic_score_gemma":0.0029049695,"teacher_disagreement_score":0.006602079,"about_ca_system_score_codex":0.0017458077,"about_ca_system_score_gemma":0.0018593081,"threshold_uncertainty_score":0.01894778},"labels":[],"label_agreement":null},{"id":"W2304562996","doi":"","title":"A Branch-and-Price Algorithm for the Vehicle Routing Problem with Deliveries, Selective Pickups and Time Windows","year":2009,"lang":"en","type":"article","venue":"RePEc: Research Papers in Economics","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal; Polytechnique Montréal; Group for Research in Decision Analysis","funders":"","keywords":"Vehicle routing problem; Pickup; Routing (electronic design automation); Computer science; Revenue; Mathematical optimization; Set (abstract data type); Operations research; Mathematics; Economics; Computer network; Artificial intelligence; Finance","score_opus":0.012272064764539913,"score_gpt":0.2622616525344074,"score_spread":0.24998958776986752,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2304562996","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010068235,0.0005319274,0.9815498,0.00031445132,0.00010914425,0.00023097114,0.000119007644,0.0008073033,0.0062691728],"genre_scores_gemma":[0.09219297,0.00066194497,0.899006,0.00013825732,0.0001100158,0.0005053309,0.00048110468,0.00030196348,0.006602346],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992418,0.00020348537,0.000038208727,0.00014982236,0.00023293319,0.00013382862],"domain_scores_gemma":[0.9993494,0.00042567085,0.000042135027,0.00004584456,0.000081472455,0.000055347748],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013804777,0.0014779486,0.00200931,0.001052149,0.0011957979,0.0019066253,0.0019947581,0.0022680492,0.010294664],"category_scores_gemma":[0.0031950867,0.0010023868,0.0010728682,0.002260388,0.00082107174,0.0026208402,0.0017387153,0.002315613,0.0020286872],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023573314,0.00026099416,0.0004479472,0.00017635203,0.00007255516,0.00016991064,0.00012427299,0.6847318,0.0015708297,0.041487176,0.011622062,0.25910038],"study_design_scores_gemma":[0.00010595166,0.00008528318,0.00008884393,0.000013334873,0.000020404543,0.000056795783,0.000022985309,0.9774531,0.00034547545,0.01914812,0.0026476071,0.000012011225],"about_ca_topic_score_codex":0.008089945,"about_ca_topic_score_gemma":0.00716767,"teacher_disagreement_score":0.010294664,"about_ca_system_score_codex":0.001364219,"about_ca_system_score_gemma":0.002725786,"threshold_uncertainty_score":0.034439147},"labels":[],"label_agreement":null},{"id":"W2311828527","doi":"10.1287/ijoc.2016.0744","title":"New Enhancements for the Exact Solution of the Vehicle Routing Problem with Time Windows","year":2017,"lang":"en","type":"article","venue":"INFORMS journal on computing","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal; Polytechnique Montréal; Group for Research in Decision Analysis; HEC Montréal","funders":"","keywords":"Vehicle routing problem; Enumeration; Routing (electronic design automation); Node (physics); Relaxation (psychology); State (computer science); Mathematics; Variable (mathematics); Computer science; Arc (geometry); Mathematical optimization; Algorithm; Combinatorics; Computer network; Engineering","score_opus":0.01893947499540653,"score_gpt":0.2695841109089779,"score_spread":0.25064463591357133,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2311828527","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009632024,0.0004575521,0.97854155,0.00036447935,0.00014732603,0.00017588364,0.00023399021,0.00086939987,0.0095777055],"genre_scores_gemma":[0.08908887,0.00060995395,0.9051247,0.00020545162,0.0001923949,0.00028255043,0.00051210256,0.0003244598,0.003659532],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99814343,0.00038384006,0.000099657,0.0003242937,0.0008392349,0.00020952738],"domain_scores_gemma":[0.99753237,0.0011034901,0.00020826812,0.000623082,0.0004390703,0.000093764895],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013398553,0.0014414033,0.00094923837,0.0011581767,0.0005486667,0.0015066249,0.0024723704,0.0009430121,0.007119032],"category_scores_gemma":[0.0078112604,0.000723348,0.0012642648,0.002020245,0.00086155924,0.0036321834,0.0018935953,0.0050123744,0.0017791383],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00045347054,0.000496976,0.00087881496,0.00042257848,0.00007234586,0.0002069821,0.00018776026,0.3764988,0.022111006,0.15906338,0.012587281,0.42702064],"study_design_scores_gemma":[0.00007174067,0.00014302143,0.00032497724,0.000051515806,0.00003716154,0.00012770291,0.000035619356,0.93568563,0.007392079,0.03833306,0.017768154,0.000029242567],"about_ca_topic_score_codex":0.005046547,"about_ca_topic_score_gemma":0.0064825793,"teacher_disagreement_score":0.007119032,"about_ca_system_score_codex":0.0011479291,"about_ca_system_score_gemma":0.0023648266,"threshold_uncertainty_score":0.023815513},"labels":[],"label_agreement":null},{"id":"W2312035630","doi":"","title":"Branch-price-and-cut algorithms for the pickup and delivery problem with time windows and multiple stacks","year":2015,"lang":"en","type":"article","venue":"PolyPublie (École Polytechnique de Montréal)","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal; Polytechnique Montréal; Group for Research in Decision Analysis","funders":"","keywords":"Pickup; FIFO and LIFO accounting; Benchmark (surveying); Stack (abstract data type); Computer science; Vehicle routing problem; Mathematical optimization; Path (computing); Travelling salesman problem; Position (finance); Shortest path problem; Algorithm; Routing (electronic design automation); Mathematics; Economics; FIFO (computing and electronics); Computer network; Theoretical computer science; Artificial intelligence; Operating system","score_opus":0.015110270455657241,"score_gpt":0.2269251546750366,"score_spread":0.21181488421937936,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2312035630","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008754941,0.00089982024,0.9846089,0.00034548368,0.0000797969,0.00016500434,0.00017112747,0.0004025266,0.004572413],"genre_scores_gemma":[0.15727173,0.0019443288,0.8309434,0.00020686368,0.00015991041,0.00082185987,0.00084003765,0.00037838827,0.007433487],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986645,0.0005653934,0.00005745346,0.00021167586,0.0002470219,0.000253888],"domain_scores_gemma":[0.99739456,0.0020188014,0.00019075122,0.00011182779,0.0001529187,0.00013115793],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026663141,0.0027719792,0.002723388,0.001604371,0.0012699807,0.0024941866,0.0032938605,0.002917016,0.0083524445],"category_scores_gemma":[0.0049717426,0.0016489618,0.0021669515,0.002578989,0.0011546083,0.003554363,0.0015964913,0.0036170082,0.0013147421],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000072627816,0.00011120562,0.00024473766,0.00010205133,0.000048317663,0.000036896585,0.000042350704,0.94155157,0.00018118562,0.025459642,0.0023405296,0.029808903],"study_design_scores_gemma":[0.000024070605,0.000025910704,0.000037179987,0.0000130006565,0.000011263337,0.000010086342,0.000010327985,0.9842135,0.000097277,0.014651818,0.00090008276,0.0000054077914],"about_ca_topic_score_codex":0.012100488,"about_ca_topic_score_gemma":0.012045849,"teacher_disagreement_score":0.012100488,"about_ca_system_score_codex":0.003307169,"about_ca_system_score_gemma":0.0036515254,"threshold_uncertainty_score":0.027941763},"labels":[],"label_agreement":null},{"id":"W2314520391","doi":"10.1109/ieem.2013.6962399","title":"Optimization of forest vehicle routing using reactive tabu search metaheuristic","year":2013,"lang":"en","type":"article","venue":"","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec en Abitibi-Témiscamingue; École de Technologie Supérieure","funders":"","keywords":"Tabu search; Metaheuristic; Vehicle routing problem; Computer science; Guided Local Search; Mathematical optimization; Routing (electronic design automation); Algorithm; Mathematics; Computer network","score_opus":0.02994774288431724,"score_gpt":0.27773371291234983,"score_spread":0.2477859700280326,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2314520391","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12428407,0.0005616557,0.86652094,0.00013239023,0.00004355221,0.00008028924,0.00009731847,0.0005375418,0.0077422266],"genre_scores_gemma":[0.8456292,0.00024618985,0.15177102,0.000046081932,0.0000114707655,0.00010177486,0.00012104808,0.00007146102,0.002001745],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997769,0.00010453077,0.000006185089,0.000022425094,0.000052419007,0.00003752017],"domain_scores_gemma":[0.9997979,0.000106804924,0.000035523855,0.0000162794,0.00003350564,0.000010075772],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004728285,0.00048475873,0.00045272955,0.0005202571,0.00030976234,0.00051050726,0.00057950756,0.0004898723,0.0009625426],"category_scores_gemma":[0.0006229022,0.00019705383,0.0004732483,0.000613388,0.0002919462,0.00043794975,0.00023587771,0.0003340349,0.0001318525],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000030538242,0.000021772334,0.00019744513,0.000023430031,0.000017710305,0.000020671725,0.000011895069,0.9804072,0.0019943267,0.002939093,0.0003096124,0.014026281],"study_design_scores_gemma":[0.0000046549503,0.000021942164,0.00005268834,0.000001439652,0.0000032633777,0.000006352128,0.000004705699,0.9985581,0.00041626903,0.00071872445,0.00021003907,0.0000018789196],"about_ca_topic_score_codex":0.006313347,"about_ca_topic_score_gemma":0.0058521796,"teacher_disagreement_score":0.006313347,"about_ca_system_score_codex":0.0005446388,"about_ca_system_score_gemma":0.0006777724,"threshold_uncertainty_score":0.012553215},"labels":[],"label_agreement":null},{"id":"W2317060951","doi":"10.1016/j.trb.2016.03.003","title":"A rough-cut approach for evaluating location-routing decisions via approximation algorithms","year":2016,"lang":"en","type":"article","venue":"Transportation Research Part B Methodological","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; Ministerio de Economía y Competitividad","keywords":"Implementation; Routing (electronic design automation); Computer science; Routing algorithm; Quality (philosophy); Cut-off; Supply chain; Simple (philosophy); Operations research; Algorithm; Mathematical optimization; Mathematics; Business; Routing protocol; Engineering; Computer network; Marketing","score_opus":0.5247866706428028,"score_gpt":0.5032388526761308,"score_spread":0.021547817966672045,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2317060951","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0030580256,0.00017876251,0.99599636,0.00008370029,0.000017043905,0.00004218174,0.00004387228,0.000068505666,0.0005114213],"genre_scores_gemma":[0.17679448,0.00053549214,0.8201865,0.000099993646,0.000089516354,0.00035130826,0.00029124867,0.000092756265,0.0015587657],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9951703,0.0022188758,0.00022539844,0.00046288644,0.0016582231,0.00026423868],"domain_scores_gemma":[0.9868326,0.010409468,0.0005394462,0.0006217891,0.0013154917,0.0002812012],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008018147,0.001992558,0.0038400055,0.003934355,0.0010603716,0.0038074248,0.0031390185,0.0028085515,0.0032284511],"category_scores_gemma":[0.021049656,0.0017515495,0.002696943,0.0030746418,0.002042754,0.0036782587,0.0019193206,0.0029984156,0.00040883545],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000073049385,0.00007202434,0.00036620852,0.00011616253,0.00009729978,0.000050243747,0.00005841443,0.92994416,0.0005074947,0.03171733,0.00076833874,0.03622915],"study_design_scores_gemma":[0.0000071154946,0.00002529559,0.000052731903,0.000013825169,0.000019688976,0.000014081506,0.000009781445,0.9831496,0.00016063737,0.016312862,0.00022847726,0.000005902987],"about_ca_topic_score_codex":0.008202894,"about_ca_topic_score_gemma":0.0052694757,"teacher_disagreement_score":0.008202894,"about_ca_system_score_codex":0.002910083,"about_ca_system_score_gemma":0.0021755758,"threshold_uncertainty_score":0.042404532},"labels":[],"label_agreement":null},{"id":"W2322772548","doi":"10.3138/infor.53.1.26","title":"Routing Courier Delivery Services with Urgent Demand","year":2015,"lang":"en","type":"article","venue":"INFOR Information Systems and Operational Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Tabu search; Vehicle routing problem; Computer science; Heuristic; Routing (electronic design automation); Set (abstract data type); Service (business); Operations research; On demand; Similarity (geometry); Computer network; Engineering; Business; Artificial intelligence","score_opus":0.05297306154814244,"score_gpt":0.3227330839743835,"score_spread":0.26976002242624103,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2322772548","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.28175208,0.0009971616,0.69695586,0.001560527,0.00027192465,0.00036614726,0.0015934582,0.0012030165,0.015299877],"genre_scores_gemma":[0.8939832,0.00041242377,0.095866695,0.00016695877,0.00008129179,0.00013880206,0.0010751579,0.0001861228,0.008089414],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99884653,0.00043717565,0.000051611383,0.00024563522,0.00017989987,0.00023913366],"domain_scores_gemma":[0.9981425,0.0011600461,0.00031510202,0.000114610135,0.00013658401,0.00013116829],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012954262,0.000723295,0.0008280867,0.00070773065,0.0007743078,0.0017138093,0.0012773083,0.0014323463,0.006172568],"category_scores_gemma":[0.0043769474,0.00057425746,0.00073389726,0.0014793688,0.00054757687,0.0012942977,0.000772476,0.0010407794,0.0006748973],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012463794,0.00007165292,0.0017159006,0.00010532206,0.00006911657,0.0003567066,0.00013468199,0.95074743,0.0011185918,0.014701713,0.005106843,0.02574742],"study_design_scores_gemma":[0.000021130218,0.000053172655,0.00052249763,0.0000066402304,0.000015212588,0.00010911632,0.00013250049,0.9851562,0.0003546433,0.009631149,0.003984459,0.0000133298645],"about_ca_topic_score_codex":0.02072307,"about_ca_topic_score_gemma":0.023212716,"teacher_disagreement_score":0.02072307,"about_ca_system_score_codex":0.0016147039,"about_ca_system_score_gemma":0.0015855219,"threshold_uncertainty_score":0.04120493},"labels":[],"label_agreement":null},{"id":"W2325833190","doi":"10.1287/moor.2015.0758","title":"Facility Location with Client Latencies: LP-Based Techniques for Minimum-Latency Problems","year":2016,"lang":"en","type":"article","venue":"Mathematics of Operations Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Facility location problem; Steiner tree problem; Latency (audio); Mathematical optimization; Approximation algorithm; Total cost; Computer science; Mathematics; Set (abstract data type); Telecommunications","score_opus":0.09349109213490026,"score_gpt":0.36359941826799386,"score_spread":0.2701083261330936,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2325833190","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003781773,0.000568739,0.98886395,0.00070469914,0.00007047895,0.00008429765,0.0002267118,0.00045275316,0.005246577],"genre_scores_gemma":[0.19711116,0.0021609487,0.7846214,0.00064135884,0.0007473101,0.00081783277,0.0012416762,0.00094505394,0.011713216],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9972631,0.0009941943,0.000107587,0.00041964397,0.0007528851,0.00046266304],"domain_scores_gemma":[0.9917706,0.0062136226,0.0005330361,0.0006320718,0.0005402024,0.00031047096],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003050425,0.002921085,0.0019985712,0.0015672282,0.0010896252,0.0029714012,0.005142733,0.0028643545,0.010980903],"category_scores_gemma":[0.014857165,0.0012134301,0.0020097375,0.003488939,0.0012791966,0.0065418924,0.0037197236,0.0064772437,0.0019570645],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001923158,0.0002824582,0.0005483596,0.0005815778,0.00007572924,0.00021614213,0.0002809888,0.7892626,0.002200478,0.12274291,0.012324061,0.071292415],"study_design_scores_gemma":[0.000035187204,0.00005225831,0.000073395415,0.00004806517,0.000020933456,0.00008222977,0.000078643025,0.8926275,0.0006272376,0.10140015,0.0049408474,0.000013498317],"about_ca_topic_score_codex":0.00436313,"about_ca_topic_score_gemma":0.0034242577,"teacher_disagreement_score":0.010980903,"about_ca_system_score_codex":0.0024819556,"about_ca_system_score_gemma":0.0022444169,"threshold_uncertainty_score":0.03673476},"labels":[],"label_agreement":null},{"id":"W2331471704","doi":"10.1061/41064(358)402","title":"Research on Optimization of Crew Scheduling of the Passenger Dedicated Line Based on a Genetic Ant Colony Algorithm","year":2009,"lang":"en","type":"article","venue":"","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"National Natural Science Foundation of China; York University","keywords":"Crew scheduling; Crew; Scheduling (production processes); Ant colony optimization algorithms; Computer science; Genetic algorithm; Operations research; Ant colony; Job shop scheduling; Engineering; Schedule; Algorithm; Operating system; Aeronautics; Operations management; Machine learning","score_opus":0.04073749363813492,"score_gpt":0.3363787996597579,"score_spread":0.295641306021623,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2331471704","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04898713,0.0008444713,0.94373816,0.00018937171,0.000070800364,0.000057630266,0.000030267933,0.00019624802,0.0058859694],"genre_scores_gemma":[0.71292317,0.0015029031,0.27878892,0.0000981139,0.000072250405,0.0001571393,0.00011418898,0.00006887511,0.0062744617],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997421,0.000064259206,0.000010744763,0.00007040788,0.00008449138,0.000027994462],"domain_scores_gemma":[0.99977094,0.00009950243,0.000032923792,0.000019512689,0.00005975731,0.000017429702],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003151219,0.00055747526,0.0008099021,0.00035918268,0.0002616377,0.0006881308,0.0009032101,0.00061740953,0.0009853978],"category_scores_gemma":[0.0008735224,0.0003081503,0.00058576325,0.00086842105,0.00035353884,0.00066315674,0.00027765718,0.00055875495,0.0001243345],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000033222335,0.000053654323,0.00047792078,0.000082856444,0.00005573854,0.00006603384,0.000046060777,0.93587583,0.0055511156,0.008731885,0.0007056532,0.048320025],"study_design_scores_gemma":[0.000006486488,0.000021620037,0.0001477453,0.0000024283422,0.0000079004385,0.000014214307,0.0000073526494,0.9978928,0.0005294559,0.000829292,0.0005375375,0.0000031807551],"about_ca_topic_score_codex":0.009450875,"about_ca_topic_score_gemma":0.004928245,"teacher_disagreement_score":0.009450875,"about_ca_system_score_codex":0.00055473205,"about_ca_system_score_gemma":0.0010746932,"threshold_uncertainty_score":0.018791735},"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,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_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","score_opus":0.0590928165349166,"score_gpt":0.34067075522544904,"score_spread":0.28157793869053244,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2332834184","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.045850232,0.0006483452,0.94769347,0.00053807447,0.00011939236,0.00009318136,0.00009830478,0.00012354016,0.0048355437],"genre_scores_gemma":[0.73301566,0.000500758,0.25819936,0.00019073031,0.00011087462,0.00037639088,0.00027590102,0.000100038575,0.007230172],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99963474,0.00017965096,0.000013339128,0.000071058246,0.000062153245,0.000038956834],"domain_scores_gemma":[0.99841666,0.0012044344,0.00012534286,0.00004367632,0.0001250717,0.000084775405],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012707115,0.0006153531,0.0015861153,0.00072242523,0.00047773955,0.0008293975,0.0018302822,0.0016635339,0.002531434],"category_scores_gemma":[0.0040223883,0.0005950771,0.00062164693,0.000804402,0.0007424799,0.0013890404,0.0014980199,0.0009332791,0.00021199005],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007383151,0.0000328002,0.00022999004,0.00003670752,0.000022267974,0.000031574345,0.000023433111,0.98371685,0.00024659347,0.005921503,0.00078853144,0.008875942],"study_design_scores_gemma":[0.000008346438,0.000012538405,0.000021117989,0.0000017537046,0.0000018534953,0.0000028979955,0.0000043618147,0.99900144,0.00001861384,0.0008315084,0.00009424696,0.0000013528826],"about_ca_topic_score_codex":0.008650142,"about_ca_topic_score_gemma":0.0062388433,"teacher_disagreement_score":0.008650142,"about_ca_system_score_codex":0.0008120207,"about_ca_system_score_gemma":0.0011875384,"threshold_uncertainty_score":0.017199636},"labels":[],"label_agreement":null},{"id":"W2335850549","doi":"10.1080/03155986.2016.1166793","title":"The pickup and delivery problem with time windows and scheduled lines","year":2016,"lang":"en","type":"article","venue":"INFOR Information Systems and Operational Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":64,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"TKI DINALOG; Technische Universiteit Eindhoven","keywords":"Pickup; Public transport; Vehicle routing problem; Computer science; Transport engineering; Integer programming; Set (abstract data type); Routing (electronic design automation); Operations research; Destinations; Line (geometry); Business; Engineering; Computer network; Tourism; Mathematics","score_opus":0.027934545557684504,"score_gpt":0.29165877861605827,"score_spread":0.2637242330583738,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2335850549","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.25908843,0.0029298912,0.70653534,0.0022595923,0.00038137822,0.000689048,0.0029523328,0.0005068922,0.024657063],"genre_scores_gemma":[0.7449967,0.0024201658,0.22640985,0.0002447558,0.00030430575,0.0006765869,0.0015833819,0.00022937181,0.02313491],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998833,0.0004933284,0.00005507211,0.0002233739,0.00014943293,0.00024574623],"domain_scores_gemma":[0.9977696,0.0016523566,0.00021958287,0.00007064242,0.00008567891,0.00020217214],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016634952,0.0020948031,0.0019430075,0.0006575205,0.00092199084,0.0023091643,0.0017777026,0.0019498643,0.005984697],"category_scores_gemma":[0.0038726896,0.0011306616,0.0016130789,0.001691527,0.0008857323,0.002936275,0.0012511988,0.0023681114,0.00040963016],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023768772,0.00020765862,0.0004790801,0.00017839736,0.00006305857,0.00023834484,0.000055758,0.94979,0.0006459592,0.034589663,0.0023037111,0.011210669],"study_design_scores_gemma":[0.00012587242,0.00011779959,0.00024582818,0.000018345821,0.00003387936,0.00006458123,0.00006963053,0.9778661,0.00048653377,0.018475367,0.0024783057,0.00001780157],"about_ca_topic_score_codex":0.014679123,"about_ca_topic_score_gemma":0.009318201,"teacher_disagreement_score":0.014679123,"about_ca_system_score_codex":0.0019844878,"about_ca_system_score_gemma":0.0025534749,"threshold_uncertainty_score":0.029187381},"labels":[],"label_agreement":null},{"id":"W2336420787","doi":"10.1111/itor.12282","title":"Sequential variable neighborhood descent variants: an empirical study on the traveling salesman problem","year":2016,"lang":"en","type":"article","venue":"International Transactions in Operational Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":77,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Group for Research in Decision Analysis; HEC Montréal","funders":"Agence Nationale de la Recherche","keywords":"Travelling salesman problem; Heuristics; Descent (aeronautics); Variable neighborhood search; Mathematical optimization; Local search (optimization); Variable (mathematics); Mathematics; Sequence (biology); Computer science; Metaheuristic; Geography","score_opus":0.13763799870457297,"score_gpt":0.42554192951133163,"score_spread":0.2879039308067587,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2336420787","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9924521,0.0007972738,0.004733601,0.00016245466,0.000021896476,0.00006384571,0.0002569971,0.00008405129,0.0014277903],"genre_scores_gemma":[0.9890537,0.0002666335,0.008932683,0.000038464874,0.000020415528,0.00006116507,0.001058545,0.000039224346,0.00052921235],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9973705,0.0016339908,0.00014676187,0.00033274762,0.00037827378,0.00013777512],"domain_scores_gemma":[0.97870123,0.017121723,0.0008145857,0.0015909296,0.0014266481,0.00034483246],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0043713134,0.0006471528,0.00078431313,0.0012461268,0.00072125683,0.000704695,0.0018770152,0.0009259542,0.0014368896],"category_scores_gemma":[0.017121688,0.00028055848,0.00068262225,0.002107295,0.0009525461,0.0013596313,0.00050987676,0.0013947829,0.00022610968],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0016810326,0.0066008503,0.13176219,0.0011508464,0.0008343954,0.0006441553,0.0008823454,0.6717011,0.0021028034,0.01136737,0.018246606,0.1530263],"study_design_scores_gemma":[0.00023891462,0.0014308209,0.023390325,0.00006065397,0.000101687656,0.00032122567,0.0006214521,0.96645945,0.0012173987,0.003125628,0.0029990922,0.000033398217],"about_ca_topic_score_codex":0.006750711,"about_ca_topic_score_gemma":0.0071936958,"teacher_disagreement_score":0.006750711,"about_ca_system_score_codex":0.00085721794,"about_ca_system_score_gemma":0.0005463819,"threshold_uncertainty_score":0.02311802},"labels":[],"label_agreement":null},{"id":"W2337514772","doi":"10.1016/j.ejor.2016.04.007","title":"A practical vehicle routing problem with desynchronized arrivals to depot","year":2016,"lang":"en","type":"article","venue":"European Journal of Operational Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval; Transport Canada","funders":"","keywords":"Computer science; Heuristic; Operations research; Vehicle routing problem; Key (lock); Routing (electronic design automation); Duration (music); Component (thermodynamics); Mathematical optimization; Artificial intelligence; Computer network; Mathematics","score_opus":0.09863036971870431,"score_gpt":0.38735915200099236,"score_spread":0.28872878228228804,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2337514772","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16481759,0.00082084053,0.8114378,0.0027911798,0.00048402284,0.00034408685,0.0008022224,0.00027086225,0.018231433],"genre_scores_gemma":[0.8974142,0.0004920841,0.084456414,0.00026750448,0.0001844777,0.000249213,0.0005230655,0.00010488681,0.016308265],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99913484,0.0003076574,0.000029026716,0.00026017556,0.000100767706,0.00016753594],"domain_scores_gemma":[0.99844295,0.001086691,0.00015176016,0.00007297368,0.00011846357,0.00012719641],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017755487,0.0013464944,0.0017612306,0.0005893622,0.0008197407,0.0019520434,0.002195137,0.0034710665,0.008254534],"category_scores_gemma":[0.004440733,0.0012098149,0.0011325193,0.0012469485,0.0010615886,0.0017778219,0.0017279948,0.0016417666,0.0004125009],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010367928,0.00003828415,0.00015503944,0.00007793783,0.000019136973,0.00016526683,0.000028465452,0.9865035,0.00036173908,0.007604997,0.0010753631,0.0038666707],"study_design_scores_gemma":[0.0000375876,0.000056247787,0.00009072804,0.000005417301,0.000012173442,0.000030161385,0.000041275216,0.9941397,0.00011392573,0.004891677,0.00057355943,0.0000076013916],"about_ca_topic_score_codex":0.008052316,"about_ca_topic_score_gemma":0.005228395,"teacher_disagreement_score":0.008254534,"about_ca_system_score_codex":0.0016383872,"about_ca_system_score_gemma":0.0017109736,"threshold_uncertainty_score":0.027614176},"labels":[],"label_agreement":null},{"id":"W2338680530","doi":"10.1080/03155986.2016.1167357","title":"Road-based goods transportation: a survey of real-world logistics applications from 2000 to 2015","year":2016,"lang":"en","type":"article","venue":"INFOR Information Systems and Operational Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":42,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"HEC Montréal; Université Laval","funders":"","keywords":"City logistics; Vehicle routing problem; Transport engineering; Point (geometry); Business; Distribution (mathematics); Computer science; Routing (electronic design automation); Engineering","score_opus":0.08966874283481639,"score_gpt":0.3823053147348017,"score_spread":0.2926365718999853,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2338680530","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.057400815,0.8432644,0.047498558,0.0035627498,0.0007421086,0.00011662732,0.002609985,0.0005737131,0.044230968],"genre_scores_gemma":[0.13984837,0.81815356,0.023108995,0.0008117802,0.00058518205,0.000076666016,0.004718623,0.0001508287,0.012545953],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99921095,0.00016148706,0.00010152751,0.0002061179,0.0002540308,0.000065891676],"domain_scores_gemma":[0.99797076,0.00094272284,0.00023480317,0.00009208846,0.00069903483,0.000060469738],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00096192834,0.0006936206,0.00049351517,0.0033047053,0.00033263187,0.0014243142,0.00092909933,0.000703896,0.0045828433],"category_scores_gemma":[0.0026634298,0.00037544468,0.0006028161,0.011140583,0.00035559334,0.0029697756,0.00070111826,0.00056553114,0.0016887682],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010736104,0.000075833144,0.0065539987,0.0049352273,0.000058838534,0.00023517442,0.00025090904,0.013473844,0.0010716245,0.013705658,0.037706815,0.92182463],"study_design_scores_gemma":[0.000008658882,0.00013570007,0.010669496,0.0019887635,0.00010966608,0.0013110269,0.00081747485,0.015761547,0.0025807132,0.004433273,0.96210366,0.000079943944],"about_ca_topic_score_codex":0.005221329,"about_ca_topic_score_gemma":0.004923024,"teacher_disagreement_score":0.005221329,"about_ca_system_score_codex":0.0008792262,"about_ca_system_score_gemma":0.0010374483,"threshold_uncertainty_score":0.015331149},"labels":[],"label_agreement":null},{"id":"W2339305992","doi":"","title":"A Proximal Trust-Region Algorithm for Column Generation Stabilization","year":2003,"lang":"en","type":"article","venue":"Les Cahiers du GERAD","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal; Group for Research in Decision Analysis","funders":"","keywords":"Column generation; Trust region; Convergence (economics); Column (typography); Mathematical optimization; Generalization; Context (archaeology); Scheduling (production processes); Algorithm; Cutting-plane method; Dimension (graph theory); Point (geometry); Computer science; Mathematics; Combinatorics; Connection (principal bundle); Geometry; Computer network","score_opus":0.01847488940365453,"score_gpt":0.23466102340845912,"score_spread":0.2161861340048046,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2339305992","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0011383422,0.000033519354,0.9983413,0.000016953056,0.00001293179,0.000013508823,0.0000053978765,0.00010820223,0.0003298731],"genre_scores_gemma":[0.22060171,0.00023376413,0.7741612,0.000084577085,0.00007382743,0.0002239996,0.00013607087,0.0002021567,0.004282749],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993795,0.00020545453,0.000024543358,0.00010969682,0.00022075902,0.000059999336],"domain_scores_gemma":[0.9988753,0.0006243684,0.00009796803,0.00011353982,0.00023299547,0.000055681943],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011629908,0.00076173904,0.0009912197,0.00060903904,0.0004139748,0.00072105305,0.0012360576,0.0009095667,0.00296059],"category_scores_gemma":[0.0030096453,0.00048012854,0.0007045862,0.0006558785,0.000982788,0.00089633744,0.001277473,0.0016501908,0.0010229504],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014846532,0.00005278309,0.00021722988,0.0001120551,0.000039549635,0.000078682206,0.00009619938,0.82832795,0.0068383366,0.03741759,0.002148855,0.12452231],"study_design_scores_gemma":[0.000009777663,0.000034779332,0.000021627542,0.0000040442283,0.000004230312,0.000018613673,0.0000032269702,0.99423164,0.0013455807,0.0033832202,0.000937098,0.0000061482674],"about_ca_topic_score_codex":0.002129971,"about_ca_topic_score_gemma":0.0011217523,"teacher_disagreement_score":0.00296059,"about_ca_system_score_codex":0.00058491493,"about_ca_system_score_gemma":0.0009946664,"threshold_uncertainty_score":0.009904146},"labels":[],"label_agreement":null},{"id":"W2340192962","doi":"","title":"Provide a Variable Neighborhood Search for Solving Multidimensional Two-Way Number Partitioning Problem","year":2014,"lang":"en","type":"article","venue":"Journal of academic and applied studies","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Variable neighborhood search; Swap (finance); Metaheuristic; Mathematical optimization; Generalization; Computer science; Set (abstract data type); Mathematics; Local search (optimization); Algorithm","score_opus":0.028330630320046988,"score_gpt":0.3137529698977157,"score_spread":0.2854223395776687,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2340192962","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016828045,0.0006195366,0.9771722,0.00014602023,0.00006278809,0.00007489487,0.000032371125,0.00011480108,0.004949442],"genre_scores_gemma":[0.30187148,0.00070993113,0.6916881,0.00012688623,0.000063601794,0.00035144237,0.00021828173,0.00008408723,0.004886242],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996141,0.00018513858,0.0000139448575,0.000059858634,0.00010054144,0.000026381755],"domain_scores_gemma":[0.9998209,0.00008506183,0.00002178886,0.000020880414,0.000041723426,0.000009702213],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00049059076,0.000537991,0.00065115275,0.0005374352,0.00042428236,0.000470474,0.0006712346,0.00076625997,0.002482507],"category_scores_gemma":[0.0012254344,0.00021424431,0.00059364224,0.0007075675,0.00027825902,0.0009731598,0.00070151454,0.0005669938,0.00043658883],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000865109,0.00013050615,0.0010351349,0.00027957026,0.000070009104,0.0001021575,0.00013144471,0.75218064,0.0048980927,0.055711616,0.0028862292,0.18248819],"study_design_scores_gemma":[0.00001654124,0.000067843015,0.00014693793,0.000015163875,0.0000124566905,0.000059115864,0.000028305383,0.98761976,0.0010003655,0.0070348936,0.003992209,0.00000633533],"about_ca_topic_score_codex":0.0012593379,"about_ca_topic_score_gemma":0.0016771938,"teacher_disagreement_score":0.002482507,"about_ca_system_score_codex":0.00032169162,"about_ca_system_score_gemma":0.00050743774,"threshold_uncertainty_score":0.008304775},"labels":[],"label_agreement":null},{"id":"W2342453569","doi":"","title":"Periodic Airline Fleet Assignment with Time Windows, Spacing Constraints, and Time Dependent Revenues","year":2003,"lang":"en","type":"article","venue":"PolyPublie (École Polytechnique de Montréal)","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal; Polytechnique Montréal; Group for Research in Decision Analysis","funders":"","keywords":"Schedule; Flow network; Computer science; Integer (computer science); Mathematical optimization; Operations research; Revenue; Assignment problem; Branch and bound; Integer programming; Mathematics; Economics; Algorithm; Finance","score_opus":0.006694028341198246,"score_gpt":0.2103561704440824,"score_spread":0.20366214210288414,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2342453569","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.50167316,0.00051591394,0.48977762,0.0004211833,0.000054862,0.00014826252,0.0015378677,0.00038365694,0.005487491],"genre_scores_gemma":[0.91747963,0.0003676791,0.07567431,0.000029079029,0.00006776085,0.00014048799,0.001188846,0.000067770416,0.0049843905],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994087,0.00023600261,0.000022298456,0.00013833331,0.00008150395,0.00011318628],"domain_scores_gemma":[0.998376,0.00076281297,0.0005064285,0.00017910829,0.000073103154,0.000102557045],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011049423,0.0006781743,0.00079597335,0.0006747404,0.00038873704,0.00094458944,0.0013567765,0.0009928156,0.0045676855],"category_scores_gemma":[0.0034954818,0.0007699567,0.0005669724,0.0014965486,0.00054142944,0.0014055449,0.00046184813,0.0007914372,0.00037674344],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000044124383,0.000024520092,0.0007872724,0.000016262655,0.000013152777,0.0000630536,0.00001501636,0.98569775,0.00017992679,0.0053340555,0.00060888776,0.0072160168],"study_design_scores_gemma":[0.000015601065,0.000017593942,0.0004906549,0.0000020214623,0.000004811132,0.000027786986,0.000008049648,0.9915475,0.000080366895,0.007381446,0.0004204887,0.0000036923423],"about_ca_topic_score_codex":0.011477931,"about_ca_topic_score_gemma":0.010359559,"teacher_disagreement_score":0.011477931,"about_ca_system_score_codex":0.0011514006,"about_ca_system_score_gemma":0.0011048797,"threshold_uncertainty_score":0.022822201},"labels":[],"label_agreement":null},{"id":"W2342688946","doi":"10.5220/0005758103800387","title":"Prize Collecting Travelling Salesman Problem","year":2016,"lang":"en","type":"article","venue":"","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Travelling salesman problem; Computer science; Algorithm","score_opus":0.022207906718143742,"score_gpt":0.24892213294514456,"score_spread":0.22671422622700083,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2342688946","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07419513,0.0012030803,0.8607731,0.0039213323,0.00082071754,0.0004319282,0.0013860873,0.00068386184,0.05658484],"genre_scores_gemma":[0.607537,0.001337762,0.24334905,0.0005776707,0.0009790226,0.0006660862,0.0018769539,0.0006482773,0.14302821],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9980585,0.00089734833,0.000086439184,0.00031554187,0.00036808877,0.000273997],"domain_scores_gemma":[0.9966654,0.0018622014,0.00020981036,0.00034167067,0.0004976905,0.0004231386],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0034355365,0.0011153027,0.0033265373,0.001335925,0.0016410907,0.00280684,0.0038140013,0.003031705,0.01919274],"category_scores_gemma":[0.008688618,0.00079851155,0.0013227334,0.0023725948,0.0010545368,0.003605552,0.002229061,0.0022296156,0.0017118867],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007782952,0.00071032834,0.0011616579,0.0009594038,0.0002464914,0.0003288662,0.00024797162,0.3755366,0.0016936382,0.30102858,0.07439958,0.24290858],"study_design_scores_gemma":[0.00011292559,0.00025397068,0.00045832765,0.000056872737,0.00006785064,0.00015455397,0.000102372775,0.7874132,0.0010867486,0.19446076,0.015796015,0.000036321155],"about_ca_topic_score_codex":0.0018246087,"about_ca_topic_score_gemma":0.0018564942,"teacher_disagreement_score":0.01919274,"about_ca_system_score_codex":0.001497888,"about_ca_system_score_gemma":0.0019790677,"threshold_uncertainty_score":0.064206064},"labels":[],"label_agreement":null},{"id":"W2343587508","doi":"","title":"Waste collection vehicle routing problem with time windows using multi-objective genetic algorithms","year":2007,"lang":"en","type":"article","venue":"","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":46,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph; Brock University","funders":"","keywords":"Vehicle routing problem; Benchmark (surveying); Cluster analysis; Computer science; Genetic algorithm; Set (abstract data type); Mathematical optimization; Routing (electronic design automation); Extension (predicate logic); Data collection; Algorithm; Mathematics; Machine learning; Computer network","score_opus":0.01627377999709144,"score_gpt":0.25576780847252706,"score_spread":0.2394940284754356,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2343587508","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0780243,0.0007485112,0.91659814,0.00036460807,0.000056014484,0.00015616426,0.0001129764,0.00018385824,0.0037554197],"genre_scores_gemma":[0.61552906,0.0008502899,0.3789283,0.00009012734,0.000061776365,0.00038683796,0.0001765717,0.000061392144,0.00391559],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99939406,0.00029083312,0.00002386325,0.00007426581,0.00014032327,0.00007669095],"domain_scores_gemma":[0.99930096,0.00045347709,0.0001106221,0.000027237544,0.00006559846,0.000042008043],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011265083,0.0013455587,0.0012609258,0.0010280267,0.0004490645,0.0011517095,0.0013383395,0.0014679024,0.0012493631],"category_scores_gemma":[0.0017400931,0.00047296146,0.001044089,0.0017675026,0.0005426071,0.0012178388,0.00057418935,0.0008089091,0.00012589786],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001783119,0.000027542997,0.00010389248,0.000025760823,0.000022110242,0.000022495875,0.000012100724,0.9902682,0.0003144686,0.0026380152,0.00013090548,0.0064166524],"study_design_scores_gemma":[0.000015448899,0.000043494103,0.000053738837,0.000004490928,0.000011565097,0.0000110313895,0.000011031579,0.9967805,0.00033654057,0.0023774167,0.0003502345,0.0000043638083],"about_ca_topic_score_codex":0.0049639074,"about_ca_topic_score_gemma":0.0031623289,"teacher_disagreement_score":0.0049639074,"about_ca_system_score_codex":0.0012638019,"about_ca_system_score_gemma":0.0013544081,"threshold_uncertainty_score":0.009870052},"labels":[],"label_agreement":null},{"id":"W2366073799","doi":"10.1016/j.omega.2016.05.001","title":"Design-balanced capacitated multicommodity network design with heterogeneous assets","year":2016,"lang":"en","type":"article","venue":"Omega","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":35,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"Program for New Century Excellent Talents in University; National Natural Science Foundation of China","keywords":"Tabu search; Network planning and design; Computer science; Mathematical optimization; Quality of service; Range (aeronautics); Metaheuristic; Mathematics; Computer network; Engineering","score_opus":0.032694311082114694,"score_gpt":0.24293606925179614,"score_spread":0.21024175816968144,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2366073799","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018595675,0.00011291676,0.9742923,0.00011965966,0.000024287923,0.00005087194,0.000057944082,0.00006483083,0.00668147],"genre_scores_gemma":[0.74924797,0.00034456,0.24079719,0.000110242945,0.00003758748,0.0003374895,0.00016843838,0.000101760575,0.008854744],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995432,0.00019983549,0.000012523747,0.00007386872,0.00010057764,0.00007010151],"domain_scores_gemma":[0.9996669,0.00016614058,0.000046181445,0.000023839097,0.00006703513,0.000029795889],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009628953,0.0011407167,0.0008547282,0.00063463545,0.00048329894,0.0010383877,0.0011539337,0.00088199344,0.003533466],"category_scores_gemma":[0.0020556834,0.0005976628,0.00052263064,0.0009241016,0.000451917,0.0012287713,0.0013431234,0.0006216707,0.00033259316],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000030066902,0.000016988026,0.00012221736,0.000031152245,0.000010303612,0.000019852303,0.000012841911,0.98107266,0.00082654733,0.008841277,0.00029613197,0.008719955],"study_design_scores_gemma":[0.0000048073393,0.000018402388,0.000030724765,0.000003545234,0.000004109665,0.0000050747017,0.0000075204925,0.99547595,0.0002400505,0.0038677827,0.00034027285,0.0000016987166],"about_ca_topic_score_codex":0.0020900837,"about_ca_topic_score_gemma":0.002284823,"teacher_disagreement_score":0.003533466,"about_ca_system_score_codex":0.0011686756,"about_ca_system_score_gemma":0.0010258622,"threshold_uncertainty_score":0.011820555},"labels":[],"label_agreement":null},{"id":"W2375815961","doi":"","title":"A Double-layer Genetic Algorithm for Sprinkle Car Routing Problem Based on Multiple Depots","year":2008,"lang":"en","type":"article","venue":"Microcomputer applications","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Genetic algorithm; Coding (social sciences); Arc routing; Algorithm; Routing (electronic design automation); Vehicle routing problem; Population; Chromosome; Layer (electronics); Mathematical optimization; Routing algorithm; Mode (computer interface); Computer network; Routing protocol; Machine learning; Mathematics; Gene; Statistics","score_opus":0.023321242855003782,"score_gpt":0.2522590006457415,"score_spread":0.22893775779073774,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2375815961","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02991291,0.00019093245,0.9663962,0.00014882234,0.000042213804,0.0000705381,0.000037340265,0.00026255642,0.0029384235],"genre_scores_gemma":[0.43608734,0.00033032158,0.5581085,0.00012559224,0.000026422298,0.00022869026,0.00018116871,0.000070173926,0.0048417607],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996736,0.000080626865,0.000014555576,0.000077528784,0.000104647435,0.00004904563],"domain_scores_gemma":[0.99978834,0.0000864715,0.000028796368,0.00001915371,0.000055487028,0.000021692942],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048497829,0.00067872263,0.0005897279,0.00065506424,0.00044834532,0.00075784663,0.0008960932,0.00089942245,0.0015200647],"category_scores_gemma":[0.000990666,0.00034720957,0.0005075698,0.0005646023,0.00043361227,0.0009661452,0.0008054061,0.00073355925,0.0002351383],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004979771,0.00005564079,0.00069356227,0.00006334158,0.00004333714,0.0000740257,0.00007780173,0.90984786,0.0043435143,0.010470086,0.0010691659,0.073211804],"study_design_scores_gemma":[0.000017448274,0.000033505014,0.00010254108,0.0000055898063,0.000009018166,0.000025281242,0.000011815026,0.9961456,0.0007295132,0.0018332527,0.0010806866,0.000005637066],"about_ca_topic_score_codex":0.005680494,"about_ca_topic_score_gemma":0.005082744,"teacher_disagreement_score":0.005680494,"about_ca_system_score_codex":0.00086909824,"about_ca_system_score_gemma":0.0013129459,"threshold_uncertainty_score":0.011294901},"labels":[],"label_agreement":null},{"id":"W2397006241","doi":"10.1080/03155986.2003.11732674","title":"A Heuristic Procedure For Path Location With Multisource Demand","year":2003,"lang":"en","type":"article","venue":"INFOR Information Systems and Operational Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Fundación Séneca","keywords":"Correctness; Mathematical optimization; Heuristic; Directed acyclic graph; Path (computing); Computer science; Relaxation (psychology); Lagrangian relaxation; Mathematics; Algorithm","score_opus":0.03506155638362248,"score_gpt":0.32169213736743707,"score_spread":0.2866305809838146,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2397006241","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0023373014,0.000048642716,0.9958269,0.000052506715,0.000015969752,0.000080328566,0.000041582643,0.00033637788,0.0012604202],"genre_scores_gemma":[0.053978182,0.000100162535,0.9434435,0.000056669713,0.000017537854,0.00028458209,0.00023293041,0.00014465225,0.0017417968],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990694,0.00030274477,0.00003127772,0.0001527271,0.0003334365,0.00011041552],"domain_scores_gemma":[0.9990183,0.00053531094,0.00009586523,0.00020699979,0.00011120433,0.000032227592],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00095819734,0.0009830265,0.000906828,0.0016005161,0.0008441906,0.0009118854,0.0019692688,0.0013542629,0.0071003702],"category_scores_gemma":[0.0027255723,0.0006487145,0.0010317969,0.0015164425,0.0010201484,0.0012452084,0.001287834,0.0020079126,0.0018143074],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019805178,0.00024514858,0.00044868578,0.00048206304,0.000116480165,0.00029509192,0.000259735,0.55836344,0.015522005,0.07246624,0.007969401,0.34363368],"study_design_scores_gemma":[0.00008454804,0.00010786081,0.00018306232,0.000046080997,0.000031646217,0.00022694368,0.000057056983,0.9522644,0.007403045,0.027249364,0.012299839,0.00004609027],"about_ca_topic_score_codex":0.0021728303,"about_ca_topic_score_gemma":0.0031716723,"teacher_disagreement_score":0.0071003702,"about_ca_system_score_codex":0.00081092113,"about_ca_system_score_gemma":0.0018917045,"threshold_uncertainty_score":0.023753107},"labels":[],"label_agreement":null},{"id":"W2400260427","doi":"10.1080/03155986.2001.11732447","title":"An Effective Lagrangian Heuristic For The Generalized Assignment Problem","year":2001,"lang":"en","type":"article","venue":"INFOR Information Systems and Operational Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Subgradient method; Lagrangian relaxation; Heuristics; Mathematical optimization; Lagrangian; Heuristic; Generalized assignment problem; Weapon target assignment problem; Relaxation (psychology); Augmented Lagrangian method; Assignment problem; Computer science; Mathematics; Applied mathematics","score_opus":0.043323036663999955,"score_gpt":0.35773603116964453,"score_spread":0.3144129945056446,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2400260427","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003729463,0.00020835502,0.99180907,0.00011349693,0.000058611895,0.000102891994,0.0000498768,0.0002497129,0.0036786248],"genre_scores_gemma":[0.07050536,0.00039213104,0.9250143,0.00010648675,0.00006237388,0.00032662568,0.00022118271,0.00013855165,0.0032330148],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99926275,0.00032153272,0.00002156869,0.00008438453,0.00021098014,0.00009884519],"domain_scores_gemma":[0.99951446,0.00025290545,0.00005751206,0.000069560476,0.0000705853,0.000035008172],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009817629,0.0012685601,0.00082677073,0.0011551963,0.00073346135,0.00087296206,0.0013400804,0.00087834615,0.0047818027],"category_scores_gemma":[0.002303642,0.0004554307,0.0007293573,0.0011511851,0.0009196321,0.0013461515,0.0011670603,0.001350731,0.0010138453],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006001364,0.00015192496,0.00021962819,0.00023244842,0.00004336932,0.00018568775,0.00012019881,0.6566196,0.0026451082,0.11832053,0.010447602,0.21095389],"study_design_scores_gemma":[0.00007529237,0.00010205601,0.00009987316,0.000043977918,0.000027108019,0.000092888054,0.000040118186,0.9297599,0.0014158821,0.05383915,0.014486186,0.000017555009],"about_ca_topic_score_codex":0.0026730811,"about_ca_topic_score_gemma":0.003703396,"teacher_disagreement_score":0.0047818027,"about_ca_system_score_codex":0.0008271939,"about_ca_system_score_gemma":0.0019876228,"threshold_uncertainty_score":0.015996754},"labels":[],"label_agreement":null},{"id":"W2408645284","doi":"10.1080/03155986.2006.11732743","title":"Steiner Tree Problems With Profits","year":2006,"lang":"en","type":"article","venue":"INFOR Information Systems and Operational Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":40,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Université Laval; HEC Montréal","funders":"","keywords":"Steiner tree problem; Revenue; Travelling salesman problem; Combinatorics; Profit (economics); Computer science; Graph; Mathematics; Tree (set theory); Mathematical optimization; Mathematical economics; Economics; Microeconomics; Finance","score_opus":0.036671311259690066,"score_gpt":0.30265446007941355,"score_spread":0.26598314881972346,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2408645284","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012642683,0.03305144,0.85335124,0.0033742306,0.0008528231,0.00015760204,0.0006542037,0.0002551049,0.09566077],"genre_scores_gemma":[0.34377047,0.09522156,0.49004757,0.0019663959,0.0048845247,0.00057234056,0.002717774,0.0005455846,0.060273826],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9982798,0.00061186403,0.0001197079,0.00026973867,0.0005783324,0.00014055891],"domain_scores_gemma":[0.9986511,0.0009102012,0.00012574242,0.000116811585,0.00014203101,0.00005414193],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012759252,0.0014893833,0.0012447608,0.0012337845,0.00096243166,0.0027973878,0.0013411454,0.001828116,0.010703458],"category_scores_gemma":[0.0045192903,0.0007524836,0.0014577946,0.00394989,0.0017505842,0.0061529684,0.0018725849,0.0030739622,0.0022766676],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003476255,0.000043355794,0.0002229188,0.00063004723,0.000042682645,0.000140405,0.00013382254,0.03863591,0.0004843379,0.8626032,0.020706788,0.076321796],"study_design_scores_gemma":[0.00001756803,0.00003275923,0.00017508493,0.0001394305,0.000017961418,0.00037812666,0.000092371396,0.048616346,0.00027546252,0.90300846,0.047229476,0.00001689662],"about_ca_topic_score_codex":0.001023782,"about_ca_topic_score_gemma":0.0010357014,"teacher_disagreement_score":0.010703458,"about_ca_system_score_codex":0.0014658051,"about_ca_system_score_gemma":0.0011124451,"threshold_uncertainty_score":0.035806596},"labels":[],"label_agreement":null},{"id":"W2412110906","doi":"10.1016/j.ijpe.2016.05.015","title":"Optimizing transshipment workloads in less-than-truckload cross-docks","year":2016,"lang":"en","type":"article","venue":"International Journal of Production Economics","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Transshipment (information security); Computer science; Workload; Truck; Scheduling (production processes); Operations research; Mathematical optimization; Job shop scheduling; Schedule; Mathematics; Engineering; Operating system","score_opus":0.022759307941540602,"score_gpt":0.2812607907588968,"score_spread":0.2585014828173562,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2412110906","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8309776,0.00038637905,0.15599555,0.0004899582,0.00022596883,0.00020802897,0.00038336893,0.00079619046,0.010536977],"genre_scores_gemma":[0.9627419,0.00009254183,0.03196029,0.000085543455,0.000035927234,0.00005478573,0.00031191763,0.0002136437,0.004503336],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99920577,0.00022340294,0.000026596672,0.00015344833,0.00006204836,0.00032874796],"domain_scores_gemma":[0.99856037,0.00062343123,0.000115584284,0.00011949217,0.00019402745,0.00038713028],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013698185,0.0013328019,0.001666822,0.0006196653,0.001002234,0.0017231256,0.0020000641,0.001317666,0.008171739],"category_scores_gemma":[0.0030699696,0.00092918176,0.0008689169,0.00091494096,0.000577954,0.0019109162,0.0013680598,0.0013624311,0.0008908814],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00056281197,0.000332621,0.0015396758,0.0000601191,0.000037456568,0.0001630063,0.000060600505,0.9743268,0.0023646115,0.001509769,0.0017871123,0.017255314],"study_design_scores_gemma":[0.000022244309,0.00012367374,0.00046570564,0.0000033967606,0.0000110619685,0.000024140832,0.0001045011,0.9976808,0.00037823722,0.0009638199,0.0002172192,0.000005163413],"about_ca_topic_score_codex":0.007990832,"about_ca_topic_score_gemma":0.01024453,"teacher_disagreement_score":0.008171739,"about_ca_system_score_codex":0.0009402502,"about_ca_system_score_gemma":0.0015361031,"threshold_uncertainty_score":0.027337193},"labels":[],"label_agreement":null},{"id":"W2421926607","doi":"10.1287/trsc.2015.0636","title":"A Benders Decomposition Approach for the Symmetric TSP with Generalized Latency Arising in the Design of Semiflexible Transit Systems","year":2016,"lang":"en","type":"article","venue":"Transportation Science","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal; École de Technologie Supérieure; Université de Montréal","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada; Centre interuniversitaire de recherche sur les reseaux d'entreprise, la logistique et le transport; Université de Montréal; Université du Québec à Montréal","keywords":"Travelling salesman problem; Solver; Mathematical optimization; Latency (audio); Routing (electronic design automation); Computer science; Class (philosophy); Mathematics","score_opus":0.048006483369849966,"score_gpt":0.29373111238555594,"score_spread":0.24572462901570596,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2421926607","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0065780696,0.000205857,0.98904943,0.00012533984,0.000022790084,0.000051253002,0.00008487569,0.0000900227,0.0037923101],"genre_scores_gemma":[0.18884687,0.00088457594,0.80324537,0.00009938771,0.00006997682,0.00026085376,0.00031848534,0.0001596265,0.006114854],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99965155,0.00011882299,0.000016814796,0.000053219435,0.00011537144,0.00004426156],"domain_scores_gemma":[0.9997359,0.00012255479,0.000042800246,0.000032190175,0.000042830095,0.00002374354],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00066053495,0.001189898,0.0006838604,0.0007425797,0.00043710976,0.0009992816,0.0007394419,0.0008542463,0.0058620363],"category_scores_gemma":[0.0010564644,0.0007406997,0.0012899173,0.0008955435,0.00050856575,0.0009334895,0.00087862235,0.0013958233,0.0006230818],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000027259584,0.000043497887,0.00016169024,0.00011245079,0.000023422579,0.000084064304,0.00006443762,0.91564435,0.0029279955,0.046228398,0.0012267873,0.033455547],"study_design_scores_gemma":[0.000013717492,0.000033708635,0.000067607754,0.000021549507,0.000011387265,0.00003466138,0.000028649458,0.9687109,0.00090997084,0.02650616,0.0036552139,0.000006470169],"about_ca_topic_score_codex":0.0031539071,"about_ca_topic_score_gemma":0.0041842684,"teacher_disagreement_score":0.0058620363,"about_ca_system_score_codex":0.0008695862,"about_ca_system_score_gemma":0.0012881556,"threshold_uncertainty_score":0.019610465},"labels":[],"label_agreement":null},{"id":"W2464648258","doi":"10.1080/02827581.2016.1206144","title":"Detailed scheduling of harvest teams and robust use of harvest and transportation resources","year":2016,"lang":"en","type":"article","venue":"Scandinavian Journal of Forest Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Scheduling (production processes); Schedule; Computer science; Procurement; Operations research; Process (computing); Industrial engineering; Operations management; Engineering; Business","score_opus":0.05462089395413105,"score_gpt":0.308446683804028,"score_spread":0.25382578984989695,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2464648258","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.23512255,0.00016936145,0.7579596,0.00021383377,0.00003579582,0.00019001547,0.00031670363,0.00032004973,0.005672103],"genre_scores_gemma":[0.8354922,0.00013604484,0.16080248,0.000032703425,0.00001190371,0.0001972111,0.0003975464,0.00006930384,0.0028605876],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991868,0.00028882056,0.00003813651,0.0001573507,0.00013082805,0.0001980498],"domain_scores_gemma":[0.9990858,0.00046068363,0.00015759714,0.000105782,0.00008884517,0.00010132126],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001410527,0.0007910629,0.0009963082,0.00041209883,0.0006723,0.0010778604,0.0009309235,0.0009844365,0.002615578],"category_scores_gemma":[0.0022961898,0.00081659056,0.00095590635,0.00072680955,0.00067637354,0.0012696293,0.0009476777,0.00077110174,0.00021972386],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000018498211,0.000011531546,0.00017385905,0.000010293282,0.000005536364,0.000017622135,0.000016449856,0.9960892,0.00046114487,0.0008701082,0.00007253457,0.0022531427],"study_design_scores_gemma":[0.000008940255,0.00003542524,0.00024827378,0.0000025361414,0.000006130324,0.000007754889,0.000031860698,0.99735814,0.00032479045,0.0016972339,0.0002744455,0.000004425949],"about_ca_topic_score_codex":0.01704797,"about_ca_topic_score_gemma":0.01989726,"teacher_disagreement_score":0.01704797,"about_ca_system_score_codex":0.0011988041,"about_ca_system_score_gemma":0.0028691376,"threshold_uncertainty_score":0.03389746},"labels":[],"label_agreement":null},{"id":"W2464795612","doi":"10.1007/s10878-018-0281-y","title":"Optimum turn-restricted paths, nested compatibility, and optimum convex polygons","year":2018,"lang":"en","type":"article","venue":"Journal of Combinatorial Optimization","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Mathematics; Theory of computation; Compatibility (geochemistry); Cutting-plane method; Regular polygon; Mathematical optimization; Combinatorics; Integer programming; Algorithm; Geometry","score_opus":0.013462062895030939,"score_gpt":0.2613608915506082,"score_spread":0.24789882865557727,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2464795612","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1351054,0.00022898661,0.83308256,0.00026968942,0.000041485495,0.00008524172,0.00023123066,0.00010656921,0.030848827],"genre_scores_gemma":[0.77201694,0.00042280508,0.21502581,0.00009227256,0.00003967182,0.00017845127,0.00034925106,0.0002079351,0.011666866],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9994318,0.00020753781,0.000016012895,0.000098667406,0.00015832233,0.00008766526],"domain_scores_gemma":[0.9987477,0.0007321474,0.00019953636,0.0001094311,0.00011882168,0.000092376446],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006847584,0.0007475901,0.0007704716,0.00081892713,0.0005363482,0.001322756,0.0008500885,0.0008956621,0.006094272],"category_scores_gemma":[0.0057028295,0.0008095592,0.00068048784,0.00075676624,0.0012272923,0.002288808,0.0015599494,0.0011817417,0.0004493463],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00027972113,0.000111447196,0.0012926066,0.00011895576,0.000032893622,0.00019874697,0.00017726148,0.59674335,0.004206613,0.36127427,0.0025918477,0.032972313],"study_design_scores_gemma":[0.00004591681,0.000104132254,0.0006184438,0.000038261816,0.000021758138,0.00020970416,0.000106728825,0.6928335,0.0021183349,0.30016187,0.003720097,0.00002132615],"about_ca_topic_score_codex":0.0010474505,"about_ca_topic_score_gemma":0.0014119147,"teacher_disagreement_score":0.006094272,"about_ca_system_score_codex":0.0005227211,"about_ca_system_score_gemma":0.00075485226,"threshold_uncertainty_score":0.020387411},"labels":[],"label_agreement":null},{"id":"W2468155677","doi":"10.1287/trsc.2015.0634","title":"Reformulations by Discretization for Piecewise Linear Integer Multicommodity Network Flow Problems","year":2016,"lang":"en","type":"article","venue":"Transportation Science","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Discretization; Mathematical optimization; Integer programming; Flow network; Lagrangian relaxation; Multi-commodity flow problem; Piecewise linear function; Mathematics; Flow (mathematics); Linear programming relaxation; Relaxation (psychology); Integer (computer science); Linear programming; Constraint (computer-aided design); Set (abstract data type); Branch and cut; Computer science","score_opus":0.020525424923256673,"score_gpt":0.2780080266119627,"score_spread":0.25748260168870607,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2468155677","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011366446,0.0003414836,0.9802229,0.00034352724,0.000054676057,0.000088515546,0.00021320848,0.0001119537,0.0072572706],"genre_scores_gemma":[0.33115572,0.001017657,0.66123194,0.00022580044,0.000110265806,0.00055458193,0.0006893464,0.0001691298,0.0048455875],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9989942,0.0005386972,0.000039539907,0.00009680052,0.00023752378,0.000093362694],"domain_scores_gemma":[0.9977962,0.0016760963,0.0001429015,0.00016164102,0.00016957485,0.0000536059],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018775557,0.00093204173,0.0008336417,0.0006227113,0.00042343122,0.0012511214,0.0010097865,0.00093540683,0.003957433],"category_scores_gemma":[0.0053400006,0.0005751256,0.0009972987,0.0010431465,0.00087359204,0.0015898432,0.0010001108,0.0023003074,0.00043987916],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000023390901,0.00003618037,0.00020081592,0.00008823938,0.000011246905,0.000064939224,0.000057912028,0.9252147,0.0005383489,0.05976378,0.0012429741,0.012757542],"study_design_scores_gemma":[0.000010945671,0.0000136309445,0.000033613524,0.0000184188,0.0000037771317,0.0000127602025,0.000026872312,0.9792069,0.00022031163,0.018699858,0.0017495388,0.000003352195],"about_ca_topic_score_codex":0.004274426,"about_ca_topic_score_gemma":0.003988698,"teacher_disagreement_score":0.004274426,"about_ca_system_score_codex":0.0013138796,"about_ca_system_score_gemma":0.001048226,"threshold_uncertainty_score":0.013238907},"labels":[],"label_agreement":null},{"id":"W2469651670","doi":"10.1007/s11590-016-1004-x","title":"General variable neighborhood search for the uncapacitated single allocation p-hub center problem","year":2016,"lang":"en","type":"article","venue":"Optimization Letters","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":39,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Military College of Canada; Royal Ottawa Mental Health Centre","funders":"Natural Sciences and Engineering Research Council of Canada; National Research University Higher School of Economics; Russian Science Foundation","keywords":"Benchmark (surveying); Variable neighborhood search; Heuristic; Mathematical optimization; Variable (mathematics); Descent (aeronautics); Computer science; Local search (optimization); Ant colony optimization algorithms; Mathematics; Metaheuristic; Engineering","score_opus":0.018576060220996726,"score_gpt":0.23410725908140795,"score_spread":0.21553119886041122,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2469651670","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03106746,0.00061317533,0.95566165,0.00063407625,0.0000794858,0.00006935208,0.0001333514,0.00010587473,0.01163553],"genre_scores_gemma":[0.6880848,0.00074348686,0.28777832,0.0002731826,0.00012715165,0.00046618452,0.00040159572,0.00022613902,0.021899194],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99971265,0.0001484234,0.0000058779337,0.000051541534,0.000047792484,0.000033717086],"domain_scores_gemma":[0.9990706,0.00069564173,0.00007276035,0.000032604956,0.0000849847,0.000043399603],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011205444,0.00060723734,0.0012255049,0.0006243393,0.00043856894,0.00092876574,0.0014071172,0.0011172025,0.0043856413],"category_scores_gemma":[0.0030906084,0.0004996167,0.000511189,0.00089140487,0.0006639758,0.001129002,0.0009970659,0.00090748916,0.00029956407],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000046843423,0.000031688196,0.00017291133,0.00004508739,0.000021075519,0.000024455378,0.000016998747,0.959724,0.00016728562,0.029263442,0.0018054874,0.008680658],"study_design_scores_gemma":[0.0000070033375,0.000005856102,0.000025940477,0.0000026207574,0.0000019885451,0.0000032795758,0.00000460744,0.99341077,0.000027179476,0.0062896386,0.00021967884,0.0000014695734],"about_ca_topic_score_codex":0.00553575,"about_ca_topic_score_gemma":0.0056730704,"teacher_disagreement_score":0.00553575,"about_ca_system_score_codex":0.0010302259,"about_ca_system_score_gemma":0.0012582301,"threshold_uncertainty_score":0.014671445},"labels":[],"label_agreement":null},{"id":"W2470126219","doi":"10.1016/j.dam.2016.05.030","title":"A heuristic for cumulative vehicle routing using column generation","year":2016,"lang":"en","type":"article","venue":"Discrete Applied Mathematics","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Lethbridge","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Column generation; Mathematical optimization; Vehicle routing problem; Rounding; Linear programming; Heuristic; Cover (algebra); Routing (electronic design automation); Scalability; Linear programming relaxation; Mathematics; Set (abstract data type); Randomized rounding; Relaxation (psychology); Computer science; Engineering","score_opus":0.050046411098905806,"score_gpt":0.2985779523229441,"score_spread":0.24853154122403828,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2470126219","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.028925156,0.00032099106,0.96267045,0.00022801719,0.00023511458,0.00023174933,0.00026174777,0.00095209427,0.0061747157],"genre_scores_gemma":[0.39515188,0.00023909188,0.5979539,0.00023214899,0.000102378304,0.00034412195,0.000585286,0.00027770994,0.005113429],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99955183,0.00014210017,0.000017380904,0.00007063266,0.00011800566,0.000100052246],"domain_scores_gemma":[0.9986094,0.0008346884,0.00009542546,0.00013529065,0.00024057613,0.000084516316],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006614875,0.0011475387,0.0013186202,0.0015214825,0.00085688726,0.0011330335,0.001624462,0.0010150718,0.008644203],"category_scores_gemma":[0.002315852,0.00066921464,0.0009274278,0.0022262956,0.00072168384,0.0009801917,0.0010261808,0.001171419,0.0007656847],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009402286,0.000099967474,0.0003459038,0.00008317033,0.000034851782,0.00006320016,0.00003380013,0.89613616,0.0016803071,0.009460584,0.003939841,0.08802818],"study_design_scores_gemma":[0.000015754347,0.000032942302,0.00006694956,0.0000062982626,0.000011553783,0.000013774144,0.000008689059,0.99586797,0.000492912,0.0028145344,0.0006607936,0.000007987487],"about_ca_topic_score_codex":0.016507454,"about_ca_topic_score_gemma":0.019656189,"teacher_disagreement_score":0.016507454,"about_ca_system_score_codex":0.0015342063,"about_ca_system_score_gemma":0.0020769048,"threshold_uncertainty_score":0.03282273},"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,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_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","score_opus":0.019460758773565952,"score_gpt":0.22406171796139102,"score_spread":0.20460095918782506,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2471190594","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017218849,0.003508821,0.95019,0.0019084545,0.00028200808,0.00028010746,0.00074143097,0.0002545334,0.025615754],"genre_scores_gemma":[0.47688818,0.009419172,0.4669193,0.000874505,0.00059794384,0.0010779141,0.001830845,0.00059524126,0.041796952],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987943,0.000508955,0.000059799564,0.0002848873,0.00022409072,0.00012794255],"domain_scores_gemma":[0.9977418,0.0016588502,0.00023181378,0.00006294154,0.00022585549,0.00007867235],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011829602,0.001690005,0.001290273,0.0011102781,0.0010092951,0.0025144038,0.0014375122,0.0023851888,0.010454129],"category_scores_gemma":[0.005096628,0.0008575419,0.0013478043,0.001813463,0.0009036246,0.0020261353,0.0012698724,0.0024647475,0.0009970469],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026388056,0.000040325023,0.00026175226,0.0002295453,0.000044627774,0.00011254464,0.00008137881,0.9235053,0.0004575845,0.053057585,0.0035989033,0.018584061],"study_design_scores_gemma":[0.000025740512,0.000040107134,0.00016621615,0.000047544945,0.000023871513,0.00008703635,0.00009885676,0.9322229,0.00028587892,0.056133,0.01084926,0.000019584159],"about_ca_topic_score_codex":0.008773268,"about_ca_topic_score_gemma":0.008408448,"teacher_disagreement_score":0.010454129,"about_ca_system_score_codex":0.0028794103,"about_ca_system_score_gemma":0.0020596343,"threshold_uncertainty_score":0.03497249},"labels":[],"label_agreement":null},{"id":"W2493658341","doi":"10.1007/978-3-319-33461-5_8","title":"Exact Algorithms for the Chance-Constrained Vehicle Routing Problem","year":2016,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Vehicle routing problem; Routing (electronic design automation); Algorithm; Mathematical optimization; Computer network; Mathematics","score_opus":0.022987443722590362,"score_gpt":0.2638290936142523,"score_spread":0.2408416498916619,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2493658341","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003470948,0.0009425126,0.98645663,0.00030739588,0.0001176207,0.00004739881,0.00011106799,0.00028872187,0.008257774],"genre_scores_gemma":[0.17209315,0.0022931695,0.8093255,0.00032655915,0.00034496732,0.0003314432,0.00059063383,0.00042241983,0.014272186],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99908125,0.00024935076,0.000045552766,0.00017073502,0.00031712692,0.00013601128],"domain_scores_gemma":[0.9981529,0.0013037617,0.00010572637,0.00018489387,0.00018405364,0.00006859124],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012389531,0.0013131058,0.0015165032,0.0008789946,0.0007104631,0.001816248,0.0026105025,0.0017893583,0.010427142],"category_scores_gemma":[0.006358456,0.000987195,0.0012156635,0.0023568876,0.0011252713,0.0028717222,0.0020074206,0.0027988993,0.0014050825],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000112860056,0.00009086083,0.00023300265,0.00021899286,0.000047978745,0.000034899123,0.000067028646,0.6796575,0.0005453854,0.14206241,0.010757436,0.16617161],"study_design_scores_gemma":[0.0000382196,0.000020408937,0.00008785894,0.000027475984,0.000011492187,0.000027767937,0.000015613403,0.8616619,0.00020626259,0.13509512,0.0027969927,0.000010838455],"about_ca_topic_score_codex":0.0074753887,"about_ca_topic_score_gemma":0.007985989,"teacher_disagreement_score":0.010427142,"about_ca_system_score_codex":0.0018042567,"about_ca_system_score_gemma":0.002237343,"threshold_uncertainty_score":0.034882307},"labels":[],"label_agreement":null},{"id":"W24951642","doi":"10.1093/brain/awu159","title":"A Two-phase Method for the Vehicle Routing Problems with Time Windows","year":2004,"lang":"en","type":"article","venue":"IE interfaces","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Vehicle routing problem; Heuristic; Mathematical optimization; Minification; Phase (matter); Computer science; Routing (electronic design automation); Service (business); Ant colony optimization algorithms; Mathematics; Computer network; Economics","score_opus":0.020034448154376893,"score_gpt":0.3088643148755291,"score_spread":0.28882986672115224,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W24951642","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00066177134,0.00007682482,0.9984706,0.00003579906,0.00007804758,0.0000614138,0.000027185677,0.00027005325,0.00031833103],"genre_scores_gemma":[0.013740771,0.00018345835,0.9815214,0.000050242525,0.00007361287,0.00031412905,0.0001712084,0.00019408506,0.0037510458],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99917763,0.00028610823,0.000065477485,0.00018023087,0.00022551927,0.00006500705],"domain_scores_gemma":[0.9988882,0.00064285105,0.00006570319,0.00014719603,0.00021877246,0.000037285372],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019759585,0.0012853183,0.00089022587,0.0012523797,0.0007600355,0.0010953811,0.0020527958,0.001669529,0.01597143],"category_scores_gemma":[0.005720209,0.0006770687,0.0013383566,0.0017129448,0.00059428473,0.0020059492,0.0013495461,0.002076971,0.0054037524],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00032653872,0.0001513234,0.0005763344,0.00032193106,0.00014376969,0.00018449464,0.00013836827,0.11665938,0.011384719,0.06325987,0.010267817,0.7965854],"study_design_scores_gemma":[0.00007239154,0.00008731062,0.00023449222,0.000024493651,0.000035829096,0.00020685933,0.000030965242,0.9577216,0.0028783255,0.018949548,0.019728104,0.00003010496],"about_ca_topic_score_codex":0.0043161223,"about_ca_topic_score_gemma":0.0047796182,"teacher_disagreement_score":0.01597143,"about_ca_system_score_codex":0.0005090614,"about_ca_system_score_gemma":0.0019302784,"threshold_uncertainty_score":0.053429723},"labels":[],"label_agreement":null},{"id":"W2502871266","doi":"","title":"The vehicle routing problem with hard time windows and stochastic service times","year":2013,"lang":"en","type":"article","venue":"PolyPublie (École Polytechnique de Montréal)","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal; Polytechnique Montréal; École de Technologie Supérieure","funders":"","keywords":"Computer science; Vehicle routing problem; Service (business); Routing (electronic design automation); Computer network; Economics","score_opus":0.006517015085209186,"score_gpt":0.20012147893694998,"score_spread":0.1936044638517408,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2502871266","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.061124295,0.0029329464,0.9226652,0.0025186858,0.00045338008,0.00010640652,0.0006121763,0.00018402589,0.009402876],"genre_scores_gemma":[0.79491955,0.0041391393,0.15245412,0.0006068906,0.0011948544,0.00036237054,0.0009757448,0.0003880364,0.04495923],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99835974,0.0006424062,0.000062509236,0.00041894338,0.0002452422,0.0002711577],"domain_scores_gemma":[0.99572587,0.0032862364,0.0004132099,0.00012594044,0.00017071654,0.0002780461],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027024676,0.0020331494,0.0021231652,0.0010430793,0.000664383,0.0031156777,0.0029869059,0.003162867,0.0037123444],"category_scores_gemma":[0.01026042,0.0019164397,0.0013932488,0.0017747934,0.001966858,0.003956189,0.0018357314,0.0031269449,0.00039645156],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013247806,0.0000469143,0.00030343884,0.00011745952,0.000060718216,0.00013451271,0.00004289365,0.9117137,0.00043009105,0.07781169,0.0018349623,0.0073709865],"study_design_scores_gemma":[0.000029792065,0.00002099889,0.00013680322,0.000010611245,0.000018384157,0.000025210422,0.000016084108,0.9607171,0.00012570097,0.038041897,0.00084476394,0.000012633667],"about_ca_topic_score_codex":0.01583598,"about_ca_topic_score_gemma":0.009281048,"teacher_disagreement_score":0.01583598,"about_ca_system_score_codex":0.0030480942,"about_ca_system_score_gemma":0.002733356,"threshold_uncertainty_score":0.031487584},"labels":[],"label_agreement":null},{"id":"W2508993989","doi":"10.1109/jsyst.2016.2601058","title":"A Game Theoretic Approach for the Real-Life Multiple-Criterion Vehicle Routing Problem With Multiple Time Windows","year":2016,"lang":"en","type":"article","venue":"IEEE Systems Journal","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":28,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"King Fahd University of Petroleum and Minerals","keywords":"Tabu search; Vehicle routing problem; Mathematical optimization; Nash equilibrium; Computer science; Routing (electronic design automation); Heuristic; Pareto principle; Multi-objective optimization; Game theory; Set (abstract data type); Operations research; Mathematics; Mathematical economics","score_opus":0.01861246419664608,"score_gpt":0.23866956915820584,"score_spread":0.22005710496155975,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2508993989","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0014367221,0.0001701659,0.99447834,0.00014128076,0.0000401835,0.000054934968,0.000024684809,0.00002283222,0.0036308914],"genre_scores_gemma":[0.2535097,0.0012670936,0.7325803,0.0002824662,0.00021871638,0.00072003255,0.000131664,0.00012379473,0.011166201],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99865913,0.0006631976,0.000041207884,0.00016512623,0.0003719325,0.000099425895],"domain_scores_gemma":[0.9991642,0.00051710365,0.00009095722,0.000044829394,0.00011817124,0.00006464711],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021243254,0.0016093266,0.0010821219,0.0011484677,0.0006961209,0.0015551124,0.0030085135,0.001482534,0.004600637],"category_scores_gemma":[0.002578551,0.00063109095,0.0016884201,0.0011808415,0.0016984328,0.002426024,0.0013324115,0.0025866393,0.00054750015],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000149710095,0.00006663844,0.00011928948,0.00009533043,0.000040260453,0.00006942094,0.000074590236,0.81029993,0.00077509676,0.1773294,0.0011573621,0.009957685],"study_design_scores_gemma":[0.000009101891,0.000034899796,0.000048130492,0.000014833162,0.000010070724,0.000035508532,0.000022622187,0.95964444,0.00014270104,0.03722332,0.0028017408,0.000012609998],"about_ca_topic_score_codex":0.004982745,"about_ca_topic_score_gemma":0.006126604,"teacher_disagreement_score":0.004982745,"about_ca_system_score_codex":0.0026947008,"about_ca_system_score_gemma":0.002340611,"threshold_uncertainty_score":0.019551456},"labels":[],"label_agreement":null},{"id":"W2514584850","doi":"10.1007/978-3-319-44953-1_21","title":"Constraint Programming for Strictly Convex Integer Quadratically-Constrained Problems","year":2016,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Mathematical optimization; Integer programming; Computer science; Heuristics; Constraint programming; Quadratically constrained quadratic program; Quadratic growth; Quadratic programming; Integer (computer science); Nonlinear programming; Mathematics; Algorithm; Stochastic programming; Nonlinear system","score_opus":0.02049755106873633,"score_gpt":0.259341411781627,"score_spread":0.23884386071289068,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2514584850","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0017391823,0.0018931506,0.9703879,0.00046317704,0.00019281723,0.00006204874,0.00019771795,0.00011915228,0.024944922],"genre_scores_gemma":[0.16225189,0.010009373,0.77822876,0.0007914332,0.0008699517,0.00094719173,0.0017514904,0.0009001363,0.044249788],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99855405,0.0005133705,0.0000762793,0.00020957922,0.0005322921,0.000114339644],"domain_scores_gemma":[0.99848664,0.0010606338,0.00008454477,0.00009494508,0.00022414156,0.000049082984],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014944011,0.0018768589,0.001269526,0.000538808,0.0004921669,0.0025528618,0.0019941013,0.0013221155,0.010582914],"category_scores_gemma":[0.0066102627,0.0009540447,0.0010324391,0.0022524216,0.0012375548,0.0020070977,0.0019978257,0.0039936514,0.0020274913],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000927375,0.00010200288,0.00017222487,0.0010676072,0.0000716128,0.0001644528,0.00014422652,0.46725014,0.002688686,0.34154624,0.030539906,0.15616012],"study_design_scores_gemma":[0.000031477677,0.000039540333,0.00012424636,0.0001495482,0.000017830896,0.00006604454,0.000029987295,0.80456704,0.00067484647,0.17511573,0.019158894,0.000024861598],"about_ca_topic_score_codex":0.005309847,"about_ca_topic_score_gemma":0.0038175774,"teacher_disagreement_score":0.010582914,"about_ca_system_score_codex":0.0013200784,"about_ca_system_score_gemma":0.0014562107,"threshold_uncertainty_score":0.03540343},"labels":[],"label_agreement":null},{"id":"W2515696021","doi":"10.1287/ijoc.2016.0706","title":"The Surgical Patient Routing Problem: A Central Planner Approach","year":2016,"lang":"en","type":"article","venue":"INFORMS journal on computing","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"University of Pittsburgh; U.S. Department of Veterans Affairs; VA Pittsburgh Healthcare System; Office of Research and Development; Division of Civil, Mechanical and Manufacturing Innovation; National Science Foundation","keywords":"Planner; Computer science; Integer programming; Solver; Scheduling (production processes); Vehicle routing problem; Health care; Routing (electronic design automation); Operations research; Mathematical optimization; Artificial intelligence; Computer network; Algorithm","score_opus":0.012822173578996073,"score_gpt":0.23385218253269335,"score_spread":0.2210300089536973,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2515696021","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02756589,0.0009978266,0.94012314,0.0034620008,0.00016618507,0.00063464383,0.0008217281,0.00052205625,0.025706492],"genre_scores_gemma":[0.49473706,0.0013507761,0.48001084,0.00076102413,0.000247841,0.0011904369,0.0010247009,0.00027191816,0.020405479],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989003,0.0005280353,0.000033341963,0.00023013505,0.00014371696,0.0001644588],"domain_scores_gemma":[0.9982279,0.0011607467,0.00016112675,0.00007243537,0.00019076263,0.00018704848],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027334276,0.0014536115,0.0013971525,0.0012829279,0.0009869115,0.0029446098,0.0028390207,0.0026389458,0.016510189],"category_scores_gemma":[0.004361177,0.0012925176,0.0011247783,0.002023191,0.001229574,0.002624507,0.0024561933,0.00228395,0.0009309585],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000109914195,0.0001131619,0.00036401965,0.000109323955,0.00004197093,0.00012339838,0.00006313659,0.94576657,0.00026165723,0.03209604,0.003575032,0.017375816],"study_design_scores_gemma":[0.000062617386,0.000041060193,0.000091691676,0.000015807123,0.000017839398,0.00003282889,0.00008054875,0.97490036,0.00016227872,0.02202549,0.0025588623,0.0000105563295],"about_ca_topic_score_codex":0.009603232,"about_ca_topic_score_gemma":0.012200999,"teacher_disagreement_score":0.016510189,"about_ca_system_score_codex":0.002819356,"about_ca_system_score_gemma":0.0059930366,"threshold_uncertainty_score":0.055232108},"labels":[],"label_agreement":null},{"id":"W2518829358","doi":"10.1002/nav.21701","title":"Column generation for stochastic green telecommunication network planning with switchable base stations","year":2016,"lang":"en","type":"article","venue":"Naval Research Logistics (NRL)","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Column generation; Base station; Mathematical optimization; Computer science; Column (typography); Base (topology); Energy consumption; Reduction (mathematics); Telecommunications network; Network planning and design; Scheme (mathematics); Operations research; Telecommunications; Mathematics; Engineering; Electrical engineering","score_opus":0.20237634617127043,"score_gpt":0.401718522681826,"score_spread":0.19934217651055558,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2518829358","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.095059775,0.0005709033,0.89354885,0.00058034685,0.00009426592,0.00018348667,0.00093903823,0.000345439,0.008677892],"genre_scores_gemma":[0.8805721,0.00039389217,0.113490224,0.00016368744,0.00004490518,0.00030128754,0.00078412174,0.00009908026,0.0041506747],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99946195,0.00024476508,0.000014527382,0.000073702926,0.000098443394,0.00010660066],"domain_scores_gemma":[0.9984907,0.0011411424,0.00013394273,0.000047781883,0.000111842804,0.00007457723],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010951478,0.0010556711,0.0012586826,0.0006221181,0.0003798192,0.0011774539,0.0008376613,0.00083093613,0.0046002306],"category_scores_gemma":[0.002021324,0.0006748642,0.0008689877,0.0013416741,0.00069658837,0.00067983416,0.0007734951,0.0012178407,0.00025547974],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000024098403,0.000012112493,0.000102902595,0.000019283649,0.000009972686,0.000033092387,0.000008046821,0.99315965,0.00014099739,0.0033585082,0.00035311744,0.0027782866],"study_design_scores_gemma":[0.0000084253525,0.000010294201,0.000038425285,0.0000027168373,0.0000032869943,0.0000041506864,0.0000058831174,0.99732375,0.000108304805,0.00231418,0.00017818918,0.0000024161334],"about_ca_topic_score_codex":0.010580223,"about_ca_topic_score_gemma":0.009302565,"teacher_disagreement_score":0.010580223,"about_ca_system_score_codex":0.0013981728,"about_ca_system_score_gemma":0.0013734258,"threshold_uncertainty_score":0.02103728},"labels":[],"label_agreement":null},{"id":"W2523297565","doi":"10.1080/00207543.2016.1231940","title":"A column generation based heuristic for the capacitated vehicle routing problem with three-dimensional loading constraints","year":2016,"lang":"en","type":"article","venue":"International Journal of Production Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":30,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Column generation; Mathematical optimization; Heuristic; FIFO and LIFO accounting; Benchmark (surveying); Tabu search; Vehicle routing problem; Computer science; Routing (electronic design automation); Computation; Algorithm; Mathematics; FIFO (computing and electronics)","score_opus":0.09263538036986169,"score_gpt":0.354699556474901,"score_spread":0.2620641761050393,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2523297565","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.026823198,0.0005505076,0.96515954,0.00016628036,0.00012791847,0.0002520852,0.0002040592,0.000785421,0.0059309425],"genre_scores_gemma":[0.37510374,0.0005171161,0.6193118,0.00026441747,0.00007744025,0.0005066397,0.0007345897,0.00020446672,0.0032797584],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997172,0.00009120044,0.000009254793,0.00003927573,0.0000795192,0.00006353194],"domain_scores_gemma":[0.9994861,0.00028548343,0.000057116726,0.000040092167,0.00009071265,0.0000405747],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034676763,0.0010806832,0.0009947821,0.0010103013,0.0006628156,0.00070014474,0.0010023506,0.00083619147,0.0040502367],"category_scores_gemma":[0.0009057257,0.00048013887,0.0006758599,0.0014540732,0.00050156045,0.00061814906,0.000633644,0.000843862,0.0004999664],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007056793,0.00011874198,0.0002934568,0.00011747775,0.000035084435,0.00013715023,0.000045837565,0.8967862,0.0034110383,0.0053734374,0.003731315,0.08987975],"study_design_scores_gemma":[0.000025445876,0.000071826835,0.00008678202,0.000008559206,0.0000145533295,0.000043205466,0.000017326387,0.9956216,0.0010364407,0.0019131361,0.001150157,0.000011032317],"about_ca_topic_score_codex":0.006409558,"about_ca_topic_score_gemma":0.0069768787,"teacher_disagreement_score":0.006409558,"about_ca_system_score_codex":0.00070591934,"about_ca_system_score_gemma":0.001398332,"threshold_uncertainty_score":0.013549328},"labels":[],"label_agreement":null},{"id":"W2524034199","doi":"10.1287/trsc.2016.0709","title":"50th Anniversary Invited Article—Future Research Directions in Stochastic Vehicle Routing","year":2016,"lang":"en","type":"article","venue":"Transportation Science","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":147,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal; Université du Québec à Montréal; Polytechnique Montréal","funders":"","keywords":"Vehicle routing problem; Computer science; Routing (electronic design automation); Operations research; Stochastic modelling; Service (business); Sketch; Key (lock); Stochastic process; Stochastic optimization; Mathematical optimization; Management science; Engineering; Mathematics; Marketing; Computer security; Algorithm; Computer network; Business","score_opus":0.03834316816566709,"score_gpt":0.3306429495440477,"score_spread":0.29229978137838064,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2524034199","genre_codex":"review","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0051749726,0.44565132,0.07401817,0.14203227,0.1987063,0.00010616034,0.00074163725,0.00090185186,0.13266732],"genre_scores_gemma":[0.064848214,0.45409876,0.057464812,0.01735264,0.17979813,0.00018989932,0.0019204757,0.0006202453,0.22370686],"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9988279,0.00034046787,0.00006344601,0.00022623807,0.00038837752,0.00015354938],"domain_scores_gemma":[0.99591136,0.0017236307,0.00017541238,0.00023943881,0.0012662122,0.0006839567],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0039375834,0.00090957317,0.0009573067,0.0016800945,0.0007390294,0.003763048,0.0016666909,0.0027958215,0.036481477],"category_scores_gemma":[0.006003482,0.0003798811,0.0009186141,0.0017140672,0.00096631,0.0048309136,0.0018478797,0.0036415828,0.012430041],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016655393,0.00012986321,0.00059832865,0.00090324687,0.000054176573,0.00020058517,0.00008703169,0.006871957,0.0010513626,0.10145102,0.5623961,0.32608974],"study_design_scores_gemma":[0.00001504062,0.00011855314,0.00045488187,0.00048359553,0.00002577483,0.00019114348,0.00010208074,0.0057741473,0.00035605906,0.047359735,0.9450837,0.000035230823],"about_ca_topic_score_codex":0.0013631735,"about_ca_topic_score_gemma":0.0020330604,"teacher_disagreement_score":0.036481477,"about_ca_system_score_codex":0.0019223768,"about_ca_system_score_gemma":0.0012069443,"threshold_uncertainty_score":0.122042656},"labels":[],"label_agreement":null},{"id":"W2531881412","doi":"10.2298/yjor160517018b","title":"Using injection points in reformulation local search for solving continuous location problems","year":2016,"lang":"en","type":"article","venue":"Yugoslav journal of operations research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Military College of Canada","funders":"Natural Sciences and Engineering Research Council of Canada; Engineering and Physical Sciences Research Council; Ministry of Education and Science of the Republic of Kazakhstan; Ministry of Education, Science and Technology; Ministerio de Economía y Competitividad","keywords":"Continuous phase modulation; Continuous modelling; Benchmark (surveying); Local search (optimization); Relaxation (psychology); Mathematical optimization; Exploit; Mathematics; Limiting; Relation (database); Computer science; Applied mathematics; Algorithm; Mathematical analysis; Data mining","score_opus":0.12156436583336688,"score_gpt":0.4073655037568511,"score_spread":0.2858011379234842,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2531881412","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009946603,0.0002785298,0.9883837,0.000108365886,0.00001474367,0.000044284934,0.0000130092585,0.00014525608,0.0010655929],"genre_scores_gemma":[0.40957722,0.000497407,0.5866063,0.00014242742,0.000060270715,0.00046000196,0.0001266498,0.00018594896,0.0023437324],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987581,0.0008142103,0.00004466781,0.00010229567,0.00020707615,0.000073711526],"domain_scores_gemma":[0.9972779,0.0019961318,0.00022482275,0.00016438629,0.0002592313,0.00007753885],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0036851014,0.001157778,0.0017378383,0.0011678849,0.0003755431,0.0012401035,0.0015005362,0.0011954033,0.0022544102],"category_scores_gemma":[0.006811182,0.000702236,0.0010509548,0.0011283488,0.0018402709,0.0019037584,0.00196771,0.0017027704,0.0004820711],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008091027,0.000063391744,0.0002763061,0.000118120275,0.000032609063,0.00005704661,0.000108261025,0.9377258,0.0013028166,0.032159608,0.0005396771,0.027535478],"study_design_scores_gemma":[0.000014933208,0.000050139075,0.000018536639,0.000009409601,0.0000050075305,0.000007749648,0.000009895819,0.9941229,0.0003682974,0.005037032,0.0003508535,0.00000513934],"about_ca_topic_score_codex":0.0026776625,"about_ca_topic_score_gemma":0.0019244408,"teacher_disagreement_score":0.0036851014,"about_ca_system_score_codex":0.0009880922,"about_ca_system_score_gemma":0.0010664739,"threshold_uncertainty_score":0.01948893},"labels":[],"label_agreement":null},{"id":"W2537704407","doi":"10.1002/atr.1417","title":"A column generation algorithm for the bus driver scheduling problem","year":2016,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Ministry of Science and Technology","keywords":"Column generation; Scheduling (production processes); Computer science; Set cover problem; Set (abstract data type); Job shop scheduling; Column (typography); Mathematical optimization; Operations research; Algorithm; Engineering; Embedded system; Mathematics; Computer network","score_opus":0.014597228855142612,"score_gpt":0.2611452104158759,"score_spread":0.2465479815607333,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2537704407","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017483348,0.00025177302,0.97703826,0.00023577009,0.000059184673,0.00017663887,0.00024321272,0.0006304374,0.0038814628],"genre_scores_gemma":[0.2332963,0.0003113835,0.76011705,0.00019519398,0.00006743279,0.0003996131,0.0011117539,0.00021725635,0.0042840596],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995747,0.00014896471,0.0000185739,0.00008417807,0.00009563528,0.00007785733],"domain_scores_gemma":[0.9990145,0.00060990505,0.00007939477,0.000061828694,0.00018180524,0.000052522395],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006687517,0.00095810916,0.0007915605,0.00076055643,0.00061191275,0.00084653625,0.00092923216,0.0008415262,0.005463963],"category_scores_gemma":[0.0018442728,0.00047723798,0.0006948009,0.001072078,0.00041111966,0.0007483328,0.00089053,0.0011842529,0.0009043455],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008840454,0.0001159713,0.00056026044,0.00010784075,0.00003306348,0.00011083529,0.00008031619,0.85375327,0.0024442924,0.013004643,0.00845716,0.121244036],"study_design_scores_gemma":[0.000022521264,0.000023202176,0.00006645263,0.000004593766,0.000005668953,0.000023402627,0.000012611869,0.99447,0.00038557447,0.003962955,0.0010177628,0.000005249213],"about_ca_topic_score_codex":0.006959207,"about_ca_topic_score_gemma":0.0067382525,"teacher_disagreement_score":0.006959207,"about_ca_system_score_codex":0.00084505783,"about_ca_system_score_gemma":0.0015283863,"threshold_uncertainty_score":0.018278778},"labels":[],"label_agreement":null},{"id":"W2538943139","doi":"10.1111/itor.12346","title":"The rescheduling arc routing problem","year":2016,"lang":"en","type":"article","venue":"International Transactions in Operational Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Transport Canada","funders":"","keywords":"Arc routing; Computer science; Integer programming; Scheduling (production processes); Operations research; Mathematical optimization; Schedule; Routing (electronic design automation); Decision maker; Computer network; Mathematics; Algorithm","score_opus":0.06704797698861954,"score_gpt":0.38720962856291435,"score_spread":0.3201616515742948,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2538943139","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11661232,0.0014375176,0.8358743,0.0019007324,0.00038805773,0.000450902,0.00093564775,0.0006961099,0.041704416],"genre_scores_gemma":[0.68913263,0.0009683591,0.28752097,0.00031274266,0.00015655135,0.00030772082,0.0011881795,0.00016339315,0.020249449],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985715,0.0007352152,0.00005082694,0.00022878221,0.00023252355,0.0001811645],"domain_scores_gemma":[0.9989874,0.0006295287,0.00010163227,0.000087428874,0.00012591702,0.000068177265],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012142435,0.0007237976,0.0008786789,0.00058596,0.00057429005,0.0011115475,0.0012476096,0.0012576616,0.005077156],"category_scores_gemma":[0.0017785659,0.00030847802,0.00074271736,0.0010218823,0.0005664739,0.0009950944,0.0005793174,0.0010950571,0.00041611787],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012660709,0.00019983828,0.00043241598,0.00020352783,0.000057564015,0.00037134858,0.000069439164,0.8525947,0.0025583594,0.062795356,0.006860358,0.07373055],"study_design_scores_gemma":[0.00004456852,0.000077220175,0.00019605193,0.00001871914,0.000017874248,0.00015063869,0.000051656625,0.95670104,0.0013632805,0.033049468,0.00831658,0.000012960745],"about_ca_topic_score_codex":0.0035564443,"about_ca_topic_score_gemma":0.0027145375,"teacher_disagreement_score":0.005077156,"about_ca_system_score_codex":0.0009697474,"about_ca_system_score_gemma":0.001642657,"threshold_uncertainty_score":0.01698476},"labels":[],"label_agreement":null},{"id":"W2546223729","doi":"10.1016/j.ejor.2018.06.046","title":"Column generation for vehicle routing problems with multiple synchronization constraints","year":2018,"lang":"en","type":"article","venue":"European Journal of Operational Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Group for Research in Decision Analysis","funders":"","keywords":"Column generation; Synchronization (alternating current); Variable (mathematics); Operations research; Routing (electronic design automation); Vehicle routing problem; Computer science; Heuristic; Mathematical optimization; Engineering; Computer network; Mathematics; Artificial intelligence","score_opus":0.08981736353026892,"score_gpt":0.3414826033651602,"score_spread":0.25166523983489125,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2546223729","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010194637,0.00044607781,0.9851598,0.00023045573,0.00012342443,0.00009588719,0.0003044706,0.00023027447,0.0032149751],"genre_scores_gemma":[0.4570352,0.0012714055,0.5263392,0.00038884275,0.0003299274,0.00066578056,0.0018784691,0.0004911757,0.0115999775],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994635,0.0002596533,0.000017719574,0.00007680901,0.0001036156,0.00007863453],"domain_scores_gemma":[0.9974553,0.0018973926,0.00018204018,0.0001270163,0.00024010635,0.00009816127],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00095464435,0.0013266823,0.0013058137,0.00085952855,0.0005280763,0.0012250696,0.0011632838,0.0010314048,0.006847198],"category_scores_gemma":[0.0033092285,0.00085937296,0.0011201798,0.0016814276,0.00069812924,0.001093447,0.0011461753,0.0017253221,0.0007888799],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000116732605,0.00008960858,0.00028647765,0.0002464618,0.00005663406,0.00009413611,0.000051195413,0.92713284,0.0014434775,0.018872255,0.0050535584,0.046556592],"study_design_scores_gemma":[0.00001618915,0.000027600669,0.000049459675,0.000009666946,0.000009812345,0.000010581633,0.000012285509,0.98939145,0.00034416153,0.00947711,0.0006450817,0.0000066036105],"about_ca_topic_score_codex":0.005832903,"about_ca_topic_score_gemma":0.0060812356,"teacher_disagreement_score":0.006847198,"about_ca_system_score_codex":0.0006986021,"about_ca_system_score_gemma":0.0010761331,"threshold_uncertainty_score":0.022906184},"labels":[],"label_agreement":null},{"id":"W2548380795","doi":"","title":"Production scheduling and routing problem in the textile industry","year":2013,"lang":"en","type":"article","venue":"Industrial Engineering and Systems Management (IESM), Proceedings of 2013 International Conference on","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Column generation; Scheduling (production processes); Solver; Integer programming; Linear programming; Mathematical optimization; Computer science; Job shop scheduling; Routing (electronic design automation); Algorithm; Mathematics; Embedded system","score_opus":0.043154000461980105,"score_gpt":0.24985874610223655,"score_spread":0.20670474564025643,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2548380795","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06156635,0.00083750125,0.92264915,0.0010449038,0.0001864154,0.00034405844,0.00047077515,0.000300529,0.012600303],"genre_scores_gemma":[0.44455433,0.0015212438,0.5336569,0.0002934115,0.000234007,0.0005311607,0.0010334714,0.00019850704,0.017977042],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989743,0.00043541196,0.000043628963,0.00024870475,0.00014839032,0.00014966032],"domain_scores_gemma":[0.9993699,0.00041451392,0.000084413696,0.000031800166,0.000059767128,0.0000395584],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011436075,0.0012179873,0.0010575076,0.00067008164,0.0007146247,0.0014591307,0.0009783515,0.0016918901,0.006288239],"category_scores_gemma":[0.0017548625,0.0006928338,0.0011261088,0.0014673504,0.0005572261,0.0014074154,0.0006873518,0.0010673587,0.0006790688],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015150801,0.0001827082,0.00057974964,0.00029380678,0.0000629117,0.00035788535,0.000090429014,0.9297837,0.004030171,0.01590528,0.0024103138,0.04615158],"study_design_scores_gemma":[0.00008283505,0.00016988772,0.00053876074,0.000022566075,0.0000359528,0.0001902888,0.0001212832,0.96831256,0.0021877193,0.02019275,0.008125047,0.000020406356],"about_ca_topic_score_codex":0.004767499,"about_ca_topic_score_gemma":0.0040869284,"teacher_disagreement_score":0.006288239,"about_ca_system_score_codex":0.001205511,"about_ca_system_score_gemma":0.0016595899,"threshold_uncertainty_score":0.021036208},"labels":[],"label_agreement":null},{"id":"W2548759626","doi":"10.1109/gol.2016.7731666","title":"Strategic planning problem represented by a three-echelon logistics network-modeling and solving","year":2016,"lang":"en","type":"article","venue":"","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Time horizon; Computer science; Heuristic; Order (exchange); Plan (archaeology); Mathematical optimization; Operations research; Product (mathematics); Minification; Total cost; Network planning and design; Flow network; Mathematics; Artificial intelligence; Economics","score_opus":0.04758450932323452,"score_gpt":0.27968365531211686,"score_spread":0.23209914598888234,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2548759626","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03817973,0.00049382483,0.9352906,0.00086942443,0.0000694222,0.00011622605,0.00093397923,0.00016032558,0.023886543],"genre_scores_gemma":[0.749407,0.0013134851,0.23242311,0.00015231826,0.00007945558,0.0004150523,0.0010414496,0.00006116696,0.01510704],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99945706,0.00025965355,0.000022655757,0.000112084046,0.00007260747,0.000075970085],"domain_scores_gemma":[0.99970394,0.00015736459,0.000051689098,0.000021213425,0.00003957487,0.000026190688],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00061004434,0.00070754625,0.00061449275,0.00064078736,0.0005838781,0.0015393917,0.00097453676,0.0011975882,0.004948453],"category_scores_gemma":[0.0008536193,0.00032789676,0.0009659311,0.0013689689,0.0006132865,0.0012866956,0.00095590996,0.0011138242,0.000495557],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000021509692,0.000019762118,0.00046787053,0.00006245245,0.000025647523,0.00013956125,0.000050633153,0.9534349,0.00036128797,0.038495447,0.00064849725,0.006272556],"study_design_scores_gemma":[0.000006271082,0.000013963062,0.00015010059,0.000009331899,0.000009694462,0.00003661116,0.00005317148,0.97352535,0.00013269026,0.024030862,0.0020256808,0.0000063454268],"about_ca_topic_score_codex":0.012334845,"about_ca_topic_score_gemma":0.012509499,"teacher_disagreement_score":0.012334845,"about_ca_system_score_codex":0.001535886,"about_ca_system_score_gemma":0.0020185057,"threshold_uncertainty_score":0.02452612},"labels":[],"label_agreement":null},{"id":"W2550943709","doi":"","title":"A Pruning based Ant Colony Algorithm for Minimum Vertex Cover Problem.","year":2009,"lang":"en","type":"article","venue":"IJCCI","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Vertex cover; Tree traversal; Graph traversal; Algorithm; Vertex (graph theory); Feedback vertex set; Edge cover; Pruning; Mathematics; Cardinality (data modeling); Ant colony optimization algorithms; Reachability; Computer science; Graph; Combinatorics; Mathematical optimization; Approximation algorithm; Data mining","score_opus":0.013454539917893494,"score_gpt":0.2624972349315403,"score_spread":0.24904269501364681,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2550943709","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020720175,0.00079887686,0.9718161,0.00026537443,0.00009126197,0.00018674556,0.000081644896,0.0006318781,0.0054078694],"genre_scores_gemma":[0.23399265,0.00047794476,0.7614166,0.00014837035,0.000045462435,0.00034843435,0.00028461812,0.00008896828,0.003196916],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999556,0.00012935337,0.000020271566,0.000060879607,0.00019368858,0.000039861065],"domain_scores_gemma":[0.99952614,0.00024407939,0.000062908024,0.000052499607,0.00008765782,0.00002670332],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044219865,0.0005727515,0.0009796713,0.0009795374,0.0005108865,0.00061382574,0.0012707727,0.001073806,0.0011349239],"category_scores_gemma":[0.0017201942,0.00032939157,0.0005548064,0.0011221734,0.00036038161,0.00067163614,0.00058966107,0.0007355977,0.00030555043],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000099245335,0.00012682201,0.0010448856,0.00016425924,0.000099205776,0.00018682705,0.00009651146,0.72977525,0.009040869,0.016754577,0.0042313104,0.23838024],"study_design_scores_gemma":[0.000022303424,0.000045893154,0.00014668606,0.000010529967,0.000012377623,0.00010880955,0.00001197223,0.9921984,0.0010308878,0.0035999937,0.0028060786,0.000006190384],"about_ca_topic_score_codex":0.0030843986,"about_ca_topic_score_gemma":0.003515841,"teacher_disagreement_score":0.0030843986,"about_ca_system_score_codex":0.0005034728,"about_ca_system_score_gemma":0.0009083447,"threshold_uncertainty_score":0.0061329007},"labels":[],"label_agreement":null},{"id":"W2552752966","doi":"10.1016/j.jal.2016.11.010","title":"A survey on the inventory-routing problem with stochastic lead times and demands","year":2016,"lang":"en","type":"article","venue":"Journal of Applied Logic","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":55,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Lead time; Operations research; Supply chain; Key (lock); Routing (electronic design automation); Inventory control; Inventory theory; Supply chain management; Control (management); Variable (mathematics); Synchronization (alternating current); Lead (geology); Field (mathematics); Mathematical optimization; Operations management; Economics; Mathematics; Business; Artificial intelligence","score_opus":0.027733129981870525,"score_gpt":0.2422598730721083,"score_spread":0.21452674309023778,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2552752966","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00730793,0.3722668,0.5703893,0.004315187,0.0012692886,0.00008906621,0.00060243235,0.00022464841,0.043535333],"genre_scores_gemma":[0.09792731,0.6698898,0.20356853,0.0020671133,0.0058628367,0.00022377259,0.001489799,0.00028647025,0.018684398],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9985689,0.00046124472,0.00012324094,0.0003151257,0.0004302955,0.00010118862],"domain_scores_gemma":[0.9979796,0.0014806582,0.00010300613,0.00012708503,0.0002495209,0.00006020914],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002104291,0.0017738551,0.002173852,0.0023896804,0.00061832403,0.0038346634,0.0022706133,0.0021892763,0.0050984565],"category_scores_gemma":[0.0038804896,0.0012663696,0.0018070189,0.008366624,0.0011100268,0.0050358316,0.0012472009,0.0026049514,0.0016416595],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012203375,0.00032391728,0.002071202,0.0051144203,0.00022554123,0.00022912138,0.00015823515,0.10752954,0.00087352685,0.37163228,0.032167695,0.47955248],"study_design_scores_gemma":[0.00004516751,0.00019635765,0.001433532,0.0012285339,0.00017199283,0.00081392634,0.0002147178,0.2528383,0.00073503586,0.48378208,0.2584536,0.000086767985],"about_ca_topic_score_codex":0.002248336,"about_ca_topic_score_gemma":0.0020107345,"teacher_disagreement_score":0.0050984565,"about_ca_system_score_codex":0.0017692426,"about_ca_system_score_gemma":0.0018347802,"threshold_uncertainty_score":0.017056048},"labels":[],"label_agreement":null},{"id":"W2556437849","doi":"10.5267/j.ijiec.2016.10.001","title":"Green open location-routing problem considering economic and environmental costs","year":2016,"lang":"en","type":"article","venue":"International Journal of Industrial Engineering Computations","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":27,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Universidad Tecnológica de Pereira; Fundação de Amparo à Pesquisa do Estado de Minas Gerais; Conselho Nacional de Desenvolvimento Científico e Tecnológico","keywords":"Routing (electronic design automation); Computer science; Mathematical optimization; Environmental economics; Natural resource economics; Business; Economics; Environmental science; Computer network; Mathematics","score_opus":0.029644109991011264,"score_gpt":0.2704808512720371,"score_spread":0.2408367412810258,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2556437849","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04162479,0.00034505888,0.94535154,0.0004886652,0.00008163408,0.000083178624,0.0002785586,0.00012494976,0.011621623],"genre_scores_gemma":[0.73747677,0.00087044196,0.23800795,0.00018007446,0.000093367344,0.0003947204,0.00048825904,0.0001340788,0.02235433],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99924845,0.00029564847,0.00002238974,0.00013058532,0.00015770865,0.00014514316],"domain_scores_gemma":[0.9994091,0.0003577538,0.00006704098,0.00003470704,0.00006749485,0.00006387975],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00092744676,0.0011257066,0.0013269186,0.00084335485,0.00054243905,0.0018122861,0.0013544332,0.002062767,0.0036349832],"category_scores_gemma":[0.0016805817,0.0005870244,0.0007981988,0.0013917092,0.00070240936,0.002124947,0.001477891,0.0010941707,0.00036488476],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000037634054,0.000035535402,0.00021585412,0.000054266107,0.00002492386,0.00012790445,0.000020350859,0.9607994,0.00057694013,0.027498838,0.0005805301,0.010027788],"study_design_scores_gemma":[0.000011582919,0.000030576644,0.00012146665,0.000007298528,0.00001267337,0.000052516087,0.000028791403,0.98237234,0.0003009363,0.015753238,0.0012993377,0.0000092410855],"about_ca_topic_score_codex":0.0037584295,"about_ca_topic_score_gemma":0.0037238644,"teacher_disagreement_score":0.0037584295,"about_ca_system_score_codex":0.0012048768,"about_ca_system_score_gemma":0.0015190092,"threshold_uncertainty_score":0.012160242},"labels":[],"label_agreement":null},{"id":"W2557690748","doi":"10.1109/cec.2016.7744235","title":"A Pareto non-dominated solution approach for the vehicle routing problem with multiple time windows","year":2016,"lang":"en","type":"article","venue":"","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Centre interuniversitaire de recherche sur les reseaux d'entreprise, la logistique et le transport; King Fahd University of Petroleum and Minerals","keywords":"Vehicle routing problem; Tabu search; Mathematical optimization; Benchmark (surveying); Pareto principle; Nash equilibrium; Computer science; Set (abstract data type); Heuristic; Variable (mathematics); Variable neighborhood search; Routing (electronic design automation); Multi-objective optimization; Metaheuristic; Mathematics","score_opus":0.012941045921025813,"score_gpt":0.2231873047745746,"score_spread":0.21024625885354878,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2557690748","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008940093,0.0003967233,0.9858166,0.00008066267,0.000034698736,0.00011579963,0.000035405923,0.00011990047,0.0044601355],"genre_scores_gemma":[0.2393679,0.00067346235,0.7527687,0.00017440277,0.0000694036,0.00063551264,0.00019116601,0.00016621359,0.005953218],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992687,0.0003763461,0.000019018484,0.00006546386,0.0002180142,0.00005246376],"domain_scores_gemma":[0.9995246,0.00029725878,0.000048602582,0.000027828572,0.000073484145,0.000028239809],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014216359,0.0010437698,0.0009938811,0.0014795638,0.00066967274,0.00083524745,0.0017349808,0.000974086,0.0026635271],"category_scores_gemma":[0.0020146403,0.00043936312,0.00094078295,0.0011466406,0.0005975329,0.00087341934,0.0007916036,0.0008967684,0.0004550059],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000041514755,0.000086425876,0.00027606738,0.00008971412,0.000058385387,0.00005380479,0.00006576834,0.9191289,0.0011332631,0.025073992,0.001111852,0.052880406],"study_design_scores_gemma":[0.000013521036,0.00006195842,0.00006557583,0.000014580099,0.000011956376,0.000028887713,0.000017155715,0.9926286,0.00034967836,0.005298243,0.0015027343,0.000007066328],"about_ca_topic_score_codex":0.0030781017,"about_ca_topic_score_gemma":0.003904673,"teacher_disagreement_score":0.0030781017,"about_ca_system_score_codex":0.0009280426,"about_ca_system_score_gemma":0.0011416015,"threshold_uncertainty_score":0.008910358},"labels":[],"label_agreement":null},{"id":"W2561257229","doi":"","title":"Variable Neighborhood Search and Local Branching","year":2004,"lang":"en","type":"article","venue":"","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Group for Research in Decision Analysis; HEC Montréal","funders":"","keywords":"Variable neighborhood search; Solver; Mathematical optimization; Integer programming; Local search (optimization); Variable (mathematics); Heuristic; Branching (polymer chemistry); Set (abstract data type); Mathematics; Limit (mathematics); Black box; Integer (computer science); Computer science; Metaheuristic; Artificial intelligence","score_opus":0.010488171469996803,"score_gpt":0.24191149265794876,"score_spread":0.23142332118795195,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2561257229","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03545224,0.00080822076,0.9530279,0.00021100354,0.000038716902,0.00007261989,0.000035258865,0.00029950406,0.010054533],"genre_scores_gemma":[0.5620121,0.00081722054,0.4298908,0.00019760986,0.00007056641,0.0003216623,0.00020801098,0.00023375108,0.0062482324],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989643,0.00048558685,0.000024735435,0.00018333958,0.00024359718,0.00009830237],"domain_scores_gemma":[0.9980236,0.0014270056,0.00017945397,0.00015148362,0.00013971452,0.00007876109],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014532047,0.00047984897,0.00097164186,0.00053873064,0.00067692966,0.0007593177,0.0013691715,0.0005946601,0.003231867],"category_scores_gemma":[0.004993276,0.00032868295,0.0005719673,0.0007423812,0.0010521886,0.0013310721,0.0015259214,0.0012467466,0.00046547392],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013457511,0.00009668652,0.0014194803,0.00013452851,0.000045191722,0.00012943319,0.00018075139,0.76804626,0.00179292,0.13521983,0.0018421491,0.09095825],"study_design_scores_gemma":[0.00004601485,0.00013626159,0.00024751676,0.000033671735,0.000024850604,0.00010907072,0.00003565287,0.9364856,0.0014949082,0.056184348,0.005188026,0.00001411898],"about_ca_topic_score_codex":0.0018762025,"about_ca_topic_score_gemma":0.002363813,"teacher_disagreement_score":0.003231867,"about_ca_system_score_codex":0.0007069319,"about_ca_system_score_gemma":0.0008478708,"threshold_uncertainty_score":0.010811627},"labels":[],"label_agreement":null},{"id":"W2563548641","doi":"10.5539/ijsp.v6n1p95","title":"Installation and Dispatch of the Traffic Patrol Service Platform","year":2016,"lang":"en","type":"article","venue":"International Journal of Statistics and Probability","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Workload; Operations research; Service (business); Construct (python library); Shortest path problem; Scale (ratio); Integer programming; Path (computing); Transport engineering; Mathematical optimization; Real-time computing; Computer network; Engineering; Geography; Mathematics; Operating system; Algorithm; Business; Graph","score_opus":0.014622230713215043,"score_gpt":0.25216208946021657,"score_spread":0.2375398587470015,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2563548641","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.039313458,0.00013461892,0.95269877,0.00034295808,0.000110754896,0.0001316866,0.0002316282,0.00063870975,0.006397333],"genre_scores_gemma":[0.8645233,0.00030572293,0.12680617,0.000060577866,0.000045519126,0.0002531737,0.0003819167,0.00013890209,0.0074847145],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99951124,0.000092901515,0.00002206516,0.00012706612,0.00014335739,0.00010335723],"domain_scores_gemma":[0.9996265,0.00010298894,0.00007915497,0.00004481204,0.0000992998,0.00004728867],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005696548,0.00079126155,0.0006370482,0.00073181465,0.00068705576,0.0013260924,0.001335635,0.00083627756,0.0031395466],"category_scores_gemma":[0.0015970682,0.0005761786,0.00070591364,0.0005598019,0.0005849919,0.002259686,0.0008666979,0.0010799082,0.0008703931],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026719368,0.000015582964,0.0008714313,0.00004067008,0.000007787883,0.000065949615,0.000040251074,0.9704874,0.0015564288,0.010498274,0.0009152112,0.015474185],"study_design_scores_gemma":[0.0000044968747,0.000015965583,0.00021812304,0.000004322969,0.000005386213,0.000019072126,0.000028227905,0.9962023,0.0007583268,0.0015913678,0.0011442724,0.000008089983],"about_ca_topic_score_codex":0.013219457,"about_ca_topic_score_gemma":0.00836026,"teacher_disagreement_score":0.013219457,"about_ca_system_score_codex":0.0017409829,"about_ca_system_score_gemma":0.0024015207,"threshold_uncertainty_score":0.026284993},"labels":[],"label_agreement":null},{"id":"W2564716879","doi":"","title":"General variable neighborhood search for the uncapacitated single allocation \\(p\\)-hub center problem","year":2015,"lang":"en","type":"article","venue":"Les Cahiers du GERAD","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Military College of Canada","funders":"","keywords":"Variable neighborhood search; Benchmark (surveying); Heuristic; Mathematical optimization; Variable (mathematics); Local search (optimization); Descent (aeronautics); Computer science; Mathematics; Ant colony optimization algorithms; Metaheuristic; Engineering; Geography","score_opus":0.030715310732639483,"score_gpt":0.24590282781208728,"score_spread":0.2151875170794478,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2564716879","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02626305,0.00032378588,0.9698102,0.00011609984,0.00002180245,0.00006175844,0.00004617516,0.00014527423,0.0032119127],"genre_scores_gemma":[0.55094063,0.000396732,0.44297194,0.000104811195,0.000040157498,0.00024232484,0.0002269012,0.00012465597,0.004951823],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997075,0.00014184877,0.000006931268,0.000060483537,0.000049290236,0.00003386089],"domain_scores_gemma":[0.9996691,0.00022094221,0.000040234878,0.000021635196,0.000030035833,0.00001806592],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006659276,0.00045158953,0.00079311966,0.00039685657,0.0002896854,0.00041365018,0.00094448816,0.0005365967,0.001484263],"category_scores_gemma":[0.00113774,0.00025976409,0.00038473817,0.00053652946,0.0004239203,0.0006359324,0.0004923142,0.00057351,0.00020464345],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000034159446,0.000025421026,0.00022830036,0.0000414512,0.000015381987,0.00003388822,0.000018283294,0.9672925,0.0005692587,0.010279181,0.0008641512,0.020598097],"study_design_scores_gemma":[0.0000090724825,0.00002138708,0.00005667496,0.0000033762974,0.0000028385136,0.0000149867665,0.000006941157,0.995453,0.00020557783,0.0036252919,0.0005982967,0.0000024923263],"about_ca_topic_score_codex":0.0034536088,"about_ca_topic_score_gemma":0.0046166424,"teacher_disagreement_score":0.0034536088,"about_ca_system_score_codex":0.0005533643,"about_ca_system_score_gemma":0.0008524931,"threshold_uncertainty_score":0.0068669915},"labels":[],"label_agreement":null},{"id":"W2566755312","doi":"","title":"Airline crew scheduling: Models, algorithms, and data sets","year":2014,"lang":"en","type":"article","venue":"PolyPublie (École Polytechnique de Montréal)","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Group for Research in Decision Analysis","funders":"","keywords":"Crew scheduling; Crew; Cockpit; Computer science; Scheduling (production processes); Column generation; Operations research; Distributed computing; Engineering; Mathematical optimization; Aeronautics; Operations management; Mathematics","score_opus":0.027306706610756546,"score_gpt":0.27041841240730485,"score_spread":0.2431117057965483,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2566755312","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10365138,0.00724712,0.8451918,0.008067368,0.00026309554,0.00085232797,0.022311453,0.0021676973,0.010247727],"genre_scores_gemma":[0.52150196,0.009160262,0.4345235,0.0007872532,0.00062633696,0.0028564746,0.023663342,0.00030030543,0.0065805297],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9970605,0.0016015784,0.00016442852,0.0005805677,0.00039677872,0.00019624399],"domain_scores_gemma":[0.9873998,0.010141368,0.0009208822,0.0007584805,0.00054396974,0.00023556323],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005643281,0.0017068858,0.0016799475,0.0023426265,0.0009818526,0.0035036416,0.0035460482,0.0031275174,0.0039270087],"category_scores_gemma":[0.013501719,0.0012017877,0.0012747092,0.00583612,0.0011787355,0.0038604322,0.0013065086,0.003040661,0.00079577236],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005813425,0.0001216306,0.0016166749,0.00014121189,0.000048550388,0.0000455342,0.000044304557,0.96541834,0.00010107447,0.0134353,0.0055105034,0.013458673],"study_design_scores_gemma":[0.000020736938,0.0000152514285,0.00034650424,0.000017526234,0.000010495977,0.000019438998,0.00002916904,0.98183006,0.00009739616,0.015564532,0.0020397499,0.000009198231],"about_ca_topic_score_codex":0.028662154,"about_ca_topic_score_gemma":0.02303713,"teacher_disagreement_score":0.028662154,"about_ca_system_score_codex":0.003873939,"about_ca_system_score_gemma":0.0024615817,"threshold_uncertainty_score":0.056990623},"labels":[],"label_agreement":null},{"id":"W2567598518","doi":"10.1016/j.cor.2019.05.008","title":"An RLT approach for solving the binary-constrained mixed linear complementarity problem","year":2019,"lang":"en","type":"article","venue":"Computers & Operations Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Group for Research in Decision Analysis; Polytechnique Montréal; Université du Québec à Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Mixed complementarity problem; Complementarity theory; Linear complementarity problem; Complementarity (molecular biology); Linear programming; Mathematical optimization; Binary number; Mathematics; Generalized linear mixed model; Applied mathematics; Computer science; Nonlinear system","score_opus":0.08351850770169487,"score_gpt":0.37789104834462384,"score_spread":0.29437254064292895,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2567598518","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00044825388,0.00007217849,0.9948909,0.00011045136,0.000032995147,0.00004050067,0.000018862025,0.00007061098,0.004315128],"genre_scores_gemma":[0.07743166,0.00032010998,0.9124141,0.00043476763,0.00012180255,0.00057634636,0.00014510046,0.00019760214,0.008358511],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9987815,0.0004868305,0.000052019044,0.00017135903,0.00042261672,0.00008572343],"domain_scores_gemma":[0.9990663,0.00056780275,0.00009136228,0.00006850887,0.00017379096,0.000032299355],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001772999,0.0013820273,0.0011330276,0.0010315783,0.00064807857,0.0012901059,0.0016605242,0.0013988501,0.012217256],"category_scores_gemma":[0.0039070793,0.00066852843,0.0015209892,0.001324135,0.0013154766,0.0014416861,0.0019627335,0.0027185436,0.0018675845],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000035086727,0.00007503371,0.00015716803,0.00032566284,0.000054627242,0.0001621888,0.000095030504,0.75189173,0.002773111,0.17650226,0.004317063,0.0636111],"study_design_scores_gemma":[0.000014445639,0.000025399473,0.00002295877,0.000018764564,0.0000077105815,0.00003724242,0.000012056668,0.96869993,0.00047361475,0.02700117,0.0036779218,0.000008868538],"about_ca_topic_score_codex":0.004017984,"about_ca_topic_score_gemma":0.0037061137,"teacher_disagreement_score":0.012217256,"about_ca_system_score_codex":0.0008568648,"about_ca_system_score_gemma":0.0016028625,"threshold_uncertainty_score":0.040870786},"labels":[],"label_agreement":null},{"id":"W2568124026","doi":"10.1155/2017/1918903","title":"Time-Dependent Vehicle Routing of Free Pickup and Delivery Service in Flight Ticket Sales Companies Based on Carbon Emissions","year":2017,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Hebei University; National Natural Science Foundation of China","keywords":"Ticket; Vehicle routing problem; Pickup; Routing (electronic design automation); Heuristic; Operations research; Computer science; Transport engineering; Traffic congestion; Service (business); Fuel efficiency; Engineering; Computer network; Business; Automotive engineering; Marketing","score_opus":0.013192115869882596,"score_gpt":0.2541595312307851,"score_spread":0.24096741536090252,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2568124026","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.817761,0.0005437297,0.16817978,0.00061048573,0.00007769772,0.00035713727,0.00059763965,0.0002114143,0.011661151],"genre_scores_gemma":[0.9468061,0.00032704967,0.04831791,0.000037823524,0.000010779704,0.00010882776,0.0003643617,0.00005612529,0.0039710756],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99964654,0.00013631763,0.000010061649,0.000058149973,0.00004592174,0.000103133716],"domain_scores_gemma":[0.99925977,0.0004602531,0.00007890258,0.000027414984,0.00005877157,0.00011490707],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007224422,0.0008900389,0.00079225143,0.0009416003,0.0009555831,0.0013129145,0.0010656294,0.001366436,0.0033440066],"category_scores_gemma":[0.0014800148,0.00067822373,0.0010183224,0.0012049284,0.000674834,0.0010501732,0.0006273447,0.0007247588,0.0001548277],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000056818622,0.000029073826,0.00023608727,0.000019505344,0.000011769865,0.000062879524,0.000018840918,0.99420315,0.00045358352,0.0024737157,0.00022162827,0.0022129605],"study_design_scores_gemma":[0.00002301791,0.000049092516,0.0002499437,0.000005563396,0.000014002017,0.000025679146,0.0000686419,0.99646866,0.00055903266,0.0021687609,0.00035848498,0.000009100857],"about_ca_topic_score_codex":0.025093934,"about_ca_topic_score_gemma":0.025590248,"teacher_disagreement_score":0.025093934,"about_ca_system_score_codex":0.0032618712,"about_ca_system_score_gemma":0.0016478781,"threshold_uncertainty_score":0.049895763},"labels":[],"label_agreement":null},{"id":"W2570390297","doi":"10.1287/moor.2015.0752","title":"An Improved Integrality Gap for Asymmetric TSP Paths","year":2016,"lang":"en","type":"article","venue":"Mathematics of Operations Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Mathematics; Combinatorics; Linear programming relaxation; Conjecture; Bounded function; Spanning tree; Path (computing); Tree (set theory); Minimum spanning tree; Relaxation (psychology); Connection (principal bundle); Space (punctuation); Linear programming; Discrete mathematics; Mathematical optimization; Computer science","score_opus":0.13116101593447474,"score_gpt":0.4304011552015642,"score_spread":0.2992401392670895,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2570390297","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.123218544,0.0029264952,0.8103635,0.0034820468,0.0004162374,0.00025690056,0.0007439486,0.002451444,0.05614097],"genre_scores_gemma":[0.6311543,0.0024693063,0.34954205,0.001474953,0.00048283132,0.00034614507,0.0017599211,0.0012642551,0.0115062585],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99607784,0.000602652,0.0001606079,0.00093995477,0.0014025469,0.0008163459],"domain_scores_gemma":[0.9896478,0.0065733423,0.00068131386,0.0016590345,0.0008239749,0.0006146213],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027962015,0.0018308747,0.001998111,0.0019302865,0.0016707313,0.0038380143,0.004117779,0.0020025661,0.010346817],"category_scores_gemma":[0.01869994,0.0009156246,0.0019210806,0.0030704602,0.0018991829,0.010332189,0.005151257,0.00778466,0.002032828],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001313043,0.0007506453,0.0029437384,0.0008090288,0.00013825763,0.0004925073,0.00094663055,0.4045121,0.018960671,0.31936568,0.01818933,0.23157838],"study_design_scores_gemma":[0.00006754313,0.00022509978,0.00064465415,0.00010708591,0.000075532844,0.00039276516,0.0001832037,0.78990793,0.0048887576,0.193439,0.010034791,0.000033615837],"about_ca_topic_score_codex":0.0024260704,"about_ca_topic_score_gemma":0.002188164,"teacher_disagreement_score":0.010346817,"about_ca_system_score_codex":0.0028810205,"about_ca_system_score_gemma":0.0026436977,"threshold_uncertainty_score":0.03461361},"labels":[],"label_agreement":null},{"id":"W2573661439","doi":"","title":"Stabilized Dynamic Constraint Aggregation for Solving Set Partitioning Problems","year":2011,"lang":"en","type":"article","venue":"PolyPublie (École Polytechnique de Montréal)","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal; Polytechnique Montréal; Group for Research in Decision Analysis","funders":"","keywords":"Degeneracy (biology); Mathematical optimization; Constraint (computer-aided design); Computer science; Scheduling (production processes); Set (abstract data type); Column generation; Dual (grammatical number); Constraint satisfaction problem; Variable (mathematics); Space (punctuation); Projection (relational algebra); Mathematics; Algorithm; Artificial intelligence","score_opus":0.023070708197838442,"score_gpt":0.24788037160537502,"score_spread":0.22480966340753658,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2573661439","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020802384,0.00022058468,0.9766718,0.00009340936,0.00001840171,0.00006409379,0.00005831806,0.00018052426,0.0018903703],"genre_scores_gemma":[0.39570278,0.00032362566,0.60172135,0.00007129933,0.00004522493,0.00042464517,0.0003031554,0.00011014909,0.0012978482],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993749,0.00030302312,0.000023371893,0.000069718386,0.0001862239,0.000042743683],"domain_scores_gemma":[0.9990289,0.0006339077,0.00008839128,0.00008398813,0.00012592909,0.000039002498],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009818152,0.00088999607,0.0007620868,0.00080091396,0.0005575306,0.00071312964,0.00063811836,0.00052292563,0.0015153425],"category_scores_gemma":[0.0023889078,0.00042461252,0.00058886217,0.0012866884,0.0004990503,0.00052566925,0.0010210396,0.0010297901,0.0002042084],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000040526233,0.000037009297,0.00033413872,0.00009368128,0.00003228101,0.00003303922,0.000055684635,0.92279136,0.002883368,0.012645628,0.00082894455,0.06022446],"study_design_scores_gemma":[0.000006432768,0.000010345081,0.000044703815,0.0000040231052,0.0000024644826,0.000005488779,0.000004915832,0.99577063,0.00061489263,0.003121758,0.00041212258,0.0000021785518],"about_ca_topic_score_codex":0.0045637325,"about_ca_topic_score_gemma":0.004848798,"teacher_disagreement_score":0.0045637325,"about_ca_system_score_codex":0.0007070842,"about_ca_system_score_gemma":0.0011284712,"threshold_uncertainty_score":0.00907433},"labels":[],"label_agreement":null},{"id":"W2577191082","doi":"10.4230/lipics.isaac.2016.56","title":"Approximation Algorithms for Capacitated k-Travelling Repairmen Problems","year":2016,"lang":"en","type":"article","venue":"DROPS (Schloss Dagstuhl – Leibniz Center for Informatics)","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Alberta Innovates","keywords":"Rounding; Vehicle routing problem; Computer science; Approximation algorithm; Latency (audio); Mathematical optimization; Algorithm; Travelling salesman problem; Constant (computer programming); Routing (electronic design automation); Mathematics; Computer network; Telecommunications","score_opus":0.027757152322522058,"score_gpt":0.25974010107693307,"score_spread":0.231982948754411,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2577191082","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.024549251,0.0029860986,0.9533998,0.0014150722,0.00021386392,0.00015193074,0.00060684583,0.0017721421,0.014905126],"genre_scores_gemma":[0.44542074,0.002745514,0.5362707,0.0007749235,0.00039551783,0.0004060601,0.002293963,0.0008213685,0.01087121],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976978,0.0006990702,0.00012515865,0.0004988988,0.00046983152,0.0005093158],"domain_scores_gemma":[0.99550134,0.0028949883,0.0003345253,0.00070067006,0.0003526115,0.00021587766],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00215116,0.0023256782,0.0018737002,0.0011642383,0.00089750526,0.0026178292,0.0042940024,0.0022850123,0.009383113],"category_scores_gemma":[0.010595344,0.00077492965,0.0017149502,0.0032519656,0.001035465,0.0053401017,0.0020781197,0.0041034217,0.0022452755],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00042871365,0.0003048293,0.0011322418,0.00045505952,0.00011677145,0.00011559498,0.00031806316,0.7835944,0.0012408033,0.089484714,0.016346138,0.10646261],"study_design_scores_gemma":[0.000055766806,0.000045298766,0.000114907874,0.000033205633,0.00002189745,0.00006469593,0.00008514817,0.9181794,0.00040170044,0.07764009,0.003345125,0.000012750761],"about_ca_topic_score_codex":0.005822957,"about_ca_topic_score_gemma":0.0068322946,"teacher_disagreement_score":0.009383113,"about_ca_system_score_codex":0.0032928337,"about_ca_system_score_gemma":0.0021274085,"threshold_uncertainty_score":0.031389654},"labels":[],"label_agreement":null},{"id":"W2579963571","doi":"","title":"Integer Linear Programming Models for a Cement Delivery Problem","year":2011,"lang":"en","type":"article","venue":"PolyPublie (École Polytechnique de Montréal)","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Integer programming; Linear programming; Mathematical optimization; Integer (computer science); Vehicle routing problem; Routing (electronic design automation); Computer science; Branch and price; Branch and cut; Mathematics; Computer network","score_opus":0.028601299560401826,"score_gpt":0.2438993260203359,"score_spread":0.21529802645993407,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2579963571","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011489666,0.0016622638,0.96474075,0.0013706519,0.00017718911,0.00022775934,0.0008772853,0.00037823006,0.019076195],"genre_scores_gemma":[0.3501286,0.0050334553,0.6014465,0.0007517396,0.0005996535,0.0017633706,0.0021292716,0.0003465369,0.037800867],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99840987,0.0007705167,0.000072315146,0.00024256694,0.00031428237,0.00019034994],"domain_scores_gemma":[0.9975587,0.0018813628,0.00022591958,0.000058344882,0.0001886369,0.00008703267],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020798242,0.0026002738,0.0013729485,0.0009217313,0.0007008086,0.0026137515,0.0020896967,0.0026530444,0.009604988],"category_scores_gemma":[0.004022033,0.0009270471,0.0015106775,0.002335698,0.0010299643,0.0017879128,0.0013681868,0.003598636,0.001666185],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000042921656,0.000102124635,0.00014332183,0.00012769208,0.000025521154,0.000112694,0.00007716495,0.9355046,0.00031218564,0.050974805,0.0031451143,0.009431861],"study_design_scores_gemma":[0.000029655077,0.000024587805,0.000036861667,0.00001493663,0.000010055703,0.00002108579,0.000025631822,0.97959775,0.00010240407,0.01693317,0.0031968115,0.0000071907234],"about_ca_topic_score_codex":0.01176545,"about_ca_topic_score_gemma":0.012033195,"teacher_disagreement_score":0.01176545,"about_ca_system_score_codex":0.0023435026,"about_ca_system_score_gemma":0.002054108,"threshold_uncertainty_score":0.03213191},"labels":[],"label_agreement":null},{"id":"W2581421322","doi":"10.1155/2017/9285403","title":"Exact Algorithm for the Capacitated Team Orienteering Problem with Time Windows","year":2017,"lang":"en","type":"article","venue":"Mathematical Problems in Engineering","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Groupe de recherche interuniversitaire en limnologie; Korea Institute of Industrial Technology","keywords":"Orienteering; Mathematical optimization; Scheme (mathematics); Heuristic; Enumeration; Computer science; Algorithm; Mathematics; Combinatorics","score_opus":0.014422961881450973,"score_gpt":0.23947638920310838,"score_spread":0.2250534273216574,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2581421322","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014578824,0.00027369428,0.9742227,0.000285383,0.00006719587,0.00012599192,0.0001947335,0.00078095385,0.009470573],"genre_scores_gemma":[0.27588245,0.00034462917,0.71431345,0.00014426002,0.000055270542,0.00041888983,0.00061775674,0.00026081217,0.007962424],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994617,0.000099326615,0.000026501384,0.0001166685,0.0001458145,0.00015000375],"domain_scores_gemma":[0.99924004,0.000433401,0.00006374772,0.0000892505,0.00012540772,0.00004820244],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007350585,0.0009855161,0.0011190206,0.0007003329,0.0007068652,0.0013672959,0.0014506426,0.0013588493,0.012524086],"category_scores_gemma":[0.0025857622,0.00056145544,0.000733142,0.0013212985,0.00061171857,0.0018429224,0.001350014,0.0014757006,0.001700202],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010962104,0.00010580134,0.00043167954,0.00015569375,0.000029100363,0.00008339959,0.00011985329,0.82422155,0.0013087367,0.043948993,0.005133966,0.124351725],"study_design_scores_gemma":[0.000039610306,0.000026677002,0.00007297244,0.00000992365,0.0000067632122,0.000024374875,0.000031806627,0.9776087,0.0002875717,0.020563444,0.0013219224,0.0000061928804],"about_ca_topic_score_codex":0.009440409,"about_ca_topic_score_gemma":0.009563188,"teacher_disagreement_score":0.012524086,"about_ca_system_score_codex":0.001395014,"about_ca_system_score_gemma":0.0028456496,"threshold_uncertainty_score":0.041897297},"labels":[],"label_agreement":null},{"id":"W2584833029","doi":"10.1016/j.cor.2017.01.020","title":"Branch-and-price and adaptive large neighborhood search for the truck and trailer routing problem with time windows","year":2017,"lang":"en","type":"article","venue":"Computers & Operations Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":69,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"Austrian Science Fund","keywords":"Truck; Trailer; Computer science; Vehicle routing problem; Metaheuristic; Routing (electronic design automation); Branch and price; Set (abstract data type); Order (exchange); Service (business); Operations research; Variable neighborhood search; Transport engineering; Mathematical optimization; Automotive engineering; Integer programming; Business; Algorithm; Engineering; Computer network; Mathematics","score_opus":0.05007025236499678,"score_gpt":0.3340841521052146,"score_spread":0.2840138997402178,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2584833029","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10769746,0.001566692,0.88104975,0.0005743165,0.00011019497,0.00019622452,0.00016410284,0.0002553236,0.00838601],"genre_scores_gemma":[0.595595,0.00091181824,0.39693555,0.00014344785,0.00011175128,0.0004910356,0.0003911074,0.00014834701,0.005271973],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99948955,0.00028944915,0.000018537918,0.00006215945,0.00007272195,0.000067525005],"domain_scores_gemma":[0.9981641,0.0014934042,0.0001286164,0.00004332534,0.00008019103,0.00009030069],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014172635,0.00088100485,0.0014042893,0.0009036801,0.00056053844,0.0008124614,0.0012917245,0.0011429802,0.003035479],"category_scores_gemma":[0.0038864918,0.00054181507,0.00065257517,0.0014313379,0.00071595225,0.001373565,0.0008056071,0.0012428045,0.0002837862],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008928894,0.00008264967,0.00030011206,0.00004467204,0.000023448109,0.000039206825,0.000021318254,0.97621506,0.00021444565,0.00828194,0.0006731073,0.014014659],"study_design_scores_gemma":[0.000012882924,0.000022576987,0.00003887643,0.000002885968,0.0000037398986,0.0000043530545,0.000005281587,0.9968592,0.000054798733,0.002842327,0.00015125393,0.0000018627027],"about_ca_topic_score_codex":0.007520092,"about_ca_topic_score_gemma":0.006748965,"teacher_disagreement_score":0.007520092,"about_ca_system_score_codex":0.0010906003,"about_ca_system_score_gemma":0.0012806297,"threshold_uncertainty_score":0.0149526},"labels":[],"label_agreement":null},{"id":"W2586734565","doi":"10.1007/s10846-017-0496-7","title":"Non-prespecified Starting Depot Formulations for Minimum-Distance Trajectory Optimization in Patrolling Problem","year":2017,"lang":"en","type":"article","venue":"Journal of Intelligent & Robotic Systems","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Concordia University","funders":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Depot; Patrolling; Mathematical optimization; Trajectory; Sequence (biology); Mathematics; Viewpoints; Computer science","score_opus":0.04332280352912892,"score_gpt":0.3005340156381482,"score_spread":0.25721121210901926,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2586734565","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015147063,0.00027066522,0.9748263,0.0001717723,0.00006526603,0.00014761847,0.0001537644,0.00008127355,0.009136294],"genre_scores_gemma":[0.510249,0.0007433029,0.4744695,0.00020745884,0.00011456168,0.0006807583,0.00078902073,0.00036783688,0.012378531],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994498,0.00022114119,0.00002917259,0.00008909275,0.00013957234,0.00007125388],"domain_scores_gemma":[0.99877125,0.0007232373,0.00011182366,0.00009738367,0.00023050593,0.00006585603],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015465328,0.0014956767,0.0014798661,0.0007134216,0.0006373634,0.0019136693,0.0022831822,0.0020497835,0.006451384],"category_scores_gemma":[0.00373812,0.00084872625,0.0014524941,0.0008213287,0.0007297763,0.0019035492,0.0017098899,0.0027564883,0.0008747727],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000055943416,0.00007982557,0.00023721403,0.00016212516,0.000022248087,0.00009930218,0.000058056397,0.9583639,0.0010935337,0.027517764,0.0012580855,0.011052027],"study_design_scores_gemma":[0.000008079194,0.000023440456,0.00006058974,0.000014664536,0.0000053255067,0.000010859361,0.000014463505,0.9934042,0.00026192612,0.005702518,0.0004895447,0.000004400611],"about_ca_topic_score_codex":0.0031614495,"about_ca_topic_score_gemma":0.004568733,"teacher_disagreement_score":0.006451384,"about_ca_system_score_codex":0.0010043982,"about_ca_system_score_gemma":0.0015923847,"threshold_uncertainty_score":0.021582067},"labels":[],"label_agreement":null},{"id":"W2587219052","doi":"10.1111/itor.12378","title":"Metaheuristics for solving the biobjective single‐path multicommodity communication flow problem","year":2017,"lang":"en","type":"article","venue":"International Transactions in Operational Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Mathematical optimization; Metaheuristic; Multi-objective optimization; Context (archaeology); Variable neighborhood search; Path (computing); Computer science; Node (physics); Pareto principle; Mathematics","score_opus":0.13915869188773056,"score_gpt":0.42198457773962467,"score_spread":0.2828258858518941,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2587219052","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05616325,0.0013516129,0.9351772,0.00039480976,0.00007826255,0.00016209095,0.000108720014,0.00022645069,0.0063375593],"genre_scores_gemma":[0.5579872,0.0008769311,0.43691286,0.00019095819,0.000068238725,0.00053618586,0.00020245886,0.000073173935,0.0031519872],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996107,0.00019034313,0.000019268244,0.00004228567,0.00008852934,0.000048834067],"domain_scores_gemma":[0.99924004,0.00052732194,0.00009313389,0.00002796888,0.000078973586,0.00003250169],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012447502,0.0011436053,0.00085681863,0.0016492851,0.0004223786,0.0009404791,0.0010683556,0.0013948141,0.0012176352],"category_scores_gemma":[0.0018778588,0.00042275948,0.00092716806,0.0013494286,0.0005860701,0.0005688895,0.0007215257,0.0009919268,0.00016626803],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002039069,0.00003515385,0.00018049298,0.00003475109,0.000027653163,0.000019039157,0.000015318363,0.9840507,0.00032685147,0.0045563844,0.0002538557,0.0104794605],"study_design_scores_gemma":[0.00000816453,0.00002172359,0.000044152108,0.000009343272,0.0000063229145,0.00000509553,0.000009329631,0.99780554,0.00013550223,0.0016895456,0.00026310992,0.0000020481816],"about_ca_topic_score_codex":0.0054847235,"about_ca_topic_score_gemma":0.004308471,"teacher_disagreement_score":0.0054847235,"about_ca_system_score_codex":0.0012628229,"about_ca_system_score_gemma":0.0014515235,"threshold_uncertainty_score":0.010905623},"labels":[],"label_agreement":null},{"id":"W2587954382","doi":"10.1109/ipdps.2017.90","title":"Distributed Vehicle Routing Approximation","year":2017,"lang":"en","type":"article","venue":"","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Vehicle routing problem; Computer science; Approximation algorithm; Set (abstract data type); Context (archaeology); Mathematical optimization; Routing (electronic design automation); Linear programming; Duality (order theory); Graph; Theoretical computer science; Algorithm; Mathematics; Combinatorics; Computer network","score_opus":0.023180131873644986,"score_gpt":0.2744738383487814,"score_spread":0.25129370647513644,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2587954382","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012281294,0.0004162507,0.97680783,0.00035208848,0.00011589108,0.0000385033,0.00014862185,0.0007018315,0.009137682],"genre_scores_gemma":[0.5965673,0.0009540624,0.38696507,0.0002792157,0.00018523446,0.00024550557,0.00081325206,0.00026163133,0.013728696],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99915767,0.00020729557,0.000028136876,0.000250558,0.00021482412,0.00014147833],"domain_scores_gemma":[0.9987031,0.0006492796,0.00010355573,0.0002586094,0.00021990006,0.00006546019],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008547723,0.0008246228,0.0012542546,0.0004981955,0.00057461363,0.0012740988,0.0018157847,0.0010437529,0.0059619504],"category_scores_gemma":[0.0036937864,0.0003045372,0.00079078623,0.0011751491,0.0005125287,0.0015161638,0.0011682054,0.0012688676,0.0010835619],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000117103205,0.00006353162,0.000450457,0.000083585954,0.000030237337,0.000057022084,0.000030141866,0.89886314,0.0009161291,0.041956823,0.0054094684,0.052022237],"study_design_scores_gemma":[0.000017137001,0.000013190632,0.000043032072,0.0000036139334,0.000005100742,0.000020650697,0.0000087638155,0.98355454,0.00023858661,0.01430398,0.0017893434,0.0000021057183],"about_ca_topic_score_codex":0.003694936,"about_ca_topic_score_gemma":0.0035384882,"teacher_disagreement_score":0.0059619504,"about_ca_system_score_codex":0.0016175176,"about_ca_system_score_gemma":0.0016774277,"threshold_uncertainty_score":0.019944727},"labels":[],"label_agreement":null},{"id":"W2589810429","doi":"10.1080/00207543.2017.1285075","title":"Physical Internet, conventional and hybrid logistic systems: a routing optimisation-based comparison using the Eastern Canada road network case study","year":2017,"lang":"en","type":"article","venue":"International Journal of Production Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":89,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Truck; Container (type theory); Modular design; Routing (electronic design automation); Computer science; The Internet; Transport engineering; Work (physics); Greenhouse gas; Operations research; Engineering; Automotive engineering; Computer network","score_opus":0.18328565131983593,"score_gpt":0.4396937165274717,"score_spread":0.25640806520763576,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2589810429","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96268106,0.0006889901,0.01661004,0.0003237508,0.000024798544,0.00021598398,0.00083903765,0.00011807956,0.018498223],"genre_scores_gemma":[0.99094534,0.0003097216,0.0057143066,0.000020216079,0.000006247587,0.00003821352,0.0003406912,0.000018956829,0.0026062974],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99949837,0.00017825204,0.000016962933,0.00006227013,0.000108792796,0.00013537765],"domain_scores_gemma":[0.9986852,0.00082558504,0.00009166284,0.000065544846,0.0002577843,0.00007430966],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009907471,0.0008078072,0.0005297301,0.0013315488,0.000660092,0.0014266506,0.0011726951,0.00092824775,0.002198828],"category_scores_gemma":[0.0022826707,0.0002559359,0.0004956198,0.0019572088,0.00095758354,0.000934495,0.0006303642,0.00059222855,0.00013921397],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000076038625,0.00006669705,0.0026770395,0.000048059974,0.000026738793,0.00008960326,0.000021934404,0.9907477,0.0001569112,0.0020337428,0.0003531469,0.0037023579],"study_design_scores_gemma":[0.000022975588,0.00010085811,0.0032706992,0.000007827405,0.00002261887,0.00002912608,0.00021752862,0.994645,0.00022274515,0.0007658358,0.00068164297,0.000013165868],"about_ca_topic_score_codex":0.5702548,"about_ca_topic_score_gemma":0.54600877,"teacher_disagreement_score":0.4297452,"about_ca_system_score_codex":0.0079899905,"about_ca_system_score_gemma":0.0031210347,"threshold_uncertainty_score":0.8645521},"labels":[],"label_agreement":null},{"id":"W2592065458","doi":"10.4018/978-1-4666-1589-2.ch018","title":"The Traveling Salesman Problem, the Vehicle Routing Problem, and Their Impact on Combinatorial Optimization","year":2012,"lang":"en","type":"book-chapter","venue":"IGI Global eBooks","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"","keywords":"Travelling salesman problem; Vehicle routing problem; Heuristics; Combinatorial optimization; Metaheuristic; Extremal optimization; Mathematical optimization; 2-opt; Lin–Kernighan heuristic; Heuristic; Routing (electronic design automation); Computer science; Quadratic assignment problem; Traveling purchaser problem; Bottleneck traveling salesman problem; Optimization problem; Mathematics; Meta-optimization","score_opus":0.014491528879275187,"score_gpt":0.24421397781514273,"score_spread":0.22972244893586755,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2592065458","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0037811294,0.26117182,0.073489845,0.0066376235,0.002937265,0.00008095589,0.00028092484,0.00028583288,0.6513346],"genre_scores_gemma":[0.04883675,0.4016383,0.10777594,0.003007544,0.0026726855,0.00019102426,0.0007497605,0.00041610596,0.4347118],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9996731,0.000061559054,0.000010426501,0.000051826253,0.00017766861,0.000025402702],"domain_scores_gemma":[0.9997522,0.00015732297,0.000013238318,0.000017643877,0.000040436567,0.00001922123],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032767552,0.0011479302,0.00056807534,0.0009369119,0.0006415738,0.0030005646,0.0011212303,0.0014216731,0.01669126],"category_scores_gemma":[0.00078597147,0.0004916279,0.0005309732,0.0028297552,0.0013673154,0.0036057457,0.0010110702,0.0025144937,0.0053884946],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000020003023,0.000075793236,0.00014450192,0.0008433398,0.000022289481,0.00014519053,0.00023732077,0.013432402,0.000809566,0.49863297,0.13800682,0.34762973],"study_design_scores_gemma":[0.00000640528,0.000026905043,0.0002720199,0.00045984093,0.000011766157,0.00027860163,0.00016658178,0.006264781,0.00027156033,0.22099105,0.77123344,0.000017107352],"about_ca_topic_score_codex":0.0021102137,"about_ca_topic_score_gemma":0.0035350865,"teacher_disagreement_score":0.01669126,"about_ca_system_score_codex":0.0012737778,"about_ca_system_score_gemma":0.0013154254,"threshold_uncertainty_score":0.05583787},"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,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_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)","score_opus":0.23291961149396032,"score_gpt":0.4196467031617125,"score_spread":0.18672709166775217,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2593704234","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.052235007,0.00071419904,0.91964096,0.0022622072,0.0002267471,0.00007115371,0.0003018459,0.00006085751,0.024486993],"genre_scores_gemma":[0.83674777,0.0015154959,0.10078695,0.00032867078,0.00029866112,0.00014160053,0.00036567333,0.000117105046,0.059698053],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994636,0.0002636517,0.000016619799,0.000124939,0.000071803384,0.00005939423],"domain_scores_gemma":[0.9991272,0.00061261526,0.000085127496,0.000043461707,0.00008262376,0.000048953934],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012416017,0.00085610256,0.00070610375,0.0005121241,0.0004247564,0.0015672924,0.0014594439,0.001416802,0.003719872],"category_scores_gemma":[0.004137509,0.00053881056,0.00064654334,0.0009065813,0.0013005865,0.002863532,0.0011918617,0.001173725,0.00028722736],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006489142,0.00004735832,0.00046933102,0.00014060742,0.000029738683,0.00016824252,0.000050026607,0.8117636,0.00059465005,0.17276983,0.0020313275,0.011870491],"study_design_scores_gemma":[0.000019215146,0.00002028713,0.00018885075,0.000010983839,0.00001273867,0.000067825276,0.00005538769,0.9147674,0.00026340358,0.082215115,0.0023653028,0.000013477024],"about_ca_topic_score_codex":0.0047602532,"about_ca_topic_score_gemma":0.0028665399,"teacher_disagreement_score":0.0047602532,"about_ca_system_score_codex":0.0015440984,"about_ca_system_score_gemma":0.0012288486,"threshold_uncertainty_score":0.012444198},"labels":[],"label_agreement":null},{"id":"W2595214333","doi":"10.1111/itor.12403","title":"Heuristics for tactical time slot management: a periodic vehicle routing problem view","year":2017,"lang":"en","type":"article","venue":"International Transactions in Operational Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":44,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Computer Research Institute of Montréal; Polytechnique Montréal; Université de Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Heuristics; Vehicle routing problem; Time horizon; Computer science; Schedule; Benchmark (surveying); Routing (electronic design automation); Heuristic; Mathematical optimization; Selection (genetic algorithm); Operations research; Plan (archaeology); Mathematics; Computer network; Artificial intelligence","score_opus":0.07925687692376843,"score_gpt":0.4127364920905471,"score_spread":0.33347961516677865,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2595214333","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07936608,0.0014599465,0.9074686,0.0005532252,0.000111600406,0.00022780643,0.00019124807,0.00031870438,0.010302745],"genre_scores_gemma":[0.6915813,0.0009643256,0.3037875,0.00011926351,0.00010368317,0.00024015893,0.00026177702,0.00007570215,0.0028662507],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99935144,0.00034382165,0.000020791345,0.00010130559,0.0000921197,0.00009048134],"domain_scores_gemma":[0.9987728,0.0008434662,0.00013737487,0.000104506296,0.00008207472,0.000059725608],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011643065,0.00066333904,0.0007592533,0.00066803483,0.00037677112,0.001061172,0.001252647,0.0008515267,0.0030442683],"category_scores_gemma":[0.0022388038,0.00044678443,0.00064533704,0.0008957014,0.00055385823,0.0009852701,0.0004596519,0.0008366669,0.00020166762],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000054991677,0.00007216341,0.0002706612,0.000087635504,0.000040117597,0.00005713487,0.00003931262,0.95560724,0.0005830539,0.02054309,0.0012984467,0.021346252],"study_design_scores_gemma":[0.000027159344,0.000071607494,0.0001459538,0.000014412077,0.000015173256,0.000032639768,0.000037686375,0.98530966,0.00039737672,0.01223629,0.0017051984,0.0000067918822],"about_ca_topic_score_codex":0.0041691572,"about_ca_topic_score_gemma":0.003894856,"teacher_disagreement_score":0.0041691572,"about_ca_system_score_codex":0.0008677693,"about_ca_system_score_gemma":0.0013368858,"threshold_uncertainty_score":0.010184109},"labels":[],"label_agreement":null},{"id":"W2595282281","doi":"","title":"Some Applications of the Generalized Vehicle Routing Problem","year":2008,"lang":"en","type":"article","venue":"Les Cahiers du GERAD","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"","keywords":"Vehicle routing problem; Travelling salesman problem; Vertex (graph theory); Routing (electronic design automation); Computer science; Mathematical optimization; Set (abstract data type); Extension (predicate logic); Variety (cybernetics); Mathematics; Theoretical computer science; Graph; Computer network; Artificial intelligence","score_opus":0.011344872875552733,"score_gpt":0.21834270917941423,"score_spread":0.20699783630386148,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2595282281","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.022268152,0.020495288,0.75131595,0.009611484,0.0010468712,0.00014037042,0.0004871287,0.00032243703,0.19431233],"genre_scores_gemma":[0.4887671,0.039731815,0.38766214,0.002577361,0.0031474577,0.00044786063,0.0012061577,0.00031796502,0.076142125],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990803,0.00037444287,0.00004630071,0.00015147448,0.00026202886,0.00008539825],"domain_scores_gemma":[0.9993012,0.0003927856,0.00006194921,0.00008047701,0.00011754134,0.00004601159],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011040311,0.0010624724,0.0006699315,0.00095920294,0.0010401589,0.001470798,0.0011401029,0.0018017053,0.0075745806],"category_scores_gemma":[0.0029954566,0.00038773697,0.0014561609,0.002324781,0.0013646706,0.0018359098,0.0017425168,0.0022549392,0.001156137],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000021701393,0.000036799876,0.00024538505,0.00015182361,0.000026573056,0.0002488249,0.00017312182,0.092484154,0.00039461273,0.85063314,0.012972552,0.042611245],"study_design_scores_gemma":[0.00001970551,0.000027068812,0.00023236679,0.000052066604,0.000010830587,0.00023920643,0.00008468307,0.10146134,0.00017208964,0.8149005,0.08278252,0.000017626448],"about_ca_topic_score_codex":0.0052081896,"about_ca_topic_score_gemma":0.0038601956,"teacher_disagreement_score":0.0075745806,"about_ca_system_score_codex":0.0013395369,"about_ca_system_score_gemma":0.0008839512,"threshold_uncertainty_score":0.025339544},"labels":[],"label_agreement":null},{"id":"W2595691866","doi":"","title":"Enhanced Branch-and-Price-and-Cut for Vehicle Routing with Split Deliveries and Time Windows","year":2010,"lang":"en","type":"other","venue":"PolyPublie (École Polytechnique de Montréal)","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Group for Research in Decision Analysis; Université Laval","funders":"","keywords":"Vehicle routing problem; Column generation; Tabu search; Path (computing); Mathematical optimization; Routing (electronic design automation); Computer science; Column (typography); Mathematics; Algorithm","score_opus":0.0060955354736997175,"score_gpt":0.21597083292230068,"score_spread":0.20987529744860095,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2595691866","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013524827,0.00027218787,0.9832417,0.000094859526,0.000034643304,0.00006735766,0.000045142555,0.00010799475,0.0026113193],"genre_scores_gemma":[0.2296649,0.0004852772,0.7641152,0.000078404286,0.000063231,0.00021699627,0.00020721575,0.00013493294,0.005033747],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991967,0.0003579612,0.000022730048,0.00008560913,0.00024538953,0.00009160376],"domain_scores_gemma":[0.9989017,0.0007563514,0.0000929933,0.000080584745,0.00012333988,0.000044984317],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013940663,0.0009343601,0.0010095624,0.00054869073,0.0004487378,0.0008564354,0.0011611604,0.000749097,0.0038770041],"category_scores_gemma":[0.003262966,0.00052056665,0.0006613743,0.0011635415,0.0006137315,0.001658587,0.0009200602,0.0016846459,0.00053068175],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012190385,0.000090960384,0.00035817712,0.00010870846,0.000041697796,0.000077411234,0.000048830618,0.8838544,0.0016635582,0.031322606,0.0016506778,0.08066103],"study_design_scores_gemma":[0.000012541332,0.000025850668,0.000050575996,0.000003979047,0.0000054986626,0.00001861723,0.0000053565022,0.99118817,0.0004033549,0.007490586,0.0007927606,0.0000025918316],"about_ca_topic_score_codex":0.003103992,"about_ca_topic_score_gemma":0.0034255078,"teacher_disagreement_score":0.0038770041,"about_ca_system_score_codex":0.000730512,"about_ca_system_score_gemma":0.001446385,"threshold_uncertainty_score":0.012969911},"labels":[],"label_agreement":null},{"id":"W2596332277","doi":"10.1007/s10951-017-0513-5","title":"Solving a wind turbine maintenance scheduling problem","year":2017,"lang":"en","type":"article","venue":"Journal of Scheduling","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":30,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Wind power; Mathematical optimization; Solver; Scheduling (production processes); Turbine; Integer programming; Constraint programming; Time horizon; Job shop scheduling; Operations research; Linear programming; Context (archaeology); Industrial engineering; Schedule; Stochastic programming; Engineering; Mathematics","score_opus":0.021650692101702274,"score_gpt":0.2777688907375572,"score_spread":0.2561181986358549,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2596332277","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2740948,0.0008433401,0.7057787,0.0014600048,0.00033673638,0.0002881641,0.00040824487,0.00023775091,0.016552273],"genre_scores_gemma":[0.7692266,0.00056353456,0.21803795,0.00018275339,0.00020579522,0.00024216849,0.00041740376,0.00012744527,0.010996322],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997303,0.00012035528,0.000012026642,0.00006465352,0.000033273984,0.00003940895],"domain_scores_gemma":[0.99894327,0.00083841005,0.00007533014,0.000032676002,0.00006225328,0.000048020767],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009262891,0.000852925,0.0011228786,0.00054952945,0.00055027846,0.00091944373,0.0010873812,0.0021565508,0.004278527],"category_scores_gemma":[0.0025258174,0.0007051431,0.00091965706,0.00080020336,0.00044165293,0.00085606374,0.00060426083,0.0010206661,0.00023306227],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000057807578,0.00007134765,0.0003455432,0.00007724083,0.000035321533,0.00007570533,0.000025974097,0.9819449,0.000541872,0.0037979914,0.0009762769,0.0120499395],"study_design_scores_gemma":[0.000027886268,0.00003500665,0.000086093234,0.0000041552657,0.000011192992,0.000012571868,0.000014115048,0.99657947,0.00013484129,0.0027650422,0.00032712246,0.000002484296],"about_ca_topic_score_codex":0.0070592985,"about_ca_topic_score_gemma":0.0048857257,"teacher_disagreement_score":0.0070592985,"about_ca_system_score_codex":0.00063787936,"about_ca_system_score_gemma":0.0012838617,"threshold_uncertainty_score":0.014313161},"labels":[],"label_agreement":null},{"id":"W2596774957","doi":"","title":"Aircrew Pairings with Possible Repetitions of the Same Flight Number","year":2009,"lang":"en","type":"article","venue":"PolyPublie (École Polytechnique de Montréal)","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Group for Research in Decision Analysis","funders":"","keywords":"Crew; Schedule; Computer science; Heuristic; Set (abstract data type); Phase (matter); Sequence (biology); Pairing; Aircrew; Mathematical optimization; Quality (philosophy); Mathematics; Engineering; Aeronautics; Artificial intelligence","score_opus":0.007451375031077562,"score_gpt":0.22384981064333542,"score_spread":0.21639843561225786,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2596774957","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5946026,0.0002506014,0.3826166,0.00032692347,0.00021886754,0.0005318454,0.00074772467,0.0004929952,0.02021182],"genre_scores_gemma":[0.8641275,0.00009822485,0.12710914,0.00008322565,0.000043797187,0.00025058974,0.0007331223,0.00007460654,0.007479832],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99906987,0.0002609991,0.000051287032,0.00024117739,0.00016768616,0.00020891162],"domain_scores_gemma":[0.9985505,0.00055986305,0.00036810097,0.00030347906,0.00011060821,0.000107469765],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00087400625,0.0006047584,0.0007205389,0.00045562044,0.00077521475,0.00089168985,0.0008343013,0.00097291043,0.007543145],"category_scores_gemma":[0.002748074,0.00042496747,0.0007983015,0.0007719476,0.0005539762,0.0010388279,0.00075981754,0.00068906887,0.0005017512],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014709143,0.0006123689,0.013107679,0.00027243106,0.00018047156,0.0019886254,0.0002994953,0.80519456,0.01356245,0.033193152,0.007513215,0.12260476],"study_design_scores_gemma":[0.00019386943,0.0009981774,0.0064006546,0.000045010416,0.00011170248,0.0011609182,0.0004862217,0.9094264,0.012295545,0.04985738,0.018929526,0.00009457826],"about_ca_topic_score_codex":0.0013910738,"about_ca_topic_score_gemma":0.003087874,"teacher_disagreement_score":0.007543145,"about_ca_system_score_codex":0.0004041743,"about_ca_system_score_gemma":0.00072508276,"threshold_uncertainty_score":0.025234342},"labels":[],"label_agreement":null},{"id":"W2596850612","doi":"","title":"Aircraft Routing Under Different Business Processes","year":2009,"lang":"en","type":"article","venue":"PolyPublie (École Polytechnique de Montréal)","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Group for Research in Decision Analysis","funders":"","keywords":"Adaptability; Robustness (evolution); Computer science; Routing (electronic design automation); Operations research; Set (abstract data type); Engineering; Mathematical optimization; Mathematics; Computer network; Economics","score_opus":0.011901509453573293,"score_gpt":0.2373497645693284,"score_spread":0.22544825511575511,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2596850612","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17457458,0.0003449388,0.8164414,0.0005846816,0.000051263625,0.00009414996,0.000077748766,0.00014168835,0.007689446],"genre_scores_gemma":[0.80294836,0.00045054135,0.19254638,0.00008848635,0.00007568263,0.00015049278,0.00016979132,0.00004374526,0.0035265211],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9968258,0.0014831207,0.00014780778,0.0006215564,0.00064124365,0.00028050618],"domain_scores_gemma":[0.99391526,0.0037649446,0.0008382045,0.0006149536,0.000560693,0.00030597224],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0040630866,0.0008610779,0.00070945435,0.0007307815,0.000925944,0.0035757225,0.0010748012,0.0023741878,0.0015525927],"category_scores_gemma":[0.010993069,0.00044059978,0.0010790157,0.001137644,0.001988956,0.0033670415,0.0017230453,0.0015094961,0.0002246919],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019747128,0.00010818388,0.0012069951,0.000055222255,0.00003082252,0.00013337284,0.00020609688,0.79214424,0.0015062699,0.17950173,0.00038123273,0.024528397],"study_design_scores_gemma":[0.00002679333,0.000034624536,0.00024818748,0.0000051667002,0.000009947266,0.000025115936,0.000089339046,0.95048714,0.000756302,0.047708806,0.0005997068,0.000008718994],"about_ca_topic_score_codex":0.0030587194,"about_ca_topic_score_gemma":0.0017582397,"teacher_disagreement_score":0.0040630866,"about_ca_system_score_codex":0.0016334762,"about_ca_system_score_gemma":0.0012878046,"threshold_uncertainty_score":0.021487892},"labels":[],"label_agreement":null},{"id":"W2597509186","doi":"","title":"Exact methods for multi-objective optimization problems and application to routing problems","year":2010,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Computer science; Mathematical optimization; Routing (electronic design automation); Mathematics; Computer network","score_opus":0.0241780881495648,"score_gpt":0.29531923297433327,"score_spread":0.2711411448247685,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2597509186","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0012196269,0.0021911617,0.99351245,0.00027525963,0.00016077388,0.000019044224,0.000030391864,0.00006696768,0.002524281],"genre_scores_gemma":[0.1536704,0.008868936,0.81564623,0.00051428104,0.00081927236,0.00051765854,0.00024863955,0.00049510755,0.019219518],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988335,0.0005148587,0.000062173945,0.00013435516,0.00039374895,0.00006136831],"domain_scores_gemma":[0.9960789,0.002931467,0.00018945131,0.00022507065,0.00049933104,0.00007586003],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026174546,0.0017307799,0.001915444,0.0013158984,0.0006135017,0.0015939038,0.0016325644,0.0024940358,0.006096192],"category_scores_gemma":[0.009078329,0.0011309604,0.0015244596,0.0023480186,0.0014946773,0.0019805094,0.0016927925,0.003872246,0.0009011071],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000033524182,0.00005635062,0.0001568816,0.0004165594,0.00007365708,0.0000623245,0.000073867486,0.8330737,0.0010556052,0.08381486,0.0028602213,0.07832242],"study_design_scores_gemma":[0.00001257096,0.000014719285,0.00006649765,0.000034007204,0.00001137798,0.000020147756,0.000011906987,0.92934334,0.00025473174,0.066730484,0.003491331,0.000008828469],"about_ca_topic_score_codex":0.0040258667,"about_ca_topic_score_gemma":0.0038592054,"teacher_disagreement_score":0.006096192,"about_ca_system_score_codex":0.0009940743,"about_ca_system_score_gemma":0.001121154,"threshold_uncertainty_score":0.020393789},"labels":[],"label_agreement":null},{"id":"W2597737709","doi":"","title":"What You Should Know about the Vehicle Routing Problem","year":2007,"lang":"en","type":"article","venue":"","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"","keywords":"Vehicle routing problem; Heuristics; Metaheuristic; Set (abstract data type); Computer science; Operations research; Routing (electronic design automation); Mathematical optimization; Engineering; Mathematics; Algorithm; Computer network","score_opus":0.02874457439702727,"score_gpt":0.2945735185899438,"score_spread":0.2658289441929165,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2597737709","genre_codex":"commentary","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0026106748,0.27530533,0.07051508,0.54102004,0.04793889,0.000073939496,0.00082498917,0.0006366461,0.061074376],"genre_scores_gemma":[0.06872439,0.44889313,0.09242706,0.24652512,0.07904764,0.00020664938,0.0018916804,0.0007097401,0.06157451],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9975897,0.00081411365,0.00016153947,0.00040402394,0.00085787446,0.00017280088],"domain_scores_gemma":[0.9902266,0.0050756,0.00051430357,0.00078134116,0.0027190344,0.0006831207],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0032657057,0.0011595401,0.0012736025,0.0011617518,0.001630999,0.005526533,0.0020606844,0.005527359,0.019567793],"category_scores_gemma":[0.016619919,0.00036493866,0.0009164835,0.0018832207,0.0032683904,0.017756024,0.0015535399,0.0077445875,0.012498477],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014873357,0.00013744147,0.0012161906,0.0024446591,0.00009471553,0.00034912632,0.00047330945,0.0025713826,0.00043969523,0.10078328,0.483317,0.40802443],"study_design_scores_gemma":[0.000022466083,0.00007075463,0.00055949984,0.0019416299,0.000030949137,0.0007477951,0.00070940837,0.0012811982,0.000305478,0.20426087,0.79000884,0.000060973594],"about_ca_topic_score_codex":0.0026339453,"about_ca_topic_score_gemma":0.0018357018,"teacher_disagreement_score":0.019567793,"about_ca_system_score_codex":0.0012854198,"about_ca_system_score_gemma":0.0018803369,"threshold_uncertainty_score":0.06546074},"labels":[],"label_agreement":null},{"id":"W2598418346","doi":"","title":"A priori optimization with recourse for the vehicle routing problem with hard time windows and stochastic service times","year":2014,"lang":"en","type":"article","venue":"PolyPublie (École Polytechnique de Montréal)","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal; Polytechnique Montréal; École de Technologie Supérieure; Group for Research in Decision Analysis","funders":"","keywords":"Vehicle routing problem; Column generation; Mathematical optimization; Benchmark (surveying); A priori and a posteriori; Computer science; Extension (predicate logic); Set (abstract data type); Routing (electronic design automation); Service (business); Mathematics; Economics","score_opus":0.007475988084122415,"score_gpt":0.2090726984807672,"score_spread":0.20159671039664478,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2598418346","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.034690525,0.00056753156,0.95929664,0.0004403513,0.000044982335,0.00010523921,0.00037599923,0.00022463924,0.0042541334],"genre_scores_gemma":[0.563594,0.00087290374,0.4249235,0.00022790098,0.00010355179,0.00054757425,0.0011964286,0.00031682133,0.008217426],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99893266,0.0005254983,0.000033485965,0.00020083133,0.00016093586,0.00014666436],"domain_scores_gemma":[0.9970475,0.0023184244,0.00024631637,0.00011163355,0.00014072115,0.00013544603],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020074092,0.0016809457,0.0016033509,0.0008881172,0.00041489178,0.0016294833,0.0015316928,0.0017876483,0.0045563],"category_scores_gemma":[0.004479481,0.0013217945,0.0017450382,0.0011030445,0.0010381993,0.0019004262,0.0011294252,0.0025056263,0.00049122504],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000042427822,0.000035456163,0.00014470756,0.000046849877,0.000019938825,0.000029719955,0.000018393064,0.98305535,0.0002290778,0.011004883,0.00049899315,0.004874158],"study_design_scores_gemma":[0.000008327118,0.00002130785,0.000064710526,0.000007764045,0.000004095432,0.000006592899,0.0000065491777,0.9923821,0.00014317172,0.0070369206,0.00031394899,0.0000044859144],"about_ca_topic_score_codex":0.009631523,"about_ca_topic_score_gemma":0.011727849,"teacher_disagreement_score":0.009631523,"about_ca_system_score_codex":0.0018971169,"about_ca_system_score_gemma":0.002519596,"threshold_uncertainty_score":0.019150913},"labels":[],"label_agreement":null},{"id":"W2600655166","doi":"","title":"A Solution Method for a Car Fleet Management Problem with Maintenance Constraints","year":2006,"lang":"en","type":"article","venue":"Archive ouverte UNIGE (University of Geneva)","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Renting; Computer science; Competitor analysis; Operations research; Tabu search; Stockout; Mathematical optimization; Engineering; Mathematics; Business; Algorithm; Marketing","score_opus":0.007470868014500835,"score_gpt":0.1990185386360051,"score_spread":0.19154767062150427,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2600655166","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007925659,0.00025426334,0.9850343,0.00028195512,0.00007750342,0.0001797637,0.00014167794,0.00054025755,0.005564602],"genre_scores_gemma":[0.07173612,0.0002456067,0.92189616,0.00010207359,0.000050280796,0.00051140325,0.0002748522,0.00015791535,0.0050255633],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999582,0.00014577073,0.000017672006,0.0000891967,0.00011138875,0.00005400728],"domain_scores_gemma":[0.9994259,0.00038069207,0.000038814207,0.000037059985,0.00009076577,0.000026759577],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00084957504,0.0008397211,0.000760592,0.0011794626,0.0007392245,0.0011174382,0.0014705922,0.0017192529,0.007787774],"category_scores_gemma":[0.0023391363,0.0005665508,0.0009436665,0.0018657403,0.0004727519,0.000994621,0.0009179053,0.001182446,0.0010244411],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013311385,0.00018170159,0.000433133,0.00040354996,0.000068506175,0.00018121532,0.00018447271,0.6193913,0.0037438183,0.049180016,0.014128498,0.3119707],"study_design_scores_gemma":[0.00006735561,0.000051762767,0.00007953759,0.000025872238,0.000014798096,0.00007644629,0.00003713892,0.98241884,0.0007807306,0.010146297,0.006291207,0.000010062633],"about_ca_topic_score_codex":0.0036873727,"about_ca_topic_score_gemma":0.004278391,"teacher_disagreement_score":0.007787774,"about_ca_system_score_codex":0.00080561795,"about_ca_system_score_gemma":0.0016049256,"threshold_uncertainty_score":0.026052654},"labels":[],"label_agreement":null},{"id":"W2601843465","doi":"","title":"Tabu Search, Partial Elementarity, and Generalized k -Path Inequalities for the Vehicle Routing Problem with Time Windows","year":2006,"lang":"en","type":"article","venue":"PolyPublie (École Polytechnique de Montréal)","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Group for Research in Decision Analysis","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); Triangle inequality; Mathematics; Combinatorics","score_opus":0.013044578428213232,"score_gpt":0.23639253800569535,"score_spread":0.2233479595774821,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2601843465","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06783153,0.002669264,0.9096949,0.0009904782,0.000118220516,0.00020010432,0.0005718394,0.00030031826,0.017623331],"genre_scores_gemma":[0.51376474,0.0030526542,0.4749787,0.0003029649,0.00022529242,0.0005727664,0.0010427495,0.00020569781,0.005854459],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991171,0.00043479406,0.000026890506,0.00008521549,0.0002074503,0.00012846384],"domain_scores_gemma":[0.9978242,0.001722101,0.0002319289,0.000084244886,0.000090787085,0.000046753514],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013112401,0.0013542649,0.00085312495,0.0010333193,0.0005568524,0.0012296899,0.0012992121,0.0010297465,0.004181116],"category_scores_gemma":[0.0059056524,0.00044888523,0.0008641087,0.0025242537,0.0012474789,0.0017083952,0.0009799588,0.0021676063,0.00035283662],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010486169,0.00007064475,0.00044499862,0.0002278106,0.000058747297,0.000080338395,0.00007284292,0.85513765,0.0007164725,0.09902816,0.002671334,0.041386195],"study_design_scores_gemma":[0.000029275318,0.000054405242,0.00022016605,0.000028746917,0.000015688342,0.000027708249,0.00002381681,0.9483116,0.0004286621,0.048888244,0.0019602845,0.000011406351],"about_ca_topic_score_codex":0.0072966623,"about_ca_topic_score_gemma":0.008951773,"teacher_disagreement_score":0.0072966623,"about_ca_system_score_codex":0.001413758,"about_ca_system_score_gemma":0.001796971,"threshold_uncertainty_score":0.014508367},"labels":[],"label_agreement":null},{"id":"W2601972902","doi":"","title":"A Theoretical Comparison of Feasibility Cuts for the Integrated Aircraft Routing and Crew Pairing Problem","year":2006,"lang":"en","type":"article","venue":"","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Group for Research in Decision Analysis","funders":"","keywords":"Crew; Routing (electronic design automation); Pairing; Set (abstract data type); Decomposition; Computer science; Mathematical optimization; Benders' decomposition; Operations research; Engineering; Aeronautics; Mathematics; Computer network","score_opus":0.023843547588331182,"score_gpt":0.29875097133638007,"score_spread":0.2749074237480489,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2601972902","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04936383,0.00217368,0.82301724,0.0026348957,0.00040695566,0.00042615127,0.0006525121,0.00038178355,0.120943025],"genre_scores_gemma":[0.552643,0.0040261936,0.4133709,0.00096332876,0.0008233323,0.0010478197,0.002372924,0.0007235415,0.024028912],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99552554,0.0020205758,0.00011208673,0.00051337236,0.0011948623,0.0006335906],"domain_scores_gemma":[0.9780442,0.017785858,0.00084738724,0.0011163177,0.001297755,0.00090837805],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0069605056,0.0016006511,0.0014776292,0.0029739237,0.0017473133,0.005817386,0.0038707033,0.002692577,0.026505375],"category_scores_gemma":[0.029859964,0.0011562906,0.0019999908,0.004053054,0.0027634336,0.006642887,0.0032763442,0.0046706414,0.001750011],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00054719206,0.00039205994,0.0008560438,0.00037037328,0.00008642293,0.00008720989,0.0001964327,0.2951124,0.0006899459,0.6290162,0.012861277,0.059784375],"study_design_scores_gemma":[0.00010368456,0.0002979983,0.00064184354,0.00016230962,0.000073931806,0.00014117624,0.00017794462,0.6434583,0.0005743367,0.34606376,0.008275569,0.000029077153],"about_ca_topic_score_codex":0.0030510519,"about_ca_topic_score_gemma":0.003475133,"teacher_disagreement_score":0.026505375,"about_ca_system_score_codex":0.0045908624,"about_ca_system_score_gemma":0.0047779945,"threshold_uncertainty_score":0.08866936},"labels":[],"label_agreement":null},{"id":"W2602117721","doi":"","title":"Branch-and-Price-and-Cut for the Split Delivery Vehicle Routing Problem with Time Windows","year":2008,"lang":"en","type":"article","venue":"PolyPublie (École Polytechnique de Montréal)","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Group for Research in Decision Analysis","funders":"","keywords":"Vehicle routing problem; Computer science; Routing (electronic design automation); Economics; Computer network","score_opus":0.010436719385956935,"score_gpt":0.21173515353165204,"score_spread":0.2012984341456951,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2602117721","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019889815,0.002014026,0.97166014,0.00057358266,0.000118923395,0.00016715894,0.00025888233,0.0001966922,0.0051207887],"genre_scores_gemma":[0.30255258,0.004570355,0.67810494,0.00018885295,0.0003137689,0.00062210136,0.00090754515,0.0003125601,0.012427277],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991303,0.00038822327,0.000033396653,0.00014081194,0.00019214231,0.00011523278],"domain_scores_gemma":[0.9983772,0.0012837638,0.00010661248,0.000053570406,0.00009113746,0.000087664426],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002111402,0.001580873,0.002081648,0.00093112927,0.00070202386,0.0016573534,0.0012877574,0.0016788825,0.005945819],"category_scores_gemma":[0.004151981,0.00090128917,0.0009477617,0.0017057161,0.00091313256,0.0022727533,0.0010583507,0.0018859135,0.00075377367],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026851235,0.00015713398,0.000502195,0.00034451013,0.000096368516,0.000120768585,0.00008701223,0.856724,0.0009935654,0.05149278,0.005126464,0.08408672],"study_design_scores_gemma":[0.000040274557,0.00006734493,0.00011623883,0.000020744084,0.000022938628,0.000029826022,0.000018656852,0.9649819,0.00028658783,0.032449268,0.0019586796,0.0000075261546],"about_ca_topic_score_codex":0.00508096,"about_ca_topic_score_gemma":0.003720169,"teacher_disagreement_score":0.005945819,"about_ca_system_score_codex":0.0013501934,"about_ca_system_score_gemma":0.001952915,"threshold_uncertainty_score":0.019890785},"labels":[],"label_agreement":null},{"id":"W2602684069","doi":"10.1016/j.ejor.2018.01.054","title":"The quadratic shortest path problem: complexity, approximability, and solution methods","year":2018,"lang":"en","type":"article","venue":"European Journal of Operational Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":37,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"Air Force Office of Scientific Research; Air Force Materiel Command; U.S. Air Force; Deutsche Forschungsgemeinschaft","keywords":"Mathematics; Shortest path problem; Quadratic equation; Mathematical optimization; Combinatorics; Regular polygon; Function (biology); Approximation algorithm; Graph","score_opus":0.16577740807923447,"score_gpt":0.4255515052571589,"score_spread":0.25977409717792443,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2602684069","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012677195,0.0020285116,0.9790435,0.0013126942,0.00012400108,0.000041179752,0.0001138964,0.0001081897,0.0045507867],"genre_scores_gemma":[0.41719422,0.005422139,0.5622953,0.00040889025,0.0011100511,0.0003930505,0.0008430146,0.00038452115,0.011948876],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9980646,0.00078895775,0.000077702796,0.0003156669,0.0005932272,0.00015990724],"domain_scores_gemma":[0.9844774,0.013485865,0.00048921374,0.0005232013,0.00081856543,0.00020573032],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0038162991,0.0013078488,0.0016656583,0.0013883606,0.0006464103,0.0025260253,0.0024197982,0.002194545,0.0047085444],"category_scores_gemma":[0.023073765,0.00079752255,0.0014836943,0.0018115143,0.0019370385,0.0059938435,0.0025258954,0.005464494,0.00046034253],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022303646,0.00019887471,0.0011070993,0.00046679864,0.000078270175,0.00008463936,0.0002102496,0.70969164,0.000921105,0.2089502,0.0077825817,0.070285484],"study_design_scores_gemma":[0.000013773757,0.000017795759,0.00009640998,0.000020165297,0.000011039706,0.00002766057,0.000028373666,0.8963659,0.00014015549,0.102144375,0.0011266483,0.000007672695],"about_ca_topic_score_codex":0.005267399,"about_ca_topic_score_gemma":0.0032556155,"teacher_disagreement_score":0.005267399,"about_ca_system_score_codex":0.0017430666,"about_ca_system_score_gemma":0.0017205718,"threshold_uncertainty_score":0.020182729},"labels":[],"label_agreement":null},{"id":"W2602703059","doi":"","title":"European Driver Rules in Vehicle Routing with Time Windows","year":2009,"lang":"en","type":"article","venue":"PolyPublie (École Polytechnique de Montréal)","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Group for Research in Decision Analysis","funders":"","keywords":"Vehicle routing problem; Tabu search; Heuristics; Computer science; Column generation; Task (project management); Routing (electronic design automation); Heuristic; Mathematical optimization; Operations research; Algorithm; Engineering; Artificial intelligence; Mathematics; Embedded system","score_opus":0.007268098677596334,"score_gpt":0.21271114448679942,"score_spread":0.20544304580920308,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2602703059","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10332378,0.0019992462,0.8420427,0.0008440457,0.00020610545,0.00022083205,0.0006560389,0.0003344937,0.050372705],"genre_scores_gemma":[0.6409323,0.0016257827,0.33160204,0.0001585592,0.000093055285,0.0002821526,0.00071056216,0.00020259866,0.024392951],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99857795,0.000690106,0.000070465256,0.00022265532,0.0002853046,0.00015345565],"domain_scores_gemma":[0.99906117,0.00057270745,0.00013238865,0.00010099395,0.00009278586,0.00003990487],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016158383,0.0005175706,0.0004553454,0.00051046663,0.00054898346,0.0016618716,0.0011284251,0.0009987578,0.0036686237],"category_scores_gemma":[0.0037571143,0.00046061774,0.0008135464,0.0009334819,0.0006957667,0.001575897,0.0009287587,0.00085270277,0.00051176036],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017592865,0.00006364802,0.0012202939,0.00013669414,0.00004673601,0.00029748725,0.00019060685,0.6352845,0.0015248933,0.27774066,0.0049830764,0.078335516],"study_design_scores_gemma":[0.00006963927,0.00007677426,0.0008330562,0.000059277012,0.00004282008,0.0002091344,0.00017527842,0.80678356,0.0031898357,0.14800178,0.040513985,0.000044866505],"about_ca_topic_score_codex":0.011458654,"about_ca_topic_score_gemma":0.010483881,"teacher_disagreement_score":0.011458654,"about_ca_system_score_codex":0.0009628783,"about_ca_system_score_gemma":0.0015670521,"threshold_uncertainty_score":0.022783935},"labels":[],"label_agreement":null},{"id":"W2602862967","doi":"","title":"Fifty Years of Vehicle Routing","year":2009,"lang":"fr","type":"article","venue":"Les Cahiers du GERAD","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"","keywords":"Vehicle routing problem; Heuristics; Metaheuristic; Truck; Routing (electronic design automation); Computer science; Decomposition; Mathematical optimization; Operations research; Mathematics; Engineering; Computer network","score_opus":0.01020903419138404,"score_gpt":0.23720877505741528,"score_spread":0.22699974086603125,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2602862967","genre_codex":"other","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007704024,0.31615996,0.13047242,0.05928887,0.02097152,0.00015612022,0.001126566,0.00052324677,0.46359733],"genre_scores_gemma":[0.21082616,0.42860854,0.073339336,0.019172437,0.0145500135,0.0003534176,0.0018143881,0.0005604309,0.2507753],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9982767,0.00049515517,0.0000921174,0.00037170463,0.0005881258,0.00017624181],"domain_scores_gemma":[0.99855644,0.0006098044,0.000087868255,0.00023590302,0.00038396337,0.00012603783],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021153863,0.0008633401,0.000837092,0.0012023303,0.0016202551,0.0041972664,0.0014756578,0.002262216,0.019950254],"category_scores_gemma":[0.0058509866,0.00047912548,0.0006138933,0.002496679,0.003668964,0.005648549,0.0028795733,0.0031923873,0.0075835045],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000060139024,0.00003717197,0.00043487852,0.0003232794,0.000023289396,0.0000906787,0.0003435838,0.0053831367,0.00022224922,0.7576823,0.05970466,0.17569456],"study_design_scores_gemma":[0.0000069944203,0.00002894438,0.0001837524,0.00040491566,0.0000062005433,0.0000970613,0.00014017326,0.0012122641,0.00011013379,0.14797547,0.8498162,0.000017868],"about_ca_topic_score_codex":0.0039853444,"about_ca_topic_score_gemma":0.0030035304,"teacher_disagreement_score":0.019950254,"about_ca_system_score_codex":0.0029879967,"about_ca_system_score_gemma":0.002227973,"threshold_uncertainty_score":0.066740215},"labels":[],"label_agreement":null},{"id":"W2603251541","doi":"10.1002/net.20332","title":"A branch‐and‐price‐based large neighborhood search algorithm for the vehicle routing problem with time windows","year":2009,"lang":"en","type":"article","venue":"Networks","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":102,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Group for Research in Decision Analysis","funders":"","keywords":"Vehicle routing problem; Benchmark (surveying); Mathematical optimization; Heuristic; Set (abstract data type); Computer science; Diversification (marketing strategy); Routing (electronic design automation); Algorithm; Mathematics","score_opus":0.009967092434046134,"score_gpt":0.24213027695610406,"score_spread":0.23216318452205792,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2603251541","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.055927396,0.0007552603,0.93719923,0.00032536793,0.000055898385,0.00017449107,0.000086170185,0.00037025954,0.0051059625],"genre_scores_gemma":[0.36354306,0.00034022468,0.6305243,0.00009100266,0.000048135193,0.0005303119,0.0002742233,0.00008938885,0.0045593223],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996319,0.00017988464,0.0000136661265,0.000058837315,0.00007589481,0.00003978665],"domain_scores_gemma":[0.99945956,0.00037956025,0.0000477751,0.00002182921,0.00005426768,0.000037027672],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009955823,0.0005275292,0.0012864475,0.000811434,0.00050363096,0.00066521304,0.001217187,0.00086942833,0.002653736],"category_scores_gemma":[0.0018076814,0.00044123904,0.0004399895,0.0009612215,0.00046093093,0.0010830015,0.0007461175,0.00066361827,0.00032517037],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021170676,0.00014774146,0.00045102602,0.00007103723,0.000044388722,0.000052648902,0.000073265706,0.8778724,0.0009377545,0.015503888,0.0026769405,0.10195711],"study_design_scores_gemma":[0.000038442562,0.0000332078,0.00004205184,0.0000037200462,0.000004808252,0.000009687506,0.0000063383277,0.997308,0.00014997672,0.001981919,0.0004191949,0.0000027261717],"about_ca_topic_score_codex":0.005556566,"about_ca_topic_score_gemma":0.0050350716,"teacher_disagreement_score":0.005556566,"about_ca_system_score_codex":0.00084777863,"about_ca_system_score_gemma":0.0012432194,"threshold_uncertainty_score":0.011048436},"labels":[],"label_agreement":null},{"id":"W2603777508","doi":"","title":"A General Variable Neighborhood Search for Solving the Uncapacitated Single Allocation p -Hub Median Problem","year":2009,"lang":"en","type":"article","venue":"RePEc: Research Papers in Economics","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Military College of Canada","funders":"","keywords":"Variable neighborhood search; Heuristics; Variable (mathematics); Mathematical optimization; PlanetLab; Descent (aeronautics); Local search (optimization); Computer science; Mathematics; Metaheuristic","score_opus":0.030557530902397968,"score_gpt":0.29201636367974876,"score_spread":0.26145883277735077,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2603777508","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013189525,0.00013167919,0.98487836,0.00006679544,0.000015360254,0.000038545473,0.000033405988,0.00015772911,0.001488613],"genre_scores_gemma":[0.2606808,0.0001885955,0.73541397,0.00009272373,0.000036223355,0.00028433517,0.0002058259,0.00010988525,0.002987679],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996555,0.00013148895,0.000009791275,0.00007744447,0.000087767294,0.000038156362],"domain_scores_gemma":[0.9997855,0.000115700605,0.000024157198,0.000025776366,0.000032005257,0.000016759379],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00056516763,0.0004213941,0.00087861484,0.00045118824,0.00029188526,0.0004180211,0.001110224,0.0006026576,0.0019657079],"category_scores_gemma":[0.001230942,0.000310253,0.0005417629,0.000686901,0.00035390185,0.0007204081,0.00067969225,0.0006641222,0.00026538232],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007686842,0.00006403081,0.000444256,0.000055924036,0.000030047526,0.000045367422,0.000045671553,0.91323143,0.002850437,0.015925897,0.0015654956,0.06566456],"study_design_scores_gemma":[0.000015929583,0.00003666592,0.00006691043,0.0000026469215,0.000003548264,0.000014803325,0.0000051118395,0.9955005,0.00034474052,0.0032705122,0.00073520903,0.0000033900294],"about_ca_topic_score_codex":0.0029530989,"about_ca_topic_score_gemma":0.0044559375,"teacher_disagreement_score":0.0029530989,"about_ca_system_score_codex":0.00047789235,"about_ca_system_score_gemma":0.0008047643,"threshold_uncertainty_score":0.006575942},"labels":[],"label_agreement":null},{"id":"W2604134726","doi":"10.5539/mas.v11n5p52","title":"Solving Multi-Objective Vehicle Routing Problem with Time Windows Using Hybrid Ants Optimization and Tabu Search Based on Performance Metrics","year":2017,"lang":"en","type":"article","venue":"Modern Applied Science","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Tabu search; Vehicle routing problem; Computer science; Mathematical optimization; Ant colony optimization algorithms; Pareto principle; Metaheuristic; Multi-objective optimization; Routing (electronic design automation); Algorithm; Mathematics","score_opus":0.029071672677627364,"score_gpt":0.2684625411401739,"score_spread":0.23939086846254656,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2604134726","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.045577545,0.00075917854,0.94895935,0.0001623638,0.000069736765,0.000079698184,0.00004893645,0.00030198583,0.004041206],"genre_scores_gemma":[0.5513182,0.0006390113,0.44474754,0.00007568907,0.00004854677,0.00022841053,0.00013551673,0.00013494206,0.002672117],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995415,0.00021217679,0.00002197978,0.000065110406,0.00011509369,0.00004424618],"domain_scores_gemma":[0.99946195,0.00030164843,0.000080319274,0.000030234209,0.000097832424,0.000028059094],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00091612566,0.00091819844,0.0007731483,0.00090152066,0.00040592693,0.0009851534,0.0009853893,0.0008589338,0.0013820779],"category_scores_gemma":[0.0016059523,0.00033325684,0.0006784982,0.0011502458,0.00034060742,0.0011750265,0.000502363,0.0006353965,0.00018344492],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006166555,0.00004707132,0.0006208433,0.000094161995,0.000075610005,0.000023381552,0.000030561645,0.9459918,0.0016865403,0.0071234195,0.00060113234,0.04364374],"study_design_scores_gemma":[0.0000061800392,0.000028788669,0.00008871095,0.0000040772643,0.00000795141,0.00001096282,0.000008842196,0.9978314,0.0003429425,0.0014052793,0.0002616913,0.0000031370002],"about_ca_topic_score_codex":0.0038927938,"about_ca_topic_score_gemma":0.003001712,"teacher_disagreement_score":0.0038927938,"about_ca_system_score_codex":0.0006717428,"about_ca_system_score_gemma":0.0011153611,"threshold_uncertainty_score":0.007740259},"labels":[],"label_agreement":null},{"id":"W2604135071","doi":"","title":"The Capacitated Team Orienteering and Profitable Tour Problems","year":2007,"lang":"en","type":"article","venue":"PolyPublie (École Polytechnique de Montréal)","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Group for Research in Decision Analysis","funders":"","keywords":"Orienteering; Heuristic; Matching (statistics); Computer science; Operations research; Engineering; Mathematical optimization; Mathematics; Artificial intelligence","score_opus":0.009517911438977173,"score_gpt":0.22581199666292132,"score_spread":0.21629408522394414,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2604135071","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.078122996,0.005185686,0.8461475,0.0019908885,0.0004265163,0.0001829881,0.00053842925,0.00021658231,0.06718846],"genre_scores_gemma":[0.7552598,0.008130251,0.17039484,0.00049159117,0.0007528574,0.00062311837,0.0010814257,0.00027571767,0.06299035],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991609,0.0003680486,0.000024034343,0.00015724132,0.00013965313,0.00015018559],"domain_scores_gemma":[0.9988606,0.0006865214,0.00013566762,0.000051482722,0.000082166596,0.00018362167],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000777967,0.0015032664,0.0010199934,0.00073993776,0.00080955256,0.0016422496,0.001833649,0.0020620653,0.0095911985],"category_scores_gemma":[0.0030979337,0.00068564195,0.0010368327,0.0015893793,0.0012290961,0.0018618486,0.0014165612,0.0016859793,0.00091683894],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013539716,0.00014499006,0.00068079337,0.00037082678,0.00011820638,0.0003500653,0.00024136611,0.607381,0.00066613714,0.33458403,0.015565262,0.039761934],"study_design_scores_gemma":[0.000048531147,0.00008352619,0.00035238554,0.00004527251,0.000024378405,0.00017057991,0.000118997916,0.7212003,0.00019843024,0.26795685,0.009774924,0.000025787313],"about_ca_topic_score_codex":0.0036514073,"about_ca_topic_score_gemma":0.0028309138,"teacher_disagreement_score":0.0095911985,"about_ca_system_score_codex":0.0014135116,"about_ca_system_score_gemma":0.0010358114,"threshold_uncertainty_score":0.032085717},"labels":[],"label_agreement":null},{"id":"W2604913432","doi":"","title":"Reaching the Elementary Lower Bound in the Vehicle Routing Problem with Time Windows","year":2013,"lang":"en","type":"article","venue":"PolyPublie (École Polytechnique de Montréal)","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Group for Research in Decision Analysis; Polytechnique Montréal; Université du Québec à Montréal","funders":"","keywords":"Column generation; Upper and lower bounds; Vehicle routing problem; Routing (electronic design automation); Relaxation (psychology); Mathematical optimization; Set (abstract data type); State space; Branch and bound; Tree (set theory); Mathematics; Path (computing); Shortest path problem; Computer science; Combinatorics","score_opus":0.007703593308363791,"score_gpt":0.21475462578448806,"score_spread":0.20705103247612427,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2604913432","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.107574984,0.001274353,0.8737515,0.00045480786,0.000057242585,0.00020262969,0.00014217621,0.00031233978,0.016229909],"genre_scores_gemma":[0.61412126,0.0016827441,0.3788238,0.00019570484,0.00007808162,0.00032974663,0.00034484259,0.00031523238,0.004108609],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982463,0.00073880685,0.00006257871,0.00024461964,0.00034655473,0.00036113674],"domain_scores_gemma":[0.9883692,0.010001422,0.00051395735,0.00057797186,0.0002961915,0.00024121178],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0036655888,0.0017054335,0.0014158012,0.0008496949,0.00077950757,0.0020355599,0.001582791,0.0011962963,0.0045781736],"category_scores_gemma":[0.012670918,0.00047141316,0.0011422042,0.0010354586,0.001219705,0.003960859,0.0018584613,0.0026055088,0.0005349442],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003339525,0.0002445487,0.0007716756,0.00037290965,0.00009280357,0.00010323368,0.00022093259,0.8225111,0.0048986105,0.1116958,0.0015596976,0.057194665],"study_design_scores_gemma":[0.00005584215,0.00023272667,0.00032623936,0.000050848495,0.000058187496,0.00006481894,0.00009005983,0.93261075,0.004404196,0.059921097,0.0021686803,0.00001653683],"about_ca_topic_score_codex":0.0021988438,"about_ca_topic_score_gemma":0.0030512766,"teacher_disagreement_score":0.0045781736,"about_ca_system_score_codex":0.0012146241,"about_ca_system_score_gemma":0.0020709538,"threshold_uncertainty_score":0.019385695},"labels":[],"label_agreement":null},{"id":"W2605705048","doi":"10.1080/03155986.2017.1303960","title":"New large-scale data instances for CARP and new variations of CARP","year":2017,"lang":"en","type":"article","venue":"INFOR Information Systems and Operational Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Samfund og Erhverv, Det Frie Forskningsråd","keywords":"Arc routing; Carp; Scale (ratio); Computer science; Time horizon; Routing (electronic design automation); Interval (graph theory); Horizon; Operations research; Fish <Actinopterygii>; Environmental science; Fishery; Geography; Mathematics; Biology; Mathematical optimization; Cartography; Computer network","score_opus":0.14214007323015554,"score_gpt":0.40551655497066014,"score_spread":0.2633764817405046,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2605705048","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7539317,0.0044051725,0.16422854,0.0059725423,0.000999885,0.0011091618,0.03360773,0.002379375,0.03336592],"genre_scores_gemma":[0.7417802,0.0010258813,0.22116253,0.0005358111,0.00032940644,0.0010696027,0.02731106,0.00056408107,0.0062213647],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99830127,0.0005476946,0.00012200207,0.00048348703,0.00029260438,0.00025290003],"domain_scores_gemma":[0.9925747,0.005331013,0.00055950007,0.00066709006,0.000544923,0.0003227312],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002577332,0.0017902368,0.001197684,0.0015331899,0.0007951868,0.0020691906,0.0032152035,0.0026540665,0.004547795],"category_scores_gemma":[0.010794095,0.0006079829,0.0016549315,0.0038972578,0.0009721836,0.0026032918,0.0013671506,0.0029120676,0.00040261092],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00031075638,0.0005037371,0.0023513364,0.0005200978,0.00012689602,0.0003019216,0.00007711238,0.9472793,0.00045873105,0.008329509,0.020442978,0.019297687],"study_design_scores_gemma":[0.00020137221,0.00012879772,0.0017562974,0.00005194656,0.00004544829,0.00023945101,0.00023809023,0.97692674,0.0010221746,0.011531589,0.007821097,0.000037048063],"about_ca_topic_score_codex":0.008755191,"about_ca_topic_score_gemma":0.0131713925,"teacher_disagreement_score":0.008755191,"about_ca_system_score_codex":0.0028044078,"about_ca_system_score_gemma":0.0015425737,"threshold_uncertainty_score":0.020347536},"labels":[],"label_agreement":null},{"id":"W2607258061","doi":"10.1007/s12293-017-0231-8","title":"A co-evolutionary approach using information about future requests for dynamic vehicle routing problem with soft time windows","year":2017,"lang":"en","type":"article","venue":"Memetic Computing","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Computer science; Vehicle routing problem; Heuristic; Routing (electronic design automation); Genetic algorithm; Evolutionary algorithm; Artificial intelligence; Machine learning; Computer network","score_opus":0.011936310361194432,"score_gpt":0.2627367152526489,"score_spread":0.2508004048914545,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2607258061","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.054733947,0.00037384033,0.9386876,0.00043805194,0.00014456385,0.00008415175,0.000042307256,0.00011234317,0.005383247],"genre_scores_gemma":[0.76151603,0.0002940592,0.2309997,0.00025441326,0.00011771912,0.000298718,0.00009614435,0.000074315845,0.0063488353],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995546,0.00012674546,0.00002757268,0.00008140125,0.00013989522,0.00006985052],"domain_scores_gemma":[0.9987704,0.00080357556,0.00009314792,0.000063755084,0.00020427811,0.00006482],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012903867,0.0006830872,0.001131538,0.0011504013,0.00057985017,0.0009439749,0.0018209388,0.0017753113,0.0022205918],"category_scores_gemma":[0.0030175291,0.0006412605,0.0009847366,0.0010137536,0.00058029604,0.0011761374,0.0009249908,0.0009804869,0.00016844495],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000032832682,0.000073176074,0.00037341283,0.000034327433,0.00005557839,0.00004940087,0.00003134503,0.9689672,0.00085124676,0.0058746785,0.00040321605,0.023253528],"study_design_scores_gemma":[0.00000270421,0.00000898284,0.00003453337,0.0000018275591,0.0000056371114,0.0000073628726,0.0000031933441,0.99915254,0.000073066854,0.00064127834,0.00006723349,0.000001615044],"about_ca_topic_score_codex":0.0042119864,"about_ca_topic_score_gemma":0.0039181197,"teacher_disagreement_score":0.0042119864,"about_ca_system_score_codex":0.00064182706,"about_ca_system_score_gemma":0.0010329693,"threshold_uncertainty_score":0.008374929},"labels":[],"label_agreement":null},{"id":"W2609748945","doi":"10.1051/ro/2017030","title":"Impact of vehicle tracking on a routing problem with dynamic travel times","year":2017,"lang":"en","type":"article","venue":"RAIRO - Operations Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Schedule; Computer science; Vehicle routing problem; Routing (electronic design automation); Adaptive routing; Dynamic positioning; Operations research; Real-time computing; Transport engineering; Static routing; Engineering; Computer network; Routing protocol; Marine engineering","score_opus":0.06383388641025729,"score_gpt":0.41219941415317757,"score_spread":0.3483655277429203,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2609748945","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.86713004,0.0006274152,0.12626584,0.000583854,0.00006423125,0.000057037225,0.00011485347,0.0002919881,0.004864775],"genre_scores_gemma":[0.98720086,0.0001404628,0.011961899,0.00003142544,0.000012752106,0.000013343041,0.000054123753,0.000020298865,0.0005648429],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99911314,0.0004956656,0.000028910139,0.00011081448,0.000123432,0.0001280581],"domain_scores_gemma":[0.9912476,0.00769352,0.0004877523,0.00018657386,0.00021263924,0.00017201502],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026599183,0.000798437,0.0005880035,0.00043071923,0.0006537514,0.0010240015,0.00059705763,0.0011703824,0.0015043175],"category_scores_gemma":[0.009428306,0.00046105354,0.0003851426,0.00053889165,0.000789516,0.001594927,0.00072631624,0.0010091349,0.0001081545],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009337221,0.000036167083,0.0006378922,0.000014876299,0.000008605301,0.000021242055,0.000011015819,0.994605,0.0005219919,0.00059601414,0.00006126239,0.0033926019],"study_design_scores_gemma":[0.000011174688,0.0000864053,0.00030509138,0.0000024745132,0.0000066529306,0.000009796528,0.00001666274,0.9986877,0.00039101706,0.00041502516,0.00006473075,0.0000033639935],"about_ca_topic_score_codex":0.011355487,"about_ca_topic_score_gemma":0.0059078196,"teacher_disagreement_score":0.011355487,"about_ca_system_score_codex":0.00091707264,"about_ca_system_score_gemma":0.0011756271,"threshold_uncertainty_score":0.022578776},"labels":[],"label_agreement":null},{"id":"W2612913298","doi":"","title":"Heuristics for an Oil Delivery Vehicle routing Problem","year":2010,"lang":"en","type":"article","venue":"PolyPublie (École Polytechnique de Montréal)","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Group for Research in Decision Analysis","funders":"","keywords":"Heuristics; Vehicle routing problem; Tabu search; Heuristic; Computer science; Metaheuristic; Mathematical optimization; Routing (electronic design automation); Column generation; Operations research; Algorithm; Artificial intelligence; Mathematics","score_opus":0.01273459076068578,"score_gpt":0.24386130164779565,"score_spread":0.23112671088710987,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2612913298","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.047091473,0.0011607992,0.9364143,0.0005220697,0.00011164812,0.0003482744,0.00044600214,0.00059939164,0.01330612],"genre_scores_gemma":[0.33749822,0.0011117948,0.6536009,0.00022389615,0.00010042864,0.000595646,0.0007316042,0.00015225685,0.005985213],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995185,0.00024957224,0.000022868719,0.00007087646,0.00006647327,0.00007166995],"domain_scores_gemma":[0.99896085,0.0007904717,0.00009631809,0.00003958114,0.0000716409,0.00004107553],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001186982,0.000953902,0.0007743635,0.0012597416,0.0005057281,0.0011671325,0.0012030125,0.0013280476,0.0039341343],"category_scores_gemma":[0.002504322,0.00047855492,0.00081196916,0.0012561214,0.0005171992,0.00083664106,0.0006566866,0.00081447297,0.00051387347],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006086451,0.00006266882,0.00029129247,0.00012174685,0.000026675863,0.000083758256,0.000053637035,0.94611514,0.0005145107,0.014788759,0.0027090767,0.035171825],"study_design_scores_gemma":[0.00005159954,0.000054618766,0.00009775162,0.000022989647,0.000016832533,0.000049073256,0.000050848226,0.9865737,0.00046528073,0.009690854,0.002918259,0.000008145552],"about_ca_topic_score_codex":0.004795128,"about_ca_topic_score_gemma":0.005011321,"teacher_disagreement_score":0.004795128,"about_ca_system_score_codex":0.0011090471,"about_ca_system_score_gemma":0.0014641498,"threshold_uncertainty_score":0.013161004},"labels":[],"label_agreement":null},{"id":"W2615340271","doi":"10.1007/s10878-020-00588-y","title":"A class of exponential neighbourhoods for the quadratic travelling salesman problem","year":2020,"lang":"en","type":"preprint","venue":"Journal of Combinatorial Optimization","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Hamiltonian path; Combinatorics; Travelling salesman problem; Mathematics; Quadratic equation; Digraph; Exponential function; Rank (graph theory); Heuristics; Discrete mathematics; Time complexity; Shortest path problem; Graph; Mathematical optimization; Mathematical analysis","score_opus":0.02603628727455053,"score_gpt":0.2718217439551514,"score_spread":0.24578545668060087,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2615340271","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.41685745,0.0017351198,0.5114479,0.0011543249,0.00023841203,0.00014133894,0.0004990047,0.00021256578,0.06771391],"genre_scores_gemma":[0.9174817,0.0012436584,0.047435813,0.00019571053,0.00019787496,0.00016061706,0.0007967513,0.00016118573,0.032326598],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995266,0.00014955082,0.00002186933,0.00010215972,0.00012723019,0.00007250381],"domain_scores_gemma":[0.99731416,0.0016949252,0.00024888793,0.0001430881,0.00032250813,0.00027643418],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010298839,0.00045967472,0.0008628137,0.00090199144,0.0011294935,0.00162222,0.0013523141,0.0010852531,0.0069909897],"category_scores_gemma":[0.007475439,0.0004357151,0.0009788334,0.0007199946,0.0012069155,0.0021814632,0.0019957845,0.0018144258,0.00053389504],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020660709,0.000102516395,0.0015415474,0.00014205613,0.0000369291,0.0002499784,0.00025352393,0.07760127,0.0013787508,0.8940413,0.004638408,0.019807106],"study_design_scores_gemma":[0.000060566756,0.00008865212,0.0008894755,0.000036541114,0.000013822606,0.00026669103,0.00015027002,0.48423663,0.0003879048,0.5075894,0.006252527,0.000027462307],"about_ca_topic_score_codex":0.001806005,"about_ca_topic_score_gemma":0.0013873634,"teacher_disagreement_score":0.0069909897,"about_ca_system_score_codex":0.00081541965,"about_ca_system_score_gemma":0.00046008546,"threshold_uncertainty_score":0.023387194},"labels":[],"label_agreement":null},{"id":"W2615412676","doi":"","title":"The Vehicle Routing Problem with Time Windows: State-of-the-Art Exact Solution Methods","year":2010,"lang":"en","type":"article","venue":"PolyPublie (École Polytechnique de Montréal)","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal; Polytechnique Montréal; Group for Research in Decision Analysis","funders":"","keywords":"Vehicle routing problem; Column generation; Solver; Benchmark (surveying); Integer programming; Mathematical optimization; Variable (mathematics); Computer science; Branch and price; Set (abstract data type); Integer (computer science); Routing (electronic design automation); Variable elimination; Mathematics; Artificial intelligence","score_opus":0.007422774126838951,"score_gpt":0.23972268725173923,"score_spread":0.23229991312490028,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2615412676","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0055422685,0.004234276,0.98367935,0.00021245585,0.00005651456,0.00006663623,0.00014117132,0.00033231766,0.005734965],"genre_scores_gemma":[0.11577276,0.008300362,0.8685862,0.00014730798,0.00015081682,0.00036144606,0.00050004176,0.0003005395,0.0058805896],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994487,0.00018229078,0.000023482113,0.000082217724,0.00021198174,0.00005139316],"domain_scores_gemma":[0.9992866,0.0005266941,0.000051984087,0.00004810778,0.000070779235,0.000015972259],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007865364,0.0012480464,0.0011705719,0.0008776205,0.00036009055,0.0011898051,0.0014121279,0.0009041319,0.0046020844],"category_scores_gemma":[0.0025312013,0.00053555716,0.00074580545,0.0022558384,0.00047317476,0.0014416312,0.0007440846,0.0014630381,0.0010153169],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000069600814,0.00007902451,0.00032059333,0.00039119748,0.000048721166,0.000037258294,0.000052929365,0.6971855,0.00091444806,0.039492555,0.004569812,0.2568383],"study_design_scores_gemma":[0.000027943403,0.000026812646,0.0001073086,0.000043175125,0.000017052194,0.000029904282,0.000018669076,0.9671477,0.0005862288,0.024893355,0.0070933728,0.000008458372],"about_ca_topic_score_codex":0.0052546826,"about_ca_topic_score_gemma":0.005157061,"teacher_disagreement_score":0.0052546826,"about_ca_system_score_codex":0.00067801436,"about_ca_system_score_gemma":0.001273403,"threshold_uncertainty_score":0.0153954625},"labels":[],"label_agreement":null},{"id":"W2617377437","doi":"10.1007/978-3-319-59250-3_17","title":"Compact, Provably-Good LPs for Orienteering and Regret-Bounded Vehicle Routing","year":2017,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo; University of Alberta","funders":"","keywords":"Orienteering; Regret; Computer science; Bounded function; Routing (electronic design automation); Mathematical optimization; Computer network; Mathematics; Machine learning","score_opus":0.022737542150065376,"score_gpt":0.2701361861080368,"score_spread":0.24739864395797143,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2617377437","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01245497,0.0010799781,0.9677046,0.0012286115,0.00018094138,0.00006931586,0.00032232061,0.0005250218,0.01643436],"genre_scores_gemma":[0.5278636,0.0029769246,0.4362336,0.0013167021,0.0009334234,0.0007189649,0.0016163226,0.0012467724,0.027093662],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99664307,0.0012279013,0.00013865059,0.00058459904,0.0009990175,0.0004066925],"domain_scores_gemma":[0.9904338,0.0068413573,0.00054406875,0.0011015717,0.0006161046,0.00046313836],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0033239631,0.0034417,0.0030887537,0.00094210287,0.0010797257,0.004346602,0.0031906248,0.0034209527,0.0075150644],"category_scores_gemma":[0.019774694,0.0013511196,0.0020286147,0.0018677055,0.003910601,0.005793621,0.0056531737,0.008973964,0.0017318298],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00036244097,0.00012844104,0.00028624586,0.000411658,0.00006989195,0.000110455,0.00021012092,0.39506587,0.0019827918,0.5530866,0.010784095,0.037501376],"study_design_scores_gemma":[0.00003563623,0.00005556406,0.00006720457,0.00005598064,0.000017005626,0.000033449312,0.000044163535,0.40702885,0.000500549,0.5897057,0.0024381075,0.000017759849],"about_ca_topic_score_codex":0.0016353171,"about_ca_topic_score_gemma":0.0017989827,"teacher_disagreement_score":0.0075150644,"about_ca_system_score_codex":0.0026174407,"about_ca_system_score_gemma":0.0023014052,"threshold_uncertainty_score":0.025140405},"labels":[],"label_agreement":null},{"id":"W2618694566","doi":"10.1016/j.ejor.2017.05.035","title":"Resource constrained routing and scheduling: Review and research prospects","year":2017,"lang":"en","type":"article","venue":"European Journal of Operational Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":108,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; National Science Foundation","keywords":"Computer science; Scheduling (production processes); Operations research; Heuristic; Routing (electronic design automation); Service (business); Resource (disambiguation); Management science; Operations management; Business; Computer network; Engineering; Marketing; Artificial intelligence","score_opus":0.14144834507892676,"score_gpt":0.42008395228908246,"score_spread":0.2786356072101557,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2618694566","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00042867984,0.9922356,0.0051000165,0.000785459,0.00035562098,0.000008217087,0.000017145394,0.000011836296,0.001057432],"genre_scores_gemma":[0.004498071,0.9895443,0.0041588005,0.00032589948,0.0010147615,0.000015238119,0.0000474088,0.000006770456,0.0003888936],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99938357,0.00016893144,0.00006577604,0.00015788138,0.00017606425,0.00004779115],"domain_scores_gemma":[0.99723756,0.0018636754,0.00022841885,0.000082232895,0.00048556994,0.00010249092],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00247994,0.0011096991,0.0018516267,0.0016857241,0.00026806464,0.0020723923,0.0019069059,0.0017354988,0.0025230509],"category_scores_gemma":[0.0035363801,0.0005191346,0.00070165464,0.0055074114,0.0010044604,0.0023057438,0.0008809982,0.001601219,0.0006899879],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015716458,0.00021049146,0.00051878445,0.014392618,0.00020501802,0.00008310665,0.00008117863,0.012941866,0.0010033685,0.027562905,0.01950872,0.9233348],"study_design_scores_gemma":[0.00012079596,0.0005140751,0.0015979928,0.008829656,0.00064413366,0.0009031343,0.00034586914,0.023416184,0.0012335874,0.056035616,0.9062331,0.00012585554],"about_ca_topic_score_codex":0.0020746961,"about_ca_topic_score_gemma":0.0020392837,"teacher_disagreement_score":0.0025230509,"about_ca_system_score_codex":0.0009089833,"about_ca_system_score_gemma":0.0025678666,"threshold_uncertainty_score":0.013115346},"labels":[],"label_agreement":null},{"id":"W2618781129","doi":"","title":"A branch-price-and-cut algorithm for the inventory-routing problem","year":2014,"lang":"en","type":"article","venue":"PolyPublie (École Polytechnique de Montréal)","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval; Polytechnique Montréal; Group for Research in Decision Analysis","funders":"","keywords":"Column generation; Branch and cut; Mathematical optimization; Benchmark (surveying); Routing (electronic design automation); Vehicle routing problem; Computer science; Branch and price; Set (abstract data type); State (computer science); Operations research; Integer programming; Algorithm; Mathematics","score_opus":0.011244336311351608,"score_gpt":0.23482880688663216,"score_spread":0.22358447057528055,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2618781129","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0076262513,0.00033381121,0.98510355,0.00037881648,0.00009594482,0.00027372467,0.0002609775,0.0005639555,0.0053628986],"genre_scores_gemma":[0.062056493,0.00031673373,0.93247795,0.00018475231,0.000060981,0.0005135431,0.0008137654,0.00019152589,0.003384286],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991622,0.00027518772,0.000040367948,0.0001838294,0.00021721917,0.00012117148],"domain_scores_gemma":[0.99874926,0.000820969,0.00009257255,0.00008721352,0.00018171589,0.00006825396],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013024806,0.001605271,0.0013206594,0.0011160519,0.00091193314,0.0013859564,0.0018270097,0.0020070241,0.007563377],"category_scores_gemma":[0.0036686307,0.00070248294,0.0009487818,0.00211335,0.0005291031,0.0015801815,0.0010462058,0.0027453913,0.0012156803],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016820294,0.00030982163,0.0005263106,0.00021270124,0.00007125292,0.00011989631,0.00006992249,0.70725685,0.0014698992,0.03022177,0.015266522,0.24430682],"study_design_scores_gemma":[0.00006781054,0.00006769218,0.00008413475,0.000015576772,0.000018143,0.00004893586,0.000016548922,0.9820108,0.0006786605,0.013917868,0.0030649605,0.000008943521],"about_ca_topic_score_codex":0.005625619,"about_ca_topic_score_gemma":0.006913358,"teacher_disagreement_score":0.007563377,"about_ca_system_score_codex":0.0017283371,"about_ca_system_score_gemma":0.0030763743,"threshold_uncertainty_score":0.025302052},"labels":[],"label_agreement":null},{"id":"W2621038882","doi":"10.1007/978-3-319-60042-0_6","title":"A New System for the Dynamic Shortest Route Problem","year":2017,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"","keywords":"Shortest path problem; K shortest path routing; Constrained Shortest Path First; Computer science; Private Network-to-Network Interface; Mathematical optimization; Routing (electronic design automation); Shortest Path Faster Algorithm; Vehicle routing problem; Link-state routing protocol; Theoretical computer science; Mathematics; Computer network; Routing protocol; Graph","score_opus":0.017406907414024283,"score_gpt":0.26238217472316294,"score_spread":0.24497526730913866,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2621038882","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0024436973,0.00040807153,0.9847252,0.00022782628,0.00056382385,0.000104075756,0.0002982345,0.000558791,0.010670195],"genre_scores_gemma":[0.08223642,0.001216372,0.8775169,0.00036496556,0.0007563805,0.00071097404,0.0016553587,0.0006406261,0.03490207],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998895,0.00028269985,0.000103491504,0.00026955502,0.00036093022,0.00008839309],"domain_scores_gemma":[0.9992274,0.00022746329,0.00005884908,0.00015171032,0.00027060873,0.00006395999],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010159498,0.0010254366,0.0010687858,0.0014429973,0.0009819607,0.002212779,0.0019540929,0.0013321401,0.017697645],"category_scores_gemma":[0.00320306,0.0004765824,0.0010380361,0.0016362485,0.00088141125,0.0030609362,0.0032290753,0.002806904,0.0067331833],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016525331,0.00010798703,0.00044764543,0.00041753083,0.0000632342,0.00023092695,0.00023638518,0.09529471,0.009089232,0.6424888,0.037514377,0.2139439],"study_design_scores_gemma":[0.00007384757,0.000103187645,0.00022738041,0.000058549922,0.00004488921,0.0002655278,0.00006302672,0.6995477,0.0022602107,0.1920665,0.10523224,0.000056989003],"about_ca_topic_score_codex":0.0020618876,"about_ca_topic_score_gemma":0.0024419334,"teacher_disagreement_score":0.017697645,"about_ca_system_score_codex":0.0011517627,"about_ca_system_score_gemma":0.0011907791,"threshold_uncertainty_score":0.05920446},"labels":[],"label_agreement":null},{"id":"W2621671707","doi":"10.1016/j.apenergy.2017.05.156","title":"Optimization of sawmill residues collection for bioenergy production","year":2017,"lang":"en","type":"article","venue":"Applied Energy","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":23,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Truck; Biomass (ecology); Bioenergy; Heuristics; Vehicle routing problem; Schedule; Environmental science; Renewable energy; Benchmark (surveying); Routing (electronic design automation); Computer science; Operations research; Pulp and paper industry; Engineering; Biofuel; Waste management; Mathematical optimization; Mathematics; Automotive engineering","score_opus":0.01780296463506549,"score_gpt":0.2528430790564614,"score_spread":0.23504011442139594,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2621671707","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.39948785,0.0010406721,0.5800757,0.00040592952,0.000116627954,0.00021939308,0.00036904865,0.00038335155,0.017901452],"genre_scores_gemma":[0.918535,0.00039786482,0.07147577,0.000046725338,0.0000165472,0.00014685746,0.00023390805,0.00012532106,0.009022066],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99981254,0.0000619197,0.0000056269837,0.00004267892,0.000033987682,0.00004334564],"domain_scores_gemma":[0.99977225,0.00012482631,0.000029620613,0.000014204714,0.000041228483,0.000017859536],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006000455,0.00083475816,0.0009141567,0.0007143306,0.0004620886,0.0012308413,0.00062151335,0.0010366307,0.0032101383],"category_scores_gemma":[0.00087249395,0.0006250374,0.00083785807,0.00086645066,0.0003665576,0.00081953756,0.0004937596,0.00063553825,0.00036721953],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000039258153,0.000032795502,0.00015818726,0.000026766884,0.000010928546,0.000015350843,0.0000059955373,0.9919247,0.0013080612,0.0005514908,0.00015430241,0.005772111],"study_design_scores_gemma":[0.0000068954496,0.000047078996,0.0001754846,0.0000036491383,0.0000071337995,0.0000043770497,0.000019027077,0.9976382,0.0011175196,0.0007075062,0.00026993343,0.0000031440534],"about_ca_topic_score_codex":0.006758206,"about_ca_topic_score_gemma":0.008928562,"teacher_disagreement_score":0.006758206,"about_ca_system_score_codex":0.0010717073,"about_ca_system_score_gemma":0.0015300725,"threshold_uncertainty_score":0.013437748},"labels":[],"label_agreement":null},{"id":"W2622363162","doi":"","title":"Solving the maximally diverse grouping problem by skewed general variable neighborhood search","year":2015,"lang":"en","type":"article","venue":"Les Cahiers du GERAD","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; Group for Research in Decision Analysis","funders":"","keywords":"Disjoint sets; Variable neighborhood search; Partition (number theory); Mathematics; Heuristic; Variable (mathematics); Set (abstract data type); Local search (optimization); Mathematical optimization; Computer science; Metaheuristic; Combinatorics","score_opus":0.01683419893696322,"score_gpt":0.22551014217170268,"score_spread":0.20867594323473945,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2622363162","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.050948378,0.00031674653,0.94547874,0.000094089475,0.000021512918,0.00006205817,0.00006340394,0.00016967517,0.0028453711],"genre_scores_gemma":[0.46989933,0.00029353463,0.5257916,0.00012457577,0.000050723917,0.00030005389,0.0004934182,0.00013785218,0.0029089472],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992669,0.0003534993,0.000024910702,0.00015642846,0.00011855829,0.00007978376],"domain_scores_gemma":[0.99949443,0.00028533614,0.000061873616,0.00007242152,0.000052277508,0.000033641227],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012097872,0.0006971921,0.0011762629,0.0006490954,0.000592791,0.00061990693,0.0014534108,0.0010193309,0.0019939665],"category_scores_gemma":[0.0019818065,0.00033396386,0.0006385116,0.0011330141,0.0004936304,0.001384256,0.0013508346,0.00074248627,0.0003209154],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013327802,0.00009051836,0.0010386729,0.00008026269,0.00006550337,0.000102121856,0.00009348805,0.9020341,0.0022134448,0.020364322,0.002063359,0.07172083],"study_design_scores_gemma":[0.000030190446,0.000054722546,0.00016872858,0.00000718109,0.000009766738,0.00004094412,0.000030885058,0.98511976,0.00049624406,0.013043392,0.0009925364,0.0000057144416],"about_ca_topic_score_codex":0.0015472037,"about_ca_topic_score_gemma":0.002309749,"teacher_disagreement_score":0.0019939665,"about_ca_system_score_codex":0.00047964096,"about_ca_system_score_gemma":0.00069541833,"threshold_uncertainty_score":0.0066704154},"labels":[],"label_agreement":null},{"id":"W2622451785","doi":"","title":"Enfoque de generación de columnas para el problema de localización y ruteo con pickup and delivery","year":2013,"lang":"es","type":"article","venue":"PolyPublie (École Polytechnique de Montréal)","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Heuristics; Column generation; Pickup; Integer programming; Shortest path problem; Routing (electronic design automation); Set (abstract data type); Mathematics; Computer science; Vehicle routing problem; Mathematical optimization; Scheme (mathematics); Integer (computer science); Algorithm; Combinatorics; Artificial intelligence; Computer network; Graph","score_opus":0.01127488069365715,"score_gpt":0.24425266358646824,"score_spread":0.2329777828928111,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2622451785","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013017926,0.00037670144,0.97471046,0.0007620856,0.000076898184,0.00013388635,0.00024836604,0.0009286831,0.009745012],"genre_scores_gemma":[0.19719371,0.0011068203,0.7583984,0.00052799744,0.00010233149,0.00043485762,0.00073892943,0.000956264,0.04054065],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99839646,0.00045061283,0.000064329644,0.00039747544,0.00041531943,0.00027568286],"domain_scores_gemma":[0.99779224,0.0011870151,0.00019664252,0.00034523473,0.000327663,0.00015123533],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015935858,0.0013306565,0.0011840797,0.00092835916,0.0012257034,0.0030396339,0.0020006124,0.0017743186,0.020445965],"category_scores_gemma":[0.004186116,0.00094452006,0.0021985748,0.0014952465,0.0014806223,0.0034131473,0.0024988295,0.0039096335,0.00218806],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00041200686,0.00045209815,0.0020545507,0.000995611,0.00009004547,0.0003602741,0.0007602218,0.5819282,0.014044968,0.19225113,0.010802247,0.19584872],"study_design_scores_gemma":[0.000069308044,0.00032174436,0.0007110917,0.00013322706,0.000068944406,0.00020597746,0.0003700007,0.8990282,0.009810941,0.0600625,0.029160094,0.000058073678],"about_ca_topic_score_codex":0.01232906,"about_ca_topic_score_gemma":0.021032652,"teacher_disagreement_score":0.020445965,"about_ca_system_score_codex":0.0028189323,"about_ca_system_score_gemma":0.0031953931,"threshold_uncertainty_score":0.068398595},"labels":[],"label_agreement":null},{"id":"W2622823936","doi":"10.1007/s10732-019-09423-y","title":"The vehicle routing problem with cross-docking and resource constraints","year":2019,"lang":"en","type":"article","venue":"Journal of Heuristics","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":34,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Saint-Gobain (Canada)","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada","keywords":"Vehicle routing problem; Heuristics; Computer science; Mathematical optimization; DOCK; Routing (electronic design automation); Integer programming; Algorithm; Mathematics; Computer network; Engineering","score_opus":0.007581508004379499,"score_gpt":0.24479528799495448,"score_spread":0.23721377999057497,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2622823936","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.099912494,0.0015958931,0.8690974,0.0014460055,0.0004792071,0.00020962839,0.0006593014,0.00029557856,0.026304452],"genre_scores_gemma":[0.77047807,0.0014059126,0.2000481,0.00038881955,0.00025296726,0.00022401934,0.00058367226,0.00024073452,0.02637764],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986255,0.00063249614,0.000049389073,0.00022732606,0.00014079262,0.000324464],"domain_scores_gemma":[0.99827313,0.0012105904,0.00014600475,0.000098349185,0.00011266868,0.00015921837],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017313443,0.0014894074,0.0020618525,0.0013380437,0.0009033822,0.0029893294,0.0024063315,0.0028707571,0.0066459556],"category_scores_gemma":[0.004538298,0.001524771,0.001503983,0.0027000126,0.0013205406,0.0036926917,0.0022180565,0.0018548631,0.00063969754],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000876715,0.00005550511,0.0003021457,0.00007676759,0.00005523443,0.0002103782,0.000022195152,0.9665551,0.00036720888,0.021437058,0.0016552033,0.009175557],"study_design_scores_gemma":[0.00003651123,0.00004993554,0.00019351664,0.000017674309,0.000029516697,0.000098727745,0.00005092019,0.9773597,0.00028006374,0.020225696,0.0016393663,0.000018297818],"about_ca_topic_score_codex":0.012304268,"about_ca_topic_score_gemma":0.008511057,"teacher_disagreement_score":0.012304268,"about_ca_system_score_codex":0.0015859657,"about_ca_system_score_gemma":0.0020830361,"threshold_uncertainty_score":0.024465322},"labels":[],"label_agreement":null},{"id":"W2658246728","doi":"10.1287/trsc.2019.0892","title":"Combining Benders’ Decomposition and Column Generation for Integrated Crew Pairing and Personalized Crew Assignment Problems","year":2019,"lang":"en","type":"article","venue":"Transportation Science","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Group for Research in Decision Analysis","funders":"","keywords":"Crew; Pairing; Crew scheduling; Column generation; Computer science; Decomposition; Scheduling (production processes); Benders' decomposition; Engineering; Aeronautics; Mathematical optimization; Mathematics; Operations management; Physics; Chemistry","score_opus":0.03136646166042128,"score_gpt":0.28734515278973816,"score_spread":0.25597869112931687,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2658246728","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005441984,0.00008083266,0.9928423,0.000063525105,0.000024099436,0.00007476383,0.00006421614,0.00021282863,0.001195353],"genre_scores_gemma":[0.12133236,0.00018422553,0.8749391,0.00012202267,0.000056042132,0.00038016852,0.00053850317,0.00020724947,0.0022402955],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99903786,0.00046473637,0.000038088845,0.0001480523,0.00018396313,0.00012720838],"domain_scores_gemma":[0.998073,0.001273537,0.00016586982,0.00017484512,0.0002270363,0.000085685024],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002029756,0.0020631342,0.0016208768,0.0014090382,0.0006859821,0.0010683959,0.0012923637,0.0014093234,0.004175553],"category_scores_gemma":[0.0036209596,0.001041084,0.001782596,0.0018293399,0.0007295909,0.0013483827,0.0012566055,0.002109522,0.00073862495],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000028892888,0.00007340124,0.00035549045,0.000047311383,0.00003107201,0.000037148162,0.000030115278,0.9629174,0.0007256128,0.006983546,0.0010500074,0.027720045],"study_design_scores_gemma":[0.0000069133694,0.000020187023,0.000038077822,0.0000035874812,0.000004865782,0.0000075067896,0.0000072667945,0.99536836,0.00021293614,0.0038259756,0.0005002053,0.0000041561293],"about_ca_topic_score_codex":0.008620347,"about_ca_topic_score_gemma":0.010585835,"teacher_disagreement_score":0.008620347,"about_ca_system_score_codex":0.0010331041,"about_ca_system_score_gemma":0.0016827857,"threshold_uncertainty_score":0.017140329},"labels":[],"label_agreement":null},{"id":"W2679747200","doi":"10.1002/net.21747","title":"Adaptive large neighborhood search algorithm for the rural postman problem with time windows","year":2017,"lang":"en","type":"article","venue":"Networks","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Transport Canada","funders":"New Jersey Department of Transportation","keywords":"Computer science; Set (abstract data type); Mathematical optimization; Algorithm; Running time; Vehicle routing problem; Mathematics; Routing (electronic design automation)","score_opus":0.013644993020114597,"score_gpt":0.2563775742917052,"score_spread":0.24273258127159061,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2679747200","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.049996577,0.0007176519,0.94145155,0.00029995112,0.00007004885,0.00017962852,0.000091687536,0.00029155888,0.006901275],"genre_scores_gemma":[0.45805076,0.00035846524,0.53347415,0.00018721995,0.0000663566,0.0004787358,0.00033742393,0.00012380254,0.006923133],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995372,0.00018774021,0.00001689127,0.00009047045,0.00010365556,0.00006396921],"domain_scores_gemma":[0.9991503,0.0005807759,0.00008721851,0.000032024815,0.00009530738,0.00005445184],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011784323,0.0007026832,0.0012941277,0.0006697126,0.0006212208,0.00057579455,0.0015755676,0.0010330155,0.0029234625],"category_scores_gemma":[0.0020894452,0.00036046407,0.00054371863,0.00091019005,0.000452387,0.0009490878,0.0008495633,0.0009047165,0.0003549796],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018414514,0.00016248411,0.00062014535,0.00007723293,0.00005620556,0.000072675706,0.00005924065,0.9153376,0.0009006606,0.017020494,0.0033513024,0.062157836],"study_design_scores_gemma":[0.000025816622,0.000038488157,0.00005529872,0.0000031248676,0.0000050880226,0.000014536578,0.0000093639665,0.9969722,0.0001154391,0.0022108173,0.000547045,0.0000028639295],"about_ca_topic_score_codex":0.0077917087,"about_ca_topic_score_gemma":0.00875541,"teacher_disagreement_score":0.0077917087,"about_ca_system_score_codex":0.0007772882,"about_ca_system_score_gemma":0.0016302841,"threshold_uncertainty_score":0.015492678},"labels":[],"label_agreement":null},{"id":"W2727820456","doi":"10.1287/trsc.2018.0836","title":"Vehicle Routing and Location Routing with Intermediate Stops: A Review","year":2019,"lang":"en","type":"review","venue":"Transportation Science","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"","keywords":"Computer science; Routing (electronic design automation); Equal-cost multi-path routing; Static routing; Computer network; Routing protocol","score_opus":0.04510220757424801,"score_gpt":0.33656426991639016,"score_spread":0.29146206234214217,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2727820456","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0002998733,0.9933435,0.0022553958,0.00038114417,0.00033581848,0.000015278485,0.000058827212,0.000022542807,0.0032876225],"genre_scores_gemma":[0.0016903109,0.9959578,0.0012463732,0.00011672865,0.00026022035,0.000011930667,0.00008593177,0.000006153834,0.0006245484],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9994425,0.00010328885,0.000069742775,0.00013922103,0.00019946085,0.00004577613],"domain_scores_gemma":[0.99819285,0.0011894836,0.00016396222,0.000048716458,0.0003514845,0.00005346345],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009847906,0.001520069,0.0015692262,0.003439225,0.00046561565,0.0020393974,0.0017130343,0.001863197,0.006564934],"category_scores_gemma":[0.0025913268,0.00076109404,0.0010259867,0.0075917384,0.000550705,0.0032245126,0.00081261015,0.0016433625,0.0027512265],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000052776235,0.00012416435,0.0004404091,0.043656748,0.00015583874,0.00018107201,0.00008674543,0.0067406017,0.0006857448,0.020089367,0.03886787,0.88891864],"study_design_scores_gemma":[0.000014466034,0.00014441396,0.00092186726,0.01392191,0.0003283768,0.00077992945,0.00019359104,0.0018925844,0.00050652766,0.010626997,0.97061723,0.000052134445],"about_ca_topic_score_codex":0.0029814865,"about_ca_topic_score_gemma":0.0030101559,"teacher_disagreement_score":0.006564934,"about_ca_system_score_codex":0.0009792639,"about_ca_system_score_gemma":0.0025415958,"threshold_uncertainty_score":0.021961927},"labels":[],"label_agreement":null},{"id":"W2728556672","doi":"","title":"An Integrated Aircraft Routing, Crew Scheduling and Flight Retiming Model","year":2005,"lang":"en","type":"article","venue":"PolyPublie (École Polytechnique de Montréal)","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Group for Research in Decision Analysis","funders":"","keywords":"Crew; Crew scheduling; Retiming; Scheduling (production processes); Schedule; Computer science; Flexibility (engineering); Benders' decomposition; Operations research; Engineering; Mathematical optimization; Aeronautics; Operating system; Operations management; Algorithm; Mathematics","score_opus":0.011666578898666879,"score_gpt":0.246834580486618,"score_spread":0.23516800158795112,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2728556672","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.107748166,0.0011035837,0.84690034,0.0011526472,0.00023547004,0.00016283861,0.0024739504,0.00073378324,0.039489195],"genre_scores_gemma":[0.8565492,0.0012181676,0.06603855,0.00022555486,0.0000978344,0.00033963964,0.0020816487,0.00019179724,0.07325756],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99969244,0.000072023744,0.000008863098,0.00009147893,0.00005669638,0.00007850888],"domain_scores_gemma":[0.9996407,0.00015209516,0.000059030954,0.000026258782,0.00006921812,0.000052764182],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006101979,0.00079711643,0.0011719932,0.0005371466,0.00054953387,0.001453224,0.002012715,0.0018728639,0.0075918203],"category_scores_gemma":[0.0014199174,0.0006769681,0.0006931531,0.0012583565,0.00061850616,0.001132618,0.0007733362,0.0013079696,0.0009751029],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019392657,0.000013121627,0.000094115916,0.000011318283,0.0000064224982,0.000014125838,0.000009140729,0.9950669,0.00015490729,0.0023676585,0.0004189585,0.0018238968],"study_design_scores_gemma":[0.000011176155,0.000013774123,0.00008429532,0.0000023752646,0.0000068742293,0.000003980973,0.000005314506,0.9985349,0.000046560606,0.0007856601,0.00050188246,0.0000031807779],"about_ca_topic_score_codex":0.065895885,"about_ca_topic_score_gemma":0.0448052,"teacher_disagreement_score":0.065895885,"about_ca_system_score_codex":0.0019554836,"about_ca_system_score_gemma":0.002692979,"threshold_uncertainty_score":0.13102466},"labels":[],"label_agreement":null},{"id":"W2728582604","doi":"10.14279/depositonce-14266","title":"On Compact Formulations for Integer Programs Solved by Column Generation","year":2003,"lang":"en","type":"article","venue":"PolyPublie (École Polytechnique de Montréal)","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Group for Research in Decision Analysis; HEC Montréal; Kronos (Canada)","funders":"","keywords":"Column generation; Mathematics; Diagonal; Mathematical optimization; Integer (computer science); Branching (polymer chemistry); Integer programming; Compatibility (geochemistry); Computer science; Engineering; Geometry","score_opus":0.021142465544789678,"score_gpt":0.25928932300944757,"score_spread":0.23814685746465789,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2728582604","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0028210825,0.00035882872,0.9904591,0.00023381898,0.000036003654,0.000081751794,0.00007060947,0.00010201768,0.0058367653],"genre_scores_gemma":[0.10245613,0.00155103,0.8865031,0.00049106346,0.00024168534,0.0010124437,0.0005675117,0.0003892378,0.006787885],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99846184,0.0007404841,0.000068907415,0.00014044263,0.00047132515,0.00011692864],"domain_scores_gemma":[0.99485403,0.004076542,0.00033232773,0.00030575084,0.0003226036,0.00010876533],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003094285,0.001982466,0.000850605,0.0011746439,0.00066898536,0.0020264788,0.0010338983,0.0011547414,0.007196734],"category_scores_gemma":[0.008910635,0.0008527365,0.001419933,0.0022579827,0.0023690984,0.002613847,0.0022925679,0.0039781565,0.0015198729],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000053991505,0.000092073824,0.0002441507,0.00022405853,0.000025793785,0.00014943517,0.0002136636,0.17704941,0.0021880863,0.7655673,0.004049207,0.050142895],"study_design_scores_gemma":[0.000049345323,0.000054918433,0.00006419924,0.000103828796,0.00001710368,0.000065726505,0.00004321703,0.44270787,0.0010739304,0.54691875,0.008885205,0.000015974405],"about_ca_topic_score_codex":0.0012282892,"about_ca_topic_score_gemma":0.0020803052,"teacher_disagreement_score":0.007196734,"about_ca_system_score_codex":0.0012303273,"about_ca_system_score_gemma":0.0010340012,"threshold_uncertainty_score":0.024075449},"labels":[],"label_agreement":null},{"id":"W2730563372","doi":"10.1007/s10479-017-2567-3","title":"A meta-heuristic for capacitated green vehicle routing problem","year":2017,"lang":"en","type":"article","venue":"Annals of Operations Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":61,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Vehicle routing problem; Mathematical optimization; Alternative fuel vehicle; Heuristic; Routing (electronic design automation); Ant colony optimization algorithms; Computer science; Ant colony; Engineering; Automotive engineering; Alternative fuels; Mathematics; Computer network","score_opus":0.484601735208781,"score_gpt":0.48273314728261624,"score_spread":0.0018685879261647886,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2730563372","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07389739,0.002147917,0.89918333,0.0007706771,0.00042299635,0.00028028994,0.00027517654,0.0005145141,0.022507751],"genre_scores_gemma":[0.53389704,0.0009764299,0.45793182,0.00029041996,0.0001605518,0.0005598557,0.00030353517,0.000129921,0.005750398],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99960095,0.00017789348,0.000017149365,0.000048058522,0.000088108514,0.00006788632],"domain_scores_gemma":[0.9994604,0.00031498322,0.000048315484,0.000044978195,0.00009190369,0.00003945159],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009828358,0.0011343528,0.0012162229,0.0016990135,0.0005868221,0.0012883573,0.0016980554,0.002150411,0.0031654765],"category_scores_gemma":[0.0016823283,0.0007005589,0.0013402241,0.001492865,0.0005362393,0.0007896884,0.0008954275,0.001261673,0.00037000477],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005476248,0.00007205404,0.00022155614,0.00006552098,0.000052409934,0.000053127445,0.00002437493,0.96253157,0.0007681427,0.006722289,0.0011321447,0.028302008],"study_design_scores_gemma":[0.000021388825,0.000037779002,0.00005818123,0.000016900438,0.000024080347,0.000015144703,0.0000103811735,0.9968072,0.00019623755,0.0021640577,0.00064406864,0.000004595253],"about_ca_topic_score_codex":0.004154815,"about_ca_topic_score_gemma":0.0038941125,"teacher_disagreement_score":0.004154815,"about_ca_system_score_codex":0.0012206476,"about_ca_system_score_gemma":0.0014561566,"threshold_uncertainty_score":0.0105896},"labels":[],"label_agreement":null},{"id":"W2731138409","doi":"","title":"A new family of facet defining inequalities for the maximum edge-weighted clique problem","year":2015,"lang":"en","type":"article","venue":"Les Cahiers du GERAD","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Clique; Mathematics; Enhanced Data Rates for GSM Evolution; Clique problem; Facet (psychology); Combinatorics; Cutting-plane method; Polynomial; Discrete mathematics; Mathematical optimization; Computer science; Graph; Artificial intelligence; Chordal graph; Integer programming; 1-planar graph","score_opus":0.03173730701773475,"score_gpt":0.25760608253380635,"score_spread":0.2258687755160716,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2731138409","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0059823254,0.0003255471,0.9813545,0.00030254977,0.00009303033,0.0001183299,0.00033845552,0.000117783406,0.011367572],"genre_scores_gemma":[0.20106457,0.0019319091,0.7833402,0.000711384,0.00040854444,0.00066042563,0.0017780565,0.0003773291,0.009727557],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99759597,0.0005609585,0.00012582968,0.00041832795,0.0010022622,0.0002966736],"domain_scores_gemma":[0.9972957,0.001557111,0.00032270612,0.00021157795,0.00043975975,0.00017325216],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021190734,0.0013550171,0.00087601173,0.0015505229,0.0010031138,0.0026861327,0.0016822129,0.0010922925,0.0053102905],"category_scores_gemma":[0.007006572,0.0005842831,0.0015439095,0.001992687,0.0012992438,0.0049044536,0.0018330546,0.0054680244,0.00066540326],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012347806,0.00016462886,0.0018318684,0.00038815825,0.00010151703,0.00033533128,0.00042677377,0.09428998,0.010502098,0.7320567,0.01682606,0.14295349],"study_design_scores_gemma":[0.0000471937,0.00017785306,0.001284827,0.0001819245,0.00006429455,0.00073144434,0.00026484756,0.5357656,0.0063529355,0.39892974,0.056112513,0.00008677692],"about_ca_topic_score_codex":0.0024746424,"about_ca_topic_score_gemma":0.0028956227,"teacher_disagreement_score":0.0053102905,"about_ca_system_score_codex":0.0014833714,"about_ca_system_score_gemma":0.0012477344,"threshold_uncertainty_score":0.017764688},"labels":[],"label_agreement":null},{"id":"W2732846998","doi":"","title":"Managing Large Fixed Costs in Vehicle Routing and Crew Scheduling Problems Solved by Column Generation","year":2005,"lang":"en","type":"article","venue":"PolyPublie (École Polytechnique de Montréal)","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Group for Research in Decision Analysis","funders":"","keywords":"Column generation; Mathematical optimization; Fixed cost; Vehicle routing problem; Computer science; Minification; Scheduling (production processes); Shortest path problem; Routing (electronic design automation); Mathematics","score_opus":0.010877686175986638,"score_gpt":0.23190354570331112,"score_spread":0.22102585952732448,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2732846998","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.34028515,0.0004571103,0.6514186,0.00047318294,0.00007822123,0.00025881073,0.00021238536,0.0006388954,0.00617772],"genre_scores_gemma":[0.7896499,0.00016377268,0.20775796,0.00011993767,0.00002140055,0.00019266577,0.0002332619,0.00012115997,0.001739933],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99944156,0.0002864424,0.000015263942,0.00005436149,0.000082101324,0.00012024332],"domain_scores_gemma":[0.99629265,0.0029026798,0.00023770881,0.00019486377,0.00025814027,0.00011406917],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012990135,0.0010752523,0.00087617454,0.00067630643,0.00059113,0.0008870947,0.00097449677,0.0010087976,0.003199723],"category_scores_gemma":[0.0032592907,0.0005168053,0.0006499459,0.00097556226,0.00081571884,0.0008931929,0.00074889883,0.00097391946,0.00024972443],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006377556,0.000061085615,0.000426191,0.000039267492,0.000017995466,0.00004500684,0.000024390209,0.9834563,0.0013055095,0.0024879565,0.0005255767,0.0115469685],"study_design_scores_gemma":[0.000016228309,0.000042337968,0.00008617208,0.000003957905,0.0000069348093,0.000009267206,0.00001552566,0.99768233,0.00073617976,0.0011951862,0.00020154368,0.000004356229],"about_ca_topic_score_codex":0.014215067,"about_ca_topic_score_gemma":0.009218794,"teacher_disagreement_score":0.014215067,"about_ca_system_score_codex":0.001098368,"about_ca_system_score_gemma":0.0012552035,"threshold_uncertainty_score":0.028264642},"labels":[],"label_agreement":null},{"id":"W2734645630","doi":"10.1162/evco_a_00215","title":"Leveraging TSP Solver Complementarity through Machine Learning","year":2017,"lang":"en","type":"article","venue":"Evolutionary Computation","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":92,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Deutscher Akademischer Austauschdienst","keywords":"Solver; Leverage (statistics); Computer science; Benchmark (surveying); Complementarity (molecular biology); Euclidean geometry; Mathematical optimization; Problem solver; Set (abstract data type); Travelling salesman problem; Parallel computing; Algorithm; Artificial intelligence; Mathematics; Computational science","score_opus":0.04305681618019019,"score_gpt":0.305783714799549,"score_spread":0.26272689861935883,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2734645630","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.23430794,0.0037766949,0.72321975,0.0017616843,0.00035845238,0.0002799329,0.0003656948,0.004779456,0.031150376],"genre_scores_gemma":[0.76293105,0.000705101,0.23130836,0.0005155154,0.00016960084,0.00019599508,0.00065979495,0.0003496003,0.003164881],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981511,0.00073215575,0.00008529182,0.0002685448,0.000564967,0.00019786529],"domain_scores_gemma":[0.99491584,0.0035612315,0.00046651866,0.0004897384,0.00044537432,0.000121315796],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029056321,0.0012996324,0.0013399286,0.0013206701,0.00060655473,0.0015282219,0.0017042803,0.0013615808,0.0027708379],"category_scores_gemma":[0.010849118,0.00046738528,0.00073120365,0.0014568391,0.0011271016,0.0019349535,0.0017089938,0.0018024507,0.00065447233],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009664924,0.00013689154,0.001914828,0.00017841598,0.000056361612,0.0000619424,0.000038113194,0.89861685,0.0010817756,0.009724985,0.0028296488,0.08526354],"study_design_scores_gemma":[0.000010476645,0.000033602402,0.000100534126,0.00000858957,0.000006807401,0.0000133715575,0.000008808895,0.99628854,0.0005001024,0.0023600662,0.0006658383,0.0000032981036],"about_ca_topic_score_codex":0.003035596,"about_ca_topic_score_gemma":0.0042817057,"teacher_disagreement_score":0.003035596,"about_ca_system_score_codex":0.00081181567,"about_ca_system_score_gemma":0.0020788428,"threshold_uncertainty_score":0.0153666735},"labels":[],"label_agreement":null},{"id":"W2734889840","doi":"10.1287/ijoc.2017.0747","title":"Numerically Safe Lower Bounds for the Capacitated Vehicle Routing Problem","year":2017,"lang":"en","type":"article","venue":"INFORMS journal on computing","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Mathematical optimization; Branch and cut; Pruning; Branch and bound; Heuristic; Mathematics; Linear programming relaxation; Integer programming; Steiner tree problem; Linear programming; Routing (electronic design automation); Upper and lower bounds; Function (biology); Integer (computer science); Key (lock); Vehicle routing problem; Dual (grammatical number); Computer science","score_opus":0.02471797659407317,"score_gpt":0.2872649552065073,"score_spread":0.26254697861243415,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2734889840","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0025774394,0.0012897869,0.9776618,0.00083006744,0.00018225625,0.00007966956,0.00017515714,0.0002991798,0.01690459],"genre_scores_gemma":[0.20313281,0.005175292,0.77532357,0.0010043975,0.0006969637,0.0010684357,0.0011823499,0.0012663846,0.011149825],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9946439,0.0017618766,0.00023995902,0.0005499347,0.0022599516,0.00054431235],"domain_scores_gemma":[0.9719453,0.022076242,0.0013043955,0.0021261154,0.0020778535,0.00047015236],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007686043,0.0035160999,0.0013316637,0.0034136046,0.0013842692,0.0053739143,0.0029425903,0.0021213233,0.012399103],"category_scores_gemma":[0.045200218,0.0009311443,0.0021674866,0.00240467,0.0032522052,0.005707631,0.0043905643,0.0092792725,0.003703016],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014163088,0.00012390513,0.0006543234,0.00050658046,0.00006969923,0.00019751476,0.00023477996,0.4596149,0.0038734353,0.45196724,0.008192864,0.074423015],"study_design_scores_gemma":[0.000019955716,0.00006083216,0.00015655125,0.00023423281,0.000029646437,0.000084959815,0.000048905426,0.667316,0.0028230064,0.31765607,0.011539985,0.00002979831],"about_ca_topic_score_codex":0.0022184881,"about_ca_topic_score_gemma":0.0036643944,"teacher_disagreement_score":0.012399103,"about_ca_system_score_codex":0.003448014,"about_ca_system_score_gemma":0.003535006,"threshold_uncertainty_score":0.04147917},"labels":[],"label_agreement":null},{"id":"W2737096452","doi":"10.1007/978-1-4615-0819-9_1","title":"Recent Algorithmic Advances for Arc Routing Problems","year":2002,"lang":"en","type":"book-chapter","venue":"International series in management science/operations research/International series in operations research & management science","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Group for Research in Decision Analysis","funders":"","keywords":"Arc routing; Heuristics; Arc (geometry); Routing (electronic design automation); Computer science; Carp; Mathematical optimization; Mathematics; Fish <Actinopterygii>; Engineering; Fishery; Biology; Computer network; Mechanical engineering","score_opus":0.07737141157025106,"score_gpt":0.40232601213038066,"score_spread":0.3249546005601296,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2737096452","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0047270884,0.28264204,0.59082866,0.010273903,0.0035499835,0.00011160067,0.00030563556,0.00044713728,0.10711409],"genre_scores_gemma":[0.080198996,0.2868401,0.56799126,0.003092516,0.015328784,0.00045239052,0.0014164702,0.0006914802,0.043987975],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9973145,0.000877786,0.00014970334,0.00037315278,0.0011352084,0.0001497296],"domain_scores_gemma":[0.9941977,0.004043154,0.000161731,0.0005970849,0.0008548882,0.00014540023],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0049586184,0.0025716152,0.0017477967,0.003365499,0.0010250746,0.004524036,0.004336642,0.0026850621,0.010150658],"category_scores_gemma":[0.012947892,0.0014127889,0.0019008522,0.006221422,0.0030915237,0.008130683,0.0030828055,0.0073086596,0.0036179826],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005688488,0.00013402064,0.00031143232,0.0010698154,0.00007535045,0.00006206262,0.00014194223,0.038288113,0.0003483502,0.6531695,0.034961373,0.27138126],"study_design_scores_gemma":[0.000030455729,0.000035148663,0.00024945123,0.00029236134,0.0000415345,0.00013519915,0.0000621272,0.10685887,0.00031445868,0.76515347,0.12680124,0.000025692025],"about_ca_topic_score_codex":0.0023947845,"about_ca_topic_score_gemma":0.0024592201,"teacher_disagreement_score":0.010150658,"about_ca_system_score_codex":0.0022821927,"about_ca_system_score_gemma":0.0018581323,"threshold_uncertainty_score":0.033957362},"labels":[],"label_agreement":null},{"id":"W2737203876","doi":"10.1007/bf03399222","title":"Location-Arc Routing Problems","year":2001,"lang":"en","type":"article","venue":"OPSEARCH","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":50,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; Group for Research in Decision Analysis; Université du Québec à Montréal","funders":"","keywords":"Arc routing; Computer science; Arc (geometry); Graph; Routing (electronic design automation); Vehicle routing problem; Routing algorithm; Garbage collection; Transport engineering; Operations research; Theoretical computer science; Computer network; Garbage; Mathematics; Routing protocol; Programming language; Engineering","score_opus":0.03531048059759525,"score_gpt":0.30215708515477113,"score_spread":0.2668466045571759,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2737203876","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016224764,0.003508279,0.9139117,0.0018364948,0.00032899948,0.0001484625,0.0011797847,0.0003465793,0.062515035],"genre_scores_gemma":[0.35729218,0.007058714,0.4765778,0.0007699038,0.00082707475,0.0005688932,0.0029085516,0.00039278143,0.15360412],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99931896,0.00027291823,0.000022338192,0.00016603651,0.00015662589,0.000063054315],"domain_scores_gemma":[0.99903226,0.0006787596,0.00008471562,0.0000686983,0.00008398328,0.000051701194],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00096333754,0.0012814874,0.0013449474,0.0010570397,0.00064949796,0.0021702761,0.0015698194,0.0027077843,0.015977217],"category_scores_gemma":[0.0041485303,0.0006637907,0.0007850074,0.002536087,0.00089930417,0.0028922367,0.0016209463,0.0018837829,0.0022819843],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008955936,0.00013748783,0.00060482766,0.0003657327,0.00008529806,0.00018800909,0.000089781715,0.499804,0.00058673753,0.34776115,0.030394625,0.119892806],"study_design_scores_gemma":[0.000053171887,0.00005104174,0.00022824679,0.00005523546,0.000038740185,0.00020681,0.00009163451,0.6790355,0.0005446151,0.2904243,0.029254833,0.000015972146],"about_ca_topic_score_codex":0.0020192463,"about_ca_topic_score_gemma":0.002396765,"teacher_disagreement_score":0.015977217,"about_ca_system_score_codex":0.0010548553,"about_ca_system_score_gemma":0.0008156558,"threshold_uncertainty_score":0.053449094},"labels":[],"label_agreement":null},{"id":"W2738714010","doi":"10.1287/ijoc.2019.0893","title":"Bilinear Assignment Problem: Large Neighborhoods and Experimental Analysis of Algorithms","year":2020,"lang":"en","type":"preprint","venue":"INFORMS journal on computing","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Heuristics; Benchmark (surveying); Heuristic; Bilinear interpolation; Generalization; Mathematical optimization; Computer science; Quadratic assignment problem; Algorithm; Quadratic equation; Variable neighborhood search; Point (geometry); Variable (mathematics); Mathematics; Combinatorial optimization; Metaheuristic","score_opus":0.02596002116034216,"score_gpt":0.30502324235123385,"score_spread":0.27906322119089166,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2738714010","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.48448256,0.002512513,0.4871582,0.0012437616,0.0003314802,0.00056300644,0.0013911548,0.0011448839,0.02117239],"genre_scores_gemma":[0.85284275,0.0004730084,0.14269462,0.000095421696,0.000047035643,0.00047750404,0.0012859639,0.00022976384,0.0018538705],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9955096,0.0023215648,0.00016474728,0.00068221474,0.0010927273,0.0002290981],"domain_scores_gemma":[0.9838778,0.010916795,0.00066036556,0.0029266807,0.001338078,0.00028027635],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004477899,0.0006220173,0.00080842245,0.00083915127,0.000965444,0.0010067928,0.0014828107,0.0010125682,0.0035517514],"category_scores_gemma":[0.023137374,0.00038210428,0.0005208731,0.0011285831,0.0013808629,0.0027689016,0.0015175518,0.0015410938,0.0004755509],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010191089,0.00201901,0.0036386987,0.0006972065,0.00012807988,0.00010615814,0.00034071738,0.78760755,0.011714351,0.08589494,0.009353311,0.09748082],"study_design_scores_gemma":[0.00007864716,0.00037459214,0.0010705402,0.000030752064,0.000017904516,0.00005948676,0.00016405314,0.9538042,0.008198313,0.033312976,0.0028682766,0.000020242689],"about_ca_topic_score_codex":0.0019073162,"about_ca_topic_score_gemma":0.0014230547,"teacher_disagreement_score":0.004477899,"about_ca_system_score_codex":0.0013760554,"about_ca_system_score_gemma":0.0007328532,"threshold_uncertainty_score":0.0236817},"labels":[],"label_agreement":null},{"id":"W2741021238","doi":"10.1287/trsc.2017.0798","title":"Branch-and-Price for the Pickup and Delivery Problem with Time Windows and Scheduled Lines","year":2018,"lang":"en","type":"article","venue":"Transportation Science","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":83,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"TKI DINALOG","keywords":"Pickup; Vehicle routing problem; Mathematical optimization; Computer science; Scheduling (production processes); Set (abstract data type); Path (computing); Shortest path problem; Longest path problem; Public transport; Job shop scheduling; Column generation; Routing (electronic design automation); Mathematics; Theoretical computer science; Engineering; Transport engineering; Computer network","score_opus":0.014703248623537248,"score_gpt":0.25421395296247146,"score_spread":0.2395107043389342,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2741021238","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011442231,0.0013315332,0.97535545,0.00058842264,0.00011623074,0.00033122126,0.00044812972,0.00029138563,0.01009551],"genre_scores_gemma":[0.194708,0.002435016,0.78651583,0.00018100376,0.00022944149,0.0012125735,0.0013988882,0.00037468033,0.012944538],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99904567,0.00041111064,0.000043747285,0.00015066893,0.00018965459,0.0001592325],"domain_scores_gemma":[0.9982835,0.0013754637,0.00010499281,0.000047790112,0.0000976446,0.00009063193],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029360503,0.0017795508,0.0019424721,0.0011587953,0.00097137893,0.0020538932,0.0015857551,0.0018301742,0.01352672],"category_scores_gemma":[0.0048280354,0.0009980962,0.0012853992,0.0027538852,0.0007974302,0.0018883608,0.0012175691,0.0029396787,0.0016665958],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002011796,0.00018238838,0.000664083,0.00044180293,0.0000632604,0.00017818103,0.000098478275,0.85645384,0.0007736071,0.06395966,0.0082286,0.068754934],"study_design_scores_gemma":[0.000054145803,0.000046369223,0.00012712226,0.000027310945,0.00001596909,0.000034308072,0.000023077764,0.972607,0.00022837534,0.023987615,0.002840653,0.000008050527],"about_ca_topic_score_codex":0.009019103,"about_ca_topic_score_gemma":0.009851693,"teacher_disagreement_score":0.01352672,"about_ca_system_score_codex":0.0018120498,"about_ca_system_score_gemma":0.0035722142,"threshold_uncertainty_score":0.04525137},"labels":[],"label_agreement":null},{"id":"W2741268399","doi":"10.1109/icsssm.2017.7996166","title":"Mobile Facility Routing Problem with Service-Time-related Demand","year":2017,"lang":"en","type":"article","venue":"","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Computer science; Metaheuristic; Mathematical optimization; Scheduling (production processes); Variable neighborhood search; Routing (electronic design automation); Service (business); Vehicle routing problem; Quality of service; Variable (mathematics); Integer programming; Computer network; Mathematics; Algorithm","score_opus":0.011353629488853453,"score_gpt":0.24255343418434572,"score_spread":0.23119980469549226,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2741268399","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.059782162,0.00062246074,0.93110853,0.0006785376,0.00014192318,0.00017741346,0.0005447865,0.00024021146,0.0067039323],"genre_scores_gemma":[0.7561881,0.0005962711,0.23110732,0.0001788217,0.00014134526,0.0005184459,0.0005869716,0.00012373464,0.010558972],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990214,0.00034027686,0.00003666267,0.0002245169,0.0001681732,0.00020895408],"domain_scores_gemma":[0.99928194,0.00041233216,0.0001221113,0.000035954283,0.0000885323,0.000059165242],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010681496,0.0011268953,0.0016053872,0.0007837471,0.00082877936,0.0011674667,0.0019918045,0.0023612147,0.0027562277],"category_scores_gemma":[0.00208445,0.0005239697,0.0011551748,0.0017169055,0.0005978471,0.0012645051,0.00088507135,0.0010821306,0.00030438686],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008394829,0.000059879214,0.00040143746,0.000104865336,0.000051067917,0.00025740924,0.000042659114,0.9583596,0.0014932981,0.021885078,0.0017834725,0.015477237],"study_design_scores_gemma":[0.000028818224,0.00006668818,0.00016008805,0.0000075922767,0.00001950826,0.000121718425,0.000030504747,0.98664945,0.0005157922,0.010322926,0.002064138,0.000012727991],"about_ca_topic_score_codex":0.006899367,"about_ca_topic_score_gemma":0.004768196,"teacher_disagreement_score":0.006899367,"about_ca_system_score_codex":0.0016193814,"about_ca_system_score_gemma":0.0013537388,"threshold_uncertainty_score":0.013718367},"labels":[],"label_agreement":null},{"id":"W2741542409","doi":"10.1007/s10479-018-2868-1","title":"Improving set partitioning problem solutions by zooming around an improving direction","year":2018,"lang":"en","type":"article","venue":"Annals of Operations Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Group for Research in Decision Analysis","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Theory of computation; Set (abstract data type); Computer science; Zoom; Mathematical optimization; Integer programming; Algorithm; Mathematics; Programming language","score_opus":0.1797821635311046,"score_gpt":0.4289831972472035,"score_spread":0.24920103371609892,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2741542409","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.050408017,0.00080105383,0.9379719,0.00067361415,0.0003857412,0.00019375578,0.00012072485,0.0015045013,0.007940631],"genre_scores_gemma":[0.17843609,0.00044311606,0.81746936,0.00039342124,0.00013417781,0.00016202369,0.00029946756,0.00033986784,0.0023224421],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993901,0.00023180382,0.000036942074,0.00012595866,0.00015649742,0.000058593592],"domain_scores_gemma":[0.997718,0.001157034,0.0001624291,0.0003761647,0.00048612477,0.00010029055],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017761573,0.0017914292,0.0011958316,0.0014662817,0.000802805,0.0010409267,0.0014987912,0.001381558,0.0070400196],"category_scores_gemma":[0.008994205,0.0006884758,0.0008339825,0.00091764395,0.0005428096,0.0021360605,0.0015908297,0.0019415055,0.001137007],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007120022,0.00052134384,0.0019676024,0.00051773974,0.00010233563,0.00010222629,0.00036484803,0.35862422,0.020196829,0.019716028,0.012654727,0.58452016],"study_design_scores_gemma":[0.00009630323,0.00028366057,0.00034266314,0.00006269392,0.000057188463,0.00007422649,0.00011656093,0.97936857,0.004745494,0.0105661545,0.0042689964,0.000017492635],"about_ca_topic_score_codex":0.0023101368,"about_ca_topic_score_gemma":0.0038883125,"teacher_disagreement_score":0.0070400196,"about_ca_system_score_codex":0.00042709452,"about_ca_system_score_gemma":0.001046792,"threshold_uncertainty_score":0.023551226},"labels":[],"label_agreement":null},{"id":"W2742471139","doi":"","title":"A Survey of Models and Algorithms for Winter Road Maintenance. Part IV: Vehicle Routing and Fleet Sizing for Plowing and Snow Disposal","year":2005,"lang":"en","type":"article","venue":"Les Cahiers du GERAD","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Group for Research in Decision Analysis","funders":"","keywords":"Sizing; Routing (electronic design automation); Vehicle routing problem; Snow; Snow removal; Transport engineering; Operations research; Fleet management; Highway maintenance; Plough; Computer science; Engineering; Geography; Meteorology","score_opus":0.02842779412637151,"score_gpt":0.26189771106823284,"score_spread":0.23346991694186134,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2742471139","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0018871535,0.02556858,0.95838404,0.00079625985,0.00031233727,0.00006051926,0.00059053494,0.0007083578,0.011692225],"genre_scores_gemma":[0.08634505,0.1006391,0.77814317,0.000673637,0.0010348648,0.0006282105,0.003027836,0.00086946203,0.02863866],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994666,0.00014436306,0.00004786199,0.00012412241,0.00017704471,0.00003996049],"domain_scores_gemma":[0.9995432,0.00024539526,0.000054614222,0.000062946485,0.0000785519,0.000015375601],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00069513044,0.0017907941,0.0013637091,0.0010305243,0.00044020134,0.0016075355,0.0020688765,0.0014989126,0.0067781857],"category_scores_gemma":[0.0017883828,0.0010020531,0.0015461147,0.0028335925,0.00048658715,0.002084228,0.00062884303,0.001689433,0.0036736312],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000049212882,0.0001372981,0.0007043232,0.00094644225,0.00010093698,0.00007456075,0.00009065042,0.5024253,0.001444766,0.11025447,0.051397853,0.33237424],"study_design_scores_gemma":[0.000025901058,0.00007159106,0.00061985175,0.0002814339,0.00006109939,0.00023571857,0.00005902943,0.6959296,0.00089902186,0.1414328,0.16034694,0.00003705413],"about_ca_topic_score_codex":0.0046170647,"about_ca_topic_score_gemma":0.0051966226,"teacher_disagreement_score":0.0067781857,"about_ca_system_score_codex":0.0013666501,"about_ca_system_score_gemma":0.0014527736,"threshold_uncertainty_score":0.022675276},"labels":[],"label_agreement":null},{"id":"W2742575432","doi":"10.1155/2017/1624328","title":"Analysis of an Automated Vehicle Routing Problem in Logistics considering Path Interruption","year":2017,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Government of Jiangsu Province; Jiangsu Science and Technology Department; National Natural Science Foundation of China","keywords":"Vehicle routing problem; Path (computing); Computer science; Routing (electronic design automation); Particle swarm optimization; Operations research; Automated guided vehicle; Shortest path problem; Transport engineering; Real-time computing; Simulation; Engineering; Embedded system; Algorithm; Artificial intelligence; Computer network","score_opus":0.029147941877763105,"score_gpt":0.33283999349427573,"score_spread":0.30369205161651264,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2742575432","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22461443,0.0012811817,0.7539075,0.00096184434,0.00010296538,0.0002339713,0.0003107412,0.00016611234,0.0184213],"genre_scores_gemma":[0.9377882,0.0009609654,0.051994827,0.00007049853,0.00009178148,0.00021460062,0.00032053664,0.000069084475,0.008489606],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994005,0.00021965691,0.000019043626,0.000101542515,0.00012929586,0.0001300315],"domain_scores_gemma":[0.9978605,0.001593794,0.00024434327,0.000031502845,0.00019610424,0.000073771385],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013044134,0.00096842524,0.001054258,0.0009318388,0.0006508407,0.0015392393,0.0009999502,0.0016969168,0.0031458775],"category_scores_gemma":[0.0032340765,0.0006089401,0.001333908,0.001064919,0.00087630557,0.0009895712,0.0006609493,0.0010200236,0.00016997172],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000022380644,0.000020243497,0.00039235578,0.000063615305,0.000021615055,0.00010223047,0.000026307114,0.99215114,0.00032326058,0.0043502552,0.0002368817,0.0022897066],"study_design_scores_gemma":[0.0000039999472,0.000024141113,0.00022933191,0.0000054874113,0.000010363524,0.000015685193,0.000029775601,0.9973851,0.00007939463,0.0019567146,0.00025623525,0.0000037483258],"about_ca_topic_score_codex":0.022799948,"about_ca_topic_score_gemma":0.010229162,"teacher_disagreement_score":0.022799948,"about_ca_system_score_codex":0.0018183318,"about_ca_system_score_gemma":0.0022573501,"threshold_uncertainty_score":0.04533446},"labels":[],"label_agreement":null},{"id":"W2742664076","doi":"10.1007/s10479-017-2601-5","title":"Solving the capacitated clustering problem with variable neighborhood search","year":2017,"lang":"en","type":"article","venue":"Annals of Operations Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":41,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Military College of Canada","funders":"Natural Sciences and Engineering Research Council of Canada; Ministarstvo Prosvete, Nauke i Tehnološkog Razvoja","keywords":"Variable neighborhood search; Heuristics; Benchmark (surveying); Mathematical optimization; Cluster analysis; Heuristic; Iterated local search; Variable (mathematics); Mathematics; Iterated function; Computer science; Metaheuristic; Artificial intelligence","score_opus":0.18524376840212,"score_gpt":0.41619169996105954,"score_spread":0.23094793155893953,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2742664076","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05194481,0.00061238755,0.93955696,0.0005486099,0.0000826601,0.00008635583,0.000113459144,0.0001850705,0.0068696844],"genre_scores_gemma":[0.6309938,0.00043890966,0.35591844,0.00017793557,0.00011851945,0.00032953135,0.00041093997,0.00021140305,0.011400475],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993278,0.0003159522,0.000023495937,0.00014258068,0.0001076387,0.000082582665],"domain_scores_gemma":[0.99808717,0.0014249634,0.00012896684,0.000092293165,0.00018457815,0.00008206705],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013876515,0.00092182047,0.0016991147,0.0011708775,0.0007313689,0.0012647875,0.002675323,0.0023977333,0.0036188029],"category_scores_gemma":[0.004664523,0.0009322837,0.001003621,0.0018044079,0.000823099,0.0017329562,0.0013879336,0.0013909288,0.00038747556],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000449489,0.00004518641,0.0002605208,0.000049220038,0.00003593406,0.000022695944,0.00002624363,0.9756739,0.00018745937,0.009393851,0.0011797943,0.01308021],"study_design_scores_gemma":[0.0000058427627,0.000008578269,0.000029708835,0.0000026192151,0.0000028390043,0.0000050541994,0.000007756801,0.9957353,0.00004957485,0.0040046563,0.00014584594,0.000002233643],"about_ca_topic_score_codex":0.011958286,"about_ca_topic_score_gemma":0.009971196,"teacher_disagreement_score":0.011958286,"about_ca_system_score_codex":0.0013663294,"about_ca_system_score_gemma":0.0018295681,"threshold_uncertainty_score":0.023777366},"labels":[],"label_agreement":null},{"id":"W2743445264","doi":"","title":"Problème de livraisons à séquence fixée","year":2014,"lang":"fr","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Humanities; Philosophy; Political science","score_opus":0.015504862831440288,"score_gpt":0.24429410367686896,"score_spread":0.22878924084542868,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2743445264","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06327232,0.0014388501,0.887047,0.003321975,0.00094296644,0.0003397675,0.0012569715,0.00081096677,0.041569315],"genre_scores_gemma":[0.4644212,0.0020264247,0.35825303,0.00060706184,0.0008802001,0.0010011183,0.0026956028,0.00078863127,0.16932671],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978516,0.0006097594,0.00009103876,0.00076395785,0.00038219045,0.0003014725],"domain_scores_gemma":[0.9942227,0.004050223,0.00023245071,0.0003283603,0.0007263576,0.00043985748],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026423314,0.0026471424,0.0021653788,0.0023614012,0.00285502,0.0042090975,0.0020171423,0.0068929046,0.03375459],"category_scores_gemma":[0.014841497,0.00092576805,0.0023537949,0.001420704,0.0026163752,0.0040904484,0.0029768408,0.0052087135,0.00405387],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009572875,0.0002375274,0.001729549,0.00092487136,0.00028335198,0.0014113988,0.00068433984,0.34965286,0.006834187,0.5173708,0.026345331,0.09356852],"study_design_scores_gemma":[0.00022130928,0.0004942655,0.00094275427,0.00023661825,0.0000956326,0.00088160933,0.00063404505,0.65149266,0.005473463,0.30101708,0.03840364,0.000106987354],"about_ca_topic_score_codex":0.011875056,"about_ca_topic_score_gemma":0.0061773118,"teacher_disagreement_score":0.03375459,"about_ca_system_score_codex":0.0027058008,"about_ca_system_score_gemma":0.0022101412,"threshold_uncertainty_score":0.112920344},"labels":[],"label_agreement":null},{"id":"W2747220382","doi":"","title":"Variable Neighborhood Search for the Maximum Clique","year":2001,"lang":"fr","type":"article","venue":"Les Cahiers du GERAD","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Group for Research in Decision Analysis; HEC Montréal","funders":"","keywords":"Heuristic; Mathematics; Clique; Variable neighborhood search; Greedy algorithm; Clique problem; Vertex (graph theory); Mathematical optimization; Simplicity; Variable (mathematics); Combinatorics; Algorithm; Metaheuristic; Graph; Chordal graph","score_opus":0.01852289698079633,"score_gpt":0.26060745756493653,"score_spread":0.24208456058414018,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2747220382","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0218722,0.0007151221,0.9691187,0.00031056424,0.00004993678,0.00007111351,0.00009963007,0.00023874249,0.00752406],"genre_scores_gemma":[0.49534473,0.0006589579,0.49618882,0.00021396628,0.0001138923,0.00036924024,0.00052745396,0.00019995766,0.0063829725],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995134,0.00024891633,0.000009254813,0.000080855934,0.00009779195,0.00004969561],"domain_scores_gemma":[0.99929047,0.0005064323,0.000040303305,0.00005556247,0.00007347618,0.00003374823],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006751267,0.0003433766,0.0007308554,0.0006811169,0.0006017869,0.0005632403,0.00079167413,0.0006409104,0.003878164],"category_scores_gemma":[0.003170919,0.0002768003,0.00046082897,0.0010346979,0.00068148895,0.0010675469,0.0009049368,0.000796826,0.00039860752],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012877039,0.00008040743,0.0005719579,0.00014229164,0.000052349264,0.00006502135,0.00008190376,0.74176466,0.0018321489,0.13767247,0.0077549918,0.10985296],"study_design_scores_gemma":[0.000027071304,0.00003287122,0.0001259452,0.000011727807,0.0000064461215,0.000028616212,0.000013587341,0.9463064,0.00041128884,0.050358366,0.0026712765,0.0000062788376],"about_ca_topic_score_codex":0.002486403,"about_ca_topic_score_gemma":0.0032801274,"teacher_disagreement_score":0.003878164,"about_ca_system_score_codex":0.00063589966,"about_ca_system_score_gemma":0.000835415,"threshold_uncertainty_score":0.012973785},"labels":[],"label_agreement":null},{"id":"W2748787255","doi":"10.1002/net.21759","title":"Solving the large‐scale min–max K‐rural postman problem for snow plowing","year":2017,"lang":"en","type":"article","venue":"Networks","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Arc routing; Computer science; Solver; Mathematical optimization; Arc (geometry); Vehicle routing problem; Travelling salesman problem; Variable (mathematics); Deck; Graph; Transformation (genetics); Heuristics; Enhanced Data Rates for GSM Evolution; Routing (electronic design automation); Mathematics; Algorithm; Theoretical computer science; Computer network; Artificial intelligence; Engineering","score_opus":0.01386496700881961,"score_gpt":0.2647827661948654,"score_spread":0.2509177991860458,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2748787255","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15142103,0.00049009407,0.840859,0.00043292186,0.000065079104,0.00017232566,0.00022409477,0.0003016619,0.0060337447],"genre_scores_gemma":[0.5932454,0.00030392918,0.39856473,0.00012002149,0.000046110647,0.00026751074,0.00039165205,0.00020946297,0.0068512144],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997073,0.00012560967,0.000010957912,0.00006926354,0.000030263729,0.000056561665],"domain_scores_gemma":[0.9992805,0.00052332465,0.00008491318,0.000030543102,0.000037879057,0.00004276533],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010008204,0.001266234,0.0010674609,0.00047547187,0.0007938709,0.0006978219,0.0012261723,0.0012487462,0.0049034697],"category_scores_gemma":[0.0016024054,0.00056179106,0.00084552856,0.0008431146,0.0006801358,0.001346699,0.00080606865,0.00072552933,0.0002927478],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006305151,0.000047255664,0.0004769213,0.000093629504,0.00003077596,0.00006884136,0.000045820805,0.97933024,0.000625872,0.0042497236,0.00071010116,0.014257719],"study_design_scores_gemma":[0.00001935439,0.000046049932,0.00015366291,0.0000057073376,0.000009429338,0.000020976919,0.000059691385,0.99345195,0.00040117896,0.0051082755,0.0007184811,0.0000052165906],"about_ca_topic_score_codex":0.008183206,"about_ca_topic_score_gemma":0.010158605,"teacher_disagreement_score":0.008183206,"about_ca_system_score_codex":0.00092000235,"about_ca_system_score_gemma":0.0013785214,"threshold_uncertainty_score":0.016403735},"labels":[],"label_agreement":null},{"id":"W2751348020","doi":"10.1016/j.cie.2017.08.036","title":"An iterated metaheuristic for the directed network design problem with relays","year":2017,"lang":"en","type":"article","venue":"Computers & Industrial Engineering","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"National Natural Science Foundation of China","keywords":"Metaheuristic; Iterated function; Computer science; Mathematical optimization; Engineering; Mathematics","score_opus":0.05211003315936165,"score_gpt":0.2552218237178717,"score_spread":0.20311179055851009,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2751348020","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03971294,0.00040986316,0.9516247,0.00023003468,0.0001042692,0.00012029454,0.000055272063,0.00032638258,0.007416237],"genre_scores_gemma":[0.50853133,0.00031471884,0.484811,0.00020717594,0.000067493755,0.0004278313,0.000111256944,0.0001443941,0.005384834],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99972945,0.00011565584,0.000011692005,0.000039249026,0.00006570367,0.000038244216],"domain_scores_gemma":[0.9992536,0.00051397,0.0000712757,0.00003756016,0.00008491851,0.000038659167],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00090413267,0.0009428118,0.0009404388,0.0008981027,0.00037154887,0.00072701526,0.0015670238,0.0017380607,0.0019367508],"category_scores_gemma":[0.0021022263,0.00049471523,0.0010491313,0.0005561363,0.00062127376,0.0006472992,0.0008728082,0.0010363094,0.00023977121],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003323649,0.00003237094,0.00015842955,0.000031800126,0.00003546255,0.00004537122,0.00002809613,0.974662,0.0007870423,0.0065902583,0.00046727672,0.017128654],"study_design_scores_gemma":[0.00001869794,0.000031023523,0.000026407655,0.0000066554453,0.000011490117,0.000009893743,0.000005629318,0.9979948,0.00017469424,0.0013628207,0.00035506743,0.0000028950476],"about_ca_topic_score_codex":0.004009124,"about_ca_topic_score_gemma":0.0038985224,"teacher_disagreement_score":0.004009124,"about_ca_system_score_codex":0.0010645402,"about_ca_system_score_gemma":0.0011140212,"threshold_uncertainty_score":0.007971585},"labels":[],"label_agreement":null},{"id":"W2753828258","doi":"10.1016/j.trb.2017.08.003","title":"Operational flexibility in the truckload trucking industry","year":2017,"lang":"en","type":"article","venue":"Transportation Research Part B Methodological","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Wilfrid Laurier University; Toronto Metropolitan University","funders":"","keywords":"Trucking industry; Flexibility (engineering); Business; Transport engineering; Engineering; Automotive engineering; Economics; Truck; Management","score_opus":0.5610344710681515,"score_gpt":0.5238044464357593,"score_spread":0.03723002463239222,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2753828258","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6959682,0.001383818,0.27140832,0.0015128716,0.00008309153,0.000031027223,0.0001455226,0.000052334457,0.029414758],"genre_scores_gemma":[0.995372,0.00018893089,0.002957089,0.000017617223,0.00001963483,0.000009742953,0.000022238679,0.000010480821,0.0014023237],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999033,0.00047079733,0.000042339972,0.00014938746,0.00013441582,0.00017003246],"domain_scores_gemma":[0.997347,0.0019738693,0.00022646606,0.00018293387,0.00011796059,0.00015174517],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017744488,0.00045424342,0.00037699818,0.0007529628,0.0004919129,0.002046056,0.00089809246,0.0005444223,0.004129538],"category_scores_gemma":[0.005845991,0.00029789,0.0006502471,0.0012117085,0.0011996991,0.0021777453,0.001217295,0.0011553878,0.00017534386],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026650314,0.000143365,0.010126597,0.000098081524,0.00008515516,0.00027883088,0.0008971814,0.6420406,0.0022376985,0.29405987,0.00081925566,0.04894679],"study_design_scores_gemma":[0.00001714137,0.00010160455,0.008873875,0.00005090041,0.00004458417,0.000079456535,0.0019088472,0.66851336,0.00085055415,0.3165046,0.0030141205,0.00004100343],"about_ca_topic_score_codex":0.0036265892,"about_ca_topic_score_gemma":0.0034046504,"teacher_disagreement_score":0.004129538,"about_ca_system_score_codex":0.0010488235,"about_ca_system_score_gemma":0.00061450794,"threshold_uncertainty_score":0.013814628},"labels":[],"label_agreement":null},{"id":"W2754261999","doi":"10.1287/trsc.2017.0771","title":"Exact Solutions for the Carrier–Vehicle Traveling Salesman Problem","year":2017,"lang":"en","type":"article","venue":"Transportation Science","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":35,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"Università di Bologna; Ministero dell’Istruzione, dell’Università e della Ricerca","keywords":"Travelling salesman problem; Range (aeronautics); Computer science; Mathematical optimization; A priori and a posteriori; Integer programming; Vehicle routing problem; Traveling purchaser problem; Integer (computer science); Conic section; 2-opt; Algorithm; Engineering; Mathematics; Routing (electronic design automation); Computer network; Aerospace engineering","score_opus":0.0518667728935645,"score_gpt":0.31759714648962245,"score_spread":0.26573037359605794,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2754261999","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.024098048,0.0015940974,0.9341713,0.0009895745,0.00018281306,0.00018794014,0.0005566215,0.00039278046,0.03782688],"genre_scores_gemma":[0.41438937,0.0017241519,0.56194884,0.00035581883,0.0002460037,0.00052321545,0.0013172164,0.0003382075,0.019157184],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993523,0.0002248049,0.00002647272,0.000116833195,0.00015045062,0.00012916184],"domain_scores_gemma":[0.99792117,0.0015689129,0.0001487573,0.000077600555,0.0002128762,0.000070814225],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012587892,0.0013521146,0.0012339607,0.0009332511,0.0007422422,0.0018234026,0.0014127265,0.0020194144,0.013086049],"category_scores_gemma":[0.0054729637,0.0005733083,0.0008252626,0.0015999409,0.0007555118,0.0014649873,0.0010192397,0.0016447151,0.0014337655],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000044836972,0.00008096395,0.00034609583,0.00016782217,0.000026104135,0.000076804994,0.000051434872,0.9115151,0.00021503179,0.04972392,0.006480691,0.031271104],"study_design_scores_gemma":[0.00001208322,0.000015326274,0.000055585304,0.000012414674,0.000005265263,0.000013793522,0.00002741948,0.9763782,0.00007643212,0.021916125,0.0014828425,0.0000044089356],"about_ca_topic_score_codex":0.011953408,"about_ca_topic_score_gemma":0.011705978,"teacher_disagreement_score":0.013086049,"about_ca_system_score_codex":0.0015895911,"about_ca_system_score_gemma":0.0024786415,"threshold_uncertainty_score":0.043777227},"labels":[],"label_agreement":null},{"id":"W2756042703","doi":"10.1139/cjce-2017-0185","title":"A case study of combined winter road snow plowing and de-icer spreading","year":2017,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Transport engineering; Computer science; Snow removal; Snow; Hierarchy; Plough; Term (time); Operations research; Plan (archaeology); Meteorology; Engineering; Geography","score_opus":0.018792628670988205,"score_gpt":0.246998523770845,"score_spread":0.22820589509985678,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2756042703","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.95898837,0.00027776303,0.029817663,0.0004309426,0.000054541226,0.00021393786,0.0003301282,0.00016696699,0.0097196875],"genre_scores_gemma":[0.9817069,0.0001449632,0.013933518,0.000037946797,0.00001442694,0.000045546272,0.00019959033,0.00003061328,0.0038865765],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9992995,0.00022743893,0.000036967904,0.00010268168,0.00014925627,0.00018417406],"domain_scores_gemma":[0.9986866,0.0006926535,0.000112118294,0.00014722515,0.00014119159,0.00022012177],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010250029,0.0007582718,0.0006127029,0.0006364993,0.0016802329,0.0009989709,0.0016731134,0.0023483313,0.0029202243],"category_scores_gemma":[0.001599599,0.00026942368,0.00092179235,0.0011349245,0.0008620454,0.00091626827,0.0007744262,0.0009787673,0.00033040272],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00054979336,0.0009852671,0.018396068,0.00035703104,0.00017605866,0.026033964,0.00079717365,0.8957013,0.013853588,0.008716097,0.0032923187,0.031141343],"study_design_scores_gemma":[0.0002603583,0.0009875868,0.014213692,0.00004848149,0.00014383548,0.0042062,0.004799908,0.93915117,0.016491946,0.0077076363,0.011913135,0.000076028075],"about_ca_topic_score_codex":0.018950505,"about_ca_topic_score_gemma":0.032909915,"teacher_disagreement_score":0.018950505,"about_ca_system_score_codex":0.0011358628,"about_ca_system_score_gemma":0.0009925356,"threshold_uncertainty_score":0.037680387},"labels":[],"label_agreement":null},{"id":"W2758864124","doi":"","title":"An Extended Branch-and-Bound Method for Locomotive Assignment","year":2003,"lang":"fr","type":"article","venue":"PolyPublie (École Polytechnique de Montréal)","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Group for Research in Decision Analysis","funders":"","keywords":"Backtracking; Branch and bound; Mathematical optimization; Computer science; Heuristic; Node (physics); Set (abstract data type); Branch and cut; Branch and price; Integer programming; Operations research; Mathematics; Engineering","score_opus":0.014757337852493618,"score_gpt":0.28410462950859844,"score_spread":0.2693472916561048,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2758864124","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002093123,0.00022407905,0.9953734,0.000044849243,0.00002340957,0.000048861115,0.000030248335,0.00028413284,0.0018777957],"genre_scores_gemma":[0.06597375,0.00030603632,0.9296662,0.00008287141,0.000050724655,0.0003004395,0.00018920636,0.00022686453,0.0032039378],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988977,0.00039653014,0.000041248873,0.00013516963,0.00042678416,0.00010265468],"domain_scores_gemma":[0.9983211,0.0010925727,0.000105690364,0.00012312438,0.00030273967,0.000054714117],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019459262,0.0012727876,0.001406343,0.0015362354,0.00065372395,0.0011463314,0.002145504,0.0014624051,0.006396064],"category_scores_gemma":[0.0043367553,0.00071714044,0.0007969357,0.0023067927,0.00072982354,0.0015309112,0.0011537408,0.0016098408,0.0015181025],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022866968,0.00012296744,0.000439019,0.00020896191,0.00007984508,0.000095754716,0.00010700125,0.6711485,0.0030449643,0.027453845,0.0035087876,0.2935618],"study_design_scores_gemma":[0.000042347732,0.00004097959,0.00007326675,0.000020089474,0.000012036987,0.00002634021,0.0000062319214,0.9878236,0.0005227016,0.008110339,0.003311918,0.000010007336],"about_ca_topic_score_codex":0.005927132,"about_ca_topic_score_gemma":0.005040663,"teacher_disagreement_score":0.006396064,"about_ca_system_score_codex":0.0009395405,"about_ca_system_score_gemma":0.0014758735,"threshold_uncertainty_score":0.021396935},"labels":[],"label_agreement":null},{"id":"W2759685288","doi":"10.1287/trsc.2017.0783","title":"A Branch-and-Cut Algorithm for the Multidepot Rural Postman Problem","year":2017,"lang":"en","type":"article","venue":"Transportation Science","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"","keywords":"Integer programming; Traverse; Mathematical optimization; Linear programming; Extension (predicate logic); Branch and cut; Mathematics; Column generation; Heuristic; Binary number; Minimum weight; Algorithm; Set (abstract data type); Computer science; Combinatorics","score_opus":0.021466535352644686,"score_gpt":0.3028421713022516,"score_spread":0.2813756359496069,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2759685288","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012711019,0.00028599423,0.9783197,0.00028777454,0.000056631914,0.00024578537,0.00021878601,0.00043438567,0.007439945],"genre_scores_gemma":[0.080028005,0.00028985625,0.9137589,0.00012014155,0.00003559244,0.0003241427,0.00058471045,0.00018588839,0.0046727546],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99944896,0.00016786021,0.000021317655,0.00011957398,0.00013281975,0.00010939207],"domain_scores_gemma":[0.99939585,0.00037060853,0.000055679357,0.00004124717,0.0000853617,0.00005133656],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007810095,0.0011006219,0.0011339919,0.00087504275,0.0010395626,0.0011632589,0.0015726613,0.0016042596,0.00906171],"category_scores_gemma":[0.0018027802,0.00069538027,0.000689353,0.0015011387,0.0005053978,0.001653103,0.0011851292,0.0017507575,0.0012032269],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022959668,0.00035381396,0.0006501316,0.00027682394,0.0000617835,0.00018686561,0.00013262956,0.6397,0.002572201,0.039243158,0.012716457,0.30387658],"study_design_scores_gemma":[0.00007941601,0.00010043625,0.00017084161,0.000023342183,0.000017626293,0.000074437,0.000056234843,0.97159374,0.0009784143,0.020627562,0.0062670344,0.0000109651],"about_ca_topic_score_codex":0.004860424,"about_ca_topic_score_gemma":0.0066647977,"teacher_disagreement_score":0.00906171,"about_ca_system_score_codex":0.0010876915,"about_ca_system_score_gemma":0.0022243995,"threshold_uncertainty_score":0.030314505},"labels":[],"label_agreement":null},{"id":"W2765241042","doi":"10.71781/11079","title":"Recourse policies in the vehicle routing problem with stochastic demands","year":2017,"lang":"en","type":"dissertation","venue":"Papyrus : Institutional Repository (Université de Montréal)","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Vehicle routing problem; Computer science; Routing (electronic design automation); Operations research; Business; Mathematical optimization; Engineering; Computer network; Mathematics","score_opus":0.007124106004110809,"score_gpt":0.19946898984464173,"score_spread":0.19234488384053092,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2765241042","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17487974,0.0019169825,0.80537486,0.0030569655,0.0001545219,0.00013874851,0.0005207887,0.00034343373,0.0136139905],"genre_scores_gemma":[0.9264335,0.0012632182,0.05826935,0.00024509314,0.00011040525,0.00018506193,0.00036373796,0.00013206522,0.012997504],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987942,0.0005325482,0.00005651324,0.0002273291,0.0001496302,0.00023970837],"domain_scores_gemma":[0.99646384,0.002672853,0.000312677,0.00008376561,0.00023469425,0.0002321802],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017010763,0.00076488365,0.0013256384,0.00048560483,0.00051891,0.0019003755,0.0011456552,0.0016032822,0.0030694546],"category_scores_gemma":[0.0061988863,0.00068707607,0.000720892,0.0007338092,0.0007556399,0.001876924,0.0009458885,0.0016144519,0.00029515545],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013580213,0.00005610719,0.0007903117,0.000105572646,0.000039506616,0.00010320203,0.000083872976,0.9468375,0.00046273644,0.03945001,0.0014790109,0.010456353],"study_design_scores_gemma":[0.000018214729,0.000041055704,0.00017231607,0.000012190889,0.000007890525,0.000022068218,0.000048593687,0.9774366,0.00012577322,0.021297695,0.00080973963,0.000007831002],"about_ca_topic_score_codex":0.008187627,"about_ca_topic_score_gemma":0.0068275337,"teacher_disagreement_score":0.008187627,"about_ca_system_score_codex":0.0015942788,"about_ca_system_score_gemma":0.0017644848,"threshold_uncertainty_score":0.016279936},"labels":[],"label_agreement":null},{"id":"W2766561679","doi":"10.5539/jms.v7n4p89","title":"A Vehicle Routing Problem with Consideration of Green Transportation","year":2017,"lang":"en","type":"article","venue":"Journal of Management and Sustainability","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Ministry of Science and Technology, Taiwan","keywords":"Fuel efficiency; Vehicle routing problem; Terrain; Routing (electronic design automation); Situated; Service (business); Consumption (sociology); Transport engineering; Operations research; Computer science; Genetic algorithm; Automotive engineering; Environmental economics; Business; Engineering; Computer network; Economics","score_opus":0.009895738117435464,"score_gpt":0.251584277456738,"score_spread":0.24168853933930254,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2766561679","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07092025,0.0012404829,0.91263163,0.001242623,0.0002615874,0.00018087414,0.00040020637,0.00014912763,0.012973125],"genre_scores_gemma":[0.7751827,0.0014354322,0.20657948,0.00024681858,0.00025420505,0.0003177783,0.0005494255,0.000108970824,0.01532519],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989348,0.00045660147,0.000033082975,0.00026822358,0.00013577129,0.00017146053],"domain_scores_gemma":[0.99923825,0.00047293326,0.000097492964,0.000034628647,0.00008854498,0.00006827559],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011101431,0.0014118347,0.001441146,0.0008439143,0.0008094874,0.001682659,0.0014008103,0.0023618224,0.0026367228],"category_scores_gemma":[0.0022203214,0.00063731667,0.001389481,0.0015499775,0.00083129486,0.0020483162,0.00106812,0.0010926109,0.00022919857],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005452797,0.00004268157,0.00037053425,0.00012339174,0.000053690117,0.00019771024,0.000047928545,0.95599973,0.0008134688,0.029568132,0.0011396882,0.011588415],"study_design_scores_gemma":[0.000019812564,0.00005818129,0.0001715543,0.0000112164,0.000028735487,0.00007536826,0.00004942847,0.9762323,0.00026618075,0.020490482,0.0025841293,0.00001262769],"about_ca_topic_score_codex":0.00664015,"about_ca_topic_score_gemma":0.0046481774,"teacher_disagreement_score":0.00664015,"about_ca_system_score_codex":0.0018002472,"about_ca_system_score_gemma":0.0018480371,"threshold_uncertainty_score":0.013202965},"labels":[],"label_agreement":null},{"id":"W2767149973","doi":"10.1016/j.trb.2018.05.015","title":"A unified framework for rich routing problems with stochastic demands","year":2018,"lang":"en","type":"article","venue":"Transportation Research Part B Methodological","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"","keywords":"Computer science; Mathematical optimization; Vehicle routing problem; Routing (electronic design automation); Operations research; Mathematics","score_opus":0.3402977632688913,"score_gpt":0.45561005971023105,"score_spread":0.11531229644133978,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2767149973","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00096634805,0.00011343275,0.99617803,0.00015546617,0.00003919114,0.000021779522,0.0000573814,0.000032078216,0.0024363478],"genre_scores_gemma":[0.105975606,0.0011886018,0.87786645,0.0003919684,0.0004831987,0.00057895813,0.0004953225,0.00036437347,0.012655508],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997422,0.0012834911,0.00014385465,0.0003522806,0.00057907775,0.00021936258],"domain_scores_gemma":[0.99691844,0.0015183511,0.00024014425,0.0005358595,0.0005637513,0.00022333923],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0058136205,0.001967194,0.0023029626,0.0024220415,0.0014001942,0.0032468352,0.00511734,0.0025671232,0.0071792193],"category_scores_gemma":[0.008906458,0.0015905071,0.0042734155,0.002667377,0.0022743316,0.0064141965,0.005353522,0.0051820134,0.0012091157],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008134517,0.00004181594,0.0001160532,0.000064549306,0.000039418064,0.000056751833,0.00006423575,0.11526708,0.00045678162,0.87725466,0.0015354147,0.0050950167],"study_design_scores_gemma":[0.000013226638,0.000025913943,0.00007637115,0.000029570081,0.000027289441,0.000034478635,0.00003178363,0.62663484,0.00012868799,0.3679742,0.005006322,0.000017423103],"about_ca_topic_score_codex":0.0041157673,"about_ca_topic_score_gemma":0.0058529954,"teacher_disagreement_score":0.0071792193,"about_ca_system_score_codex":0.0019900647,"about_ca_system_score_gemma":0.0028292197,"threshold_uncertainty_score":0.030745685},"labels":[],"label_agreement":null},{"id":"W2767827514","doi":"10.1007/s00291-017-0494-y","title":"Alternative formulations and improved bounds for the multi-depot fleet size and mix vehicle routing problem","year":2017,"lang":"en","type":"article","venue":"OR Spectrum","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":30,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Center for Interuniversity Research and Analysis on Organizations; Université Laval; GLS Industries (Canada)","funders":"Natural Sciences and Engineering Research Council of Canada; Alfaisal University","keywords":"Vehicle routing problem; Solver; Mathematical optimization; Computer science; Routing (electronic design automation); Lexicographical order; Set (abstract data type); Variable (mathematics); Index (typography); Mathematics","score_opus":0.028535133580071145,"score_gpt":0.30224196025263855,"score_spread":0.2737068266725674,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2767827514","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03666268,0.01296775,0.89207435,0.0025700289,0.0008875195,0.00034854875,0.0012585848,0.00080013723,0.05243034],"genre_scores_gemma":[0.29414052,0.008685576,0.6852851,0.0012152401,0.0008607359,0.0006890912,0.002116641,0.00078220177,0.006224833],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9910069,0.0036524546,0.0003565401,0.0009254882,0.0031290266,0.0009296441],"domain_scores_gemma":[0.9814641,0.01376817,0.0014085299,0.0013265935,0.0016763108,0.00035635484],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008336602,0.0029739367,0.001669305,0.0035054218,0.0009432633,0.0056925723,0.0042934027,0.0029778457,0.012470629],"category_scores_gemma":[0.021801805,0.001084793,0.0029677786,0.003946567,0.0015060052,0.007208604,0.0025561848,0.0063058427,0.0014049822],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00033969615,0.00027599937,0.0008887779,0.0008231957,0.00019732105,0.00014754554,0.00018507292,0.79146606,0.001594334,0.122735575,0.008785235,0.07256115],"study_design_scores_gemma":[0.000096703894,0.00020960179,0.00043881134,0.00029975575,0.000094497365,0.00013020218,0.0001544907,0.9319537,0.0016924761,0.05150627,0.013382264,0.00004109492],"about_ca_topic_score_codex":0.0034134067,"about_ca_topic_score_gemma":0.0050924346,"teacher_disagreement_score":0.012470629,"about_ca_system_score_codex":0.004789684,"about_ca_system_score_gemma":0.0032860162,"threshold_uncertainty_score":0.04408872},"labels":[],"label_agreement":null},{"id":"W2767831630","doi":"10.1016/j.cor.2017.11.003","title":"Vehicle routing with backhauls: Review and research perspectives","year":2017,"lang":"en","type":"article","venue":"Computers & Operations Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":120,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Backhaul (telecommunications); Vehicle routing problem; Computer science; Heuristic; Operations research; Routing (electronic design automation); Computer network; Artificial intelligence; Mathematics","score_opus":0.11812769133609638,"score_gpt":0.44026408235878545,"score_spread":0.3221363910226891,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2767831630","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0005819479,0.990119,0.006971889,0.0003740893,0.0002483529,0.00001096877,0.000020505966,0.000015730884,0.0016575076],"genre_scores_gemma":[0.0073671816,0.98660034,0.0046239323,0.0001743451,0.0004960093,0.000015075186,0.00004767357,0.000009074829,0.0006663693],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.999428,0.00013346199,0.000056708846,0.00015442814,0.00018089171,0.00004654131],"domain_scores_gemma":[0.9987072,0.00079880824,0.00014052671,0.000047193935,0.00026909445,0.000037255937],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015320143,0.0011568784,0.0021488185,0.0018260785,0.0002533068,0.0022545755,0.0019585786,0.0014653871,0.0021878632],"category_scores_gemma":[0.002021377,0.00058065425,0.0008216109,0.004307019,0.00074092986,0.0025336456,0.00071910315,0.0013839646,0.0009008328],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009838048,0.00012255108,0.0004499902,0.01853598,0.00024886953,0.000105730716,0.00006478369,0.012553361,0.0010872217,0.020700848,0.009050819,0.9369815],"study_design_scores_gemma":[0.0000903288,0.0005492182,0.0014579227,0.009427314,0.00088595925,0.0014802907,0.00038070595,0.02344354,0.0027289023,0.038028125,0.9213842,0.0001436396],"about_ca_topic_score_codex":0.0017348041,"about_ca_topic_score_gemma":0.0016739061,"teacher_disagreement_score":0.0022545755,"about_ca_system_score_codex":0.0007411537,"about_ca_system_score_gemma":0.0016803794,"threshold_uncertainty_score":0.008102179},"labels":[],"label_agreement":null},{"id":"W2769122157","doi":"10.1155/2017/9387302","title":"Finding <mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" id=\"M1\"><mml:mrow><mml:mi>p</mml:mi></mml:mrow></mml:math>-Hub Median Locations: An Empirical Study on Problems and Solution Techniques","year":2017,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":39,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Scalability; Computer science; Algorithm; Relaxation (psychology); Database","score_opus":0.028900912177476167,"score_gpt":0.2980729017373702,"score_spread":0.2691719895598941,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2769122157","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5910935,0.0027030797,0.34943017,0.0025649995,0.00023462309,0.000766755,0.01459126,0.0070925895,0.03152298],"genre_scores_gemma":[0.60109454,0.00091325137,0.37611723,0.00015896391,0.000034271154,0.00039940586,0.013317349,0.0008952229,0.0070698154],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986247,0.0005303918,0.00008268772,0.00041763403,0.00021079397,0.0001337076],"domain_scores_gemma":[0.9949274,0.0032327787,0.00044148133,0.00067179074,0.00056492526,0.00016168231],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025816222,0.0010199293,0.0007508988,0.0021722647,0.0010629932,0.0017987029,0.0014525474,0.0014277569,0.011027087],"category_scores_gemma":[0.0099102305,0.00034936864,0.0010489299,0.003717012,0.00048676346,0.0026153466,0.0009816501,0.0009656298,0.0024759667],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00042241658,0.0010475351,0.029400101,0.0018811923,0.0002463739,0.00027278028,0.0004819746,0.40483418,0.0026209364,0.022776624,0.06162735,0.47438854],"study_design_scores_gemma":[0.00018378592,0.0003411528,0.012899203,0.00021562929,0.00012867416,0.0003768073,0.0018368413,0.9121609,0.01080396,0.024169704,0.036816977,0.000066464214],"about_ca_topic_score_codex":0.014055842,"about_ca_topic_score_gemma":0.023667876,"teacher_disagreement_score":0.014055842,"about_ca_system_score_codex":0.0016855844,"about_ca_system_score_gemma":0.0028519076,"threshold_uncertainty_score":0.036889315},"labels":[],"label_agreement":null},{"id":"W2772307536","doi":"10.1016/j.tre.2017.11.008","title":"Service level, cost and environmental optimization of collaborative transportation","year":2017,"lang":"en","type":"article","venue":"Transportation Research Part E Logistics and Transportation Review","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":75,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Transport engineering; Service (business); Business; Total cost; Operations research; Operational costs; Computer science; Environmental economics; Operations management; Engineering; Marketing; Economics","score_opus":0.12142294007334825,"score_gpt":0.3747008007721558,"score_spread":0.25327786069880753,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2772307536","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.059566494,0.018977659,0.89345884,0.001334249,0.00033971315,0.00006514783,0.00015607002,0.00009977456,0.026002167],"genre_scores_gemma":[0.86387223,0.019042537,0.100656204,0.00014320189,0.0004575063,0.00016339484,0.00028312526,0.00014499247,0.01523676],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990914,0.00034879457,0.000032412794,0.00012547817,0.0002985852,0.00010336192],"domain_scores_gemma":[0.99939775,0.00037716742,0.00004996604,0.000040306746,0.00009738187,0.000037505084],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015385168,0.0010349888,0.0014056835,0.000798054,0.000346281,0.0016963539,0.0019018286,0.0013648226,0.0019344821],"category_scores_gemma":[0.002328654,0.00046454082,0.0012556245,0.0022980683,0.0008690668,0.0023176586,0.000971063,0.0011590238,0.00026051534],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000024906578,0.000041700685,0.00021046987,0.00012284328,0.00006402938,0.00001885858,0.000017659455,0.9244339,0.00039851514,0.04299254,0.0011244897,0.030550051],"study_design_scores_gemma":[0.0000063967077,0.000039700288,0.00044305917,0.000016952637,0.000027344975,0.000024502293,0.000029351875,0.9618184,0.00031976207,0.034127783,0.003134573,0.000012157055],"about_ca_topic_score_codex":0.0057219695,"about_ca_topic_score_gemma":0.003787143,"teacher_disagreement_score":0.0057219695,"about_ca_system_score_codex":0.0021582115,"about_ca_system_score_gemma":0.0012898258,"threshold_uncertainty_score":0.015658975},"labels":[],"label_agreement":null},{"id":"W2773247030","doi":"10.1016/j.tre.2017.10.007","title":"The importance of considering non-linear layover and delay costs for local truckers","year":2017,"lang":"en","type":"article","venue":"Transportation Research Part E Logistics and Transportation Review","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Wilfrid Laurier University; Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Discretization; Nonlinear system; Computer science; Mathematical optimization; Linear programming; Integer programming; Operations research; Engineering; Mathematics; Algorithm","score_opus":0.09995997690386282,"score_gpt":0.4001760545754898,"score_spread":0.300216077671627,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2773247030","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3740347,0.025657715,0.5473379,0.0043937466,0.0010847737,0.0002091693,0.0007274205,0.00022268483,0.04633187],"genre_scores_gemma":[0.9392709,0.008826153,0.029252632,0.00016811257,0.00029658963,0.00006791345,0.00034519963,0.00015493725,0.021617418],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994968,0.00014867593,0.000021676446,0.000108050845,0.00010090934,0.00012387913],"domain_scores_gemma":[0.998599,0.0007336644,0.00018394002,0.000094330004,0.00030376427,0.000085248415],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001170496,0.0012235763,0.0011866699,0.00058556284,0.0006629204,0.0020268317,0.0031030686,0.0016274404,0.0060955943],"category_scores_gemma":[0.002822535,0.00062282,0.0013353474,0.0013894949,0.0006247349,0.00427568,0.00083899806,0.0017330659,0.00046330743],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000116281015,0.00013063956,0.005197159,0.00076606544,0.00022923453,0.00029477023,0.00008992076,0.8775362,0.001207142,0.025376491,0.0030774504,0.0859787],"study_design_scores_gemma":[0.000016583926,0.00021390893,0.01049749,0.00021112748,0.00038983047,0.0003110149,0.00082912797,0.9398868,0.001983436,0.03222999,0.013351847,0.00007880445],"about_ca_topic_score_codex":0.0153490035,"about_ca_topic_score_gemma":0.025094291,"teacher_disagreement_score":0.0153490035,"about_ca_system_score_codex":0.0016962473,"about_ca_system_score_gemma":0.0019548936,"threshold_uncertainty_score":0.030519307},"labels":[],"label_agreement":null},{"id":"W2782124575","doi":"10.1007/978-3-319-69215-9_3","title":"Cumulative VRP: A Simplified Model of Green Vehicle Routing","year":2017,"lang":"en","type":"book-chapter","venue":"Springer optimization and its applications","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Lethbridge","funders":"","keywords":"Vehicle routing problem; Context (archaeology); Mathematical optimization; Column generation; Cumulative distribution function; Routing (electronic design automation); Integer programming; Computer science; Mathematics; Statistics; Geography; Probability density function","score_opus":0.040998494797837445,"score_gpt":0.27718368582240277,"score_spread":0.2361851910245653,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2782124575","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0112104,0.0017332118,0.940049,0.00074439513,0.00036015757,0.000049223923,0.00076516985,0.00032761868,0.044760812],"genre_scores_gemma":[0.6767352,0.005944783,0.18124698,0.0005545677,0.0005468631,0.000313845,0.0013384777,0.0007970881,0.13252215],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99968565,0.000090365065,0.000010316584,0.000055664437,0.00010943529,0.000048602997],"domain_scores_gemma":[0.99977607,0.000084583415,0.000026362814,0.000034288267,0.000054237564,0.000024521412],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003605651,0.0008724524,0.0010584918,0.00068745104,0.0004352603,0.0020473914,0.0025517254,0.0015196385,0.00913247],"category_scores_gemma":[0.0014621761,0.0005521594,0.00093527307,0.0018265418,0.0009002404,0.0019929078,0.0010942082,0.0015473593,0.001791836],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013520669,0.000009603128,0.00006533124,0.000039801052,0.000010501668,0.00004217273,0.000019302008,0.7982947,0.00044931078,0.18597691,0.0037875313,0.011291262],"study_design_scores_gemma":[0.0000035228832,0.000005991906,0.000048672453,0.000006944551,0.0000050490266,0.000022708155,0.0000060735906,0.9193771,0.0000829512,0.07605943,0.0043746745,0.0000069068506],"about_ca_topic_score_codex":0.0112334285,"about_ca_topic_score_gemma":0.00870361,"teacher_disagreement_score":0.0112334285,"about_ca_system_score_codex":0.0015927722,"about_ca_system_score_gemma":0.001264163,"threshold_uncertainty_score":0.030551195},"labels":[],"label_agreement":null},{"id":"W2787686159","doi":"10.1109/ssci.2017.8285385","title":"An age layered population structure genetic algorithm for the multi-depot vehicle problem","year":2017,"lang":"en","type":"article","venue":"","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brock University","funders":"","keywords":"Vehicle routing problem; Genetic algorithm; Benchmark (surveying); Ranking (information retrieval); Pareto principle; Mathematical optimization; Computer science; Population; Routing (electronic design automation); Mathematics; Artificial intelligence; Geography; Computer network","score_opus":0.027715621526807035,"score_gpt":0.3046774116243873,"score_spread":0.27696179009758026,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2787686159","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.043263033,0.0004751213,0.94878215,0.00023943014,0.00006244794,0.00008350101,0.0000629985,0.00027285976,0.006758524],"genre_scores_gemma":[0.5403533,0.00062638597,0.45287818,0.00024833335,0.000055452474,0.00029006044,0.00023737432,0.000061974075,0.005248951],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996977,0.00011804224,0.000011782859,0.000040620387,0.00009173967,0.00004016947],"domain_scores_gemma":[0.99970144,0.00014071452,0.000039996612,0.000020934902,0.000071674796,0.000025240057],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006179885,0.0005667509,0.00052871066,0.0005819513,0.0003539589,0.00079997344,0.0008978987,0.0009225699,0.0015089917],"category_scores_gemma":[0.001612018,0.00021671549,0.0005994753,0.00062466704,0.00038651912,0.000767917,0.00088193,0.0008130241,0.00027701704],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002681047,0.000041383024,0.00095457764,0.00003968378,0.000035736644,0.00007881336,0.00007866243,0.92269766,0.002089668,0.014663388,0.0009444516,0.058349166],"study_design_scores_gemma":[0.000008209056,0.000033526616,0.00012933141,0.000006140693,0.0000073267875,0.00002622135,0.000011853034,0.9959279,0.00027609442,0.0027578115,0.0008111427,0.0000043913187],"about_ca_topic_score_codex":0.0036254835,"about_ca_topic_score_gemma":0.0033468443,"teacher_disagreement_score":0.0036254835,"about_ca_system_score_codex":0.0006627249,"about_ca_system_score_gemma":0.0011650011,"threshold_uncertainty_score":0.0072087646},"labels":[],"label_agreement":null},{"id":"W2789761009","doi":"10.5267/j.dsl.2018.2.002","title":"A stochastic time-dependent green capacitated vehicle routing and scheduling problem with time window, resiliency and reliability: a case study","year":2018,"lang":"en","type":"article","venue":"Decision Science Letters","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"University of Tehran","keywords":"Scheduling (production processes); Vehicle routing problem; Reliability (semiconductor); Computer science; Mathematical optimization; Operations research; Reliability engineering; Engineering; Routing (electronic design automation); Mathematics; Computer network","score_opus":0.011872862016939803,"score_gpt":0.264793870505356,"score_spread":0.2529210084884162,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2789761009","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.77307636,0.0009586089,0.20882352,0.0015457721,0.0001499232,0.00024861802,0.0010803617,0.00021514577,0.013901704],"genre_scores_gemma":[0.9743018,0.0002841062,0.022227075,0.000041788695,0.00003235765,0.00009718443,0.00019160098,0.000019280513,0.0028047233],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992781,0.00031154783,0.000028020082,0.00009239448,0.00013653105,0.00015339257],"domain_scores_gemma":[0.9988066,0.0007469497,0.00015037178,0.0000632949,0.00009663828,0.00013612203],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001193055,0.0010971522,0.0008669271,0.0008194357,0.00093409885,0.0012448326,0.0013832848,0.0031268736,0.0019564182],"category_scores_gemma":[0.0016669189,0.00040931688,0.0013008249,0.0016271129,0.00092768454,0.00087349257,0.00081023254,0.0010593149,0.00013419616],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006610789,0.00010355932,0.00091887655,0.000060399336,0.000035570793,0.0011453776,0.00003594464,0.98676884,0.0005871006,0.0068364893,0.00055691926,0.0028848073],"study_design_scores_gemma":[0.000030073517,0.000092732305,0.00053319806,0.0000065258296,0.000023839735,0.00024147668,0.00009362135,0.9943329,0.00058253156,0.0032485316,0.000800737,0.00001386945],"about_ca_topic_score_codex":0.010760548,"about_ca_topic_score_gemma":0.0075556743,"teacher_disagreement_score":0.010760548,"about_ca_system_score_codex":0.0015004535,"about_ca_system_score_gemma":0.0012159646,"threshold_uncertainty_score":0.021395802},"labels":[],"label_agreement":null},{"id":"W2790139429","doi":"10.1007/s11750-018-0468-5","title":"Comments on: Disruption management in vehicle routing and scheduling for road freight transport: a review","year":2018,"lang":"en","type":"review","venue":"Top","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Vehicle routing problem; Computer science; Scheduling (production processes); Transport engineering; Operations research; Routing (electronic design automation); Operations management; Computer network; Engineering","score_opus":0.0571982485782732,"score_gpt":0.3558061633112891,"score_spread":0.2986079147330159,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2790139429","genre_codex":"review","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00029707013,0.6583299,0.0015092021,0.17552403,0.15398619,0.000083401355,0.0012427033,0.00016019176,0.008867312],"genre_scores_gemma":[0.0026454516,0.72067094,0.001475512,0.15750103,0.09268248,0.00009540265,0.00090353366,0.00011012712,0.023915635],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9985777,0.00024685843,0.00023967242,0.00018828365,0.00063678715,0.00011078066],"domain_scores_gemma":[0.9877686,0.004444941,0.0013574966,0.00020050182,0.005648703,0.0005796334],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029232441,0.00089296175,0.0015487766,0.0028510713,0.0006011085,0.0023155105,0.001704746,0.003604351,0.028646015],"category_scores_gemma":[0.015351425,0.0003409913,0.0012360981,0.0037012412,0.0008215068,0.00472926,0.0014391636,0.003883705,0.013167618],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000049331982,0.000017705497,0.000084983636,0.0057526343,0.00003804152,0.000058575584,0.000035808924,0.00011675771,0.00019209196,0.0015867786,0.89454013,0.09752711],"study_design_scores_gemma":[0.000018597368,0.000032042542,0.0003619451,0.0035994228,0.00006270832,0.00011039102,0.0000542449,0.000044121574,0.00009262468,0.0016571963,0.99394727,0.000019389652],"about_ca_topic_score_codex":0.0021998032,"about_ca_topic_score_gemma":0.005721359,"teacher_disagreement_score":0.028646015,"about_ca_system_score_codex":0.0009934016,"about_ca_system_score_gemma":0.0039866073,"threshold_uncertainty_score":0.0958305},"labels":[],"label_agreement":null},{"id":"W2790986720","doi":"10.1155/2018/9848104","title":"Robust Solution Approach for the Dynamic and Stochastic Vehicle Routing Problem","year":2018,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Conselho Nacional de Desenvolvimento Científico e Tecnológico","keywords":"A priori and a posteriori; Mathematical optimization; Computer science; Vehicle routing problem; Stochastic programming; Robustness (evolution); Routing (electronic design automation); Robust optimization; Benchmark (surveying); Control reconfiguration; Set (abstract data type); Operations research; Mathematics","score_opus":0.017370511382564416,"score_gpt":0.2563082079329629,"score_spread":0.23893769655039848,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2790986720","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0050039715,0.00028437946,0.992432,0.0001978158,0.00002585148,0.000043736203,0.00009720046,0.00013624689,0.0017787901],"genre_scores_gemma":[0.4540371,0.0009207929,0.53840697,0.00026846904,0.0001627337,0.000588801,0.00085194677,0.0003397708,0.004423422],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988102,0.00045223173,0.00005521289,0.00026352206,0.00027803815,0.00014079272],"domain_scores_gemma":[0.9982223,0.0012029689,0.00021523713,0.0000733232,0.00022524263,0.000060851682],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002165556,0.0015297097,0.0014373576,0.0010476535,0.00042362785,0.0013569153,0.0012875188,0.0017078643,0.0040804143],"category_scores_gemma":[0.0039829644,0.00083740783,0.0019325101,0.00096383056,0.00081724057,0.0010395477,0.0014192951,0.0022065425,0.00043423355],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016492755,0.000013306809,0.0001184802,0.000043904285,0.000026803084,0.0000275191,0.000013692338,0.9845633,0.0002998231,0.008178015,0.00039725337,0.006301366],"study_design_scores_gemma":[0.000004988764,0.000013480381,0.000025810441,0.000004768832,0.000004415174,0.0000063539715,0.000004598653,0.9955071,0.00009270273,0.004010453,0.0003227356,0.000002594354],"about_ca_topic_score_codex":0.008359524,"about_ca_topic_score_gemma":0.0056713196,"teacher_disagreement_score":0.008359524,"about_ca_system_score_codex":0.0014376561,"about_ca_system_score_gemma":0.0026242526,"threshold_uncertainty_score":0.016621709},"labels":[],"label_agreement":null},{"id":"W2794584159","doi":"10.1016/j.trc.2018.03.012","title":"Robust supply vessel routing and scheduling","year":2018,"lang":"en","type":"article","venue":"Transportation Research Part C Emerging Technologies","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":41,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"","keywords":"Robustness (evolution); Schedule; Computer science; Scheduling (production processes); Operations research; Supply chain; Metaheuristic; Engineering; Operations management; Business","score_opus":0.08231002441420703,"score_gpt":0.3483272193273699,"score_spread":0.26601719491316284,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2794584159","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005544202,0.00020400394,0.9906026,0.00022910173,0.0000742649,0.000023531855,0.00008586942,0.00014052136,0.0030959367],"genre_scores_gemma":[0.66229844,0.0010394008,0.31145152,0.00017252969,0.0003776461,0.00022387215,0.00065041095,0.00043667483,0.02334946],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991604,0.00028959455,0.000031595606,0.00021918674,0.00021888863,0.000080359154],"domain_scores_gemma":[0.99886096,0.0005874823,0.00022072066,0.00010972504,0.00017390032,0.000047253314],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014300614,0.0011726537,0.0015880641,0.00075372984,0.00039628436,0.0015619582,0.0013217566,0.001496005,0.0040722024],"category_scores_gemma":[0.0048489794,0.00094142806,0.0009961032,0.0011166831,0.000864754,0.0016142597,0.0011917322,0.0012380466,0.0007262373],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000048065427,0.000016792867,0.00008966421,0.00004138843,0.000030690437,0.000021222584,0.000011749188,0.95390475,0.0008862876,0.025967598,0.0015935675,0.017388195],"study_design_scores_gemma":[0.000004652914,0.00001187384,0.00003970396,0.0000026799237,0.0000054738525,0.000005587871,0.000003729271,0.9899234,0.0002734846,0.009126186,0.0005995582,0.0000037037657],"about_ca_topic_score_codex":0.004298878,"about_ca_topic_score_gemma":0.0021419423,"teacher_disagreement_score":0.004298878,"about_ca_system_score_codex":0.0013831147,"about_ca_system_score_gemma":0.0014559272,"threshold_uncertainty_score":0.01362282},"labels":[],"label_agreement":null},{"id":"W2794851298","doi":"","title":"The Senior Transportation Problem.","year":2017,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science","score_opus":0.11009375435604372,"score_gpt":0.35990514637520304,"score_spread":0.2498113920191593,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2794851298","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.038300674,0.01042813,0.14368999,0.036165476,0.004725399,0.00018518486,0.0077617913,0.00056444027,0.75817883],"genre_scores_gemma":[0.51621294,0.0075359503,0.03608233,0.002342134,0.0019662038,0.00023619199,0.0072254413,0.00041025894,0.4279885],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99944013,0.00018398018,0.00003901182,0.00013853451,0.00010691537,0.00009155576],"domain_scores_gemma":[0.9991844,0.00025455616,0.00007465488,0.00008175381,0.0001655135,0.00023903052],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00093570136,0.00095316075,0.00085076917,0.0007862719,0.0013141449,0.002994648,0.0010351263,0.0021040577,0.05700046],"category_scores_gemma":[0.0040816544,0.00034407276,0.00052137853,0.0020131299,0.0008386392,0.004164543,0.0014488961,0.0020105345,0.008422976],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013764846,0.00011071012,0.0009830412,0.00025846303,0.00004778068,0.00014276706,0.00017465117,0.018788861,0.00017180134,0.49412164,0.35870954,0.12635311],"study_design_scores_gemma":[0.00005823429,0.00006047109,0.0007004963,0.00013399169,0.00002793046,0.00026232534,0.00038372318,0.052689284,0.00026624696,0.62540907,0.31998748,0.000020677364],"about_ca_topic_score_codex":0.005324341,"about_ca_topic_score_gemma":0.005379565,"teacher_disagreement_score":0.05700046,"about_ca_system_score_codex":0.0016036388,"about_ca_system_score_gemma":0.0015718356,"threshold_uncertainty_score":0.19068551},"labels":[],"label_agreement":null},{"id":"W2800758442","doi":"10.1093/forsci/fxy001","title":"Identifying Key Factors for the Success of a Regional Logistic Center","year":2018,"lang":"en","type":"article","venue":"Forest Science","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Center (category theory); Key (lock); Logistic regression; Geography; Medicine; Biology; Ecology","score_opus":0.09065658436735671,"score_gpt":0.34740818640079074,"score_spread":0.25675160203343406,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2800758442","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9679405,0.00026577836,0.012253563,0.0011363304,0.000031055795,0.00012395042,0.00023268448,0.000077341865,0.017938852],"genre_scores_gemma":[0.9974106,0.00006826123,0.0014893019,0.00001840321,0.000010922317,0.000011801678,0.000057207948,0.000015390162,0.0009180193],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9977691,0.00057508604,0.00009459741,0.00026210005,0.00040442025,0.00089477055],"domain_scores_gemma":[0.98047024,0.00926842,0.004445647,0.00045124465,0.0029102569,0.0024541575],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0032753916,0.00051443704,0.0005626638,0.0015436286,0.0014077914,0.0053142137,0.000900608,0.0014873594,0.008337142],"category_scores_gemma":[0.017968211,0.00034471333,0.0004576199,0.0016662374,0.0014644592,0.0026844726,0.0014260447,0.0013982853,0.0011954255],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00066304154,0.0004804356,0.7565638,0.0003547551,0.0005526896,0.0013661833,0.00080400903,0.1407699,0.005657954,0.03411605,0.0057279584,0.052943274],"study_design_scores_gemma":[0.00009774894,0.0008520235,0.6224381,0.0002264635,0.00055151916,0.000596463,0.017066458,0.31246424,0.006386646,0.027959501,0.011185463,0.00017533735],"about_ca_topic_score_codex":0.015625326,"about_ca_topic_score_gemma":0.033109304,"teacher_disagreement_score":0.015625326,"about_ca_system_score_codex":0.002329493,"about_ca_system_score_gemma":0.005175417,"threshold_uncertainty_score":0.031068742},"labels":[],"label_agreement":null},{"id":"W2800829962","doi":"10.1016/j.cor.2018.05.004","title":"A decomposition heuristic for a rich production routing problem","year":2018,"lang":"en","type":"article","venue":"Computers & Operations Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":46,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"Conselho Nacional de Desenvolvimento Científico e Tecnológico; Fundação de Amparo à Pesquisa do Estado de São Paulo","keywords":"Solver; Mathematical optimization; Heuristic; Computer science; Benchmark (surveying); Routing (electronic design automation); Vehicle routing problem; Context (archaeology); Set (abstract data type); Production (economics); Decomposition; Mathematics","score_opus":0.06507195492111406,"score_gpt":0.4036858702288438,"score_spread":0.3386139153077297,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2800829962","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.040729128,0.0004162109,0.9491515,0.00026149064,0.00008439142,0.00011128284,0.00017307639,0.0002588329,0.0088140005],"genre_scores_gemma":[0.26436964,0.00037921933,0.72998965,0.00012648609,0.000049972747,0.00021035902,0.00035231814,0.00018845081,0.0043338933],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997049,0.00011829056,0.0000117167865,0.000048881444,0.000057071837,0.000059103808],"domain_scores_gemma":[0.9993892,0.0003945047,0.00004106944,0.000053662458,0.00006421906,0.000057363653],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007724782,0.0009127029,0.0009154746,0.0010875153,0.000564756,0.0009650067,0.0008789887,0.0013305234,0.0048116497],"category_scores_gemma":[0.0018055919,0.00071624503,0.001076352,0.0012446222,0.0005280909,0.001005941,0.001242202,0.0011545318,0.00051007216],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010576352,0.00010581558,0.00023360793,0.0001281148,0.000039470542,0.00011194541,0.00006248152,0.91784215,0.003135495,0.015795283,0.00262206,0.059817817],"study_design_scores_gemma":[0.000028440667,0.00003271232,0.00007381592,0.000017480063,0.00001653794,0.000026678477,0.000021388545,0.9895739,0.00035287702,0.008861713,0.0009891449,0.0000052327728],"about_ca_topic_score_codex":0.0034685854,"about_ca_topic_score_gemma":0.004153616,"teacher_disagreement_score":0.0048116497,"about_ca_system_score_codex":0.00081439264,"about_ca_system_score_gemma":0.0009794487,"threshold_uncertainty_score":0.016096532},"labels":[],"label_agreement":null},{"id":"W2801518913","doi":"10.1007/978-3-319-91641-5_6","title":"Collaborative Agent Teams (CAT): From the Paradigm to Implementation Guidelines","year":2018,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Computer science; Key (lock); Representation (politics); Solver; Problem solver; Architecture; Multi-agent system; Software engineering; Distributed computing; Theoretical computer science; Artificial intelligence; Programming language; Computer security","score_opus":0.027408213938519277,"score_gpt":0.3262787265091211,"score_spread":0.2988705125706018,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2801518913","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0015636812,0.010826835,0.89241934,0.007799092,0.0011214712,0.00019534004,0.00013933695,0.0009651059,0.084969796],"genre_scores_gemma":[0.09811477,0.014335224,0.84620273,0.0019098507,0.0009864907,0.00095392007,0.00034264522,0.00061373523,0.03654058],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.996852,0.0014590956,0.00028568742,0.0003776548,0.000888533,0.00013702936],"domain_scores_gemma":[0.9973959,0.001260191,0.00012437152,0.00052104035,0.00048261802,0.0002159719],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0036001203,0.00092069036,0.00055895123,0.0009526627,0.0010803137,0.0063884053,0.0030467645,0.0037222481,0.007977425],"category_scores_gemma":[0.0062474017,0.0007718932,0.00069387746,0.0019190895,0.0034030408,0.006533187,0.0039920774,0.0041184453,0.0042019067],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013777022,0.000032483276,0.00009581142,0.00029367823,0.000010811831,0.00007200863,0.00041310204,0.0040869303,0.00032041827,0.8869818,0.02140252,0.086276636],"study_design_scores_gemma":[0.000021185768,0.000047925798,0.00007408133,0.00048917305,0.000014314079,0.00034741807,0.00026477728,0.020668168,0.0009122667,0.6668997,0.31023353,0.00002747762],"about_ca_topic_score_codex":0.0016872525,"about_ca_topic_score_gemma":0.0017716924,"teacher_disagreement_score":0.007977425,"about_ca_system_score_codex":0.0014714566,"about_ca_system_score_gemma":0.0023506593,"threshold_uncertainty_score":0.026687145},"labels":[],"label_agreement":null},{"id":"W2806899243","doi":"10.1007/978-3-319-93031-2_35","title":"The WeightedCircuitsLmax Constraint","year":2018,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Travelling salesman problem; Constraint (computer-aided design); Computer science; Mathematical optimization; Relaxation (psychology); Bounded function; Graph; Constraint satisfaction problem; Bottleneck traveling salesman problem; 2-opt; Mathematics; Algorithm; Theoretical computer science; Artificial intelligence","score_opus":0.01652795363498932,"score_gpt":0.24617602208944842,"score_spread":0.2296480684544591,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2806899243","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0075681894,0.0019574866,0.5774182,0.001700646,0.001127438,0.00011083906,0.0015402875,0.00068278244,0.40789416],"genre_scores_gemma":[0.30987084,0.0063239024,0.21585059,0.0022428147,0.0009149702,0.0006121132,0.0034417335,0.0021422345,0.45860082],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9994272,0.000088849854,0.000024102781,0.00016755762,0.00020260479,0.00008974429],"domain_scores_gemma":[0.999514,0.0001606856,0.000033623633,0.0001291241,0.00013741467,0.00002528425],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037875792,0.0010937867,0.0005563717,0.00051809056,0.0006758486,0.0018147236,0.0018894272,0.0010816942,0.060220987],"category_scores_gemma":[0.0016238171,0.00047042425,0.0005893509,0.0012097907,0.00068591355,0.0031006034,0.0015137856,0.0020572003,0.011030973],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010195362,0.000053246153,0.000210122,0.0005989553,0.00003400819,0.0002747209,0.000057598412,0.029952697,0.011132294,0.67018664,0.064996846,0.22240095],"study_design_scores_gemma":[0.000027737815,0.0001011585,0.00039506488,0.00025482153,0.00006353513,0.0009106801,0.00006565601,0.07846173,0.016931398,0.43816918,0.46458623,0.00003276237],"about_ca_topic_score_codex":0.0012044837,"about_ca_topic_score_gemma":0.0030561164,"teacher_disagreement_score":0.060220987,"about_ca_system_score_codex":0.0008645266,"about_ca_system_score_gemma":0.0008732325,"threshold_uncertainty_score":0.20145929},"labels":[],"label_agreement":null},{"id":"W2807082804","doi":"10.1007/s10732-018-9374-0","title":"On the empirical scaling of running time for finding optimal solutions to the TSP","year":2018,"lang":"en","type":"article","venue":"Journal of Heuristics","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Belgian Federal Science Policy Office; Fonds De La Recherche Scientifique - FNRS","keywords":"Scaling; Mathematics; Mathematical optimization; Computer science; Geometry","score_opus":0.10556523700704039,"score_gpt":0.35214306271432305,"score_spread":0.24657782570728265,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2807082804","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.81989425,0.014036142,0.13031112,0.005833247,0.0011233455,0.00021181222,0.004615898,0.0039770566,0.019997086],"genre_scores_gemma":[0.9408738,0.0014787967,0.04979403,0.0004942634,0.00034697133,0.0001640656,0.003836859,0.001137889,0.001873303],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.98327315,0.008060735,0.001331684,0.0030553944,0.0032059245,0.0010731448],"domain_scores_gemma":[0.65742326,0.29817796,0.0067745065,0.025725722,0.009628088,0.002270354],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.015135426,0.0013702641,0.0013274356,0.004002226,0.0013750048,0.0023716514,0.0029600607,0.0033834632,0.005737657],"category_scores_gemma":[0.22823985,0.00090767944,0.0010595198,0.0052946676,0.0030620452,0.006368378,0.0020476845,0.003989242,0.0012499165],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0026855837,0.0012319336,0.069369175,0.0014326049,0.0007108227,0.0005442162,0.0008888763,0.681981,0.007235079,0.023413101,0.028484114,0.18202342],"study_design_scores_gemma":[0.00017629491,0.0004919653,0.024192464,0.00017332881,0.00018659847,0.0010295043,0.0004968358,0.948473,0.003382355,0.017367033,0.003935114,0.00009555216],"about_ca_topic_score_codex":0.0061830166,"about_ca_topic_score_gemma":0.0052238884,"teacher_disagreement_score":0.015135426,"about_ca_system_score_codex":0.0020809458,"about_ca_system_score_gemma":0.0019011744,"threshold_uncertainty_score":0.08004475},"labels":[],"label_agreement":null},{"id":"W2807264637","doi":"10.1007/978-3-319-93031-2_30","title":"Modelling and Solving the Senior Transportation Problem","year":2018,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Mathematical optimization; Decomposition; Integer programming; Heuristic; Transportation theory; Routing (electronic design automation); Operations research; Profit (economics); Heuristics; Constraint (computer-aided design); Artificial intelligence; Algorithm; Mathematics","score_opus":0.017596636783663223,"score_gpt":0.2364923174511141,"score_spread":0.21889568066745088,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2807264637","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.028572157,0.001650806,0.8961756,0.0021179584,0.0003754758,0.000058551694,0.0006710639,0.00021793466,0.070160545],"genre_scores_gemma":[0.60077465,0.0043087336,0.2565726,0.00040312656,0.00048884354,0.00025504138,0.002310745,0.0003563083,0.1345299],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996575,0.000121172015,0.00001686465,0.00007647057,0.00006549276,0.00006240655],"domain_scores_gemma":[0.9997104,0.00012476087,0.000036275673,0.000031201194,0.00005450362,0.000042948395],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00058589247,0.0008294383,0.0007225988,0.00048452563,0.0005408567,0.0018761337,0.001128304,0.0015400046,0.009966363],"category_scores_gemma":[0.0017138484,0.00044015583,0.0010746327,0.0011534244,0.0007170543,0.0021903715,0.0013422737,0.0014017245,0.001545481],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000041784562,0.00005246599,0.0006015296,0.00017185112,0.000028039776,0.000105395884,0.0001329056,0.4917316,0.00057829317,0.437753,0.016135648,0.05266755],"study_design_scores_gemma":[0.000010804572,0.000029605784,0.0002078734,0.000039616993,0.000010544506,0.00006982916,0.00008892504,0.66930854,0.00029898615,0.30116397,0.028757103,0.000014152154],"about_ca_topic_score_codex":0.008225892,"about_ca_topic_score_gemma":0.0064511183,"teacher_disagreement_score":0.009966363,"about_ca_system_score_codex":0.0010780242,"about_ca_system_score_gemma":0.0014488485,"threshold_uncertainty_score":0.03334081},"labels":[],"label_agreement":null},{"id":"W2808768722","doi":"10.1002/net.21826","title":"Integer programming formulations for minimum deficiency interval coloring","year":2018,"lang":"en","type":"article","venue":"Networks","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Office of Naval Research; National Science Foundation","keywords":"Mathematics; Combinatorics; Graph coloring; Discrete mathematics; Edge coloring; Interval graph; Fractional coloring; Integer programming; Greedy coloring; Vertex (graph theory); Graph; Line graph; Pathwidth; Graph power; Algorithm","score_opus":0.025214227599909756,"score_gpt":0.2909210732592273,"score_spread":0.2657068456593175,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2808768722","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008373642,0.0003792962,0.98316735,0.00037376705,0.000049175116,0.00008770276,0.00026154335,0.00006295031,0.0072445],"genre_scores_gemma":[0.28584477,0.0013360672,0.70281935,0.0003145178,0.00018746172,0.0007978318,0.0007408851,0.0001725852,0.0077865548],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99886334,0.000483792,0.000053709573,0.00018064596,0.0002824893,0.00013604696],"domain_scores_gemma":[0.99598855,0.003101811,0.00029219728,0.00014672297,0.00037004045,0.000100623634],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026335632,0.0015997917,0.0011436729,0.00095858215,0.00049395656,0.0020568303,0.00163052,0.0013566194,0.004246611],"category_scores_gemma":[0.008590885,0.00079176424,0.0010465757,0.0016841767,0.0011041383,0.0021192783,0.0010469008,0.0029416152,0.0005066712],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000429565,0.00008698738,0.0003642983,0.00013570342,0.000018403683,0.00008148587,0.00010683214,0.83875203,0.000599328,0.1407152,0.0023820158,0.01671476],"study_design_scores_gemma":[0.000013978621,0.00002228135,0.00006596931,0.000021192871,0.0000061519927,0.000022664759,0.00003180814,0.9451454,0.00018718981,0.05318689,0.0012904954,0.0000059501617],"about_ca_topic_score_codex":0.0030808481,"about_ca_topic_score_gemma":0.0030609958,"teacher_disagreement_score":0.004246611,"about_ca_system_score_codex":0.0020541893,"about_ca_system_score_gemma":0.0016885125,"threshold_uncertainty_score":0.014904201},"labels":[],"label_agreement":null},{"id":"W2809160987","doi":"10.1002/net.21827","title":"A two‐phase Pareto local search heuristic for the bi‐objective pollution‐routing problem","year":2018,"lang":"en","type":"article","venue":"Networks","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Group for Research in Decision Analysis","funders":"Conselho Nacional de Desenvolvimento Científico e Tecnológico","keywords":"Pareto principle; Heuristic; Mathematical optimization; Routing (electronic design automation); Pollution; Phase (matter); Pareto optimal; Computer science; Multi-objective optimization; Mathematics; Chemistry; Ecology; Biology","score_opus":0.022229289596827535,"score_gpt":0.31266925528611006,"score_spread":0.29043996568928254,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2809160987","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023102386,0.0003580789,0.97021693,0.00014002238,0.000052225685,0.0001794451,0.000055625824,0.00027573138,0.005619636],"genre_scores_gemma":[0.38535705,0.0004120466,0.6060351,0.00027461495,0.000058322625,0.00094485626,0.00032089883,0.00019235852,0.0064047547],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995285,0.00021340683,0.00001809528,0.000050319966,0.00012392087,0.000065790904],"domain_scores_gemma":[0.99940014,0.0003889621,0.000049245155,0.00002917498,0.00009110789,0.000041421743],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012494362,0.0011863128,0.0011425877,0.0012678324,0.0005227657,0.0007606351,0.0013986236,0.0015134476,0.0034171524],"category_scores_gemma":[0.0016323676,0.00057500286,0.0008946626,0.0009656846,0.0006565902,0.00084595685,0.0011123959,0.0010402665,0.0005145712],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007290499,0.000117553514,0.00015082861,0.00009509955,0.000029075787,0.000059205187,0.000039336086,0.9548991,0.0011712405,0.005830081,0.0010680051,0.03646755],"study_design_scores_gemma":[0.000035012174,0.00006125403,0.0000476564,0.000009237654,0.000008385741,0.000012604516,0.0000130717945,0.99746895,0.00029922562,0.0015447102,0.0004939769,0.0000059590757],"about_ca_topic_score_codex":0.0030039772,"about_ca_topic_score_gemma":0.0034904843,"teacher_disagreement_score":0.0034171524,"about_ca_system_score_codex":0.0008113138,"about_ca_system_score_gemma":0.001735683,"threshold_uncertainty_score":0.011431575},"labels":[],"label_agreement":null},{"id":"W28167682","doi":"10.1184/r1/6705596","title":"Flexible Milk-Runs for Stochastic Vehicle Routing","year":2018,"lang":"en","type":"article","venue":"Journal of Cell Science","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Booth University College","funders":"","keywords":"Vehicle routing problem; Robustness (evolution); Mathematical optimization; Computer science; Routing (electronic design automation); Key (lock); Column generation; Stochastic programming; Set (abstract data type); Operations research; Engineering; Mathematics; Computer network; Computer security","score_opus":0.02149614779217763,"score_gpt":0.29220186188904357,"score_spread":0.27070571409686595,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W28167682","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08789607,0.00054873753,0.90596,0.00047360672,0.00006723744,0.00006690266,0.0001772285,0.0002814484,0.0045287153],"genre_scores_gemma":[0.82188576,0.00048345546,0.17103589,0.00017200776,0.00006044556,0.0002480576,0.00035837735,0.00026012232,0.005495911],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99899596,0.000548318,0.000032591608,0.00014844054,0.00015776175,0.00011689539],"domain_scores_gemma":[0.9974605,0.0018061879,0.00030239273,0.00014947116,0.00012218377,0.00015924661],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019889448,0.0012120918,0.0012255014,0.0006138509,0.00050983543,0.0009604178,0.0014228955,0.0012823555,0.0029791472],"category_scores_gemma":[0.0052332906,0.00064789236,0.0013228303,0.0006973625,0.0012258143,0.0012700332,0.0014704318,0.0018094184,0.00024321313],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000025640482,0.0000118035505,0.00015184465,0.000017404884,0.0000142895315,0.000025612084,0.000015372432,0.988882,0.0002576037,0.008251291,0.00021013858,0.0021370146],"study_design_scores_gemma":[0.000005534986,0.000022777,0.000043732987,0.0000032910787,0.0000038316844,0.000008988188,0.000007580226,0.9898477,0.00013142645,0.009597573,0.00032368154,0.0000039304814],"about_ca_topic_score_codex":0.0027763904,"about_ca_topic_score_gemma":0.002517411,"teacher_disagreement_score":0.0029791472,"about_ca_system_score_codex":0.0011609149,"about_ca_system_score_gemma":0.0008359625,"threshold_uncertainty_score":0.01051867},"labels":[],"label_agreement":null},{"id":"W2883994353","doi":"10.1016/j.cor.2018.07.012","title":"The green mixed fleet vehicle routing problem with partial battery recharging and time windows","year":2018,"lang":"en","type":"article","venue":"Computers & Operations Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":180,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Vehicle routing problem; Computer science; Battery (electricity); Heuristic; Routing (electronic design automation); Automotive engineering; Set (abstract data type); Electric vehicle; Mathematical optimization; Computer network; Engineering; Mathematics; Artificial intelligence","score_opus":0.03899741903171219,"score_gpt":0.31642982938420966,"score_spread":0.2774324103524975,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2883994353","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20509991,0.0015421212,0.77847314,0.0021268774,0.00028365155,0.00020661176,0.0011417898,0.00027639905,0.01084946],"genre_scores_gemma":[0.89045423,0.00079611863,0.08606885,0.0003036024,0.00015668933,0.00021037635,0.0005719391,0.00017068557,0.021267481],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991579,0.00036857353,0.000027543607,0.00016250025,0.00010831201,0.00017523058],"domain_scores_gemma":[0.99880385,0.00073836726,0.00016634626,0.000059118494,0.00008270839,0.00014955203],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018111669,0.001839876,0.0022388774,0.0009835651,0.0006531407,0.0020820738,0.0023487278,0.0028165355,0.004021712],"category_scores_gemma":[0.003354228,0.001453831,0.0013937372,0.0014146913,0.0011879688,0.003719637,0.0014357154,0.0014517537,0.00026780614],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00028459626,0.00006233806,0.0003791363,0.000102429345,0.00010155753,0.0002102219,0.000033392305,0.96996677,0.0008679733,0.018216783,0.0014193165,0.0083554685],"study_design_scores_gemma":[0.00003096636,0.000056654266,0.00020921549,0.000009577345,0.000030307381,0.000059455433,0.000033179058,0.9840443,0.00024086157,0.014593204,0.0006801732,0.00001213596],"about_ca_topic_score_codex":0.0072441124,"about_ca_topic_score_gemma":0.006545035,"teacher_disagreement_score":0.0072441124,"about_ca_system_score_codex":0.0016673998,"about_ca_system_score_gemma":0.0015273322,"threshold_uncertainty_score":0.01440388},"labels":[],"label_agreement":null},{"id":"W2884232159","doi":"10.1177/0361198118786620","title":"Fuel Consumption Optimization Model for the Multi-Period Inventory Routing Problem","year":2018,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Fuel efficiency; Variable (mathematics); Consumption (sociology); Integer programming; Routing (electronic design automation); Computer science; Operations research; Environmental science; Automotive engineering; Engineering; Mathematics","score_opus":0.15659592623825086,"score_gpt":0.40161239244870445,"score_spread":0.2450164662104536,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2884232159","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023661358,0.00094953773,0.9432179,0.0008534319,0.0001329047,0.00018558459,0.0006567337,0.0002318874,0.030110594],"genre_scores_gemma":[0.76234895,0.0022563462,0.17921236,0.00034812765,0.00012469554,0.00093656604,0.0009204013,0.00018956546,0.053663015],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993343,0.00022270413,0.000024348981,0.00015212309,0.00013595104,0.00013066479],"domain_scores_gemma":[0.9995821,0.00022766861,0.000064923246,0.000015773478,0.000078935715,0.000030683223],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010364441,0.0016421253,0.0011135563,0.0006881491,0.0004497142,0.0016864415,0.002474049,0.0020824792,0.0068399576],"category_scores_gemma":[0.0015883173,0.0006773514,0.0010690392,0.001227836,0.0005979628,0.0014406288,0.0008780207,0.0017560732,0.0007139964],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002466957,0.000026235992,0.0001487218,0.000055248707,0.000013500353,0.00006367437,0.000020486208,0.9822463,0.00029116037,0.012932171,0.00067624805,0.003501575],"study_design_scores_gemma":[0.000005931803,0.0000143731495,0.000058130703,0.0000052500463,0.0000064870173,0.000011079656,0.000010206757,0.99614763,0.00007224348,0.0029642642,0.0007008014,0.000003516495],"about_ca_topic_score_codex":0.011187333,"about_ca_topic_score_gemma":0.008801266,"teacher_disagreement_score":0.011187333,"about_ca_system_score_codex":0.0024946397,"about_ca_system_score_gemma":0.0019387132,"threshold_uncertainty_score":0.022881925},"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,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_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)","score_opus":0.021965773158420066,"score_gpt":0.26770347160931873,"score_spread":0.24573769845089866,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2884929083","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09504169,0.00071408984,0.87051433,0.0016924054,0.00030546953,0.00038005132,0.0009021446,0.00040225533,0.03004747],"genre_scores_gemma":[0.8044582,0.0007308566,0.15015714,0.0002702566,0.00019244701,0.00040887424,0.00073959195,0.00024641005,0.0427962],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995615,0.00018296365,0.000013798042,0.00011467767,0.000058073707,0.00006902787],"domain_scores_gemma":[0.9995435,0.00028328344,0.000051215426,0.000026504109,0.000061426224,0.00003391725],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00088946836,0.0018908961,0.0017245971,0.00083593023,0.0006039335,0.0016959034,0.0019849052,0.0026195827,0.008419918],"category_scores_gemma":[0.0019570822,0.00094153255,0.0011679416,0.0014307214,0.0006625951,0.0016570891,0.00085824204,0.0011814242,0.00059643225],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000079196536,0.000051055988,0.00018619095,0.00007425741,0.000036846428,0.00011468573,0.00002280323,0.9799999,0.0006933903,0.00879083,0.0014712029,0.008479541],"study_design_scores_gemma":[0.00003066755,0.00005347965,0.00012415148,0.000008660548,0.000030334235,0.00003490011,0.000033132375,0.9913809,0.00039986183,0.0067633023,0.001133018,0.0000075231865],"about_ca_topic_score_codex":0.0053827036,"about_ca_topic_score_gemma":0.003883973,"teacher_disagreement_score":0.008419918,"about_ca_system_score_codex":0.0012455418,"about_ca_system_score_gemma":0.001198613,"threshold_uncertainty_score":0.028167486},"labels":[],"label_agreement":null},{"id":"W2884960304","doi":"10.1016/j.omega.2018.07.006","title":"Solving a large multi-product production-routing problem with delivery time windows","year":2018,"lang":"en","type":"article","venue":"Omega","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":93,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"","keywords":"Computer science; Supply chain; Production (economics); Sizing; Vendor; Routing (electronic design automation); Product (mathematics); Integer programming; Mathematical optimization; Set (abstract data type); Vehicle routing problem; Lead time; Operations research; Linear programming; Operations management; Engineering; Mathematics; Algorithm; Economics; Business","score_opus":0.014760608332868318,"score_gpt":0.24134078328042902,"score_spread":0.2265801749475607,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2884960304","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11028871,0.000660686,0.8813182,0.000872381,0.00013257432,0.00012527905,0.0003569246,0.000296227,0.0059490134],"genre_scores_gemma":[0.6710633,0.00066171703,0.31481937,0.00019117777,0.00014910196,0.00038914234,0.00045337153,0.0002596801,0.012013185],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99946123,0.00022673284,0.00002436959,0.00013811055,0.000075809585,0.00007376243],"domain_scores_gemma":[0.9957859,0.003715088,0.0001810477,0.000086667635,0.00011633945,0.00011498548],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002467619,0.0014090744,0.0017629082,0.00089262024,0.00061767374,0.0014516218,0.0015205193,0.0024634886,0.004727963],"category_scores_gemma":[0.0057018646,0.0014305061,0.001054353,0.0013813167,0.0008493978,0.0022088166,0.0015909283,0.001732614,0.0003100998],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000057093577,0.00004764064,0.00023261712,0.00008784611,0.000022812865,0.0000572052,0.000017745087,0.9889058,0.00032106828,0.00298408,0.0004371094,0.0068290737],"study_design_scores_gemma":[0.000011756689,0.00002198549,0.0000548233,0.000003184052,0.0000063333305,0.0000074003756,0.000012421506,0.99753726,0.00012661154,0.0020715282,0.00014417482,0.0000025499978],"about_ca_topic_score_codex":0.008020958,"about_ca_topic_score_gemma":0.0054604863,"teacher_disagreement_score":0.008020958,"about_ca_system_score_codex":0.0014412098,"about_ca_system_score_gemma":0.0019440091,"threshold_uncertainty_score":0.015948534},"labels":[],"label_agreement":null},{"id":"W2885484731","doi":"10.1287/ijoc.2020.0974","title":"Addressing Orientation Symmetry in the Time Window Assignment Vehicle Routing Problem","year":2020,"lang":"en","type":"article","venue":"INFORMS journal on computing","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Group for Research in Decision Analysis","funders":"","keywords":"Orientation (vector space); Vehicle routing problem; Computer science; Window (computing); Routing (electronic design automation); Artificial intelligence; Mathematics; Geometry; Computer network","score_opus":0.03841780626790474,"score_gpt":0.2893322890560845,"score_spread":0.25091448278817974,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2885484731","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.23963705,0.00058039976,0.7457736,0.00069400563,0.000121141056,0.00020435371,0.00028207456,0.00063325756,0.012074154],"genre_scores_gemma":[0.712853,0.0005379287,0.28255895,0.00018139563,0.00007938264,0.00016983337,0.0005164268,0.0002095155,0.0028934986],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99936134,0.00022589267,0.00002638414,0.00012139934,0.000118031,0.00014704332],"domain_scores_gemma":[0.99863416,0.00083258236,0.00021583436,0.0001444945,0.000085513864,0.00008743522],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011667891,0.0007444449,0.0008918164,0.0004395347,0.0005627692,0.0010675762,0.00088815857,0.00078379834,0.0028224515],"category_scores_gemma":[0.0033402261,0.0003992009,0.00069949013,0.000982886,0.000566191,0.0018265567,0.0011120162,0.0013420065,0.00042941942],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002505205,0.00022262236,0.0029182853,0.00016227724,0.000046203735,0.000233278,0.00016137765,0.8167467,0.006070686,0.03710617,0.0039414763,0.13214028],"study_design_scores_gemma":[0.000082016064,0.0001198251,0.00060787034,0.000019312947,0.000032040578,0.000115585324,0.0001178711,0.9645128,0.0036819798,0.027854756,0.002839157,0.000016741098],"about_ca_topic_score_codex":0.0038132113,"about_ca_topic_score_gemma":0.0029770955,"teacher_disagreement_score":0.0038132113,"about_ca_system_score_codex":0.000629419,"about_ca_system_score_gemma":0.0015997674,"threshold_uncertainty_score":0.009442091},"labels":[],"label_agreement":null},{"id":"W2885599890","doi":"10.1061/9780784481288.047","title":"Reaction Time Optimization Based on Sensor Data-Driven Simulation for Snow Removal Projects","year":2018,"lang":"en","type":"article","venue":"Construction Research Congress 2018","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Truck; Snow; Computer science; Global Positioning System; Snow removal; Real-time computing; Software; Real-time data; Environmental science; Track (disk drive); Simulation; Meteorology; Automotive engineering; Engineering","score_opus":0.11514414012687699,"score_gpt":0.39234901595416,"score_spread":0.27720487582728304,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2885599890","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.39967042,0.00025020336,0.5884251,0.00023291688,0.000053350424,0.00023987245,0.00055103644,0.00057765585,0.009999414],"genre_scores_gemma":[0.9526382,0.00010545616,0.04518605,0.000026672753,0.0000058186433,0.00023174098,0.00025672463,0.000046436373,0.0015028914],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99972934,0.00010610909,0.0000141046185,0.00004266548,0.00005410237,0.000053683798],"domain_scores_gemma":[0.9989932,0.00068845437,0.00010396556,0.000031083004,0.00013548638,0.000047782778],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008892844,0.0007648472,0.00084395654,0.0006069995,0.0003420817,0.0007183206,0.0007934682,0.0007478505,0.0014045975],"category_scores_gemma":[0.0018383695,0.0006803507,0.0008700756,0.0006248653,0.00047527134,0.00040119083,0.0006188703,0.0006324026,0.00011529825],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000005588155,0.0000046182354,0.00009594065,0.0000034820462,0.0000023296675,0.0000038069295,0.0000032397484,0.9993992,0.00006460838,0.00010950749,0.000010704223,0.0002969699],"study_design_scores_gemma":[0.0000012854931,0.0000036255803,0.000027976304,5.4548855e-7,9.0614793e-7,4.5404607e-7,0.0000022274394,0.99983716,0.00005489533,0.000045197026,0.00002515152,7.0530496e-7],"about_ca_topic_score_codex":0.0320535,"about_ca_topic_score_gemma":0.021782625,"teacher_disagreement_score":0.0320535,"about_ca_system_score_codex":0.0012910381,"about_ca_system_score_gemma":0.0015798874,"threshold_uncertainty_score":0.063733876},"labels":[],"label_agreement":null},{"id":"W2887647726","doi":"10.1007/978-3-319-95104-1_16","title":"Solving the Home Health Care Problem with Temporal Precedence and Synchronization","year":2018,"lang":"en","type":"book-chapter","venue":"Studies in computational intelligence","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Chicoutimi","funders":"","keywords":"Heuristic; Task (project management); Computer science; Synchronization (alternating current); Home health; Routing (electronic design automation); Term (time); Health care; Operations research; Artificial intelligence; Engineering; Computer network; Economics","score_opus":0.046888362037004916,"score_gpt":0.33125520669338737,"score_spread":0.28436684465638246,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2887647726","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021000216,0.0010093007,0.9558635,0.0011467696,0.00026802017,0.00006622606,0.00017092709,0.00016773843,0.020307321],"genre_scores_gemma":[0.513214,0.0021830339,0.45320213,0.00037466173,0.00044187557,0.00021362591,0.0005851444,0.00023144134,0.029554067],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995865,0.00014156484,0.000020607968,0.00008538426,0.000081385304,0.00008452111],"domain_scores_gemma":[0.99934155,0.0004860256,0.000046147925,0.000035022884,0.00004430754,0.000046977446],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009823622,0.00095634535,0.0008856247,0.00033108066,0.0005127234,0.0011062904,0.0013707699,0.0013653218,0.007221426],"category_scores_gemma":[0.0024281114,0.0005084894,0.0008686487,0.0008552097,0.00067471113,0.0014835846,0.0015401628,0.0017305912,0.00033162243],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010914758,0.000086219836,0.00043948786,0.00023472791,0.00005699223,0.00018587057,0.00013056904,0.8092896,0.0010045154,0.12311755,0.007894107,0.057451162],"study_design_scores_gemma":[0.000041324303,0.000038073074,0.00018222742,0.000025648276,0.000017970371,0.000062473635,0.00008414552,0.9230639,0.0004234794,0.07205515,0.0039943624,0.000011227439],"about_ca_topic_score_codex":0.008460674,"about_ca_topic_score_gemma":0.006900867,"teacher_disagreement_score":0.008460674,"about_ca_system_score_codex":0.00062057516,"about_ca_system_score_gemma":0.0017458966,"threshold_uncertainty_score":0.02415812},"labels":[],"label_agreement":null},{"id":"W2888546274","doi":"10.1016/j.ijpe.2018.08.020","title":"Trade-offs between environmental and economic performance in production and inventory-routing problems","year":2018,"lang":"en","type":"article","venue":"International Journal of Production Economics","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":80,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Supply chain; Production (economics); Sustainability; Environmental economics; Time horizon; Routing (electronic design automation); Computer science; Business; Operations research; Economics; Microeconomics; Marketing; Engineering","score_opus":0.01708886336193741,"score_gpt":0.23533347463246637,"score_spread":0.21824461127052897,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2888546274","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8321435,0.0030465736,0.13818917,0.003512656,0.00014649403,0.000117135656,0.00038382135,0.00020014272,0.022260515],"genre_scores_gemma":[0.9923888,0.00041386296,0.0053862566,0.00006220297,0.0000520184,0.000023893264,0.00008000948,0.00004173936,0.0015511619],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977003,0.0014434332,0.00009702168,0.00016077013,0.0002624136,0.0003361461],"domain_scores_gemma":[0.9763569,0.020954823,0.0010528495,0.0003584833,0.0005943071,0.00068252825],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0082621565,0.0012179421,0.0013797304,0.0014089557,0.0007379773,0.0032943976,0.0010662626,0.0026967495,0.0031736193],"category_scores_gemma":[0.022055384,0.0006479615,0.0007539877,0.0013687009,0.0017510178,0.003874033,0.0016205991,0.0011791268,0.00027015212],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00052403356,0.00022279243,0.0031798326,0.00016284226,0.00009654675,0.00012781234,0.00005030345,0.96729714,0.001316172,0.015772035,0.00056520273,0.01068527],"study_design_scores_gemma":[0.00006610958,0.00041284988,0.0032056049,0.00005399459,0.000083557585,0.00011176945,0.00020458174,0.96007615,0.0014062944,0.03398523,0.00036191792,0.000031866533],"about_ca_topic_score_codex":0.00071424514,"about_ca_topic_score_gemma":0.00081575767,"teacher_disagreement_score":0.0082621565,"about_ca_system_score_codex":0.0013174308,"about_ca_system_score_gemma":0.0007805289,"threshold_uncertainty_score":0.043694973},"labels":[],"label_agreement":null},{"id":"W2889549077","doi":"10.1287/trsc.2018.0878","title":"Exact Branch-Price-and-Cut Algorithms for Vehicle Routing","year":2019,"lang":"en","type":"article","venue":"Transportation Science","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal; Polytechnique Montréal; Université de Montréal; Group for Research in Decision Analysis; HEC Montréal","funders":"","keywords":"Vehicle routing problem; Computer science; Algorithm; Branch and cut; Routing (electronic design automation); Integer programming; Computer network","score_opus":0.021958453004788953,"score_gpt":0.29102438433255134,"score_spread":0.2690659313277624,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2889549077","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0018681485,0.0038479045,0.9885273,0.00027305694,0.00012162406,0.000058090234,0.0001170184,0.00018761381,0.004999298],"genre_scores_gemma":[0.12374613,0.011670155,0.85494965,0.00034684714,0.00043434397,0.00042327895,0.00091941457,0.00035947582,0.007150725],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976044,0.0008134281,0.00013223724,0.00038084123,0.00083960046,0.00022948104],"domain_scores_gemma":[0.99688137,0.0022233543,0.00020536552,0.00025805592,0.0003621163,0.00006978945],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024588162,0.0020781523,0.0022967686,0.0012674882,0.0008374374,0.0024714626,0.0025054144,0.0022656552,0.0069970433],"category_scores_gemma":[0.00834533,0.0009358133,0.0013742003,0.004397824,0.0012171941,0.0034065698,0.0017428586,0.003678224,0.0019782928],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000055815573,0.00009790465,0.00030671,0.00041605765,0.0000818895,0.00004217389,0.000062813684,0.6856169,0.00039649,0.11203555,0.00828944,0.19259821],"study_design_scores_gemma":[0.000027099002,0.000033602875,0.00009767255,0.00006259078,0.000023022392,0.000044684246,0.000020254025,0.84706503,0.0003161505,0.14497109,0.0073271324,0.000011635014],"about_ca_topic_score_codex":0.0040573003,"about_ca_topic_score_gemma":0.0039251572,"teacher_disagreement_score":0.0069970433,"about_ca_system_score_codex":0.0017879622,"about_ca_system_score_gemma":0.0024183218,"threshold_uncertainty_score":0.02340746},"labels":[],"label_agreement":null},{"id":"W2890582430","doi":"10.1016/j.ifacol.2018.08.478","title":"A Fix-and-Optimize Variable Neighborhood Search for the Biomedical Sample Transportation Problem","year":2018,"lang":"en","type":"article","venue":"IFAC-PapersOnLine","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Université Laval; HEC Montréal; Université du Québec à Montréal; Transport Canada","funders":"","keywords":"Computer science; Vehicle routing problem; Variable neighborhood search; Sample (material); Decomposition; Cluster analysis; Medical diagnosis; Variable (mathematics); Linear programming; Closing (real estate); Mathematical optimization; Duration (music); Quality (philosophy); Routing (electronic design automation); Operations research; Metaheuristic; Mathematics; Machine learning; Artificial intelligence; Algorithm; Medicine","score_opus":0.019341752699110114,"score_gpt":0.27599475139191176,"score_spread":0.25665299869280167,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2890582430","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019064166,0.00063966424,0.9761203,0.00047203817,0.00008121018,0.00012360455,0.0001242925,0.00017087841,0.0032038377],"genre_scores_gemma":[0.25439504,0.0006029902,0.7384579,0.00031755754,0.00012698659,0.0006528932,0.0005871605,0.00019558125,0.004663973],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992644,0.00039151032,0.000028851355,0.00015235406,0.00008664347,0.000076240874],"domain_scores_gemma":[0.99781847,0.0017213161,0.00014120265,0.000058179914,0.00016766613,0.0000931109],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019527661,0.0014312256,0.0015159776,0.0009163848,0.00060907967,0.00090457697,0.0015284482,0.0016577794,0.003526847],"category_scores_gemma":[0.0058317524,0.0007128637,0.0011822754,0.0009957863,0.0009048782,0.0012178604,0.0012848116,0.0018849595,0.00034441575],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000044174805,0.00005245148,0.00038702248,0.000064361244,0.00003185314,0.00004241716,0.000026747779,0.97996956,0.00027297725,0.0060016983,0.0011475473,0.011959302],"study_design_scores_gemma":[0.000010307529,0.00001949444,0.000048286027,0.0000060144444,0.0000046484975,0.0000062742124,0.000011667536,0.99744606,0.00006098276,0.0020870927,0.00029671588,0.0000025359443],"about_ca_topic_score_codex":0.010233638,"about_ca_topic_score_gemma":0.007994516,"teacher_disagreement_score":0.010233638,"about_ca_system_score_codex":0.0011610969,"about_ca_system_score_gemma":0.0019664583,"threshold_uncertainty_score":0.020348132},"labels":[],"label_agreement":null},{"id":"W2891416252","doi":"10.1007/978-981-13-0860-4_19","title":"Ant Colony Algorithm for Routing Alternate Fuel Vehicles in Multi-depot Vehicle Routing Problem","year":2018,"lang":"en","type":"book-chapter","venue":"Asset analytics","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":36,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Vehicle routing problem; Ant colony optimization algorithms; Mathematical optimization; Constraint (computer-aided design); Ant colony; Routing (electronic design automation); Metaheuristic; Engineering; Operations research; Computer science; Mathematics; Computer network","score_opus":0.041872811536891326,"score_gpt":0.2958504325081746,"score_spread":0.2539776209712833,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2891416252","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012069634,0.0015244234,0.9712423,0.00042873202,0.00028058325,0.000056366265,0.00009684156,0.00026476636,0.014036361],"genre_scores_gemma":[0.36422905,0.0028565542,0.603195,0.00022236216,0.00017492232,0.00029180772,0.00041690285,0.00025133142,0.028361922],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998311,0.00005061632,0.000006476504,0.000034476012,0.000057329064,0.00002005343],"domain_scores_gemma":[0.99981207,0.000096580516,0.00001718357,0.000013398259,0.000047806763,0.000012995207],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025434935,0.0008658041,0.00090654625,0.00040886295,0.00038941336,0.0008122448,0.0011457616,0.0008874692,0.002613325],"category_scores_gemma":[0.00078642654,0.0003452945,0.00050720415,0.00092705456,0.00037131304,0.0007539507,0.0006792879,0.0012574508,0.0005096916],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000035380435,0.000033871507,0.00017923531,0.000090517446,0.000027567508,0.00005448506,0.000039426468,0.9147124,0.0009926953,0.015843157,0.0065056686,0.061485678],"study_design_scores_gemma":[0.0000037031857,0.000007816293,0.00003056647,0.0000050425656,0.0000034773618,0.00001298655,0.000008488375,0.9936745,0.00011827552,0.004831725,0.0013012548,0.0000022038164],"about_ca_topic_score_codex":0.0060505094,"about_ca_topic_score_gemma":0.004692567,"teacher_disagreement_score":0.0060505094,"about_ca_system_score_codex":0.0005234855,"about_ca_system_score_gemma":0.00076081854,"threshold_uncertainty_score":0.012030542},"labels":[],"label_agreement":null},{"id":"W2893812993","doi":"10.1155/2019/9063232","title":"Optimization of Base Location and Patrol Routes for Unmanned Aerial Vehicles in Border Intelligence, Surveillance, and Reconnaissance","year":2019,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":56,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Heuristic; Base station; Integer programming; Computer science; Base (topology); Drone; Military Base; Routing (electronic design automation); Location model; Real-time computing; Operations research; Artificial intelligence; Engineering; Algorithm; Computer network; Mathematics","score_opus":0.009330241177657544,"score_gpt":0.26348460247772265,"score_spread":0.2541543613000651,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2893812993","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.24081826,0.0011561502,0.74487644,0.0006176437,0.00009422945,0.00029130536,0.00039430507,0.00023220992,0.011519335],"genre_scores_gemma":[0.86763334,0.00067302736,0.12600654,0.00005451758,0.000027791664,0.00024970694,0.00033233018,0.000062600164,0.0049600685],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995752,0.00019490208,0.000012342733,0.000086337546,0.00004470026,0.00008642981],"domain_scores_gemma":[0.9993813,0.00037429197,0.00010541213,0.000026565041,0.000051169085,0.00006115995],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00084737805,0.0011534909,0.0011653634,0.0007522721,0.0005531681,0.0011299574,0.0009665811,0.0013331192,0.002003772],"category_scores_gemma":[0.0015655115,0.0005631896,0.0007590038,0.0010502198,0.0004982451,0.0010441999,0.0006665368,0.00070727116,0.0002011491],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000028685838,0.000030131154,0.0002822297,0.00003650108,0.000014962393,0.00004836123,0.000018982904,0.9905496,0.0004467103,0.0019038927,0.00032306733,0.0063168914],"study_design_scores_gemma":[0.000009904008,0.00005270843,0.00017770122,0.0000050068343,0.000008242744,0.000015896732,0.000043157557,0.99805415,0.00026880953,0.0010283651,0.0003320292,0.0000040454734],"about_ca_topic_score_codex":0.009581203,"about_ca_topic_score_gemma":0.007901354,"teacher_disagreement_score":0.009581203,"about_ca_system_score_codex":0.0012496642,"about_ca_system_score_gemma":0.0013365727,"threshold_uncertainty_score":0.019050896},"labels":[],"label_agreement":null},{"id":"W2894610790","doi":"10.1287/ijoc.2017.0800","title":"The Vehicle Routing Problem with Floating Targets: Formulation and Solution Approaches","year":2018,"lang":"en","type":"article","venue":"INFORMS journal on computing","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":34,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Vehicle routing problem; Mathematical optimization; Lagrangian relaxation; Routing (electronic design automation); Limit (mathematics); Exploit; Integer programming; Branch and cut; Generalization; Computer science; Integer (computer science); Relaxation (psychology); Mathematics","score_opus":0.02495445842993473,"score_gpt":0.24189110339968825,"score_spread":0.2169366449697535,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2894610790","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005463231,0.001930859,0.98265326,0.0006979616,0.00010664706,0.00011483087,0.0001893864,0.0000685593,0.008775222],"genre_scores_gemma":[0.22610076,0.008784633,0.749985,0.00038720047,0.00048097983,0.00068275566,0.00081461144,0.00017766934,0.012586515],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99928147,0.00024201398,0.000029778805,0.00014213138,0.00020999428,0.00009457757],"domain_scores_gemma":[0.99952257,0.00025885442,0.00006959827,0.000027965369,0.000087286295,0.000033702192],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012567557,0.0013953847,0.0009799607,0.0007807286,0.00053700584,0.0023924443,0.0020038253,0.0014542277,0.0032640237],"category_scores_gemma":[0.0017701295,0.0007418861,0.0011013593,0.001723942,0.0007385338,0.0017312552,0.0015184741,0.0025378112,0.00048289757],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002717227,0.000107510656,0.00021705286,0.00024223373,0.000036734644,0.00013887804,0.00009002998,0.85212106,0.0006936847,0.10255932,0.004649251,0.03911703],"study_design_scores_gemma":[0.0000130289145,0.00002936156,0.000077874734,0.000039107385,0.000011135807,0.000060189388,0.000055675642,0.9530183,0.000258106,0.039667998,0.006758723,0.000010558848],"about_ca_topic_score_codex":0.0063791564,"about_ca_topic_score_gemma":0.004727173,"teacher_disagreement_score":0.0063791564,"about_ca_system_score_codex":0.0014198432,"about_ca_system_score_gemma":0.0020207008,"threshold_uncertainty_score":0.012684047},"labels":[],"label_agreement":null},{"id":"W2896787456","doi":"10.2139/ssrn.3179994","title":"Data-Driven Order Assignment for Last Mile Delivery","year":2018,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Mile; Order (exchange); Last mile (transportation); Computer science; Geography; Business","score_opus":0.022994081824839502,"score_gpt":0.28264060656855844,"score_spread":0.25964652474371896,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2896787456","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009998715,0.00012264303,0.9867757,0.0001711164,0.00010045657,0.00012686977,0.00030884016,0.00029121625,0.0021044947],"genre_scores_gemma":[0.5658687,0.00041159158,0.4192594,0.00018056972,0.00015693586,0.00044218055,0.0011466645,0.00033100616,0.012202926],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991191,0.00026158683,0.00004731148,0.00016009207,0.0002520036,0.0001598684],"domain_scores_gemma":[0.99762976,0.0012671342,0.00019707436,0.00026378655,0.00048656994,0.0001557245],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00195556,0.00107267,0.0016079647,0.0010817462,0.0008015163,0.0016551993,0.0023136178,0.0010982534,0.007779579],"category_scores_gemma":[0.006456545,0.0010203749,0.00092162506,0.002091799,0.000728539,0.0018545345,0.0015733106,0.0017326517,0.0012014307],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014278365,0.00008486382,0.00022827383,0.00009871864,0.0000165611,0.000042332376,0.000042592274,0.951255,0.00069779315,0.012879422,0.0022362797,0.032275435],"study_design_scores_gemma":[0.000007684752,0.000020700978,0.000028827399,0.0000037723846,0.0000029749158,0.000005514402,0.000009355745,0.99332124,0.00017617342,0.006007321,0.00041336525,0.0000031534516],"about_ca_topic_score_codex":0.008084571,"about_ca_topic_score_gemma":0.006654735,"teacher_disagreement_score":0.008084571,"about_ca_system_score_codex":0.0015839512,"about_ca_system_score_gemma":0.0023538743,"threshold_uncertainty_score":0.026025236},"labels":[],"label_agreement":null},{"id":"W2897824369","doi":"10.1016/j.dam.2018.08.026","title":"A comparison of integer programming models for the partial directed weighted improper coloring problem","year":2018,"lang":"en","type":"article","venue":"Discrete Applied Mathematics","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure; Polytechnique Montréal","funders":"","keywords":"Mathematics; Combinatorics; Knapsack problem; Vertex (graph theory); Integer programming; Integer (computer science); Upper and lower bounds; Discrete mathematics; Function (biology); Graph; Mathematical optimization; Computer science","score_opus":0.04151401789022738,"score_gpt":0.31289705484824093,"score_spread":0.2713830369580136,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2897824369","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.072331615,0.0019944722,0.89649254,0.001168391,0.0003944435,0.00020471773,0.00045181438,0.000366395,0.026595619],"genre_scores_gemma":[0.61740905,0.0039114277,0.36418173,0.0006168372,0.00027103175,0.00046909918,0.0009471,0.0005917905,0.011601933],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9974178,0.0015839528,0.00007563007,0.00021292918,0.00046320635,0.0002464666],"domain_scores_gemma":[0.9917291,0.006313505,0.00042584733,0.00053974567,0.0006808458,0.00031093086],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0051587056,0.0013288251,0.001448454,0.001388136,0.00053669774,0.0029477654,0.0026333868,0.0012434372,0.006161392],"category_scores_gemma":[0.010767814,0.0006666937,0.0016947913,0.0023500524,0.0007835448,0.0040625753,0.001254797,0.0022554563,0.00045891074],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00033809358,0.000316659,0.00064641685,0.00023570168,0.00008351701,0.000030584888,0.000086288754,0.8343415,0.0004390986,0.115713954,0.0033675644,0.04440071],"study_design_scores_gemma":[0.000016514838,0.00006412573,0.00011478754,0.000023936316,0.000021727503,0.000010957161,0.000034205314,0.9779407,0.00012513167,0.020730438,0.0009094573,0.000008055831],"about_ca_topic_score_codex":0.005270641,"about_ca_topic_score_gemma":0.006288826,"teacher_disagreement_score":0.006161392,"about_ca_system_score_codex":0.003084771,"about_ca_system_score_gemma":0.0034992958,"threshold_uncertainty_score":0.027282178},"labels":[],"label_agreement":null},{"id":"W2898596225","doi":"10.1016/j.cor.2019.02.014","title":"Electric Vehicle Routing Problem with Time-Dependent Waiting Times at Recharging Stations","year":2019,"lang":"en","type":"article","venue":"Computers & Operations Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":223,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"","keywords":"Queueing theory; Queue; Computer science; Routing (electronic design automation); Driving range; Mathematical optimization; Range (aeronautics); Vehicle routing problem; Electric vehicle; Real-time computing; Operations research; Computer network; Mathematics; Engineering","score_opus":0.025915186573026647,"score_gpt":0.3053319213757771,"score_spread":0.2794167348027504,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2898596225","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18898892,0.001142401,0.78809375,0.0029700266,0.0005112521,0.00030069755,0.0021391695,0.00042945784,0.015424284],"genre_scores_gemma":[0.90643734,0.00076035527,0.05111463,0.00029990592,0.0002708174,0.000252926,0.0012040278,0.0002254469,0.03943466],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987035,0.00035110896,0.00005632555,0.00040548423,0.00016666157,0.00031689455],"domain_scores_gemma":[0.99667054,0.0022676736,0.00038611103,0.0001080296,0.00030619785,0.00026151154],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002310872,0.002095162,0.0025109225,0.0012392286,0.0009371753,0.0027413643,0.0037274414,0.003714876,0.0072738566],"category_scores_gemma":[0.0063861175,0.0018926502,0.0014533035,0.0023148828,0.001416226,0.0029650133,0.0015397685,0.0020433054,0.0005645499],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015700374,0.00006949666,0.00046504638,0.00009703366,0.00006295125,0.0002695665,0.00004071278,0.9830313,0.0007572822,0.00930429,0.0012835002,0.0044617103],"study_design_scores_gemma":[0.000042866355,0.00006002225,0.00030743072,0.0000072134853,0.000040175735,0.0000669488,0.00004508459,0.99299556,0.00033856474,0.0055887536,0.00048880576,0.000018649931],"about_ca_topic_score_codex":0.016051032,"about_ca_topic_score_gemma":0.009134407,"teacher_disagreement_score":0.016051032,"about_ca_system_score_codex":0.0024741183,"about_ca_system_score_gemma":0.002243838,"threshold_uncertainty_score":0.031915188},"labels":[],"label_agreement":null},{"id":"W2898820646","doi":"10.1155/2018/3743710","title":"Reoptimization Heuristic for the Capacitated Vehicle Routing Problem","year":2018,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Comisión Nacional de Investigación Científica y Tecnológica; Universidad del Bío-Bío; Universidad del Valle","keywords":"Vehicle routing problem; Mathematical optimization; Heuristic; Routing (electronic design automation); Computer science; Context (archaeology); Time horizon; Metaheuristic; Algorithm; Mathematics","score_opus":0.013588291512896549,"score_gpt":0.2631056191549325,"score_spread":0.24951732764203596,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2898820646","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07391573,0.0009551885,0.9160838,0.00026867198,0.00006474711,0.00021562414,0.0001346652,0.00046975617,0.007891789],"genre_scores_gemma":[0.71060085,0.00049710553,0.28412113,0.00013367317,0.00003932717,0.00030464606,0.00037339597,0.0001939358,0.0037360096],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99946886,0.00020214135,0.000020299512,0.00009165956,0.000112758884,0.00010444397],"domain_scores_gemma":[0.999564,0.00023864451,0.00007422569,0.000034059987,0.00005889923,0.00003016161],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006872166,0.0013227226,0.001061974,0.00090981845,0.00041040385,0.00073618017,0.0011569958,0.0007959195,0.001983688],"category_scores_gemma":[0.0012596198,0.0005013671,0.000790213,0.0009032428,0.00048332242,0.000786771,0.00068224897,0.0011343765,0.00023380344],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000031297943,0.000032221553,0.00014124905,0.000029345163,0.00001876837,0.000044825647,0.000022010583,0.9841055,0.0008547227,0.0021788331,0.0004899712,0.01205128],"study_design_scores_gemma":[0.000005962374,0.000027573315,0.000053745567,0.0000040931495,0.0000059088024,0.000013023185,0.000012227442,0.9980478,0.00036629682,0.0010824208,0.00037729685,0.0000036499925],"about_ca_topic_score_codex":0.0061313557,"about_ca_topic_score_gemma":0.005387679,"teacher_disagreement_score":0.0061313557,"about_ca_system_score_codex":0.001282389,"about_ca_system_score_gemma":0.001233929,"threshold_uncertainty_score":0.012191296},"labels":[],"label_agreement":null},{"id":"W2899109642","doi":"10.1155/2018/1904340","title":"A Synchronous Optimization Model for Multiship Shuttle Tanker Fleet Design and Scheduling Considering Hard Time Window Constraint","year":2018,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Ningbo University; National Natural Science Foundation of China","keywords":"Column generation; Scheduling (production processes); Integer programming; Job shop scheduling; Mathematical optimization; Computer science; Vehicle routing problem; Fleet management; Constraint programming; Engineering; Real-time computing; Routing (electronic design automation); Stochastic programming; Algorithm; Embedded system; Mathematics","score_opus":0.02364987430930877,"score_gpt":0.2664668767632441,"score_spread":0.24281700245393534,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2899109642","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013099686,0.00017419245,0.9824686,0.00012144934,0.000038605005,0.00005104324,0.00012987024,0.00010709673,0.0038093675],"genre_scores_gemma":[0.77015215,0.0008452059,0.2165279,0.00010606853,0.00006394278,0.0005037035,0.00046787944,0.00012703886,0.011206154],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996283,0.000105567065,0.000013940548,0.00009379556,0.000090268055,0.00006804486],"domain_scores_gemma":[0.9996829,0.00015308081,0.00005876697,0.000017804,0.000053367887,0.000034089717],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000765927,0.001218231,0.0009346646,0.0004882912,0.00045696052,0.0011306825,0.0011818323,0.00096563215,0.0040893164],"category_scores_gemma":[0.0008409691,0.000618543,0.0009793801,0.00088402873,0.00044813185,0.0010784217,0.00063656515,0.0010055241,0.00036542956],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013719397,0.000010587566,0.00009156385,0.000027265396,0.000006633774,0.000026760024,0.000007873205,0.99245775,0.0005566285,0.00390803,0.00023445106,0.0026587774],"study_design_scores_gemma":[0.000003371914,0.000012284915,0.00002827513,0.000001088422,0.0000026839616,0.0000030522988,0.0000037784612,0.99888295,0.00009130467,0.00080381025,0.0001658576,0.000001480539],"about_ca_topic_score_codex":0.009337051,"about_ca_topic_score_gemma":0.008793398,"teacher_disagreement_score":0.009337051,"about_ca_system_score_codex":0.0009811476,"about_ca_system_score_gemma":0.0020064588,"threshold_uncertainty_score":0.018565357},"labels":[],"label_agreement":null},{"id":"W2899182135","doi":"10.5267/j.ijiec.2018.6.003","title":"A mixed integer linear programming formulation for the vehicle routing problem with backhauls","year":2018,"lang":"en","type":"article","venue":"International Journal of Industrial Engineering Computations","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Vehicle routing problem; Integer programming; Mathematical optimization; Linear programming; Mathematics; Integer (computer science); Computer science; Routing (electronic design automation); Computer network","score_opus":0.04464265567407138,"score_gpt":0.29323809542789636,"score_spread":0.24859543975382498,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2899182135","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004435258,0.0005892018,0.9831867,0.0005691563,0.00012300737,0.00016909224,0.00038897202,0.0001396701,0.010398866],"genre_scores_gemma":[0.19419268,0.0016514803,0.78378844,0.0005730177,0.0003588381,0.001573784,0.0010786585,0.00018784539,0.016595304],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988335,0.00049822644,0.00004912762,0.0002611659,0.00022274541,0.00013517952],"domain_scores_gemma":[0.9991129,0.0006306949,0.00008998024,0.000033322704,0.000093938186,0.000039124057],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011822704,0.0018829985,0.0012092319,0.00065843225,0.00045411373,0.0020957892,0.0016927514,0.0021826988,0.0071750986],"category_scores_gemma":[0.0023576634,0.00077354966,0.001296276,0.0014638193,0.00062628905,0.0014846871,0.0012374453,0.0027593551,0.001259642],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000571545,0.00014922167,0.00027856856,0.00040368023,0.00004405422,0.00025127243,0.00008489528,0.8959943,0.0012347226,0.0629467,0.0045500365,0.03400544],"study_design_scores_gemma":[0.000024620162,0.00007950032,0.00007138478,0.000036587993,0.000020079793,0.00007622996,0.00004503783,0.9790485,0.00032437788,0.014875864,0.0053879586,0.000009804811],"about_ca_topic_score_codex":0.0031621438,"about_ca_topic_score_gemma":0.004240705,"teacher_disagreement_score":0.0071750986,"about_ca_system_score_codex":0.0011169353,"about_ca_system_score_gemma":0.0019674543,"threshold_uncertainty_score":0.024003088},"labels":[],"label_agreement":null},{"id":"W2901773049","doi":"10.1287/ijoc.2018.0810","title":"A Joint Vehicle Routing and Speed Optimization Problem","year":2018,"lang":"en","type":"article","venue":"INFORMS journal on computing","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Mathematical optimization; Solver; Computer science; Vehicle routing problem; Benchmark (surveying); Fuel efficiency; Optimization problem; Routing (electronic design automation); Convex function; Regular polygon; Algorithm; Mathematics; Engineering","score_opus":0.02059373518829806,"score_gpt":0.25878763751141537,"score_spread":0.23819390232311732,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2901773049","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04216323,0.0004607975,0.93547463,0.001166752,0.00017388185,0.00031340105,0.0010182257,0.00045571887,0.01877337],"genre_scores_gemma":[0.56677645,0.00061536575,0.40808603,0.000360321,0.00022053513,0.00072525506,0.0017167378,0.0004635517,0.021035785],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99819535,0.000586168,0.000069503265,0.0005360156,0.00030608833,0.0003069062],"domain_scores_gemma":[0.99887985,0.00064410554,0.00010026906,0.00008291529,0.0001653203,0.0001276096],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018633486,0.0020476175,0.0019662394,0.0010605282,0.00088079553,0.0025392093,0.0023264517,0.0036740464,0.00952294],"category_scores_gemma":[0.0035490843,0.0010508538,0.0014337247,0.0018843577,0.0009601525,0.0035005582,0.0017782104,0.002122035,0.0011157708],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008606846,0.00012158417,0.0005396132,0.00014230513,0.000048008496,0.00014124619,0.00005313904,0.92612624,0.0010268267,0.034451064,0.0050584446,0.03220548],"study_design_scores_gemma":[0.000031944575,0.00007097285,0.00024332681,0.000014606085,0.000026077027,0.00010356105,0.000058603466,0.96799266,0.0007155962,0.026512975,0.0042128596,0.000016741009],"about_ca_topic_score_codex":0.003953758,"about_ca_topic_score_gemma":0.0028049243,"teacher_disagreement_score":0.00952294,"about_ca_system_score_codex":0.0018724129,"about_ca_system_score_gemma":0.0023085647,"threshold_uncertainty_score":0.03185743},"labels":[],"label_agreement":null},{"id":"W2902620155","doi":"10.1007/978-1-4614-6940-7_12","title":"Variable Neighborhood Search","year":2013,"lang":"en","type":"book-chapter","venue":"","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":107,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal; Group for Research in Decision Analysis","funders":"","keywords":"Variable neighborhood search; Heuristics; Maxima and minima; Scheme (mathematics); Metaheuristic; Mathematical optimization; Variable (mathematics); Descent (aeronautics); Exploit; Local search (optimization); Decomposition; Computer science; Gradient descent; Mathematics; Algorithm; Artificial intelligence; Geography; Artificial neural network","score_opus":0.021530523238376058,"score_gpt":0.23671686916165446,"score_spread":0.2151863459232784,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2902620155","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0027146486,0.015466536,0.6631833,0.0008568913,0.0013224268,0.000071747825,0.0003935915,0.0010946295,0.31489623],"genre_scores_gemma":[0.0681319,0.016112557,0.3500694,0.0006049631,0.000682775,0.00027922168,0.0017299819,0.0013038473,0.5610854],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99979097,0.000032795553,0.000006353696,0.00005035733,0.00010411558,0.00001542954],"domain_scores_gemma":[0.9999143,0.000028463164,0.000004868897,0.000019017816,0.00002756099,0.000005775257],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023220768,0.0009538184,0.0008698708,0.0006605463,0.00045802246,0.0009245868,0.00095406966,0.00071923545,0.022662522],"category_scores_gemma":[0.0006066166,0.0003789125,0.000466327,0.0013432314,0.0005544736,0.0011775224,0.0008926828,0.0013530105,0.008720147],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000320868,0.000045932335,0.00013796893,0.00020532585,0.000032806143,0.00004407646,0.00005783677,0.06795054,0.0014381341,0.14378059,0.12547304,0.6608016],"study_design_scores_gemma":[0.00002052414,0.00005016491,0.00044074387,0.00022021183,0.00003288273,0.0002399711,0.00006239088,0.21788405,0.0026784115,0.19810936,0.5802214,0.000039875325],"about_ca_topic_score_codex":0.0026390033,"about_ca_topic_score_gemma":0.004560785,"teacher_disagreement_score":0.022662522,"about_ca_system_score_codex":0.0006254865,"about_ca_system_score_gemma":0.0005879152,"threshold_uncertainty_score":0.07581371},"labels":[],"label_agreement":null},{"id":"W2902921897","doi":"10.1504/ijor.2019.10017910","title":"An integrated production and distribution problem with direct shipment: a case from Moroccan bottled-water market","year":2018,"lang":"en","type":"article","venue":"International Journal of Operational Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Bottled water; Production (economics); Context (archaeology); Product (mathematics); Vehicle routing problem; Routing (electronic design automation); Operations research; Computer science; Distribution (mathematics); Integer programming; Business; Operations management; Environmental science; Mathematics; Economics; Microeconomics; Environmental engineering; Algorithm","score_opus":0.03279020252808257,"score_gpt":0.35582834798141716,"score_spread":0.3230381454533346,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2902921897","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9731895,0.00058833277,0.010994235,0.00064181694,0.000048758433,0.00023052015,0.00074066274,0.000065570915,0.013500744],"genre_scores_gemma":[0.98724127,0.00022180579,0.0089437105,0.000042972653,0.000021913294,0.00008841962,0.00032211884,0.000017788456,0.003100067],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993895,0.0001929477,0.000021027361,0.000078240046,0.00006652126,0.0002518368],"domain_scores_gemma":[0.99867105,0.00087195553,0.00010343713,0.000055516484,0.00012292567,0.0001751118],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012128819,0.0014740573,0.0011075621,0.0011418734,0.0019980664,0.0021513237,0.0018153846,0.00348193,0.0059043146],"category_scores_gemma":[0.0020959836,0.0005225891,0.0011954899,0.0016035646,0.001055467,0.0012438175,0.0012877544,0.0010927343,0.0002929578],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007484366,0.00067113683,0.004666452,0.00038967584,0.000081337115,0.0075354637,0.00026599254,0.9612158,0.0023222463,0.010873942,0.0023963156,0.008833238],"study_design_scores_gemma":[0.00031948093,0.00029482614,0.0040295897,0.00003813873,0.00008496322,0.0005565728,0.0011144878,0.9847274,0.0015132825,0.0038282543,0.003440822,0.000052082552],"about_ca_topic_score_codex":0.08072841,"about_ca_topic_score_gemma":0.058191072,"teacher_disagreement_score":0.08072841,"about_ca_system_score_codex":0.00402463,"about_ca_system_score_gemma":0.0015945663,"threshold_uncertainty_score":0.16051704},"labels":[],"label_agreement":null},{"id":"W2903252607","doi":"10.1016/j.ejor.2019.10.007","title":"A branch-and-cut algorithm for an assembly routing problem","year":2019,"lang":"en","type":"article","venue":"European Journal of Operational Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Group for Research in Decision Analysis; HEC Montréal","funders":"HEC Montréal","keywords":"Branch and cut; Linear programming relaxation; Integer programming; Context (archaeology); Mathematical optimization; Routing (electronic design automation); Set (abstract data type); Linear programming; Algorithm; Production planning; Production (economics); Relaxation (psychology); Computer science; Lagrangian relaxation; Product (mathematics); Integer (computer science); Column generation; Mathematics; Programming language","score_opus":0.07423329463476477,"score_gpt":0.37031368853167523,"score_spread":0.29608039389691043,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2903252607","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011288134,0.0002578189,0.98187584,0.00029707703,0.00009344673,0.00014810787,0.00012082817,0.00051674224,0.0054020057],"genre_scores_gemma":[0.07194856,0.0002665338,0.9222714,0.0001077465,0.000068666384,0.000326517,0.00034427296,0.00019786965,0.0044684755],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999503,0.00014731514,0.000021125872,0.0000891071,0.0001614617,0.00007796104],"domain_scores_gemma":[0.9986609,0.00095452997,0.000059780916,0.000056102443,0.00018416329,0.00008447635],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012111362,0.0014843995,0.0017099238,0.001333116,0.0011410194,0.0014698257,0.0016850437,0.0027283889,0.009761672],"category_scores_gemma":[0.0030328352,0.0010648485,0.0010915628,0.0018891979,0.00073065166,0.0015041749,0.001451046,0.0024283377,0.0011980559],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002356367,0.0002609568,0.0004234097,0.00019539555,0.000053437627,0.00010142954,0.00008952542,0.7382346,0.0026894254,0.014684829,0.006566964,0.23646438],"study_design_scores_gemma":[0.000045792625,0.000056475743,0.0000739144,0.0000119879005,0.000013191106,0.000019327934,0.0000141013625,0.99330574,0.000329906,0.0050939033,0.0010296907,0.0000060082034],"about_ca_topic_score_codex":0.0074387756,"about_ca_topic_score_gemma":0.0069054537,"teacher_disagreement_score":0.009761672,"about_ca_system_score_codex":0.0010646654,"about_ca_system_score_gemma":0.0023275176,"threshold_uncertainty_score":0.032656014},"labels":[],"label_agreement":null},{"id":"W2905319009","doi":"10.1111/itor.12620","title":"The electric vehicle routing problem with shared charging stations","year":2018,"lang":"en","type":"article","venue":"International Transactions in Operational Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":123,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Agence Nationale de la Recherche","keywords":"Vehicle routing problem; Benchmark (surveying); Heuristic; Mathematical optimization; Computer science; Routing (electronic design automation); Electric vehicle; Integer (computer science); Mathematics; Computer network; Power (physics)","score_opus":0.0507784722135999,"score_gpt":0.3735352907591384,"score_spread":0.3227568185455385,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2905319009","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4791583,0.0011235607,0.48190498,0.001332978,0.00024130107,0.00025605995,0.0011947001,0.0003138581,0.034474306],"genre_scores_gemma":[0.94915575,0.0002524486,0.04231073,0.000066410335,0.000040333183,0.000102836086,0.00049004663,0.00004328283,0.00753825],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991092,0.00030822924,0.00004134797,0.00020605327,0.00014327622,0.00019192105],"domain_scores_gemma":[0.99924505,0.00040270528,0.00009467574,0.000086456974,0.000091572634,0.000079544436],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00081931305,0.0010955418,0.0011574317,0.00044249953,0.00054109254,0.001475702,0.0014099409,0.0012605787,0.006419268],"category_scores_gemma":[0.0019276206,0.00047145516,0.00082948763,0.0010739532,0.0007267393,0.0022370163,0.0016477572,0.0007954486,0.0003037798],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000109089364,0.0000669225,0.00055922876,0.00007729651,0.000052505384,0.00028780443,0.00003194035,0.9598795,0.0007285811,0.018718356,0.0015896647,0.017899068],"study_design_scores_gemma":[0.000094813266,0.00010643259,0.00043223426,0.000019115882,0.00003737183,0.00021321901,0.00016576952,0.9494159,0.0011634742,0.04335642,0.00497669,0.000018522253],"about_ca_topic_score_codex":0.0035709145,"about_ca_topic_score_gemma":0.003378738,"teacher_disagreement_score":0.006419268,"about_ca_system_score_codex":0.0010374315,"about_ca_system_score_gemma":0.0011252445,"threshold_uncertainty_score":0.0214746},"labels":[],"label_agreement":null},{"id":"W2907244310","doi":"10.1080/24725854.2018.1552820","title":"Integrated order allocation and order routing problem for e-order fulfillment","year":2019,"lang":"en","type":"article","venue":"IISE Transactions","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":30,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"Shanghai Education Development Foundation; Shanghai Municipal Education Commission; National Natural Science Foundation of China","keywords":"Order (exchange); Solver; Computer science; Order fulfillment; Mathematical optimization; Routing (electronic design automation); Integer programming; Heuristic; Benchmarking; Quality (philosophy); Operations research; Supply chain; Engineering; Mathematics; Economics; Business; Marketing; Algorithm; Artificial intelligence; Computer network","score_opus":0.0124421512583515,"score_gpt":0.24792089944327028,"score_spread":0.2354787481849188,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2907244310","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06134494,0.0005660635,0.9227886,0.0006819085,0.00011124501,0.00029678352,0.000451613,0.0002536905,0.013505195],"genre_scores_gemma":[0.5641177,0.0006597748,0.4137082,0.00023591204,0.00015391388,0.00046316083,0.0010143035,0.0002274961,0.019419529],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99899536,0.00040302434,0.000040655465,0.00021297776,0.00020289906,0.00014509949],"domain_scores_gemma":[0.9991047,0.0005550287,0.000101718775,0.00005405145,0.00010637258,0.00007817912],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011296275,0.0013359053,0.0015965322,0.0008611402,0.00076307374,0.0017451306,0.0015444536,0.0020820026,0.008496146],"category_scores_gemma":[0.0023816933,0.00076247804,0.0011392583,0.0016177809,0.00070086506,0.0019497417,0.0010800029,0.00130199,0.0007040707],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013679215,0.00018552897,0.00050809275,0.00014072098,0.000052016687,0.00016988572,0.0000465684,0.95589995,0.0012032544,0.01613451,0.0023644255,0.023158288],"study_design_scores_gemma":[0.000027076338,0.00006387524,0.00018113482,0.000009000163,0.00001770032,0.00005215578,0.000043224107,0.9919012,0.0005742128,0.0054702335,0.0016516004,0.0000084919375],"about_ca_topic_score_codex":0.007186394,"about_ca_topic_score_gemma":0.007667743,"teacher_disagreement_score":0.008496146,"about_ca_system_score_codex":0.0017137473,"about_ca_system_score_gemma":0.0024450459,"threshold_uncertainty_score":0.028422415},"labels":[],"label_agreement":null},{"id":"W2908756765","doi":"10.1155/2019/5364201","title":"A Hybrid Simulated Annealing Heuristic for Multistage Heterogeneous Fleet Scheduling with Fleet Sizing Decisions","year":2019,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Sizing; Simulated annealing; Mathematical optimization; Scheduling (production processes); Integer programming; Heuristic; Vehicle routing problem; Computer science; Job shop scheduling; Routing (electronic design automation); Mathematics","score_opus":0.013350608733517962,"score_gpt":0.27466590032402344,"score_spread":0.2613152915905055,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2908756765","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05041485,0.00029594047,0.94284415,0.00012714848,0.000056930297,0.000114942275,0.00009105282,0.00045612833,0.005598921],"genre_scores_gemma":[0.6689344,0.00015609385,0.32666597,0.00008101148,0.000025885367,0.00023809406,0.0001860518,0.00007177247,0.003640684],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996501,0.00014148004,0.000013891497,0.00006187098,0.00007368664,0.000058909358],"domain_scores_gemma":[0.99974054,0.00014688548,0.000030592913,0.000028669903,0.00003228431,0.000020911175],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004832987,0.00054431387,0.00075695163,0.0005410866,0.00041670873,0.00050867285,0.00085432647,0.00070522714,0.001585251],"category_scores_gemma":[0.0006869402,0.00042502192,0.0008121797,0.00052758976,0.00034604606,0.00045406263,0.0004738554,0.00051403145,0.0001850441],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000039115315,0.000029510473,0.00017729179,0.000023829261,0.000027043687,0.000028351973,0.000021175354,0.97478205,0.0018782363,0.0028647578,0.00036579132,0.019762713],"study_design_scores_gemma":[0.000010289741,0.000029919664,0.00007062118,0.000002420145,0.000005732278,0.000008743155,0.000005247698,0.997936,0.0005183925,0.0008340293,0.0005751847,0.0000033616539],"about_ca_topic_score_codex":0.0065240352,"about_ca_topic_score_gemma":0.0070453743,"teacher_disagreement_score":0.0065240352,"about_ca_system_score_codex":0.00082763744,"about_ca_system_score_gemma":0.0011079757,"threshold_uncertainty_score":0.0129721165},"labels":[],"label_agreement":null},{"id":"W2911388047","doi":"10.1016/j.heliyon.2019.e01158","title":"Vehicle routing for a mid-day meal delivery distribution system","year":2019,"lang":"en","type":"article","venue":"Heliyon","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Vehicle routing problem; Food delivery; Meal; Routing (electronic design automation); Delivery system; Distribution (mathematics); Set (abstract data type); Transfer (computing); Food distribution; Computer science; Day to day; Business; Operations research; Transport engineering; Operations management; Engineering; Computer network; Mathematics; Marketing; Medicine; Food science; Operating system; Biology","score_opus":0.012508411937791631,"score_gpt":0.23666581191706032,"score_spread":0.2241573999792687,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2911388047","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.030268589,0.0007310811,0.954769,0.0007327773,0.00011878947,0.00014933637,0.000367607,0.00028903128,0.012573771],"genre_scores_gemma":[0.6736401,0.0014289295,0.28978154,0.00018149814,0.00010311955,0.0003168473,0.0006650846,0.00012108139,0.033761863],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995028,0.00016899267,0.000017157936,0.0001349194,0.000065784196,0.00011039728],"domain_scores_gemma":[0.99969614,0.00014156417,0.000051783623,0.000013382226,0.00006370218,0.000033395878],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006270255,0.0008139345,0.0008098554,0.00053669774,0.0009105813,0.0014586152,0.0014009599,0.0013316427,0.006624684],"category_scores_gemma":[0.0009530912,0.00052834715,0.0007309184,0.00095877907,0.0005122923,0.0009083218,0.0008637494,0.00097473356,0.00066154235],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000038486844,0.000024751529,0.00021400888,0.00007246365,0.000014481842,0.00012101815,0.000053228974,0.9724325,0.0008413726,0.014903123,0.0012563237,0.010028204],"study_design_scores_gemma":[0.000010207519,0.000027059812,0.00010273191,0.000005153199,0.0000057356497,0.000037907637,0.000055606593,0.99158233,0.00017388839,0.005516326,0.0024767814,0.0000061402757],"about_ca_topic_score_codex":0.016057208,"about_ca_topic_score_gemma":0.012731904,"teacher_disagreement_score":0.016057208,"about_ca_system_score_codex":0.0019069931,"about_ca_system_score_gemma":0.001859947,"threshold_uncertainty_score":0.031927466},"labels":[],"label_agreement":null},{"id":"W2912438787","doi":"10.1016/j.tre.2019.01.008","title":"Reliable single-allocation hub location problem with disruptions","year":2019,"lang":"en","type":"article","venue":"Transportation Research Part E Logistics and Transportation Review","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":98,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Heuristic; Mathematical optimization; Pareto principle; Operations research; Pareto optimal; Facility location problem; Natural disaster; Location-allocation; Multi-objective optimization; Engineering; Mathematics; Geography","score_opus":0.0698358171683551,"score_gpt":0.34064801630546465,"score_spread":0.2708121991371095,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2912438787","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03512483,0.0023521937,0.95533574,0.00092501845,0.00018252758,0.00008442908,0.00032843044,0.00044917114,0.0052176397],"genre_scores_gemma":[0.86139727,0.004181935,0.12088269,0.00016879464,0.0003716726,0.00024289788,0.0006458855,0.00029956523,0.011809213],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985266,0.00046536291,0.000082824336,0.00031498255,0.00037076854,0.00023940636],"domain_scores_gemma":[0.99766135,0.0011783646,0.00035389324,0.00033466757,0.00035978897,0.00011192194],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024011482,0.0018605492,0.0027760484,0.0010387498,0.0007471424,0.0018708059,0.0041593965,0.0020660087,0.0028273384],"category_scores_gemma":[0.005189223,0.00111707,0.0012449352,0.0035725185,0.0017120781,0.003123721,0.001587646,0.0016958888,0.0005510729],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017638782,0.000033846314,0.00027412287,0.00031934987,0.00008904586,0.0002102651,0.000074961245,0.92871636,0.0011662005,0.03406467,0.0029930705,0.031881813],"study_design_scores_gemma":[0.000055834793,0.00010043517,0.0004085754,0.000039531264,0.00006718385,0.0002495426,0.00008451555,0.92506534,0.0011823056,0.06974752,0.0029718045,0.000027455982],"about_ca_topic_score_codex":0.0040899883,"about_ca_topic_score_gemma":0.0027581442,"teacher_disagreement_score":0.0041593965,"about_ca_system_score_codex":0.0019239088,"about_ca_system_score_gemma":0.0019128314,"threshold_uncertainty_score":0.013958991},"labels":[],"label_agreement":null},{"id":"W2912451127","doi":"10.1109/access.2019.2894681","title":"Brainstorming-Based Ant Colony Optimization for Vehicle Routing With Soft Time Windows","year":2019,"lang":"en","type":"article","venue":"IEEE Access","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":42,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"Natural Science Foundation of Hubei Province; Ministry of Education of the People's Republic of China; National Natural Science Foundation of China","keywords":"Ant colony optimization algorithms; Brainstorming; Simulated annealing; Computer science; Vehicle routing problem; Metaheuristic; Convergence (economics); Mathematical optimization; Ant colony; Routing (electronic design automation); Algorithm; Artificial intelligence; Mathematics","score_opus":0.014646939018490589,"score_gpt":0.26544929526132355,"score_spread":0.250802356242833,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2912451127","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023235971,0.00024451,0.97336584,0.0001003468,0.000042580334,0.000040962463,0.000009628933,0.00016763949,0.0027924627],"genre_scores_gemma":[0.74512994,0.00040687423,0.24925584,0.00014022656,0.000051280884,0.00022067846,0.000052186784,0.000086041386,0.0046570147],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997694,0.00005846771,0.000010519476,0.00004189216,0.00008907697,0.000030732666],"domain_scores_gemma":[0.99976045,0.00011619896,0.00003785167,0.0000158262,0.00005358312,0.00001602447],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003631673,0.0006700704,0.00061730976,0.00044377104,0.0004076246,0.00053155934,0.0010247036,0.00067741115,0.001145468],"category_scores_gemma":[0.0008130202,0.00029042986,0.0005946967,0.0004243129,0.0005066111,0.00090258126,0.00060624786,0.0006730667,0.0001502463],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005468066,0.000038171063,0.00038244,0.00006805001,0.000047597397,0.00007574041,0.00007201819,0.92366123,0.0053954474,0.0077708135,0.0007233983,0.06171046],"study_design_scores_gemma":[0.0000072145476,0.000021651871,0.000051686155,0.0000016789442,0.0000059266467,0.000013303478,0.000008341698,0.9976803,0.0005145182,0.001338942,0.0003525814,0.000003825392],"about_ca_topic_score_codex":0.0035127383,"about_ca_topic_score_gemma":0.0036425502,"teacher_disagreement_score":0.0035127383,"about_ca_system_score_codex":0.0004641405,"about_ca_system_score_gemma":0.00077429746,"threshold_uncertainty_score":0.0069845915},"labels":[],"label_agreement":null},{"id":"W2914448801","doi":"","title":"Proceedings of the 14th international conference on Integer Programming and Combinatorial Optimization","year":2010,"lang":"en","type":"article","venue":"Integer Programming and Combinatorial Optimization","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Integer programming; Computer science; Combinatorial optimization; Integer (computer science); Mathematical optimization; Theoretical computer science; Mathematics; Programming language; Algorithm","score_opus":0.01127464220673921,"score_gpt":0.2481053551520198,"score_spread":0.2368307129452806,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2914448801","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011926991,0.055644736,0.5668547,0.011619795,0.056921396,0.00024515679,0.00083153066,0.0011546977,0.294801],"genre_scores_gemma":[0.10051501,0.06001198,0.36227006,0.0023226265,0.022326153,0.00052801135,0.0035263859,0.0020672774,0.44643262],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9985073,0.00051103544,0.00009685748,0.00020001447,0.00053511956,0.00014960318],"domain_scores_gemma":[0.9974268,0.0011205287,0.00009424588,0.00035899226,0.00078094477,0.00021838177],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0035316213,0.00192259,0.002432059,0.0012108865,0.00070520927,0.006271424,0.0017280327,0.0014382806,0.037485406],"category_scores_gemma":[0.0039574667,0.0006718756,0.0015987669,0.001769016,0.001220776,0.0025943457,0.0015515307,0.0041856254,0.011477407],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00078719284,0.00060208625,0.0009141561,0.0006136722,0.0002887647,0.0002332551,0.0002102697,0.022812774,0.0036086626,0.10402598,0.3628724,0.5030308],"study_design_scores_gemma":[0.00011777601,0.00023786622,0.0010623928,0.0005313596,0.00017788075,0.00038391072,0.00017519179,0.19313559,0.0044367295,0.07296267,0.72671556,0.000063092506],"about_ca_topic_score_codex":0.002698421,"about_ca_topic_score_gemma":0.0030736686,"teacher_disagreement_score":0.037485406,"about_ca_system_score_codex":0.0011714452,"about_ca_system_score_gemma":0.0022564214,"threshold_uncertainty_score":0.12540114},"labels":[],"label_agreement":null},{"id":"W2915502992","doi":"10.20944/preprints201902.0183.v1","title":"Two-Echelon Routing Problem for Parcel Delivery by Cooperated Truck and Drone","year":2019,"lang":"en","type":"preprint","venue":"Preprints.org","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Distinguished Young Scholar Foundation of Hunan Province; National Natural Science Foundation of China","keywords":"Drone; Truck; Simulated annealing; Routing (electronic design automation); Tabu search; Computer science; Vehicle routing problem; Heuristic; Process (computing); Operations research; Transport engineering; Engineering; Computer network; Artificial intelligence; Automotive engineering; Algorithm","score_opus":0.06495944926391745,"score_gpt":0.320628277966875,"score_spread":0.25566882870295754,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2915502992","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16510727,0.0013978715,0.80874026,0.0012089075,0.00026242895,0.0005212971,0.0016488812,0.0003295505,0.020783544],"genre_scores_gemma":[0.82079357,0.00090008945,0.1593183,0.00018955127,0.000107068285,0.0006238633,0.0013580916,0.00016039518,0.016549109],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99884844,0.00044051965,0.000047570087,0.00032661017,0.0001384612,0.00019827184],"domain_scores_gemma":[0.9988569,0.0007132896,0.00013215448,0.00006855561,0.000104903935,0.00012418495],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016932655,0.0016333121,0.002344987,0.0010128601,0.0008899918,0.0021128864,0.0024593591,0.002378876,0.00699648],"category_scores_gemma":[0.002331218,0.0008323062,0.0015672692,0.0019821038,0.0007587528,0.0019136921,0.0013286739,0.0013270292,0.00039853322],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006283605,0.000058318547,0.00037711518,0.00012251582,0.000053621447,0.000198471,0.000037055735,0.9848031,0.0005519654,0.006571335,0.0008460681,0.0063176774],"study_design_scores_gemma":[0.000024948919,0.000052634336,0.00021549925,0.000006443585,0.000015551099,0.000060149177,0.000055316716,0.99454236,0.00021602727,0.0038461126,0.0009553429,0.000009514867],"about_ca_topic_score_codex":0.0123029705,"about_ca_topic_score_gemma":0.009171264,"teacher_disagreement_score":0.0123029705,"about_ca_system_score_codex":0.0023253895,"about_ca_system_score_gemma":0.0016400629,"threshold_uncertainty_score":0.0244627},"labels":[],"label_agreement":null},{"id":"W2916653839","doi":"10.1002/net.21925","title":"Primal column generation framework for vehicle and crew scheduling problems","year":2020,"lang":"en","type":"article","venue":"Networks","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Group for Research in Decision Analysis","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Crew scheduling; Column generation; Scheduling (production processes); Crew; Column (typography); Computer science; Operations research; Aeronautics; Engineering; Operations management; Mathematics; Mathematical optimization","score_opus":0.03948768764023189,"score_gpt":0.26363793227351073,"score_spread":0.22415024463327884,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2916653839","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0055320077,0.00022356349,0.9907279,0.0001000496,0.0000332256,0.000052915355,0.00007498115,0.00020978326,0.0030456595],"genre_scores_gemma":[0.28437066,0.00052552344,0.70879066,0.00015549621,0.00009004306,0.00039537853,0.00042759138,0.00019636458,0.0050482834],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994479,0.0002777247,0.00001470947,0.000057407302,0.00013907367,0.00006325743],"domain_scores_gemma":[0.99923515,0.0004498773,0.0000612943,0.000060764658,0.000149665,0.00004329851],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011423635,0.00088149525,0.0008453699,0.00085794076,0.00046292492,0.00092103204,0.000986106,0.0007517368,0.005080483],"category_scores_gemma":[0.0015007707,0.0005990292,0.0005891012,0.0010483793,0.00068688067,0.00069012865,0.0009755953,0.0012959588,0.0006457006],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000037737726,0.000046662266,0.00015515332,0.00007528872,0.000014311364,0.00004708003,0.000031513973,0.9392505,0.0011007156,0.023912558,0.0015400407,0.033788506],"study_design_scores_gemma":[0.0000074855284,0.000011529442,0.000020076244,0.0000042004986,0.0000020853395,0.000008423593,0.0000045646884,0.9945475,0.00026996646,0.00432547,0.0007962458,0.0000024620774],"about_ca_topic_score_codex":0.0042621368,"about_ca_topic_score_gemma":0.0037951006,"teacher_disagreement_score":0.005080483,"about_ca_system_score_codex":0.00081185316,"about_ca_system_score_gemma":0.0013379131,"threshold_uncertainty_score":0.016995907},"labels":[],"label_agreement":null},{"id":"W2917261650","doi":"10.3390/a12020039","title":"A Hybrid Adaptive Large Neighborhood Heuristic for a Real-Life Dial-a-Ride Problem","year":2019,"lang":"en","type":"article","venue":"Algorithms","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Computer science; Heuristic; Benchmark (surveying); Adaptability; Vehicle routing problem; Routing (electronic design automation); Mathematical optimization; Heuristics; Real-time computing; Artificial intelligence; Mathematics; Computer network","score_opus":0.013412085036412865,"score_gpt":0.2512406010561653,"score_spread":0.23782851601975244,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2917261650","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15713686,0.0009458849,0.8318,0.00036145523,0.000078709,0.0002272721,0.00016257979,0.00036650014,0.008920681],"genre_scores_gemma":[0.69001746,0.000262801,0.30475074,0.00010690174,0.000029634144,0.00027902707,0.00028148797,0.00007321034,0.004198667],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996351,0.0001755294,0.000013418417,0.000059644604,0.000065376094,0.000050772014],"domain_scores_gemma":[0.99946934,0.00034342357,0.000053229025,0.000026483902,0.00006323472,0.000044223114],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00081083574,0.0005124396,0.0008096764,0.00067888334,0.0004585589,0.0006046803,0.0015353813,0.0008549922,0.0016267512],"category_scores_gemma":[0.0014671518,0.0002944668,0.0004577444,0.0006670462,0.00046379305,0.0007345302,0.00053769926,0.00039068895,0.00015567745],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000047131085,0.0000577537,0.00036321708,0.000032553322,0.000017768867,0.00005235341,0.000022571317,0.9804105,0.00040386006,0.0026730509,0.000639574,0.01527965],"study_design_scores_gemma":[0.000010685084,0.000024595587,0.00006104086,0.0000031575348,0.0000037840477,0.000010790075,0.00001333828,0.99874216,0.00012395027,0.00067597954,0.0003277739,0.0000027437536],"about_ca_topic_score_codex":0.01363849,"about_ca_topic_score_gemma":0.016031938,"teacher_disagreement_score":0.01363849,"about_ca_system_score_codex":0.0010144777,"about_ca_system_score_gemma":0.0011980303,"threshold_uncertainty_score":0.027118206},"labels":[],"label_agreement":null},{"id":"W2922070351","doi":"10.1007/978-3-030-19212-9_9","title":"A Constraint Programming Approach to Electric Vehicle Routing with Time Windows","year":2019,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Vehicle routing problem; Mathematical optimization; Constraint programming; Electric vehicle; Integer programming; Linear programming; Routing (electronic design automation); Scheduling (production processes); Transformation (genetics); Range (aeronautics); Minification; Algorithm; Computer network; Stochastic programming; Mathematics; Engineering","score_opus":0.012218359804833804,"score_gpt":0.225919186827576,"score_spread":0.2137008270227422,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2922070351","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0009477878,0.0008094791,0.98435104,0.0002740581,0.00015719367,0.000034489436,0.00010412445,0.00007266563,0.013249224],"genre_scores_gemma":[0.08567556,0.0052925833,0.87085205,0.0003874613,0.0005151819,0.00031297284,0.00030773334,0.00030197873,0.036354493],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99942136,0.00017537076,0.00003347169,0.00009765778,0.00022411102,0.000048078626],"domain_scores_gemma":[0.99931526,0.00045676663,0.000048575304,0.000041453233,0.00011135637,0.000026527388],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00083785644,0.0013310608,0.00089467445,0.0007184549,0.0005913002,0.0021269033,0.0026012694,0.0011912805,0.008782184],"category_scores_gemma":[0.002594336,0.0010002671,0.0012741243,0.0029237627,0.00077555137,0.0021161735,0.0009659279,0.0029045786,0.0010311278],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000032537544,0.000057007117,0.00008429132,0.00015483968,0.000049204828,0.00014351624,0.000060928123,0.51141226,0.0013500418,0.4206999,0.0076201246,0.05833532],"study_design_scores_gemma":[0.000013106716,0.00002010242,0.00006492644,0.00004267631,0.000023551022,0.000055827226,0.00001985566,0.81680095,0.0005887565,0.1641276,0.018221742,0.000020887546],"about_ca_topic_score_codex":0.0117323315,"about_ca_topic_score_gemma":0.009992218,"teacher_disagreement_score":0.0117323315,"about_ca_system_score_codex":0.0014215304,"about_ca_system_score_gemma":0.001569318,"threshold_uncertainty_score":0.029379308},"labels":[],"label_agreement":null},{"id":"W2923724034","doi":"10.1016/j.trc.2018.12.011","title":"A Lagrangian heuristic and GRASP for the hub-and-spoke network system with economies-of-scale and congestion","year":2019,"lang":"en","type":"article","venue":"Transportation Research Part C Emerging Technologies","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":54,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"GRASP; Mathematical optimization; Heuristic; Nonlinear system; Function (biology); Lagrangian relaxation; Computer science; Benchmark (surveying); Convergence (economics); Mathematics; Economics","score_opus":0.029146225791938155,"score_gpt":0.2952903331790177,"score_spread":0.26614410738707955,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2923724034","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.047002163,0.0008265736,0.9321134,0.00089056045,0.00016681528,0.0002344582,0.00034115647,0.00047302566,0.017951937],"genre_scores_gemma":[0.68744695,0.00061928213,0.30156422,0.0002119275,0.00008584518,0.000384938,0.00037404845,0.00017580167,0.009136909],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99953127,0.00017442458,0.00001556586,0.00006910238,0.00008346824,0.00012602932],"domain_scores_gemma":[0.99909806,0.00056139956,0.00008701439,0.000061186256,0.00008637306,0.000105931096],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012741375,0.0010613677,0.002113686,0.0012776454,0.00094683614,0.0018609831,0.0017650198,0.0023135485,0.0061558913],"category_scores_gemma":[0.0029598344,0.0009369418,0.001151218,0.0015234113,0.0012166122,0.001918421,0.0022732215,0.0015172845,0.00050844555],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003726791,0.000022055336,0.00007138021,0.000038014336,0.000012371002,0.000032312568,0.000018745417,0.9807091,0.00013892473,0.011148852,0.0010681006,0.006702907],"study_design_scores_gemma":[0.000015780668,0.000017884706,0.000024972538,0.0000075755556,0.000004786043,0.000007753689,0.000015976908,0.99387604,0.00003667237,0.005648824,0.0003390642,0.000004582628],"about_ca_topic_score_codex":0.011736761,"about_ca_topic_score_gemma":0.009809886,"teacher_disagreement_score":0.011736761,"about_ca_system_score_codex":0.0020552443,"about_ca_system_score_gemma":0.0033794926,"threshold_uncertainty_score":0.023336887},"labels":[],"label_agreement":null},{"id":"W2925837702","doi":"10.1155/2019/1812543","title":"An Integrated Problem of <mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" id=\"M1\"><mml:mrow><mml:mi>p</mml:mi></mml:mrow></mml:math>-Hub Location and Revenue Management with Multiple Capacity Levels under Disruptions","year":2019,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Revenue; Profit maximization; Profit (economics); Computer science; Stochastic programming; Mathematical optimization; Star (game theory); Algorithm; Operations research; Mathematics; Economics; Finance","score_opus":0.01523716657415934,"score_gpt":0.2449827766764912,"score_spread":0.22974561010233188,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2925837702","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03707803,0.0002526698,0.95621276,0.00044693027,0.00005641401,0.00010593579,0.00019043816,0.000116846524,0.0055398527],"genre_scores_gemma":[0.828451,0.0005005765,0.15596326,0.00012432844,0.00010290807,0.00030673374,0.00036741426,0.000118610806,0.014065233],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982344,0.00059934624,0.00006242883,0.0005539944,0.00024650284,0.00030330897],"domain_scores_gemma":[0.9984863,0.00094222714,0.0001917553,0.000079924226,0.00017678707,0.00012306245],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017780411,0.0011537034,0.001638647,0.00066144305,0.00066264876,0.0023282398,0.0016986998,0.00220798,0.004517785],"category_scores_gemma":[0.0029323143,0.0008305751,0.0016556042,0.0011715094,0.0009124989,0.0021954242,0.0014050249,0.0016727201,0.00039204027],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005976509,0.00007419572,0.00041047417,0.00007081844,0.00005376107,0.000092985894,0.000034476587,0.97025543,0.0008412052,0.01596025,0.0006893637,0.011457331],"study_design_scores_gemma":[0.000010320284,0.00006393735,0.00020442672,0.0000067838905,0.000022242164,0.000023288123,0.000025968235,0.99240464,0.00052894576,0.0060951696,0.00060498854,0.000009147982],"about_ca_topic_score_codex":0.008781134,"about_ca_topic_score_gemma":0.0067524947,"teacher_disagreement_score":0.008781134,"about_ca_system_score_codex":0.0022679684,"about_ca_system_score_gemma":0.0026251702,"threshold_uncertainty_score":0.017460048},"labels":[],"label_agreement":null},{"id":"W2934965048","doi":"10.5267/j.dsl.2018.11.001","title":"Vehicle routing problems in rice-for-the-poor distribution","year":2019,"lang":"en","type":"article","venue":"Decision Science Letters","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Kementerian Riset, Teknologi dan Pendidikan Tinggi; Institut Pertanian Bogor","keywords":"Vehicle routing problem; Integer programming; Distribution (mathematics); Routing (electronic design automation); Mathematical optimization; Homogeneous; Linear programming; Computer science; Integer (computer science); Operations research; Mathematics","score_opus":0.018459451644066833,"score_gpt":0.2790806962669723,"score_spread":0.2606212446229055,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2934965048","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1365104,0.0015491464,0.8475903,0.0013909932,0.000096295844,0.00016371574,0.0006047471,0.00015930738,0.011935049],"genre_scores_gemma":[0.9276978,0.0014768629,0.061001662,0.0001341484,0.00005738208,0.00015879657,0.00028245096,0.000041317915,0.009149536],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99915516,0.00043391544,0.000028297187,0.00016611144,0.000070719405,0.00014570847],"domain_scores_gemma":[0.9992999,0.0004242085,0.00014908078,0.00002667096,0.00005077876,0.000049441845],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012576573,0.00058640755,0.00096144277,0.0005431993,0.0005957658,0.0016650733,0.0009694333,0.0013290878,0.0022515226],"category_scores_gemma":[0.0020060712,0.00057073415,0.00075576926,0.0013789055,0.0011354305,0.0011599532,0.0010154006,0.00089250586,0.000146538],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000022920294,0.00002115695,0.00055389915,0.000055358647,0.000021163589,0.00013254609,0.000029550865,0.9683926,0.00016543045,0.025862733,0.00068392913,0.0040587247],"study_design_scores_gemma":[0.00002321771,0.000057548732,0.00048808873,0.000016214295,0.000017221337,0.000108113185,0.0002029823,0.9618172,0.00022940268,0.034361582,0.0026617676,0.000016617369],"about_ca_topic_score_codex":0.01013442,"about_ca_topic_score_gemma":0.008764125,"teacher_disagreement_score":0.01013442,"about_ca_system_score_codex":0.0017843907,"about_ca_system_score_gemma":0.0015206845,"threshold_uncertainty_score":0.0201509},"labels":[],"label_agreement":null},{"id":"W2936461448","doi":"10.1080/03155986.2019.1575686","title":"Mixed integer formulations for a coupled lot-scheduling and vehicle routing problem in furniture settings","year":2019,"lang":"en","type":"article","venue":"INFOR Information Systems and Operational Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Fundação de Amparo à Pesquisa do Estado de São Paulo","keywords":"Time horizon; Scheduling (production processes); Computer science; Vehicle routing problem; Integer programming; Production (economics); Production planning; Mathematical optimization; Operations research; Set (abstract data type); Job shop scheduling; Routing (electronic design automation); Industrial engineering; Engineering; Algorithm; Mathematics; Economics","score_opus":0.03983700142863135,"score_gpt":0.3221490325116731,"score_spread":0.28231203108304176,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2936461448","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.024412427,0.00125705,0.95978487,0.0007760878,0.000111765694,0.00016217704,0.00035836516,0.00012826422,0.013009078],"genre_scores_gemma":[0.6087642,0.0022264607,0.37079397,0.00054690364,0.00023710114,0.0010308479,0.0007124565,0.00021313039,0.015474971],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99902046,0.0004526709,0.000035594883,0.00016387641,0.00017826166,0.0001490418],"domain_scores_gemma":[0.9973901,0.002037868,0.00025306057,0.000065761684,0.00014466725,0.00010864068],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021564355,0.0022069425,0.0012213029,0.000961796,0.0004759861,0.0026317928,0.0019165191,0.0026892691,0.005544162],"category_scores_gemma":[0.0041667763,0.0010240312,0.0015891492,0.0013565717,0.0009948167,0.0014924344,0.0014525723,0.0018738294,0.0005393285],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000024324747,0.000043033484,0.00014163293,0.00006784841,0.00002167151,0.000043540247,0.00002421222,0.98171896,0.00018553542,0.01432052,0.0004946474,0.0029140757],"study_design_scores_gemma":[0.00001071422,0.00002315044,0.0000549516,0.000009597535,0.000008929343,0.0000118725175,0.000016901531,0.9950717,0.00006427723,0.0042336886,0.0004899967,0.000004177498],"about_ca_topic_score_codex":0.0065318127,"about_ca_topic_score_gemma":0.0068434794,"teacher_disagreement_score":0.0065318127,"about_ca_system_score_codex":0.0020006923,"about_ca_system_score_gemma":0.0020552464,"threshold_uncertainty_score":0.018547058},"labels":[],"label_agreement":null},{"id":"W2937321088","doi":"10.1287/ijoc.2019.0915","title":"A Branch-Price-and-Cut Procedure for the Discrete Ordered Median Problem","year":2020,"lang":"en","type":"article","venue":"INFORMS journal on computing","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Belgian Federal Science Policy Office","keywords":"Mathematics; Sorting; Column generation; Mathematical optimization; Branch and cut; Relaxation (psychology); Linear programming relaxation; Set (abstract data type); Position (finance); Variable (mathematics); Exponential function; Integer programming; Algorithm; Computer science","score_opus":0.020375786052066303,"score_gpt":0.265880337222943,"score_spread":0.24550455117087672,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2937321088","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0024551179,0.00005711723,0.9946833,0.00013648659,0.000032889722,0.00015208343,0.00012737037,0.0003015862,0.0020540673],"genre_scores_gemma":[0.051704228,0.00013611298,0.94473904,0.000097768956,0.00004688774,0.0004593748,0.00048270143,0.00020125847,0.002132665],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99910164,0.00028903174,0.000039349175,0.00014937652,0.0002922584,0.00012842841],"domain_scores_gemma":[0.99880743,0.0007426042,0.0000872038,0.00010459467,0.00018042253,0.00007786185],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013113748,0.001113794,0.0013952192,0.0013063648,0.0010455078,0.0013150452,0.0014904736,0.001490236,0.011622165],"category_scores_gemma":[0.0035384006,0.0007109596,0.0011648302,0.0023530459,0.0006575728,0.0012974198,0.0014435353,0.0033401723,0.0015693584],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012620368,0.00030163347,0.00039451238,0.00020580417,0.000066481145,0.0002033679,0.000085088686,0.6646436,0.00490155,0.06387657,0.013277058,0.2519182],"study_design_scores_gemma":[0.000051618325,0.0000686713,0.000109969216,0.000015300195,0.0000147770415,0.00006703149,0.000023442484,0.9525108,0.0015490165,0.04178469,0.0037901443,0.000014507294],"about_ca_topic_score_codex":0.003737557,"about_ca_topic_score_gemma":0.004476558,"teacher_disagreement_score":0.011622165,"about_ca_system_score_codex":0.0013420946,"about_ca_system_score_gemma":0.002666276,"threshold_uncertainty_score":0.03887999},"labels":[],"label_agreement":null},{"id":"W2937396187","doi":"10.1177/0954410019842487","title":"Optimal scheduling for aerial recovery of multiple unmanned aerial vehicles using genetic algorithm","year":2019,"lang":"en","type":"article","venue":"Proceedings of the Institution of Mechanical Engineers Part G Journal of Aerospace Engineering","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Scheduling (production processes); Genetic algorithm; Drone; Greedy algorithm; Real-time computing; Algorithm; Mathematical optimization; Machine learning; Mathematics","score_opus":0.014378231345519929,"score_gpt":0.23235224859443065,"score_spread":0.21797401724891072,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2937396187","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07907306,0.00028130057,0.9155629,0.00021945452,0.000042651747,0.000079142155,0.00004747182,0.00024731935,0.004446683],"genre_scores_gemma":[0.8110675,0.00026070004,0.18495837,0.00006185216,0.000017525128,0.00016134462,0.00010434173,0.00005149222,0.0033168604],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998078,0.000050622526,0.000006145562,0.000044535504,0.000040771996,0.000050169932],"domain_scores_gemma":[0.999728,0.00014139747,0.000056878525,0.000013782257,0.000037810576,0.000022212413],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041767993,0.00063420285,0.00068063056,0.00063079956,0.00044130636,0.0005501122,0.00074427295,0.0006842874,0.0015716539],"category_scores_gemma":[0.00096841366,0.00031645712,0.00052157516,0.00065879663,0.00048742554,0.0004933853,0.00046869606,0.000497956,0.00015214397],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001514523,0.00001605118,0.00014848208,0.000012576064,0.000008087274,0.000022441913,0.00001829707,0.98691154,0.0006597832,0.0025974202,0.00024935673,0.009340775],"study_design_scores_gemma":[0.0000054842594,0.0000131830575,0.000040220337,0.0000014878525,0.000002861949,0.000003901597,0.000007791247,0.9986486,0.00015546902,0.0009750743,0.00014423927,0.0000017090163],"about_ca_topic_score_codex":0.013467542,"about_ca_topic_score_gemma":0.009605301,"teacher_disagreement_score":0.013467542,"about_ca_system_score_codex":0.0010962151,"about_ca_system_score_gemma":0.0019346101,"threshold_uncertainty_score":0.02677834},"labels":[],"label_agreement":null},{"id":"W2938533079","doi":"","title":"Valid Inequalities and a Branch-and-Cut Algorithm for Asymmetric Multi-Depot Routing Problems","year":2019,"lang":"en","type":"article","venue":"","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Travelling salesman problem; Branch and cut; Vehicle routing problem; Mathematical optimization; Routing (electronic design automation); Benchmark (surveying); Set (abstract data type); Triangle inequality; Mathematics; Computer science; Node (physics); Traveling purchaser problem; Column generation; Algorithm; 2-opt; Integer programming; Combinatorics; Engineering","score_opus":0.03342638280313788,"score_gpt":0.28106554293530933,"score_spread":0.24763916013217147,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2938533079","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0051326347,0.0002394924,0.9907907,0.00015881543,0.000047872494,0.00011199943,0.00019730396,0.00021149647,0.0031096998],"genre_scores_gemma":[0.09532583,0.0004581924,0.9001941,0.00015976623,0.00006260702,0.00033584374,0.0009430349,0.00020349622,0.0023172058],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982262,0.0005767823,0.00012934572,0.00023813178,0.0005268578,0.00030268464],"domain_scores_gemma":[0.9975454,0.0014704667,0.0002583887,0.00023747652,0.00035020552,0.00013802946],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019942445,0.0018031537,0.0011417608,0.0012548204,0.0007742333,0.0020315535,0.0023451662,0.0012623371,0.004198512],"category_scores_gemma":[0.004954806,0.0008248432,0.0013611219,0.002023813,0.00077796885,0.0020731762,0.0020527793,0.0034510118,0.0008604496],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018614545,0.00023802335,0.0007155364,0.0003197371,0.00007870499,0.00021215841,0.000128352,0.685681,0.0033384098,0.09932799,0.006073663,0.20370042],"study_design_scores_gemma":[0.000048351212,0.000053488176,0.00010181441,0.000028804036,0.000023426159,0.000045267076,0.000027388895,0.95182014,0.0015782776,0.042964328,0.003296122,0.0000125561355],"about_ca_topic_score_codex":0.0040792013,"about_ca_topic_score_gemma":0.0045686048,"teacher_disagreement_score":0.004198512,"about_ca_system_score_codex":0.0014805829,"about_ca_system_score_gemma":0.0026089277,"threshold_uncertainty_score":0.014045477},"labels":[],"label_agreement":null},{"id":"W2939963135","doi":"10.1007/s43069-020-00042-z","title":"The Inventory Routing Problem with Demand Moves","year":2021,"lang":"en","type":"article","venue":"Operations Research Forum","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Group for Research in Decision Analysis","funders":"Nederlandse Organisatie voor Wetenschappelijk Onderzoek","keywords":"Time horizon; Routing (electronic design automation); Operations research; Inventory theory; Computer science; Holding cost; Demand forecasting; Perpetual inventory; Total cost; Inventory cost; Inventory control; Operations management; Business; Economics; Mathematical optimization; Supply chain; Microeconomics; Marketing; Engineering; Computer network; Mathematics","score_opus":0.03831725747215989,"score_gpt":0.3362077405177609,"score_spread":0.29789048304560106,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2939963135","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22105683,0.001904222,0.7332681,0.0030042042,0.0003380597,0.00041403278,0.0017628397,0.000600547,0.037651226],"genre_scores_gemma":[0.82874966,0.0008845248,0.14840326,0.00034311783,0.00014651453,0.00026975453,0.0013668742,0.00013831229,0.01969794],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99911493,0.0003695433,0.000036691636,0.00016833383,0.00012440827,0.00018604801],"domain_scores_gemma":[0.99928206,0.00046622293,0.00006872779,0.00004143605,0.000072039715,0.00006953847],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00084074034,0.0011465843,0.0011949013,0.0005353681,0.0007185945,0.0018615305,0.001481438,0.0022167736,0.0065773223],"category_scores_gemma":[0.001545161,0.00070401636,0.0009874414,0.0013851112,0.0009394921,0.0016319402,0.0009569163,0.0016895818,0.000651774],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017237951,0.00008265088,0.00035275353,0.00012057637,0.00004283669,0.00033913934,0.00007064732,0.941506,0.0013741032,0.037471678,0.0036798588,0.014787325],"study_design_scores_gemma":[0.000044817487,0.00007411647,0.00020652429,0.000015808097,0.000023576133,0.00011521488,0.00008005132,0.9724309,0.00058907096,0.022513015,0.0038923465,0.0000145503855],"about_ca_topic_score_codex":0.0059894924,"about_ca_topic_score_gemma":0.0035048688,"teacher_disagreement_score":0.0065773223,"about_ca_system_score_codex":0.0017031813,"about_ca_system_score_gemma":0.0010729418,"threshold_uncertainty_score":0.022003293},"labels":[],"label_agreement":null},{"id":"W2940300938","doi":"10.1007/s10732-019-09412-1","title":"Three multi-start data-driven evolutionary heuristics for the vehicle routing problem with multiple time windows","year":2019,"lang":"en","type":"article","venue":"Journal of Heuristics","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":29,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"","keywords":"Heuristics; Benchmark (surveying); Vehicle routing problem; Computer science; Routing (electronic design automation); Set (abstract data type); Mathematical optimization; Evolutionary algorithm; Genetic algorithm; Machine learning; Mathematics","score_opus":0.03366536894842708,"score_gpt":0.26658316919298636,"score_spread":0.23291780024455927,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2940300938","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06854966,0.00063203386,0.9221539,0.00034280107,0.00020601056,0.0004603392,0.0001643296,0.00055332866,0.0069375993],"genre_scores_gemma":[0.4994416,0.00027520495,0.49529517,0.00021094778,0.000059593916,0.0005352441,0.00024951695,0.00017377512,0.0037589697],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99888307,0.0003808264,0.000064289154,0.00012300373,0.00027485358,0.00027394656],"domain_scores_gemma":[0.99729484,0.0015348555,0.00024689682,0.00019208055,0.0004012237,0.00033005633],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024031,0.0012005223,0.001705052,0.0013093905,0.0007567147,0.0015754333,0.0033827655,0.0020686216,0.0033686915],"category_scores_gemma":[0.005538717,0.000862651,0.0013717323,0.0011661879,0.0007370499,0.0013775554,0.0019999403,0.0018638116,0.00036421142],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004025416,0.00026155563,0.00075950654,0.00010033248,0.000082860905,0.0000723387,0.00007889252,0.93328977,0.0018909913,0.008120505,0.00084149605,0.05409918],"study_design_scores_gemma":[0.00008916627,0.00016544727,0.00021213686,0.000013378225,0.000026220087,0.000018858118,0.000023526229,0.9961886,0.00081479974,0.0019832025,0.0004462174,0.00001850332],"about_ca_topic_score_codex":0.0048947614,"about_ca_topic_score_gemma":0.006495995,"teacher_disagreement_score":0.0048947614,"about_ca_system_score_codex":0.0017475387,"about_ca_system_score_gemma":0.0023540447,"threshold_uncertainty_score":0.012708962},"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,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_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","score_opus":0.026667245177416754,"score_gpt":0.24072319847424806,"score_spread":0.2140559532968313,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2943956374","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008394098,0.44505072,0.5011335,0.0037275096,0.001646528,0.00030532738,0.00072863704,0.0004859494,0.038527783],"genre_scores_gemma":[0.091947325,0.552095,0.33266973,0.0019674036,0.004378202,0.0006491488,0.0030020373,0.00046537383,0.012825767],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9979303,0.0005884366,0.00020500385,0.0005774631,0.00051470083,0.0001840712],"domain_scores_gemma":[0.99836665,0.0011062514,0.00011321068,0.00011139431,0.00023731755,0.00006526189],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021738415,0.003583003,0.0025573934,0.0027516966,0.0008721976,0.003804682,0.003163745,0.0028548734,0.0048751934],"category_scores_gemma":[0.0039732433,0.0012914472,0.0030155156,0.009270006,0.000976704,0.0044734166,0.0020115853,0.0031186487,0.0018214397],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016907787,0.00041676636,0.0015656854,0.0069014737,0.0003157681,0.0002994924,0.00029297807,0.15689537,0.0011263633,0.082626574,0.04015636,0.70923406],"study_design_scores_gemma":[0.000117943084,0.0006236405,0.0022276754,0.0026467652,0.000368829,0.0015188734,0.00087685644,0.41930726,0.001077063,0.24804538,0.32302862,0.00016107554],"about_ca_topic_score_codex":0.004232106,"about_ca_topic_score_gemma":0.0027675051,"teacher_disagreement_score":0.0048751934,"about_ca_system_score_codex":0.0016349731,"about_ca_system_score_gemma":0.0022374175,"threshold_uncertainty_score":0.016309083},"labels":[],"label_agreement":null},{"id":"W2944565881","doi":"10.1016/j.ejor.2019.04.047","title":"The Steiner Traveling Salesman Problem and its extensions","year":2019,"lang":"en","type":"article","venue":"European Journal of Operational Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":24,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Ministerio de Economía y Competitividad","keywords":"Travelling salesman problem; Bottleneck traveling salesman problem; Mathematics; Steiner tree problem; Mathematical optimization; Integer programming; Extension (predicate logic); Graph; Branch and cut; 2-opt; Combinatorics; Computer science","score_opus":0.0678517191682732,"score_gpt":0.3429520143905892,"score_spread":0.275100295222316,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2944565881","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11924121,0.012229738,0.7608086,0.0041152593,0.00059137645,0.00011460148,0.0008147176,0.00017501706,0.101909444],"genre_scores_gemma":[0.6865389,0.018795418,0.22937815,0.0008875829,0.002403017,0.0003588003,0.0013872902,0.0002671244,0.059983633],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988859,0.00045810762,0.00006282871,0.00021323243,0.00026392788,0.000116044845],"domain_scores_gemma":[0.99695253,0.0017943928,0.00047827067,0.00022156481,0.00036373912,0.0001894277],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001940155,0.0011579264,0.0011688938,0.0015100361,0.0009035174,0.002088822,0.0021201083,0.0018264166,0.0065652723],"category_scores_gemma":[0.0074273497,0.0007468805,0.0018733159,0.0032677287,0.0016471659,0.005759165,0.0025083595,0.0033849687,0.0011074883],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010079048,0.00016404982,0.00056895433,0.00025940355,0.000051426774,0.00019056501,0.00023944394,0.15336041,0.0006422565,0.8015001,0.009420374,0.03350231],"study_design_scores_gemma":[0.000021890073,0.00006314476,0.00040233083,0.000050723145,0.000023797369,0.00024998462,0.00017094494,0.3344413,0.00015377982,0.65534186,0.009055911,0.000024343037],"about_ca_topic_score_codex":0.0022335239,"about_ca_topic_score_gemma":0.0018892122,"teacher_disagreement_score":0.0065652723,"about_ca_system_score_codex":0.00092687213,"about_ca_system_score_gemma":0.001146391,"threshold_uncertainty_score":0.021963},"labels":[],"label_agreement":null},{"id":"W2947091483","doi":"10.1080/03155986.2019.1607806","title":"An improved tabu search algorithm for the petrol-station replenishment problem with adjustable demands","year":2019,"lang":"en","type":"article","venue":"INFOR Information Systems and Operational Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Tabu search; Benchmark (surveying); Computer science; Mathematical optimization; Statistic; Process (computing); Gasoline; Operations research; Vehicle routing problem; Order (exchange); Routing (electronic design automation); Algorithm; Mathematics; Statistics; Economics; Engineering","score_opus":0.027317348647659195,"score_gpt":0.321159388669695,"score_spread":0.29384204002203584,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2947091483","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.043452337,0.0014839752,0.9379461,0.00041932936,0.00021034964,0.00031600866,0.00039236847,0.0018060518,0.013973582],"genre_scores_gemma":[0.20668532,0.00051989435,0.7850603,0.00029003256,0.0001023599,0.00044030452,0.00091041776,0.00031203212,0.0056793476],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999482,0.00019004926,0.000023837552,0.00009031262,0.00011984245,0.00009404641],"domain_scores_gemma":[0.9993722,0.00036798252,0.00005109124,0.000047524358,0.00012518805,0.00003605199],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00097388047,0.0010227306,0.0012245178,0.0013449263,0.00062476256,0.0010350262,0.0019453927,0.0017343112,0.006280946],"category_scores_gemma":[0.0025906786,0.0005142314,0.0009171768,0.002227119,0.00047028033,0.0010909666,0.00080609863,0.0012697147,0.001119663],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016161759,0.00017623814,0.0004883199,0.00016110361,0.00005249648,0.00008755402,0.00008288286,0.8380503,0.001501731,0.010530708,0.006889117,0.14181794],"study_design_scores_gemma":[0.000053490112,0.00004314931,0.000094013805,0.000010933435,0.000010147924,0.00002633203,0.0000134924685,0.99573106,0.00023520437,0.0023457413,0.0014305388,0.0000059537097],"about_ca_topic_score_codex":0.010577788,"about_ca_topic_score_gemma":0.009978067,"teacher_disagreement_score":0.010577788,"about_ca_system_score_codex":0.0009854715,"about_ca_system_score_gemma":0.002467429,"threshold_uncertainty_score":0.021032453},"labels":[],"label_agreement":null},{"id":"W2947585677","doi":"10.1016/j.cor.2019.05.026","title":"Exact solution methods for the multi-period vehicle routing problem with due dates","year":2019,"lang":"en","type":"article","venue":"Computers & Operations Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":44,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"Fondo Nacional de Desarrollo Científico y Tecnológico; Natural Sciences and Engineering Research Council of Canada; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Vehicle routing problem; Period (music); Computer science; Mathematical optimization; Routing (electronic design automation); Operations research; Mathematics; Computer network","score_opus":0.07459327942086358,"score_gpt":0.4055867442912805,"score_spread":0.3309934648704169,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2947585677","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0026325462,0.00084847835,0.9927844,0.00019786206,0.000121570505,0.000034030174,0.000049604358,0.000055359247,0.0032762224],"genre_scores_gemma":[0.2905096,0.003710604,0.6845987,0.0003062948,0.00041245055,0.0005632294,0.00027536,0.00032378416,0.01930005],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99914956,0.000345813,0.00004041393,0.00010314234,0.0002671645,0.00009392443],"domain_scores_gemma":[0.99750644,0.0018054291,0.00017939793,0.00013273997,0.00031191832,0.00006403857],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022177799,0.0010119568,0.0011645139,0.0010754444,0.0005459722,0.0013380941,0.0016674902,0.0016057969,0.006367539],"category_scores_gemma":[0.0073541203,0.00085613923,0.0011572741,0.00154167,0.0008090598,0.0022187626,0.0013141856,0.002156412,0.0005932333],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000050020793,0.00004701247,0.00017290855,0.00016998712,0.00003820454,0.00004061414,0.00005103944,0.8784796,0.00039073496,0.069647506,0.0022037306,0.048708558],"study_design_scores_gemma":[0.000012681929,0.000012339666,0.000048237023,0.00001875766,0.00000700746,0.000010995188,0.00001116345,0.9717942,0.000091277405,0.026768899,0.001219569,0.0000048630864],"about_ca_topic_score_codex":0.0077894204,"about_ca_topic_score_gemma":0.006476369,"teacher_disagreement_score":0.0077894204,"about_ca_system_score_codex":0.0014369744,"about_ca_system_score_gemma":0.0023535306,"threshold_uncertainty_score":0.021301508},"labels":[],"label_agreement":null},{"id":"W2949203745","doi":"10.1016/j.ejor.2019.10.010","title":"A concise guide to existing and emerging vehicle routing problem variants","year":2019,"lang":"en","type":"preprint","venue":"European Journal of Operational Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":30,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Natural Sciences and Engineering Research Council of Canada; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior","keywords":"Vehicle routing problem; Computer science; Diversity (politics); Focus (optics); Routing (electronic design automation); Data science; Management science; Operations research; Risk analysis (engineering); Engineering; Business; Political science; Computer network","score_opus":0.10251774212373478,"score_gpt":0.40096161314748013,"score_spread":0.29844387102374537,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2949203745","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0013939065,0.060403783,0.8631028,0.001856735,0.0046296734,0.00031424215,0.003177446,0.0009789991,0.06414252],"genre_scores_gemma":[0.019328631,0.07697727,0.80170774,0.0024320686,0.005386457,0.001504535,0.0062411157,0.0017102238,0.084712],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9985505,0.0004459675,0.00019281438,0.00028725096,0.00045063058,0.00007273037],"domain_scores_gemma":[0.99900347,0.00049390265,0.00006489012,0.00017558874,0.0002237084,0.00003851442],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018436507,0.0028774957,0.0014748465,0.0033843268,0.0008522669,0.004145281,0.002945349,0.0021250248,0.027152833],"category_scores_gemma":[0.0043694377,0.0010738571,0.0019349137,0.006738361,0.0012063041,0.004972981,0.0023520745,0.005572226,0.022664417],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004896669,0.00022410523,0.00020090293,0.0016610667,0.000065748514,0.00024939427,0.00013044813,0.017273823,0.0024489497,0.5027441,0.17057934,0.30437323],"study_design_scores_gemma":[0.000022406948,0.00011025142,0.0002610586,0.0003954456,0.000035161967,0.0007009528,0.00006519106,0.03559224,0.00064544386,0.35036033,0.6117538,0.000057596288],"about_ca_topic_score_codex":0.0012068391,"about_ca_topic_score_gemma":0.0021834695,"teacher_disagreement_score":0.027152833,"about_ca_system_score_codex":0.0009908595,"about_ca_system_score_gemma":0.0011926845,"threshold_uncertainty_score":0.09083527},"labels":[],"label_agreement":null},{"id":"W2951794473","doi":"10.48550/arxiv.1708.01335","title":"Compact, Provably-Good LPs for Orienteering and Regret-Bounded Vehicle Routing","year":2017,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo; University of Alberta","funders":"","keywords":"Orienteering; Regret; Rounding; Mathematics; Linear programming relaxation; Approximation algorithm; Routing (electronic design automation); Bounded function; Path (computing); Mathematical optimization; Combinatorics; Node (physics); Point (geometry); Linear programming; Discrete mathematics; Computer science; Statistics; Computer network; Geometry; Engineering","score_opus":0.0782951958348076,"score_gpt":0.22094772364564436,"score_spread":0.14265252781083676,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2951794473","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014859428,0.00049207866,0.97281986,0.00095658965,0.00012003649,0.0001081533,0.00051648496,0.00082090864,0.009306506],"genre_scores_gemma":[0.33465758,0.0011328226,0.65053135,0.0008561811,0.00039839992,0.00066139124,0.0020682153,0.00072340097,0.008970703],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9977133,0.00075985846,0.00009794079,0.00048874615,0.0005137075,0.00042653477],"domain_scores_gemma":[0.99570787,0.0026985134,0.00041199286,0.00063242007,0.00029624035,0.00025294087],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002176467,0.0024337338,0.002004757,0.00076667184,0.0008682416,0.0027684579,0.0022197973,0.002199209,0.008085255],"category_scores_gemma":[0.011121158,0.00092546997,0.0018092126,0.0016569654,0.0015876128,0.004048262,0.0029818392,0.005057577,0.0016377814],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00036591824,0.00022910503,0.00033161588,0.00034587856,0.0000561294,0.00014171915,0.00017493822,0.7647597,0.0022508767,0.1690779,0.011949688,0.050316475],"study_design_scores_gemma":[0.00007101492,0.000088462046,0.00010335625,0.000041096086,0.00002126262,0.00005641537,0.000079821606,0.8491969,0.0010474491,0.14499427,0.004279257,0.000020656313],"about_ca_topic_score_codex":0.0027899079,"about_ca_topic_score_gemma":0.0029165086,"teacher_disagreement_score":0.008085255,"about_ca_system_score_codex":0.0019622357,"about_ca_system_score_gemma":0.0020745578,"threshold_uncertainty_score":0.027047873},"labels":[],"label_agreement":null},{"id":"W2953891001","doi":"10.1287/trsc.2018.0876","title":"A Rule-Based Recourse for the Vehicle Routing Problem with Stochastic Demands","year":2019,"lang":"en","type":"article","venue":"Transportation Science","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":29,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal; Polytechnique Montréal; Université de Montréal; Transport Canada","funders":"","keywords":"Vehicle routing problem; Computer science; TRIPS architecture; Operations research; Routing (electronic design automation); Mathematical optimization; Residual; Engineering; Mathematics","score_opus":0.014085415359261281,"score_gpt":0.25906495470527746,"score_spread":0.24497953934601618,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2953891001","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019327056,0.00037031743,0.97495484,0.00043342495,0.00007023346,0.000114297894,0.00012328061,0.00015277386,0.0044537303],"genre_scores_gemma":[0.64062685,0.0007516386,0.35022068,0.00028361444,0.00012721188,0.00036519062,0.00037065087,0.00011891027,0.0071352534],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985813,0.00055159273,0.00006806744,0.00032008762,0.00030367982,0.00017529838],"domain_scores_gemma":[0.9979231,0.0012838806,0.00022979199,0.00013011991,0.00030285458,0.00013019987],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018255849,0.00084278936,0.0012634655,0.0006280862,0.00042700738,0.0016785365,0.0020160829,0.0020259137,0.0026472367],"category_scores_gemma":[0.004786807,0.00057406945,0.00087578397,0.00080297334,0.0009674034,0.001489372,0.001126582,0.0019801096,0.00039858735],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000033643682,0.00004668687,0.00020605205,0.000051891544,0.000018041323,0.000080801496,0.000028306515,0.97700953,0.00035927325,0.013562001,0.00047076715,0.008132972],"study_design_scores_gemma":[0.0000119508595,0.000030908635,0.000039314164,0.00000964322,0.000004712274,0.000021704676,0.000008849116,0.99289,0.0001404977,0.00623519,0.00060214545,0.0000050445465],"about_ca_topic_score_codex":0.005877802,"about_ca_topic_score_gemma":0.004130273,"teacher_disagreement_score":0.005877802,"about_ca_system_score_codex":0.000926579,"about_ca_system_score_gemma":0.0019063106,"threshold_uncertainty_score":0.01168716},"labels":[],"label_agreement":null},{"id":"W2953949769","doi":"10.1016/j.tre.2019.05.014","title":"A Lagrangean decomposition approach for a novel two-echelon node-based location-routing problem in an offshore oil and gas supply chain","year":2019,"lang":"en","type":"article","venue":"Transportation Research Part E Logistics and Transportation Review","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":44,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Node (physics); Supply chain; Submarine pipeline; Decomposition; Oil supply; Vehicle routing problem; Petroleum engineering; Computer science; Mathematical optimization; Routing (electronic design automation); Engineering; Business; Computer network; Chemistry; Mathematics; Structural engineering","score_opus":0.07540011802285368,"score_gpt":0.36748203541960733,"score_spread":0.29208191739675365,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2953949769","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0066310484,0.000396073,0.987455,0.00034716437,0.000080491554,0.000049448678,0.00009577935,0.000031399915,0.004913544],"genre_scores_gemma":[0.23591039,0.0017656968,0.74705845,0.00024783367,0.00022854292,0.00044240046,0.00037319516,0.0001939382,0.01377947],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993388,0.0003185313,0.00002165393,0.00010542634,0.0001416132,0.00007392028],"domain_scores_gemma":[0.9992391,0.00049191207,0.00006179908,0.00003847827,0.00011593485,0.000052858046],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018948006,0.0011192588,0.0014206547,0.0008896374,0.00073818193,0.0018774755,0.0017839788,0.0019450301,0.0047534085],"category_scores_gemma":[0.002801513,0.0010325866,0.0016221242,0.0017355739,0.00094207993,0.0020243474,0.002033788,0.0021979462,0.00058820104],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000028367134,0.000062367384,0.00022698611,0.00013514771,0.000036567275,0.00009084791,0.000056711357,0.94008386,0.00077514764,0.039035443,0.002108388,0.017360266],"study_design_scores_gemma":[0.0000040014993,0.000010052949,0.00004481828,0.000009032158,0.0000058412284,0.000012205298,0.000016960204,0.99174327,0.000080696525,0.0074207895,0.0006479629,0.0000043490863],"about_ca_topic_score_codex":0.009644026,"about_ca_topic_score_gemma":0.00925032,"teacher_disagreement_score":0.009644026,"about_ca_system_score_codex":0.0015655272,"about_ca_system_score_gemma":0.0023839264,"threshold_uncertainty_score":0.019175768},"labels":[],"label_agreement":null},{"id":"W2954693089","doi":"10.22119/ijte.2019.94586.1361","title":"Sustainable vehicle-routing problem with time windows by heterogeneous fleet of vehicles and separated compartments: Application in waste collection problem","year":2019,"lang":"en","type":"article","venue":"International Journal of Transportation Engineering","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Vehicle routing problem; Solver; Variable (mathematics); Mathematical optimization; Integer programming; Computer science; Operations research; Fleet management; Constraint programming; Constraint (computer-aided design); Routing (electronic design automation); Engineering; Transport engineering; Mathematics","score_opus":0.003690886833348476,"score_gpt":0.21545884997712114,"score_spread":0.21176796314377266,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2954693089","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11011293,0.001764561,0.8766754,0.0011399308,0.00012565868,0.00026921756,0.0006925908,0.00017908034,0.009040581],"genre_scores_gemma":[0.82574856,0.0015190975,0.16056454,0.00013918255,0.00009492113,0.00039906584,0.000622684,0.000088529574,0.010823439],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99908185,0.0003225678,0.000046040113,0.00021904768,0.0001279291,0.00020252881],"domain_scores_gemma":[0.99927205,0.00044425638,0.00010967949,0.000025635,0.00006354667,0.000084896434],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011970556,0.0013450818,0.0015355714,0.000823775,0.000825868,0.0015680373,0.0014143573,0.001964438,0.0032284707],"category_scores_gemma":[0.0015816356,0.00073523994,0.0015153801,0.0020762407,0.000645913,0.0019708823,0.0010961107,0.0010504038,0.00016339614],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005414907,0.000041484243,0.0003751181,0.0001045887,0.00004422132,0.00022411549,0.000028127097,0.98271126,0.0006025234,0.009032048,0.00052167696,0.006260823],"study_design_scores_gemma":[0.000018825833,0.00006881207,0.00024455143,0.000013935123,0.000026188029,0.000086356114,0.000076823206,0.9878154,0.00040541866,0.010222395,0.0010096636,0.000011764794],"about_ca_topic_score_codex":0.008452394,"about_ca_topic_score_gemma":0.005813355,"teacher_disagreement_score":0.008452394,"about_ca_system_score_codex":0.0015961577,"about_ca_system_score_gemma":0.001573065,"threshold_uncertainty_score":0.016806364},"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,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_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","score_opus":0.16889241896232268,"score_gpt":0.3911624713733028,"score_spread":0.2222700524109801,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2955472062","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.051160716,0.0016704741,0.92942965,0.0030958818,0.00017188232,0.000048778496,0.0004601896,0.000056989946,0.013905511],"genre_scores_gemma":[0.8849723,0.0022701668,0.094709754,0.00031756057,0.000291584,0.00013598286,0.00046024873,0.00009645295,0.01674595],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99894077,0.00053148053,0.00003392843,0.00026081136,0.0001456122,0.00008732464],"domain_scores_gemma":[0.99769044,0.0019052187,0.00017658014,0.000066288194,0.00010492984,0.0000565234],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021599436,0.0008665559,0.0010617044,0.00064114475,0.00038914787,0.001815764,0.001433354,0.0016087106,0.002072253],"category_scores_gemma":[0.0064382157,0.0008274044,0.00085475924,0.0014455011,0.0012176074,0.0032522485,0.0011376272,0.0014700147,0.00011781624],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000032056316,0.000020936151,0.00025877354,0.000058560472,0.000042527947,0.000071604605,0.000024455561,0.91670823,0.00014102063,0.07630053,0.0006745745,0.0056667794],"study_design_scores_gemma":[0.000011718408,0.000017742543,0.00019403786,0.00001459718,0.000019587393,0.000032269676,0.00003612454,0.90472966,0.0001488465,0.09338602,0.0013983749,0.000011089814],"about_ca_topic_score_codex":0.0054624965,"about_ca_topic_score_gemma":0.0030210398,"teacher_disagreement_score":0.0054624965,"about_ca_system_score_codex":0.00177966,"about_ca_system_score_gemma":0.0011952439,"threshold_uncertainty_score":0.012912393},"labels":[],"label_agreement":null},{"id":"W2955754236","doi":"10.1287/trsc.2018.0881","title":"Exact Solution of Several Families of Location-Arc Routing Problems","year":2019,"lang":"en","type":"article","venue":"Transportation Science","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"Ministerio de Economía y Competitividad","keywords":"Arc routing; Mathematical optimization; Traverse; Routing (electronic design automation); Graph; Computer science; Set (abstract data type); Vehicle routing problem; Mathematics; Mixed graph; Social connectedness; Constraint (computer-aided design); Theoretical computer science; Line graph","score_opus":0.01570143438517315,"score_gpt":0.25899625266381293,"score_spread":0.24329481827863977,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2955754236","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07077488,0.0012663367,0.9066696,0.00078667665,0.000078501325,0.00013109227,0.0005247901,0.00026425763,0.019503877],"genre_scores_gemma":[0.6133684,0.0013381151,0.37611124,0.00017797024,0.00009563118,0.00029089267,0.00086193497,0.00013429148,0.0076215114],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99932265,0.0002604134,0.000029091123,0.00015760158,0.00011727782,0.000113046],"domain_scores_gemma":[0.9985695,0.0010319888,0.0001621167,0.000101732956,0.00008122911,0.000053475258],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010775056,0.0010685842,0.0009901744,0.0007281205,0.00055838015,0.0011805311,0.0011664218,0.0020371277,0.005925637],"category_scores_gemma":[0.0033498902,0.0005816022,0.0010084213,0.0012207737,0.00075579627,0.0016904025,0.0009785603,0.0012482669,0.0003384442],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002858797,0.00005207346,0.0004145862,0.000083403436,0.000020717758,0.00008899671,0.00004070259,0.956032,0.00033666857,0.0237602,0.0014118088,0.017730365],"study_design_scores_gemma":[0.000014958665,0.000023965918,0.00014956933,0.000012754572,0.0000074191853,0.0000500527,0.000030089368,0.97926223,0.00021920846,0.01887988,0.0013450864,0.0000047188273],"about_ca_topic_score_codex":0.0037572444,"about_ca_topic_score_gemma":0.0041364385,"teacher_disagreement_score":0.005925637,"about_ca_system_score_codex":0.0011045777,"about_ca_system_score_gemma":0.00088619196,"threshold_uncertainty_score":0.019823194},"labels":[],"label_agreement":null},{"id":"W2956133935","doi":"10.1016/j.tre.2019.06.015","title":"The time-dependent location-routing problem","year":2019,"lang":"en","type":"article","venue":"Transportation Research Part E Logistics and Transportation Review","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":30,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Heuristics; Computer science; Routing (electronic design automation); Set (abstract data type); Mathematical optimization; Vehicle routing problem; Mathematics; Computer network","score_opus":0.050619391118946436,"score_gpt":0.3429221920774899,"score_spread":0.2923028009585435,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2956133935","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02502004,0.007899512,0.9357275,0.0050407364,0.00095350604,0.00011372503,0.0015796198,0.00019886688,0.023466457],"genre_scores_gemma":[0.65194213,0.024526317,0.2489499,0.0012415791,0.0018775966,0.00042707293,0.0037769817,0.0002794913,0.06697892],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99857116,0.0005904405,0.00005487143,0.00043441603,0.00023309475,0.00011597133],"domain_scores_gemma":[0.9985876,0.00090685143,0.0001717239,0.000089991096,0.00014754265,0.00009631019],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013561932,0.001617178,0.0017369152,0.0008816326,0.0005067502,0.0023381829,0.003424863,0.0035287263,0.0051291888],"category_scores_gemma":[0.0038619805,0.0010056463,0.0013180289,0.002872031,0.0016667482,0.0039808047,0.0015447896,0.0022915811,0.0008428286],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000098890465,0.00009544757,0.00053808617,0.00049664866,0.00015193014,0.0003591967,0.00007077641,0.6397948,0.0007893967,0.29773372,0.012289507,0.04758157],"study_design_scores_gemma":[0.00008266494,0.00008365996,0.00054829003,0.00007323359,0.00008711214,0.00038232267,0.0000919848,0.75596344,0.00052955205,0.22414987,0.017959248,0.000048683327],"about_ca_topic_score_codex":0.004324996,"about_ca_topic_score_gemma":0.003225253,"teacher_disagreement_score":0.0051291888,"about_ca_system_score_codex":0.0020711746,"about_ca_system_score_gemma":0.0018599117,"threshold_uncertainty_score":0.017158866},"labels":[],"label_agreement":null},{"id":"W2957530701","doi":"","title":"Heuristics for the dynamic facility location problem with modular capacities","year":2019,"lang":"en","type":"article","venue":"PolyPublie (École Polytechnique de Montréal)","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Group for Research in Decision Analysis; Université Laval","funders":"","keywords":"Heuristics; Benchmark (surveying); Mathematical optimization; Modular design; Heuristic; Time horizon; Genetic algorithm; Computer science; Facility location problem; Variable (mathematics); Integer programming; Point (geometry); Mathematics","score_opus":0.008236871715568514,"score_gpt":0.2170081944015468,"score_spread":0.2087713226859783,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2957530701","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09468014,0.00046186446,0.8984454,0.0002541591,0.000045500776,0.00020307962,0.0002186434,0.00041575712,0.005275523],"genre_scores_gemma":[0.63108426,0.00024496802,0.3661599,0.00008726012,0.000038145015,0.00021831629,0.0003035928,0.00007436038,0.0017893169],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99921906,0.00040627376,0.000024006467,0.00012251538,0.00010275383,0.00012526475],"domain_scores_gemma":[0.998086,0.0014135375,0.00021369942,0.00010886359,0.00009898691,0.000078956364],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001735141,0.0007760345,0.00068511383,0.0009975672,0.00033165436,0.00073077006,0.0015256657,0.0009242997,0.002662062],"category_scores_gemma":[0.003399766,0.00047529014,0.0006555598,0.0012046334,0.00069478503,0.0008355683,0.0009147798,0.000750961,0.00021799187],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004082483,0.000041249532,0.0002820464,0.000049599097,0.000015573343,0.000039549013,0.000019733787,0.9765354,0.000497298,0.0059509897,0.0005577015,0.015970144],"study_design_scores_gemma":[0.000023997778,0.000043918142,0.000119350814,0.000011544166,0.0000071400864,0.000022704957,0.000018571087,0.99489486,0.00042187943,0.0038506363,0.00058006623,0.0000052913765],"about_ca_topic_score_codex":0.004196298,"about_ca_topic_score_gemma":0.0045997566,"teacher_disagreement_score":0.004196298,"about_ca_system_score_codex":0.001293658,"about_ca_system_score_gemma":0.00096793904,"threshold_uncertainty_score":0.009386182},"labels":[],"label_agreement":null},{"id":"W2958866644","doi":"10.5267/j.ijiec.2019.6.001","title":"The electric vehicle routing problem with backhauls","year":2019,"lang":"en","type":"article","venue":"International Journal of Industrial Engineering Computations","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":32,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Backhaul (telecommunications); Vehicle routing problem; Computer science; Initialization; Mathematical optimization; Electric vehicle; Linear programming; Routing (electronic design automation); Computer network; Mathematics; Algorithm","score_opus":0.015070319391829097,"score_gpt":0.24871662331391664,"score_spread":0.23364630392208754,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2958866644","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17675851,0.0033669122,0.7750678,0.0022346592,0.0005122369,0.0007046341,0.004243549,0.00082873355,0.036282912],"genre_scores_gemma":[0.72509307,0.0030794446,0.24306013,0.00050580245,0.00032480856,0.00071277755,0.0048825406,0.00027198755,0.022069432],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990465,0.00035788416,0.000044456967,0.0002593564,0.0001414889,0.00015035154],"domain_scores_gemma":[0.9992842,0.00045333998,0.00009872954,0.00005036282,0.00006269003,0.00005075439],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006716759,0.00140123,0.0011058468,0.0005180415,0.0005245929,0.0022893276,0.0013685067,0.0019856866,0.0048065013],"category_scores_gemma":[0.0020126365,0.0005118272,0.0010951395,0.0017664665,0.00063253497,0.00212223,0.0008246495,0.0013622417,0.00048764326],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014948494,0.00016656274,0.0005482064,0.00032254957,0.000078820376,0.0003313273,0.00008601064,0.908457,0.0011661757,0.034050215,0.005383856,0.049259838],"study_design_scores_gemma":[0.000084889674,0.00014705381,0.0005276641,0.000042216605,0.0000410538,0.00033901545,0.00024488164,0.9381998,0.001134286,0.042221706,0.016987419,0.000029913883],"about_ca_topic_score_codex":0.005104117,"about_ca_topic_score_gemma":0.0046464945,"teacher_disagreement_score":0.005104117,"about_ca_system_score_codex":0.001147277,"about_ca_system_score_gemma":0.0013124864,"threshold_uncertainty_score":0.016079307},"labels":[],"label_agreement":null},{"id":"W2960400114","doi":"10.1287/trsc.2020.0988","title":"Variable Fixing for Two-Arc Sequences in Branch-Price-and-Cut Algorithms on Path-Based Models","year":2020,"lang":"en","type":"article","venue":"Transportation Science","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Group for Research in Decision Analysis","funders":"","keywords":"Path (computing); Algorithm; Arc (geometry); Variable (mathematics); Mathematics; Computer science; Geometry","score_opus":0.04511196812750818,"score_gpt":0.29447120525812354,"score_spread":0.24935923713061536,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2960400114","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017695712,0.0002157314,0.9787658,0.00011411053,0.000032406944,0.000089812034,0.00005845017,0.00051832426,0.0025097316],"genre_scores_gemma":[0.27238813,0.000499906,0.72196555,0.00011866192,0.000055070464,0.00041894117,0.0004085897,0.00038483768,0.0037603893],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986915,0.0007087238,0.00003105839,0.00013970166,0.00024382486,0.00018524574],"domain_scores_gemma":[0.99751425,0.0018844287,0.0001825111,0.00020866291,0.00012583571,0.00008428622],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0034394339,0.0015735008,0.0013761838,0.001642883,0.000650181,0.0011142042,0.0019044037,0.0012917233,0.00587744],"category_scores_gemma":[0.006516474,0.0010524512,0.0014682317,0.0021519084,0.0010529191,0.0018962377,0.0013465829,0.003277863,0.00080798933],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008886714,0.000096368276,0.00036725216,0.00007148578,0.000029616913,0.000053589116,0.000064848035,0.92093146,0.0008134561,0.03128553,0.0010285107,0.04516902],"study_design_scores_gemma":[0.000026274165,0.00005139642,0.0000524878,0.00001580182,0.000012059651,0.000015207162,0.000008952183,0.9831081,0.00044981702,0.015371321,0.00088239886,0.0000061084334],"about_ca_topic_score_codex":0.007034501,"about_ca_topic_score_gemma":0.0067287046,"teacher_disagreement_score":0.007034501,"about_ca_system_score_codex":0.0013836964,"about_ca_system_score_gemma":0.002067982,"threshold_uncertainty_score":0.019661963},"labels":[],"label_agreement":null},{"id":"W2963443214","doi":"10.1287/ijoc.2018.0869","title":"A Flexible, Natural Formulation for the Network Design Problem with Vulnerability Constraints","year":2019,"lang":"en","type":"article","venue":"INFORMS journal on computing","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"","keywords":"Bounded function; Network planning and design; Backup; Mathematical optimization; Mathematics; Enhanced Data Rates for GSM Evolution; Computer science; Benchmark (surveying); Graph; Hop (telecommunications); Algorithm; Discrete mathematics","score_opus":0.023120167328931438,"score_gpt":0.27374386999094885,"score_spread":0.2506237026620174,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2963443214","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0059179985,0.00013891958,0.98870647,0.00045546223,0.000056906396,0.00014173954,0.000274287,0.000077124576,0.0042311316],"genre_scores_gemma":[0.11227688,0.00037357683,0.88163775,0.00028989665,0.000089001776,0.0007404529,0.0005513889,0.00011857734,0.0039224965],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986558,0.0005661434,0.00006078088,0.00029281748,0.00030991613,0.000114532464],"domain_scores_gemma":[0.99826247,0.001162085,0.0001758488,0.0001461436,0.00018388609,0.00006957373],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018356539,0.0015418563,0.00066196616,0.00072833634,0.0005582322,0.0015156181,0.0016136543,0.0014149507,0.0052363495],"category_scores_gemma":[0.0056483964,0.0006870804,0.0011052092,0.0012869794,0.001217139,0.002158149,0.001439082,0.0024451378,0.00051332836],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004424504,0.0000993265,0.0003248443,0.0002720255,0.00002352913,0.00014619931,0.00009841932,0.863596,0.0016102744,0.10017489,0.005459114,0.028151104],"study_design_scores_gemma":[0.000053245447,0.000088333276,0.00013646949,0.000054592452,0.000015465665,0.000143618,0.000076440614,0.9217994,0.0006795052,0.066498786,0.010436283,0.00001779819],"about_ca_topic_score_codex":0.0027144975,"about_ca_topic_score_gemma":0.006582771,"teacher_disagreement_score":0.0052363495,"about_ca_system_score_codex":0.0013325302,"about_ca_system_score_gemma":0.0022739214,"threshold_uncertainty_score":0.017517388},"labels":[],"label_agreement":null},{"id":"W2963813097","doi":"10.1109/tits.2018.2865893","title":"Joint Ground and Aerial Package Delivery Services: A Stochastic Optimization Approach","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Transportation Systems","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":112,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"Singapore Institute of Manufacturing Technology","keywords":"Drone; Computer science; Benchmark (surveying); Suite; Operations research; Stochastic programming; Takeoff; Mathematical optimization; Engineering; Mathematics","score_opus":0.026419990522462487,"score_gpt":0.23868458060270087,"score_spread":0.21226459008023837,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2963813097","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019520659,0.0009819529,0.97396076,0.000544217,0.0000793392,0.00011632073,0.00035666264,0.00030079225,0.0041393065],"genre_scores_gemma":[0.7313384,0.0019966208,0.25786167,0.00030700298,0.00025757987,0.00044037713,0.0012009981,0.00018885444,0.0064084856],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988427,0.00043065124,0.000042696647,0.00017606246,0.0002644551,0.00024331725],"domain_scores_gemma":[0.9989882,0.0006139456,0.0001290392,0.00004332647,0.00013120734,0.00009422594],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017145093,0.0016334926,0.0018127579,0.0012165328,0.00043454638,0.0015623686,0.0016249786,0.0013107168,0.0027565658],"category_scores_gemma":[0.0020962958,0.000981942,0.0017575652,0.0017227288,0.0006796677,0.0011896618,0.0012670832,0.0015889261,0.00026925214],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013488257,0.000018673087,0.00017744872,0.000024817455,0.000018437773,0.000023824052,0.0000065033514,0.99090415,0.00011949426,0.0034633246,0.0004955252,0.004734334],"study_design_scores_gemma":[0.000002890324,0.000008802736,0.000048624206,0.0000020991065,0.0000046945693,0.000004153418,0.0000038036635,0.99855036,0.00003215829,0.0011453113,0.0001953307,0.000001856199],"about_ca_topic_score_codex":0.022192935,"about_ca_topic_score_gemma":0.014988755,"teacher_disagreement_score":0.022192935,"about_ca_system_score_codex":0.0020208335,"about_ca_system_score_gemma":0.0029936272,"threshold_uncertainty_score":0.044127524},"labels":[],"label_agreement":null},{"id":"W2964487007","doi":"10.1016/j.ejor.2019.07.049","title":"Multi-objective optimization of a two-echelon vehicle routing problem with vehicle synchronization and ‘grey zone’ customers arising in urban logistics","year":2019,"lang":"en","type":"article","venue":"European Journal of Operational Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":150,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; Université du Québec à Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Österreichische Forschungsförderungsgesellschaft","keywords":"Vehicle routing problem; Computer science; Operations research; Synchronization (alternating current); City logistics; Routing (electronic design automation); Business; Mathematical optimization; Transport engineering; Computer network; Mathematics; Engineering","score_opus":0.038374522156203794,"score_gpt":0.3206628979252961,"score_spread":0.2822883757690923,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2964487007","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.49915725,0.0012303166,0.4778867,0.0010634258,0.00012125939,0.0002243973,0.0005500294,0.00021079511,0.019555787],"genre_scores_gemma":[0.9404747,0.00024215576,0.054530274,0.00009688156,0.000025197209,0.00014279425,0.00018770684,0.00004760841,0.0042526596],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993674,0.00029962338,0.000019369738,0.000118006275,0.000070465576,0.00012508959],"domain_scores_gemma":[0.99900514,0.0007015099,0.00010797236,0.000033083983,0.00006320101,0.00008905303],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011822731,0.0011538013,0.0013808038,0.00075915206,0.00052668864,0.0016374182,0.0011624232,0.0018896312,0.003626472],"category_scores_gemma":[0.0021636733,0.00062875677,0.0010542851,0.0010861254,0.00093428453,0.0010008634,0.0013936284,0.0009172422,0.00023400258],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000030452455,0.000021369593,0.00018671066,0.00003271592,0.000017308612,0.00006361082,0.000015670961,0.9957562,0.00027304966,0.0015983892,0.00013256154,0.0018719652],"study_design_scores_gemma":[0.000017197797,0.000044465873,0.00017037564,0.0000056418157,0.000008957925,0.00001646322,0.00003461104,0.99752516,0.00019769502,0.0017691859,0.00020457522,0.0000056003882],"about_ca_topic_score_codex":0.007944125,"about_ca_topic_score_gemma":0.004357611,"teacher_disagreement_score":0.007944125,"about_ca_system_score_codex":0.0013844867,"about_ca_system_score_gemma":0.001134155,"threshold_uncertainty_score":0.015795767},"labels":[],"label_agreement":null},{"id":"W2964779613","doi":"10.1155/2019/3201656","title":"A New Ant Colony Optimization Algorithm to Solve the Periodic Capacitated Arc Routing Problem with Continuous Moves","year":2019,"lang":"en","type":"article","venue":"Mathematical Problems in Engineering","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval; Center for Interuniversity Research and Analysis on Organizations","funders":"Coordenação de Aperfeiçoamento de Pessoal de Nível Superior","keywords":"Arc routing; Tabu search; Ant colony optimization algorithms; Mathematical optimization; Routing (electronic design automation); Arc (geometry); Computer science; Ant colony; Algorithm; Metaheuristic; Mathematics; Computer network","score_opus":0.007159781880296249,"score_gpt":0.20567763480277604,"score_spread":0.19851785292247978,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2964779613","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0072751227,0.0002934024,0.98660463,0.00014721141,0.0001142686,0.00007599208,0.000043831275,0.0003567172,0.005088766],"genre_scores_gemma":[0.15135548,0.00031044256,0.8409563,0.00014170125,0.00006912768,0.00036565788,0.00018544745,0.00014151151,0.0064743455],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996817,0.00008064137,0.000016327602,0.00006505591,0.00012265127,0.000033648168],"domain_scores_gemma":[0.99964845,0.00017507117,0.0000367478,0.000027823553,0.0000833331,0.000028652968],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000454687,0.00081171544,0.00093800423,0.0006161157,0.0004702335,0.0008508895,0.0015378497,0.0012529388,0.0029199342],"category_scores_gemma":[0.00120401,0.00045670517,0.0005768866,0.0008958687,0.00047622912,0.0008929562,0.00081046997,0.0010650448,0.0006046322],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005032859,0.000061441286,0.00021786733,0.00009408252,0.000040985746,0.00008450151,0.000050308725,0.901522,0.0021234795,0.011017554,0.0030216458,0.08171574],"study_design_scores_gemma":[0.000014972242,0.000015935695,0.000021827385,0.000003163528,0.0000037179786,0.000016252634,0.000004594652,0.9971616,0.0001533077,0.0013488705,0.0012527603,0.0000029853582],"about_ca_topic_score_codex":0.0046034064,"about_ca_topic_score_gemma":0.0043765297,"teacher_disagreement_score":0.0046034064,"about_ca_system_score_codex":0.00040827208,"about_ca_system_score_gemma":0.0010395136,"threshold_uncertainty_score":0.009768188},"labels":[],"label_agreement":null},{"id":"W2964913780","doi":"10.1016/j.omega.2019.07.009","title":"Strategic and operational decision-making in expanding supply chains for LNG as a fuel","year":2019,"lang":"en","type":"article","venue":"Omega","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":18,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"Luonnontieteiden ja Tekniikan Tutkimuksen Toimikunta; Natural Sciences and Engineering Research Council of Canada","keywords":"Bunker; Truck; Liquefied natural gas; Supply chain; Operations research; Greenhouse gas; Offset (computer science); Transport engineering; Engineering; Computer science; Natural gas; Waste management; Business; Automotive engineering; Coal","score_opus":0.018471173390760213,"score_gpt":0.30275367998788977,"score_spread":0.28428250659712956,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2964913780","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.19315697,0.000650753,0.78050274,0.0016992134,0.00011752498,0.00019334635,0.0002880973,0.00010845954,0.0232829],"genre_scores_gemma":[0.8686742,0.00063736766,0.113508545,0.00017618494,0.00008637885,0.00020850454,0.00019486614,0.00007256825,0.016441345],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988072,0.0008146154,0.00002114566,0.00015401814,0.000075394186,0.00012760301],"domain_scores_gemma":[0.9960731,0.0033105458,0.00025497787,0.00006006381,0.00016046644,0.00014096909],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0031481795,0.00088482804,0.0011103827,0.0009843854,0.00065591757,0.0022982354,0.0011652436,0.0017678023,0.0055388906],"category_scores_gemma":[0.0068003647,0.00091338885,0.000798814,0.0012985243,0.0011288552,0.002553956,0.0010645782,0.0016205619,0.0002747953],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000055804107,0.000032045267,0.0004146596,0.00003377999,0.000020552417,0.000031247728,0.000043035758,0.97285044,0.00015360837,0.019449629,0.00035601965,0.0065592416],"study_design_scores_gemma":[0.000005159357,0.000015506137,0.00008349706,0.000008461777,0.0000071117356,0.0000036778986,0.00005150216,0.9862061,0.00008191847,0.013274181,0.00025832868,0.0000044765247],"about_ca_topic_score_codex":0.016713697,"about_ca_topic_score_gemma":0.015552531,"teacher_disagreement_score":0.016713697,"about_ca_system_score_codex":0.0023392388,"about_ca_system_score_gemma":0.0026556524,"threshold_uncertainty_score":0.033232868},"labels":[],"label_agreement":null},{"id":"W2965363856","doi":"10.24963/ijcai.2019/782","title":"On Computational Complexity of Pickup-and-Delivery Problems with Precedence Constraints or Time Windows","year":2019,"lang":"en","type":"article","venue":"","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"Social Sciences and Humanities Research Council of Canada; Natural Sciences and Engineering Research Council of Canada","keywords":"Heuristics; Computer science; Vehicle routing problem; Solver; Pickup; Set (abstract data type); Mathematical optimization; Computational complexity theory; Metaheuristic; Theoretical computer science; Routing (electronic design automation); Algorithm; Mathematics; Artificial intelligence","score_opus":0.02812422643871574,"score_gpt":0.2472558387060989,"score_spread":0.21913161226738317,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2965363856","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.24364102,0.010563848,0.6381433,0.014098595,0.00071354,0.00082156766,0.003986459,0.0011911418,0.08684056],"genre_scores_gemma":[0.7483603,0.0060850126,0.22578125,0.0014197988,0.0008399171,0.0009949609,0.0037965162,0.00073656713,0.011985786],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99599695,0.0017258299,0.00014244155,0.00086181005,0.0005822319,0.0006906955],"domain_scores_gemma":[0.9495455,0.046979576,0.001117043,0.001118947,0.00069799676,0.00054094876],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0042332774,0.002854127,0.0020723853,0.0019708073,0.0014691156,0.0048261317,0.0026613253,0.0022120913,0.013871669],"category_scores_gemma":[0.025063882,0.0007883414,0.0024401555,0.003184655,0.0032751146,0.0075185616,0.0028132992,0.005977544,0.00089411036],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005705347,0.00055224815,0.0030706285,0.0009696501,0.00020137933,0.00019895671,0.0003153654,0.81637204,0.0015103321,0.11988337,0.012470703,0.043884836],"study_design_scores_gemma":[0.00009194843,0.00006574618,0.00065798743,0.00006163954,0.000056853605,0.000066997585,0.000116751515,0.8640616,0.00060858123,0.13231316,0.0018784849,0.000020385363],"about_ca_topic_score_codex":0.010252768,"about_ca_topic_score_gemma":0.011135016,"teacher_disagreement_score":0.013871669,"about_ca_system_score_codex":0.00425386,"about_ca_system_score_gemma":0.0043695085,"threshold_uncertainty_score":0.046405375},"labels":[],"label_agreement":null},{"id":"W2965762698","doi":"10.1016/j.ins.2019.08.017","title":"Solving the traveling repairman problem with profits: A Novel variable neighborhood search approach","year":2019,"lang":"en","type":"article","venue":"Information Sciences","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":48,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Military College of Canada","funders":"Ministarstvo Prosvete, Nauke i Tehnološkog Razvoja; National Natural Science Foundation of China","keywords":"Variable neighborhood search; Heuristics; Mathematical optimization; Benchmark (surveying); Variable (mathematics); Replicate; Computer science; Heuristic; Local search (optimization); Profit (economics); Mathematics; Metaheuristic; Operations research; Statistics; Economics","score_opus":0.018931491975604054,"score_gpt":0.2396039903047542,"score_spread":0.22067249832915015,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2965762698","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011470112,0.0003409474,0.98220587,0.00021176224,0.00005255458,0.000045805926,0.0000319733,0.00007026031,0.0055706725],"genre_scores_gemma":[0.37167734,0.0006847223,0.6155659,0.00016994306,0.00015575232,0.00030564793,0.00015608643,0.00014328447,0.011141259],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99959975,0.00016377578,0.000012695448,0.000075923715,0.000105629006,0.000042243606],"domain_scores_gemma":[0.9993192,0.00048510983,0.000055897523,0.00003104275,0.000081984246,0.000026857755],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00092578435,0.0007172942,0.0010639684,0.0008594807,0.00043813433,0.00085014635,0.0018515545,0.001407764,0.00311747],"category_scores_gemma":[0.00258288,0.0004771208,0.00073624647,0.00081711286,0.0006035935,0.0013639332,0.0010210775,0.0009821375,0.00033962607],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000033893702,0.00006870571,0.00028761328,0.000072896255,0.000034509296,0.000053529446,0.000043158434,0.90922517,0.0006451086,0.05010947,0.0017051375,0.03772084],"study_design_scores_gemma":[0.0000061586047,0.000014118633,0.00002765283,0.000004372622,0.000004663385,0.000011635388,0.000007996663,0.99251646,0.0000853482,0.006866068,0.000453146,0.0000024201493],"about_ca_topic_score_codex":0.0047750636,"about_ca_topic_score_gemma":0.003935811,"teacher_disagreement_score":0.0047750636,"about_ca_system_score_codex":0.00069575873,"about_ca_system_score_gemma":0.0011301527,"threshold_uncertainty_score":0.010428965},"labels":[],"label_agreement":null},{"id":"W2966130377","doi":"10.1287/ijoc.2020.1039","title":"Integer Programming, Constraint Programming, and Hybrid Decomposition Approaches to Discretizable Distance Geometry Problems","year":2021,"lang":"en","type":"preprint","venue":"INFORMS journal on computing","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Mathematics; Vertex (graph theory); Discretization; Integer programming; Constraint programming; Combinatorics; Mathematical optimization; Discrete mathematics; Graph","score_opus":0.04288099152760674,"score_gpt":0.27726845819022056,"score_spread":0.2343874666626138,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2966130377","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005780597,0.0005949672,0.9873338,0.000489342,0.00005289989,0.000047901674,0.00014266078,0.00009682423,0.0054610125],"genre_scores_gemma":[0.18441024,0.001651667,0.80792814,0.00041414125,0.00021061965,0.00043440887,0.0004438759,0.00016876702,0.004338071],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99889404,0.0005219847,0.000048497765,0.00017323352,0.00024349574,0.00011877394],"domain_scores_gemma":[0.998109,0.0013536745,0.00018596424,0.00012734585,0.00013338121,0.0000905893],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001922748,0.0016158951,0.0010338535,0.000957646,0.00047011557,0.002246559,0.0016783159,0.0012495671,0.0044962065],"category_scores_gemma":[0.0035898858,0.0006322492,0.0013301945,0.0022814688,0.0013352896,0.0020899028,0.0017630896,0.0038594108,0.00057852885],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004402645,0.00009154074,0.00028997744,0.00017652987,0.000045303554,0.00006295633,0.000063310414,0.84479177,0.0005682055,0.11933938,0.0022464637,0.032280535],"study_design_scores_gemma":[0.000013649138,0.00002178459,0.00005112127,0.000021375079,0.000008193815,0.000019970446,0.000023197295,0.95044094,0.0002748026,0.047165193,0.0019529865,0.0000068423437],"about_ca_topic_score_codex":0.0034368085,"about_ca_topic_score_gemma":0.003459307,"teacher_disagreement_score":0.0044962065,"about_ca_system_score_codex":0.001695202,"about_ca_system_score_gemma":0.0015816122,"threshold_uncertainty_score":0.015041351},"labels":[],"label_agreement":null},{"id":"W2970460007","doi":"10.1016/j.ijpe.2019.08.007","title":"The multi-plant perishable food production routing with packaging consideration","year":2019,"lang":"en","type":"article","venue":"International Journal of Production Economics","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":52,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Computer science; Food spoilage; Production (economics); Supply chain; Routing (electronic design automation); Mathematical optimization; Integer (computer science); Vehicle routing problem; Operations research; Process (computing); Integer programming; Selection (genetic algorithm); Algorithm; Mathematics; Business; Economics; Artificial intelligence","score_opus":0.01566775008233696,"score_gpt":0.2307604342598984,"score_spread":0.21509268417756144,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2970460007","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.061681345,0.0006666927,0.9260691,0.00037702514,0.00014745175,0.00009474226,0.00022326062,0.00014331259,0.010597049],"genre_scores_gemma":[0.8273166,0.001038145,0.14361401,0.00012491283,0.00011590765,0.00021380125,0.00033561228,0.00014469966,0.027096346],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996045,0.00013795486,0.000010014626,0.00013090222,0.00006451586,0.00005220624],"domain_scores_gemma":[0.9996793,0.00016020444,0.0000587986,0.000026103618,0.00005047339,0.000025154122],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00056836405,0.0013871837,0.0013783514,0.000616491,0.00057565793,0.0012295991,0.001742914,0.0018050926,0.0055758683],"category_scores_gemma":[0.0010947728,0.0010673357,0.0015158117,0.0013463155,0.00048680237,0.001646019,0.0008103286,0.0009470518,0.00042068327],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000048260532,0.00002915654,0.0002636161,0.0000914773,0.000027825796,0.00012889164,0.00001765205,0.98314327,0.0012660314,0.0052298266,0.00054476026,0.009209156],"study_design_scores_gemma":[0.0000051074567,0.000045022945,0.00019190481,0.0000044090584,0.00001609049,0.000028966526,0.000011271839,0.99701643,0.00023698054,0.0020561637,0.0003816465,0.0000060211028],"about_ca_topic_score_codex":0.0045091463,"about_ca_topic_score_gemma":0.0035534427,"teacher_disagreement_score":0.0055758683,"about_ca_system_score_codex":0.00088679045,"about_ca_system_score_gemma":0.0007983023,"threshold_uncertainty_score":0.018653154},"labels":[],"label_agreement":null},{"id":"W2973679003","doi":"10.5267/j.dsl.2019.7.003","title":"A novel two-phase approach for solving the multi-compartment vehicle routing problem with a heterogeneous fleet of vehicles: a case study on fuel delivery","year":2019,"lang":"en","type":"article","venue":"Decision Science Letters","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Khon Kaen University","keywords":"Vehicle routing problem; Computer science; Phase (matter); Routing (electronic design automation); Transport engineering; Operations research; Automotive engineering; Engineering; Computer network","score_opus":0.04887680044610048,"score_gpt":0.3264399826008187,"score_spread":0.2775631821547182,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2973679003","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.040236633,0.00038942022,0.9539115,0.00027267233,0.00005352519,0.0002679135,0.00007873639,0.0001558933,0.0046338323],"genre_scores_gemma":[0.38840643,0.0004236091,0.60663444,0.00010958235,0.000033777327,0.00037831068,0.00016731353,0.00004600617,0.00380047],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995777,0.00016572226,0.000019387156,0.00009007714,0.00007001627,0.000076960474],"domain_scores_gemma":[0.9995797,0.00023720041,0.00005084013,0.000028150533,0.00005989318,0.000044126846],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00073024095,0.0009234925,0.0008406394,0.0007644333,0.00080054766,0.0010248066,0.0012101901,0.0014575134,0.0020178477],"category_scores_gemma":[0.00092962454,0.0005497728,0.0011056762,0.00094657677,0.00042209646,0.0009548026,0.0008316071,0.0008160605,0.00018099134],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000649551,0.00017439474,0.00055747904,0.00016957716,0.00004980855,0.0002908754,0.00007013461,0.9456971,0.0031456265,0.009646256,0.00094344345,0.03919036],"study_design_scores_gemma":[0.000021784334,0.000081916216,0.00009074539,0.0000061161754,0.000013626738,0.00007910841,0.00004419777,0.99602664,0.00069967995,0.0020010162,0.00092760444,0.0000075661005],"about_ca_topic_score_codex":0.0060559805,"about_ca_topic_score_gemma":0.007918147,"teacher_disagreement_score":0.0060559805,"about_ca_system_score_codex":0.0007508626,"about_ca_system_score_gemma":0.0019404208,"threshold_uncertainty_score":0.01204145},"labels":[],"label_agreement":null},{"id":"W2974438822","doi":"10.1287/ijoc.2018.0881","title":"An MDD-Based Lagrangian Approach to the Multicommodity Pickup-and-Delivery TSP","year":2019,"lang":"en","type":"article","venue":"INFORMS journal on computing","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Pickup; Mathematical optimization; Lagrangian relaxation; Lagrangian; Mathematics; Travelling salesman problem; Branch and bound; Computer science; Applied mathematics; Artificial intelligence","score_opus":0.01822810706720267,"score_gpt":0.25650438329130326,"score_spread":0.23827627622410058,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2974438822","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0046034306,0.00015258024,0.992268,0.00020091786,0.000038898514,0.0000426973,0.000107564294,0.00015720775,0.0024288397],"genre_scores_gemma":[0.1678857,0.00046666918,0.82692677,0.00013951932,0.00006871086,0.00017747357,0.00040184226,0.0001365688,0.003796815],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994785,0.00018098543,0.000023554452,0.000080864374,0.00017245539,0.00006365333],"domain_scores_gemma":[0.9994854,0.00027083996,0.0000516575,0.000059337268,0.0000935424,0.000039240713],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000664561,0.00062254164,0.0007921213,0.00078846986,0.00042499683,0.00090376753,0.001320445,0.0006190189,0.0031888566],"category_scores_gemma":[0.0015445447,0.00049708126,0.0011417582,0.0009427952,0.00045007546,0.0007943609,0.0011111711,0.001464124,0.0005679219],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003153027,0.00006101866,0.00038863334,0.00012529032,0.000030047086,0.00008277709,0.000063838364,0.8754787,0.002205467,0.058260158,0.0026065109,0.060666066],"study_design_scores_gemma":[0.000009434189,0.00001611114,0.00003732198,0.000007764944,0.000007668129,0.000019119227,0.000009136346,0.9872858,0.00042321612,0.009426632,0.0027538778,0.0000038545677],"about_ca_topic_score_codex":0.0047707055,"about_ca_topic_score_gemma":0.006739912,"teacher_disagreement_score":0.0047707055,"about_ca_system_score_codex":0.0010663687,"about_ca_system_score_gemma":0.0020116197,"threshold_uncertainty_score":0.010667741},"labels":[],"label_agreement":null},{"id":"W2978059148","doi":"10.1016/j.tre.2019.09.016","title":"An exact algorithm for the inventory routing problem with logistic ratio","year":2019,"lang":"en","type":"article","venue":"Transportation Research Part E Logistics and Transportation Review","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Time horizon; Mathematical optimization; Routing (electronic design automation); Algorithm; Computer science; Function (biology); Distribution (mathematics); Mathematics","score_opus":0.09468338248980823,"score_gpt":0.3723578190204224,"score_spread":0.2776744365306142,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2978059148","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0047781197,0.00042013044,0.9869354,0.00023042235,0.00013900323,0.00011186587,0.00009214279,0.00087639515,0.0064166277],"genre_scores_gemma":[0.07888181,0.0005055573,0.91548765,0.00015379723,0.00012429024,0.00030240128,0.00020698996,0.00024617778,0.004091347],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988901,0.00027198254,0.0000616278,0.0002291271,0.0003841446,0.00016303343],"domain_scores_gemma":[0.9987973,0.00072742626,0.000078400815,0.0001680098,0.00017627388,0.00005264917],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013341404,0.0014957336,0.0018513325,0.001236684,0.0009471199,0.0019313148,0.0028513041,0.0019704425,0.009293293],"category_scores_gemma":[0.0043002493,0.0010144874,0.0014004603,0.0020625237,0.001126672,0.0031926103,0.0022765181,0.0021557289,0.0019046539],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00027915192,0.0002514159,0.0004415621,0.00029570644,0.00007174485,0.00011167963,0.00012413658,0.567972,0.0021860243,0.049038135,0.011089612,0.36813876],"study_design_scores_gemma":[0.00017719343,0.00007918031,0.00015467074,0.00002631955,0.000026590562,0.00011539756,0.00003594366,0.9571487,0.0006250246,0.03745065,0.004137165,0.000023161867],"about_ca_topic_score_codex":0.006399784,"about_ca_topic_score_gemma":0.0046779513,"teacher_disagreement_score":0.009293293,"about_ca_system_score_codex":0.0018696701,"about_ca_system_score_gemma":0.0035549053,"threshold_uncertainty_score":0.031089127},"labels":[],"label_agreement":null},{"id":"W2978804656","doi":"10.1002/9780470400531.eorms1107","title":"Continuous Optimization by Variable Neighborhood Search","year":2011,"lang":"en","type":"other","venue":"Wiley Encyclopedia of Operations Research and Management Science","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal; Royal Military College of Canada; Group for Research in Decision Analysis; Royal Ottawa Mental Health Centre","funders":"","keywords":"Metaheuristic; Variable neighborhood search; Mathematical optimization; Continuous variable; Variable (mathematics); Computer science; Continuous optimization; Local search (optimization); Section (typography); Optimization problem; Mathematics; Multi-swarm optimization","score_opus":0.021397446484316636,"score_gpt":0.2968403791596312,"score_spread":0.27544293267531456,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2978804656","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005904128,0.0012170624,0.9819516,0.00016593211,0.00009127811,0.000028089571,0.00003396797,0.000101089434,0.010506974],"genre_scores_gemma":[0.45532987,0.0025337872,0.5245378,0.0001813263,0.00026076497,0.00035180265,0.00019924334,0.00020134488,0.016404172],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995431,0.00021628416,0.000013376873,0.000058251797,0.00013916373,0.000029848394],"domain_scores_gemma":[0.99962485,0.00024243933,0.000026750104,0.00003469633,0.00005518451,0.000016152044],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000927594,0.0006561483,0.0008415498,0.00060981157,0.000273795,0.0007102567,0.00074393605,0.0007302987,0.0034226128],"category_scores_gemma":[0.0018209005,0.00026940723,0.000659625,0.0011440711,0.0009151885,0.000762502,0.0008967261,0.0010056822,0.00050244015],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004142787,0.00002714754,0.00018234646,0.00010043253,0.000039824117,0.000035584104,0.00002888471,0.81429315,0.0010660099,0.122350894,0.00256177,0.05927251],"study_design_scores_gemma":[0.000011056097,0.000015345451,0.000044715744,0.000009148447,0.0000034558839,0.000009158905,0.0000024185924,0.9771542,0.00017763919,0.020509528,0.0020598944,0.0000034826962],"about_ca_topic_score_codex":0.002687698,"about_ca_topic_score_gemma":0.0019217005,"teacher_disagreement_score":0.0034226128,"about_ca_system_score_codex":0.0006359736,"about_ca_system_score_gemma":0.00059696595,"threshold_uncertainty_score":0.011449814},"labels":[],"label_agreement":null},{"id":"W2978805836","doi":"10.4018/ijamc.2020010102","title":"An Efficient VNS Algorithm to Solve the Multi-Attribute Technician Routing and Scheduling Problem","year":2019,"lang":"en","type":"article","venue":"International Journal of Applied Metaheuristic Computing","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Variable neighborhood search; Computer science; Tabu search; Mathematical optimization; Technician; Meta heuristic; Job shop scheduling; Heuristic; Metaheuristic; Solver; Overtime; Scheduling (production processes); Algorithm; Routing (electronic design automation); Artificial intelligence; Mathematics; Engineering","score_opus":0.010689592422019686,"score_gpt":0.27545001590261187,"score_spread":0.2647604234805922,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2978805836","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014751445,0.00039741176,0.97790205,0.00017449252,0.00012428104,0.000092155824,0.00010621576,0.00039745707,0.0060544712],"genre_scores_gemma":[0.23438399,0.00037709222,0.7574845,0.00013593574,0.00007689985,0.0003973293,0.00048526452,0.00011580897,0.0065432205],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996363,0.00012757465,0.000016876678,0.00005806598,0.00009952357,0.00006164209],"domain_scores_gemma":[0.9996786,0.00017084021,0.000033718316,0.000022692786,0.00006816695,0.00002598443],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007296712,0.0007653652,0.0008723501,0.00078722654,0.00047201366,0.0005597344,0.0012506966,0.00093725964,0.003156391],"category_scores_gemma":[0.0012298451,0.00040617603,0.0006975403,0.0012136931,0.00040421245,0.00062072277,0.0006911103,0.00076443073,0.00045949794],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007665252,0.000059495556,0.0003197689,0.00006911301,0.000036266938,0.000046127745,0.000031299045,0.90407604,0.0012618287,0.010125449,0.002591245,0.081306584],"study_design_scores_gemma":[0.000021583704,0.000031184598,0.000058345737,0.0000064326614,0.0000052834657,0.00002397733,0.000011238367,0.9948979,0.00024087468,0.003159807,0.0015399427,0.0000035633038],"about_ca_topic_score_codex":0.0049323114,"about_ca_topic_score_gemma":0.0054355077,"teacher_disagreement_score":0.0049323114,"about_ca_system_score_codex":0.0007795079,"about_ca_system_score_gemma":0.0016918491,"threshold_uncertainty_score":0.010559201},"labels":[],"label_agreement":null},{"id":"W2981013975","doi":"10.1287/trsc.2019.0913","title":"Improving Air Crew Rostering by Considering Crew Preferences in the Crew Pairing Problem","year":2019,"lang":"en","type":"article","venue":"Transportation Science","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":52,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Group for Research in Decision Analysis","funders":"","keywords":"Crew; Crew scheduling; Computer science; Operations research; Crew resource management; Schedule; Column generation; Scheduling (production processes); Aviation; Mathematical optimization; Aeronautics; Operations management; Engineering; Mathematics","score_opus":0.018296338483712423,"score_gpt":0.25626094630158225,"score_spread":0.23796460781786982,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2981013975","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.19937952,0.00041953576,0.78964823,0.00036404404,0.00009111792,0.0003509321,0.00022694211,0.00052848435,0.008991173],"genre_scores_gemma":[0.7547672,0.00025892665,0.2405222,0.0001513177,0.000062915555,0.0003071713,0.00039011525,0.00015926342,0.0033808495],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99920565,0.00027218266,0.0000319762,0.00016750817,0.0001499127,0.000172806],"domain_scores_gemma":[0.9991874,0.000333446,0.00013968974,0.00011401677,0.0001007829,0.0001246843],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011009135,0.0011636894,0.0012124435,0.0005898476,0.0004981846,0.00083927676,0.0011696585,0.0009493962,0.0047971564],"category_scores_gemma":[0.002544011,0.00045645385,0.00074462936,0.00088194804,0.00044934367,0.0015700221,0.0013050267,0.0014237528,0.00054943335],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021296993,0.00035487593,0.002648153,0.00014870816,0.00006189184,0.0001923447,0.00013338686,0.89532757,0.0057140575,0.008397122,0.0023817439,0.08442709],"study_design_scores_gemma":[0.0000487107,0.00031322765,0.0010300168,0.000014679473,0.000032116594,0.00010682604,0.00010784651,0.9864196,0.0020755858,0.007264486,0.0025699316,0.000016985829],"about_ca_topic_score_codex":0.0019143423,"about_ca_topic_score_gemma":0.0019028526,"teacher_disagreement_score":0.0047971564,"about_ca_system_score_codex":0.00044352195,"about_ca_system_score_gemma":0.0009536131,"threshold_uncertainty_score":0.016048074},"labels":[],"label_agreement":null},{"id":"W2981178732","doi":"","title":"A general variable neighborhood search for the travelling salesman problem with draft limits","year":2013,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Travelling salesman problem; Traveling purchaser problem; Variable neighborhood search; Mathematical optimization; Limit (mathematics); Hull; 2-opt; Set (abstract data type); Variable (mathematics); Computer science; Context (archaeology); Bottleneck traveling salesman problem; Mathematics; Metaheuristic; Engineering; Geography","score_opus":0.019853393059819625,"score_gpt":0.2370067501492878,"score_spread":0.21715335708946817,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2981178732","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.055236,0.0014265327,0.9214598,0.0012530602,0.00020619245,0.000118966986,0.00021922274,0.00011508155,0.019965032],"genre_scores_gemma":[0.590754,0.0014875429,0.36931038,0.00033472155,0.00031570348,0.00047658719,0.0006085809,0.00025649543,0.03645598],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992918,0.0003516671,0.000018358964,0.00015005436,0.000115691124,0.00007241838],"domain_scores_gemma":[0.9981173,0.00136774,0.00014185347,0.00008436533,0.00016478375,0.00012400156],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001625108,0.00055689324,0.0014868014,0.0008362955,0.00062765565,0.0015890382,0.0022935716,0.0019214903,0.006335018],"category_scores_gemma":[0.007298918,0.00061321264,0.0009508849,0.001323284,0.0010869157,0.0022973435,0.0016334498,0.0015722825,0.0004481528],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012823597,0.000081034916,0.0005219377,0.00017515315,0.0000439354,0.00013728486,0.000083228886,0.7840525,0.0006585241,0.18696202,0.0054501514,0.02170597],"study_design_scores_gemma":[0.000015200524,0.00002244543,0.00006059943,0.000010661523,0.0000055309247,0.000018039624,0.000012907383,0.96716726,0.000064765045,0.03150928,0.001108094,0.0000052088712],"about_ca_topic_score_codex":0.0045803427,"about_ca_topic_score_gemma":0.0035739273,"teacher_disagreement_score":0.006335018,"about_ca_system_score_codex":0.0010135421,"about_ca_system_score_gemma":0.0011630462,"threshold_uncertainty_score":0.02119273},"labels":[],"label_agreement":null},{"id":"W2982060258","doi":"10.1051/matecconf/201929602009","title":"Optimization of DND Multi-Depot Split-Load Pickup-Delivery Problem","year":2019,"lang":"en","type":"article","venue":"MATEC Web of Conferences","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Public Safety Canada; Defence Research and Development Canada","funders":"","keywords":"Pickup; Heuristic; Computer science; Descent (aeronautics); Mathematical optimization; Ground transportation; Vehicle routing problem; Depot; Range (aeronautics); Greedy algorithm; Operations research; Transport engineering; Engineering; Routing (electronic design automation); Computer network; Mathematics; Algorithm","score_opus":0.018466011410109128,"score_gpt":0.24346451760024254,"score_spread":0.22499850619013342,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2982060258","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.062080253,0.00073476176,0.9251392,0.000466465,0.00013291163,0.00026092966,0.0006199609,0.00027465992,0.010291009],"genre_scores_gemma":[0.68372077,0.00065456,0.30257657,0.00019978645,0.00007900083,0.00045080762,0.0009369577,0.00018932582,0.011192185],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999456,0.00020439789,0.000022230799,0.00012759022,0.00008491069,0.00010474446],"domain_scores_gemma":[0.99945277,0.00030797522,0.00006858173,0.000029379842,0.00007715728,0.00006407675],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011888059,0.0013087111,0.0016834443,0.0010083647,0.00054355076,0.0012901243,0.0016330502,0.0017462948,0.0044136886],"category_scores_gemma":[0.0014826074,0.0006956029,0.0008483038,0.0010970007,0.0005850228,0.00091432,0.001234886,0.0009876193,0.00047489954],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004464628,0.00003349907,0.00031035647,0.000090440844,0.000023856444,0.000078956014,0.00001674587,0.9849861,0.0005508484,0.0026536083,0.0008114986,0.010399309],"study_design_scores_gemma":[0.00001028863,0.000034158667,0.00009833067,0.000005810477,0.0000068233726,0.0000162177,0.000018759043,0.9977102,0.00020480527,0.0013979493,0.0004934609,0.000003198996],"about_ca_topic_score_codex":0.006423198,"about_ca_topic_score_gemma":0.005225896,"teacher_disagreement_score":0.006423198,"about_ca_system_score_codex":0.0012751732,"about_ca_system_score_gemma":0.0015607442,"threshold_uncertainty_score":0.014765263},"labels":[],"label_agreement":null},{"id":"W2982515303","doi":"10.3390/su11216055","title":"A Memetic Algorithm for the Green Vehicle Routing Problem","year":2019,"lang":"en","type":"article","venue":"Sustainability","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":35,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Crossover; Memetic algorithm; Mathematical optimization; Vehicle routing problem; Population; Computer science; Local search (optimization); Bees algorithm; Routing (electronic design automation); Metaheuristic; Artificial intelligence; Mathematics","score_opus":0.008642215360106072,"score_gpt":0.2598593622576075,"score_spread":0.2512171468975014,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2982515303","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01810025,0.0006713942,0.9692945,0.00070404063,0.00018391763,0.00014760083,0.00007482515,0.00029816417,0.0105253495],"genre_scores_gemma":[0.36914733,0.00085621496,0.6192573,0.00046680524,0.00018135693,0.00073718396,0.00016060026,0.0000967796,0.009096468],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996724,0.00012393467,0.000016025124,0.000057643505,0.00008405473,0.00004604519],"domain_scores_gemma":[0.9996176,0.00022977992,0.000042273212,0.000028084372,0.00006209068,0.000020189907],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000963696,0.0008501987,0.00082286686,0.00093930465,0.0008074974,0.00090752076,0.0013654681,0.0016229136,0.0020732463],"category_scores_gemma":[0.0018243341,0.0004031457,0.00079663127,0.0009241209,0.0007130373,0.000842864,0.0008685647,0.0011044113,0.00034222344],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004914196,0.000066308974,0.00042600243,0.000090151145,0.00007000752,0.000120948534,0.00007515189,0.9056176,0.0014818608,0.027155107,0.003506882,0.061340842],"study_design_scores_gemma":[0.00002373409,0.000028431763,0.00005595181,0.000010467894,0.000010807341,0.000045102875,0.000013999282,0.9909259,0.0003801243,0.0062297024,0.0022699395,0.000005828872],"about_ca_topic_score_codex":0.0022440762,"about_ca_topic_score_gemma":0.0019404236,"teacher_disagreement_score":0.0022440762,"about_ca_system_score_codex":0.00081983133,"about_ca_system_score_gemma":0.0012948606,"threshold_uncertainty_score":0.0069357157},"labels":[],"label_agreement":null},{"id":"W2982586778","doi":"10.1016/j.cor.2020.105085","title":"A new constraint programming model and a linear programming-based adaptive large neighborhood search for the vehicle routing problem with synchronization constraints","year":2020,"lang":"en","type":"preprint","venue":"Computers & Operations Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Vehicle routing problem; Constraint programming; Mathematical optimization; Linear programming; Computer science; Constraint (computer-aided design); Synchronization (alternating current); Constraint satisfaction; Goal programming; Routing (electronic design automation); Mathematics; Stochastic programming; Artificial intelligence","score_opus":0.07258725483866049,"score_gpt":0.34493051542850567,"score_spread":0.27234326058984515,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2982586778","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0016522625,0.00010199268,0.9960211,0.00017790943,0.000043632608,0.000030867766,0.000042409923,0.000033412067,0.0018964187],"genre_scores_gemma":[0.22649677,0.0007655672,0.75708735,0.00042221035,0.00026538852,0.000862345,0.00043417665,0.00026775416,0.013398425],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991381,0.00035838594,0.000032422133,0.00015504673,0.0002639361,0.00005207291],"domain_scores_gemma":[0.99898404,0.0006248423,0.000092823386,0.00006184827,0.00018474858,0.00005172843],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013184281,0.00095124287,0.0012698247,0.00070514844,0.00052884244,0.0015434339,0.002864973,0.00211184,0.005314151],"category_scores_gemma":[0.0038642243,0.0007968901,0.0011491396,0.0015650257,0.0009275908,0.0023628552,0.0018016822,0.0028093997,0.00065881544],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026659274,0.00004461166,0.00010156051,0.000057368507,0.000024127223,0.000057069963,0.000026258353,0.927412,0.0008593676,0.059136078,0.0018862234,0.010368776],"study_design_scores_gemma":[0.0000035032804,0.000005937344,0.000008225579,0.0000019711572,0.0000015729215,0.0000037424047,0.0000017487084,0.9970107,0.000044022963,0.0026215499,0.0002946288,0.0000023335388],"about_ca_topic_score_codex":0.009232475,"about_ca_topic_score_gemma":0.0083416095,"teacher_disagreement_score":0.009232475,"about_ca_system_score_codex":0.0011097398,"about_ca_system_score_gemma":0.0017839082,"threshold_uncertainty_score":0.018357456},"labels":[],"label_agreement":null},{"id":"W2986923212","doi":"10.1016/j.omega.2019.102151","title":"A continuous-time supply-driven inventory-constrained routing problem","year":2019,"lang":"en","type":"article","venue":"Omega","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"Nederlandse Organisatie voor Wetenschappelijk Onderzoek; Commonwealth Scientific and Industrial Research Organisation","keywords":"Routing (electronic design automation); Container (type theory); Operations research; Vehicle routing problem; Computer science; Supply chain; Inventory theory; Biogas; Operations management; Business; Inventory control; Economics; Engineering; Computer network; Waste management; Marketing","score_opus":0.007621246591284409,"score_gpt":0.2210714830864677,"score_spread":0.2134502364951833,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2986923212","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.048744924,0.000636893,0.9296149,0.0019619502,0.00024142166,0.00011578369,0.0008300585,0.00014229446,0.017711824],"genre_scores_gemma":[0.79182017,0.0012521712,0.1647082,0.00049669703,0.00025478928,0.00033835732,0.00092608685,0.00015531896,0.040048197],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992556,0.00027258726,0.000023820246,0.00022593685,0.00012950232,0.000092499606],"domain_scores_gemma":[0.99864143,0.0009142264,0.00013438713,0.00004569053,0.00013984488,0.000124387],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015132467,0.001001222,0.0013359955,0.0008171693,0.00048781667,0.0022863802,0.0017118149,0.003013995,0.0055994648],"category_scores_gemma":[0.003443868,0.00095125096,0.00082759594,0.0014271996,0.0011596737,0.0016413571,0.0015349099,0.001632855,0.00040622556],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008915198,0.00007146545,0.00037066083,0.00012706821,0.000038549926,0.00014215923,0.000044168126,0.95094055,0.0006482207,0.037140764,0.0023208042,0.008066456],"study_design_scores_gemma":[0.000018485725,0.000028645918,0.00008894999,0.000009146945,0.000009528314,0.00002283817,0.000017557335,0.99030674,0.00010744571,0.008418396,0.0009658543,0.0000064026462],"about_ca_topic_score_codex":0.0070022997,"about_ca_topic_score_gemma":0.004008048,"teacher_disagreement_score":0.0070022997,"about_ca_system_score_codex":0.0017633237,"about_ca_system_score_gemma":0.0022290524,"threshold_uncertainty_score":0.01873207},"labels":[],"label_agreement":null},{"id":"W2987023879","doi":"10.1016/j.ejor.2019.11.015","title":"Formulations, branch-and-cut and a hybrid heuristic algorithm for an inventory routing problem with perishable products","year":2019,"lang":"en","type":"article","venue":"European Journal of Operational Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":55,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"Conselho Nacional de Desenvolvimento Científico e Tecnológico; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior; Fundação de Amparo à Pesquisa do Estado de São Paulo","keywords":"Iterated local search; Mathematical optimization; Computer science; Heuristic; Routing (electronic design automation); Metaheuristic; Product (mathematics); Time horizon; Set (abstract data type); Quality (philosophy); USable; Vehicle routing problem; Algorithm; Mathematics","score_opus":0.053666287729541004,"score_gpt":0.32640920298315945,"score_spread":0.27274291525361843,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2987023879","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.031211028,0.0005609047,0.9574652,0.00044918148,0.00014553194,0.00022483623,0.0001733345,0.0003180596,0.009451918],"genre_scores_gemma":[0.24200891,0.0006194501,0.74764407,0.00023055599,0.00012730825,0.00076332473,0.00035282603,0.00016150695,0.008092096],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99939656,0.00029548485,0.00002090131,0.00006261768,0.00015313274,0.00007140014],"domain_scores_gemma":[0.99867487,0.00095742795,0.000084767,0.000053834498,0.00016757894,0.000061539],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00173565,0.0014122865,0.001467606,0.0013759742,0.00094266585,0.0022250854,0.0019742553,0.0027765527,0.0054933294],"category_scores_gemma":[0.0025828395,0.0010905842,0.001145862,0.0019970671,0.000847752,0.0016816452,0.0010880801,0.0020276466,0.00051725813],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000107944645,0.00012640664,0.00016273992,0.00007014573,0.000028169437,0.00003904507,0.000040970543,0.9546938,0.0005062075,0.013859799,0.0013722616,0.028992578],"study_design_scores_gemma":[0.000020213132,0.000030757717,0.000033929486,0.000006670684,0.000008742564,0.000008465651,0.000011355507,0.9959394,0.00012554564,0.0034583614,0.00035204322,0.000004603951],"about_ca_topic_score_codex":0.01182762,"about_ca_topic_score_gemma":0.012703705,"teacher_disagreement_score":0.01182762,"about_ca_system_score_codex":0.0017848179,"about_ca_system_score_gemma":0.0025518273,"threshold_uncertainty_score":0.023517549},"labels":[],"label_agreement":null},{"id":"W2989947400","doi":"10.1287/trsc.2021.1111","title":"The Electric Vehicle Routing Problem with Capacitated Charging Stations","year":2021,"lang":"en","type":"article","venue":"Transportation Science","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":113,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"Agence Nationale de la Recherche","keywords":"Vehicle routing problem; Iterated local search; Routing (electronic design automation); Electric vehicle; Mathematical optimization; Set (abstract data type); Generator (circuit theory); Computer science; Charging station; Iterated function; Local search (optimization); Mathematics","score_opus":0.013352103453035993,"score_gpt":0.24868157888305245,"score_spread":0.23532947543001645,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2989947400","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13877094,0.0018175825,0.80495375,0.0021179214,0.00036251923,0.0005159828,0.0023164044,0.000687414,0.048457462],"genre_scores_gemma":[0.72693825,0.0019504755,0.2441788,0.00035221406,0.00025353354,0.0004881998,0.0019459235,0.0002612537,0.023631355],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99884903,0.0004447831,0.000052611344,0.00026867006,0.00016990602,0.00021500676],"domain_scores_gemma":[0.99858236,0.0009804599,0.00016182887,0.00008087042,0.00009884046,0.00009578739],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011010834,0.0021122582,0.0014108858,0.00088273553,0.0008707651,0.0026734597,0.0022784306,0.0030377673,0.0075775567],"category_scores_gemma":[0.003024397,0.0009033069,0.001499425,0.0025988717,0.0009344011,0.002595359,0.001182629,0.0017662974,0.0006649791],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004464026,0.000057688463,0.00023540923,0.00007524072,0.000029578801,0.000095906275,0.000019752666,0.974113,0.0002356363,0.014424587,0.0015038161,0.009164711],"study_design_scores_gemma":[0.00005800067,0.000068380796,0.00015424774,0.000014397167,0.000025091342,0.00012084839,0.0000681833,0.9739314,0.00034060012,0.020466156,0.0047366633,0.00001606448],"about_ca_topic_score_codex":0.008702466,"about_ca_topic_score_gemma":0.007659526,"teacher_disagreement_score":0.008702466,"about_ca_system_score_codex":0.0018439043,"about_ca_system_score_gemma":0.0021040647,"threshold_uncertainty_score":0.025349498},"labels":[],"label_agreement":null},{"id":"W2991187918","doi":"10.1016/j.ejor.2019.11.043","title":"A branch-and-price heuristic for the crew pairing problem with language constraints","year":2019,"lang":"en","type":"article","venue":"European Journal of Operational Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":35,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal; Group for Research in Decision Analysis; Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Crew; Computer science; Heuristic; Set (abstract data type); Schedule; Operations research; Tree (set theory); Mathematical optimization; Artificial intelligence; Programming language; Mathematics; Engineering","score_opus":0.0438936097016484,"score_gpt":0.3347566218631179,"score_spread":0.2908630121614695,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2991187918","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05463396,0.0005023018,0.92340416,0.00066705275,0.00026240986,0.00066975295,0.0003300762,0.0012172367,0.018313041],"genre_scores_gemma":[0.27671835,0.00033439155,0.7120813,0.00027430418,0.00010196765,0.00060272473,0.0006121226,0.00044416008,0.008830652],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99916446,0.00029130117,0.000029585475,0.0001240959,0.0001354188,0.00025522997],"domain_scores_gemma":[0.9984218,0.0010495915,0.000073941526,0.00011209915,0.00013761093,0.00020499648],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014070242,0.0014177002,0.0027188396,0.0015714729,0.0015005113,0.0021193235,0.002758955,0.0032762852,0.016204415],"category_scores_gemma":[0.0034170826,0.0011876945,0.001552082,0.002118143,0.0010623523,0.002599528,0.0021947236,0.0024987366,0.0017368427],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00037233272,0.00062097097,0.00045610502,0.00019353909,0.00006540238,0.00028648076,0.000181882,0.8281305,0.0022721943,0.02528623,0.009487809,0.13264658],"study_design_scores_gemma":[0.00013776887,0.00011092126,0.00009663567,0.00002050235,0.000026780044,0.000042505562,0.000087435365,0.9857474,0.00052455364,0.011304089,0.0018839017,0.00001762098],"about_ca_topic_score_codex":0.011030095,"about_ca_topic_score_gemma":0.009986639,"teacher_disagreement_score":0.016204415,"about_ca_system_score_codex":0.0015424283,"about_ca_system_score_gemma":0.0041150693,"threshold_uncertainty_score":0.054209173},"labels":[],"label_agreement":null},{"id":"W2991685881","doi":"","title":"Transportation optimization for the collection of end-of-life vehicles","year":2019,"lang":"en","type":"article","venue":"Espace École de technologie supérieure (École de technologie supérieure)","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Purchasing; Vehicle routing problem; Integer programming; Operations research; Heuristic; Linear programming; Routing (electronic design automation); Order (exchange); Computer science; Heuristics; Transport engineering; Mathematical optimization; Engineering; Operations management; Business; Mathematics; Computer network","score_opus":0.014024788042035509,"score_gpt":0.25425514609940664,"score_spread":0.24023035805737114,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2991685881","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08614572,0.0015765069,0.89784145,0.0007846133,0.00011942931,0.00027202614,0.0007255857,0.0002707755,0.0122638345],"genre_scores_gemma":[0.67232454,0.0018243067,0.3034064,0.00015732423,0.00007871746,0.0004751868,0.0012803007,0.00018713385,0.020266052],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994838,0.00022565249,0.000015569547,0.00011385107,0.00007252415,0.00008849531],"domain_scores_gemma":[0.99934274,0.00040116435,0.0001028239,0.000024668465,0.000082938925,0.000045638884],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009995035,0.0012036436,0.0009391539,0.0009791707,0.00066561095,0.0013480643,0.0012237827,0.0012546596,0.0037562486],"category_scores_gemma":[0.0015219012,0.00071096775,0.0013113868,0.00152256,0.0006369043,0.0009952736,0.0006890322,0.0011120071,0.00035454414],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000025826716,0.000031331765,0.00029391295,0.000050038085,0.000023741279,0.00004267374,0.00001728952,0.98841673,0.0003149445,0.0037540058,0.0005465248,0.0064829974],"study_design_scores_gemma":[0.000008991827,0.000034741326,0.00018623636,0.0000072276575,0.000010248893,0.000013784541,0.000027680615,0.9961845,0.00022073151,0.0022097616,0.0010914333,0.0000045963266],"about_ca_topic_score_codex":0.027729345,"about_ca_topic_score_gemma":0.023112051,"teacher_disagreement_score":0.027729345,"about_ca_system_score_codex":0.002262612,"about_ca_system_score_gemma":0.0022950822,"threshold_uncertainty_score":0.055135846},"labels":[],"label_agreement":null},{"id":"W2992897731","doi":"10.1093/forestscience/49.1.1","title":"An Indirect Search Algorithm for Harvest-Scheduling Under Adjacency Constraints","year":2003,"lang":"en","type":"article","venue":"Forest Science","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Tabu search; Adjacency list; Simulated annealing; Mathematical optimization; Best-first search; Hill climbing; Search algorithm; Algorithm; Guided Local Search; Computer science; Greedy algorithm; Scheduling (production processes); Integer programming; Beam search; Mathematics","score_opus":0.03399996158010347,"score_gpt":0.3140991011701756,"score_spread":0.2800991395900721,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2992897731","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019910816,0.00020376439,0.9719652,0.00017725638,0.00007169774,0.000106829924,0.00010719385,0.00056583836,0.0068913936],"genre_scores_gemma":[0.22281902,0.0001875125,0.7681898,0.00014625677,0.000069738584,0.0005504098,0.00037401263,0.00025559062,0.0074077784],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99971443,0.00009587322,0.00001235431,0.000055040287,0.000078196914,0.000044076973],"domain_scores_gemma":[0.9990421,0.00067070103,0.00006459816,0.000059058762,0.0001050798,0.000058513666],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009794431,0.0008379502,0.001469098,0.0011921291,0.0007966119,0.0009600807,0.0015402532,0.0014684869,0.0064684427],"category_scores_gemma":[0.0025421919,0.00076518836,0.00079199293,0.0014054908,0.0007195755,0.0013415797,0.0013710781,0.0014645123,0.0007344563],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019109831,0.00017667116,0.0006068564,0.00010785539,0.000060211234,0.00005301429,0.00011460908,0.83256817,0.0014538271,0.025140153,0.003463128,0.13606445],"study_design_scores_gemma":[0.000051935247,0.000037337093,0.00009581358,0.000008687449,0.0000142745275,0.000012071294,0.000013696046,0.99110585,0.00019495352,0.0075994264,0.00085996854,0.00000590624],"about_ca_topic_score_codex":0.005552684,"about_ca_topic_score_gemma":0.010356142,"teacher_disagreement_score":0.0064684427,"about_ca_system_score_codex":0.0010242118,"about_ca_system_score_gemma":0.0022256614,"threshold_uncertainty_score":0.021639109},"labels":[],"label_agreement":null},{"id":"W2993393857","doi":"10.1109/kse.2019.8919421","title":"On the Traveling Salesman Problem with Hierarchical Objective Function","year":2019,"lang":"en","type":"article","venue":"","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Travelling salesman problem; Mathematical optimization; Computer science; 2-opt; Traveling purchaser problem; Genetic algorithm; Transformation (genetics); Function (biology); Bottleneck traveling salesman problem; Lin–Kernighan heuristic; Group (periodic table); Mathematics","score_opus":0.008224644849638498,"score_gpt":0.20786119378277734,"score_spread":0.19963654893313884,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2993393857","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025634589,0.0013822507,0.9596979,0.0005498284,0.000112950904,0.00010726417,0.00019185855,0.00013850494,0.012184891],"genre_scores_gemma":[0.55913854,0.0034729121,0.41856837,0.00036200584,0.00035626788,0.00030397627,0.0006416006,0.0001823355,0.016974064],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993043,0.0002919775,0.000023599856,0.00013818187,0.0001314192,0.00011056479],"domain_scores_gemma":[0.99949086,0.00029902035,0.000060987437,0.00003393336,0.000071153925,0.000044004963],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010096581,0.0012107032,0.00096347893,0.0007541839,0.0005952016,0.0009463315,0.0011413324,0.0012497207,0.0062590176],"category_scores_gemma":[0.001850846,0.00041452312,0.00096001715,0.0016326002,0.0008327405,0.0016334623,0.0010978349,0.0014499223,0.000700103],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000050871364,0.0000872322,0.0003471492,0.00018142353,0.00004346518,0.00016318909,0.00006552078,0.89470506,0.0009654836,0.06843972,0.0033030102,0.031647895],"study_design_scores_gemma":[0.000014053491,0.000044620945,0.00012440799,0.000011559183,0.000012355555,0.00004162582,0.000025826223,0.9754091,0.00022323265,0.021777702,0.0023057463,0.000009695952],"about_ca_topic_score_codex":0.0077705747,"about_ca_topic_score_gemma":0.0043371445,"teacher_disagreement_score":0.0077705747,"about_ca_system_score_codex":0.001144167,"about_ca_system_score_gemma":0.0011852595,"threshold_uncertainty_score":0.020938516},"labels":[],"label_agreement":null},{"id":"W2998840417","doi":"10.1287/ijoc.2022.1174","title":"Integral Column Generation for Set Partitioning Problems with Side Constraints","year":2022,"lang":"en","type":"article","venue":"INFORMS journal on computing","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Group for Research in Decision Analysis","funders":"","keywords":"Column generation; Mathematical optimization; Heuristics; Column (typography); Heuristic; Integer (computer science); Set (abstract data type); Computer science; Algorithm; Integer programming; Vehicle routing problem; Mathematics; Routing (electronic design automation)","score_opus":0.03675847513803565,"score_gpt":0.27204631779692334,"score_spread":0.2352878426588877,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2998840417","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007820217,0.0002100735,0.9893519,0.00006884436,0.000028879143,0.00005249138,0.00004440426,0.0002951527,0.0021280502],"genre_scores_gemma":[0.17694683,0.0003335396,0.8202117,0.00011687166,0.00005023607,0.00016656784,0.00032910152,0.00022041668,0.0016247261],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99927074,0.00029045358,0.000028488552,0.00009757147,0.00022172026,0.00009106483],"domain_scores_gemma":[0.9987097,0.0007329889,0.000098092096,0.00021648007,0.00018199433,0.000060675276],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010387265,0.000690005,0.00074984477,0.000713663,0.0004576901,0.0009664319,0.0010343337,0.0005641722,0.0036118082],"category_scores_gemma":[0.0024904844,0.00042597292,0.00076546404,0.0011778488,0.00071353687,0.0009200133,0.0011756797,0.0016104041,0.00069114193],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011940236,0.000112644426,0.000740511,0.00030074714,0.00005590072,0.00011877,0.00014053038,0.7031719,0.00830256,0.06630212,0.0050766016,0.21555829],"study_design_scores_gemma":[0.000019732259,0.00003146519,0.00008331998,0.000015103574,0.000009268234,0.00005119687,0.000015789541,0.9794144,0.0029778378,0.014512816,0.0028622064,0.0000068354275],"about_ca_topic_score_codex":0.0016884466,"about_ca_topic_score_gemma":0.0022888875,"teacher_disagreement_score":0.0036118082,"about_ca_system_score_codex":0.00074098003,"about_ca_system_score_gemma":0.0012606189,"threshold_uncertainty_score":0.012082696},"labels":[],"label_agreement":null},{"id":"W3000079527","doi":"10.1155/2020/2431763","title":"Multimodal Capacitated Hub Location Problems with Multi-Commodities: An Application in Freight Transport","year":2020,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Corporación de Fomento de la Producción","keywords":"Solver; Truck; Computer science; Integer programming; Train; Operations research; Process (computing); Multimodal transport; Commodity; Facility location problem; Linear programming; Mathematical optimization; Transport engineering; Engineering; Business; Mathematics","score_opus":0.016710931128499792,"score_gpt":0.2469352076798134,"score_spread":0.2302242765513136,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3000079527","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3650514,0.003485785,0.60373145,0.001625716,0.00020488854,0.0002844677,0.0010390829,0.00037238267,0.024204765],"genre_scores_gemma":[0.863185,0.0015756927,0.12792571,0.0000881189,0.00009296024,0.00019373395,0.00035210565,0.00008410488,0.0065026837],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996026,0.00020559605,0.000012446346,0.000056893197,0.000047350044,0.00007521413],"domain_scores_gemma":[0.9990207,0.00076859334,0.000073734984,0.000024530966,0.000057340152,0.00005515216],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00071944244,0.0014778747,0.0009209574,0.0008534311,0.0006758145,0.0012797535,0.0009825442,0.0020884373,0.004510411],"category_scores_gemma":[0.0018188216,0.00046440953,0.0010296999,0.0021150652,0.0006028478,0.0012155987,0.001158653,0.0012570783,0.00022518412],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003644664,0.00006577253,0.00041806887,0.00009804284,0.00002223449,0.00016520331,0.000029747842,0.9855828,0.00031584754,0.004786357,0.00069845066,0.0077809948],"study_design_scores_gemma":[0.00000947221,0.000031001447,0.00015478568,0.000011081499,0.000010365535,0.000040694365,0.00006353949,0.9941881,0.00024866257,0.004471048,0.00076407497,0.0000071254262],"about_ca_topic_score_codex":0.009461675,"about_ca_topic_score_gemma":0.009600593,"teacher_disagreement_score":0.009461675,"about_ca_system_score_codex":0.0013530306,"about_ca_system_score_gemma":0.0009543593,"threshold_uncertainty_score":0.018813193},"labels":[],"label_agreement":null},{"id":"W3000972540","doi":"10.1287/trsc.2019.0904","title":"The Vehicle Routing Problem with Stochastic Two-Dimensional Items","year":2020,"lang":"en","type":"article","venue":"Transportation Science","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":30,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Université de Montréal; Transport Canada; Université Laval","funders":"","keywords":"Vehicle routing problem; Mathematical optimization; Routing (electronic design automation); Integer (computer science); Integer programming; Probability distribution; Mathematics; Computer science; Statistics","score_opus":0.01835321259663467,"score_gpt":0.2561111963444445,"score_spread":0.23775798374780985,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3000972540","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14869513,0.0006856489,0.84199005,0.0009247706,0.00014603925,0.00022664457,0.0011405869,0.00023565102,0.0059555266],"genre_scores_gemma":[0.85697836,0.00091912586,0.13043179,0.0002010277,0.00012811569,0.0004924829,0.0014324399,0.000118194206,0.00929861],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981481,0.0007378311,0.00009776161,0.0004212477,0.00026992898,0.00032511464],"domain_scores_gemma":[0.9977895,0.0013648188,0.00033883916,0.00011121827,0.00017712003,0.0002184491],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018673607,0.0015912554,0.00211502,0.0007822276,0.0006645252,0.001928049,0.0024483616,0.002306482,0.0030821462],"category_scores_gemma":[0.003809945,0.0012156687,0.001565095,0.0024090584,0.0014401877,0.001964335,0.0013591833,0.0016411305,0.00033221164],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004389399,0.000034992434,0.0003316879,0.000048892438,0.000033112923,0.00013495682,0.00001936434,0.98328066,0.00036038214,0.012575167,0.00039564312,0.0027412234],"study_design_scores_gemma":[0.000024745257,0.00004755856,0.00014527903,0.0000058661126,0.000012911252,0.000047654583,0.000016819882,0.98947734,0.00022201451,0.00938588,0.0006014279,0.000012511545],"about_ca_topic_score_codex":0.008016581,"about_ca_topic_score_gemma":0.0052391547,"teacher_disagreement_score":0.008016581,"about_ca_system_score_codex":0.0021183768,"about_ca_system_score_gemma":0.0017312354,"threshold_uncertainty_score":0.015939832},"labels":[],"label_agreement":null},{"id":"W3002119673","doi":"10.5267/j.dsl.2020.1.002","title":"Bi objective hybrid vehicle routing problem with alternative paths and reliability","year":2020,"lang":"en","type":"article","venue":"Decision Science Letters","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Mathematical optimization; Simulated annealing; Vehicle routing problem; Reliability (semiconductor); Fuel efficiency; Hybrid algorithm (constraint satisfaction); Routing (electronic design automation); Nonlinear programming; Computer science; Nonlinear system; Engineering; Constraint programming; Mathematics; Automotive engineering; Stochastic programming","score_opus":0.015172443608376997,"score_gpt":0.26131042787723296,"score_spread":0.24613798426885597,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3002119673","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06348813,0.0005196625,0.9260238,0.00036902443,0.00006128364,0.00011880097,0.0002907463,0.00016533428,0.008963233],"genre_scores_gemma":[0.77445495,0.00050986535,0.21076329,0.00011868424,0.00004484582,0.00042150472,0.0004166973,0.000082037084,0.013188132],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99910396,0.00042688858,0.00003302433,0.00014885978,0.00016916287,0.00011813752],"domain_scores_gemma":[0.9992465,0.0004693827,0.0000927132,0.00003418133,0.00010053121,0.00005680604],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011434898,0.0012660928,0.000988384,0.00094150304,0.0005446105,0.0014027046,0.0011973663,0.0014182046,0.0031448577],"category_scores_gemma":[0.0014733115,0.0006425174,0.0007517321,0.0014118447,0.0005711597,0.0013374412,0.0010396097,0.0010256866,0.00029585528],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000059989055,0.000030324181,0.00025965748,0.000050902,0.00004265886,0.00010858989,0.000026983724,0.978593,0.0010190263,0.01143406,0.00043165247,0.007943178],"study_design_scores_gemma":[0.000008950637,0.000033264754,0.00010821181,0.0000032873209,0.000008719905,0.000037002366,0.000014884355,0.994726,0.00022122797,0.004287575,0.0005446953,0.0000061952846],"about_ca_topic_score_codex":0.0034480419,"about_ca_topic_score_gemma":0.0028655601,"teacher_disagreement_score":0.0034480419,"about_ca_system_score_codex":0.0009626812,"about_ca_system_score_gemma":0.0009946655,"threshold_uncertainty_score":0.010520577},"labels":[],"label_agreement":null},{"id":"W3002241203","doi":"10.1155/2020/9743841","title":"Presenting a Multi-Start Hybrid Heuristic for Solving the Problem of Two-Echelon Location-Routing Problem with Simultaneous Pickup and Delivery (2E-LRPSPD)","year":2020,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Heuristics; Mathematical optimization; Pickup; Vehicle routing problem; Computer science; Heuristic; Routing (electronic design automation); Path (computing); Mathematics; Artificial intelligence","score_opus":0.01784759339996823,"score_gpt":0.25696146778215445,"score_spread":0.23911387438218623,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3002241203","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01896281,0.00032434333,0.9759586,0.00011638816,0.000063734835,0.0001378145,0.0000642971,0.00021725432,0.0041547236],"genre_scores_gemma":[0.33610505,0.00030296663,0.65907466,0.0001172523,0.000034868382,0.0003248168,0.00020057429,0.00006610738,0.0037737489],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996631,0.00012043032,0.000013324631,0.00006238453,0.000091636466,0.00004903338],"domain_scores_gemma":[0.9997154,0.0001661385,0.000033267843,0.000022197879,0.000038229315,0.000024709787],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00053369155,0.00070532656,0.0007013071,0.0007157172,0.00044379575,0.00077377673,0.0010665242,0.001038178,0.002585194],"category_scores_gemma":[0.0008958512,0.0004466397,0.00086918235,0.0006985682,0.0003560657,0.0006847209,0.00075191224,0.00072653353,0.0003549664],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000055181295,0.00007239872,0.00030409134,0.00012313288,0.000033669203,0.000095024116,0.00003984309,0.95679224,0.0023396492,0.0076541537,0.0007531216,0.0317375],"study_design_scores_gemma":[0.000018582801,0.000049004848,0.00006041286,0.000006703399,0.000007091695,0.000021961934,0.000015992786,0.9965958,0.00079447,0.0014447466,0.0009788102,0.000006479639],"about_ca_topic_score_codex":0.0033750692,"about_ca_topic_score_gemma":0.0042179367,"teacher_disagreement_score":0.0033750692,"about_ca_system_score_codex":0.0005374951,"about_ca_system_score_gemma":0.0013324484,"threshold_uncertainty_score":0.008648336},"labels":[],"label_agreement":null},{"id":"W3002434250","doi":"10.48550/arxiv.2001.09297","title":"Vehicle Scheduling Problem","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; University of Waterloo","funders":"","keywords":"Scheduling (production processes); Mathematical optimization; Job shop scheduling; Integer programming; Computer science; Linear programming; Heuristic; Operations research; Mathematics; Computer network","score_opus":0.0769270011065532,"score_gpt":0.1993015847559991,"score_spread":0.1223745836494459,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3002434250","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020042693,0.005344728,0.76685977,0.0077936314,0.0032272823,0.0011315685,0.01033623,0.0013232586,0.18394083],"genre_scores_gemma":[0.38436404,0.011816003,0.45347157,0.003152353,0.0027240496,0.0018532326,0.01808469,0.0007782896,0.12375587],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977095,0.0007828145,0.00015944292,0.00059208716,0.00040642516,0.00034966672],"domain_scores_gemma":[0.9990376,0.00041796616,0.00012804866,0.0001018765,0.00019446929,0.00012005256],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001143186,0.0018729351,0.001497516,0.0009802151,0.0013105334,0.0034157159,0.0022916137,0.0020984355,0.024372447],"category_scores_gemma":[0.0026421957,0.00042236736,0.0011130621,0.0020580648,0.0008648027,0.0025060244,0.0018355289,0.0023591735,0.0057556005],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017667517,0.00024475617,0.0012107018,0.00087880733,0.00017111767,0.0006371359,0.00024494977,0.22933015,0.0019750483,0.51191676,0.11454063,0.13867325],"study_design_scores_gemma":[0.00011851462,0.00016077826,0.00057564606,0.00014732133,0.000058990016,0.00080534845,0.00041029236,0.26500306,0.0010740847,0.31496656,0.41662544,0.00005413682],"about_ca_topic_score_codex":0.0042298757,"about_ca_topic_score_gemma":0.0028317473,"teacher_disagreement_score":0.024372447,"about_ca_system_score_codex":0.001791216,"about_ca_system_score_gemma":0.0027180705,"threshold_uncertainty_score":0.08153397},"labels":[],"label_agreement":null},{"id":"W3003938257","doi":"10.1109/tfuzz.2020.2970834","title":"Sentiment Analysis for Driver Selection in Fuzzy Capacitated Vehicle Routing Problem With Simultaneous Pick-Up and Drop in Shared Transportation","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Fuzzy Systems","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Selection (genetic algorithm); Fuzzy logic; Vehicle routing problem; Process (computing); Routing (electronic design automation); Operations research; Fuzzy set; Artificial intelligence; Machine learning; Data mining; Engineering","score_opus":0.016651655313582717,"score_gpt":0.23319210546419988,"score_spread":0.21654045015061718,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3003938257","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4259657,0.00074562075,0.56447303,0.0011390183,0.00010312411,0.00029587155,0.00031964076,0.00015618553,0.0068018213],"genre_scores_gemma":[0.9680578,0.00017568185,0.030222123,0.00007407753,0.000036558144,0.00012012748,0.0002036459,0.000015563086,0.0010945109],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992656,0.00035058154,0.00004432277,0.00011569726,0.00009034639,0.00013355514],"domain_scores_gemma":[0.9979662,0.0014925136,0.0001752574,0.000029919256,0.000249058,0.000087105924],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002492678,0.0010094076,0.0014163798,0.00077781465,0.0006338714,0.0012784384,0.0011085368,0.0014084432,0.0019764344],"category_scores_gemma":[0.003224565,0.0005046372,0.0009531898,0.0005454594,0.00052606576,0.0007545908,0.0006294973,0.0008743719,0.00012631128],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018698744,0.00013184205,0.0032268965,0.00014455155,0.00009759296,0.00015492152,0.00011854356,0.9711076,0.001026574,0.0029125318,0.0011518343,0.019740123],"study_design_scores_gemma":[0.000004626416,0.000019648534,0.0001912115,0.0000029266448,0.000007767964,0.0000045040524,0.000022687585,0.9992417,0.00006644308,0.00039487577,0.000041130203,0.000002399702],"about_ca_topic_score_codex":0.011510136,"about_ca_topic_score_gemma":0.0068263886,"teacher_disagreement_score":0.011510136,"about_ca_system_score_codex":0.0016376901,"about_ca_system_score_gemma":0.0011801196,"threshold_uncertainty_score":0.022886276},"labels":[],"label_agreement":null},{"id":"W3004319648","doi":"10.1155/2020/5748680","title":"Product Service Scheduling Problem with Service Matching Based on Tabu Search Method","year":2020,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Department of Education of Liaoning Province","keywords":"Tabu search; Mathematical optimization; Job shop scheduling; Travelling salesman problem; Scheduling (production processes); Computer science; Adaptability; Guided Local Search; Operations research; Engineering; Mathematics; Schedule; Economics","score_opus":0.01869334838219953,"score_gpt":0.2827049988874803,"score_spread":0.26401165050528075,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3004319648","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.030548815,0.00074467366,0.9592064,0.00023055717,0.00008299433,0.00019969756,0.00014411756,0.00031813158,0.008524766],"genre_scores_gemma":[0.5884823,0.0012067399,0.40181068,0.00014717871,0.00012676725,0.00057533704,0.0005515666,0.00015877129,0.006940714],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991491,0.00036493049,0.000035450663,0.000111849215,0.00020860642,0.00013008536],"domain_scores_gemma":[0.99968874,0.0001541156,0.00004230437,0.000022348524,0.000062622414,0.000029903122],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010832574,0.0009945054,0.0017836365,0.0011427607,0.00084329216,0.0011858478,0.0015190012,0.0012636918,0.0035546739],"category_scores_gemma":[0.001562446,0.00046426992,0.0012045898,0.002489237,0.00056235306,0.0012272156,0.00074639276,0.0007319797,0.00041124746],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000058654736,0.00006416029,0.0003485037,0.00012988177,0.000044087214,0.0000541716,0.000046622743,0.9452969,0.0008453918,0.015377785,0.0016034619,0.036130466],"study_design_scores_gemma":[0.0000136814715,0.000030267764,0.00006257312,0.0000062410027,0.0000093102835,0.000020589801,0.000011842157,0.9947284,0.00018161212,0.004123519,0.00080724776,0.0000046482187],"about_ca_topic_score_codex":0.010716644,"about_ca_topic_score_gemma":0.0046105194,"teacher_disagreement_score":0.010716644,"about_ca_system_score_codex":0.0012647506,"about_ca_system_score_gemma":0.0023311425,"threshold_uncertainty_score":0.021308541},"labels":[],"label_agreement":null},{"id":"W3005635975","doi":"10.1111/itor.12911","title":"Selective arc‐ng pricing for vehicle routing","year":2020,"lang":"en","type":"article","venue":"International Transactions in Operational Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal; Polytechnique Montréal; Group for Research in Decision Analysis","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Column generation; Mathematical optimization; Solver; Vehicle routing problem; Path (computing); Set (abstract data type); Relaxation (psychology); Computer science; Routing (electronic design automation); Mathematics","score_opus":0.10321937624915684,"score_gpt":0.40502112560386316,"score_spread":0.30180174935470633,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3005635975","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0795212,0.00016066435,0.9142611,0.00022252068,0.000031776537,0.00008451872,0.000105662795,0.00028947784,0.0053231292],"genre_scores_gemma":[0.7708479,0.00016567472,0.22518578,0.00010219702,0.000038297578,0.00010889704,0.00016111432,0.000097115495,0.0032930665],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99937683,0.00028095918,0.000017354872,0.00006337654,0.00016401635,0.00009738586],"domain_scores_gemma":[0.99862754,0.00089680945,0.000097783384,0.00016700686,0.00013621166,0.00007471873],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010078389,0.000510544,0.0005493388,0.000518511,0.00038161394,0.0006565195,0.0011303218,0.00050313387,0.003657434],"category_scores_gemma":[0.0023164505,0.00028422487,0.00045752476,0.00066847965,0.0006302501,0.0010297456,0.0007932664,0.0009265874,0.00025510587],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009891123,0.00009846221,0.00052123703,0.0000820869,0.000025252964,0.000070594346,0.000038205202,0.890009,0.0040689097,0.048588548,0.0021330249,0.054265812],"study_design_scores_gemma":[0.000007653174,0.000019954192,0.000055086406,0.0000022537786,0.0000023170667,0.000011046329,0.000004776335,0.9887039,0.0007465896,0.010072882,0.0003714055,0.0000020601008],"about_ca_topic_score_codex":0.0024286811,"about_ca_topic_score_gemma":0.002967598,"teacher_disagreement_score":0.003657434,"about_ca_system_score_codex":0.0007938013,"about_ca_system_score_gemma":0.0007792083,"threshold_uncertainty_score":0.012235343},"labels":[],"label_agreement":null},{"id":"W3006796855","doi":"10.1155/2020/3153201","title":"A Branch-and-Price-and-Cut Algorithm for the Integrated Scheduling and Rostering Problem of Bus Drivers","year":2020,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Ministry of Science and Technology, Taiwan","keywords":"Crew scheduling; Computer science; Scheduling (production processes); Mathematical optimization; Benchmark (surveying); Fleet management; Job shop scheduling; Algorithm; Operations research; Schedule; Engineering; Mathematics","score_opus":0.012713217930857678,"score_gpt":0.24730868024559557,"score_spread":0.2345954623147379,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3006796855","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023418916,0.0002852612,0.9719883,0.00022803557,0.000045200177,0.00021277988,0.00012890117,0.00033885977,0.0033537222],"genre_scores_gemma":[0.278208,0.00039058356,0.7164382,0.0001372727,0.000054182052,0.0005850178,0.0005830049,0.00017772456,0.00342609],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99954754,0.00014549293,0.000020574487,0.00010330541,0.00009526802,0.000087677836],"domain_scores_gemma":[0.99936515,0.00038766235,0.000058514674,0.000027310512,0.00009972856,0.00006166446],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010354227,0.0011485487,0.0012536788,0.0008353947,0.000713406,0.0010901833,0.001472233,0.0014908349,0.003949509],"category_scores_gemma":[0.0020020676,0.0006961829,0.00095777615,0.0012652322,0.000363722,0.0010070273,0.0010150969,0.0015856525,0.0004055757],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009321042,0.0001449836,0.0007447343,0.00010298432,0.000045040186,0.00007261195,0.000044817978,0.9199317,0.0008963425,0.0066330777,0.0023383738,0.068952106],"study_design_scores_gemma":[0.000017148444,0.000030749765,0.00006755371,0.000004136316,0.000006850817,0.000011419864,0.000012329813,0.99735165,0.0001467896,0.0019265041,0.00042161686,0.0000032326482],"about_ca_topic_score_codex":0.011416919,"about_ca_topic_score_gemma":0.010420222,"teacher_disagreement_score":0.011416919,"about_ca_system_score_codex":0.0009516303,"about_ca_system_score_gemma":0.003172767,"threshold_uncertainty_score":0.022700906},"labels":[],"label_agreement":null},{"id":"W3007930322","doi":"10.1007/s11590-020-01551-w","title":"Variable neighborhood search based algorithms to solve a rich k-travelling repairmen problem","year":2020,"lang":"en","type":"article","venue":"Optimization Letters","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Variable neighborhood search; Benchmark (surveying); Computational intelligence; Variable (mathematics); Algorithm; Set (abstract data type); Computer science; Mathematical optimization; Database transaction; Local search (optimization); Mathematics; Metaheuristic; Artificial intelligence; Database","score_opus":0.01777942290356581,"score_gpt":0.2336952618307448,"score_spread":0.21591583892717897,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3007930322","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023217965,0.0004680719,0.97035855,0.000184822,0.00008466748,0.000051655894,0.00004704902,0.00011562634,0.0054715923],"genre_scores_gemma":[0.4656701,0.00052391767,0.5194028,0.00015576546,0.000087864,0.00034552757,0.00021956179,0.00017972919,0.013414732],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996891,0.00015345025,0.000013784205,0.000048465878,0.00006267526,0.000032464373],"domain_scores_gemma":[0.9988753,0.000852941,0.00008048132,0.000045845194,0.00010888384,0.000036496847],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012124989,0.0006711668,0.0010264843,0.0007448796,0.0005295534,0.00066074776,0.0015334162,0.0013668344,0.0028995685],"category_scores_gemma":[0.003309021,0.0005221616,0.0007565067,0.00088789203,0.00074443006,0.0010221618,0.0010210936,0.0011323381,0.00034270436],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004023739,0.00003462569,0.00018467395,0.000050897146,0.00003049418,0.000024166147,0.00003343661,0.96266824,0.00031998847,0.017646682,0.0010744752,0.017892186],"study_design_scores_gemma":[0.0000058978794,0.000011426916,0.000027512158,0.0000031457002,0.00000334625,0.0000045092834,0.0000050478607,0.99691075,0.00005096501,0.0027122379,0.0002629951,0.0000020267676],"about_ca_topic_score_codex":0.0057219015,"about_ca_topic_score_gemma":0.006142343,"teacher_disagreement_score":0.0057219015,"about_ca_system_score_codex":0.00068851945,"about_ca_system_score_gemma":0.0007973915,"threshold_uncertainty_score":0.011377156},"labels":[],"label_agreement":null},{"id":"W3009064098","doi":"10.1016/j.omega.2020.102304","title":"Inventory routing under stochastic supply and demand","year":2020,"lang":"en","type":"article","venue":"Omega","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"Coordenação de Aperfeiçoamento de Pessoal de Nível Superior; Fundação de Amparo à Pesquisa do Estado de São Paulo","keywords":"Supply chain; Computer science; Context (archaeology); Stochastic programming; Reservation; Operations research; Mathematical optimization; Routing (electronic design automation); Benchmark (surveying); Inventory theory; Heuristic; Set (abstract data type); Inventory control; Mathematics; Business; Artificial intelligence","score_opus":0.023338424295900252,"score_gpt":0.24432199237118943,"score_spread":0.22098356807528918,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3009064098","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017431132,0.00032567847,0.9767496,0.0006028429,0.000081662394,0.000035150337,0.00018809641,0.00013423838,0.004451614],"genre_scores_gemma":[0.74745417,0.0017712833,0.21231973,0.00043099772,0.00036690498,0.00034390885,0.0007459792,0.00041544292,0.0361515],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99863094,0.0007545857,0.000047722286,0.00023406236,0.00020789824,0.00012484338],"domain_scores_gemma":[0.99566627,0.0033284293,0.00047250517,0.00011965736,0.00028006447,0.00013300378],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003495972,0.0014158356,0.001788192,0.0011750706,0.0004981395,0.0021132173,0.0018194312,0.002014527,0.0035968446],"category_scores_gemma":[0.010055373,0.0018339254,0.0010140052,0.0016982721,0.0012821222,0.0029403248,0.0019440223,0.0016737905,0.000438774],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000024985091,0.000013926988,0.0001599948,0.000024730181,0.000019549121,0.000020156407,0.000019738669,0.96650356,0.00010863591,0.029255606,0.00061106915,0.0032380973],"study_design_scores_gemma":[0.0000032408907,0.000004308758,0.00002372859,0.0000023402686,0.0000024330861,0.0000035694025,0.000004517762,0.98994154,0.000019140807,0.009849738,0.00014332459,0.0000021813535],"about_ca_topic_score_codex":0.0074038515,"about_ca_topic_score_gemma":0.004095327,"teacher_disagreement_score":0.0074038515,"about_ca_system_score_codex":0.00251287,"about_ca_system_score_gemma":0.00184063,"threshold_uncertainty_score":0.018488705},"labels":[],"label_agreement":null},{"id":"W3009897573","doi":"10.1016/j.ejtl.2020.100020","title":"Machine learning in airline crew pairing to construct initial clusters for dynamic constraint aggregation","year":2020,"lang":"en","type":"article","venue":"EURO Journal on Transportation and Logistics","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Mila - Quebec Artificial Intelligence Institute; Université de Montréal; Group for Research in Decision Analysis","funders":"Natural Sciences and Engineering Research Council of Canada; Institut de Valorisation des Données","keywords":"Solver; Crew; Heuristics; Computer science; Pairing; Sequence (biology); Aggregate (composite); Reduction (mathematics); Constraint (computer-aided design); Baseline (sea); Mathematical optimization; Set (abstract data type); Mathematics; Engineering","score_opus":0.032987308182205494,"score_gpt":0.28849050009934984,"score_spread":0.2555031919171443,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3009897573","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09157637,0.00055009965,0.895721,0.00066178944,0.00009629479,0.00024457433,0.0005026372,0.0018063775,0.008840881],"genre_scores_gemma":[0.55399436,0.00016702189,0.44036266,0.00031202994,0.000064494816,0.00035655912,0.0010300411,0.00032801152,0.0033847315],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991061,0.00029700968,0.000047847647,0.00025294392,0.00014662968,0.0001494651],"domain_scores_gemma":[0.99678886,0.0022065544,0.00025245087,0.00026752564,0.00035177657,0.00013290548],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013757993,0.0012637097,0.0012120549,0.00090734,0.0005781455,0.0012743608,0.001422653,0.00127798,0.0066634407],"category_scores_gemma":[0.0065100957,0.00077951496,0.001074163,0.001303392,0.0008647343,0.0014737219,0.0012110658,0.0019713861,0.00075786037],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000047841404,0.000055507266,0.0007698822,0.000053286112,0.000025119572,0.000036744015,0.000045079258,0.9673692,0.00043233164,0.0040709856,0.0013976789,0.025696224],"study_design_scores_gemma":[0.000011374163,0.0000124537,0.000094931376,0.0000062303397,0.0000034328311,0.0000062711556,0.000013827952,0.9964012,0.00027684247,0.0026777878,0.0004932343,0.0000023880884],"about_ca_topic_score_codex":0.013113371,"about_ca_topic_score_gemma":0.015698675,"teacher_disagreement_score":0.013113371,"about_ca_system_score_codex":0.0016254399,"about_ca_system_score_gemma":0.0025865606,"threshold_uncertainty_score":0.026074111},"labels":[],"label_agreement":null},{"id":"W3013962532","doi":"","title":"Computational study of a branching algorithm for the maximum \\(k\\)-cut problem","year":2020,"lang":"en","type":"article","venue":"Les Cahiers du GERAD","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Branch and bound; Maximum cut; Branching (polymer chemistry); Cutting-plane method; Algorithm; Mathematics; Mathematical optimization; Relaxation (psychology); Metaheuristic; Graph; Node (physics); Linear programming relaxation; Linear programming; Combinatorics; Integer programming","score_opus":0.01582555029809753,"score_gpt":0.24318568317454362,"score_spread":0.22736013287644607,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3013962532","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3231077,0.00074604177,0.6558977,0.0008630886,0.000085016436,0.00026489928,0.0001754513,0.00065533613,0.018204784],"genre_scores_gemma":[0.6140191,0.00018834368,0.38334483,0.000118890006,0.00003338086,0.00023921693,0.00024219454,0.000111502166,0.0017025635],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994116,0.00033446684,0.00001698773,0.00006959014,0.00009414405,0.00007325879],"domain_scores_gemma":[0.9928664,0.0062704505,0.00023483834,0.00015757882,0.00027163327,0.00019912707],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002443166,0.00070774485,0.0008829945,0.0007485877,0.0005598883,0.0010256628,0.001242247,0.001167361,0.0043830853],"category_scores_gemma":[0.007377818,0.00035747312,0.00055298995,0.000908375,0.0007383227,0.00096234225,0.0008682126,0.0014374905,0.0002786219],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00034600653,0.00018524034,0.0011994528,0.00015460206,0.000034861638,0.000058510563,0.00007727882,0.9374374,0.0014313002,0.023544263,0.0013735882,0.034157507],"study_design_scores_gemma":[0.000024568884,0.000030944713,0.00006617733,0.0000057825437,0.000003949886,0.000007725274,0.000009185879,0.99655765,0.00014760069,0.0030330843,0.000111845686,0.0000014390855],"about_ca_topic_score_codex":0.0032341443,"about_ca_topic_score_gemma":0.0026220807,"teacher_disagreement_score":0.0043830853,"about_ca_system_score_codex":0.0010134227,"about_ca_system_score_gemma":0.0014459373,"threshold_uncertainty_score":0.014662862},"labels":[],"label_agreement":null},{"id":"W3016159714","doi":"10.1111/itor.12797","title":"Solving the clustered traveling salesman problem with ‐relaxed priority rule","year":2020,"lang":"en","type":"article","venue":"International Transactions in Operational Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Travelling salesman problem; Iterated local search; Mathematical optimization; Computer science; Class (philosophy); Iterated function; Traveling purchaser problem; 2-opt; Constraint (computer-aided design); Integer (computer science); Metaheuristic; Mathematics; Artificial intelligence","score_opus":0.07497900821825791,"score_gpt":0.3582295158604328,"score_spread":0.28325050764217485,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3016159714","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08667408,0.0002781743,0.90706265,0.0002611001,0.00008573942,0.00014114931,0.00011993887,0.00026908892,0.005108162],"genre_scores_gemma":[0.6646601,0.00021941176,0.33056676,0.00011733384,0.000055041528,0.0001686,0.00029133886,0.00008220286,0.0038391908],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99927145,0.0002252958,0.000047536614,0.00015500443,0.00014113866,0.0001595263],"domain_scores_gemma":[0.99895006,0.0005561115,0.00011819336,0.0000859102,0.00021051263,0.00007920194],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010448019,0.00087402825,0.0013775877,0.00055556017,0.0005379687,0.0011746573,0.001718123,0.0013860415,0.003324339],"category_scores_gemma":[0.0019328017,0.0005261911,0.0011328548,0.0009002335,0.00048371672,0.0010953753,0.00075453473,0.001166033,0.00028898256],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000084975145,0.00009803272,0.0003973937,0.000086041524,0.000043721855,0.000111547946,0.000044129534,0.96968824,0.0016040487,0.008062906,0.001107512,0.018671423],"study_design_scores_gemma":[0.000016134574,0.000030893134,0.000048608672,0.0000036139213,0.000005885586,0.000015083906,0.000017548156,0.99700314,0.0003236642,0.0023067747,0.0002245504,0.0000040802256],"about_ca_topic_score_codex":0.010572105,"about_ca_topic_score_gemma":0.005466645,"teacher_disagreement_score":0.010572105,"about_ca_system_score_codex":0.0007115795,"about_ca_system_score_gemma":0.0015704047,"threshold_uncertainty_score":0.021021128},"labels":[],"label_agreement":null},{"id":"W3016459552","doi":"10.1007/978-3-030-39694-7_14","title":"Integration of User’s Preferences into the Home Healthcare Routing and Scheduling Multi-objective Problem: A Hierarchical Approach with Pareto-Optimal Alternative Solutions","year":2020,"lang":"en","type":"book-chapter","venue":"Springer proceedings in mathematics & statistics","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"CARE Canada; Polytechnique Montréal","funders":"","keywords":"Scheduling (production processes); Pareto principle; Health care; Decision maker; Computer science; Operations research; Heuristic; Pareto optimal; Multi-objective optimization; Operations management; Engineering; Economics; Artificial intelligence; Machine learning","score_opus":0.04330043294735896,"score_gpt":0.27533835176008314,"score_spread":0.23203791881272418,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3016459552","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018273912,0.0006026705,0.9689845,0.0003383865,0.000057707166,0.00008999134,0.00020172614,0.00009429816,0.011356938],"genre_scores_gemma":[0.5492885,0.0013846112,0.43977517,0.00020121377,0.00013463132,0.00027006384,0.00038541062,0.0001560357,0.008404301],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982851,0.0008781092,0.00007341591,0.00017127141,0.00043386064,0.0001581419],"domain_scores_gemma":[0.99871325,0.000778361,0.00009502995,0.00010059386,0.00024198552,0.00007072811],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024821244,0.0010136589,0.0015026339,0.0011979367,0.0005989096,0.0022871676,0.0017896771,0.0013146927,0.0041018515],"category_scores_gemma":[0.0035473949,0.0007668585,0.0018411287,0.0024048474,0.000539052,0.0025820313,0.0017980558,0.0015343123,0.00042849715],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000085528525,0.00020047335,0.0017016532,0.00027282294,0.00020771136,0.00021889411,0.00018327351,0.84439605,0.0021551724,0.059619043,0.0027531777,0.08820619],"study_design_scores_gemma":[0.0000052538303,0.000051374605,0.000704542,0.000028501625,0.00003521788,0.000049829272,0.000093725546,0.97731507,0.00034169495,0.020126803,0.0012323787,0.00001565269],"about_ca_topic_score_codex":0.0056487503,"about_ca_topic_score_gemma":0.007928724,"teacher_disagreement_score":0.0056487503,"about_ca_system_score_codex":0.0010652904,"about_ca_system_score_gemma":0.0012447265,"threshold_uncertainty_score":0.0137220025},"labels":[],"label_agreement":null},{"id":"W3017984166","doi":"10.3390/su12083500","title":"Multi-Depot Green Vehicle Routing Problem to Minimize Carbon Emissions","year":2020,"lang":"en","type":"article","venue":"Sustainability","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":59,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Ant colony optimization algorithms; Vehicle routing problem; Mathematical optimization; Engineering; Routing (electronic design automation); Ant colony; Operations research; Mathematics; Embedded system","score_opus":0.021513893174764206,"score_gpt":0.2794227487170579,"score_spread":0.2579088555422937,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3017984166","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07686982,0.0015189128,0.90217936,0.00095899,0.00022173114,0.00032344257,0.0009950389,0.0003662628,0.016566442],"genre_scores_gemma":[0.6384157,0.0015213079,0.3420215,0.0002830644,0.00011313656,0.0004986794,0.0013260569,0.0001987628,0.0156217925],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993104,0.00026172053,0.000025705878,0.00018534585,0.00009526704,0.00012151118],"domain_scores_gemma":[0.9995129,0.00027551255,0.00006684843,0.000026583139,0.000063981744,0.00005410825],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006520002,0.0015434377,0.0014393266,0.0008757981,0.0007113774,0.0013193733,0.001198436,0.0019359656,0.0032892677],"category_scores_gemma":[0.001055728,0.0005280134,0.0009890081,0.001832978,0.0005599652,0.0011358291,0.000884118,0.0009414828,0.00039011452],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000613943,0.00006965589,0.0003859638,0.00018551412,0.000059547638,0.00016534812,0.00003447487,0.96811277,0.0013663797,0.009578792,0.0017224608,0.018257614],"study_design_scores_gemma":[0.000022173921,0.000058875048,0.00020403987,0.000013649719,0.000022676719,0.000070281865,0.00004923987,0.9867422,0.0007684322,0.0094264215,0.0026126357,0.000009411019],"about_ca_topic_score_codex":0.0050562215,"about_ca_topic_score_gemma":0.005710424,"teacher_disagreement_score":0.0050562215,"about_ca_system_score_codex":0.001401838,"about_ca_system_score_gemma":0.0018016729,"threshold_uncertainty_score":0.011003733},"labels":[],"label_agreement":null},{"id":"W3019441366","doi":"10.1111/itor.12801","title":"The uncapacitated <i>r</i>‐allocation <i>p</i>‐hub center problem","year":2020,"lang":"en","type":"article","venue":"International Transactions in Operational Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Military College of Canada","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Heuristics; Variable neighborhood search; Mathematical optimization; Benchmark (surveying); Solver; Generalization; Equivalence (formal languages); Mathematics; Computer science; Variable (mathematics); Integer programming; Binary number; Metaheuristic; Discrete mathematics","score_opus":0.07830413859196639,"score_gpt":0.3652832162998267,"score_spread":0.2869790777078603,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3019441366","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08637541,0.00052646897,0.8811406,0.00065673824,0.00012780525,0.00017982823,0.00046938882,0.00053915323,0.029984664],"genre_scores_gemma":[0.8336077,0.00032623796,0.15392642,0.00017151216,0.000059471986,0.0001532499,0.00039247543,0.00015129083,0.01121171],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99936277,0.00023127062,0.000017100565,0.00016450231,0.000079514975,0.00014491052],"domain_scores_gemma":[0.99946004,0.0002315836,0.000093788,0.00007541254,0.000080065365,0.000059127608],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00069072674,0.00064725697,0.0007607337,0.00035030398,0.00045749272,0.0009956117,0.0018815778,0.0007562602,0.0062723598],"category_scores_gemma":[0.0010732695,0.0002658693,0.000479141,0.0009354282,0.0005698791,0.0012200176,0.0008244012,0.0009187786,0.00049613975],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015087557,0.00013943863,0.00050883024,0.00019506918,0.000040540894,0.00022575179,0.000034059998,0.8899818,0.0021913222,0.043620713,0.009566772,0.05334491],"study_design_scores_gemma":[0.000022505226,0.000057501624,0.00030612905,0.000016056694,0.000010145294,0.0001040399,0.000046003403,0.97977793,0.0018437038,0.01418481,0.0036192625,0.000011817649],"about_ca_topic_score_codex":0.005202989,"about_ca_topic_score_gemma":0.00468749,"teacher_disagreement_score":0.0062723598,"about_ca_system_score_codex":0.00091794075,"about_ca_system_score_gemma":0.0014849395,"threshold_uncertainty_score":0.0209831},"labels":[],"label_agreement":null},{"id":"W3020867067","doi":"10.1287/opre.2020.2033","title":"Asymmetric Multidepot Vehicle Routing Problems: Valid Inequalities and a Branch-and-Cut Algorithm","year":2021,"lang":"en","type":"article","venue":"Operations Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada; Nederlandse Organisatie voor Wetenschappelijk Onderzoek","keywords":"Branch and cut; Routing (electronic design automation); Vehicle routing problem; Solver; Reduction (mathematics); Mathematical optimization; Path (computing); Computer science; Mathematics; Node (physics); Inequality; Upper and lower bounds; Algorithm; Integer programming","score_opus":0.08454693968172562,"score_gpt":0.3695019839405457,"score_spread":0.28495504425882007,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3020867067","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0038005977,0.0005505654,0.9903515,0.00035889825,0.000077534256,0.00007995915,0.00008517973,0.00009053736,0.0046051354],"genre_scores_gemma":[0.16363558,0.0014757981,0.8284003,0.00036603608,0.00021046984,0.00034493953,0.00052405725,0.00018236635,0.004860277],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99803704,0.00082385726,0.000093296374,0.0002366431,0.0005555879,0.00025363418],"domain_scores_gemma":[0.9976757,0.0016232729,0.00016332357,0.0002126741,0.00022916566,0.000095893556],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002418407,0.0019655863,0.0011642,0.0010009253,0.00067328167,0.0019357244,0.0030661996,0.0018052299,0.0040572695],"category_scores_gemma":[0.0047609173,0.0009568667,0.0011807987,0.0022501564,0.0011595456,0.0031622583,0.0019780602,0.004638934,0.000649619],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002427709,0.0002683152,0.0005653872,0.0003623762,0.000103361104,0.0001855924,0.00014241865,0.5206536,0.0027242608,0.28951338,0.009878224,0.1753604],"study_design_scores_gemma":[0.000040833398,0.000043484088,0.000094772775,0.000045639958,0.000023829043,0.00004077553,0.00002760292,0.89458907,0.001268737,0.09839493,0.0054179104,0.000012472095],"about_ca_topic_score_codex":0.0028988253,"about_ca_topic_score_gemma":0.0034729608,"teacher_disagreement_score":0.0040572695,"about_ca_system_score_codex":0.0017401155,"about_ca_system_score_gemma":0.0017590291,"threshold_uncertainty_score":0.013572872},"labels":[],"label_agreement":null},{"id":"W3022257994","doi":"","title":"Selective pricing in branch-price-and-cut algorithms for vehicle routing","year":2016,"lang":"en","type":"article","venue":"PolyPublie (École Polytechnique de Montréal)","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal; Polytechnique Montréal; Group for Research in Decision Analysis","funders":"","keywords":"Vehicle routing problem; Mathematical optimization; Heuristic; Column generation; Context (archaeology); Branch and cut; Branch and bound; Branch and price; Path (computing); Computer science; Mathematics; Routing (electronic design automation); Algorithm; Integer programming","score_opus":0.012191029225512188,"score_gpt":0.24728491598619065,"score_spread":0.23509388676067847,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3022257994","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011089507,0.00028696394,0.9842174,0.00019144837,0.000034679666,0.000102868544,0.00004299956,0.00026025806,0.0037739177],"genre_scores_gemma":[0.2097034,0.0005820645,0.78491616,0.00019721045,0.000070256625,0.0004792825,0.00029489154,0.00023455666,0.0035221472],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987795,0.0005510864,0.000045174456,0.0001445872,0.0003203161,0.00015946859],"domain_scores_gemma":[0.99826306,0.0012370705,0.000104493,0.00018518233,0.00014719219,0.00006305333],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018461207,0.0010204823,0.0009343169,0.0008051904,0.0008710469,0.0010152913,0.0015717347,0.0010766735,0.0036719611],"category_scores_gemma":[0.0045757643,0.0006404845,0.0008304082,0.0018551745,0.00091986515,0.002170529,0.0011749364,0.002187068,0.00074324623],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001397781,0.00018116529,0.00053779985,0.00015686073,0.00004405954,0.00006751645,0.00010152436,0.75534344,0.0026976373,0.08192019,0.0040879957,0.154722],"study_design_scores_gemma":[0.00002489213,0.00004526936,0.00007418792,0.000008886716,0.000010871922,0.000022760996,0.000014917335,0.96883404,0.0010282566,0.027570853,0.0023595642,0.0000055888977],"about_ca_topic_score_codex":0.0039443127,"about_ca_topic_score_gemma":0.0047548884,"teacher_disagreement_score":0.0039443127,"about_ca_system_score_codex":0.0014305543,"about_ca_system_score_gemma":0.0018700395,"threshold_uncertainty_score":0.012283981},"labels":[],"label_agreement":null},{"id":"W3023001328","doi":"","title":"A time-expanded network for the biomedical sample transportation problem","year":2019,"lang":"en","type":"article","venue":"","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Humanities; Political science; Philosophy","score_opus":0.011131934831249083,"score_gpt":0.24776028536215572,"score_spread":0.23662835053090664,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3023001328","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.030925883,0.0013973352,0.9491949,0.003503873,0.000382299,0.0003551822,0.0016614185,0.00029879576,0.012280242],"genre_scores_gemma":[0.4678976,0.0026070978,0.48188424,0.00085212034,0.00051667745,0.00159991,0.0035031617,0.00034159463,0.04079754],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99821883,0.0009398907,0.000062741616,0.00039564288,0.00019990446,0.00018297961],"domain_scores_gemma":[0.9955279,0.0034240463,0.00027640548,0.0001428772,0.00042043833,0.00020824563],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003528562,0.0014730836,0.0013345007,0.0013354314,0.00084266043,0.00154875,0.0024902946,0.0025304637,0.012881859],"category_scores_gemma":[0.0094200615,0.00065363396,0.0015209726,0.0015035957,0.0008838519,0.0031147553,0.0019572806,0.002510181,0.00071194104],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014697458,0.000072067116,0.0006551356,0.0001574089,0.000050635674,0.00009026406,0.0000539591,0.9321058,0.00027451682,0.04205956,0.005006239,0.019327542],"study_design_scores_gemma":[0.000016401282,0.00002031052,0.00009686133,0.000013298956,0.00000833279,0.000017439419,0.000016366797,0.98381156,0.000055555294,0.014226835,0.0017116863,0.000005378206],"about_ca_topic_score_codex":0.01666065,"about_ca_topic_score_gemma":0.015153952,"teacher_disagreement_score":0.01666065,"about_ca_system_score_codex":0.0028454144,"about_ca_system_score_gemma":0.0025523931,"threshold_uncertainty_score":0.04309404},"labels":[],"label_agreement":null},{"id":"W3025850278","doi":"10.1016/j.cor.2020.104944","title":"The two-echelon inventory-routing problem with fleet management","year":2020,"lang":"en","type":"article","venue":"Computers & Operations Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":24,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval; Transport Canada","funders":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior","keywords":"Vehicle routing problem; Computer science; Vendor-managed inventory; Remanufacturing; Inventory theory; Renting; Product (mathematics); Supply chain; Operations research; Fleet management; Vendor; Inventory management; Set (abstract data type); Inventory control; Routing (electronic design automation); Supply chain management; Operations management; Business; Manufacturing engineering; Mathematics; Economics; Engineering","score_opus":0.06683703304782425,"score_gpt":0.3465967150423648,"score_spread":0.27975968199454054,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3025850278","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15645848,0.0025688699,0.7942313,0.0045881327,0.00057405856,0.0003544663,0.0026596526,0.00032204762,0.03824305],"genre_scores_gemma":[0.8275528,0.0014355845,0.13333195,0.00040458108,0.00045849025,0.00029476613,0.001015136,0.00017093393,0.03533566],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987859,0.00055309234,0.000046235447,0.00029471345,0.00014033046,0.00017977577],"domain_scores_gemma":[0.99863786,0.00088327995,0.00013460712,0.0000921074,0.00006913127,0.00018299218],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020286534,0.0013936874,0.0022174972,0.0010045398,0.0008750211,0.0031828557,0.0027811474,0.003696892,0.008921253],"category_scores_gemma":[0.004853199,0.0010463333,0.0012016698,0.0024389045,0.0012177762,0.0046171574,0.0020418037,0.0017614554,0.0006449611],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022070952,0.00017642943,0.0005795713,0.00019935015,0.00009034056,0.00034466304,0.000048986236,0.9384675,0.00061697897,0.036197197,0.0038312403,0.019226993],"study_design_scores_gemma":[0.000050336777,0.00006205403,0.0003397755,0.000015429436,0.000019906405,0.000086819506,0.000046671263,0.9641128,0.00022171385,0.033653382,0.0013707262,0.000020358077],"about_ca_topic_score_codex":0.0072951457,"about_ca_topic_score_gemma":0.0062809405,"teacher_disagreement_score":0.008921253,"about_ca_system_score_codex":0.0021573706,"about_ca_system_score_gemma":0.0016109125,"threshold_uncertainty_score":0.029844582},"labels":[],"label_agreement":null},{"id":"W3027262244","doi":"10.1016/j.ejor.2020.05.005","title":"Alternating Lagrangian decomposition for integrated airline crew scheduling problem","year":2020,"lang":"en","type":"article","venue":"European Journal of Operational Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":37,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Group for Research in Decision Analysis","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Crew; Crew scheduling; Column generation; Computer science; Scheduling (production processes); Mathematical optimization; Set (abstract data type); Constraint programming; Operations research; Decomposition; Context (archaeology); Assignment problem; Engineering; Mathematics; Aeronautics","score_opus":0.0954391724302661,"score_gpt":0.38727626805254606,"score_spread":0.29183709562227994,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3027262244","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.030072376,0.00037261323,0.95783937,0.00036865074,0.000116465504,0.000074083444,0.00024296377,0.00010114492,0.010812379],"genre_scores_gemma":[0.54666704,0.00067690766,0.43655416,0.0002561624,0.00016708698,0.00046202927,0.00085892493,0.000240154,0.014117478],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995722,0.00019501586,0.00001147887,0.000066354856,0.00008123239,0.00007372315],"domain_scores_gemma":[0.99936587,0.00037110617,0.00006400089,0.000035400175,0.00009959898,0.00006408548],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009408645,0.0010137255,0.00107464,0.0004998425,0.00034877876,0.0010571107,0.0010851347,0.0010830634,0.0042697242],"category_scores_gemma":[0.0017778152,0.0005609636,0.0008550659,0.0006688505,0.00048601767,0.0009861679,0.0010978124,0.0014786883,0.00039087053],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000089802306,0.00008283737,0.00029438979,0.00011550501,0.0000402594,0.000073780866,0.00003466403,0.9619767,0.0011325821,0.018125298,0.0025279385,0.0155062415],"study_design_scores_gemma":[0.000010530615,0.000019509604,0.000058079844,0.000005184772,0.000006191226,0.0000089488685,0.0000080322925,0.995073,0.00009757919,0.004331223,0.00037922297,0.0000024509375],"about_ca_topic_score_codex":0.006027306,"about_ca_topic_score_gemma":0.00429526,"teacher_disagreement_score":0.006027306,"about_ca_system_score_codex":0.0006563331,"about_ca_system_score_gemma":0.0016334746,"threshold_uncertainty_score":0.014283657},"labels":[],"label_agreement":null},{"id":"W3028512226","doi":"10.1016/j.cor.2020.104987","title":"A review of vehicle routing with simultaneous pickup and delivery","year":2020,"lang":"en","type":"review","venue":"Computers & Operations Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":200,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"Luonnontieteiden ja Tekniikan Tutkimuksen Toimikunta; Natural Sciences and Engineering Research Council of Canada","keywords":"Pickup; Vehicle routing problem; Computer science; Routing (electronic design automation); Destinations; Operations research; Tourism; Artificial intelligence; Computer network; Mathematics; Geography","score_opus":0.08792970271494478,"score_gpt":0.389522005941624,"score_spread":0.30159230322667924,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3028512226","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00018323609,0.99080276,0.004877643,0.00025242634,0.00043760287,0.000020077687,0.000073324045,0.000032452004,0.0033204018],"genre_scores_gemma":[0.001291589,0.9928363,0.0039256685,0.00016186017,0.00031085228,0.000020293577,0.0001116084,0.000011690856,0.0013302527],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9994893,0.0000903592,0.00007290206,0.00011931692,0.00018915038,0.00003885737],"domain_scores_gemma":[0.9992391,0.0004178378,0.000084450796,0.000034297052,0.00019304323,0.00003121222],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001029063,0.0016945363,0.0022148422,0.0031207157,0.00038732414,0.0015048548,0.0019192266,0.0013483501,0.007483081],"category_scores_gemma":[0.0017613497,0.0009790447,0.0010624652,0.005873605,0.00049638574,0.0026102297,0.0009566999,0.001403117,0.0034954506],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000045586567,0.00009341846,0.000110907655,0.02217848,0.00010675043,0.000049460064,0.000018930268,0.0045502395,0.0007297678,0.011131153,0.027041076,0.9339443],"study_design_scores_gemma":[0.00001953771,0.00017392429,0.0004574326,0.0051254216,0.00026099788,0.00039468255,0.000043961652,0.0024913135,0.0007643405,0.005642622,0.98457664,0.00004910136],"about_ca_topic_score_codex":0.002799517,"about_ca_topic_score_gemma":0.0033307772,"teacher_disagreement_score":0.007483081,"about_ca_system_score_codex":0.00090129895,"about_ca_system_score_gemma":0.0020756135,"threshold_uncertainty_score":0.025033474},"labels":[],"label_agreement":null},{"id":"W3033405475","doi":"10.1016/j.ejor.2020.05.054","title":"The two-echelon production-routing problem","year":2020,"lang":"en","type":"article","venue":"European Journal of Operational Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":56,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval; Transport Canada","funders":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior","keywords":"Computer science; Mathematical optimization; Routing (electronic design automation); Production (economics); Supply chain; Vendor-managed inventory; Vendor; Supply chain management; Mathematics; Economics","score_opus":0.11010586751565572,"score_gpt":0.367330615983832,"score_spread":0.2572247484681763,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3033405475","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17305839,0.0024886073,0.74427795,0.0062007653,0.0007552401,0.0003357426,0.0038494272,0.0003537191,0.06868019],"genre_scores_gemma":[0.8347874,0.0011290639,0.12271347,0.00052190875,0.00048276177,0.00027284442,0.0014650701,0.0001850344,0.0384424],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99853504,0.00063618086,0.00004710685,0.00041700838,0.00015686073,0.000207692],"domain_scores_gemma":[0.99794906,0.0014531028,0.00014949872,0.00012600659,0.000108543594,0.00021383971],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019172042,0.0013712269,0.0021049238,0.000916161,0.0008132763,0.0031954327,0.00278019,0.0037511042,0.015147136],"category_scores_gemma":[0.005604171,0.0009401114,0.0010302662,0.0018984788,0.0013727458,0.0036998352,0.0024611226,0.0019525514,0.0009935271],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00036619327,0.00025246033,0.0010083967,0.00034540208,0.00011726843,0.00060690084,0.00008407767,0.8813922,0.0010525844,0.07476532,0.007183127,0.032826107],"study_design_scores_gemma":[0.000096448246,0.00006759501,0.00043501065,0.0000199221,0.000022159395,0.00013007775,0.0000815797,0.93011874,0.00036358702,0.06590742,0.0027337365,0.00002380001],"about_ca_topic_score_codex":0.00446411,"about_ca_topic_score_gemma":0.0034809043,"teacher_disagreement_score":0.015147136,"about_ca_system_score_codex":0.0018057722,"about_ca_system_score_gemma":0.0013801471,"threshold_uncertainty_score":0.050672293},"labels":[],"label_agreement":null},{"id":"W3034726805","doi":"10.1287/ijoc.2021.1119","title":"Exact Branch-Price-and-Cut for a Hospital Therapist Scheduling Problem with Flexible Service Locations and Time-Dependent Location Capacity","year":2021,"lang":"en","type":"article","venue":"INFORMS journal on computing","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Group for Research in Decision Analysis; HEC Montréal","funders":"Bayerische Forschungsallianz","keywords":"Vehicle routing problem; Mathematical optimization; Scheduling (production processes); Computer science; Branch and price; Branch and cut; Synchronization (alternating current); Mathematics; Routing (electronic design automation); Linear programming; Computer network","score_opus":0.014642493236050096,"score_gpt":0.24660680146249273,"score_spread":0.23196430822644262,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3034726805","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08518552,0.00044817556,0.89813495,0.0009381137,0.0000774177,0.00023468795,0.0006772165,0.0003628275,0.013941109],"genre_scores_gemma":[0.4547311,0.00048572815,0.5366458,0.00020287187,0.000052019586,0.00035054891,0.0010673162,0.00018823742,0.0062764063],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994777,0.00019838652,0.000018685765,0.00011160381,0.00009286341,0.000100788144],"domain_scores_gemma":[0.9983455,0.0012867928,0.00012262021,0.000056653153,0.00009780482,0.00009065001],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010770295,0.0008803743,0.0010379297,0.0005118355,0.00052683876,0.0011890631,0.0010917162,0.0013463905,0.007709117],"category_scores_gemma":[0.0030410949,0.0006403389,0.00073442346,0.0011183966,0.0006020336,0.0011013877,0.000658624,0.0015597645,0.00046941347],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006197135,0.000051969266,0.00037612554,0.00007110988,0.000016445973,0.00006344604,0.000027587903,0.97614837,0.00050866976,0.008887507,0.0014670233,0.012319739],"study_design_scores_gemma":[0.00002347869,0.000025456797,0.00011167383,0.000005330526,0.0000061133787,0.000020327,0.000018329758,0.9918611,0.00022742951,0.0069685113,0.0007284198,0.000003799788],"about_ca_topic_score_codex":0.014046084,"about_ca_topic_score_gemma":0.011901276,"teacher_disagreement_score":0.014046084,"about_ca_system_score_codex":0.0020873703,"about_ca_system_score_gemma":0.00294695,"threshold_uncertainty_score":0.02792865},"labels":[],"label_agreement":null},{"id":"W3036044801","doi":"10.3390/app10124154","title":"Cooperative Path Planning for Aerial Recovery of a UAV Swarm Using Genetic Algorithm and Homotopic Approach","year":2020,"lang":"en","type":"article","venue":"Applied Sciences","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Motion planning; Computer science; Swarm behaviour; Scheduling (production processes); Rendezvous; Real-time computing; Path (computing); Flexibility (engineering); Distributed computing; Mathematical optimization; Artificial intelligence; Engineering; Mathematics; Robot; Aerospace engineering; Spacecraft","score_opus":0.051461689233457224,"score_gpt":0.2816755440561116,"score_spread":0.23021385482265438,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3036044801","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.029070333,0.00015549167,0.9667165,0.00011308981,0.000025763802,0.000052434843,0.000021868602,0.00015889754,0.0036856635],"genre_scores_gemma":[0.74960005,0.00031105778,0.24451903,0.0000709887,0.000019815707,0.0002838476,0.00010481409,0.00005765234,0.0050327177],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998704,0.000031835607,0.0000053325157,0.000034935376,0.00003081581,0.000026571377],"domain_scores_gemma":[0.99985886,0.00005765134,0.000030307545,0.000011265018,0.00002801421,0.0000138902105],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031997383,0.0006547497,0.0005096423,0.0006307242,0.0005427263,0.00046445732,0.0007473646,0.0005613209,0.0013727229],"category_scores_gemma":[0.00052730145,0.00029892017,0.000635866,0.0004928666,0.00055852573,0.00039528566,0.0007140689,0.00050881616,0.00013766723],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013614098,0.000013599292,0.00021190137,0.000019646373,0.00001326043,0.000055977558,0.00005003966,0.98249507,0.0013805712,0.0041138334,0.00026843947,0.01136413],"study_design_scores_gemma":[0.00000368923,0.000015801077,0.00004926344,0.0000020021912,0.0000040310633,0.000009743705,0.000015372289,0.99848384,0.00021295031,0.0009779013,0.00022257553,0.000002868406],"about_ca_topic_score_codex":0.012184647,"about_ca_topic_score_gemma":0.0070267413,"teacher_disagreement_score":0.012184647,"about_ca_system_score_codex":0.0007392569,"about_ca_system_score_gemma":0.0014182708,"threshold_uncertainty_score":0.02422744},"labels":[],"label_agreement":null},{"id":"W3036776288","doi":"10.1287/trsc.2019.0956","title":"Vehicle Routing Problems with Synchronized Visits and Stochastic Travel and Service Times: Applications in Healthcare","year":2020,"lang":"en","type":"article","venue":"Transportation Science","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":70,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Concordia University","funders":"","keywords":"Bounding overwatch; Scheduling (production processes); Mathematical optimization; Vehicle routing problem; Branch and cut; Integer programming; Linear programming; Computer science; Stochastic programming; Travelling salesman problem; Stochastic optimization; Routing (electronic design automation); Mathematics","score_opus":0.018078489015077453,"score_gpt":0.258140197037406,"score_spread":0.24006170802232854,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3036776288","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018619198,0.00086385274,0.97666126,0.0008679825,0.0000858193,0.000052188032,0.00013499959,0.00012956264,0.0025850625],"genre_scores_gemma":[0.65560275,0.0033802646,0.333159,0.0003987588,0.00029823105,0.00029144716,0.00049187994,0.0001973953,0.0061802804],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988123,0.0005680267,0.000045965844,0.00026740154,0.00016235127,0.00014395478],"domain_scores_gemma":[0.9978934,0.0015828065,0.00025519886,0.00006486804,0.000106785235,0.000097001044],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014599535,0.0012301637,0.0011172104,0.0005151788,0.0005022907,0.0011609917,0.0012011371,0.001534701,0.0025756801],"category_scores_gemma":[0.0035659536,0.0008706179,0.0014212346,0.0014851856,0.00084468664,0.0014710628,0.00096931774,0.0013433261,0.0002517423],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026909289,0.000030257868,0.00040324597,0.00007533702,0.000034587025,0.000073491334,0.0000334116,0.9544542,0.00042829502,0.028988142,0.0010969624,0.01435516],"study_design_scores_gemma":[0.000017768734,0.000036513327,0.00020368198,0.000012542447,0.000015171469,0.000061416096,0.00004835915,0.96307313,0.0003043925,0.034125566,0.0020904117,0.000010987666],"about_ca_topic_score_codex":0.0065806755,"about_ca_topic_score_gemma":0.004908174,"teacher_disagreement_score":0.0065806755,"about_ca_system_score_codex":0.0017021869,"about_ca_system_score_gemma":0.0021970503,"threshold_uncertainty_score":0.013084769},"labels":[],"label_agreement":null},{"id":"W3037143117","doi":"10.5267/j.ijiec.2020.6.003","title":"Parameter tuning of the HCSCROCFO-3Opt algorithm for solving the capacitated vehicle routing problem","year":2020,"lang":"en","type":"article","venue":"International Journal of Industrial Engineering Computations","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Vehicle routing problem; Mathematical optimization; Computer science; Routing (electronic design automation); Algorithm; Mathematics; Embedded system","score_opus":0.04260207365858089,"score_gpt":0.2687201777290583,"score_spread":0.22611810407047744,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3037143117","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12629646,0.0009289475,0.84467834,0.00035453713,0.00012141149,0.00023056094,0.00019300959,0.0014672057,0.025729515],"genre_scores_gemma":[0.7118209,0.00028388534,0.2839133,0.0001306268,0.000025438543,0.00033494495,0.00032260898,0.00017158176,0.0029966973],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999785,0.00006925637,0.00000994995,0.0000414339,0.000056356712,0.000038022103],"domain_scores_gemma":[0.99965286,0.00017742762,0.000041384985,0.000035596084,0.00007316796,0.00001958615],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005207145,0.00082125637,0.0005204967,0.00058210164,0.00036619836,0.00055266777,0.00093723513,0.00091464503,0.002500476],"category_scores_gemma":[0.001496642,0.00024545324,0.00047889148,0.00040965472,0.0003493197,0.00045385343,0.000494998,0.0006410042,0.00037123906],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000069275105,0.000054900822,0.0009783607,0.000108208405,0.000030653402,0.00005068425,0.000040521496,0.94169873,0.0023166887,0.004310391,0.001453795,0.04888778],"study_design_scores_gemma":[0.000014725479,0.00003885879,0.00016114081,0.000009140154,0.00000607954,0.000026933594,0.000017184433,0.9967519,0.0011355191,0.0008038973,0.0010287152,0.000005905882],"about_ca_topic_score_codex":0.0051822094,"about_ca_topic_score_gemma":0.0059742415,"teacher_disagreement_score":0.0051822094,"about_ca_system_score_codex":0.00044442888,"about_ca_system_score_gemma":0.001179293,"threshold_uncertainty_score":0.010304093},"labels":[],"label_agreement":null},{"id":"W3037764692","doi":"10.1002/net.21964","title":"Routing electric vehicles with a single recharge per route","year":2020,"lang":"en","type":"article","venue":"Networks","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Group for Research in Decision Analysis","funders":"Deutsche Forschungsgemeinschaft","keywords":"Groundwater recharge; Computer science; Driving range; Routing (electronic design automation); Heuristic; Range (aeronautics); Parking lot; Suite; Operations research; Mathematical optimization; Power (physics); Electric vehicle; Engineering; Mathematics; Computer network","score_opus":0.01957702120302509,"score_gpt":0.2200015023775971,"score_spread":0.200424481174572,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3037764692","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.35885867,0.00038259942,0.6250323,0.00041924816,0.00011902318,0.00019533739,0.0004966005,0.0007595735,0.013736583],"genre_scores_gemma":[0.8921197,0.0001021926,0.10130721,0.000039290095,0.000017154605,0.00007322841,0.00021038728,0.0000498554,0.006080899],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997671,0.00007065859,0.00001147082,0.00005219651,0.000037680016,0.000060886192],"domain_scores_gemma":[0.99965847,0.00016988322,0.000055176894,0.00003672077,0.00004422503,0.00003550793],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039520973,0.00042923403,0.0006744074,0.0004279326,0.00033421008,0.00078870694,0.0009223155,0.0007131355,0.004842601],"category_scores_gemma":[0.0013156929,0.00036204004,0.0004428576,0.00063096045,0.0004124435,0.00087773707,0.00057738024,0.00043352696,0.00029410882],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000084668965,0.000025624948,0.00041269851,0.000025216894,0.000015518028,0.00004604426,0.000023196046,0.979392,0.0010232589,0.00497917,0.00057952106,0.0133930715],"study_design_scores_gemma":[0.000012321796,0.00003474029,0.00009282163,0.000003011815,0.0000050350172,0.000014130903,0.000021607717,0.9954151,0.00042264545,0.0033103093,0.00066424086,0.0000039801407],"about_ca_topic_score_codex":0.006556589,"about_ca_topic_score_gemma":0.005553042,"teacher_disagreement_score":0.006556589,"about_ca_system_score_codex":0.00077357434,"about_ca_system_score_gemma":0.0006289006,"threshold_uncertainty_score":0.016200125},"labels":[],"label_agreement":null},{"id":"W3046352921","doi":"10.1155/2020/8815983","title":"A Fissile Ripple Spreading Algorithm to Solve Time-Dependent Vehicle Routing Problem via Coevolutionary Path Optimization","year":2020,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Mathematical optimization; Shortest path problem; Computer science; Constrained Shortest Path First; Routing (electronic design automation); Path (computing); Vehicle routing problem; Trajectory; Traffic congestion; Engineering; Computer network; Mathematics; Transport engineering; K shortest path routing","score_opus":0.007878574087836812,"score_gpt":0.2298517935174723,"score_spread":0.2219732194296355,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3046352921","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.029226286,0.00030418613,0.9667399,0.00015548251,0.000039971066,0.00005223372,0.000018483131,0.00014471407,0.0033187247],"genre_scores_gemma":[0.6747801,0.0004983888,0.31816798,0.00018081484,0.000038140013,0.0003534591,0.00013513287,0.00007013035,0.005775845],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997745,0.0000718508,0.0000119406695,0.000042830074,0.00006347798,0.000035417146],"domain_scores_gemma":[0.99963665,0.00020178889,0.00002827402,0.000023101511,0.000085513384,0.000024637722],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006338421,0.000754561,0.0006560717,0.0006700631,0.00043888297,0.000620892,0.0009296551,0.0010389116,0.0014955635],"category_scores_gemma":[0.0014335124,0.0003148138,0.00068409997,0.00065717625,0.0005049586,0.0006492275,0.000926681,0.0008944989,0.00019078025],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000031238098,0.0000331005,0.0007156963,0.00003952682,0.00004815557,0.00006060903,0.00006820113,0.9472151,0.0018777916,0.00840108,0.0006905984,0.04081902],"study_design_scores_gemma":[0.0000039794713,0.000013221169,0.000038650207,0.0000019771521,0.000003856484,0.000007873802,0.0000044425597,0.9987153,0.00013535908,0.00085703377,0.00021605373,0.0000022693814],"about_ca_topic_score_codex":0.007653294,"about_ca_topic_score_gemma":0.005149527,"teacher_disagreement_score":0.007653294,"about_ca_system_score_codex":0.00050625723,"about_ca_system_score_gemma":0.0011422412,"threshold_uncertainty_score":0.015217483},"labels":[],"label_agreement":null},{"id":"W3046951078","doi":"10.1007/s10479-020-03743-0","title":"Optimal scheduling of airport ferry vehicles based on capacity network","year":2020,"lang":"en","type":"article","venue":"Annals of Operations Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":30,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria; Fisheries and Oceans Canada","funders":"Natural Science Foundation of Shandong Province; National Natural Science Foundation of China","keywords":"Integer programming; Computer science; Scheduling (production processes); Operations research; Linear programming; Flow network; Mathematical optimization; Engineering; Mathematics; Algorithm","score_opus":0.2884538788196349,"score_gpt":0.4198422238996973,"score_spread":0.13138834508006236,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3046951078","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2594032,0.00073207816,0.7186135,0.00069813826,0.0003601448,0.0002517546,0.00042569143,0.00044058939,0.019074993],"genre_scores_gemma":[0.9496009,0.00022123307,0.044833906,0.000045595938,0.00007331531,0.000076484255,0.0001883596,0.00008141008,0.0048787603],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99941766,0.0002312957,0.0000127711,0.00010330174,0.000064697924,0.00017034127],"domain_scores_gemma":[0.9990771,0.00055210607,0.00011100362,0.00003812665,0.00010529816,0.00011641735],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00077286846,0.0009509175,0.0013705897,0.0010086189,0.0007142111,0.0011793919,0.0012923589,0.0008946927,0.0036215186],"category_scores_gemma":[0.0021793654,0.000612587,0.00055755983,0.0010372229,0.0006224995,0.0011996975,0.00057510595,0.00072529836,0.0002093309],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007630104,0.000022965049,0.00011453444,0.000019998653,0.000009993869,0.000018498517,0.000013574109,0.9899058,0.00059225725,0.004213214,0.0005404387,0.004472351],"study_design_scores_gemma":[0.000005202709,0.000016792708,0.00006544122,0.0000019361837,0.0000037112898,0.0000029035361,0.000007972714,0.9978064,0.00012085574,0.001807027,0.00015912236,0.0000027513681],"about_ca_topic_score_codex":0.018622778,"about_ca_topic_score_gemma":0.012560802,"teacher_disagreement_score":0.018622778,"about_ca_system_score_codex":0.0024429753,"about_ca_system_score_gemma":0.0016824219,"threshold_uncertainty_score":0.03702879},"labels":[],"label_agreement":null},{"id":"W3048626276","doi":"10.1155/2020/3030197","title":"Time-Dependent Electric Vehicle Routing Problem with Time Windows and Path Flexibility","year":2020,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Flexibility (engineering); Mathematical optimization; Path (computing); Routing (electronic design automation); Vehicle routing problem; Computer science; Electric vehicle; Energy consumption; Shortest path problem; Integer programming; Simulation; Real-time computing; Engineering; Mathematics; Computer network","score_opus":0.008577693196341687,"score_gpt":0.22932060219763753,"score_spread":0.22074290900129584,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3048626276","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.074747615,0.0008444338,0.9146077,0.000750173,0.00011856982,0.00020415078,0.000535521,0.00021241,0.0079793725],"genre_scores_gemma":[0.84274554,0.0011017494,0.14330655,0.00013965581,0.00007841134,0.000392515,0.0007587357,0.00009436987,0.011382555],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989022,0.00034382692,0.000058189245,0.00034060705,0.00016452157,0.00019057735],"domain_scores_gemma":[0.9991529,0.0005001699,0.00013742191,0.000041444997,0.0000891721,0.000078787816],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010197563,0.0015260173,0.0014273965,0.0006261085,0.00070333946,0.0014368301,0.0017644959,0.0017151772,0.0028755576],"category_scores_gemma":[0.0019925002,0.00077951944,0.0013174778,0.0014879955,0.00058942504,0.0019839809,0.0010932721,0.0015356526,0.00020998958],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000062079554,0.000033809836,0.00034847314,0.000064010295,0.00002608322,0.00013948188,0.00003795314,0.98087436,0.0005579882,0.009166006,0.00062303664,0.008066743],"study_design_scores_gemma":[0.000014744084,0.000032462838,0.00013595504,0.000003964545,0.000011952439,0.000043961783,0.000026128819,0.99440104,0.00019735025,0.004528774,0.0005959061,0.000007810088],"about_ca_topic_score_codex":0.009371358,"about_ca_topic_score_gemma":0.006228381,"teacher_disagreement_score":0.009371358,"about_ca_system_score_codex":0.0012913144,"about_ca_system_score_gemma":0.0015261888,"threshold_uncertainty_score":0.018633664},"labels":[],"label_agreement":null},{"id":"W3079159037","doi":"10.1007/s43069-020-00016-1","title":"Dynamic Constraint Aggregation for Solving Very Large-scale Airline Crew Pairing Problems","year":2020,"lang":"en","type":"article","venue":"Operations Research Forum","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Group for Research in Decision Analysis","funders":"","keywords":"Crew; Constraint (computer-aided design); Computer science; Set (abstract data type); Degeneracy (biology); Exploit; Mathematical optimization; Scale (ratio); Column generation; Pairing; Algorithm; Operations research; Mathematics; Engineering; Aeronautics","score_opus":0.05093061371344633,"score_gpt":0.3443331500059634,"score_spread":0.29340253629251706,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3079159037","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0147702,0.00042098376,0.97803247,0.00035026018,0.00015979442,0.000092062255,0.00020445464,0.00025911434,0.0057106065],"genre_scores_gemma":[0.5186863,0.0005797656,0.47029713,0.00035710353,0.00028602357,0.0005553888,0.0008947188,0.00031222156,0.0080313785],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987704,0.0005496098,0.000046797668,0.00018460923,0.00027480212,0.00017372692],"domain_scores_gemma":[0.99689686,0.0021271105,0.00018922411,0.00026517193,0.00035267283,0.00016894893],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019704285,0.0012197405,0.0019265063,0.00087977055,0.0009874306,0.0017021779,0.0017740104,0.0015976104,0.0049997196],"category_scores_gemma":[0.005954269,0.00088076363,0.0011050613,0.0019777017,0.0007471631,0.0019164964,0.0026830938,0.002568657,0.00051897456],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000048071524,0.000084719206,0.00028796273,0.00007344839,0.000046392863,0.000059502046,0.000031377782,0.95694613,0.00039348655,0.011835535,0.0032990968,0.026894255],"study_design_scores_gemma":[0.000006397449,0.0000105701465,0.000040005267,0.000004044922,0.0000032654161,0.0000056389376,0.000008869213,0.9943974,0.00007079132,0.0050598267,0.00039053956,0.0000025918775],"about_ca_topic_score_codex":0.008798017,"about_ca_topic_score_gemma":0.008598934,"teacher_disagreement_score":0.008798017,"about_ca_system_score_codex":0.0009286091,"about_ca_system_score_gemma":0.0017260769,"threshold_uncertainty_score":0.017493606},"labels":[],"label_agreement":null},{"id":"W3082078888","doi":"10.1016/j.asoc.2020.106681","title":"A heuristic approach for optimal integrated airline schedule design and fleet assignment with demand recapture","year":2020,"lang":"en","type":"article","venue":"Applied Soft Computing","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":26,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Schedule; Mathematical optimization; Computer science; Genetic algorithm; Integer programming; Scheduling (production processes); Heuristic; Linear programming; Population; Scale (ratio); Operations research; Engineering; Mathematics","score_opus":0.02200079983770911,"score_gpt":0.23153715455458043,"score_spread":0.20953635471687132,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3082078888","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011105501,0.00012756669,0.9846674,0.0000881369,0.000045024466,0.00009152986,0.00005566687,0.00023530712,0.0035838466],"genre_scores_gemma":[0.32543692,0.00018481861,0.6689929,0.00014486711,0.0000687121,0.00043727062,0.00019941202,0.00013856143,0.0043966644],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99946314,0.00018003995,0.000020368178,0.000097854754,0.00014490988,0.00009372079],"domain_scores_gemma":[0.9991892,0.0004960022,0.000076334,0.00004881217,0.00013703661,0.000052664407],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011736881,0.0012229664,0.0014375923,0.0014701344,0.000675137,0.0012833329,0.0020688383,0.0015375073,0.003636973],"category_scores_gemma":[0.0022810814,0.0013021462,0.0015131026,0.001451641,0.00074289076,0.000960378,0.0011401279,0.0011694935,0.0004670615],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000022334689,0.000028533448,0.000078414654,0.000026481775,0.000021836415,0.000029235263,0.000016288814,0.98378146,0.00051198434,0.0027765252,0.00030607238,0.012400732],"study_design_scores_gemma":[0.000005445683,0.000017407301,0.000021994652,0.0000031248526,0.000005998913,0.000006463487,0.0000048370794,0.9988117,0.00013441144,0.00080625986,0.00017936941,0.0000029073933],"about_ca_topic_score_codex":0.009930275,"about_ca_topic_score_gemma":0.0107940845,"teacher_disagreement_score":0.009930275,"about_ca_system_score_codex":0.0013280647,"about_ca_system_score_gemma":0.0021555892,"threshold_uncertainty_score":0.019744992},"labels":[],"label_agreement":null},{"id":"W3083618710","doi":"10.1016/j.eswa.2020.113959","title":"Waiting strategy for the vehicle routing problem with simultaneous pickup and delivery using genetic algorithm","year":2020,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":106,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"National Research Foundation of Korea","keywords":"Computer science; Genetic algorithm; Pickup; Routing (electronic design automation); Vehicle routing problem; Operations research; Point (geometry); Delivery Performance; Set (abstract data type); Decision maker; Artificial intelligence; Industrial engineering; Machine learning; Computer network","score_opus":0.029393816094489624,"score_gpt":0.259616303172509,"score_spread":0.23022248707801937,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3083618710","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03314631,0.00039372215,0.9611732,0.00035990987,0.00008511525,0.00010386605,0.00007286629,0.00020624342,0.0044588065],"genre_scores_gemma":[0.7513312,0.0006166831,0.23195148,0.00027629462,0.00009702049,0.0003835097,0.00027616645,0.00020150543,0.014866114],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99939847,0.00018518462,0.000024675981,0.00009792721,0.00013642722,0.00015729491],"domain_scores_gemma":[0.99842924,0.0011347446,0.00011726979,0.00003207372,0.00018477827,0.00010193785],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016672671,0.0012760819,0.00202742,0.0011432383,0.0006379365,0.0014857309,0.002533065,0.002459269,0.004666914],"category_scores_gemma":[0.0030563504,0.000978991,0.0010783687,0.0014213712,0.00087924174,0.0013337993,0.0008431355,0.0015987114,0.00042504564],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000679053,0.00005064107,0.0001487832,0.000049682287,0.000026569436,0.000042021093,0.00003193432,0.98165244,0.0005594489,0.007670757,0.0007168961,0.008982969],"study_design_scores_gemma":[0.000010935947,0.000018224537,0.000026200694,0.0000029609184,0.0000054453967,0.0000037312084,0.0000045798197,0.99849534,0.00007071105,0.0012518473,0.00010691917,0.000002983666],"about_ca_topic_score_codex":0.0160911,"about_ca_topic_score_gemma":0.0070377695,"teacher_disagreement_score":0.0160911,"about_ca_system_score_codex":0.0016477266,"about_ca_system_score_gemma":0.0023909416,"threshold_uncertainty_score":0.03199488},"labels":[],"label_agreement":null},{"id":"W3083807878","doi":"10.1287/ijoc.2020.0974","title":"Addressing orientation symmetry in the time window assignment vehicle routing problem","year":2021,"lang":"en","type":"article","venue":"EUR Research Repository (Erasmus University Rotterdam)","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Group for Research in Decision Analysis","funders":"","keywords":"Vehicle routing problem; Benchmark (surveying); Computer science; Mathematical optimization; Routing (electronic design automation); Tree (set theory); Orientation (vector space); Algorithm; Mathematics","score_opus":0.05342564923841636,"score_gpt":0.3072336184337487,"score_spread":0.25380796919533233,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3083807878","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17718713,0.00049347396,0.8100704,0.0005404623,0.000101617974,0.000177123,0.00022735541,0.00042737173,0.010775093],"genre_scores_gemma":[0.6576355,0.0005906677,0.33749133,0.00017484439,0.000085316184,0.00020697236,0.00048281567,0.000211004,0.0031216117],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993599,0.0002483828,0.000025376343,0.00011841348,0.00011651607,0.00013144416],"domain_scores_gemma":[0.99857175,0.00091564853,0.00021906303,0.0001261622,0.00008615403,0.00008123455],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013008508,0.00075193413,0.0008709821,0.00046825464,0.0005054826,0.0010447577,0.0008696748,0.00079988386,0.0027151986],"category_scores_gemma":[0.0036457235,0.00040741774,0.00072461134,0.0009792855,0.00057418685,0.0016223437,0.0010954688,0.0013055743,0.00040860052],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020070189,0.00018714379,0.0025292186,0.0001473488,0.000043849468,0.00020099398,0.00014688299,0.8426646,0.0044537345,0.036425922,0.0029870034,0.110012576],"study_design_scores_gemma":[0.00007252897,0.00010376888,0.00052149437,0.00002076193,0.00003010501,0.00009606658,0.000093857845,0.96640635,0.0027745995,0.027333,0.0025328887,0.00001459989],"about_ca_topic_score_codex":0.0035124961,"about_ca_topic_score_gemma":0.0026482707,"teacher_disagreement_score":0.0035124961,"about_ca_system_score_codex":0.0006233176,"about_ca_system_score_gemma":0.0015617581,"threshold_uncertainty_score":0.009083271},"labels":[],"label_agreement":null},{"id":"W3084549995","doi":"10.1002/net.21984","title":"The mixed capacitated general routing problem with <scp>time‐dependent</scp> demands","year":2020,"lang":"en","type":"article","venue":"Networks","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Mathematical optimization; Metaheuristic; Computer science; Integer programming; Routing (electronic design automation); Set (abstract data type); Graph; Mathematics; Theoretical computer science; Computer network","score_opus":0.01018283057323346,"score_gpt":0.2019510239074673,"score_spread":0.19176819333423384,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3084549995","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.39557245,0.00093317404,0.5775897,0.0009802242,0.0001989457,0.00026474168,0.0011720108,0.00038870852,0.022899952],"genre_scores_gemma":[0.8846835,0.00024815177,0.107760824,0.000114513496,0.00006160196,0.0001864945,0.000532695,0.00007583063,0.006336406],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992067,0.00033331575,0.00003651453,0.00013817022,0.00012852918,0.0001567471],"domain_scores_gemma":[0.9990375,0.00058749673,0.00012171932,0.000069232585,0.00009826992,0.00008578532],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009574673,0.0015259476,0.0008524261,0.0008736445,0.0005296751,0.0013471767,0.0017653847,0.0013332347,0.0024851274],"category_scores_gemma":[0.0015924005,0.0005537635,0.0011329963,0.0015878577,0.0006097973,0.0014513253,0.00074794417,0.0008915399,0.00015777409],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001471251,0.000093932074,0.0005425814,0.00010784268,0.00006873856,0.00030189898,0.000027803497,0.9707718,0.0021029215,0.014493549,0.0012577481,0.010084022],"study_design_scores_gemma":[0.0000244578,0.00006529163,0.00023230999,0.000007802503,0.000019080007,0.000103878025,0.00002917283,0.99144566,0.0010554682,0.005874669,0.0011333072,0.000008818851],"about_ca_topic_score_codex":0.0064323037,"about_ca_topic_score_gemma":0.0062152282,"teacher_disagreement_score":0.0064323037,"about_ca_system_score_codex":0.0014103196,"about_ca_system_score_gemma":0.0010207769,"threshold_uncertainty_score":0.012789726},"labels":[],"label_agreement":null},{"id":"W3085334717","doi":"10.1080/10556788.2020.1817447","title":"Improving dynamic programming for travelling salesman with precedence constraints: parallel Morin–Marsten bounding","year":2020,"lang":"en","type":"article","venue":"Optimization methods & software","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Bounding overwatch; Travelling salesman problem; Morin; Computer science; Mathematical optimization; Mathematics; Algorithm; Artificial intelligence","score_opus":0.025209316645392842,"score_gpt":0.301731733635452,"score_spread":0.2765224169900592,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3085334717","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.037256718,0.0013630487,0.9309194,0.0009758616,0.00022310771,0.00017715259,0.00030592296,0.0029258109,0.025853],"genre_scores_gemma":[0.19613272,0.0005436771,0.79498464,0.00045554544,0.00014051267,0.00034926302,0.00066012994,0.0011809624,0.00555243],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981371,0.00046507205,0.000097113414,0.0003714443,0.0005689104,0.00036036345],"domain_scores_gemma":[0.9980081,0.0010022981,0.00014953154,0.00047006528,0.00025660958,0.0001134551],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001976123,0.0018397841,0.001498535,0.0013542668,0.0009767867,0.0022584521,0.002349821,0.0012354308,0.009079669],"category_scores_gemma":[0.0075242925,0.00074408355,0.0019124681,0.0017108603,0.0009920412,0.0033702832,0.0031967498,0.0034105303,0.0018113236],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002211452,0.00022022903,0.0011300093,0.00024948912,0.00009737968,0.000121594545,0.00027863757,0.67833877,0.0045493995,0.101739444,0.011910984,0.20114289],"study_design_scores_gemma":[0.000029127366,0.000026793778,0.000102081875,0.000022513444,0.000011397774,0.000019656942,0.000017517776,0.9699555,0.0007663588,0.023982946,0.005056179,0.000009880758],"about_ca_topic_score_codex":0.011420277,"about_ca_topic_score_gemma":0.014360816,"teacher_disagreement_score":0.011420277,"about_ca_system_score_codex":0.002396481,"about_ca_system_score_gemma":0.00270569,"threshold_uncertainty_score":0.030374527},"labels":[],"label_agreement":null},{"id":"W3086889218","doi":"10.1016/j.cie.2020.106832","title":"A new bi-objective vehicle routing-scheduling problem with cross-docking: Mathematical model and algorithms","year":2020,"lang":"en","type":"article","venue":"Computers & Industrial Engineering","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":50,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Tardiness; Mathematical optimization; Vehicle routing problem; Sorting; Computer science; Integer programming; Scheduling (production processes); Genetic algorithm; Job shop scheduling; Multi-objective optimization; Pareto principle; Algorithm; Routing (electronic design automation); Mathematics","score_opus":0.036964806310519835,"score_gpt":0.25109734866772104,"score_spread":0.2141325423572012,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3086889218","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.026732067,0.0010736389,0.9597747,0.00070146815,0.0003424403,0.00014447053,0.00034099774,0.00017498196,0.010715358],"genre_scores_gemma":[0.64351577,0.0018918557,0.30831966,0.00057469425,0.00046143908,0.00065561204,0.0010233189,0.00029739973,0.043260228],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99864656,0.00045279003,0.000056917324,0.0003174403,0.0003048319,0.00022148479],"domain_scores_gemma":[0.99849916,0.0007448187,0.00025617555,0.00008084217,0.00021982181,0.00019922394],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025874882,0.0023470714,0.003172764,0.0020745767,0.0009529115,0.003891387,0.003740401,0.0046118964,0.0068662944],"category_scores_gemma":[0.0034768723,0.0015926273,0.002016645,0.003985763,0.0012852675,0.0037655106,0.0030083836,0.0028270641,0.0008179784],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000053829586,0.000078384204,0.00020361217,0.00009606416,0.000047773403,0.00011357056,0.0000183928,0.9816706,0.00040006262,0.0095805125,0.0012469523,0.0064902897],"study_design_scores_gemma":[0.000011250328,0.000021823436,0.00006670568,0.0000050298695,0.000010557866,0.000023570694,0.0000085906495,0.99741864,0.00007205206,0.0020195472,0.00033541548,0.000006762758],"about_ca_topic_score_codex":0.0076212785,"about_ca_topic_score_gemma":0.0060083666,"teacher_disagreement_score":0.0076212785,"about_ca_system_score_codex":0.0020320679,"about_ca_system_score_gemma":0.0019602897,"threshold_uncertainty_score":0.02297008},"labels":[],"label_agreement":null},{"id":"W3089996180","doi":"10.1016/j.ejor.2021.07.056","title":"Meta partial benders decomposition for the logistics service network design problem","year":2021,"lang":"en","type":"preprint","venue":"European Journal of Operational Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"Agence Nationale de la Recherche","keywords":"Benders' decomposition; Decomposition; Computer science; Benchmark (surveying); Mathematical optimization; Service (business); Reverse logistics; Network planning and design; Operations research; Product (mathematics); Supply chain; Engineering; Mathematics; Computer network; Economics; Business","score_opus":0.33625220473001965,"score_gpt":0.4159091840486648,"score_spread":0.07965697931864513,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3089996180","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008647186,0.00032681928,0.98475176,0.00029692004,0.000055698827,0.000064930005,0.00017069494,0.00008397673,0.0056020166],"genre_scores_gemma":[0.2259704,0.0010532225,0.758495,0.00028456215,0.00020182863,0.00063887786,0.00076486164,0.00031381793,0.012277433],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992601,0.0003932295,0.000024773266,0.00009585113,0.00016449593,0.000061457205],"domain_scores_gemma":[0.9992055,0.00050657376,0.00008278482,0.000055224842,0.000091220725,0.0000586865],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015282669,0.0016952802,0.0012976181,0.0011941162,0.00045816437,0.0013486217,0.00088241545,0.0015721561,0.0060008103],"category_scores_gemma":[0.0025787435,0.00093056477,0.0021040246,0.0010788707,0.0005902239,0.0012048176,0.0011362225,0.0022419612,0.00080120086],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008808974,0.00007100324,0.00022265874,0.000172641,0.000084870655,0.00005765453,0.000043917047,0.9182258,0.0016261456,0.043116152,0.0019952122,0.034295868],"study_design_scores_gemma":[0.000021776312,0.000052407413,0.00007930781,0.00003451431,0.000025221028,0.0000285261,0.000023345867,0.9545517,0.00046115226,0.042375907,0.0023395582,0.0000066052125],"about_ca_topic_score_codex":0.0016696181,"about_ca_topic_score_gemma":0.0016635029,"teacher_disagreement_score":0.0060008103,"about_ca_system_score_codex":0.00091951905,"about_ca_system_score_gemma":0.0012724076,"threshold_uncertainty_score":0.020074725},"labels":[],"label_agreement":null},{"id":"W3093308000","doi":"10.1155/2020/8831746","title":"The Fourth-Party Logistics Routing Problem Using Ant Colony System-Improved Grey Wolf Optimization","year":2020,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":35,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Natural Science Foundation of China; Fundamental Research Funds for the Central Universities; National Science Foundation","keywords":"Ant colony optimization algorithms; Computer science; Node (physics); Operations research; Vehicle routing problem; Routing (electronic design automation); Optimization problem; Convergence (economics); Transport engineering; Engineering; Computer network; Economics; Artificial intelligence","score_opus":0.020695206598193495,"score_gpt":0.257597793447659,"score_spread":0.2369025868494655,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3093308000","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.059971523,0.00042573488,0.9319943,0.00045659524,0.00006537223,0.00009590493,0.000059523398,0.00018076795,0.006750191],"genre_scores_gemma":[0.89750415,0.00031995677,0.09734378,0.00009200258,0.00002497189,0.00020027024,0.00010772266,0.00004887874,0.0043582707],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999466,0.00019973092,0.000024033729,0.00009421421,0.00013218539,0.00008386052],"domain_scores_gemma":[0.99944335,0.00032995516,0.000071556526,0.000023570028,0.00009162069,0.000039830848],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00084087695,0.00091475225,0.0013373188,0.0006791636,0.00058913947,0.0011353343,0.00092946907,0.0013709795,0.001345597],"category_scores_gemma":[0.0013959138,0.00050111744,0.0009766704,0.000784216,0.0007973338,0.0010090441,0.0010883574,0.00090004184,0.00013816236],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015179294,0.000010339983,0.0001705571,0.000023498518,0.000016991056,0.00004359249,0.00001797155,0.99260885,0.00041251903,0.002159807,0.00022263562,0.004298123],"study_design_scores_gemma":[0.000004174902,0.000010531237,0.000028578044,0.0000013550809,0.0000033921074,0.0000048475645,0.0000043399327,0.9986665,0.00006220347,0.0011136933,0.000098522585,0.0000018180153],"about_ca_topic_score_codex":0.0125246765,"about_ca_topic_score_gemma":0.005768656,"teacher_disagreement_score":0.0125246765,"about_ca_system_score_codex":0.0011159643,"about_ca_system_score_gemma":0.0017387932,"threshold_uncertainty_score":0.024903536},"labels":[],"label_agreement":null},{"id":"W3093338685","doi":"10.3390/math8101788","title":"A Method for Transportation Planning and Profit Sharing in Collaborative Multi-Carrier Vehicle Routing","year":2020,"lang":"en","type":"article","venue":"Mathematics","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Heuristics; Shapley value; Vehicle routing problem; Computer science; Mathematical optimization; Profit (economics); Routing (electronic design automation); Genetic algorithm; Route planning; Profit sharing; Operations research; Distributed computing; Game theory; Computer network; Engineering; Mathematics; Microeconomics; Economics; Mathematical economics","score_opus":0.04895802467706618,"score_gpt":0.3282797199960404,"score_spread":0.2793216953189742,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3093338685","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00074212096,0.000066863955,0.99659145,0.00006859899,0.000035741297,0.00003661831,0.0000135393575,0.000042180665,0.002402792],"genre_scores_gemma":[0.10690246,0.00033093692,0.8856155,0.00009565307,0.00010421494,0.00031830536,0.00007013053,0.0000977778,0.006464947],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989222,0.000402249,0.000042975927,0.00016919003,0.00039613014,0.00006723018],"domain_scores_gemma":[0.99928063,0.0003707162,0.000052916228,0.00011993697,0.00013552551,0.000040315095],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016460402,0.0007581301,0.0006754105,0.001118705,0.0009965602,0.0011378988,0.0020186873,0.0012332328,0.003957775],"category_scores_gemma":[0.0024956546,0.00033541536,0.0012872124,0.0013297638,0.0012965649,0.0018624222,0.001470747,0.0014819694,0.00066260557],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003741496,0.00007401333,0.00018841137,0.00013694297,0.00005287729,0.00010483962,0.00015446829,0.39841944,0.002943362,0.48702356,0.0039374614,0.10692711],"study_design_scores_gemma":[0.000016679169,0.00003059964,0.000060222643,0.000016785027,0.000013437514,0.00006329385,0.000022969734,0.90270174,0.0009442637,0.08732354,0.008790647,0.000015864578],"about_ca_topic_score_codex":0.0029391942,"about_ca_topic_score_gemma":0.0028284516,"teacher_disagreement_score":0.003957775,"about_ca_system_score_codex":0.0017063,"about_ca_system_score_gemma":0.0017556897,"threshold_uncertainty_score":0.013240099},"labels":[],"label_agreement":null},{"id":"W3094207985","doi":"10.1155/2020/8843397","title":"Collaborative Multidepot Petrol Station Replenishment Problem with Multicompartments and Time Window Assignment","year":2020,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Science Foundation of Ministry of Education of China; Ministry of Education of the People's Republic of China; National Natural Science Foundation of China","keywords":"Shapley value; Computer science; Operations research; Mathematical optimization; Particle swarm optimization; Heuristic; Profit (economics); Transport engineering; Engineering; Game theory; Economics","score_opus":0.007228364623000414,"score_gpt":0.23794464172279306,"score_spread":0.23071627709979264,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3094207985","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.101893485,0.00044401904,0.8900731,0.0004079468,0.0000794085,0.00025205855,0.00029066956,0.00024165289,0.0063176993],"genre_scores_gemma":[0.86747175,0.00038961743,0.12439496,0.00007611694,0.000050559516,0.00029410876,0.0003326922,0.00006583057,0.006924396],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985312,0.00040441394,0.000094639145,0.0003881909,0.00025104,0.00033042388],"domain_scores_gemma":[0.99873644,0.00063609803,0.0001965405,0.00011112674,0.0001556042,0.00016416155],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001536245,0.0013614265,0.0022857687,0.0009196387,0.0012918382,0.001819354,0.0028359583,0.0021606374,0.0036667],"category_scores_gemma":[0.002109786,0.0009769528,0.0019417271,0.0018991703,0.0006932671,0.0023738085,0.0020539078,0.0012411713,0.00032115597],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019643332,0.00013534259,0.0010224514,0.00016874302,0.000092572074,0.0005288376,0.00010532091,0.9660664,0.0020905384,0.012420055,0.001048235,0.01612506],"study_design_scores_gemma":[0.000024332225,0.00006585057,0.00020331315,0.000005392842,0.000029524519,0.00006557076,0.000055583412,0.99393106,0.00065918645,0.0043298523,0.0006189901,0.000011460663],"about_ca_topic_score_codex":0.010187769,"about_ca_topic_score_gemma":0.00817252,"teacher_disagreement_score":0.010187769,"about_ca_system_score_codex":0.0014936563,"about_ca_system_score_gemma":0.0019088701,"threshold_uncertainty_score":0.020256937},"labels":[],"label_agreement":null},{"id":"W3098142126","doi":"","title":"A Branch-and-Check Approach for a Tourist Trip Design Problem with Rich Constraints","year":2019,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"","keywords":"Computer science; Tourism; Geography","score_opus":0.02164509327999898,"score_gpt":0.2318065279226187,"score_spread":0.21016143464261972,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3098142126","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009038808,0.0002443282,0.98553395,0.00025174263,0.00005140182,0.00017670497,0.00019162822,0.00022832195,0.0042830887],"genre_scores_gemma":[0.28065825,0.0005052096,0.70892227,0.00025129612,0.00014506932,0.0006001859,0.0006109811,0.00033993556,0.007966697],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987398,0.0005514315,0.00004679677,0.000203317,0.00028793598,0.00017081475],"domain_scores_gemma":[0.9926651,0.0061189015,0.0002802658,0.00021980933,0.00046546193,0.0002505721],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0036832043,0.002120302,0.002851848,0.0022340117,0.0013218896,0.0024739536,0.0026381465,0.003183019,0.013689208],"category_scores_gemma":[0.008924921,0.001962303,0.0023684583,0.0021675124,0.0014888913,0.0018742738,0.0023434432,0.0036871366,0.0009570591],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011555735,0.00009071074,0.00030628135,0.0001818281,0.00005925102,0.00012517115,0.00006713539,0.9548456,0.0008230768,0.013136052,0.0020571554,0.028192123],"study_design_scores_gemma":[0.000027158514,0.00003866443,0.000050751754,0.000019964318,0.000021110265,0.00001680172,0.000015028266,0.99080884,0.0001810439,0.008331018,0.00048267393,0.0000068643185],"about_ca_topic_score_codex":0.01217056,"about_ca_topic_score_gemma":0.011010782,"teacher_disagreement_score":0.013689208,"about_ca_system_score_codex":0.0014175572,"about_ca_system_score_gemma":0.0030076697,"threshold_uncertainty_score":0.045795023},"labels":[],"label_agreement":null},{"id":"W3102154833","doi":"10.5267/j.ijiec.2020.10.003","title":"Time-dependent vehicle routing problem with backhaul with FIFO assumption: Variable neighborhood search and mat-heuristic variable neighborhood search algorithms","year":2020,"lang":"en","type":"article","venue":"International Journal of Industrial Engineering Computations","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Backhaul (telecommunications); Vehicle routing problem; FIFO (computing and electronics); Variable neighborhood search; Heuristic; FIFO and LIFO accounting; Mathematical optimization; Variable (mathematics); Computer science; Routing (electronic design automation); Reduction (mathematics); Algorithm; Null-move heuristic; Mathematics; Metaheuristic; Computer network","score_opus":0.0249850087606199,"score_gpt":0.25282262619365276,"score_spread":0.22783761743303285,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3102154833","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.035593834,0.0006567998,0.9594042,0.00025731314,0.000044466393,0.000057658162,0.00007509263,0.000073038995,0.0038376378],"genre_scores_gemma":[0.7867611,0.0008821634,0.20654581,0.00008744477,0.00006228815,0.00020850598,0.00016445066,0.00004375586,0.005244548],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996605,0.00015861445,0.000011684076,0.00005414933,0.000067655295,0.000047354002],"domain_scores_gemma":[0.9994404,0.00039181588,0.00007985181,0.000018792683,0.000044509754,0.000024587678],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008420091,0.00056550873,0.000796777,0.00045596677,0.00035991587,0.0009163177,0.0011301769,0.0008251923,0.00094323274],"category_scores_gemma":[0.0013356336,0.00031810722,0.00067145954,0.0007310943,0.00044130592,0.0009512061,0.00045157268,0.0006647495,0.00010273156],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000025449115,0.00002167988,0.00018924095,0.000028863738,0.000015476691,0.00003536604,0.000013527104,0.9861346,0.00024691276,0.006246649,0.00027521426,0.006767098],"study_design_scores_gemma":[0.0000030311264,0.000012476567,0.000040327814,0.0000024870694,0.000002517159,0.000008711817,0.00000551418,0.99821174,0.00006715514,0.0014895041,0.00015500758,0.0000015440665],"about_ca_topic_score_codex":0.0057882946,"about_ca_topic_score_gemma":0.0042446093,"teacher_disagreement_score":0.0057882946,"about_ca_system_score_codex":0.00080647314,"about_ca_system_score_gemma":0.00089349336,"threshold_uncertainty_score":0.01150918},"labels":[],"label_agreement":null},{"id":"W3104464641","doi":"10.1016/j.cor.2023.106526","title":"Unified Branch-and-Benders-Cut for two-stage stochastic mixed-integer programs","year":2024,"lang":"en","type":"article","venue":"Computers & Operations Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"Fonds de recherche du Québec – Nature et technologies; Canada Foundation for Innovation; Ministère de l'Économie, de la Science et de l'Innovation - Québec","keywords":"Mathematical optimization; Benders' decomposition; Integer (computer science); Decomposition; Generality; Heuristic; Branch and bound; Heuristics; Discretization; Stochastic programming; Mathematics; Branch and cut; Computer science; Integer programming","score_opus":0.09352603497581218,"score_gpt":0.3962700240992904,"score_spread":0.3027439891234782,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3104464641","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0057873824,0.00027988155,0.98943955,0.00013431373,0.00005953722,0.00013636734,0.00015545623,0.00022959762,0.0037779182],"genre_scores_gemma":[0.2642554,0.0006598782,0.7233946,0.00025164054,0.00017770409,0.0012420538,0.00093482464,0.00052256027,0.008561364],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982822,0.00071024284,0.00006544271,0.00019389296,0.00044714686,0.0003011433],"domain_scores_gemma":[0.9972,0.002008444,0.00014561458,0.00011335858,0.00038429297,0.00014826767],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004202725,0.0027296958,0.0039382717,0.0018573597,0.0010670811,0.0031466864,0.0027437077,0.0031594168,0.00947805],"category_scores_gemma":[0.0062757013,0.0020444102,0.002906731,0.0020228175,0.0011665362,0.0020754691,0.002349252,0.0034995337,0.0010852519],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011094307,0.00013873294,0.00022196496,0.00016165506,0.000062385894,0.000053123098,0.00004372492,0.9415851,0.00060770253,0.027499083,0.0017362096,0.027779426],"study_design_scores_gemma":[0.000016336327,0.000023825181,0.00003265806,0.000009611749,0.000010590404,0.0000036560418,0.000003836391,0.99335444,0.00012861127,0.00611846,0.00029379167,0.0000040862246],"about_ca_topic_score_codex":0.008991485,"about_ca_topic_score_gemma":0.010531978,"teacher_disagreement_score":0.00947805,"about_ca_system_score_codex":0.0022689418,"about_ca_system_score_gemma":0.0041488176,"threshold_uncertainty_score":0.031707287},"labels":[],"label_agreement":null},{"id":"W3107516874","doi":"10.1007/s11590-020-01675-z","title":"An efficient heuristic for a hub location routing problem","year":2020,"lang":"en","type":"article","venue":"Optimization Letters","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":23,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Military College of Canada","funders":"","keywords":"Travelling salesman problem; Mathematical optimization; Heuristic; Variable neighborhood search; Computer science; Computational intelligence; Selection (genetic algorithm); Local search (optimization); Fraction (chemistry); Steiner tree problem; Mathematics; Metaheuristic; Artificial intelligence","score_opus":0.01530435585602781,"score_gpt":0.24606985011928661,"score_spread":0.23076549426325882,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3107516874","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023942064,0.00048588397,0.9588479,0.00035714332,0.00021268705,0.0002292043,0.00017672004,0.00038447074,0.015364031],"genre_scores_gemma":[0.27337772,0.0004496345,0.7183431,0.00020576561,0.0001002812,0.00045116345,0.00030527145,0.00017849289,0.006588561],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995291,0.00018045511,0.000016826563,0.00006506974,0.00012345324,0.000085162224],"domain_scores_gemma":[0.9992562,0.0004770397,0.000055546152,0.00006174611,0.00010120305,0.00004821687],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008821679,0.0010517316,0.0012615595,0.0013228535,0.00073633104,0.001264142,0.001511671,0.0020631175,0.0062535712],"category_scores_gemma":[0.002451234,0.00075170136,0.0009265684,0.0014887863,0.0007357173,0.00106001,0.0010962976,0.001143681,0.0006639504],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009312381,0.00011290204,0.00015483417,0.0001087641,0.000028865912,0.00009035549,0.000042800708,0.92156947,0.0014285507,0.017647298,0.0031742984,0.0555488],"study_design_scores_gemma":[0.000039726183,0.0000312822,0.000055631685,0.0000127592075,0.000012191941,0.00002217443,0.000017249069,0.9924206,0.00024602027,0.0059307683,0.0012051252,0.0000063932985],"about_ca_topic_score_codex":0.005159768,"about_ca_topic_score_gemma":0.00686917,"teacher_disagreement_score":0.0062535712,"about_ca_system_score_codex":0.0012940869,"about_ca_system_score_gemma":0.0018919295,"threshold_uncertainty_score":0.020920277},"labels":[],"label_agreement":null},{"id":"W3108622340","doi":"10.1016/j.dib.2020.106568","title":"Data for a meta-analysis of the adaptive layer in adaptive large neighborhood search","year":2020,"lang":"en","type":"article","venue":"Data in Brief","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University; Université Laval","funders":"Universiteit Antwerpen","keywords":"Computer science; Metaheuristic; Meta-analysis; Set (abstract data type); Replicate; Implementation; Domain (mathematical analysis); Variety (cybernetics); Range (aeronautics); Data mining; Machine learning; Artificial intelligence; Statistics; Mathematics; Software engineering","score_opus":0.30977380902254176,"score_gpt":0.3708319806133107,"score_spread":0.06105817159076893,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3108622340","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011559436,0.017953716,0.031152723,0.0039214846,0.00051250146,0.0066954056,0.9148521,0.0013414412,0.012011221],"genre_scores_gemma":[0.16429172,0.013238582,0.14319883,0.004898138,0.00041427303,0.068634585,0.59646195,0.0016591725,0.0072028143],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9687942,0.015869513,0.005680877,0.002709254,0.006214116,0.00073195325],"domain_scores_gemma":[0.79579735,0.17419678,0.011515967,0.00971504,0.007985174,0.00078971835],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.028289946,0.001861613,0.0029357362,0.013775411,0.0007783403,0.0035477525,0.0027161401,0.0024211188,0.058392588],"category_scores_gemma":[0.18020266,0.0010555132,0.010739564,0.0144953,0.0005777045,0.0019840316,0.0020963696,0.0030145755,0.0060582743],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.008115512,0.00088071753,0.026600473,0.21868391,0.044779506,0.0009335816,0.0009669885,0.036954135,0.0025118804,0.027388858,0.42670503,0.2054794],"study_design_scores_gemma":[0.007633979,0.0022794604,0.033367656,0.06060634,0.038073156,0.001057214,0.0010743313,0.011207074,0.0056659984,0.037020355,0.8015984,0.0004159809],"about_ca_topic_score_codex":0.0033665344,"about_ca_topic_score_gemma":0.0068849744,"teacher_disagreement_score":0.058392588,"about_ca_system_score_codex":0.0021470166,"about_ca_system_score_gemma":0.0041302145,"threshold_uncertainty_score":0.19534266},"labels":[],"label_agreement":null},{"id":"W3108778051","doi":"10.1016/j.ejtl.2020.100024","title":"Introduction to the special issue on combining optimization and machine learning: Application in vehicle routing, network design and crew scheduling","year":2020,"lang":"en","type":"article","venue":"EURO Journal on Transportation and Logistics","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Group for Research in Decision Analysis; HEC Montréal","funders":"Agence Nationale de la Recherche","keywords":"Vehicle routing problem; Crew; Computer science; Crew scheduling; Scheduling (production processes); Routing (electronic design automation); Engineering; Embedded system; Aeronautics; Operations management","score_opus":0.02748217703064264,"score_gpt":0.2509191413029552,"score_spread":0.22343696427231252,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3108778051","genre_codex":"editorial","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00090142083,0.15041314,0.06497415,0.022409767,0.7217529,0.000114194656,0.00085748726,0.000583904,0.037993163],"genre_scores_gemma":[0.0053553195,0.08506145,0.01903321,0.011279703,0.7651014,0.00013898883,0.001261645,0.0010096404,0.11175862],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9988066,0.00020781129,0.00013147166,0.00036913026,0.0004071748,0.00007786119],"domain_scores_gemma":[0.9959204,0.0019219341,0.00022093518,0.00029016353,0.001165827,0.00048065974],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019703235,0.0020995182,0.0025930074,0.003271267,0.000841752,0.0041329144,0.0017139667,0.002882243,0.045218375],"category_scores_gemma":[0.0048541753,0.0006990873,0.0019342131,0.0033316053,0.0010115847,0.003263369,0.0015223005,0.0056633335,0.02335609],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000039485647,0.0000925723,0.0002197459,0.00063984114,0.00006722958,0.00009204683,0.000020910538,0.0014811335,0.0006926211,0.0060675326,0.88538235,0.10520456],"study_design_scores_gemma":[0.000012380517,0.000096865966,0.00086565595,0.00035388247,0.000046323952,0.000296976,0.00002418042,0.0041616065,0.0003444611,0.011635239,0.9821253,0.000036984067],"about_ca_topic_score_codex":0.00093273906,"about_ca_topic_score_gemma":0.0021338267,"teacher_disagreement_score":0.045218375,"about_ca_system_score_codex":0.0009133527,"about_ca_system_score_gemma":0.0011230251,"threshold_uncertainty_score":0.15127057},"labels":[],"label_agreement":null},{"id":"W3110999205","doi":"10.1287/ijoc.2020.1003","title":"The Rank-One Quadratic Assignment Problem","year":2020,"lang":"en","type":"article","venue":"INFORMS journal on computing","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Simon Fraser University","funders":"","keywords":"Quadratic assignment problem; Heuristics; Mathematical optimization; Metaheuristic; Integer programming; Quadratic equation; Pairwise comparison; Computer science; Quadratic programming; Rank (graph theory); Generalized assignment problem; Benchmark (surveying); Mathematics; Combinatorial optimization; Optimization problem; Artificial intelligence; Combinatorics","score_opus":0.026991940439963214,"score_gpt":0.2570781168276537,"score_spread":0.23008617638769047,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3110999205","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.058604445,0.0010137449,0.9110089,0.0016396601,0.00018957088,0.0001772798,0.0006096735,0.0004127768,0.02634396],"genre_scores_gemma":[0.707286,0.0014810227,0.27155387,0.0004331928,0.00031810068,0.00023970703,0.0011156851,0.0002395439,0.01733279],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977245,0.00092981325,0.00007985829,0.000526597,0.0004203774,0.00031894917],"domain_scores_gemma":[0.9963199,0.002161104,0.0005383953,0.00033865616,0.00044133802,0.0002005727],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018623574,0.0010219358,0.0009476389,0.0007010634,0.00093710615,0.0018485677,0.0016407679,0.0011949035,0.009035223],"category_scores_gemma":[0.0058687762,0.0003776309,0.00062614225,0.0016355873,0.001118891,0.0025700182,0.0012013424,0.0018334935,0.0013697643],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00035402595,0.00047037555,0.0023873886,0.00094771327,0.0001222783,0.00052183965,0.00030972747,0.5736713,0.0042408556,0.25175795,0.019495804,0.14572078],"study_design_scores_gemma":[0.00004606931,0.00025227046,0.00086055393,0.00004689961,0.000040766445,0.0004808941,0.00022277675,0.81821465,0.0020239302,0.16077231,0.01699788,0.00004102291],"about_ca_topic_score_codex":0.0030613758,"about_ca_topic_score_gemma":0.0023583802,"teacher_disagreement_score":0.009035223,"about_ca_system_score_codex":0.0011747921,"about_ca_system_score_gemma":0.0016958129,"threshold_uncertainty_score":0.030225873},"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,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Group for Research in Decision Analysis","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","score_opus":0.013839727611325623,"score_gpt":0.2045635052047349,"score_spread":0.19072377759340928,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W311217234","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0026123272,0.0070438627,0.7845934,0.0009537805,0.00085572765,0.00008242282,0.00038451343,0.00047330747,0.2030007],"genre_scores_gemma":[0.052655846,0.021245718,0.6310285,0.00040671442,0.000773782,0.00039743408,0.0014679049,0.00079602003,0.29122806],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99973017,0.000052577216,0.000009353208,0.000052507214,0.00013239079,0.000023006685],"domain_scores_gemma":[0.9998392,0.000089554494,0.000008737496,0.000023606002,0.000031473603,0.000007497598],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031661708,0.001794728,0.0009516303,0.0005778078,0.0004500997,0.0012587724,0.0016361292,0.00094129844,0.015071284],"category_scores_gemma":[0.0008206165,0.0006845122,0.0006255147,0.0019498949,0.0006275606,0.0023397906,0.0007868189,0.0021143137,0.0045148754],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000023035129,0.00007275205,0.000074093565,0.000577942,0.000037047503,0.00008281735,0.000091129856,0.14790677,0.0017382042,0.3607278,0.094956554,0.39371184],"study_design_scores_gemma":[0.000020611322,0.000031691387,0.00013779325,0.00020017753,0.00002593054,0.00018772704,0.00006767105,0.18693595,0.0017286409,0.49450228,0.316135,0.00002653648],"about_ca_topic_score_codex":0.0015274596,"about_ca_topic_score_gemma":0.0029703758,"teacher_disagreement_score":0.015071284,"about_ca_system_score_codex":0.0007612286,"about_ca_system_score_gemma":0.0008742882,"threshold_uncertainty_score":0.050418437},"labels":[],"label_agreement":null},{"id":"W3112475217","doi":"10.1139/cjce-2020-0262","title":"Optimized route to clear diverging diamond interchange using discrete optimization method","year":2020,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Mathematical optimization; Clearing; Graph; Algorithm; Mathematics; Theoretical computer science","score_opus":0.02619415702868113,"score_gpt":0.2522113426855539,"score_spread":0.22601718565687276,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3112475217","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.112033404,0.00032773337,0.87346214,0.00023116304,0.00009473151,0.00015723049,0.00020840664,0.0002501767,0.013235043],"genre_scores_gemma":[0.76528174,0.00020672615,0.22879113,0.000050254053,0.000015534551,0.0001974684,0.00025072426,0.000065113076,0.005141404],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997987,0.00006719522,0.000009454376,0.00004627474,0.000045607743,0.000032770546],"domain_scores_gemma":[0.9996044,0.00020993095,0.00004949948,0.000021286252,0.000092708375,0.000022235668],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00058854563,0.0005859344,0.0007523961,0.0007309316,0.00039524198,0.0009857627,0.00065589196,0.00079339714,0.003011115],"category_scores_gemma":[0.0012043914,0.00035326628,0.0006314366,0.0006725928,0.00043431058,0.000556328,0.0004128723,0.00061737065,0.00023115685],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000023170236,0.00001822924,0.0002688029,0.000037350972,0.0000062038966,0.000019704816,0.000014215807,0.9903931,0.00049369887,0.0023629966,0.00022354146,0.006138977],"study_design_scores_gemma":[0.0000037254786,0.000011572323,0.000061843224,0.000001935815,0.0000026924283,0.0000028351246,0.000008926516,0.99908066,0.0001419277,0.0005362347,0.00014570734,0.0000020390803],"about_ca_topic_score_codex":0.012386578,"about_ca_topic_score_gemma":0.008690356,"teacher_disagreement_score":0.012386578,"about_ca_system_score_codex":0.0011245158,"about_ca_system_score_gemma":0.0014973298,"threshold_uncertainty_score":0.024628997},"labels":[],"label_agreement":null},{"id":"W3112745349","doi":"10.1111/itor.12911","title":"Selective arc-ng pricing for vehicle routing","year":2021,"lang":"en","type":"article","venue":"PolyPublie (École Polytechnique de Montréal)","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal; Polytechnique Montréal; Group for Research in Decision Analysis","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Column generation; Solver; Mathematical optimization; Vehicle routing problem; Path (computing); Computer science; Set (abstract data type); Relaxation (psychology); Routing (electronic design automation); Mathematics","score_opus":0.012099992376166312,"score_gpt":0.24637388085833553,"score_spread":0.2342738884821692,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3112745349","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05777927,0.00013690046,0.9369509,0.00017478487,0.000025773788,0.0000725875,0.00008118614,0.00023772259,0.0045408034],"genre_scores_gemma":[0.7180162,0.000172516,0.27731055,0.00010894839,0.000039621238,0.00013164162,0.0001573038,0.000110566696,0.003952644],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994103,0.00027280863,0.00001603483,0.000060532017,0.00015470208,0.00008559934],"domain_scores_gemma":[0.99872524,0.00083795056,0.000090735884,0.0001506134,0.00012939126,0.00006602592],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009974136,0.0004984696,0.0005248714,0.00051847764,0.0003651003,0.0006210838,0.0010815555,0.00050653407,0.0036638076],"category_scores_gemma":[0.0022852274,0.00028491943,0.00044510534,0.00064555206,0.00061003276,0.0009397943,0.000807948,0.00089973916,0.00026446418],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000089757996,0.00009277651,0.0005004662,0.0000771838,0.000024878847,0.00006205898,0.000038083534,0.8865471,0.0038624618,0.05156153,0.0020173904,0.055126294],"study_design_scores_gemma":[0.00000680771,0.000016738719,0.000047724705,0.0000022061095,0.0000020724005,0.0000086493765,0.000004030274,0.98953855,0.0006635357,0.0093603125,0.00034762328,0.0000017963426],"about_ca_topic_score_codex":0.002344939,"about_ca_topic_score_gemma":0.002947362,"teacher_disagreement_score":0.0036638076,"about_ca_system_score_codex":0.0007923443,"about_ca_system_score_gemma":0.00076139293,"threshold_uncertainty_score":0.012256682},"labels":[],"label_agreement":null},{"id":"W3114589794","doi":"10.1155/2020/8834502","title":"A New Hybrid Butterfly Optimization Algorithm for Green Vehicle Routing Problem","year":2020,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":52,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Tabu search; Vehicle routing problem; Algorithm; Metaheuristic; Mathematical optimization; Computer science; Routing (electronic design automation); Mathematics","score_opus":0.012817209095655266,"score_gpt":0.24777536887085788,"score_spread":0.23495815977520262,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3114589794","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.030694358,0.00063097005,0.96046346,0.0002964349,0.00008481492,0.000095287796,0.00012863627,0.0003729793,0.007233076],"genre_scores_gemma":[0.4146166,0.000709608,0.57268345,0.0003008127,0.00007803206,0.00037707613,0.0005623283,0.00013735103,0.010534682],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997881,0.000054124543,0.000011431135,0.00004902361,0.00006141027,0.00003591299],"domain_scores_gemma":[0.9998209,0.00009388988,0.000018101124,0.0000095430805,0.000046571913,0.000010939484],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040946266,0.00079348823,0.0007164266,0.0007216807,0.0004904514,0.00074136455,0.0010181086,0.0011105626,0.0027887987],"category_scores_gemma":[0.00069736753,0.00032167998,0.00063903246,0.0008248303,0.00035674457,0.0009273107,0.0007126229,0.00064703525,0.00034350593],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000076803226,0.000044749133,0.00041290134,0.00007057942,0.000041728206,0.000050584833,0.00004883824,0.9124613,0.0019577763,0.0062419153,0.002449837,0.07614294],"study_design_scores_gemma":[0.0000141227865,0.000023473942,0.00004785995,0.000004017125,0.0000054146303,0.000013639329,0.0000088360775,0.99755764,0.00023977173,0.001151025,0.0009308945,0.0000033765054],"about_ca_topic_score_codex":0.007582077,"about_ca_topic_score_gemma":0.0074621937,"teacher_disagreement_score":0.007582077,"about_ca_system_score_codex":0.0006141125,"about_ca_system_score_gemma":0.001001302,"threshold_uncertainty_score":0.015075862},"labels":[],"label_agreement":null},{"id":"W3116025892","doi":"10.18280/jesa.530604","title":"Modelling of Agent-Based Vehicle Routing Problem Using Unified Modelling Language","year":2020,"lang":"en","type":"article","venue":"Journal Européen des Systèmes Automatisés","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Vehicle routing problem; Computer science; Exploit; Unified Modeling Language; Visualization; Routing (electronic design automation); Constraint (computer-aided design); Modeling language; Process (computing); Operations research; Distributed computing; Engineering; Data mining; Computer network; Software","score_opus":0.05934378831447326,"score_gpt":0.26962164200769784,"score_spread":0.21027785369322458,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3116025892","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007860286,0.00055607484,0.98258245,0.00023339053,0.000053626147,0.0000902013,0.00018528079,0.00028894655,0.008149681],"genre_scores_gemma":[0.4168166,0.0026948748,0.5660837,0.00011950255,0.00010463084,0.0010914019,0.0009687477,0.00016573255,0.01195474],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99938786,0.0002497601,0.000053155996,0.00008929606,0.00015338513,0.0000666554],"domain_scores_gemma":[0.9996195,0.00019388458,0.00007155944,0.000025403107,0.00007195883,0.000017614586],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00074826216,0.00080000923,0.0007935287,0.0006631912,0.00050221104,0.0018315372,0.0016474702,0.0011389767,0.0021316956],"category_scores_gemma":[0.0011395562,0.00048428905,0.0013979911,0.00069573557,0.000640523,0.0010197491,0.00094125455,0.0009589937,0.0004304941],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019057918,0.000022697828,0.000317785,0.00012873147,0.000034322282,0.00019596267,0.00014423636,0.8900946,0.0018957331,0.098148644,0.00070623535,0.008292032],"study_design_scores_gemma":[0.000011921623,0.000015260004,0.000089896195,0.000016893622,0.000015128043,0.00003607091,0.00002277608,0.9804494,0.0003589893,0.013679978,0.0052953092,0.000008491382],"about_ca_topic_score_codex":0.010875452,"about_ca_topic_score_gemma":0.0078079095,"teacher_disagreement_score":0.010875452,"about_ca_system_score_codex":0.00081518537,"about_ca_system_score_gemma":0.0017761227,"threshold_uncertainty_score":0.021624327},"labels":[],"label_agreement":null},{"id":"W3117154257","doi":"10.1093/jcde/qwaa089","title":"A biobjective home health care logistics considering the working time and route balancing: a self-adaptive social engineering optimizer","year":2020,"lang":"en","type":"article","venue":"Journal of Computational Design and Engineering","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":68,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Metaheuristic; Vehicle routing problem; Scheduling (production processes); Computer science; Population; Operations research; Operations management; Routing (electronic design automation); Engineering; Artificial intelligence; Medicine; Computer network","score_opus":0.02356356349678254,"score_gpt":0.23335147664918524,"score_spread":0.2097879131524027,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3117154257","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09556397,0.0008489063,0.88831997,0.0009921503,0.00016506563,0.00022356819,0.00015358072,0.00022517257,0.01350767],"genre_scores_gemma":[0.8534458,0.0005349874,0.13916355,0.0003478933,0.000089247085,0.00044561335,0.00019476707,0.00007795726,0.005700063],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99948514,0.00021285306,0.000020488149,0.00009089346,0.00010508641,0.00008545567],"domain_scores_gemma":[0.9991561,0.0004972095,0.000098613666,0.00003392371,0.00015151573,0.00006268141],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011610317,0.0012568483,0.001206919,0.00082913955,0.00052464136,0.0014487354,0.0010205616,0.0016748434,0.0021822096],"category_scores_gemma":[0.002044992,0.00049400626,0.0011657558,0.0006159349,0.0006405727,0.0007298312,0.0012006344,0.0011499133,0.00019662986],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000024896905,0.000053346914,0.00043150116,0.000038006026,0.00003629678,0.000054859163,0.000021042457,0.9894806,0.00041044908,0.0031032225,0.00044242258,0.005903341],"study_design_scores_gemma":[0.0000045257216,0.000021047174,0.00005873368,0.000004828332,0.000006897776,0.0000054113602,0.000011135553,0.9989661,0.00006304138,0.0006500027,0.00020629016,0.0000020471543],"about_ca_topic_score_codex":0.006698887,"about_ca_topic_score_gemma":0.0029952829,"teacher_disagreement_score":0.006698887,"about_ca_system_score_codex":0.0009266006,"about_ca_system_score_gemma":0.0014036855,"threshold_uncertainty_score":0.01331979},"labels":[],"label_agreement":null},{"id":"W3118945546","doi":"10.1007/s43069-020-00044-x","title":"Branch-and-Price for a Multi-attribute Technician Routing and Scheduling Problem","year":2021,"lang":"en","type":"article","venue":"Operations Research Forum","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Université du Québec à Montréal; Université de Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Branch and price; Arc routing; Technician; Column generation; Solver; Mathematical optimization; Integer programming; Scheduling (production processes); Algorithm; Routing (electronic design automation); Mathematics; Engineering; Computer network","score_opus":0.08663084792724975,"score_gpt":0.3906069132587584,"score_spread":0.30397606533150867,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3118945546","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03802107,0.0011907691,0.94611526,0.0023289314,0.00025261586,0.00045146848,0.0007800639,0.00041846035,0.01044135],"genre_scores_gemma":[0.5403734,0.002606156,0.42280138,0.00041583288,0.00082038797,0.0012072719,0.0017187782,0.0006266004,0.029430332],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9958936,0.0022073777,0.00013897335,0.00060063094,0.0005935465,0.000565814],"domain_scores_gemma":[0.98829067,0.010094854,0.00039438793,0.00025953594,0.00039090068,0.0005696273],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01052483,0.002888871,0.0064382395,0.004049375,0.0018872343,0.005710316,0.0052411137,0.007924191,0.017780406],"category_scores_gemma":[0.014838792,0.0029009748,0.0029102585,0.0059467466,0.0029198884,0.006207505,0.0026772844,0.005792259,0.0014466709],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00033670713,0.00030091385,0.0007190707,0.00027912486,0.00012561156,0.0001451918,0.000078180776,0.9409974,0.00045480378,0.030062256,0.0036241554,0.022876514],"study_design_scores_gemma":[0.00004322018,0.00005324961,0.00014195104,0.000013971396,0.000027650285,0.000023984983,0.00001662244,0.9842589,0.000095099866,0.014875844,0.00043778218,0.0000116715355],"about_ca_topic_score_codex":0.013596967,"about_ca_topic_score_gemma":0.007978225,"teacher_disagreement_score":0.017780406,"about_ca_system_score_codex":0.005571755,"about_ca_system_score_gemma":0.005223881,"threshold_uncertainty_score":0.059481323},"labels":[],"label_agreement":null},{"id":"W3119461075","doi":"10.1109/access.2021.3051741","title":"A Fast and Robust Heuristic Algorithm for the Minimum Weight Vertex Cover Problem","year":2021,"lang":"en","type":"article","venue":"IEEE Access","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Tabu search; Computer science; Algorithm; Guided Local Search; Evolutionary algorithm; Benchmark (surveying); Vertex cover; Robustness (evolution); Local search (optimization); Memetic algorithm; Mathematical optimization; Combinatorial optimization; Best-first search; Search algorithm; Mathematics; Approximation algorithm; Beam search; Artificial intelligence","score_opus":0.025941904011549687,"score_gpt":0.27988977420800404,"score_spread":0.2539478701964544,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3119461075","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017702186,0.0007276095,0.9705366,0.00025126978,0.00014195513,0.000280645,0.00016905241,0.0018362169,0.008354556],"genre_scores_gemma":[0.1933013,0.0005050074,0.79976445,0.00024633488,0.00011522758,0.00055246975,0.0008719307,0.0003093134,0.004333913],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993068,0.00015553649,0.000031955813,0.00015034166,0.00023757387,0.00011780977],"domain_scores_gemma":[0.99951506,0.00022163872,0.000064141765,0.00006831138,0.000100841724,0.000030051548],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006349246,0.0013466803,0.0011877238,0.0015912729,0.0007840624,0.0010709867,0.0016239664,0.0016937549,0.0039977957],"category_scores_gemma":[0.002591369,0.0004968634,0.0011520562,0.0017558284,0.0005039539,0.0008952897,0.000967898,0.0013593024,0.0010808263],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000116904,0.00014789682,0.00068186026,0.00020444438,0.00008688338,0.00018224161,0.000084396146,0.6656006,0.0048229,0.016663909,0.008843236,0.30256474],"study_design_scores_gemma":[0.000046884576,0.000053463602,0.000131474,0.000017752425,0.00001623186,0.000096551266,0.000026051282,0.98940337,0.0010400071,0.0055694254,0.0035884902,0.000010284824],"about_ca_topic_score_codex":0.004626869,"about_ca_topic_score_gemma":0.004872506,"teacher_disagreement_score":0.004626869,"about_ca_system_score_codex":0.0008901113,"about_ca_system_score_gemma":0.001928965,"threshold_uncertainty_score":0.013373971},"labels":[],"label_agreement":null},{"id":"W3121559560","doi":"","title":"The Integrated Production and Transportation Scheduling Problem for a Product with a Short Lifespan","year":2007,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"","keywords":"Mathematical optimization; Job shop scheduling; Production schedule; Scheduling (production processes); Computer science; Heuristic; Operations research; Schedule; Production (economics); Memetic algorithm; Genetic algorithm; Engineering; Mathematics; Economics","score_opus":0.008334488040680722,"score_gpt":0.2456455639661238,"score_spread":0.2373110759254431,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3121559560","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1883648,0.0009766784,0.79635656,0.0010607832,0.00014110014,0.00068848056,0.0013478972,0.00041915456,0.010644527],"genre_scores_gemma":[0.5604016,0.0011636161,0.42247772,0.00016038655,0.00016586755,0.0010268323,0.0019568545,0.00021159965,0.012435468],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993641,0.00015473137,0.00003724576,0.00021729374,0.000115412855,0.00011119509],"domain_scores_gemma":[0.9991246,0.00047953895,0.00016492016,0.00005968696,0.000068049616,0.000103221624],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011455526,0.0015154615,0.0016212565,0.0009445452,0.0009777271,0.0014509673,0.0015143825,0.0018854738,0.008284139],"category_scores_gemma":[0.0023204156,0.0006826226,0.0012668557,0.0018461128,0.0008330711,0.0020635182,0.0009235364,0.001030276,0.0007645688],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004960807,0.00036750137,0.001682536,0.0007125974,0.00017438452,0.00073850784,0.00023609192,0.8698842,0.010604829,0.02882709,0.0040281015,0.08224814],"study_design_scores_gemma":[0.00017785517,0.00083023997,0.0016205314,0.000050121398,0.000114864124,0.0005881909,0.00025273568,0.94901615,0.0041017123,0.034018043,0.009183831,0.00004567794],"about_ca_topic_score_codex":0.0035782212,"about_ca_topic_score_gemma":0.0030005807,"teacher_disagreement_score":0.008284139,"about_ca_system_score_codex":0.0014134188,"about_ca_system_score_gemma":0.0023363994,"threshold_uncertainty_score":0.02771324},"labels":[],"label_agreement":null},{"id":"W3121630490","doi":"10.2139/ssrn.2460366","title":"A Bi-Objective Model for the Used Oil Location-Routing Problem","year":2014,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Download; Computer science; Routing (electronic design automation); Computer network; World Wide Web","score_opus":0.01347704958455576,"score_gpt":0.25484008521588164,"score_spread":0.24136303563132588,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3121630490","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.030036973,0.0009507299,0.9499294,0.0007192602,0.00019410459,0.0001630861,0.001086386,0.00016408031,0.016755963],"genre_scores_gemma":[0.74141836,0.001885517,0.2025953,0.00032535498,0.0001355092,0.0009321083,0.0015102769,0.00017006537,0.051027488],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990459,0.000406202,0.00004341749,0.00016668532,0.00021045795,0.00012726401],"domain_scores_gemma":[0.99924576,0.0003924274,0.000105773724,0.000037845064,0.0001484966,0.00006968584],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016700967,0.0016823355,0.0016564068,0.0012171031,0.00055370777,0.0024660444,0.002962095,0.0031381082,0.008008838],"category_scores_gemma":[0.0023944187,0.0008342377,0.0012151593,0.0025026025,0.000830405,0.001776006,0.0016527175,0.0022693197,0.0011552386],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000022643317,0.000027373344,0.00012823787,0.00004578811,0.000021222291,0.00005310106,0.000010501489,0.9897736,0.00020346303,0.0066233296,0.0003764228,0.0027143531],"study_design_scores_gemma":[0.0000066509338,0.000015236412,0.00004741127,0.0000044394224,0.000007519201,0.000008561374,0.00000710438,0.99748766,0.000036031466,0.0020231137,0.00035238313,0.000003901602],"about_ca_topic_score_codex":0.012531459,"about_ca_topic_score_gemma":0.012101895,"teacher_disagreement_score":0.012531459,"about_ca_system_score_codex":0.0015241825,"about_ca_system_score_gemma":0.001594656,"threshold_uncertainty_score":0.026792228},"labels":[],"label_agreement":null},{"id":"W3122240186","doi":"","title":"Optimum Turn-Restricted Paths, Nested Compatibility, and Optimum Convex Polygons","year":2016,"lang":"en","type":"preprint","venue":"RePEc: Research Papers in Economics","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Compatibility (geochemistry); Mathematics; Mathematical optimization; Regular polygon; Cutting-plane method; Integer programming; Combinatorics; Convex polygon; Path (computing); Computer science; Geometry; Engineering","score_opus":0.03557851359454558,"score_gpt":0.3186559526578178,"score_spread":0.28307743906327226,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3122240186","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.042965364,0.00032104147,0.94122356,0.00031700468,0.00002813555,0.00007783461,0.0001832654,0.00008167515,0.014802208],"genre_scores_gemma":[0.49589446,0.001069394,0.49186563,0.0001375165,0.00008332387,0.00032265615,0.0005139393,0.0002074543,0.0099056605],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99943537,0.00016764674,0.000021800013,0.00013481385,0.00015508324,0.00008527386],"domain_scores_gemma":[0.9989453,0.0006714869,0.00014340115,0.00010608342,0.0000661976,0.00006757486],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006521965,0.0009968061,0.00081630895,0.00056024815,0.00037475722,0.0013800852,0.0010683811,0.0009948389,0.00729432],"category_scores_gemma":[0.003714052,0.00065285136,0.00078347384,0.0008440643,0.0011854339,0.0034154316,0.0014806244,0.0017816637,0.00052642386],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000116577976,0.00010348359,0.00052646204,0.00020445461,0.00002951083,0.00017689112,0.00015264333,0.5623828,0.0035945873,0.39272338,0.0019517032,0.03803748],"study_design_scores_gemma":[0.00004552644,0.00012406224,0.0003521873,0.000053834363,0.000018676157,0.0001572307,0.00011518426,0.6788864,0.0029414985,0.30979007,0.0074929176,0.000022397031],"about_ca_topic_score_codex":0.0010912394,"about_ca_topic_score_gemma":0.0014195964,"teacher_disagreement_score":0.00729432,"about_ca_system_score_codex":0.0008089091,"about_ca_system_score_gemma":0.0006534097,"threshold_uncertainty_score":0.024401963},"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,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_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","score_opus":0.01662834278793372,"score_gpt":0.23147513826401478,"score_spread":0.21484679547608107,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3122956505","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.029771848,0.0014594705,0.95909464,0.0006918155,0.00010370363,0.0001926568,0.0002937416,0.00028909987,0.008103068],"genre_scores_gemma":[0.42425883,0.0029271203,0.55836874,0.00024124388,0.00021450006,0.0010056834,0.0009442765,0.00029129593,0.0117483055],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983317,0.0008809216,0.00006390107,0.00020477596,0.0002689625,0.00024967227],"domain_scores_gemma":[0.9953241,0.0037776507,0.0003614108,0.00013445223,0.00022805536,0.00017441277],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0036358626,0.00215142,0.0021334095,0.0014159314,0.0010311311,0.0037107929,0.002458785,0.002351137,0.008164994],"category_scores_gemma":[0.007110833,0.001793191,0.001631629,0.00254125,0.0014418318,0.0041324333,0.001740441,0.00377647,0.0007569885],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008504208,0.00006971102,0.00017114592,0.000056420122,0.000030618092,0.000038201655,0.000051136387,0.94261837,0.00019476816,0.04624123,0.0011816634,0.009261613],"study_design_scores_gemma":[0.000023807943,0.000015656185,0.000023540539,0.000009629061,0.000007919782,0.000005808871,0.000011588975,0.9787162,0.00011141826,0.020483783,0.0005871232,0.0000035474286],"about_ca_topic_score_codex":0.01087178,"about_ca_topic_score_gemma":0.007358776,"teacher_disagreement_score":0.01087178,"about_ca_system_score_codex":0.0032,"about_ca_system_score_gemma":0.002538991,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W3123584973","doi":"","title":"Less-than-Truckload Carrier Collaboration Problem: Modeling Framework and Solution Approach","year":2015,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Motor carrier; Business; Computer science; Engineering; Automotive engineering","score_opus":0.02214227710273034,"score_gpt":0.2627927840904783,"score_spread":0.24065050698774795,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3123584973","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019157242,0.00056080514,0.9616025,0.0013041989,0.00013718393,0.00020544195,0.00038204275,0.000093188006,0.016557273],"genre_scores_gemma":[0.7168462,0.002227831,0.23995194,0.0004916215,0.0003934822,0.0008645829,0.00079728145,0.0001463295,0.038280636],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99870455,0.00044621332,0.000043122433,0.00033433732,0.00021510938,0.00025660044],"domain_scores_gemma":[0.9986243,0.0007659426,0.00017269328,0.00006996964,0.00020696965,0.00016001155],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00232434,0.0015234896,0.0023988397,0.0014216914,0.0011581283,0.0027635782,0.0049284277,0.0051884553,0.00858762],"category_scores_gemma":[0.0040102554,0.00087828503,0.0019667896,0.0022598428,0.001270291,0.003443557,0.0032611783,0.0025124098,0.000573857],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000042376592,0.00012397082,0.0004247242,0.00012753968,0.0000508362,0.00020047557,0.000067688416,0.88426274,0.00047337194,0.10405928,0.002881585,0.0072853393],"study_design_scores_gemma":[0.000013211271,0.000027959035,0.00010456684,0.000013828165,0.000015777387,0.00004508886,0.000059930746,0.9707982,0.00011533823,0.027563168,0.0012307367,0.000012238861],"about_ca_topic_score_codex":0.011069947,"about_ca_topic_score_gemma":0.0075641824,"teacher_disagreement_score":0.011069947,"about_ca_system_score_codex":0.0019186364,"about_ca_system_score_gemma":0.00351356,"threshold_uncertainty_score":0.028728485},"labels":[],"label_agreement":null},{"id":"W3125310543","doi":"10.1002/net.10067","title":"The single‐vehicle routing problem with unrestricted backhauls","year":2003,"lang":"en","type":"article","venue":"Networks","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":47,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Solver; Computer science; Backhaul (telecommunications); Vehicle routing problem; Mathematical optimization; Set (abstract data type); Revenue; Routing (electronic design automation); Mathematics; Computer network; Economics","score_opus":0.010873064014029878,"score_gpt":0.21076196657672738,"score_spread":0.1998889025626975,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3125310543","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.39165747,0.0017084616,0.57412404,0.0019912308,0.00027197294,0.0004490794,0.0014465506,0.000420002,0.027931197],"genre_scores_gemma":[0.8746674,0.00080129947,0.10423633,0.00014362685,0.00012156969,0.0003795148,0.00074695447,0.00011801472,0.018785369],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99952686,0.00015210455,0.000022687957,0.00011414843,0.00006347939,0.00012070306],"domain_scores_gemma":[0.99918824,0.0005074983,0.000110549416,0.0000399259,0.000052366267,0.000101410056],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00068270747,0.0012325476,0.0013064792,0.00060128834,0.0007992728,0.001793517,0.0015101483,0.0017413824,0.0061213677],"category_scores_gemma":[0.0015349779,0.0007974196,0.0009993598,0.0011861693,0.0007172207,0.0018115117,0.0009791469,0.0009897612,0.00060639],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016341328,0.000083013634,0.00027216293,0.00015441925,0.000053623502,0.00026071465,0.000055152694,0.95742965,0.0012194496,0.02074067,0.001893228,0.01767451],"study_design_scores_gemma":[0.00006024494,0.0000783631,0.00017924632,0.000017699938,0.000024849945,0.0000962592,0.00007703857,0.9730829,0.00090527325,0.023256678,0.002205532,0.000015952757],"about_ca_topic_score_codex":0.007178061,"about_ca_topic_score_gemma":0.0054401164,"teacher_disagreement_score":0.007178061,"about_ca_system_score_codex":0.0010221457,"about_ca_system_score_gemma":0.0013278744,"threshold_uncertainty_score":0.02047801},"labels":[],"label_agreement":null},{"id":"W3125430804","doi":"10.5267/j.ijiec.2020.11.001","title":"Designing optimal route for the distribution chain of a rural LPG delivery system","year":2021,"lang":"en","type":"article","venue":"International Journal of Industrial Engineering Computations","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Context (archaeology); Vehicle routing problem; Benchmark (surveying); Computer science; Constraint (computer-aided design); Mathematical optimization; Pickup; Homogeneous; Integer programming; Liquefied petroleum gas; Linear programming; Routing (electronic design automation); Simulation; Algorithm; Mathematics; Engineering; Artificial intelligence; Embedded system; Mechanical engineering","score_opus":0.02889744660939638,"score_gpt":0.26814512367465065,"score_spread":0.23924767706525427,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3125430804","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.112033494,0.00037339333,0.87621516,0.00038534583,0.00004081444,0.00027975364,0.00052901043,0.00035638138,0.0097866515],"genre_scores_gemma":[0.77938354,0.00063695834,0.20816854,0.00006048175,0.000021992199,0.00036142697,0.00060404104,0.000096582946,0.0106664235],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997024,0.00009685011,0.000008814363,0.00007665568,0.000043124863,0.00007211113],"domain_scores_gemma":[0.99965274,0.00012663176,0.00008908,0.000022272658,0.00006540238,0.000043817097],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005441256,0.0009547594,0.00091353845,0.00083374657,0.0010684946,0.0015129073,0.0011019034,0.001626306,0.008020389],"category_scores_gemma":[0.0011284767,0.00062653446,0.000605402,0.0012579382,0.00074885355,0.0010857546,0.0009578254,0.00092243723,0.00073408824],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000040550956,0.000018730814,0.0003124462,0.000070710455,0.0000065987533,0.000087446504,0.000040097486,0.9869812,0.0011118886,0.0048884787,0.0004659379,0.0059759216],"study_design_scores_gemma":[0.000009422174,0.000055136894,0.00010254243,0.000007639272,0.000006846454,0.000031396554,0.00008579294,0.9948665,0.0005114824,0.0034910124,0.00082597433,0.0000064120136],"about_ca_topic_score_codex":0.013232956,"about_ca_topic_score_gemma":0.01043039,"teacher_disagreement_score":0.013232956,"about_ca_system_score_codex":0.0018142072,"about_ca_system_score_gemma":0.0022436716,"threshold_uncertainty_score":0.026830912},"labels":[],"label_agreement":null},{"id":"W3125828786","doi":"10.5267/j.ijiec.2020.11.003","title":"A specialized genetic algorithm for the fuel consumption heterogeneous fleet vehicle routing problem with bidimensional packing constraints","year":2021,"lang":"en","type":"article","venue":"International Journal of Industrial Engineering Computations","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Universidad Tecnológica de Pereira","keywords":"Vehicle routing problem; Benchmark (surveying); Fuel efficiency; GRASP; Genetic algorithm; Routing (electronic design automation); Computer science; Set (abstract data type); Mathematical optimization; Reduction (mathematics); Consumption (sociology); Engineering; Automotive engineering; Mathematics; Embedded system","score_opus":0.03647383700614815,"score_gpt":0.28103662421572534,"score_spread":0.2445627872095772,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3125828786","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.029109914,0.0004011039,0.96397746,0.00017686684,0.00008677207,0.00014505416,0.000084509185,0.0003941823,0.0056242053],"genre_scores_gemma":[0.30530417,0.0005846561,0.6877291,0.00021482771,0.000074071744,0.00067729154,0.00047295532,0.0001301353,0.004812858],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996859,0.00009649486,0.000012225922,0.00006738985,0.00007935922,0.0000585793],"domain_scores_gemma":[0.9996654,0.00018637453,0.000040040963,0.000023080283,0.00006082515,0.000024388475],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00056272413,0.0011816196,0.00106683,0.0008995428,0.00039443516,0.0009218854,0.0011094913,0.0018974032,0.002498908],"category_scores_gemma":[0.0017649562,0.00036723542,0.000909741,0.0009973295,0.00047519116,0.0005265378,0.0008254844,0.0009879542,0.00043734504],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002821825,0.000056984823,0.00032889593,0.00005940461,0.000027882264,0.00007960117,0.00004672951,0.9454425,0.0014150501,0.0054554553,0.000990962,0.046068344],"study_design_scores_gemma":[0.000015015965,0.00003776453,0.00007218497,0.000008648077,0.000009380777,0.000025120235,0.000012162676,0.99731916,0.00026852707,0.0013256124,0.0009023626,0.000004090103],"about_ca_topic_score_codex":0.005490886,"about_ca_topic_score_gemma":0.0040086214,"teacher_disagreement_score":0.005490886,"about_ca_system_score_codex":0.00072737597,"about_ca_system_score_gemma":0.0017261428,"threshold_uncertainty_score":0.010917902},"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,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_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","score_opus":0.008366144430866654,"score_gpt":0.20464250949066126,"score_spread":0.19627636505979462,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3125883116","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17392835,0.0010498284,0.81815755,0.0019819427,0.00019290496,0.00014861983,0.0007214821,0.0005375013,0.0032818418],"genre_scores_gemma":[0.9247585,0.0006268418,0.070579074,0.00015012972,0.00014305982,0.00010925482,0.0005282921,0.000084493964,0.003020346],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99888414,0.0005639038,0.000031511594,0.00021962049,0.00013298482,0.00016772245],"domain_scores_gemma":[0.9935603,0.0044167284,0.00083182706,0.00041234563,0.0004873643,0.00029133732],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0039511956,0.0011077889,0.0015135601,0.00069904426,0.00043250923,0.0012911293,0.0018216374,0.0010642072,0.0025237796],"category_scores_gemma":[0.014051877,0.00078170275,0.00058988197,0.0013272915,0.0007813106,0.0021379478,0.001078693,0.0024711392,0.00032905542],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006206209,0.00006331701,0.0012608368,0.000026426798,0.000018412073,0.000015821419,0.000013196115,0.98564446,0.00010041674,0.0035616534,0.0004671031,0.008766322],"study_design_scores_gemma":[0.0000037308155,0.000018210805,0.00018116632,0.0000024899705,0.0000044852954,0.0000025370477,0.000005092189,0.99836737,0.00008294012,0.0012120731,0.00011734977,0.0000025475726],"about_ca_topic_score_codex":0.021839479,"about_ca_topic_score_gemma":0.0140139805,"teacher_disagreement_score":0.021839479,"about_ca_system_score_codex":0.0016947832,"about_ca_system_score_gemma":0.0024082817,"threshold_uncertainty_score":0.043424726},"labels":[],"label_agreement":null},{"id":"W3126046128","doi":"10.1007/s10479-017-2725-7","title":"Negative dependence in matrix arrangement problems","year":2017,"lang":"en","type":"article","venue":"Annals of Operations Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Eidgenössische Technische Hochschule Zürich; Swiss Finance Institute","keywords":"Theory of computation; Mathematical optimization; Maximization; Computer science; Minification; Matrix (chemical analysis); Scheduling (production processes); Mathematics; Algorithm","score_opus":0.2819698716354554,"score_gpt":0.49960532239658373,"score_spread":0.21763545076112834,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3126046128","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09753111,0.003065684,0.84323263,0.0049818326,0.00043563914,0.00007696941,0.00036856494,0.00015921277,0.050148323],"genre_scores_gemma":[0.84075314,0.0041357493,0.0898259,0.00089915906,0.0008858836,0.0003418049,0.0010133866,0.0003667664,0.061778117],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982576,0.0010222594,0.000051364525,0.00019995625,0.00033392647,0.0001348215],"domain_scores_gemma":[0.98253757,0.01398341,0.0010988549,0.00053981197,0.0011308348,0.00070949167],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026361546,0.0012062122,0.0014011861,0.0014517453,0.0010241908,0.0020513146,0.0020000204,0.0017822345,0.007370586],"category_scores_gemma":[0.019487083,0.0012788743,0.00071828364,0.0021596393,0.00311598,0.0048497296,0.002062553,0.004069032,0.00066310336],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009710108,0.00013159732,0.00095474266,0.00021753272,0.000043328495,0.000154373,0.00017617042,0.13213873,0.0006438094,0.8376084,0.009834083,0.018000117],"study_design_scores_gemma":[0.000021208194,0.00002568591,0.00033124659,0.00002598849,0.000013657461,0.00005328933,0.000048172933,0.3283128,0.00017777481,0.66863143,0.0023412695,0.000017448378],"about_ca_topic_score_codex":0.003047977,"about_ca_topic_score_gemma":0.003673146,"teacher_disagreement_score":0.007370586,"about_ca_system_score_codex":0.0012589883,"about_ca_system_score_gemma":0.0011857045,"threshold_uncertainty_score":0.02465707},"labels":[],"label_agreement":null},{"id":"W3127947492","doi":"","title":"Can Machine Learning Help in Solving Cargo Capacity Management Booking Control Problems","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; Polytechnique Montréal","funders":"","keywords":"Revenue management; Computer science; Operations research; Revenue; Dynamic programming; Reinforcement learning; Profit (economics); Vehicle routing problem; Control (management); Mathematical optimization; Capacity management; Focus (optics); Time horizon; Routing (electronic design automation); Economics; Artificial intelligence; Engineering; Mathematics; Microeconomics; Finance; Algorithm","score_opus":0.0425783939065098,"score_gpt":0.17502310269506727,"score_spread":0.13244470878855746,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3127947492","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07226206,0.0022066303,0.90439284,0.0065864087,0.00042279126,0.00010102815,0.00021623906,0.0013964352,0.01241544],"genre_scores_gemma":[0.8659809,0.00093093,0.12602253,0.0009975123,0.0004223314,0.0001715669,0.00046092598,0.00016310689,0.0048502525],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99951315,0.00018761898,0.000022422942,0.00012864488,0.00007115294,0.0000770407],"domain_scores_gemma":[0.99640524,0.0028223319,0.00021728604,0.00015919514,0.00028336822,0.000112563874],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015695086,0.0010409554,0.0011776872,0.0004838184,0.00032885003,0.0011752509,0.0009985152,0.0022840286,0.004307923],"category_scores_gemma":[0.008938175,0.0004128676,0.00048028486,0.00043457787,0.0007546822,0.0014757118,0.00066340074,0.0018828749,0.00066185987],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008096162,0.00018402394,0.0013112619,0.00011078158,0.000044799646,0.000033142907,0.00002402434,0.94106764,0.00035824254,0.00733645,0.0024701734,0.04697862],"study_design_scores_gemma":[0.000007917298,0.000014798217,0.00007829384,0.000009447368,0.0000031944874,0.0000034874054,0.0000058075407,0.9928733,0.00011139648,0.006620452,0.00026995857,0.0000020333437],"about_ca_topic_score_codex":0.0053421846,"about_ca_topic_score_gemma":0.0041091684,"teacher_disagreement_score":0.0053421846,"about_ca_system_score_codex":0.0007171879,"about_ca_system_score_gemma":0.0015316235,"threshold_uncertainty_score":0.014411449},"labels":[],"label_agreement":null},{"id":"W3128085259","doi":"10.1016/j.tcs.2021.02.003","title":"Improved hardness and approximation results for single allocation hub location problems","year":2021,"lang":"en","type":"article","venue":"Theoretical Computer Science","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Combinatorics; Hardness of approximation; Mathematics; Clique; Graph; Node (physics); Metric (unit); Approximation algorithm; Discrete mathematics; Integer (computer science); Computer science; Physics","score_opus":0.01807884128422667,"score_gpt":0.25378342178509666,"score_spread":0.23570458050086998,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3128085259","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07512045,0.006137659,0.8310179,0.013421319,0.0011817729,0.00043021515,0.002607816,0.0015125668,0.0685703],"genre_scores_gemma":[0.68857235,0.0054858956,0.25736317,0.0030804768,0.002854722,0.0011559575,0.003817169,0.0016660907,0.036004107],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99310416,0.0023146647,0.00026034427,0.001420966,0.0016356427,0.0012642046],"domain_scores_gemma":[0.96213293,0.030380586,0.0012741996,0.0038021763,0.0013720744,0.0010380633],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0055911206,0.0041450644,0.0050209686,0.0032599058,0.002883715,0.00660354,0.010120299,0.004399029,0.019892922],"category_scores_gemma":[0.035333343,0.0021398256,0.0052936804,0.0058449483,0.0049400236,0.019406449,0.0064415196,0.01540871,0.002570021],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0018844032,0.0011482077,0.003336872,0.0018081992,0.00044936725,0.00042393172,0.0009395958,0.43192303,0.0028598476,0.4074554,0.058260035,0.08951109],"study_design_scores_gemma":[0.00018231997,0.000103389015,0.0008083721,0.000103817234,0.00015368438,0.0001357295,0.00020650103,0.49381685,0.0009262805,0.4978594,0.005651574,0.000052106185],"about_ca_topic_score_codex":0.006326814,"about_ca_topic_score_gemma":0.006772066,"teacher_disagreement_score":0.019892922,"about_ca_system_score_codex":0.0057161856,"about_ca_system_score_gemma":0.0043606055,"threshold_uncertainty_score":0.06654847},"labels":[],"label_agreement":null},{"id":"W3128538413","doi":"10.1155/2021/8886572","title":"Vehicle Routing Problem with Transshipment: Mathematical Model and Algorithm","year":2021,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Transshipment (information security); Heuristics; Economic shortage; Computer science; Routing (electronic design automation); Heuristic; Vehicle routing problem; Operations research; Integer programming; Mathematical optimization; Algorithm; Mathematics; Artificial intelligence; Computer network","score_opus":0.009899326220585324,"score_gpt":0.242173556055023,"score_spread":0.23227422983443768,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3128538413","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00979032,0.0031612024,0.9682889,0.0011124742,0.00022900745,0.00017578245,0.00043503215,0.00026124154,0.016546119],"genre_scores_gemma":[0.37976798,0.011613844,0.5658568,0.00058471016,0.00057111256,0.0013030571,0.0020929673,0.00023632696,0.03797318],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992174,0.00027750924,0.000034837798,0.00020036777,0.00013830829,0.00013159488],"domain_scores_gemma":[0.9994742,0.00029280435,0.00008858362,0.000031329324,0.000076279866,0.000036842026],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008803479,0.0016116189,0.0016418345,0.00092795375,0.0007657705,0.0026877092,0.0026821054,0.0022284035,0.0045892824],"category_scores_gemma":[0.001398623,0.00079647714,0.0017710752,0.0023443275,0.00093922904,0.0027668062,0.0015736708,0.0028183614,0.0009623505],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000029249146,0.00006063541,0.00026072364,0.00016621797,0.000037873448,0.00017996081,0.000053451447,0.9304372,0.00036959388,0.04854753,0.0036122939,0.016245257],"study_design_scores_gemma":[0.000017025352,0.000029222641,0.00007133856,0.000020112737,0.0000117799755,0.00010517477,0.00003918104,0.9731328,0.000109763394,0.021664172,0.0047863284,0.000013161705],"about_ca_topic_score_codex":0.00946649,"about_ca_topic_score_gemma":0.008116966,"teacher_disagreement_score":0.00946649,"about_ca_system_score_codex":0.0019020113,"about_ca_system_score_gemma":0.001702135,"threshold_uncertainty_score":0.01882279},"labels":[],"label_agreement":null},{"id":"W3128705935","doi":"10.1111/itor.13111","title":"GRASP‐ILS and set cover hybrid heuristic for the synchronized team orienteering problem with time windows","year":2022,"lang":"en","type":"preprint","venue":"International Transactions in Operational Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"Agence Nationale de la Recherche","keywords":"Orienteering; GRASP; Heuristic; Cover (algebra); Set (abstract data type); Computer science; Set cover problem; Operations research; Artificial intelligence; Mathematical optimization; Engineering; Mathematics; Software engineering; Programming language","score_opus":0.04690838405420756,"score_gpt":0.36247496320102846,"score_spread":0.3155665791468209,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3128705935","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21525283,0.0027230363,0.76601815,0.000583708,0.00020965956,0.0005962407,0.0005302463,0.0010501129,0.013036005],"genre_scores_gemma":[0.7989039,0.00061159715,0.19603974,0.00015307321,0.00006623334,0.00054849515,0.0005460378,0.0001411608,0.0029898367],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99932873,0.0002613537,0.000028141667,0.00010377745,0.00012516625,0.0001528676],"domain_scores_gemma":[0.99840254,0.0010880454,0.00017274354,0.000061460756,0.00011221764,0.0001630524],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011803292,0.0020865719,0.002104359,0.0013156114,0.00047214213,0.0010506685,0.001958009,0.0017669862,0.004358238],"category_scores_gemma":[0.0022834223,0.0006144904,0.0014032231,0.0012279599,0.00054603076,0.0010195854,0.0011035169,0.001270982,0.00030928725],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000066873195,0.00009189385,0.00026552335,0.000094317664,0.000032924665,0.00007319273,0.000024076397,0.98619616,0.00033687524,0.0016709997,0.0006181226,0.0105291335],"study_design_scores_gemma":[0.000022201386,0.00006467071,0.00008051257,0.000013351972,0.000012062294,0.000012933317,0.000020353425,0.99842775,0.00015487413,0.0009212242,0.00026552263,0.0000046151013],"about_ca_topic_score_codex":0.0071188062,"about_ca_topic_score_gemma":0.0046840673,"teacher_disagreement_score":0.0071188062,"about_ca_system_score_codex":0.0011102447,"about_ca_system_score_gemma":0.0018470075,"threshold_uncertainty_score":0.014579713},"labels":[],"label_agreement":null},{"id":"W3129185417","doi":"10.1016/j.trpro.2021.01.007","title":"The Synchronized Location-Transshipment Problem","year":2021,"lang":"en","type":"article","venue":"Transportation research procedia","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Transshipment (information security); Computer science; Synchronization (alternating current); Integer programming; Operations research; Linear programming; Mathematical optimization; Engineering; Mathematics; Computer network; Computer security; Channel (broadcasting); Algorithm","score_opus":0.04130024121777702,"score_gpt":0.34486838691013294,"score_spread":0.3035681456923559,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3129185417","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.085146934,0.00046486672,0.8986524,0.0012053614,0.00015855991,0.00031620672,0.0010920953,0.00021574348,0.0127478475],"genre_scores_gemma":[0.8063895,0.0009634513,0.17525768,0.00032111915,0.00013871402,0.000642944,0.001682248,0.00017733098,0.0144269755],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980963,0.000910514,0.00008486354,0.0005567026,0.00014536096,0.00020625546],"domain_scores_gemma":[0.99819237,0.0011061558,0.00026960726,0.0001212896,0.0001528151,0.00015776782],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017348776,0.001774165,0.0014082172,0.00062361144,0.0007211964,0.0015352996,0.0018193836,0.0022362054,0.010537276],"category_scores_gemma":[0.003770119,0.0008073516,0.0011724667,0.0014621643,0.0010180285,0.003555356,0.0021616397,0.0017002828,0.0007445584],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003106437,0.0001364685,0.0010481705,0.00025998108,0.00013941221,0.0006414448,0.00012965286,0.87846076,0.0017662274,0.08910206,0.004484218,0.023520947],"study_design_scores_gemma":[0.00014532967,0.00024338208,0.00051241124,0.00004273169,0.00006476996,0.0002887,0.00024250492,0.92348725,0.0014138875,0.068231136,0.00529088,0.00003711146],"about_ca_topic_score_codex":0.0031986132,"about_ca_topic_score_gemma":0.0018925609,"teacher_disagreement_score":0.010537276,"about_ca_system_score_codex":0.0011628352,"about_ca_system_score_gemma":0.0014712876,"threshold_uncertainty_score":0.035250664},"labels":[],"label_agreement":null},{"id":"W3129729546","doi":"10.1287/trsc.2020.1013","title":"The Robust Vehicle Routing Problem with Time Window Assignments","year":2021,"lang":"en","type":"article","venue":"Transportation Science","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":38,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Group for Research in Decision Analysis; HEC Montréal","funders":"Nederlandse Organisatie voor Wetenschappelijk Onderzoek; Compute Canada","keywords":"Subgradient method; Vehicle routing problem; Window (computing); Mathematical optimization; Computer science; Travel time; Routing (electronic design automation); Service (business); Operations research; Mathematics; Engineering; Transport engineering; Economics; Computer network","score_opus":0.013936633328072743,"score_gpt":0.23757625810749416,"score_spread":0.22363962477942143,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3129729546","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01827887,0.00033082237,0.9777945,0.00024808518,0.000041996213,0.00012052404,0.00035090322,0.00031273815,0.0025215144],"genre_scores_gemma":[0.5488907,0.0009283028,0.44090536,0.00014780102,0.0001256996,0.00057013874,0.0011685597,0.00033188128,0.006931548],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99816704,0.0006418,0.0000998881,0.0005489452,0.00028430193,0.00025807132],"domain_scores_gemma":[0.9983144,0.0009549234,0.0003323068,0.00012677714,0.00017090507,0.00010074153],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022445067,0.00188472,0.0016727861,0.000803291,0.00048397662,0.0015260195,0.0017905324,0.0018198886,0.004157912],"category_scores_gemma":[0.0050680656,0.0009821076,0.0013122489,0.0012428002,0.00074406544,0.0025656216,0.0014170955,0.0018923429,0.00061351014],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009551155,0.00003412478,0.00016016849,0.00009024269,0.000032926106,0.00006773041,0.000033464683,0.968151,0.0011306504,0.011993499,0.00089703716,0.017313672],"study_design_scores_gemma":[0.000022813436,0.00005723801,0.000110765825,0.000009283458,0.000013831139,0.000037679634,0.000018340643,0.98660755,0.00068229844,0.011456694,0.00097332965,0.0000102803015],"about_ca_topic_score_codex":0.0046949508,"about_ca_topic_score_gemma":0.0022906174,"teacher_disagreement_score":0.0046949508,"about_ca_system_score_codex":0.0012699522,"about_ca_system_score_gemma":0.0017858513,"threshold_uncertainty_score":0.013909578},"labels":[],"label_agreement":null},{"id":"W3132725877","doi":"10.1007/978-3-030-60104-1_3","title":"A Fast Electric Vehicle Planner Using Clustering","year":2021,"lang":"en","type":"book-chapter","venue":"Studies in classification, data analysis, and knowledge organization","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Cluster analysis; Computation; Speedup; Computer science; Shortest path problem; Planner; Range (aeronautics); Electric vehicle; Graph; Path (computing); Mathematical optimization; Real-time computing; Algorithm; Engineering; Mathematics; Artificial intelligence; Parallel computing; Theoretical computer science; Computer network","score_opus":0.11164230897041949,"score_gpt":0.3458162574810454,"score_spread":0.2341739485106259,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3132725877","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005915695,0.0001153718,0.98472726,0.00007432911,0.000038808248,0.00007448452,0.00012221524,0.0022577557,0.0066740303],"genre_scores_gemma":[0.16360883,0.00018453544,0.82478625,0.00006771318,0.000027741771,0.0001698834,0.00057686004,0.0005145424,0.010063627],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99978095,0.000027518654,0.000010286834,0.00007398833,0.000073443916,0.000033922202],"domain_scores_gemma":[0.99974257,0.000091822214,0.000017406948,0.000050637813,0.00007972511,0.000017846598],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038883134,0.0010663845,0.0011982858,0.00094304304,0.0009069762,0.0009915314,0.0017047916,0.00089313206,0.007101955],"category_scores_gemma":[0.0008559378,0.0007184949,0.00074753765,0.0016697994,0.00045878932,0.001263081,0.0010804583,0.0009121056,0.0013208241],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007216022,0.000044484274,0.00021490757,0.000049735474,0.00002969566,0.00004357169,0.00004819769,0.766833,0.0018632173,0.009511465,0.005915882,0.21537371],"study_design_scores_gemma":[0.000007661538,0.000012718749,0.00006318346,0.0000051609995,0.0000052779496,0.000013038743,0.000018180946,0.99193794,0.0006942983,0.005278104,0.0019584976,0.0000058269065],"about_ca_topic_score_codex":0.02017766,"about_ca_topic_score_gemma":0.021107944,"teacher_disagreement_score":0.02017766,"about_ca_system_score_codex":0.0011494686,"about_ca_system_score_gemma":0.0015485907,"threshold_uncertainty_score":0.040120423},"labels":[],"label_agreement":null},{"id":"W3132873744","doi":"10.1287/opre.2020.2037","title":"Branch-Price-and-Cut Algorithms for the Vehicle Routing Problem with Stochastic and Correlated Travel Times","year":2021,"lang":"en","type":"article","venue":"Operations Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure; Group for Research in Decision Analysis; Wilfrid Laurier University; Polytechnique Montréal","funders":"","keywords":"Vehicle routing problem; Computer science; Branch and cut; Routing (electronic design automation); Mathematical optimization; Algorithm; Mathematics; Integer programming; Computer network","score_opus":0.049962690972596556,"score_gpt":0.3392341381493094,"score_spread":0.2892714471767129,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3132873744","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01072089,0.00049907615,0.9857883,0.0004420575,0.000083270956,0.00011192046,0.00013312005,0.00027154494,0.0019497556],"genre_scores_gemma":[0.20030941,0.0009338919,0.790784,0.00020029864,0.00021224622,0.00060764956,0.00071487814,0.00041391456,0.0058238422],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986197,0.0006838398,0.00006540101,0.00019203604,0.0002687193,0.0001704083],"domain_scores_gemma":[0.9901008,0.008440965,0.00038373165,0.00026342698,0.0005389836,0.00027202605],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0048170984,0.001840934,0.0026234328,0.0018310229,0.0012895653,0.0022390892,0.0028614276,0.0034278298,0.0057198834],"category_scores_gemma":[0.012167762,0.0018626464,0.0013814857,0.0032313708,0.0011660934,0.0036888148,0.0016008202,0.004595754,0.0007391889],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007038885,0.00010883735,0.00032511863,0.000057068446,0.000039342824,0.000028680906,0.000032558204,0.9497936,0.00014615417,0.014519282,0.001859162,0.033019945],"study_design_scores_gemma":[0.000012430287,0.000009806681,0.000031388474,0.000004537468,0.0000049969785,0.0000032399232,0.0000037345883,0.9903106,0.00004081713,0.009427901,0.0001484967,0.0000021119129],"about_ca_topic_score_codex":0.014338593,"about_ca_topic_score_gemma":0.015421201,"teacher_disagreement_score":0.014338593,"about_ca_system_score_codex":0.0024883896,"about_ca_system_score_gemma":0.004291049,"threshold_uncertainty_score":0.028510273},"labels":[],"label_agreement":null},{"id":"W3134425920","doi":"10.1287/ijoc.2020.1027","title":"Multirow Intersection Cuts Based on the Infinity Norm","year":2021,"lang":"en","type":"article","venue":"INFORMS journal on computing","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Intersection (aeronautics); Mathematics; Integer programming; Linear programming relaxation; Linear programming; Norm (philosophy); Cutting-plane method; Mathematical optimization; Relaxation (psychology); Row; Algorithm; Computer science","score_opus":0.02295085128851744,"score_gpt":0.2621619497504528,"score_spread":0.2392110984619354,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3134425920","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008938587,0.00014294256,0.98604393,0.000070771835,0.000027498472,0.00008793395,0.00011116137,0.00028739288,0.004289803],"genre_scores_gemma":[0.13487825,0.00022542439,0.8609465,0.00010775262,0.000037791735,0.00044214708,0.0007217282,0.00040199235,0.0022384766],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9967077,0.0011428185,0.00018914256,0.00047227368,0.0012013826,0.00028665413],"domain_scores_gemma":[0.9941759,0.0036216632,0.00047311786,0.000710083,0.0008517429,0.00016745075],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0033909709,0.0013185348,0.0012787976,0.0018482149,0.000782505,0.0024551582,0.0020435872,0.0011878896,0.008276],"category_scores_gemma":[0.009493457,0.00062623265,0.0015369605,0.0021079206,0.0015522172,0.0036824953,0.0028281894,0.0036680049,0.0014224084],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004309052,0.00019838386,0.001238533,0.00038541775,0.00007587419,0.00018435845,0.00027518478,0.5220007,0.00916067,0.21031053,0.006143952,0.24959552],"study_design_scores_gemma":[0.00003534765,0.00018800361,0.00021322057,0.00010131063,0.000021725156,0.00011982591,0.00011111389,0.90139705,0.009075608,0.08197341,0.0067290612,0.000034354933],"about_ca_topic_score_codex":0.0009866849,"about_ca_topic_score_gemma":0.0013338501,"teacher_disagreement_score":0.008276,"about_ca_system_score_codex":0.0010331647,"about_ca_system_score_gemma":0.0010424799,"threshold_uncertainty_score":0.02768594},"labels":[],"label_agreement":null},{"id":"W3136996413","doi":"10.1007/s10107-022-01844-1","title":"Disjunctive cuts in Mixed-Integer Conic Optimization","year":2022,"lang":"en","type":"article","venue":"Archivio istituzionale della ricerca (Alma Mater Studiorum Università di Bologna)","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"Mitacs","keywords":"Conic section; Normalization (sociology); Conic optimization; Mathematics; Mathematical optimization; Solver; Integer programming; Context (archaeology); Duality (order theory); Discrete mathematics; Geometry","score_opus":0.012955250657911692,"score_gpt":0.2247089888048701,"score_spread":0.21175373814695841,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3136996413","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010799509,0.0005044917,0.97741735,0.000316099,0.00005099507,0.00010097853,0.000110637426,0.00016092186,0.010538995],"genre_scores_gemma":[0.23533073,0.00088278577,0.75407684,0.00041277715,0.00012210259,0.000412762,0.0005154943,0.00024561016,0.008000929],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9983541,0.0006400116,0.00006950623,0.0002672182,0.000489911,0.00017941858],"domain_scores_gemma":[0.9974185,0.0018947585,0.00017228124,0.00019000159,0.0002468907,0.00007759994],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003173185,0.0012479115,0.000786515,0.000933969,0.00054319546,0.0020820503,0.0011698384,0.0009493975,0.0046025063],"category_scores_gemma":[0.0065372176,0.0007206591,0.00081177196,0.0017017404,0.0019159138,0.0020774137,0.001746693,0.00351537,0.00072008104],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012897476,0.0001224885,0.00065811543,0.00034333573,0.000045641846,0.00014984065,0.00018745496,0.47258362,0.0018663476,0.43032324,0.0031580487,0.09043297],"study_design_scores_gemma":[0.000037834987,0.000057721623,0.00013357971,0.000120288314,0.000018794448,0.00008541538,0.00008643195,0.78949785,0.0024671734,0.19770838,0.009773287,0.000013229184],"about_ca_topic_score_codex":0.0017733384,"about_ca_topic_score_gemma":0.0022977118,"teacher_disagreement_score":0.0046025063,"about_ca_system_score_codex":0.0014300859,"about_ca_system_score_gemma":0.0014309175,"threshold_uncertainty_score":0.016781628},"labels":[],"label_agreement":null},{"id":"W3140856702","doi":"10.1287/trsc.2021.1084","title":"An Improved Integral Column Generation Algorithm Using Machine Learning for Aircrew Pairing","year":2021,"lang":"en","type":"article","venue":"Transportation Science","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Group for Research in Decision Analysis","funders":"","keywords":"Pairing; Crew; Aircrew; Column generation; Computer science; Algorithm; Scheduling (production processes); Crew scheduling; Process (computing); Set (abstract data type); Engineering; Mathematics; Mathematical optimization; Aeronautics; Physics; Programming language","score_opus":0.03862218519718917,"score_gpt":0.3131411882160839,"score_spread":0.2745190030188947,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3140856702","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.029009284,0.00030947942,0.96461797,0.00022500791,0.00009603615,0.00012225968,0.00013299963,0.0017071993,0.0037796795],"genre_scores_gemma":[0.30567113,0.00017080778,0.68803614,0.00031046316,0.00007521489,0.00029346865,0.00069541216,0.00032889287,0.004418446],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995586,0.000090111076,0.000020938958,0.00011297095,0.0001171937,0.00010000256],"domain_scores_gemma":[0.9990539,0.00053697155,0.00009313785,0.00010060744,0.00015715492,0.000058184876],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00076420204,0.00096794084,0.0012756797,0.0008147427,0.00059755886,0.0007069835,0.0016172857,0.0010207653,0.005515467],"category_scores_gemma":[0.0020827923,0.0005442052,0.0006920017,0.0010778229,0.000599385,0.001259297,0.0009856429,0.0014788511,0.0009466615],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000106831205,0.00012321176,0.00068505935,0.00006870775,0.000024549885,0.00007837457,0.000048792324,0.7770655,0.0019724339,0.007481476,0.005028368,0.20731667],"study_design_scores_gemma":[0.000012381831,0.000015278616,0.000037013117,0.0000027757535,0.0000029857692,0.000010337182,0.0000036448648,0.9976718,0.0003500681,0.0015112805,0.0003796302,0.0000027211493],"about_ca_topic_score_codex":0.007826508,"about_ca_topic_score_gemma":0.008290673,"teacher_disagreement_score":0.007826508,"about_ca_system_score_codex":0.0009935333,"about_ca_system_score_gemma":0.0021560828,"threshold_uncertainty_score":0.018451035},"labels":[],"label_agreement":null},{"id":"W3143580828","doi":"10.1155/2021/6665539","title":"Modeling and Solution of Vehicle Routing Problem with Grey Time Windows and Multiobjective Constraints","year":2021,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Department of Education, Fujian Province","keywords":"Vehicle routing problem; Customer satisfaction; Routing (electronic design automation); Computer science; Mathematical optimization; Order (exchange); Operations research; Simulation; Mathematics; Economics","score_opus":0.008225670799943506,"score_gpt":0.23393888837125842,"score_spread":0.2257132175713149,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3143580828","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06498546,0.00061226974,0.9288962,0.00032284833,0.00006188855,0.00006644734,0.000123763,0.00006280707,0.0048682694],"genre_scores_gemma":[0.9319951,0.00067002664,0.0627051,0.00005757339,0.00003415896,0.00017246988,0.00012860217,0.000039699447,0.0041973884],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994629,0.00016328007,0.000020230627,0.00010861591,0.00012802979,0.000116829855],"domain_scores_gemma":[0.99944335,0.00032598482,0.00009838327,0.000017291626,0.000081131584,0.00003375201],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008721211,0.0010222392,0.0009298592,0.00056778715,0.0003866481,0.0009587251,0.0010148614,0.001119253,0.0016980342],"category_scores_gemma":[0.0013755405,0.0004986235,0.0011966258,0.00066895445,0.000765118,0.0011186827,0.00073152006,0.001106566,0.00008994715],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009672461,0.000006566633,0.00017125416,0.000027996546,0.000013194976,0.00003817216,0.000013487368,0.99340963,0.00035950198,0.004656687,0.00008579747,0.0012081476],"study_design_scores_gemma":[0.000003250276,0.000009550177,0.00007997166,0.0000022323254,0.000004417547,0.00000564743,0.000005867757,0.9978829,0.00010824541,0.0018061008,0.00008901129,0.0000027369101],"about_ca_topic_score_codex":0.014126571,"about_ca_topic_score_gemma":0.00600184,"teacher_disagreement_score":0.014126571,"about_ca_system_score_codex":0.001330356,"about_ca_system_score_gemma":0.0014602761,"threshold_uncertainty_score":0.028088689},"labels":[],"label_agreement":null},{"id":"W3147792716","doi":"10.5267/j.uscm.2021.2.007","title":"Multi-product multi-vehicle inventory routing problem with vehicle compatibility and site dependency: A case study in the restaurant chain industry","year":2021,"lang":"en","type":"article","venue":"Uncertain Supply Chain Management","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Office of Naval Research; Japan Society for the Promotion of Science","keywords":"Vehicle routing problem; Heuristics; Computer science; Integer programming; Compatibility (geochemistry); Mathematical optimization; Dependency (UML); Supply chain; Operations research; Column generation; Routing (electronic design automation); Mathematics; Engineering; Algorithm; Artificial intelligence","score_opus":0.036795674175107734,"score_gpt":0.28659657764442037,"score_spread":0.24980090346931264,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3147792716","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8908241,0.000502603,0.10069364,0.00058900955,0.00003771036,0.00017131561,0.00020718992,0.00007236741,0.0069021042],"genre_scores_gemma":[0.9669862,0.00026116442,0.030795515,0.000030303212,0.0000145944605,0.000060056187,0.00009119764,0.00001208733,0.0017488876],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992778,0.00034146084,0.00002678865,0.000107177395,0.00011526991,0.00013154361],"domain_scores_gemma":[0.9986051,0.0009484172,0.00014916334,0.000068895875,0.00009928664,0.00012922032],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013028386,0.0008772062,0.0007763111,0.0007847687,0.0012736771,0.00121317,0.0012301591,0.002935404,0.001688883],"category_scores_gemma":[0.0015168908,0.0005716403,0.0012882052,0.0014586152,0.00082218344,0.0015917586,0.00071557536,0.0009788497,0.00014008365],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016738409,0.00040808727,0.00565003,0.00017473263,0.00006610041,0.0035419553,0.00018587851,0.9682102,0.002631565,0.00836707,0.0008141305,0.00978289],"study_design_scores_gemma":[0.00005309041,0.00024394305,0.001565679,0.000011649477,0.000050172268,0.00041802236,0.0005206725,0.99191076,0.0020363799,0.0020412279,0.0011215524,0.000026948823],"about_ca_topic_score_codex":0.012222204,"about_ca_topic_score_gemma":0.011702067,"teacher_disagreement_score":0.012222204,"about_ca_system_score_codex":0.0015274552,"about_ca_system_score_gemma":0.0009784587,"threshold_uncertainty_score":0.024302125},"labels":[],"label_agreement":null},{"id":"W3149352661","doi":"10.1080/00207543.2021.1902013","title":"Integrated production-inventory-routing problem for multi-perishable products under uncertainty by meta-heuristic algorithms","year":2021,"lang":"en","type":"article","venue":"International Journal of Production Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":76,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"","keywords":"Mathematical optimization; Computer science; Production (economics); Profit (economics); Vehicle routing problem; Integer programming; Fuzzy logic; Differential evolution; Routing (electronic design automation); Operations research; Mathematics; Economics; Artificial intelligence","score_opus":0.1940955073617104,"score_gpt":0.418685039166351,"score_spread":0.2245895318046406,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3149352661","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03819401,0.0011866186,0.9546159,0.00032008465,0.000056119283,0.0001463365,0.00014156326,0.0001440069,0.0051953997],"genre_scores_gemma":[0.6977683,0.0009872683,0.2961012,0.00012207989,0.00007891389,0.0005630357,0.00033122592,0.00007725703,0.003970661],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99928683,0.00032801612,0.00003147089,0.0001256649,0.00011156133,0.00011645357],"domain_scores_gemma":[0.9987741,0.00090077287,0.00014626438,0.00003787586,0.000086060776,0.00005496818],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017381725,0.001670499,0.0018583747,0.0014130245,0.00057757134,0.00197845,0.0016134015,0.0020701224,0.0018065383],"category_scores_gemma":[0.0021765376,0.0010490932,0.0017516796,0.0017361086,0.00069581746,0.0012562787,0.0011348433,0.0013665103,0.00018492098],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001697616,0.000015957714,0.00014983403,0.000027463784,0.000023538516,0.00003388735,0.000011724878,0.9932995,0.000090744,0.0021899547,0.00010842887,0.004031911],"study_design_scores_gemma":[0.000007052176,0.000015435928,0.00003295586,0.00000577444,0.000008334176,0.000007534968,0.000008629463,0.99787533,0.000055830547,0.0018052744,0.00017526538,0.000002556356],"about_ca_topic_score_codex":0.0071380804,"about_ca_topic_score_gemma":0.0056404695,"teacher_disagreement_score":0.0071380804,"about_ca_system_score_codex":0.0016339194,"about_ca_system_score_gemma":0.0021071767,"threshold_uncertainty_score":0.014193058},"labels":[],"label_agreement":null},{"id":"W3149471625","doi":"10.1016/j.ejor.2021.03.031","title":"An inverse optimization approach for a capacitated vehicle routing problem","year":2021,"lang":"en","type":"article","venue":"European Journal of Operational Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":30,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"National Natural Science Foundation of China","keywords":"Vehicle routing problem; Computer science; Mathematical optimization; Routing (electronic design automation); Operations research; Multiplicative function; Process (computing); Inverse; Mathematics","score_opus":0.0969225648087,"score_gpt":0.35160653743997106,"score_spread":0.25468397263127107,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3149471625","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002823699,0.00015740475,0.99087054,0.00020356326,0.00007024688,0.000020725685,0.000023998347,0.00003381719,0.005796012],"genre_scores_gemma":[0.28584182,0.0011513821,0.68894774,0.00035681098,0.00030837176,0.00036151533,0.00029255124,0.00028553675,0.02245424],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996087,0.00013586145,0.000013529056,0.00006564764,0.00014153775,0.00003463256],"domain_scores_gemma":[0.99937195,0.00040237058,0.000044963366,0.000034451376,0.00011649248,0.000029815803],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00082702364,0.0012062076,0.0011621509,0.0010864773,0.0005539997,0.0015390033,0.0014255084,0.0018517995,0.004115601],"category_scores_gemma":[0.002620292,0.0008635593,0.001674373,0.0009348481,0.0012238288,0.0013238443,0.001941026,0.0025757991,0.0005149522],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000020000745,0.000043123317,0.00013372001,0.000102193815,0.000030133104,0.000076224285,0.00007202922,0.9024287,0.001260453,0.07761613,0.001171997,0.01704533],"study_design_scores_gemma":[0.0000039865563,0.000013060051,0.000024270561,0.000009231899,0.0000063617317,0.000019998013,0.000012552161,0.98069394,0.00013255366,0.017941644,0.0011361284,0.000006288335],"about_ca_topic_score_codex":0.0055895196,"about_ca_topic_score_gemma":0.004034591,"teacher_disagreement_score":0.0055895196,"about_ca_system_score_codex":0.0008448307,"about_ca_system_score_gemma":0.0015509827,"threshold_uncertainty_score":0.013768077},"labels":[],"label_agreement":null},{"id":"W3151788990","doi":"10.1109/wsc.2011.6147959","title":"Modeling and simulation of military tactical logistics distribution","year":2011,"lang":"en","type":"article","venue":"","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Defence Research and Development Canada","funders":"","keywords":"Truck; Computer science; Military logistics; Operations research; Integrated logistics support; Humanitarian Logistics; Systems engineering; Transport engineering; Engineering; Operations management; Automotive engineering","score_opus":0.05568092365549123,"score_gpt":0.28831719802683686,"score_spread":0.23263627437134562,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3151788990","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.28043312,0.00092503306,0.658418,0.0014143385,0.00017029594,0.00019627431,0.0010642639,0.0009390873,0.056439627],"genre_scores_gemma":[0.9306595,0.0007565424,0.054995675,0.000085379375,0.000033495264,0.0002798993,0.00057210604,0.000105271814,0.0125120785],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996886,0.000122068086,0.000015524514,0.00003654423,0.00007020886,0.000067041256],"domain_scores_gemma":[0.99939585,0.00037788626,0.000058100806,0.00003723615,0.00008429763,0.000046558194],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005610398,0.00063635036,0.0007490823,0.0005778144,0.000559195,0.0013625862,0.001077714,0.0016905738,0.00320229],"category_scores_gemma":[0.001525909,0.00050902355,0.0008570607,0.0008320589,0.00070873875,0.0010022073,0.00072243524,0.0008820166,0.0004028113],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008074142,0.000008042939,0.000120748904,0.0000043813607,0.0000037199168,0.000012256021,0.0000059492986,0.99713635,0.00010099817,0.0019239228,0.00006926432,0.0006063478],"study_design_scores_gemma":[0.00000396134,0.000004446639,0.00003908867,0.0000012350321,0.000001381732,0.0000026888922,0.0000045920196,0.99901676,0.00007104392,0.0006062562,0.00024683558,0.000001676163],"about_ca_topic_score_codex":0.026398256,"about_ca_topic_score_gemma":0.0122471405,"teacher_disagreement_score":0.026398256,"about_ca_system_score_codex":0.0012658053,"about_ca_system_score_gemma":0.0015726022,"threshold_uncertainty_score":0.05248916},"labels":[],"label_agreement":null},{"id":"W3152731649","doi":"10.3390/app11083346","title":"Randomized and Generated Instances Fitting with the Home Health Care Problem Subjected to Certain Constraints","year":2021,"lang":"en","type":"article","venue":"Applied Sciences","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Chicoutimi","funders":"","keywords":"Benchmark (surveying); Computer science; Generator (circuit theory); Workload; Set (abstract data type); Confidentiality; Health care; Synchronization (alternating current); Risk analysis (engineering); Operations research; Power (physics); Computer security; Medicine; Engineering","score_opus":0.01466948788174533,"score_gpt":0.26013073658135333,"score_spread":0.245461248699608,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3152731649","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7892973,0.0015815326,0.14585365,0.0025634991,0.0009942477,0.002571462,0.022777963,0.0021394829,0.03222077],"genre_scores_gemma":[0.7139397,0.00057478127,0.25739563,0.0004650525,0.00009749765,0.0025991725,0.01874198,0.00030838882,0.005877708],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99864596,0.0005910312,0.000076086944,0.00026665648,0.00020936676,0.00021093279],"domain_scores_gemma":[0.99249506,0.005524174,0.00032024868,0.00053318514,0.0009369357,0.00019041669],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00208022,0.001176371,0.00062642636,0.0010772697,0.0004928847,0.0008925012,0.00177742,0.0014465001,0.0045713596],"category_scores_gemma":[0.010242272,0.0002950882,0.0010440542,0.0014442028,0.0006799401,0.00064796244,0.00065412035,0.0015996188,0.0003920345],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007181694,0.0012611541,0.005536571,0.0010804941,0.00016041161,0.0005431563,0.00017087489,0.8985261,0.002304299,0.016487308,0.027025122,0.046186417],"study_design_scores_gemma":[0.00038744585,0.0003517495,0.002386664,0.00012298574,0.0000592968,0.000115070165,0.000341327,0.9717116,0.002973974,0.01182372,0.009699098,0.000027137568],"about_ca_topic_score_codex":0.0052607474,"about_ca_topic_score_gemma":0.008423927,"teacher_disagreement_score":0.0052607474,"about_ca_system_score_codex":0.0015815535,"about_ca_system_score_gemma":0.0021858301,"threshold_uncertainty_score":0.015292704},"labels":[],"label_agreement":null},{"id":"W3155585954","doi":"10.5267/j.ijiec.2021.2.002","title":"A biobjective capacitated vehicle routing problem using metaheuristic ILS and decomposition","year":2021,"lang":"en","type":"article","venue":"International Journal of Industrial Engineering Computations","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Mathematical optimization; Vehicle routing problem; Sorting; Benchmark (surveying); Metaheuristic; Multi-objective optimization; Decomposition; Tabu search; Set (abstract data type); Computer science; Iterated local search; Routing (electronic design automation); Genetic algorithm; Mathematics; Algorithm","score_opus":0.034552938549428196,"score_gpt":0.2983848979352959,"score_spread":0.2638319593858677,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3155585954","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.028390672,0.00037628578,0.96403706,0.00014783427,0.00003967977,0.00006949845,0.00009696651,0.00015235142,0.006689621],"genre_scores_gemma":[0.4469676,0.0006492216,0.5470221,0.0001036705,0.000032719294,0.00036978623,0.00039579213,0.00008239315,0.0043767355],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996119,0.0001573159,0.000017036911,0.000055893106,0.00011238336,0.000045507124],"domain_scores_gemma":[0.9996846,0.00018597151,0.00004649549,0.00002209013,0.00004254022,0.000018352175],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006233782,0.001125104,0.0008734634,0.00088662567,0.00046121728,0.0011980343,0.00077512837,0.0011869627,0.0012728592],"category_scores_gemma":[0.0009934268,0.00040241424,0.001005154,0.0012432957,0.00051432184,0.0007488532,0.0006779808,0.0007840994,0.00016965825],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001594692,0.000024377889,0.00017249961,0.000057180325,0.000017172217,0.000036418893,0.000019978108,0.9793945,0.0011083189,0.0053493874,0.00032227722,0.013481964],"study_design_scores_gemma":[0.0000049833848,0.000025819765,0.00006253263,0.000007858874,0.0000058303635,0.000024091469,0.000017052369,0.99680746,0.00040715234,0.0019913118,0.00064242387,0.00000354433],"about_ca_topic_score_codex":0.0048419503,"about_ca_topic_score_gemma":0.0035402842,"teacher_disagreement_score":0.0048419503,"about_ca_system_score_codex":0.0008267049,"about_ca_system_score_gemma":0.0015802898,"threshold_uncertainty_score":0.009627521},"labels":[],"label_agreement":null},{"id":"W3156359523","doi":"10.1155/2021/5531500","title":"Vehicle Routing Problem for Collaborative Multidepot Petrol Replenishment under Emergency Conditions","year":2021,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Science Foundation of Ministry of Education of China; Chongqing Municipal Education Commission; Ministry of Education of the People's Republic of China; National Natural Science Foundation of China","keywords":"Truck; Vehicle routing problem; Routing (electronic design automation); Computer science; Genetic algorithm; Operations research; Transport engineering; Particle swarm optimization; Engineering; Computer network; Automotive engineering","score_opus":0.015592656698369243,"score_gpt":0.29327924102817887,"score_spread":0.27768658432980964,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3156359523","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.054245763,0.0006610185,0.93415064,0.00066647364,0.00011773166,0.00018209335,0.0004039053,0.0002080458,0.009364362],"genre_scores_gemma":[0.84440815,0.0007099282,0.13972889,0.0001696782,0.000064990454,0.00041457757,0.00066175737,0.00012849679,0.013713541],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99913234,0.00031864108,0.000035932862,0.00024836196,0.000103729355,0.0001610221],"domain_scores_gemma":[0.9993944,0.00034601308,0.00008964306,0.000029366363,0.00008769769,0.00005289107],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001047668,0.0012283437,0.0014864944,0.0006547578,0.00073491526,0.0015195285,0.0015277046,0.001820093,0.003719556],"category_scores_gemma":[0.001605803,0.0007071728,0.0013629664,0.0010030268,0.0005348771,0.001106486,0.0010548694,0.0010782557,0.00038032967],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000050967166,0.000031441763,0.0004349387,0.00006417478,0.0000371208,0.00013673265,0.000039408267,0.9852873,0.0005401475,0.0055052578,0.0008048634,0.007067637],"study_design_scores_gemma":[0.000011827157,0.000023802773,0.000117150186,0.000003621891,0.000010220363,0.000023543082,0.00003344902,0.99594814,0.00015459798,0.002970728,0.0006980526,0.000004901769],"about_ca_topic_score_codex":0.01088356,"about_ca_topic_score_gemma":0.007773432,"teacher_disagreement_score":0.01088356,"about_ca_system_score_codex":0.0016062312,"about_ca_system_score_gemma":0.0016506024,"threshold_uncertainty_score":0.02164042},"labels":[],"label_agreement":null},{"id":"W3157158781","doi":"10.1287/trsc.2022.1168","title":"The Fragility-Constrained Vehicle Routing Problem with Time Windows","year":2022,"lang":"en","type":"article","venue":"Transportation Science","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Polytechnique Montréal; École de Technologie Supérieure; Group for Research in Decision Analysis","funders":"","keywords":"Vehicle routing problem; Fragility; Column generation; Constraint (computer-aided design); Mathematical optimization; Routing (electronic design automation); Path (computing); Computer science; Algorithm; Mathematics","score_opus":0.009692242060243708,"score_gpt":0.2352825047397383,"score_spread":0.22559026267949459,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3157158781","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13101551,0.000719114,0.8537216,0.0009387046,0.00015089521,0.00035386242,0.0015120989,0.0006393618,0.010948868],"genre_scores_gemma":[0.66269183,0.00090541434,0.3226311,0.00020268852,0.00011224813,0.00051581743,0.0016876854,0.00035918097,0.010894014],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991968,0.00025004812,0.00003073775,0.00020260813,0.0001322301,0.00018759287],"domain_scores_gemma":[0.9979735,0.00139799,0.0002584757,0.00010412572,0.00011299507,0.00015278872],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010371573,0.001520567,0.001144499,0.0006869815,0.0006686986,0.0014103121,0.001854651,0.0017333076,0.007352766],"category_scores_gemma":[0.0032809956,0.0007591889,0.0010470724,0.0014426832,0.00085303513,0.0023812347,0.001057295,0.0015845341,0.00055673247],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008459324,0.000038915052,0.00027251666,0.0000825866,0.000027320015,0.000120282835,0.000031837237,0.96979517,0.0009084531,0.015662726,0.0013687633,0.011606882],"study_design_scores_gemma":[0.000033827622,0.000057167825,0.0001549778,0.00001101021,0.000015304806,0.00006565603,0.000036830574,0.9807411,0.00067392807,0.016205113,0.0019942743,0.000010838531],"about_ca_topic_score_codex":0.008015473,"about_ca_topic_score_gemma":0.007092418,"teacher_disagreement_score":0.008015473,"about_ca_system_score_codex":0.0013111032,"about_ca_system_score_gemma":0.0017787055,"threshold_uncertainty_score":0.024597466},"labels":[],"label_agreement":null},{"id":"W3162944938","doi":"10.5267/j.dsl.2021.4.003","title":"A new distributed optimization approach for home healthcare routing and scheduling problem","year":2021,"lang":"en","type":"article","venue":"Decision Science Letters","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Scheduling (production processes); Health care; Home health; Technician; Vehicle routing problem; Operations research; Routing (electronic design automation); Nurse scheduling problem; Job shop scheduling; Mathematical optimization; Engineering; Computer network; Mathematics; Economics","score_opus":0.026161131746409657,"score_gpt":0.2946441322966472,"score_spread":0.26848300055023755,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3162944938","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0044676187,0.00022136711,0.9927608,0.00027276858,0.000054984313,0.000039002098,0.000035765737,0.000060213857,0.0020875663],"genre_scores_gemma":[0.4289013,0.00083973614,0.5570415,0.00037643441,0.00027136522,0.0005967505,0.00036447856,0.00013193679,0.011476462],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99920624,0.00030909872,0.000037015816,0.00021255272,0.00014876161,0.00008627144],"domain_scores_gemma":[0.9993618,0.0003794504,0.000055999364,0.000029892744,0.00013064448,0.00004217547],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013624383,0.00096103945,0.0011604471,0.0007141573,0.0006829261,0.0011755868,0.0013528037,0.0015123507,0.0027345445],"category_scores_gemma":[0.0016559582,0.00044608992,0.00089310645,0.00092932873,0.0006388072,0.0010151434,0.001095881,0.0012432389,0.00031031156],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000031643296,0.000035507077,0.00024124833,0.0000542483,0.00003211963,0.000037836257,0.000024001749,0.9658788,0.00045891484,0.013276184,0.0012120449,0.018717417],"study_design_scores_gemma":[0.000010477667,0.000013541458,0.000034934055,0.000002401288,0.0000044073417,0.000008791349,0.000006128811,0.9952956,0.000059505302,0.004024979,0.0005370424,0.0000021901897],"about_ca_topic_score_codex":0.0052723284,"about_ca_topic_score_gemma":0.0039534727,"teacher_disagreement_score":0.0052723284,"about_ca_system_score_codex":0.0013053357,"about_ca_system_score_gemma":0.0017494997,"threshold_uncertainty_score":0.010483265},"labels":[],"label_agreement":null},{"id":"W3163275984","doi":"10.1155/2021/8280686","title":"Two-Echelon Location-Routing Problem with Time Windows and Transportation Resource Sharing","year":2021,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Chongqing Municipal Education Commission; Ministry of Education of the People's Republic of China; National Natural Science Foundation of China","keywords":"Sorting; Genetic algorithm; Computer science; Mathematical optimization; Particle swarm optimization; Vehicle routing problem; Multi-objective optimization; Resource (disambiguation); Cluster analysis; Process (computing); Service (business); Routing (electronic design automation); Algorithm; Mathematics","score_opus":0.008240355208725375,"score_gpt":0.2414700889973975,"score_spread":0.23322973378867212,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3163275984","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16051507,0.0008725813,0.8256564,0.0006925442,0.00018818669,0.0002148471,0.00075148646,0.00040075762,0.0107081],"genre_scores_gemma":[0.84454066,0.00037844552,0.14507037,0.00010900249,0.000066885535,0.00026945618,0.00066470745,0.000089658395,0.008810727],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99855167,0.00047128866,0.00008458803,0.00043193757,0.00019302839,0.00026735725],"domain_scores_gemma":[0.9992304,0.00040259917,0.00011899333,0.00007415529,0.000080316444,0.000093610455],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011742058,0.0012193008,0.0019361564,0.00082813355,0.00081392255,0.0017679048,0.0017705804,0.0018324894,0.0046471274],"category_scores_gemma":[0.0018169563,0.0007950508,0.001454011,0.0018207724,0.000589035,0.0022247,0.00136579,0.0010144182,0.0004053233],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007811009,0.000058010577,0.0005214658,0.0000771209,0.00006813974,0.00021915355,0.000031445317,0.97882277,0.00084821554,0.006832521,0.00065343425,0.011789606],"study_design_scores_gemma":[0.000028792956,0.000046052628,0.00025917296,0.0000045972565,0.0000158708,0.00008342681,0.000043315365,0.9934429,0.00042682455,0.004857642,0.0007782729,0.000013219287],"about_ca_topic_score_codex":0.006762547,"about_ca_topic_score_gemma":0.0050087087,"teacher_disagreement_score":0.006762547,"about_ca_system_score_codex":0.0013032437,"about_ca_system_score_gemma":0.0013723616,"threshold_uncertainty_score":0.015546203},"labels":[],"label_agreement":null},{"id":"W3163654962","doi":"10.1016/j.cie.2021.107396","title":"Robust optimization for the hierarchical mixed capacitated general routing problem applied to winter road maintenance","year":2021,"lang":"en","type":"article","venue":"Computers & Industrial Engineering","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"Fonds de recherche du Québec – Nature et technologies","keywords":"Mathematical optimization; Computer science; Vehicle routing problem; Metaheuristic; Hierarchy; Routing (electronic design automation); Robust optimization; Operations research; Engineering; Mathematics","score_opus":0.03923158368135803,"score_gpt":0.2291408364209234,"score_spread":0.18990925273956535,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3163654962","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019065648,0.00026496444,0.9771121,0.00022366826,0.000039551698,0.000051560546,0.000104592335,0.00013373328,0.0030041991],"genre_scores_gemma":[0.7746809,0.0004628502,0.21684285,0.00013632528,0.000091631024,0.00021384623,0.00042139192,0.0002698793,0.0068803406],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99933356,0.00032980597,0.000021427373,0.00011366191,0.00011215359,0.000089373665],"domain_scores_gemma":[0.99841094,0.0010529697,0.00021455613,0.000085230495,0.00017529538,0.000060889073],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021435379,0.0012485654,0.0011659347,0.00091913634,0.0004110264,0.0011128114,0.0015957307,0.0013959495,0.0032060805],"category_scores_gemma":[0.005151919,0.0007446344,0.0010486156,0.0008458316,0.0008898439,0.0011418636,0.0013629964,0.0010623503,0.00024738308],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000024624322,0.000012201694,0.000091855894,0.000029022567,0.000018048282,0.000013329802,0.000008390844,0.9895517,0.00032780995,0.005069534,0.00030824426,0.0045452802],"study_design_scores_gemma":[0.0000022598956,0.000008527375,0.000033645658,0.0000017403966,0.0000024987678,0.0000019903305,0.0000025685824,0.9980268,0.000060840226,0.0017822512,0.00007519725,0.0000015344041],"about_ca_topic_score_codex":0.012008255,"about_ca_topic_score_gemma":0.009445833,"teacher_disagreement_score":0.012008255,"about_ca_system_score_codex":0.0017148417,"about_ca_system_score_gemma":0.0016200237,"threshold_uncertainty_score":0.023876727},"labels":[],"label_agreement":null},{"id":"W3166781248","doi":"10.1155/2021/5182989","title":"Multi-Depot Pickup and Delivery Problem with Resource Sharing","year":2021,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Chongqing Municipal Education Commission; Fundamental Research Funds for the Central Universities; Ministry of Education of the People's Republic of China; National Natural Science Foundation of China","keywords":"Sorting; Mathematical optimization; Benchmark (surveying); Computer science; Genetic algorithm; Particle swarm optimization; Cluster analysis; Multi-objective optimization; Pareto principle; Operator (biology); Pickup; Selection (genetic algorithm); Algorithm; Mathematics","score_opus":0.012240130949841102,"score_gpt":0.24067441416789895,"score_spread":0.22843428321805784,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3166781248","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.055026267,0.0010040293,0.9357062,0.00042728128,0.00011278763,0.0002276633,0.000333367,0.0002920897,0.006870314],"genre_scores_gemma":[0.8439733,0.00080301677,0.14728372,0.00014914629,0.000065406726,0.00046730106,0.00037166013,0.000070332164,0.006816227],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989196,0.0003226126,0.000060920098,0.00023769209,0.0002169284,0.00024221448],"domain_scores_gemma":[0.99945337,0.0002808259,0.00009386612,0.000036326164,0.00007170466,0.00006384567],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013850256,0.001406166,0.0021393623,0.0008834894,0.00082347944,0.0014463651,0.0016924251,0.0015170188,0.0021552767],"category_scores_gemma":[0.0012812463,0.0008211358,0.0015608907,0.0014935604,0.0005920821,0.0014197762,0.0015434618,0.0010832476,0.00022075807],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006156335,0.00005386068,0.00038321898,0.0001335048,0.000059097994,0.00018059203,0.000032764437,0.9757144,0.0009736362,0.00689249,0.00074321707,0.014771746],"study_design_scores_gemma":[0.000018308041,0.00004536053,0.000163911,0.000006835046,0.00001721414,0.000040568997,0.000023811877,0.99544865,0.00037916895,0.0031380015,0.0007094569,0.000008750449],"about_ca_topic_score_codex":0.008454237,"about_ca_topic_score_gemma":0.00505162,"teacher_disagreement_score":0.008454237,"about_ca_system_score_codex":0.001314394,"about_ca_system_score_gemma":0.002431092,"threshold_uncertainty_score":0.01681006},"labels":[],"label_agreement":null},{"id":"W3167803807","doi":"10.1016/j.scs.2021.103074","title":"Mixed fleet based green clustered logistics problem under carbon emission cap","year":2021,"lang":"en","type":"article","venue":"Sustainable Cities and Society","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":39,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada; University of Manitoba","keywords":"Benchmark (surveying); Metaheuristic; Particle swarm optimization; Computer science; Vehicle routing problem; Mathematical optimization; Operations research; Engineering; Algorithm; Mathematics; Routing (electronic design automation)","score_opus":0.017666043508759385,"score_gpt":0.2346691047409842,"score_spread":0.21700306123222482,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3167803807","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.23708801,0.0011422805,0.7315649,0.0026409458,0.00034127507,0.00040263662,0.0016945556,0.00041139396,0.024713993],"genre_scores_gemma":[0.87326854,0.0005118991,0.09516893,0.00042070745,0.00013124122,0.00036005958,0.0010987932,0.00022064116,0.028819075],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992167,0.00032299123,0.000021783628,0.00018445567,0.000089248926,0.00016486256],"domain_scores_gemma":[0.9986002,0.0008353862,0.00017799406,0.000063345586,0.00014878457,0.00017434223],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016575633,0.0021136557,0.0026755307,0.001473684,0.0009547986,0.0027089743,0.0029544665,0.0040170657,0.007441889],"category_scores_gemma":[0.0026700534,0.0014614098,0.0016746633,0.002534664,0.001132822,0.0024559035,0.0021249743,0.0016262905,0.00038083],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000063879845,0.00004047066,0.0001832,0.000053545165,0.000039880055,0.000087327215,0.0000116877845,0.9904857,0.00019557506,0.005937506,0.00079067936,0.0021105697],"study_design_scores_gemma":[0.000011113719,0.000022492031,0.00007488498,0.0000053413783,0.00001081075,0.000012738504,0.000018394176,0.99611026,0.000077727906,0.0034369566,0.00021460955,0.000004605118],"about_ca_topic_score_codex":0.01802815,"about_ca_topic_score_gemma":0.013264628,"teacher_disagreement_score":0.01802815,"about_ca_system_score_codex":0.0030812845,"about_ca_system_score_gemma":0.0020937098,"threshold_uncertainty_score":0.035846412},"labels":[],"label_agreement":null},{"id":"W3171114511","doi":"10.5267/j.jpm.2021.5.002","title":"Solving open travelling salesman subset-tour problem through a hybrid genetic algorithm","year":2021,"lang":"en","type":"article","venue":"Journal of Project Management","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Travelling salesman problem; Tree traversal; Crossover; Christofides algorithm; Benchmark (surveying); 2-opt; Computer science; Selection (genetic algorithm); Genetic algorithm; Traverse; Set (abstract data type); Bottleneck traveling salesman problem; Lin–Kernighan heuristic; Nearest neighbour algorithm; Mathematical optimization; Mutation; Permutation (music); Algorithm; Mathematics; Artificial intelligence; Machine learning; Geography; Biology","score_opus":0.03150600752234208,"score_gpt":0.29639048382187294,"score_spread":0.2648844762995309,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3171114511","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08439012,0.00052273483,0.9066107,0.00021615706,0.00006840036,0.000114031114,0.00007489948,0.0005525273,0.007450389],"genre_scores_gemma":[0.6217559,0.00049211417,0.37180015,0.00016255076,0.0000415998,0.00026416694,0.0002761935,0.00007826743,0.0051290547],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997379,0.000066932815,0.000011303837,0.000051608793,0.00008156011,0.000050634386],"domain_scores_gemma":[0.9998103,0.00008900335,0.000022789342,0.000014663895,0.000046082383,0.000017204085],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034739551,0.0006060682,0.0007973885,0.0007310295,0.0004185602,0.0007510526,0.0010424202,0.00081970356,0.0014492328],"category_scores_gemma":[0.0006855348,0.0003210978,0.00077944295,0.0008583193,0.00033653015,0.00059440883,0.0005961404,0.00047660538,0.00017922677],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000054919765,0.00007477255,0.00089083874,0.000055445817,0.00006601984,0.00010816659,0.000071273585,0.9170251,0.0026734022,0.006929716,0.0012269407,0.0708234],"study_design_scores_gemma":[0.0000110874735,0.00003283008,0.00011675985,0.000005029847,0.000012232599,0.000035197092,0.000016768812,0.9975084,0.00034337613,0.0012796227,0.0006342837,0.000004314106],"about_ca_topic_score_codex":0.006919732,"about_ca_topic_score_gemma":0.00478004,"teacher_disagreement_score":0.006919732,"about_ca_system_score_codex":0.0004968002,"about_ca_system_score_gemma":0.0010468634,"threshold_uncertainty_score":0.013758957},"labels":[],"label_agreement":null},{"id":"W3171468365","doi":"10.5267/j.ijiec.2021.4.002","title":"Solution of capacitated vehicle routing problem with invasive weed and hybrid algorithms","year":2021,"lang":"en","type":"article","venue":"International Journal of Industrial Engineering Computations","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Algorithm; Vehicle routing problem; Metaheuristic; Mathematical optimization; Set (abstract data type); Genetic algorithm; Computer science; Routing (electronic design automation); Hybrid algorithm (constraint satisfaction); Weed; Mathematics","score_opus":0.02588579521446102,"score_gpt":0.2501091145931175,"score_spread":0.2242233193786565,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3171468365","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.24293303,0.0004599921,0.7428264,0.0002673564,0.000046426576,0.000120549266,0.00011556604,0.00040934733,0.012821258],"genre_scores_gemma":[0.75986266,0.0002018152,0.23647457,0.00006597318,0.000022518801,0.00018687226,0.00017969837,0.000059407725,0.0029463673],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997274,0.0000897703,0.00001082099,0.000046979945,0.000076406955,0.000048587557],"domain_scores_gemma":[0.99962914,0.00022987595,0.000047469082,0.000026044892,0.000048339356,0.000019109608],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005800577,0.0006846684,0.00052396994,0.0008988685,0.00030464088,0.00059962075,0.0009047528,0.00065061776,0.0012479348],"category_scores_gemma":[0.0010901329,0.00026922903,0.0006184876,0.0009795816,0.00036757204,0.00069736387,0.0005587714,0.00045074162,0.00011645625],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000052408854,0.00006549495,0.00060701335,0.000045247034,0.000030304189,0.00004974721,0.00004203125,0.9570883,0.0017633083,0.0059801554,0.0005399447,0.0337361],"study_design_scores_gemma":[0.000008232824,0.000032784836,0.00010590474,0.0000029093476,0.000005394241,0.000011748949,0.000015599884,0.9977689,0.00036031092,0.0014324355,0.0002532523,0.0000025697686],"about_ca_topic_score_codex":0.0045955298,"about_ca_topic_score_gemma":0.003913331,"teacher_disagreement_score":0.0045955298,"about_ca_system_score_codex":0.0005551467,"about_ca_system_score_gemma":0.0009097605,"threshold_uncertainty_score":0.009137511},"labels":[],"label_agreement":null},{"id":"W3172443659","doi":"10.21203/rs.3.rs-315613/v1","title":"An optimization approach for green tourist trip design","year":2021,"lang":"en","type":"preprint","venue":"Research Square","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Tourism; Business; Computer science; Transport engineering; Architectural engineering; Engineering; Geography","score_opus":0.1338843360683134,"score_gpt":0.4060421232160737,"score_spread":0.2721577871477603,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3172443659","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0025996726,0.00023209253,0.990153,0.00013883511,0.00005561282,0.000045339155,0.00005770792,0.000051970397,0.006665779],"genre_scores_gemma":[0.27597392,0.001355198,0.6915529,0.00031627144,0.00019862158,0.00065350975,0.0003202951,0.00042103117,0.029208388],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996251,0.00016946137,0.00001106707,0.00005634318,0.00010220497,0.000035793895],"domain_scores_gemma":[0.99959666,0.00023035523,0.000031186548,0.00001881788,0.00009609615,0.000026925694],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008848352,0.001430585,0.0011087244,0.0010651504,0.0005057773,0.0010665592,0.0013532238,0.0014048263,0.006303054],"category_scores_gemma":[0.0017355417,0.0008731917,0.001251899,0.0011187056,0.0007186093,0.00078049797,0.0012445914,0.0013980396,0.00071465503],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014589015,0.000025576577,0.00007561103,0.00006263988,0.000024792056,0.000022730906,0.000023591769,0.96478903,0.00060784904,0.01975457,0.0010007812,0.013598271],"study_design_scores_gemma":[0.0000036934205,0.000009432847,0.000021132515,0.0000061045334,0.0000050610574,0.0000043183672,0.000005765469,0.9918806,0.000077990764,0.0066325064,0.0013509457,0.0000025618979],"about_ca_topic_score_codex":0.009157453,"about_ca_topic_score_gemma":0.007762876,"teacher_disagreement_score":0.009157453,"about_ca_system_score_codex":0.0010955835,"about_ca_system_score_gemma":0.001541907,"threshold_uncertainty_score":0.021085799},"labels":[],"label_agreement":null},{"id":"W3172863303","doi":"10.1007/s40314-021-01548-w","title":"Integrated hub location and flow processing schedule problem under renewable capacity constraint","year":2021,"lang":"en","type":"article","venue":"Computational and Applied Mathematics","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"","keywords":"Tardiness; Mathematical optimization; Computer science; Lagrangian relaxation; Schedule; Flow network; Integer programming; Scheduling (production processes); Profitability index; Linear programming; Operations research; Job shop scheduling; Engineering; Mathematics","score_opus":0.02633252222274936,"score_gpt":0.24093024207064648,"score_spread":0.2145977198478971,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3172863303","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12119596,0.0004203272,0.8607161,0.0005525026,0.00017258561,0.00021711717,0.0013277847,0.00047227007,0.014925289],"genre_scores_gemma":[0.80551213,0.00034824593,0.17315128,0.00010722162,0.00013091693,0.00026160484,0.001311595,0.00022249937,0.018954543],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993331,0.00015224787,0.00001767214,0.00023004271,0.00011714859,0.00014981661],"domain_scores_gemma":[0.9990368,0.00047312977,0.00016236916,0.000080437145,0.00013689398,0.0001104964],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012320852,0.001044187,0.0021771425,0.0009251221,0.00058307615,0.0017900901,0.0023516684,0.0019265118,0.0084163435],"category_scores_gemma":[0.0022680743,0.0012417058,0.0011347715,0.0022415025,0.000726482,0.0018637177,0.0010684773,0.0013372119,0.0006550751],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011672918,0.00006417861,0.00023688537,0.00006776939,0.000030840467,0.000100334386,0.000024104376,0.9823737,0.00066779606,0.006543351,0.0010175273,0.008756798],"study_design_scores_gemma":[0.000023024111,0.000043738324,0.00021546338,0.0000041884823,0.000016514143,0.000017588098,0.000019728739,0.99445456,0.0003424289,0.0044836528,0.00037258392,0.000006535928],"about_ca_topic_score_codex":0.0127146,"about_ca_topic_score_gemma":0.009903486,"teacher_disagreement_score":0.0127146,"about_ca_system_score_codex":0.0016501177,"about_ca_system_score_gemma":0.0024784224,"threshold_uncertainty_score":0.028155506},"labels":[],"label_agreement":null},{"id":"W3173319990","doi":"10.1609/aaai.v35i5.16512","title":"Parameterizing Branch-and-Bound Search Trees to Learn Branching Policies","year":2021,"lang":"en","type":"article","venue":"","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":53,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; Polytechnique Montréal","funders":"Institut de Valorisation des Données; Compute Canada; Canadian Institute for Advanced Research","keywords":"Branching (polymer chemistry); Generalization; Branch and bound; Computer science; Tree (set theory); Benchmark (surveying); Search tree; Integer programming; Mathematical optimization; Theoretical computer science; Artificial intelligence; Mathematics; Search algorithm; Algorithm; Combinatorics","score_opus":0.030937053087930263,"score_gpt":0.3004092617432398,"score_spread":0.2694722086553095,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3173319990","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.045986407,0.00041867222,0.94893616,0.00029567938,0.00005450133,0.000096481854,0.0000798621,0.0010389369,0.0030932592],"genre_scores_gemma":[0.7653028,0.0003637733,0.23128556,0.0003040924,0.000049510327,0.0003923919,0.00033409134,0.00024877142,0.0017189669],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99924195,0.00031483476,0.000047782825,0.000153873,0.0001420836,0.00009954153],"domain_scores_gemma":[0.9953805,0.0034661952,0.00034564125,0.0003559858,0.00029179646,0.0001599271],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022455684,0.0012545137,0.0012976057,0.00068082585,0.00039517207,0.001056626,0.0015911631,0.0016082849,0.002334263],"category_scores_gemma":[0.012402694,0.00064222474,0.00069123146,0.00074211316,0.0010643911,0.001876875,0.0014792039,0.0029247804,0.0006779152],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000041591724,0.000039955325,0.000563046,0.000029058998,0.000014320067,0.000015610274,0.000026177242,0.96731436,0.00047597752,0.004672766,0.0005415222,0.026265768],"study_design_scores_gemma":[0.000004777346,0.000010956823,0.000018681034,0.000004434516,0.0000018976727,0.0000028677323,0.0000019445026,0.99737394,0.0001494596,0.0023246198,0.00010482574,0.0000015903976],"about_ca_topic_score_codex":0.0031486466,"about_ca_topic_score_gemma":0.0032575657,"teacher_disagreement_score":0.0031486466,"about_ca_system_score_codex":0.0011118153,"about_ca_system_score_gemma":0.0018634523,"threshold_uncertainty_score":0.011875868},"labels":[],"label_agreement":null},{"id":"W3173475768","doi":"10.1287/trsc.2021.1045","title":"Machine-learning-based column selection for column generation","year":2021,"lang":"en","type":"article","venue":"Archivio istituzionale della ricerca (Alma Mater Studiorum Università di Bologna)","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":109,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Group for Research in Decision Analysis","funders":"","keywords":"Column generation; Column (typography); Crew scheduling; Scheduling (production processes); Computer science; Selection (genetic algorithm); Vehicle routing problem; Mathematical optimization; Row and column spaces; Routing (electronic design automation); Artificial intelligence; Mathematics; Row","score_opus":0.01780381453618663,"score_gpt":0.2344738495249914,"score_spread":0.21667003498880477,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3173475768","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006654343,0.00030375764,0.98969567,0.00009546907,0.00005702127,0.0001268732,0.00014189637,0.0019428293,0.0009820868],"genre_scores_gemma":[0.16924597,0.0003348239,0.8259078,0.0002676349,0.000112064816,0.0004946786,0.0011490742,0.0004872765,0.0020005826],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993625,0.00024906208,0.000033659664,0.00011716556,0.0001652104,0.000072318384],"domain_scores_gemma":[0.99782974,0.0013989261,0.00014162906,0.00024907096,0.00032340654,0.00005719663],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008717526,0.0015782312,0.0013636695,0.0011652353,0.0005356733,0.00078936486,0.0010811548,0.00078195456,0.006323736],"category_scores_gemma":[0.0033201596,0.000566034,0.0010644753,0.0014020597,0.00061382196,0.00086488103,0.0007722832,0.0018482783,0.0016684828],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000247045,0.00027162762,0.0011179105,0.0003575313,0.00011858896,0.0001583686,0.00007523229,0.53447044,0.012920585,0.0096186185,0.013060209,0.42758384],"study_design_scores_gemma":[0.000032120082,0.000063598796,0.00010054048,0.000010098997,0.000013010327,0.000034459,0.000008001774,0.991418,0.0036112135,0.0031756624,0.0015248224,0.000008480187],"about_ca_topic_score_codex":0.002922024,"about_ca_topic_score_gemma":0.0048134034,"teacher_disagreement_score":0.006323736,"about_ca_system_score_codex":0.0005224222,"about_ca_system_score_gemma":0.0015748289,"threshold_uncertainty_score":0.02115494},"labels":[],"label_agreement":null},{"id":"W3175587957","doi":"10.3390/su13126940","title":"Optimization of Conventional and Green Vehicles Composition under Carbon Emission Cap","year":2021,"lang":"en","type":"article","venue":"Sustainability","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Benchmark (surveying); Vehicle routing problem; Metaheuristic; Ant colony optimization algorithms; Computer science; Mathematical optimization; Variable (mathematics); Composition (language); Routing (electronic design automation); Operations research; Engineering; Algorithm; Mathematics; Embedded system","score_opus":0.00933232368733773,"score_gpt":0.25987730161910505,"score_spread":0.25054497793176733,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3175587957","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6955446,0.0008333287,0.28874215,0.00029781534,0.00006507519,0.00015206714,0.00032933417,0.00017577174,0.013859829],"genre_scores_gemma":[0.94483626,0.00022280256,0.051658496,0.00002848673,0.000007876679,0.00008522315,0.00015637907,0.000034943438,0.002969464],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997893,0.000072513045,0.0000062737013,0.000040047533,0.000036975445,0.00005485178],"domain_scores_gemma":[0.9997739,0.00011374421,0.000042927375,0.000013151022,0.000029842675,0.000026461797],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005279286,0.0008361098,0.0006239072,0.0006099127,0.0002740086,0.0007844013,0.00062822003,0.000799004,0.0012860915],"category_scores_gemma":[0.0009833131,0.00036904638,0.0006094778,0.0006268235,0.00031541917,0.0007305733,0.0004590652,0.00039368364,0.00012207282],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000028041857,0.000028863351,0.00031662564,0.000025112384,0.000012158086,0.000024550618,0.000004290191,0.9935401,0.00094551605,0.0007786323,0.00009981079,0.0041963225],"study_design_scores_gemma":[0.0000071571717,0.000044180117,0.0002159506,0.0000035507014,0.000008855384,0.000011132729,0.000015819329,0.99816716,0.00062942883,0.00064860756,0.00024562737,0.0000025480408],"about_ca_topic_score_codex":0.00575738,"about_ca_topic_score_gemma":0.00657812,"teacher_disagreement_score":0.00575738,"about_ca_system_score_codex":0.0007934648,"about_ca_system_score_gemma":0.00097277836,"threshold_uncertainty_score":0.011447787},"labels":[],"label_agreement":null},{"id":"W3179633369","doi":"","title":"A Branch-And-Check Solution Method for a Tourist Trip Design Problem with Rich Constraints","year":2019,"lang":"fr","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"","keywords":"Computer science; Tourism; Mathematical optimization; Mathematics; Geography","score_opus":0.02312201320111373,"score_gpt":0.25865185113404404,"score_spread":0.2355298379329303,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3179633369","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005000567,0.000129652,0.9913606,0.000113827184,0.00004706961,0.00010028234,0.00009455109,0.00023764768,0.0029157996],"genre_scores_gemma":[0.16033615,0.00026446307,0.8322011,0.00014681356,0.00008123154,0.0005197544,0.00031718504,0.00028809826,0.0058451644],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993918,0.0002394548,0.000024738989,0.000096405216,0.00016800193,0.00007954573],"domain_scores_gemma":[0.996725,0.0025972163,0.00012133582,0.00009923865,0.00034714877,0.00011004911],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021183528,0.0015523502,0.0019100823,0.0013825775,0.00088790385,0.0013526567,0.0015827669,0.0026678918,0.012517472],"category_scores_gemma":[0.005099748,0.0012682206,0.0015777474,0.001393505,0.0008823646,0.000957897,0.0015666464,0.0028668777,0.0010711954],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012061045,0.000085539235,0.0002706217,0.00019380343,0.000049621976,0.00010040151,0.00007751194,0.9170968,0.0017184841,0.009907837,0.0027017405,0.067677096],"study_design_scores_gemma":[0.000030519735,0.000030421306,0.000040417424,0.000016015027,0.000013239599,0.000011336239,0.000009772477,0.99578947,0.00021757484,0.003245452,0.0005907192,0.000004976226],"about_ca_topic_score_codex":0.011242191,"about_ca_topic_score_gemma":0.010215513,"teacher_disagreement_score":0.012517472,"about_ca_system_score_codex":0.00089719915,"about_ca_system_score_gemma":0.002687432,"threshold_uncertainty_score":0.041875124},"labels":[],"label_agreement":null},{"id":"W3181596661","doi":"10.1287/trsc.2022.1176","title":"Exponential-Size Neighborhoods for the Pickup-and-Delivery Traveling Salesman Problem","year":2022,"lang":"en","type":"article","venue":"Transportation Science","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Travelling salesman problem; Pickup; Metaheuristic; Computer science; Vehicle routing problem; Mathematical optimization; Combinatorial optimization; Routing (electronic design automation); Traveling purchaser problem; Heuristic; 2-opt; Mathematics; Artificial intelligence; Computer network","score_opus":0.020268344787622763,"score_gpt":0.25714307200236775,"score_spread":0.23687472721474498,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3181596661","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04863736,0.0020244557,0.9344935,0.0009737485,0.00016181465,0.00026919323,0.00026304642,0.0004884015,0.012688606],"genre_scores_gemma":[0.4368705,0.0019818372,0.5516025,0.00028824396,0.00014731001,0.0007468235,0.00050276995,0.00031049334,0.007549627],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993587,0.00023619075,0.000028973198,0.00013550493,0.00018034366,0.00006031224],"domain_scores_gemma":[0.9981779,0.0012998085,0.00018138606,0.00009455307,0.00016250844,0.00008390962],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010652005,0.00072824926,0.0009900725,0.000693204,0.00065393647,0.0010166605,0.0013416716,0.00094246643,0.004205885],"category_scores_gemma":[0.004824208,0.00042916817,0.000989781,0.00083466986,0.0006256137,0.0015251427,0.0012502909,0.0017271874,0.0006005687],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022017748,0.00019139258,0.0010629237,0.00035351384,0.000040409355,0.00021139174,0.00011793425,0.8184664,0.0021879137,0.087441474,0.007932125,0.08177435],"study_design_scores_gemma":[0.000035128094,0.00008635146,0.0001923225,0.000034933928,0.000020801119,0.00010076358,0.000037015012,0.96100533,0.0008070506,0.031643666,0.006022133,0.00001441228],"about_ca_topic_score_codex":0.0023021619,"about_ca_topic_score_gemma":0.0022092222,"teacher_disagreement_score":0.004205885,"about_ca_system_score_codex":0.0011291148,"about_ca_system_score_gemma":0.0014906306,"threshold_uncertainty_score":0.014070094},"labels":[],"label_agreement":null},{"id":"W3183470606","doi":"","title":"Multiperiod Dispatching and Routing for On-Time Delivery in a Dynamic and Stochastic Environment.","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Routing (electronic design automation); Set (abstract data type); Markov decision process; Mathematical optimization; TRIPS architecture; Operations research; Engineering; Computer network; Markov process; Mathematics","score_opus":0.025875795795027284,"score_gpt":0.17797134776877654,"score_spread":0.15209555197374927,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3183470606","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011746409,0.00051044323,0.98348826,0.0004196866,0.00009726913,0.0001057492,0.00021764127,0.00016655338,0.0032480701],"genre_scores_gemma":[0.45775688,0.0011258166,0.5302691,0.0002737012,0.00015303536,0.00036989187,0.00078351673,0.0003017778,0.008966378],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99922884,0.0004116682,0.000026376718,0.00012270332,0.0001160632,0.00009449668],"domain_scores_gemma":[0.9982993,0.001183817,0.00022937405,0.000080680664,0.00011162319,0.00009524201],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017806083,0.0012718695,0.00096892775,0.00064589636,0.0005170738,0.0011424054,0.001225285,0.0012697642,0.003953435],"category_scores_gemma":[0.003511013,0.00076196634,0.0010414252,0.0010742428,0.0007612203,0.0012306795,0.0008640576,0.0017658317,0.000477546],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017449749,0.000023324503,0.00018912923,0.00003776824,0.000014171901,0.000018677674,0.000015809732,0.98560506,0.0001852606,0.0071537415,0.00089684816,0.005842772],"study_design_scores_gemma":[0.0000031723866,0.0000074851123,0.000038003076,0.0000031517782,0.000002292305,0.00000533832,0.000006881714,0.9961904,0.000049925813,0.0032423588,0.00044915883,0.0000016879056],"about_ca_topic_score_codex":0.010517661,"about_ca_topic_score_gemma":0.01108842,"teacher_disagreement_score":0.010517661,"about_ca_system_score_codex":0.0018937647,"about_ca_system_score_gemma":0.0020343203,"threshold_uncertainty_score":0.020912886},"labels":[],"label_agreement":null},{"id":"W3184432724","doi":"10.4230/lipics.icalp.2021.67","title":"Constant-Factor Approximation to Deadline TSP and Related Problems in (Almost) Quasi-Polytime","year":2021,"lang":"en","type":"article","venue":"DROPS (Schloss Dagstuhl – Leibniz Center for Informatics)","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo; University of Alberta","funders":"","keywords":"Rounding; Approximation algorithm; Combinatorics; Orienteering; Metric space; Path (computing); Constant (computer programming); Mathematics; Travelling salesman problem; Binary logarithm; Randomized rounding; Metric (unit); Point (geometry); Time complexity; Computer science; Discrete mathematics; Mathematical optimization","score_opus":0.014313825941000835,"score_gpt":0.2530143342341167,"score_spread":0.23870050829311584,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3184432724","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.084075086,0.0032486622,0.88043845,0.0055603953,0.00061561674,0.00022785296,0.0014102577,0.0039081923,0.020515397],"genre_scores_gemma":[0.56560105,0.0019166495,0.4133964,0.001805966,0.0005814577,0.0003995054,0.0023796968,0.0012754776,0.012643774],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9974942,0.00064681267,0.00013132436,0.0006262383,0.0005653361,0.00053620536],"domain_scores_gemma":[0.9934012,0.0039743283,0.0005557872,0.0012501706,0.0003840178,0.00043445828],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029916654,0.00213689,0.0022184597,0.0007486209,0.0013058075,0.0030228256,0.0033127377,0.0018905263,0.009164559],"category_scores_gemma":[0.014150272,0.0007441321,0.0018500059,0.002196005,0.0014853694,0.0063338084,0.0021243487,0.004213823,0.002169186],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0017006204,0.00068203546,0.0028545947,0.0011397011,0.00023395543,0.00032892346,0.0005992653,0.6829309,0.0059009157,0.17857772,0.032890473,0.0921609],"study_design_scores_gemma":[0.00019083532,0.00016502306,0.0003877639,0.000037849863,0.000039245,0.00012605316,0.00008581842,0.8159744,0.0007250037,0.17574474,0.006505449,0.000017693106],"about_ca_topic_score_codex":0.0071256044,"about_ca_topic_score_gemma":0.008726786,"teacher_disagreement_score":0.009164559,"about_ca_system_score_codex":0.0033483447,"about_ca_system_score_gemma":0.0028543305,"threshold_uncertainty_score":0.030658543},"labels":[],"label_agreement":null},{"id":"W3186291209","doi":"10.1287/opre.2021.2185","title":"Single Allocation Hub Location with Heterogeneous Economies of Scale","year":2021,"lang":"en","type":"article","venue":"Operations Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Group for Research in Decision Analysis; HEC Montréal; Wilfrid Laurier University; Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Compute Canada","keywords":"Mathematical optimization; Quadratic growth; Linear programming; Piecewise; Lagrangian relaxation; Column generation; Economies of scale; Computer science; Constant function; Scale (ratio); Returns to scale; Consolidation (business); Scalability; Integer programming; Constant (computer programming); Mathematics; Production (economics); Economics; Algorithm","score_opus":0.05497484970647972,"score_gpt":0.33477250748693355,"score_spread":0.27979765778045385,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3186291209","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07687218,0.00026315675,0.9075569,0.0002990871,0.000058812202,0.00012008506,0.000223059,0.00013867996,0.014467966],"genre_scores_gemma":[0.88687617,0.00029547905,0.10534427,0.00007785894,0.0000445911,0.00016540985,0.00015987793,0.000053990643,0.0069823693],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987382,0.00042495027,0.000041068022,0.00035787336,0.00018394778,0.0002539053],"domain_scores_gemma":[0.9987336,0.00060454325,0.00019717336,0.00024387326,0.000118035176,0.00010284397],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011422554,0.0010113737,0.0012753474,0.00063204323,0.0005381646,0.0018881498,0.001978045,0.0014063128,0.004901999],"category_scores_gemma":[0.0032700128,0.00073196884,0.0010165336,0.0015502224,0.0010424401,0.0025911864,0.0017267113,0.0014889005,0.0003964599],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000052926127,0.000039458046,0.00033973547,0.000059665446,0.00003192309,0.00013883723,0.000022258722,0.91897714,0.0009033704,0.06727109,0.0009903414,0.011173197],"study_design_scores_gemma":[0.00001870761,0.000044553683,0.0002915707,0.000009568918,0.000017347907,0.00005275921,0.000033137192,0.9671053,0.00057308323,0.030220864,0.0016212683,0.000011713221],"about_ca_topic_score_codex":0.0028638968,"about_ca_topic_score_gemma":0.0026180209,"teacher_disagreement_score":0.004901999,"about_ca_system_score_codex":0.0016199766,"about_ca_system_score_gemma":0.0008469481,"threshold_uncertainty_score":0.016398847},"labels":[],"label_agreement":null},{"id":"W3187408980","doi":"10.48550/arxiv.2009.14628","title":"Meta Partial Benders Decomposition for the Logistics Service Network\\n Design Problem","year":2020,"lang":"","type":"article","venue":"arXiv (Cornell University)","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"Agence Nationale de la Recherche","keywords":"Benders' decomposition; Decomposition; Computer science; Benchmark (surveying); Mathematical optimization; Service (business); Operations research; Scheme (mathematics); Product (mathematics); Reverse logistics; Decomposition method (queueing theory); Network planning and design; Supply chain; Engineering; Mathematics; Computer network","score_opus":0.2511091973975603,"score_gpt":0.23398495898147642,"score_spread":0.017124238416083903,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3187408980","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010404104,0.0005088554,0.98077685,0.00038036657,0.000056712623,0.00012181007,0.00029851345,0.00016420516,0.007288587],"genre_scores_gemma":[0.22715554,0.0011016667,0.76022667,0.0002960562,0.00012520039,0.0007958544,0.0014440205,0.00028671557,0.00856826],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992655,0.00032562358,0.000033295742,0.00013701951,0.00016172891,0.00007675763],"domain_scores_gemma":[0.9990553,0.0006382992,0.000088798246,0.00006579721,0.000097635515,0.000054058262],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016362851,0.0019863208,0.0011086853,0.0012285536,0.0005446712,0.0013183992,0.0009213757,0.0013223366,0.006732944],"category_scores_gemma":[0.002993122,0.00081765495,0.0018703367,0.0013011972,0.0008751041,0.0013088102,0.001303523,0.0021728799,0.00080436066],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000033102646,0.000037959097,0.00029143487,0.00011341609,0.000036619036,0.000051389674,0.000031248703,0.95970494,0.0005799142,0.019068714,0.0014562665,0.018594988],"study_design_scores_gemma":[0.000012598438,0.00002665472,0.000062826126,0.000023540078,0.000011151404,0.00001781004,0.00002003535,0.97835743,0.0002878161,0.01933754,0.0018378162,0.000004721171],"about_ca_topic_score_codex":0.0037727798,"about_ca_topic_score_gemma":0.0052532796,"teacher_disagreement_score":0.006732944,"about_ca_system_score_codex":0.0012766594,"about_ca_system_score_gemma":0.002016478,"threshold_uncertainty_score":0.02252394},"labels":[],"label_agreement":null},{"id":"W3187835696","doi":"10.1109/cec45853.2021.9504700","title":"Caching and Vectorization Schemes to Accelerate Local Search Algorithms for Assignment Problems","year":2021,"lang":"en","type":"article","venue":"","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Quadratic assignment problem; Leverage (statistics); Solver; Parallel computing; Vectorization (mathematics); Assignment problem; Local search (optimization); Weapon target assignment problem; Block (permutation group theory); Algorithm; Optimization problem; Mathematical optimization; Mathematics; Artificial intelligence","score_opus":0.04627111951563646,"score_gpt":0.30776134058308147,"score_spread":0.261490221067445,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3187835696","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.022297649,0.00028922912,0.9705987,0.00019835665,0.00007444719,0.00007522726,0.00005724167,0.0022577143,0.004151343],"genre_scores_gemma":[0.2791659,0.00031865854,0.7148691,0.0001854618,0.000056221797,0.0002407439,0.00026225616,0.0005333209,0.0043683043],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99950314,0.00015663907,0.00003661398,0.00007083388,0.00015778285,0.0000750184],"domain_scores_gemma":[0.9983714,0.0007303582,0.00012440723,0.00038023765,0.00032897474,0.00006469014],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010741635,0.00081469107,0.0006877338,0.0006382339,0.00050452346,0.0009349563,0.0015010494,0.0006381986,0.0071773506],"category_scores_gemma":[0.004435184,0.00042709714,0.000489616,0.0012892724,0.0006371159,0.0024172235,0.0011040842,0.0014351276,0.0013205651],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002285907,0.00019906687,0.0010984509,0.0001553152,0.000058512993,0.00007357794,0.00018201808,0.6458468,0.0143922465,0.05544779,0.008150059,0.27416763],"study_design_scores_gemma":[0.000029706376,0.000047351285,0.000071833885,0.0000070919955,0.000007478391,0.000017166567,0.000017215341,0.98959535,0.0026672606,0.0055550872,0.0019776754,0.000006879503],"about_ca_topic_score_codex":0.0044675125,"about_ca_topic_score_gemma":0.008745954,"teacher_disagreement_score":0.0071773506,"about_ca_system_score_codex":0.00084159715,"about_ca_system_score_gemma":0.0012725263,"threshold_uncertainty_score":0.024010658},"labels":[],"label_agreement":null},{"id":"W3190147417","doi":"10.1016/j.eswa.2021.115683","title":"Solving the battery swap station location-routing problem with a mixed fleet of electric and conventional vehicles using a heuristic branch-and-price algorithm with an adaptive selection scheme","year":2021,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":71,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"National Key Research and Development Program of China; Ministry of Science and Technology of the People's Republic of China; National Natural Science Foundation of China","keywords":"Swap (finance); Computer science; Scheme (mathematics); Selection (genetic algorithm); Heuristic; Mathematical optimization; Vehicle routing problem; Routing (electronic design automation); Algorithm; Computer network; Artificial intelligence; Mathematics; Finance","score_opus":0.015929789995386485,"score_gpt":0.2521296384471959,"score_spread":0.23619984845180939,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3190147417","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16618732,0.00028072263,0.8254728,0.00032287446,0.00006442031,0.0001591228,0.00012412817,0.00022849049,0.007160206],"genre_scores_gemma":[0.78061104,0.00013070602,0.21249318,0.00008270007,0.000052341402,0.00025559534,0.00016627023,0.000061690895,0.0061464976],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999706,0.0001233681,0.000013011808,0.0000582539,0.000045962675,0.00005343849],"domain_scores_gemma":[0.99916697,0.00064829754,0.00005590223,0.000021691456,0.0000621007,0.000045122903],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011458008,0.0010139409,0.0014213313,0.00088076445,0.00061250327,0.0011209319,0.001277115,0.0017707472,0.0037692885],"category_scores_gemma":[0.0017457005,0.00094793335,0.000882183,0.0011454718,0.00058547047,0.0012609822,0.0008514451,0.0007912344,0.00022423179],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006386268,0.00003945291,0.00031897557,0.00002659566,0.000031144555,0.000043356995,0.000013796424,0.98809147,0.00038646322,0.0017486599,0.0002448924,0.008991267],"study_design_scores_gemma":[0.000013162437,0.00002189433,0.00005933706,0.0000012755057,0.0000061306887,0.0000071450822,0.0000067432825,0.9990207,0.00008427917,0.000722626,0.0000548752,0.0000018177429],"about_ca_topic_score_codex":0.009604942,"about_ca_topic_score_gemma":0.009251648,"teacher_disagreement_score":0.009604942,"about_ca_system_score_codex":0.00091357384,"about_ca_system_score_gemma":0.0015710546,"threshold_uncertainty_score":0.019098043},"labels":[],"label_agreement":null},{"id":"W3190827665","doi":"10.5267/j.dsl.2021.6.001","title":"A hybrid FJA-ALNS algorithm for solving the multi-compartment vehicle routing problem with a heterogeneous fleet of vehicles for the fuel delivery problem","year":2021,"lang":"en","type":"article","venue":"Decision Science Letters","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Khon Kaen University","keywords":"Algorithm; Vehicle routing problem; Hybrid algorithm (constraint satisfaction); Computer science; Heuristic; Mathematical optimization; Mathematics; Routing (electronic design automation)","score_opus":0.02836788013487987,"score_gpt":0.2811055917875958,"score_spread":0.25273771165271597,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3190827665","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.048682917,0.00037583508,0.945269,0.00012298192,0.00005295807,0.00012353403,0.00008992402,0.0006456125,0.0046372474],"genre_scores_gemma":[0.27330005,0.00018201729,0.7218865,0.000106331456,0.000027967453,0.0002686726,0.00037835634,0.000089771864,0.0037603094],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99957484,0.00010274348,0.00003367921,0.00010752677,0.0001236104,0.000057565423],"domain_scores_gemma":[0.99970454,0.00013728708,0.00003151276,0.00002557107,0.00007617094,0.000024907305],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00063110056,0.00076313625,0.00069942884,0.0009528624,0.000641204,0.00061467517,0.0014440832,0.00090128963,0.0021624095],"category_scores_gemma":[0.0010319719,0.000421475,0.0009957929,0.00074836274,0.00034690666,0.0009850966,0.00065964763,0.000556808,0.00032685278],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013309234,0.00011017707,0.001381892,0.00010542869,0.00007790463,0.000082730316,0.00007225245,0.8458019,0.0036345234,0.0068513704,0.0013193483,0.14042939],"study_design_scores_gemma":[0.000029198703,0.000045101777,0.0001379371,0.0000066370753,0.000010917566,0.00002926307,0.000022800516,0.99628484,0.00075727305,0.0013251511,0.0013444982,0.0000063258803],"about_ca_topic_score_codex":0.012792422,"about_ca_topic_score_gemma":0.01646799,"teacher_disagreement_score":0.012792422,"about_ca_system_score_codex":0.00068344444,"about_ca_system_score_gemma":0.0017487985,"threshold_uncertainty_score":0.025435925},"labels":[],"label_agreement":null},{"id":"W3191258758","doi":"10.5267/j.dsl.2021.5.003","title":"An efficient genetic algorithm for solving open multiple travelling salesman problem with load balancing constraint","year":2021,"lang":"en","type":"article","venue":"Decision Science Letters","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Travelling salesman problem; Mathematical optimization; Computer science; Genetic algorithm; Constraint (computer-aided design); Tree traversal; 2-opt; Integer programming; Algorithm; Mathematics","score_opus":0.019935957969724697,"score_gpt":0.2902609772048175,"score_spread":0.27032501923509283,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3191258758","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.030228512,0.0006267521,0.9611025,0.00023454502,0.00008837933,0.000113425594,0.00006481615,0.0004648518,0.00707625],"genre_scores_gemma":[0.43367785,0.00086050516,0.5578045,0.00022758452,0.00006611809,0.00040615362,0.00038566327,0.00010749276,0.006464079],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997392,0.00007040284,0.000010380141,0.000053318294,0.00007346972,0.00005323588],"domain_scores_gemma":[0.9998462,0.00007507235,0.000023576875,0.000008824439,0.0000354177,0.000010823742],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039609705,0.00090070366,0.00087886775,0.00084703043,0.0005181696,0.00075536635,0.0010613995,0.0012019416,0.0020229858],"category_scores_gemma":[0.0008162954,0.00037421242,0.0007312138,0.0011259726,0.00043335152,0.00063806784,0.0006022617,0.0008942266,0.00028248187],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000035816454,0.000058529644,0.00047258538,0.00006319559,0.000038524773,0.000099425175,0.000056727684,0.92575717,0.001877468,0.009820337,0.001530635,0.060189493],"study_design_scores_gemma":[0.000016001577,0.000032298623,0.00008092197,0.0000066487355,0.000010348572,0.000027780212,0.000016979486,0.9964193,0.00025887176,0.0022077619,0.0009185389,0.000004659821],"about_ca_topic_score_codex":0.009837879,"about_ca_topic_score_gemma":0.0069172187,"teacher_disagreement_score":0.009837879,"about_ca_system_score_codex":0.00067498226,"about_ca_system_score_gemma":0.001762733,"threshold_uncertainty_score":0.019561231},"labels":[],"label_agreement":null},{"id":"W3191774642","doi":"10.1016/j.jii.2021.100246","title":"Bi-level programming for home health care supply chain considering outsourcing","year":2021,"lang":"en","type":"article","venue":"Journal of Industrial Information Integration","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":115,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"","keywords":"Stackelberg competition; Heuristics; Outsourcing; Supply chain; Health care; Novelty; Business; Operations management; Computer science; Operations research; Marketing; Economics; Microeconomics; Engineering; Psychology","score_opus":0.06443391656776795,"score_gpt":0.303664331238838,"score_spread":0.23923041467107006,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3191774642","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.040403742,0.0006818398,0.9389276,0.0019189295,0.00020930302,0.00022252543,0.00046313682,0.00019128853,0.016981686],"genre_scores_gemma":[0.75691396,0.0011301488,0.2067201,0.000551986,0.0002038231,0.000884762,0.0007770877,0.0003273295,0.03249073],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998198,0.0008902204,0.000058387504,0.00021291268,0.0002541579,0.00038637925],"domain_scores_gemma":[0.9965127,0.0024159388,0.00022063752,0.00012137986,0.0004268092,0.00030257856],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0038860526,0.0016594582,0.0030701212,0.0014099913,0.0012189251,0.0038596382,0.002745189,0.0037542165,0.012413714],"category_scores_gemma":[0.00684747,0.0019969058,0.0023174589,0.0022722818,0.0014824824,0.0023589781,0.00345228,0.0037794996,0.00081923115],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000041877807,0.00006049447,0.00034373667,0.00007776338,0.000045525783,0.00009294613,0.00004539853,0.97636175,0.00014964431,0.018954879,0.00090071966,0.002925263],"study_design_scores_gemma":[0.000006682916,0.00001108588,0.000050235863,0.000008539734,0.000008590918,0.000004564474,0.000022164731,0.9958359,0.000028496499,0.003811377,0.00020859274,0.0000037325983],"about_ca_topic_score_codex":0.027938996,"about_ca_topic_score_gemma":0.017152231,"teacher_disagreement_score":0.027938996,"about_ca_system_score_codex":0.0028419462,"about_ca_system_score_gemma":0.004322551,"threshold_uncertainty_score":0.05555272},"labels":[],"label_agreement":null},{"id":"W3192094171","doi":"10.1007/978-3-030-76724-2_30","title":"Home Health Care Services Management: Districting Problem Revisited","year":2021,"lang":"en","type":"book-chapter","venue":"Lecture notes in management and industrial engineering","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University; University of Ottawa","funders":"","keywords":"Heuristic; Workload; Mathematical optimization; Computer science; Crew; Integer (computer science); Integer programming; Operations research; Assignment problem; Function (biology); Mathematics; Engineering","score_opus":0.014480454550387897,"score_gpt":0.22654076287118943,"score_spread":0.21206030832080153,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3192094171","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0888298,0.024011876,0.44768372,0.083618484,0.0025011331,0.00034481546,0.0024657107,0.0003057274,0.3502387],"genre_scores_gemma":[0.84290254,0.012173163,0.05229467,0.0023780551,0.0018758454,0.00017255527,0.000501336,0.00017513546,0.08752668],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99871695,0.0005919506,0.00003513698,0.00022628199,0.00012736437,0.0003023492],"domain_scores_gemma":[0.9988475,0.0006643844,0.000089565634,0.00006231301,0.00013432399,0.00020199026],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015695863,0.00052687566,0.002018569,0.000783706,0.0011883554,0.004603616,0.0034692346,0.00342081,0.02437041],"category_scores_gemma":[0.0029726713,0.0007079344,0.0009140382,0.0037199832,0.0019072518,0.0032795176,0.0018310788,0.0031120467,0.0007242351],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006434558,0.00014176096,0.0009690005,0.0003519331,0.00006359744,0.00018361563,0.0001784223,0.12688752,0.00022426964,0.7484598,0.047546867,0.07492886],"study_design_scores_gemma":[0.00007954185,0.000089540204,0.0031205097,0.00032563054,0.000064662505,0.0003861817,0.0017543372,0.17907396,0.0004426894,0.738172,0.07643561,0.000055278295],"about_ca_topic_score_codex":0.014217941,"about_ca_topic_score_gemma":0.023315236,"teacher_disagreement_score":0.02437041,"about_ca_system_score_codex":0.00373283,"about_ca_system_score_gemma":0.002598522,"threshold_uncertainty_score":0.081527114},"labels":[],"label_agreement":null},{"id":"W3196241247","doi":"10.1007/s11356-021-15907-x","title":"A multiobjective model for the green capacitated location-routing problem considering drivers’ satisfaction and time window with uncertain demand","year":2021,"lang":"en","type":"article","venue":"Environmental Science and Pollution Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":32,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Mathematical optimization; Genetic algorithm; Vehicle routing problem; Sorting; Computer science; Routing (electronic design automation); Facility location problem; Integer programming; Operations research; Mathematics; Algorithm","score_opus":0.03675962874608778,"score_gpt":0.29468325688427316,"score_spread":0.25792362813818537,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3196241247","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11479769,0.001063406,0.8656721,0.0013197699,0.00028118154,0.00016700257,0.0012113021,0.00026265197,0.01522481],"genre_scores_gemma":[0.93146735,0.00062984374,0.04519997,0.00018280285,0.00008990937,0.0003122681,0.00065743824,0.00009999533,0.021360328],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99896264,0.0003339288,0.000037269736,0.00025451952,0.00016014441,0.00025149717],"domain_scores_gemma":[0.99890876,0.00058709894,0.00017570927,0.000033643355,0.00016888337,0.00012589294],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014591028,0.0017079747,0.0021293508,0.0011362728,0.0007063995,0.0026196898,0.0033328086,0.00336735,0.0049405703],"category_scores_gemma":[0.0021245293,0.0013093357,0.0015606195,0.0018499056,0.0010563079,0.0017015226,0.0016159182,0.001990167,0.0005609668],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002177946,0.000018217459,0.00015568189,0.00002244935,0.00002070227,0.00005792329,0.000015812348,0.9946743,0.00020026378,0.0036210346,0.00020889279,0.0009829823],"study_design_scores_gemma":[0.000006022135,0.0000122086785,0.00006593494,0.0000027546298,0.000007142997,0.0000067320047,0.000010531236,0.9985568,0.00003313274,0.001128036,0.00016609115,0.0000046320133],"about_ca_topic_score_codex":0.024113113,"about_ca_topic_score_gemma":0.019447366,"teacher_disagreement_score":0.024113113,"about_ca_system_score_codex":0.0028170028,"about_ca_system_score_gemma":0.0021055709,"threshold_uncertainty_score":0.04794556},"labels":[],"label_agreement":null},{"id":"W3199751886","doi":"10.5539/jmr.v13n5p14","title":"A Comprehensive Comparison of Label Setting Algorithm and Dynamic Programming Algorithm in Solving Shortest Path Problems","year":2021,"lang":"en","type":"article","venue":"Journal of Mathematics Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Shortest path problem; Computer science; Algorithm; Dynamic programming; K shortest path routing; Path (computing); Mathematical optimization; Mathematics; Theoretical computer science; Graph; Computer network","score_opus":0.07689474368349493,"score_gpt":0.4005593809347281,"score_spread":0.3236646372512332,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3199751886","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011179589,0.0032506657,0.9749883,0.00036142953,0.00023606405,0.000095848096,0.00006192739,0.000565124,0.009261013],"genre_scores_gemma":[0.21730794,0.0057635866,0.7697109,0.00030382827,0.0002512387,0.00041324287,0.00054008665,0.00032867576,0.00538049],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9973127,0.0009685871,0.0001497836,0.00052502204,0.00083832943,0.00020554531],"domain_scores_gemma":[0.99778265,0.0012880367,0.000091861744,0.00016139315,0.0005856039,0.00009057852],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002233934,0.0011698562,0.0014283818,0.001596302,0.0012784896,0.0018656772,0.0017079862,0.0018961339,0.0036423649],"category_scores_gemma":[0.0060099103,0.0004402152,0.0010206676,0.0031915787,0.0007026755,0.0034019998,0.0012221787,0.0024363548,0.00069406995],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026114617,0.00036400734,0.0013916371,0.0006488569,0.00011623976,0.00009541172,0.00016763383,0.43269965,0.0022533503,0.058025,0.006590708,0.49738637],"study_design_scores_gemma":[0.000043340824,0.00014414503,0.00034631367,0.000048804694,0.000036570913,0.000093519986,0.000091061076,0.9703522,0.0017577048,0.019843165,0.00721405,0.000029180464],"about_ca_topic_score_codex":0.004820073,"about_ca_topic_score_gemma":0.0029746394,"teacher_disagreement_score":0.004820073,"about_ca_system_score_codex":0.0012070973,"about_ca_system_score_gemma":0.0028172897,"threshold_uncertainty_score":0.012184858},"labels":[],"label_agreement":null},{"id":"W3202848248","doi":"10.1016/j.cie.2021.107723","title":"Efficient flow models for the uncapacitated multiple allocation p-hub median problem on non-triangular networks","year":2021,"lang":"en","type":"article","venue":"Computers & Industrial Engineering","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Military College of Canada","funders":"","keywords":"Solver; Mathematical optimization; Triangle inequality; Mathematics; Flow network; Maximum flow problem; Linear programming; Flow (mathematics); Graph; Integer programming; Computer science; Combinatorics","score_opus":0.03162924463575501,"score_gpt":0.22251291745337867,"score_spread":0.19088367281762367,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3202848248","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.032292046,0.00039684286,0.95011425,0.00068711856,0.00008770994,0.00008946109,0.00035244136,0.0001471477,0.015833052],"genre_scores_gemma":[0.8304933,0.00097495277,0.13699882,0.00024073732,0.00011878126,0.0003614309,0.00059652515,0.00026854296,0.029946893],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994617,0.00024459537,0.000014900192,0.000085017076,0.000085105494,0.00010871879],"domain_scores_gemma":[0.9985178,0.0010063007,0.0001361485,0.00006594683,0.00017745365,0.000096315765],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014173533,0.0009118862,0.0014409053,0.00089592545,0.0006714381,0.002332822,0.0022208982,0.0017085781,0.009549779],"category_scores_gemma":[0.0039619952,0.0007041647,0.0009876428,0.0015644699,0.0010852603,0.0026297697,0.0012373268,0.0018771386,0.0005642263],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002231076,0.000021357462,0.00006534049,0.000018709477,0.0000060991197,0.000015216966,0.00001687236,0.95675087,0.000116305084,0.039964363,0.0007917795,0.0022107135],"study_design_scores_gemma":[0.0000026406397,0.0000033017693,0.00001068932,0.0000019847216,0.0000010928296,0.0000015038034,0.0000053777217,0.9886002,0.000019511386,0.011216824,0.00013557053,0.0000013350663],"about_ca_topic_score_codex":0.011881677,"about_ca_topic_score_gemma":0.013167947,"teacher_disagreement_score":0.011881677,"about_ca_system_score_codex":0.003149386,"about_ca_system_score_gemma":0.001919243,"threshold_uncertainty_score":0.031947136},"labels":[],"label_agreement":null},{"id":"W3204221696","doi":"10.1016/j.orl.2022.01.018","title":"Guidelines for the computational testing of machine learning approaches to vehicle routing problems","year":2022,"lang":"en","type":"article","venue":"Operations Research Letters","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":35,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Heuristics; Vehicle routing problem; Computer science; Routing (electronic design automation); Work (physics); Machine learning; Management science; Operations research; Engineering","score_opus":0.38072239786165063,"score_gpt":0.3906359972719764,"score_spread":0.009913599410325769,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3204221696","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002424718,0.0017839678,0.96705276,0.0055903816,0.0005682458,0.0013907128,0.0012723038,0.0023978774,0.017519103],"genre_scores_gemma":[0.01718924,0.00090780354,0.97165316,0.0012834024,0.00027108844,0.0029740294,0.0013667154,0.0009976702,0.0033570463],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.94756186,0.03334121,0.00648848,0.0014418947,0.010311168,0.0008554549],"domain_scores_gemma":[0.79203403,0.13679744,0.004797647,0.023224842,0.041511584,0.0016345228],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03480418,0.0028222518,0.0018826545,0.0050651454,0.0021167598,0.0054733274,0.009508629,0.0062874854,0.016307212],"category_scores_gemma":[0.22342108,0.0019115884,0.0024283149,0.0037764895,0.003831315,0.0048766276,0.004796619,0.009947669,0.009663689],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003188351,0.0012034421,0.0023799767,0.002140524,0.00017872208,0.00094953616,0.0009797759,0.07924627,0.004850587,0.47966582,0.14235109,0.28573552],"study_design_scores_gemma":[0.00049203396,0.00039623195,0.0011474135,0.003254214,0.00008609419,0.0005663827,0.0005380748,0.28002393,0.009020852,0.55775213,0.14652206,0.0002006506],"about_ca_topic_score_codex":0.006499689,"about_ca_topic_score_gemma":0.009153382,"teacher_disagreement_score":0.03480418,"about_ca_system_score_codex":0.0018153706,"about_ca_system_score_gemma":0.0059181,"threshold_uncertainty_score":0.18406427},"labels":[],"label_agreement":null},{"id":"W3205122045","doi":"10.1002/net.22112","title":"The consistent production routing problem","year":2022,"lang":"en","type":"article","venue":"Networks","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Group for Research in Decision Analysis; HEC Montréal","funders":"","keywords":"Mathematical optimization; Consistency (knowledge bases); Computer science; Routing (electronic design automation); Local consistency; Production (economics); Heuristic; Consistency model; Linear programming; Mathematics; Data consistency; Distributed computing; Constraint satisfaction; Probabilistic logic","score_opus":0.011754787796061596,"score_gpt":0.22163534045340202,"score_spread":0.20988055265734043,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3205122045","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07459269,0.00058818073,0.90301806,0.0014541289,0.00012589003,0.00037430655,0.00070664124,0.00027039906,0.018869761],"genre_scores_gemma":[0.6182631,0.0005758697,0.371409,0.00028759785,0.000112731635,0.00050620286,0.0012405803,0.00017685907,0.007428027],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99704045,0.0014319487,0.00013656546,0.00063419057,0.00045730683,0.00029945385],"domain_scores_gemma":[0.99617803,0.0026446,0.0003861625,0.0002928502,0.0003606935,0.00013770688],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003076032,0.00096071244,0.0011047176,0.0008448662,0.0009697894,0.0023930478,0.0018291371,0.0021443886,0.006327956],"category_scores_gemma":[0.007630444,0.0007085233,0.000999058,0.0016417479,0.0015354346,0.0023865178,0.0013134643,0.0017830534,0.00046144438],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012355472,0.00012904881,0.0010212287,0.00021578553,0.00007502683,0.0005234945,0.00014160688,0.83253634,0.0014037223,0.11833686,0.004122218,0.041371096],"study_design_scores_gemma":[0.00009701592,0.000116728166,0.00035914473,0.000048792168,0.000040164516,0.00025246033,0.00015003096,0.79579324,0.0012652134,0.19400088,0.007850972,0.000025268926],"about_ca_topic_score_codex":0.0026064138,"about_ca_topic_score_gemma":0.0016889699,"teacher_disagreement_score":0.006327956,"about_ca_system_score_codex":0.0013499144,"about_ca_system_score_gemma":0.0023424078,"threshold_uncertainty_score":0.021169126},"labels":[],"label_agreement":null},{"id":"W3205317538","doi":"10.5267/j.ijiec.2021.6.001","title":"A granular tabu search for the refrigerated vehicle routing problem with homogeneous fleet","year":2021,"lang":"en","type":"article","venue":"International Journal of Industrial Engineering Computations","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Tabu search; Vehicle routing problem; Benchmark (surveying); Energy consumption; Homogeneous; Computer science; Mathematical optimization; Selection (genetic algorithm); Routing (electronic design automation); Truck; Operations research; Engineering; Automotive engineering; Mathematics; Artificial intelligence; Computer network","score_opus":0.033558854195274,"score_gpt":0.2816800808341932,"score_spread":0.24812122663891922,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3205317538","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1556705,0.0008686855,0.834802,0.00029881636,0.0000803981,0.00022213408,0.00031207848,0.00048664847,0.0072587063],"genre_scores_gemma":[0.7013775,0.00033973999,0.29546902,0.00009950204,0.000032684897,0.00031428336,0.00038319256,0.000076857024,0.0019072588],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996338,0.00016434338,0.000015396365,0.000059946477,0.00006323313,0.000063264975],"domain_scores_gemma":[0.99951625,0.00029369848,0.000060053808,0.000042917778,0.000047185527,0.000039873965],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00078171573,0.00058412325,0.0009909086,0.0007535786,0.0004978822,0.0010929868,0.0011241742,0.0010003557,0.002555143],"category_scores_gemma":[0.0020407827,0.00040448012,0.00057933945,0.0013369801,0.00048435462,0.0008785768,0.0007583628,0.0006491446,0.00023202642],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007548696,0.0000359207,0.0003086008,0.000060866696,0.000020104886,0.000047952377,0.000031749405,0.9756479,0.0005011511,0.0053977747,0.0005751638,0.017297357],"study_design_scores_gemma":[0.000016584761,0.00003765278,0.00008725269,0.000008640983,0.0000060434336,0.000012660799,0.000019491894,0.99613416,0.00010957066,0.0032852178,0.00027902998,0.0000036968145],"about_ca_topic_score_codex":0.0050371243,"about_ca_topic_score_gemma":0.004407407,"teacher_disagreement_score":0.0050371243,"about_ca_system_score_codex":0.00073734316,"about_ca_system_score_gemma":0.000826138,"threshold_uncertainty_score":0.010015607},"labels":[],"label_agreement":null},{"id":"W3205680747","doi":"10.1155/2021/6619539","title":"Two-Echelon Multidepot Logistics Network Design with Resource Sharing","year":2021,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Chongqing Municipal Education Commission; National Natural Science Foundation of China","keywords":"Crossover; Mathematical optimization; Computer science; Sorting; Genetic algorithm; Greedy algorithm; Operations research; Cluster analysis; Engineering; Mathematics; Algorithm","score_opus":0.023175313279072973,"score_gpt":0.2695580455629636,"score_spread":0.24638273228389063,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3205680747","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.050886065,0.0004755723,0.9374531,0.00022978893,0.000054450094,0.00016941664,0.00017862167,0.00017230414,0.010380628],"genre_scores_gemma":[0.8401893,0.0006006488,0.15094739,0.00010176319,0.000021303684,0.00043433494,0.0001892166,0.000049725466,0.007466322],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99923325,0.0002466001,0.000031880867,0.00019642702,0.00015893998,0.00013285594],"domain_scores_gemma":[0.99962115,0.0001228971,0.00008207529,0.000031614218,0.00008214075,0.00006009139],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000845274,0.0010611628,0.0010534915,0.0006829811,0.0006239539,0.0015004199,0.0017977195,0.0011466979,0.0032136801],"category_scores_gemma":[0.00092549063,0.0006185878,0.0010824827,0.0010624719,0.0005126096,0.0014490446,0.0016016398,0.0007503697,0.00026585336],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000021597049,0.00002418448,0.00019762482,0.000041503226,0.00001894928,0.000068581045,0.000015372501,0.9876632,0.00090066605,0.0043113315,0.00021621046,0.006520687],"study_design_scores_gemma":[0.0000054753586,0.000029435283,0.000057992114,0.0000036584463,0.0000063211683,0.00001617071,0.000015400488,0.997413,0.0002702663,0.0017126243,0.0004656177,0.0000040743735],"about_ca_topic_score_codex":0.005335633,"about_ca_topic_score_gemma":0.004685935,"teacher_disagreement_score":0.005335633,"about_ca_system_score_codex":0.0017644151,"about_ca_system_score_gemma":0.0014118736,"threshold_uncertainty_score":0.012801826},"labels":[],"label_agreement":null},{"id":"W3207420437","doi":"10.5267/j.ijiec.2021.8.001","title":"Cockpit crew pairing Pareto optimisation in a budget airline","year":2021,"lang":"en","type":"article","venue":"International Journal of Industrial Engineering Computations","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Chulalongkorn University","keywords":"Crew; Cockpit; Crew scheduling; Pareto principle; Computer science; Operations research; Reliability engineering; Mathematical optimization; Aeronautics; Engineering; Operations management; Mathematics","score_opus":0.03306707408553632,"score_gpt":0.287297977414064,"score_spread":0.25423090332852766,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3207420437","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.62359506,0.0005344917,0.36266044,0.0002514666,0.00006332049,0.00013106618,0.00013486682,0.00017710098,0.012452224],"genre_scores_gemma":[0.9514002,0.00015457037,0.04509926,0.000032221364,0.000009054204,0.000057307392,0.00009056652,0.0000276305,0.0031291866],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996575,0.00013587509,0.000008251948,0.000043717926,0.00006632622,0.00008829747],"domain_scores_gemma":[0.9997216,0.00012480699,0.00004727847,0.000020475452,0.0000419807,0.000043899166],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007778173,0.0007344703,0.00063769124,0.00054799643,0.00051857525,0.00080624345,0.00039297985,0.0007641573,0.001894926],"category_scores_gemma":[0.0010395308,0.00033226263,0.00055947783,0.00047756833,0.0003736011,0.00063118513,0.0006892584,0.0006014947,0.00015829186],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000095336974,0.00003614296,0.0009093223,0.00003317873,0.000016338146,0.00008665047,0.000028958384,0.97909683,0.002358255,0.0026437943,0.00032397153,0.014371312],"study_design_scores_gemma":[0.0000146963885,0.00019499069,0.0011555246,0.000011531379,0.000010911403,0.000051474006,0.00010349944,0.9939615,0.0014303915,0.0019151744,0.0011412752,0.000009131141],"about_ca_topic_score_codex":0.008570732,"about_ca_topic_score_gemma":0.0061066225,"teacher_disagreement_score":0.008570732,"about_ca_system_score_codex":0.00068513706,"about_ca_system_score_gemma":0.00091899966,"threshold_uncertainty_score":0.017041683},"labels":[],"label_agreement":null},{"id":"W3211240821","doi":"10.1155/2021/8711964","title":"Study on the Optimization of Hub-and-Spoke Logistics Network regarding Traffic Congestion","year":2021,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Spoke-hub distribution paradigm; Network traffic control; Computer science; Network planning and design; Traffic congestion; Flow network; Operations research; Transport engineering; Computer network; Engineering; Mathematical optimization","score_opus":0.024728834219215574,"score_gpt":0.27574437810458213,"score_spread":0.25101554388536657,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3211240821","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15769684,0.0013576237,0.8296967,0.00049843267,0.0001212894,0.000103305865,0.00009725831,0.00013749501,0.010291096],"genre_scores_gemma":[0.9705231,0.001027981,0.024413556,0.000062147956,0.000037706104,0.00010917919,0.00007618478,0.00004116144,0.0037089363],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996177,0.00013730513,0.000010795652,0.000083403844,0.000056509583,0.000094196876],"domain_scores_gemma":[0.99959105,0.00022733741,0.000059276914,0.000014506994,0.00007155795,0.00003623138],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010348832,0.0010417377,0.001190041,0.0006316458,0.0006139009,0.0012870934,0.0007256537,0.0011597626,0.001524771],"category_scores_gemma":[0.0017651108,0.0005177877,0.0008049921,0.00086229754,0.00075615133,0.0015398631,0.0007878672,0.00087832235,0.00008490831],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015679176,0.000010476926,0.00020763584,0.000029280547,0.000011585801,0.000037101694,0.000011969077,0.99408203,0.00036482792,0.0031336097,0.0001361304,0.0019596133],"study_design_scores_gemma":[0.0000029870462,0.000022449729,0.00009553777,0.0000027871129,0.000006610741,0.000006689112,0.000017095457,0.9986298,0.0001014201,0.0009806574,0.00013132885,0.000002665802],"about_ca_topic_score_codex":0.0097345365,"about_ca_topic_score_gemma":0.0040892344,"teacher_disagreement_score":0.0097345365,"about_ca_system_score_codex":0.0012846629,"about_ca_system_score_gemma":0.0015701634,"threshold_uncertainty_score":0.019355774},"labels":[],"label_agreement":null},{"id":"W3213457727","doi":"10.48550/arxiv.2111.04536","title":"Network Migration Problem: A Logic-based Benders Decomposition Approach Driven by Column Generation and Constraint Programming","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Column generation; Constraint programming; Computer science; Upgrade; Purchasing; Mathematical optimization; Integer programming; Constraint logic programming; Node (physics); Constraint (computer-aided design); Stochastic programming; Engineering; Mathematics; Operations management; Algorithm","score_opus":0.05995537691616206,"score_gpt":0.20210791226161792,"score_spread":0.14215253534545585,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3213457727","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0042606215,0.000099928584,0.9921556,0.00017973976,0.000028859602,0.0000732709,0.00011838363,0.00011639419,0.0029672296],"genre_scores_gemma":[0.11200802,0.00027410217,0.882245,0.00020736897,0.00006536334,0.00035566385,0.00048477494,0.00015266548,0.0042070844],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99950004,0.00020365154,0.00001898899,0.00008790324,0.000121040226,0.00006837298],"domain_scores_gemma":[0.9994424,0.00035481722,0.00004972668,0.000033039454,0.00008597164,0.00003403604],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010777869,0.001238782,0.00076120545,0.0008878717,0.0006031978,0.0012719145,0.0012371753,0.0009811213,0.005729334],"category_scores_gemma":[0.0016097242,0.00068303186,0.0013961218,0.0011657058,0.00062934787,0.001096247,0.0010618258,0.0018544304,0.0006571515],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003168269,0.000054700413,0.00026667028,0.00008005924,0.000026437192,0.00006407568,0.000045679764,0.93956846,0.0012848874,0.024593811,0.0021562763,0.031827252],"study_design_scores_gemma":[0.000008483562,0.000013277645,0.000037338254,0.000008524478,0.0000058311566,0.000013012032,0.000015220704,0.9894791,0.0003568925,0.00900039,0.001057905,0.0000041197236],"about_ca_topic_score_codex":0.0071594794,"about_ca_topic_score_gemma":0.008039368,"teacher_disagreement_score":0.0071594794,"about_ca_system_score_codex":0.0011809748,"about_ca_system_score_gemma":0.0018404305,"threshold_uncertainty_score":0.019166589},"labels":[],"label_agreement":null},{"id":"W3213598897","doi":"10.1108/srt-08-2021-0008","title":"Modeling a robust multi-objective locating-routing problem with bounded delivery time using meta-heuristic algorithms","year":2021,"lang":"en","type":"article","venue":"Smart and Resilient Transport","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Vehicle routing problem; Mathematical optimization; Computer science; Tardiness; Sorting; Genetic algorithm; Particle swarm optimization; Bottleneck; Routing (electronic design automation); Heuristic; Algorithm; Mathematics; Job shop scheduling","score_opus":0.03633847495880669,"score_gpt":0.24317006625681004,"score_spread":0.20683159129800335,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3213598897","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04888453,0.0008654187,0.93731326,0.00037248997,0.00006584942,0.00018502177,0.00016589469,0.00016295712,0.011984496],"genre_scores_gemma":[0.7728251,0.0009820337,0.21921259,0.00010778717,0.00005660199,0.00054715853,0.00021085437,0.000066940556,0.0059908726],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99946636,0.00024492238,0.000024031859,0.00009142143,0.000099603494,0.00007365961],"domain_scores_gemma":[0.9991879,0.0005337205,0.00013207062,0.000027176684,0.00008987978,0.000029162054],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011565594,0.0012303529,0.0010666284,0.001052346,0.00041679616,0.001984569,0.0014144414,0.0015239213,0.0024974726],"category_scores_gemma":[0.0019369407,0.0006974868,0.0014637741,0.0010516088,0.00055527483,0.00092049735,0.000748074,0.001023903,0.00026075018],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000066630705,0.000010566207,0.000109127715,0.0000272415,0.00001316916,0.000018708264,0.0000078500425,0.9957879,0.00010096332,0.0021544788,0.00006587746,0.0016974472],"study_design_scores_gemma":[0.0000032411685,0.000012037051,0.00003485871,0.0000074356412,0.000006578178,0.000004402154,0.000010430362,0.9985776,0.00005903598,0.0011054315,0.00017710804,0.0000017929212],"about_ca_topic_score_codex":0.009603455,"about_ca_topic_score_gemma":0.0070498926,"teacher_disagreement_score":0.009603455,"about_ca_system_score_codex":0.0015263072,"about_ca_system_score_gemma":0.0022435882,"threshold_uncertainty_score":0.019095063},"labels":[],"label_agreement":null},{"id":"W3214868035","doi":"10.1016/j.cor.2021.105643","title":"Hybrid Genetic Search for the CVRP: Open-Source Implementation and SWAP* Neighborhood","year":2020,"lang":"en","type":"article","venue":"arXiv (Cornell University)","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":197,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"Conselho Nacional de Desenvolvimento Científico e Tecnológico; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior; Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro; Compute Canada","keywords":"Swap (finance); Computer science; Vehicle routing problem; Sophistication; Metaheuristic; Mathematical optimization; Open source; Simplicity; Operations research; Routing (electronic design automation); Algorithm; Mathematics; Software","score_opus":0.084428929637477,"score_gpt":0.23377888125092935,"score_spread":0.14934995161345235,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3214868035","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.024193188,0.00051104464,0.944751,0.00030007,0.0001721976,0.00021476473,0.00026785114,0.013207249,0.016382627],"genre_scores_gemma":[0.15955709,0.0003603038,0.8318043,0.00018411575,0.00003746897,0.00052823295,0.00094882515,0.0016998905,0.004879764],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994473,0.000186363,0.000028822202,0.00008397539,0.00018878866,0.00006465327],"domain_scores_gemma":[0.9991708,0.00045924273,0.00004839582,0.0001539344,0.0001300098,0.00003766738],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011058816,0.00068630686,0.0005050895,0.0006612915,0.00034033103,0.0009729915,0.002553668,0.001699757,0.008346041],"category_scores_gemma":[0.003766888,0.00034198718,0.00068131945,0.0010354039,0.0005810839,0.0008781508,0.0016217553,0.001324176,0.002354923],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00035723002,0.0005506767,0.0021395436,0.0005797133,0.00014447604,0.0003636236,0.0002928099,0.50408614,0.0072318567,0.07968153,0.019351585,0.3852209],"study_design_scores_gemma":[0.00013292088,0.0000941482,0.00029439738,0.000053226562,0.00002256856,0.000109514294,0.0000435885,0.9606062,0.0034648997,0.015643787,0.019513376,0.000021344204],"about_ca_topic_score_codex":0.0028550865,"about_ca_topic_score_gemma":0.003304176,"teacher_disagreement_score":0.008346041,"about_ca_system_score_codex":0.00051087694,"about_ca_system_score_gemma":0.001132719,"threshold_uncertainty_score":0.027920306},"labels":[],"label_agreement":null},{"id":"W35147626","doi":"10.1039/d1fo02409d","title":"On Export Intermodal Transportation Problem","year":2012,"lang":"en","type":"article","venue":"Food & Function","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Business; Transport engineering; Engineering","score_opus":0.01648786916203184,"score_gpt":0.2288026113357905,"score_spread":0.21231474217375865,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W35147626","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21283855,0.010977818,0.109566815,0.058835275,0.0046321526,0.00089272094,0.012624869,0.0018598672,0.58777183],"genre_scores_gemma":[0.73723364,0.008134627,0.028070604,0.0045737145,0.0012144659,0.0002901009,0.01002192,0.000391209,0.21006964],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9992519,0.00016032251,0.000041704774,0.00020333902,0.00012131973,0.00022147961],"domain_scores_gemma":[0.9993698,0.00014149473,0.00013661887,0.000057513338,0.00016225516,0.0001323322],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00071894424,0.0008987542,0.00068148994,0.0013316054,0.0026397917,0.0034637873,0.0013106439,0.0030406164,0.06038174],"category_scores_gemma":[0.0013711407,0.00025307192,0.00064412836,0.0025392917,0.0008331573,0.0038119417,0.0022131905,0.0021379264,0.0049090497],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00047876142,0.00053820177,0.022222579,0.0009619027,0.0002448702,0.0063115954,0.0009955062,0.07246715,0.0031082507,0.22202997,0.38634738,0.28429377],"study_design_scores_gemma":[0.000083728635,0.00024173931,0.011825257,0.0005682627,0.00009911959,0.003509165,0.0096364785,0.14924543,0.0020361424,0.07541691,0.7472314,0.00010635836],"about_ca_topic_score_codex":0.022335451,"about_ca_topic_score_gemma":0.013781884,"teacher_disagreement_score":0.06038174,"about_ca_system_score_codex":0.0027800577,"about_ca_system_score_gemma":0.0017726303,"threshold_uncertainty_score":0.20199704},"labels":[],"label_agreement":null},{"id":"W407282568","doi":"","title":"枝コストに制限を加えたk-Canadian Traveller Problemの競合比解析","year":2010,"lang":"ja","type":"article","venue":"電子情報通信学会技術研究報告. COMP, コンピュテーション","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Computer science","score_opus":0.01567056958695733,"score_gpt":0.24469134619955182,"score_spread":0.22902077661259448,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W407282568","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.30974674,0.0035360076,0.37631565,0.014635687,0.00055843336,0.0004080648,0.0043958235,0.00029049796,0.29011315],"genre_scores_gemma":[0.8513179,0.0014847975,0.065521955,0.00036849966,0.000097608616,0.00016019806,0.0010403646,0.00008746043,0.07992122],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995732,0.000098713645,0.00001091659,0.000105855215,0.000078029,0.00013324863],"domain_scores_gemma":[0.99961984,0.00014567534,0.000035345103,0.000014930874,0.000113127295,0.00007106823],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00073621736,0.0006953583,0.00055397226,0.00067393325,0.0015655049,0.0027390162,0.0014291056,0.0016643299,0.011732475],"category_scores_gemma":[0.0019654264,0.0003847191,0.0005199108,0.0011380276,0.0011678899,0.0019001785,0.0009967359,0.0013320321,0.0005197374],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00030125323,0.000095247626,0.0021682414,0.0003094729,0.00007177282,0.00025848692,0.00026021266,0.50788665,0.000700684,0.40476942,0.027925441,0.05525318],"study_design_scores_gemma":[0.00010626319,0.000076811986,0.0018614467,0.00009887434,0.000061836814,0.00018590977,0.0011120962,0.7490925,0.0013057396,0.214664,0.03134161,0.000092865346],"about_ca_topic_score_codex":0.3193151,"about_ca_topic_score_gemma":0.26689735,"teacher_disagreement_score":0.3193151,"about_ca_system_score_codex":0.007386096,"about_ca_system_score_gemma":0.008468086,"threshold_uncertainty_score":0.63491297},"labels":[],"label_agreement":null},{"id":"W41672188","doi":"","title":"School bus selection, routing and scheduling.","year":2005,"lang":"en","type":"article","venue":"Scholarship at UWindsor (University of Windsor)","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Scheduling (production processes); Computer science; Selection (genetic algorithm); School bus; Engineering; Operations management; Artificial intelligence; Transport engineering","score_opus":0.013201532391812345,"score_gpt":0.21982026003128138,"score_spread":0.20661872763946904,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W41672188","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03927939,0.015599937,0.53751594,0.00673692,0.0022461463,0.0012089788,0.010995675,0.004842168,0.38157487],"genre_scores_gemma":[0.2615064,0.019082574,0.32491836,0.0007369582,0.00084663427,0.0009185953,0.014692731,0.0010101658,0.37628752],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99926823,0.00021459378,0.00004140727,0.00015401901,0.00024048587,0.00008116115],"domain_scores_gemma":[0.9995801,0.0001023336,0.00008280014,0.000053095588,0.00010862658,0.00007304078],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006581471,0.0007799159,0.00073631835,0.0011141925,0.00063736556,0.002088836,0.0008064904,0.0007353079,0.04657727],"category_scores_gemma":[0.0017078096,0.000376175,0.00056252436,0.0027617076,0.0002773902,0.0014443211,0.000761525,0.0007995698,0.014086223],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012578315,0.00019173055,0.003129793,0.0007532259,0.000064276166,0.00017058253,0.00013531005,0.081078045,0.0017930791,0.11719023,0.18167177,0.61369616],"study_design_scores_gemma":[0.00007521248,0.00023654847,0.0054793027,0.00021721776,0.00005809211,0.0004357673,0.0005994153,0.12968048,0.003070004,0.12810813,0.7319969,0.000042945077],"about_ca_topic_score_codex":0.00334503,"about_ca_topic_score_gemma":0.008082781,"teacher_disagreement_score":0.04657727,"about_ca_system_score_codex":0.0012758571,"about_ca_system_score_gemma":0.0021982067,"threshold_uncertainty_score":0.1558165},"labels":[],"label_agreement":null},{"id":"W4200307786","doi":"10.18280/jesa.540614","title":"A Heterogeneous Vehicle Routing Problem with Soft Time Windows for 3PL Company’s Deliveries: A Case Study","year":2021,"lang":"en","type":"article","venue":"Journal Européen des Systèmes Automatisés","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Vehicle routing problem; Consolidation (business); Operations research; Computer science; Interval (graph theory); Service (business); Fleet management; Process (computing); Routing (electronic design automation); Mathematical optimization; Engineering; Business; Mathematics; Computer network","score_opus":0.020891603396323382,"score_gpt":0.2612998627086541,"score_spread":0.2404082593123307,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200307786","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.763647,0.00079103204,0.21056159,0.0015727733,0.00012251551,0.0003249829,0.0011245464,0.00023201118,0.021623563],"genre_scores_gemma":[0.943775,0.0003217698,0.047201425,0.00006444387,0.0000372013,0.000117245836,0.00036756796,0.000041401076,0.008073927],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994593,0.00021582039,0.000019207098,0.00008047163,0.000072608884,0.00015260439],"domain_scores_gemma":[0.99910396,0.00058353203,0.000085394495,0.00004171634,0.00006931699,0.00011600057],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00076774985,0.00084249786,0.0006433342,0.00060553267,0.0010261099,0.0013168193,0.0011588793,0.002976276,0.003971438],"category_scores_gemma":[0.0011876395,0.0004014248,0.0011438477,0.0011543026,0.000591575,0.0009102486,0.0007461552,0.0009894534,0.00028356686],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013283032,0.00025265585,0.001623268,0.00010197758,0.000036853122,0.0022368317,0.000095916184,0.9748633,0.0012291808,0.0077534965,0.0020343435,0.009639299],"study_design_scores_gemma":[0.000043622545,0.00012567757,0.0009057669,0.000010657722,0.000023042909,0.00033632756,0.00029668,0.99192625,0.0011145788,0.0029754648,0.0022233573,0.000018639763],"about_ca_topic_score_codex":0.017206034,"about_ca_topic_score_gemma":0.014997755,"teacher_disagreement_score":0.017206034,"about_ca_system_score_codex":0.0017211959,"about_ca_system_score_gemma":0.0010118238,"threshold_uncertainty_score":0.034211814},"labels":[],"label_agreement":null},{"id":"W4205362405","doi":"10.1108/ijicc-08-2021-0163","title":"Designing a locating-routing three-echelon supply chain network under uncertainty","year":2022,"lang":"en","type":"article","venue":"International Journal of Intelligent Computing and Cybernetics","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Vehicle routing problem; Genetic algorithm; Sorting; Mathematical optimization; Particle swarm optimization; Supply chain; Routing (electronic design automation); Operations research; Distributor; Service (business); Algorithm; Mathematics","score_opus":0.018780576492913353,"score_gpt":0.27131172033245793,"score_spread":0.25253114383954456,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4205362405","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07591797,0.0002511273,0.9162361,0.0003846631,0.0000250026,0.00016470713,0.00016108407,0.00017188142,0.006687382],"genre_scores_gemma":[0.82534975,0.00038766064,0.17035042,0.00007725025,0.000015608292,0.0002258603,0.00018289086,0.000030337269,0.0033801917],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993198,0.0002267531,0.000033707627,0.0001861073,0.00012219655,0.00011139033],"domain_scores_gemma":[0.999398,0.00027233036,0.0001202858,0.000038790586,0.000121046905,0.000049484817],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010203171,0.00056609116,0.0009114575,0.0008970586,0.0009073374,0.0016150429,0.0012986541,0.0013004136,0.0036541184],"category_scores_gemma":[0.0021699818,0.00057251216,0.00070788484,0.0015939425,0.00072085863,0.001729863,0.0015040761,0.000713355,0.00032150888],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001549214,0.000011548506,0.00041010638,0.00002923276,0.00001071242,0.00004760074,0.000026520422,0.98720074,0.0005069388,0.0033162609,0.00014274198,0.008282133],"study_design_scores_gemma":[0.0000039451743,0.000021531552,0.00010072573,0.0000072414778,0.000005553759,0.00001409766,0.00004760452,0.9954391,0.00021952318,0.003781389,0.000354294,0.0000049845144],"about_ca_topic_score_codex":0.010944527,"about_ca_topic_score_gemma":0.0072141173,"teacher_disagreement_score":0.010944527,"about_ca_system_score_codex":0.0018128055,"about_ca_system_score_gemma":0.0020998078,"threshold_uncertainty_score":0.021761596},"labels":[],"label_agreement":null},{"id":"W4205537357","doi":"10.1016/j.ejor.2021.12.050","title":"A branch-and-cut algorithm for the vehicle routing problem with two-dimensional loading constraints","year":2022,"lang":"en","type":"article","venue":"European Journal of Operational Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":32,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Transport Canada","funders":"Shanghai Jiao Tong University; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China; Compute Canada; Polytechnique Montréal","keywords":"Vehicle routing problem; Branch and cut; Computer science; Mathematical optimization; Routing (electronic design automation); Integer programming; Branch and bound; Algorithm; Mathematics; Computer network","score_opus":0.05341610305665129,"score_gpt":0.3333075957489059,"score_spread":0.2798914926922546,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4205537357","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006356324,0.00025005534,0.989267,0.00023092816,0.000079124664,0.00011578966,0.00011738605,0.00053836184,0.0030451068],"genre_scores_gemma":[0.051042765,0.0002716659,0.9443787,0.00011317961,0.000062807136,0.00032445262,0.00038079853,0.00021780994,0.003207771],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9994351,0.00016388067,0.000025772839,0.0000948445,0.00019036904,0.00008999784],"domain_scores_gemma":[0.9986602,0.00092080137,0.000062615705,0.00005788091,0.00020896741,0.000089599416],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011228278,0.0015370387,0.002137789,0.0013755405,0.0010833653,0.0015952059,0.001892306,0.0028057005,0.0107866945],"category_scores_gemma":[0.002953763,0.0011508398,0.0010816735,0.0023578317,0.00071456376,0.001721191,0.0016735874,0.0026835944,0.0016269021],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002060543,0.00026596276,0.00037974044,0.0001747898,0.00006628746,0.00009264956,0.00008473703,0.7084747,0.0025100328,0.013301311,0.007871174,0.26657256],"study_design_scores_gemma":[0.000056360845,0.000045045308,0.00006854462,0.0000124958615,0.000011975326,0.000021098725,0.000014923217,0.9925175,0.00031649886,0.0058944905,0.0010336859,0.000007405339],"about_ca_topic_score_codex":0.009892887,"about_ca_topic_score_gemma":0.009137001,"teacher_disagreement_score":0.0107866945,"about_ca_system_score_codex":0.0011465609,"about_ca_system_score_gemma":0.0029139263,"threshold_uncertainty_score":0.03608507},"labels":[],"label_agreement":null},{"id":"W4205600329","doi":"10.5267/j.ijiec.2021.10.001","title":"Hybrid algorithm for the solution of the periodic vehicle routing problem with variable service frequency","year":2022,"lang":"en","type":"article","venue":"International Journal of Industrial Engineering Computations","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Vehicle routing problem; Mathematical optimization; Integer programming; Time horizon; Ant colony optimization algorithms; Computer science; Metaheuristic; Routing (electronic design automation); Scheduling (production processes); Linear programming; Algorithm; Simulated annealing; Variable (mathematics); Set (abstract data type); Mathematics","score_opus":0.02123064897644581,"score_gpt":0.2451079673216813,"score_spread":0.2238773183452355,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4205600329","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011927378,0.00026813333,0.98305964,0.00009064378,0.000056331028,0.000085508465,0.000056413282,0.0004804561,0.003975607],"genre_scores_gemma":[0.22048983,0.00023063242,0.7733328,0.000094763884,0.00005390212,0.0005856974,0.00029351737,0.00011748575,0.0048014657],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997002,0.000078352336,0.0000147090295,0.00006793491,0.0000864032,0.000052533487],"domain_scores_gemma":[0.99963045,0.00022960524,0.00003969219,0.000022989872,0.000059140002,0.000018194136],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00063319545,0.0007984438,0.0007767743,0.0008222085,0.00045098248,0.0006612423,0.0012163208,0.0009779973,0.004088239],"category_scores_gemma":[0.0011295172,0.00034641192,0.00061141804,0.00087724754,0.00036535258,0.0006002454,0.00075708586,0.0007381186,0.000567267],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009045723,0.00007213792,0.00033761436,0.00008291542,0.000038704155,0.000041491032,0.000044213964,0.88994735,0.0012020556,0.012192324,0.001725545,0.09422524],"study_design_scores_gemma":[0.000020980246,0.00003001083,0.000042947056,0.000004479016,0.0000048590055,0.000012241638,0.0000074946192,0.9968651,0.00017325269,0.002039069,0.00079681305,0.0000028070694],"about_ca_topic_score_codex":0.0048753414,"about_ca_topic_score_gemma":0.00547974,"teacher_disagreement_score":0.0048753414,"about_ca_system_score_codex":0.0006517426,"about_ca_system_score_gemma":0.0013778402,"threshold_uncertainty_score":0.013676584},"labels":[],"label_agreement":null},{"id":"W4205601287","doi":"10.5267/j.ijiec.2021.10.002","title":"An exact algorithm for constrained k-cardinality unbalanced assignment problem","year":2022,"lang":"en","type":"article","venue":"International Journal of Industrial Engineering Computations","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Cardinality (data modeling); Assignment problem; Set (abstract data type); Generalized assignment problem; Weapon target assignment problem; Mathematical optimization; Algorithm; Computer science; Basis (linear algebra); Mathematics; Data mining","score_opus":0.03363362699043817,"score_gpt":0.3011088124539641,"score_spread":0.26747518546352594,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4205601287","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014418797,0.00032841243,0.97446096,0.00037372127,0.00008313736,0.00025075927,0.00035830704,0.0019311713,0.0077948277],"genre_scores_gemma":[0.13562052,0.00028336231,0.8558488,0.0001787016,0.0000628172,0.0005054142,0.0012904321,0.00025978842,0.005950216],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993568,0.00012942505,0.000033801076,0.00017587896,0.00014855224,0.00015554234],"domain_scores_gemma":[0.9995208,0.00020588766,0.000056714816,0.00008055391,0.00009776376,0.000038318958],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005206704,0.0011022944,0.0012065369,0.0010691966,0.0010350908,0.0011178661,0.0017662285,0.0013478647,0.012786895],"category_scores_gemma":[0.001804116,0.00047227627,0.00075726403,0.0019264964,0.00041215442,0.0016124363,0.0015695011,0.0012359099,0.0022167948],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003375207,0.00032798474,0.0012369112,0.00038359876,0.000053466414,0.00022765793,0.00019588486,0.49090603,0.0047489847,0.031454477,0.02248392,0.44764358],"study_design_scores_gemma":[0.00009186666,0.00006252115,0.00025124926,0.000020494394,0.000014177989,0.0001520646,0.00007005376,0.97764814,0.0007814343,0.016142333,0.00475129,0.000014314503],"about_ca_topic_score_codex":0.006169141,"about_ca_topic_score_gemma":0.0071183546,"teacher_disagreement_score":0.012786895,"about_ca_system_score_codex":0.0011243557,"about_ca_system_score_gemma":0.0029008992,"threshold_uncertainty_score":0.042776406},"labels":[],"label_agreement":null},{"id":"W4205882819","doi":"10.5267/j.ijiec.2021.12.002","title":"Clustering and heuristics algorithm for the vehicle routing problem with time windows","year":2022,"lang":"en","type":"article","venue":"International Journal of Industrial Engineering Computations","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Universidad Nacional de Colombia; Massachusetts Institute of Technology","keywords":"Vehicle routing problem; Heuristics; Cluster analysis; Computer science; Set (abstract data type); Mathematical optimization; Algorithm; Routing (electronic design automation); Mathematics; Artificial intelligence","score_opus":0.021013029719480766,"score_gpt":0.2563518208738407,"score_spread":0.2353387911543599,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4205882819","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0060601267,0.00033887234,0.98887783,0.00007920329,0.00007674479,0.00014444177,0.00011702674,0.00053525687,0.0037704855],"genre_scores_gemma":[0.07886906,0.0003125143,0.91688764,0.000070203045,0.000043145053,0.00028959467,0.00038425988,0.00015576046,0.002987804],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995672,0.00011220721,0.000023811637,0.0001131848,0.00012355194,0.000059988062],"domain_scores_gemma":[0.99969757,0.00013198673,0.000042461495,0.00003495573,0.000073145144,0.000019900315],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00057861983,0.0011862607,0.0007524882,0.001385187,0.00077897403,0.00086997193,0.0012534095,0.0009562401,0.0029872155],"category_scores_gemma":[0.0011991422,0.00046278088,0.001158888,0.0016676497,0.00042653017,0.000779372,0.0006369587,0.0010742699,0.000950927],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001318484,0.0001182286,0.00056881184,0.00017822796,0.00012736575,0.0000964975,0.00012916006,0.76228875,0.004055119,0.03665396,0.0065753106,0.18907669],"study_design_scores_gemma":[0.000032517622,0.00004874486,0.00021139259,0.000015309986,0.000021914235,0.00006386599,0.00003957777,0.984189,0.0014282246,0.008613515,0.0053192284,0.000016666767],"about_ca_topic_score_codex":0.0098709315,"about_ca_topic_score_gemma":0.009144883,"teacher_disagreement_score":0.0098709315,"about_ca_system_score_codex":0.0011602008,"about_ca_system_score_gemma":0.0022438576,"threshold_uncertainty_score":0.019626975},"labels":[],"label_agreement":null},{"id":"W4205930499","doi":"10.1287/ijoc.2021.1110","title":"Learning-Based Branch-and-Price Algorithms for the Vehicle Routing Problem with Time Windows and Two-Dimensional Loading Constraints","year":2021,"lang":"en","type":"article","venue":"INFORMS journal on computing","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":39,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Transport Canada","funders":"","keywords":"Vehicle routing problem; Computer science; Column generation; Algorithm; Mathematical optimization; Branch and cut; Routing (electronic design automation); Integer programming; Mathematics","score_opus":0.013169023414246587,"score_gpt":0.253346230159968,"score_spread":0.2401772067457214,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4205930499","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03610787,0.00095548754,0.95687705,0.0007101268,0.000115726245,0.00023155351,0.00013207216,0.0007306573,0.0041395132],"genre_scores_gemma":[0.27901024,0.0006381664,0.71468973,0.00035710496,0.00017763337,0.00052901765,0.0005684919,0.0002604327,0.0037691407],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986588,0.0005086055,0.00009390041,0.0002330465,0.00025057953,0.00025505628],"domain_scores_gemma":[0.9927925,0.0056434404,0.00046180622,0.00034442774,0.0005145504,0.00024324098],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0030056858,0.002107691,0.0029038785,0.0011710173,0.00074700394,0.0020786468,0.002869548,0.0026084126,0.005779065],"category_scores_gemma":[0.008484128,0.0010108631,0.0014448102,0.0024751483,0.0009977387,0.003679047,0.0015848276,0.003858014,0.0008627829],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001861283,0.00031704092,0.00073257607,0.00014644962,0.00006223539,0.000049308837,0.00005678834,0.87145793,0.00045872194,0.013834455,0.0026538803,0.11004444],"study_design_scores_gemma":[0.00002039462,0.000029695482,0.000037712387,0.0000044290446,0.0000054471475,0.0000068807003,0.000006046476,0.99523085,0.00009634568,0.0044213794,0.00013789869,0.0000028823367],"about_ca_topic_score_codex":0.006683962,"about_ca_topic_score_gemma":0.006973748,"teacher_disagreement_score":0.006683962,"about_ca_system_score_codex":0.002336576,"about_ca_system_score_gemma":0.003890191,"threshold_uncertainty_score":0.019332886},"labels":[],"label_agreement":null},{"id":"W4206161370","doi":"10.5267/j.ijiec.2021.12.003","title":"How to start a heuristic? Utilizing lower bounds for solving the quadratic assignment problem","year":2022,"lang":"en","type":"article","venue":"International Journal of Industrial Engineering Computations","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Quadratic assignment problem; Metaheuristic; Mathematical optimization; Heuristic; Bounding overwatch; Computation; Computer science; Weapon target assignment problem; Quadratic equation; Quadratic programming; Combinatorial optimization; Optimization problem; Algorithm; Mathematics; Artificial intelligence","score_opus":0.042516963782441256,"score_gpt":0.28286103164726606,"score_spread":0.24034406786482482,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4206161370","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010350021,0.00068177894,0.9808461,0.0007221112,0.00014571543,0.00006304633,0.000039419123,0.00039352136,0.006758333],"genre_scores_gemma":[0.1704372,0.00082114944,0.82461077,0.00045554413,0.0001075175,0.0001790362,0.00021097614,0.00049679686,0.0026810023],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990846,0.00035030465,0.00003949653,0.00013088368,0.00025951967,0.00013515612],"domain_scores_gemma":[0.9983479,0.00094822765,0.0001530221,0.0001993515,0.0002683801,0.00008314012],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015647449,0.0009998097,0.001184649,0.0010754372,0.0008149535,0.0016962383,0.0011745006,0.0015988477,0.0055304],"category_scores_gemma":[0.0071618324,0.0007758843,0.00095893664,0.0010599076,0.0011279887,0.0024044733,0.0013000049,0.0023183245,0.0017265568],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016301493,0.00015115894,0.0007002895,0.0003469486,0.00007684375,0.00013649066,0.00020529557,0.7644235,0.0044432273,0.07453218,0.0065175854,0.14830352],"study_design_scores_gemma":[0.000038369195,0.00008582796,0.00014656328,0.00013860606,0.000024081506,0.00006328521,0.00010316401,0.93711996,0.0024444612,0.050129637,0.009685367,0.000020744752],"about_ca_topic_score_codex":0.0027336683,"about_ca_topic_score_gemma":0.0028316327,"teacher_disagreement_score":0.0055304,"about_ca_system_score_codex":0.0008030116,"about_ca_system_score_gemma":0.0015676692,"threshold_uncertainty_score":0.018500984},"labels":[],"label_agreement":null},{"id":"W4210545694","doi":"10.1108/ecam-02-2021-0177","title":"A multiproject scheduling and resource management model in projects construction","year":2022,"lang":"en","type":"article","venue":"Engineering Construction & Architectural Management","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia, Okanagan Campus; University of British Columbia","funders":"","keywords":"Profit (economics); Scheduling (production processes); Exploit; Computer science; Multimodal transport; Operations research; Project management; Transport engineering; Operations management; Engineering; Systems engineering; Computer security","score_opus":0.009417982647277704,"score_gpt":0.21047230024238253,"score_spread":0.20105431759510484,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4210545694","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11478188,0.00042881776,0.86445534,0.00084542,0.000096251526,0.0003413539,0.00054372574,0.0003458279,0.018161483],"genre_scores_gemma":[0.86384314,0.00052287383,0.11887213,0.00006896713,0.00004533966,0.0005990075,0.00033671653,0.00007734434,0.015634578],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99883956,0.00051597395,0.00004778117,0.00022630826,0.00017178131,0.00019859974],"domain_scores_gemma":[0.9993759,0.00023626014,0.0001004252,0.0000438629,0.00012613524,0.00011748938],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016927521,0.00069464865,0.0008202845,0.0007383177,0.0008543216,0.0020530783,0.0017914539,0.0011916409,0.0063020317],"category_scores_gemma":[0.0013968493,0.00067976676,0.0011302658,0.0011853735,0.00071184436,0.001621106,0.0012012973,0.0011173004,0.0005280023],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005400223,0.00005971281,0.00080222497,0.00004901584,0.00001941327,0.00011303393,0.00006215957,0.97726506,0.0004372352,0.013974664,0.00050586363,0.0066576595],"study_design_scores_gemma":[0.000012501566,0.00005242896,0.00021944185,0.0000062499394,0.000007965714,0.000018293962,0.000042861557,0.99486536,0.00011462737,0.0037234083,0.0009299386,0.0000068622917],"about_ca_topic_score_codex":0.015099417,"about_ca_topic_score_gemma":0.010990451,"teacher_disagreement_score":0.015099417,"about_ca_system_score_codex":0.0021256346,"about_ca_system_score_gemma":0.0034507676,"threshold_uncertainty_score":0.030023038},"labels":[],"label_agreement":null},{"id":"W4210652853","doi":"10.1155/2022/5538113","title":"Dynamic Multicompartment Refrigerated Vehicle Routing Problem with Multigraph Based on Real-Time Traffic Information","year":2022,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Multigraph; Computer science; Routing (electronic design automation); Scheme (mathematics); Path (computing); Vehicle routing problem; Selection (genetic algorithm); Variable (mathematics); Adaptive routing; Chaotic; Genetic algorithm; Operations research; Mathematical optimization; Real-time computing; Routing protocol; Computer network; Static routing; Artificial intelligence; Engineering; Machine learning; Mathematics; Theoretical computer science","score_opus":0.00497834148271386,"score_gpt":0.2267176048472236,"score_spread":0.22173926336450975,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4210652853","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17762187,0.0005574582,0.81579,0.0005613893,0.000059776055,0.00013175118,0.00035071245,0.00018063118,0.004746456],"genre_scores_gemma":[0.9321666,0.000385948,0.062548816,0.00005250912,0.00003172108,0.00012011378,0.00030671371,0.00004504886,0.004342534],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99951625,0.00014363989,0.000017282537,0.00016298323,0.00006733575,0.00009259073],"domain_scores_gemma":[0.9994623,0.0003036108,0.00010097709,0.000034345503,0.000053138163,0.000045513247],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00054675236,0.0010071995,0.0012082095,0.0009327675,0.0006846048,0.0010908523,0.0015719427,0.0013703606,0.0014492397],"category_scores_gemma":[0.0010573409,0.00070353615,0.00093138346,0.0017517529,0.00076387695,0.0019498559,0.0007626902,0.00079063704,0.00009850492],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000046670644,0.000023400627,0.000313631,0.00004521227,0.000031229134,0.00007789453,0.000024543537,0.9884466,0.0007569551,0.005137288,0.00032381734,0.0047728294],"study_design_scores_gemma":[0.000009313014,0.000029139064,0.00018764794,0.0000028436477,0.000014008943,0.000027588207,0.000018010413,0.9954045,0.00028687812,0.0037043872,0.0003089572,0.00000677827],"about_ca_topic_score_codex":0.014004799,"about_ca_topic_score_gemma":0.009812402,"teacher_disagreement_score":0.014004799,"about_ca_system_score_codex":0.0025634249,"about_ca_system_score_gemma":0.001394674,"threshold_uncertainty_score":0.027846575},"labels":[],"label_agreement":null},{"id":"W4211174428","doi":"10.1007/978-3-319-91086-4_3","title":"Variable Neighborhood Search","year":2018,"lang":"en","type":"book-chapter","venue":"International series in management science/operations research/International series in operations research & management science","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":106,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Group for Research in Decision Analysis; Royal Military College of Canada; HEC Montréal","funders":"","keywords":"Variable neighborhood search; Metaheuristic; Mathematical optimization; Mathematical proof; Mathematics; Combinatorial optimization; Guided Local Search; Integer programming; Linear programming; Nonlinear programming; Nonlinear system; Computer science","score_opus":0.060288739394379905,"score_gpt":0.398312244958024,"score_spread":0.3380235055636441,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4211174428","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016286403,0.0072951694,0.8766678,0.0008012822,0.0009552577,0.00012276066,0.00045576337,0.0010778708,0.09633776],"genre_scores_gemma":[0.2725217,0.0034617705,0.57344645,0.0006855296,0.00046967267,0.0003611127,0.002279866,0.0009232686,0.1458506],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99964035,0.00009830202,0.000013390926,0.00009807841,0.00011626724,0.000033631764],"domain_scores_gemma":[0.9996699,0.00014993345,0.000019893921,0.000062354084,0.00007864117,0.000019302326],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005311521,0.00065705006,0.0010747807,0.0008822621,0.00071044354,0.0010785578,0.0011825473,0.001087247,0.015148248],"category_scores_gemma":[0.001961417,0.00038718389,0.00060938334,0.0013155362,0.0005754991,0.0011644579,0.0010727677,0.0013013807,0.0031122926],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013883479,0.00012283902,0.00061081396,0.0001857478,0.000094129886,0.00006156052,0.00007492682,0.2087592,0.0016697975,0.16371343,0.05691356,0.56765515],"study_design_scores_gemma":[0.000053082436,0.000089201574,0.000508674,0.00008560795,0.000045282824,0.00013389096,0.00004247012,0.8271889,0.0015588368,0.11137744,0.058894973,0.000021684045],"about_ca_topic_score_codex":0.0025649667,"about_ca_topic_score_gemma":0.003653849,"teacher_disagreement_score":0.015148248,"about_ca_system_score_codex":0.00062989,"about_ca_system_score_gemma":0.0006306412,"threshold_uncertainty_score":0.05067593},"labels":[],"label_agreement":null},{"id":"W4211246340","doi":"10.1007/978-3-030-32177-2_12","title":"Hub Location Problems","year":2019,"lang":"en","type":"book-chapter","venue":"Location Science","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":46,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Network planning and design; Computer science; Cover (algebra); Integer programming; Key (lock); Facility location problem; Network topology; Operations research; Class (philosophy); Focus (optics); Modal; Flow network; Mathematical optimization; Engineering; Computer network; Artificial intelligence; Mathematics; Computer security","score_opus":0.028624032868462122,"score_gpt":0.266979153600706,"score_spread":0.23835512073224388,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4211246340","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004460158,0.005726006,0.29438987,0.0021868753,0.0011839482,0.00006335366,0.0007209156,0.000443565,0.6908252],"genre_scores_gemma":[0.09206928,0.010779917,0.05743352,0.0005778031,0.0009494831,0.0001356091,0.0012660963,0.00039677147,0.8363915],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99977213,0.000048440605,0.0000067780884,0.00006315907,0.000078860714,0.00003062836],"domain_scores_gemma":[0.9998683,0.000042709842,0.000011392935,0.000028519862,0.00003365782,0.000015456979],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000275043,0.0011333439,0.00080652576,0.00072279776,0.0006237993,0.0018876896,0.0011670074,0.0013012041,0.04224056],"category_scores_gemma":[0.0007470887,0.0004052273,0.000573939,0.0014314178,0.0008041667,0.0020238736,0.0011495267,0.0018986046,0.011418027],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002040199,0.000042975225,0.000103310784,0.0001846209,0.000017136588,0.000077619596,0.000065486034,0.02769318,0.00067737536,0.7437999,0.10477136,0.12254663],"study_design_scores_gemma":[0.000012973067,0.000027355032,0.00025305653,0.00012318838,0.000018761006,0.00021682901,0.0001260753,0.04565306,0.0010066633,0.56987464,0.3826707,0.00001666299],"about_ca_topic_score_codex":0.0012717479,"about_ca_topic_score_gemma":0.0019357529,"teacher_disagreement_score":0.04224056,"about_ca_system_score_codex":0.0011297986,"about_ca_system_score_gemma":0.0007042211,"threshold_uncertainty_score":0.14130872},"labels":[],"label_agreement":null},{"id":"W4214807431","doi":"10.1016/j.cor.2022.105761","title":"The doubly open park-and-loop routing problem","year":2022,"lang":"en","type":"article","venue":"Computers & Operations Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"HEC Montréal; Canada First Research Excellence Fund","keywords":"Vehicle routing problem; Computer science; Routing (electronic design automation); Heuristic; Extension (predicate logic); Mathematical optimization; Set (abstract data type); Quality (philosophy); Loop (graph theory); Operations research; Mathematics; Artificial intelligence; Computer network","score_opus":0.0847938384632459,"score_gpt":0.38310976878355396,"score_spread":0.29831593032030806,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4214807431","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.120460466,0.0009838078,0.8376192,0.0022774024,0.0003063395,0.00012369953,0.00072458124,0.00012838696,0.03737605],"genre_scores_gemma":[0.87370664,0.0009801219,0.093997985,0.00030728607,0.00027311625,0.00014048391,0.0006750222,0.00009456466,0.02982479],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994685,0.00021807526,0.000019008177,0.00012968045,0.00008100875,0.00008372569],"domain_scores_gemma":[0.9987716,0.0007951206,0.00013425,0.00007570448,0.00009503787,0.00012828186],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007667007,0.0005892461,0.00096297055,0.0004421664,0.0005730128,0.0016936476,0.0010762059,0.0019376792,0.007563298],"category_scores_gemma":[0.0036632875,0.00044855243,0.0005283507,0.000593649,0.0011094365,0.0025027965,0.0017092956,0.0011732264,0.00047853848],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00040203548,0.00026070763,0.0013007453,0.0003003047,0.000066827226,0.0004687947,0.00015907931,0.5668921,0.001924459,0.3464566,0.010361203,0.071407124],"study_design_scores_gemma":[0.00007811784,0.00011286255,0.00025416218,0.000021248048,0.000023082295,0.00015732243,0.00011712725,0.808763,0.0005905327,0.18398935,0.005872325,0.000020894124],"about_ca_topic_score_codex":0.0014006364,"about_ca_topic_score_gemma":0.0014675166,"teacher_disagreement_score":0.007563298,"about_ca_system_score_codex":0.000516821,"about_ca_system_score_gemma":0.0008309805,"threshold_uncertainty_score":0.025301754},"labels":[],"label_agreement":null},{"id":"W4220699348","doi":"10.1155/2022/9241112","title":"Advanced Phasmatodea Population Evolution Algorithm for Capacitated Vehicle Routing Problem","year":2022,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Natural Science Foundation of Fujian Province; National Natural Science Foundation of China","keywords":"Local optimum; Convergence (economics); Vehicle routing problem; Population; Algorithm; Jump; Computer science; Closing (real estate); Mathematical optimization; Metaheuristic; Routing (electronic design automation); Mathematics","score_opus":0.009819368436302486,"score_gpt":0.25512469360238754,"score_spread":0.24530532516608505,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4220699348","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.029898528,0.00091205904,0.9595631,0.0003370219,0.00010419831,0.000097119526,0.00011543936,0.00041974382,0.00855285],"genre_scores_gemma":[0.47582376,0.0015589767,0.50732106,0.00037617516,0.000100253266,0.0006860401,0.00087205734,0.00014374772,0.013117914],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996389,0.00008933876,0.000019621193,0.000083421204,0.00011963971,0.000048998347],"domain_scores_gemma":[0.99973243,0.00011901418,0.000021702346,0.000019417399,0.00009014282,0.000017350703],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005045386,0.0009216018,0.0008653608,0.0008228923,0.00057531486,0.00076252274,0.0010499386,0.0009763155,0.0026079216],"category_scores_gemma":[0.0011331355,0.00034479023,0.00090249395,0.001168444,0.00037109398,0.0009023826,0.00091611507,0.0011896187,0.00028999714],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004140208,0.00006323445,0.0010969455,0.000106671076,0.000060656806,0.00008906481,0.000078436555,0.8638735,0.002251512,0.011646929,0.0028587617,0.11783287],"study_design_scores_gemma":[0.000017387383,0.000023137776,0.00012690607,0.000006300585,0.000009251029,0.000036145128,0.000011210497,0.99512607,0.0004836554,0.0022557348,0.0018987076,0.0000055369696],"about_ca_topic_score_codex":0.006752405,"about_ca_topic_score_gemma":0.004686709,"teacher_disagreement_score":0.006752405,"about_ca_system_score_codex":0.0006618069,"about_ca_system_score_gemma":0.0012887863,"threshold_uncertainty_score":0.013426185},"labels":[],"label_agreement":null},{"id":"W4220718688","doi":"10.1007/s00291-022-00670-3","title":"Mobile healthcare services in rural areas: an application with periodic location routing problem","year":2022,"lang":"en","type":"article","venue":"OR Spectrum","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":24,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Health care; Christian ministry; Heuristic; Computer science; Routing (electronic design automation); Population; Rural area; Focus (optics); Operations research; Business; Computer network; Mathematics; Artificial intelligence; Economic growth; Medicine; Economics; Environmental health","score_opus":0.007366936327757205,"score_gpt":0.25197933688144364,"score_spread":0.24461240055368644,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4220718688","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.39560828,0.0017772333,0.57830566,0.003423195,0.00038975678,0.00021115081,0.0005826531,0.00037832657,0.019323679],"genre_scores_gemma":[0.9155262,0.00087475026,0.07420968,0.00011836414,0.0001267736,0.000072088485,0.00019807316,0.00006181887,0.008812282],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995927,0.00022002208,0.000010002957,0.00007107125,0.00004507498,0.00006115913],"domain_scores_gemma":[0.9990965,0.0006651378,0.00008227062,0.000025537098,0.000072111965,0.000058471727],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008155828,0.0008770002,0.0007611368,0.0005607305,0.00062688923,0.0010283868,0.0009648426,0.0019637444,0.0035764975],"category_scores_gemma":[0.0018185973,0.00037684332,0.00081843144,0.0011083981,0.00047042605,0.00082018913,0.00070919143,0.00064539665,0.00016610751],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008677464,0.00011254861,0.0010541028,0.00009386523,0.00003300335,0.00034756577,0.00004833906,0.97376347,0.0006939353,0.008510029,0.0019519937,0.013304424],"study_design_scores_gemma":[0.000011481735,0.000032843265,0.000208721,0.000003935969,0.000010335253,0.000053381256,0.00006307919,0.99663925,0.00011818069,0.0023873122,0.00046676976,0.000004635781],"about_ca_topic_score_codex":0.012307885,"about_ca_topic_score_gemma":0.0076012933,"teacher_disagreement_score":0.012307885,"about_ca_system_score_codex":0.00088111404,"about_ca_system_score_gemma":0.00085618946,"threshold_uncertainty_score":0.024472475},"labels":[],"label_agreement":null},{"id":"W4221015978","doi":"10.5325/transportationj.61.1.0060","title":"Sustainable Pricing-Production-Workforce-Routing Problem for Perishable Products by Considering Demand Uncertainty; A Case Study from the Dairy Industry","year":2022,"lang":"en","type":"article","venue":"Transportation Journal","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Supply chain; Greenhouse gas; Vehicle routing problem; Production (economics); Pareto principle; Profit (economics); Environmental economics; Workforce; Production planning; Computer science; Business; Operations research; Routing (electronic design automation); Economics; Operations management; Microeconomics; Engineering; Marketing","score_opus":0.025502268843888787,"score_gpt":0.2715234489447433,"score_spread":0.2460211801008545,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4221015978","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8645328,0.00055455207,0.124536976,0.00072107837,0.000050728486,0.00022636361,0.00054221734,0.00008368668,0.0087515535],"genre_scores_gemma":[0.97951883,0.00017855049,0.01824098,0.000025698608,0.000009139678,0.00007684211,0.00015450206,0.000009431583,0.00178606],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994018,0.00028764285,0.000021503209,0.000091685586,0.00008473064,0.00011269359],"domain_scores_gemma":[0.9983632,0.0012994617,0.000108040746,0.000049108894,0.000090097084,0.00009002109],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014331923,0.0009703208,0.0007742455,0.0009549737,0.00094047684,0.0012937789,0.0010767197,0.0023953505,0.0020735955],"category_scores_gemma":[0.0017310574,0.0005074851,0.00087463396,0.0015397789,0.0007604206,0.0010054082,0.00063314807,0.00096557144,0.00009980879],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005082446,0.000099284145,0.0013575323,0.00005141508,0.00001698804,0.000587587,0.000041663017,0.9902733,0.00048877596,0.0026131258,0.00028550002,0.00413401],"study_design_scores_gemma":[0.000020214455,0.00008502807,0.0006916103,0.0000069935613,0.000014112623,0.000067196444,0.00018811772,0.9957775,0.00043545454,0.0022481903,0.00045335354,0.000012090614],"about_ca_topic_score_codex":0.016783671,"about_ca_topic_score_gemma":0.015513745,"teacher_disagreement_score":0.016783671,"about_ca_system_score_codex":0.002121201,"about_ca_system_score_gemma":0.0016223846,"threshold_uncertainty_score":0.033371985},"labels":[],"label_agreement":null},{"id":"W4221122161","doi":"10.1287/trsc.2022.1129","title":"Vehicle Routing with Stochastic Demands and Partial Reoptimization","year":2022,"lang":"en","type":"article","venue":"Transportation Science","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Mathematical optimization; Computer science; Vehicle routing problem; A priori and a posteriori; Context (archaeology); Operations research; Routing (electronic design automation); Stochastic dominance; Mathematics","score_opus":0.011874176218330378,"score_gpt":0.24106467431340253,"score_spread":0.22919049809507216,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4221122161","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.24764892,0.00039128718,0.7423143,0.0006575783,0.00008640452,0.000094924864,0.0005096411,0.00043672518,0.007860218],"genre_scores_gemma":[0.9298592,0.00018963986,0.06699079,0.000075497286,0.00003489746,0.000073612086,0.00032562818,0.000090278656,0.0023603998],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991037,0.0003263421,0.000028958735,0.00013897131,0.00017865656,0.0002233665],"domain_scores_gemma":[0.9986216,0.00080802024,0.00017785975,0.000115507406,0.00014883516,0.00012819398],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012771101,0.00088455115,0.0011281617,0.00050931325,0.00041781674,0.0011255082,0.0010431261,0.0008229449,0.0024450123],"category_scores_gemma":[0.003595809,0.00061739184,0.0009974687,0.0009077443,0.00086155924,0.001258501,0.00089157687,0.0012878451,0.00022015144],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000027978705,0.000012848123,0.00018351815,0.000010862668,0.000008622186,0.000024991703,0.000008689347,0.9931184,0.00021880722,0.0041679586,0.00018971002,0.0020276872],"study_design_scores_gemma":[0.000004324601,0.000012354409,0.000050084484,0.0000012127423,0.0000020187722,0.000005420816,0.000005578907,0.99576133,0.000121822995,0.0039072367,0.00012616937,0.0000023189814],"about_ca_topic_score_codex":0.00885634,"about_ca_topic_score_gemma":0.006425341,"teacher_disagreement_score":0.00885634,"about_ca_system_score_codex":0.0012247914,"about_ca_system_score_gemma":0.0012703944,"threshold_uncertainty_score":0.017609537},"labels":[],"label_agreement":null},{"id":"W4224271542","doi":"10.1007/s11750-022-00632-6","title":"Analysis of the selective traveling salesman problem with time-dependent profits","year":2022,"lang":"en","type":"article","venue":"Top","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal; Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada; Junta de Andalucía; European Regional Development Fund; Ministerio de Ciencia, Innovación y Universidades","keywords":"Travelling salesman problem; Solver; Profit (economics); Mathematical optimization; Computer science; Time horizon; Graph; Piecewise; Mathematics; Combinatorics; Operations research; Economics","score_opus":0.007919825672221409,"score_gpt":0.22200185495626706,"score_spread":0.21408202928404566,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4224271542","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3853734,0.0008537528,0.5953319,0.0008511838,0.000068400244,0.00020260003,0.0005517504,0.00024179374,0.016525155],"genre_scores_gemma":[0.92341566,0.0004576623,0.07181604,0.00006718888,0.000040559684,0.00012785372,0.00033187683,0.0000783319,0.003664867],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99952734,0.00015907275,0.000016938397,0.000070359194,0.00010077105,0.00012547214],"domain_scores_gemma":[0.99834573,0.0012161504,0.0001600852,0.000061600906,0.00012885749,0.00008749946],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001041028,0.0007171394,0.0005973951,0.00054248114,0.00039861831,0.0007971119,0.0011951239,0.00070615247,0.003889022],"category_scores_gemma":[0.0028469504,0.00037852835,0.00073372386,0.0008310984,0.00068945513,0.0011570421,0.00074199506,0.00069971185,0.00016413255],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000405774,0.000028614642,0.00048081024,0.000053844367,0.000025009549,0.00009825338,0.000024535859,0.98046726,0.0004808506,0.012261749,0.00055923086,0.005479326],"study_design_scores_gemma":[0.0000080685595,0.000024194429,0.0001748273,0.000004566068,0.000007856834,0.000030470615,0.000026757305,0.99297214,0.00021064683,0.0061503635,0.00038752233,0.0000025940585],"about_ca_topic_score_codex":0.006865553,"about_ca_topic_score_gemma":0.00485068,"teacher_disagreement_score":0.006865553,"about_ca_system_score_codex":0.0012385177,"about_ca_system_score_gemma":0.0013090526,"threshold_uncertainty_score":0.013651192},"labels":[],"label_agreement":null},{"id":"W4224281875","doi":"10.1109/sm55505.2022.9758346","title":"Multi-criteria Optimal Routing for Last-mile Parcel Delivery","year":2022,"lang":"en","type":"article","venue":"","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"General Motors (Canada); University of Toronto","funders":"","keywords":"Simulated annealing; Travelling salesman problem; Computer science; Genetic algorithm; Metaheuristic; Mathematical optimization; Cluster analysis; Vehicle routing problem; Routing (electronic design automation); Adaptive simulated annealing; Algorithm; Artificial intelligence; Mathematics; Computer network","score_opus":0.031500689323713874,"score_gpt":0.2871309209264886,"score_spread":0.25563023160277476,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4224281875","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.026934946,0.00050912594,0.9653815,0.00019078581,0.000070870556,0.00009398248,0.00016327556,0.00033344747,0.006322075],"genre_scores_gemma":[0.7304226,0.0004456668,0.26060328,0.000090311674,0.000036033816,0.00018757524,0.0003649007,0.00019300991,0.0076565803],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99931,0.00029297298,0.00002080625,0.000103221486,0.00016667598,0.0001062537],"domain_scores_gemma":[0.9996593,0.00015018373,0.000049857485,0.00002751515,0.000068512934,0.000044645523],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00074336596,0.00084823195,0.00090525026,0.0007686851,0.0006566816,0.00105169,0.0011895105,0.00080338476,0.003810544],"category_scores_gemma":[0.0011520853,0.0003425567,0.00076372956,0.0012482901,0.0003680954,0.0007366384,0.00058507576,0.0006110723,0.00042155583],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000053900185,0.000040966246,0.00029964728,0.00007765544,0.000035325655,0.000117939635,0.000038729137,0.95927167,0.0020661769,0.0092315655,0.0016383271,0.027128207],"study_design_scores_gemma":[0.000004868515,0.000041280677,0.00013410997,0.0000048403163,0.000006262532,0.00003495896,0.00002247939,0.9946924,0.00052596757,0.0028632837,0.0016641162,0.000005489104],"about_ca_topic_score_codex":0.005741864,"about_ca_topic_score_gemma":0.005280941,"teacher_disagreement_score":0.005741864,"about_ca_system_score_codex":0.0014176827,"about_ca_system_score_gemma":0.00096716284,"threshold_uncertainty_score":0.012747586},"labels":[],"label_agreement":null},{"id":"W4224298808","doi":"10.1007/s43069-022-00136-w","title":"Distributed Integral Column Generation for Set Partitioning Problems","year":2022,"lang":"en","type":"article","venue":"Operations Research Forum","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal; Polytechnique Montréal; Group for Research in Decision Analysis","funders":"","keywords":"Column generation; Computer science; Mathematical optimization; Scheduling (production processes); Heuristic; Crew scheduling; Column (typography); Heuristics; Set (abstract data type); Tree (set theory); Integer programming; Algorithm; Mathematics; Combinatorics","score_opus":0.12749409430182423,"score_gpt":0.3847809595954399,"score_spread":0.25728686529361566,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4224298808","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0074404716,0.00017562209,0.9892396,0.00013357119,0.000051192794,0.000034907156,0.000059908114,0.00020963798,0.0026550484],"genre_scores_gemma":[0.42973348,0.00039122824,0.562399,0.000193257,0.00013973653,0.00022561292,0.00046171714,0.00032022607,0.0061356444],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99945587,0.00020756022,0.000017138993,0.000061802,0.00018681232,0.00007084005],"domain_scores_gemma":[0.9985561,0.00086177373,0.00007663549,0.0001687893,0.00027535355,0.00006133237],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009858655,0.0006638577,0.0009950977,0.0006638343,0.0004841337,0.0010379095,0.00113605,0.0006628639,0.00447872],"category_scores_gemma":[0.003105351,0.00048359178,0.00059030944,0.0012708518,0.00063886406,0.0009433379,0.001156007,0.0013579463,0.0005068798],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019318283,0.00014191393,0.00026845472,0.00015422676,0.000041372274,0.000054635046,0.00006224975,0.82362765,0.0038789525,0.04009843,0.0054602134,0.12601873],"study_design_scores_gemma":[0.000014448383,0.0000141716155,0.000026433197,0.000004409493,0.000005696868,0.000008964858,0.0000058690016,0.9895286,0.00064921257,0.009289963,0.00044950753,0.000002639988],"about_ca_topic_score_codex":0.0023488465,"about_ca_topic_score_gemma":0.00405874,"teacher_disagreement_score":0.00447872,"about_ca_system_score_codex":0.0006971074,"about_ca_system_score_gemma":0.0009836237,"threshold_uncertainty_score":0.01498282},"labels":[],"label_agreement":null},{"id":"W4225279627","doi":"10.1155/2022/4124950","title":"Expected Length of the Shortest Path of the Traveling Salesman Problem in 3D Space","year":2022,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"China Postdoctoral Science Foundation; National Natural Science Foundation of China","keywords":"Shortest path problem; Travelling salesman problem; Constrained Shortest Path First; Yen's algorithm; Path length; K shortest path routing; Euclidean shortest path; Mathematical optimization; Path (computing); Any-angle path planning; Longest path problem; Shortest Path Faster Algorithm; Mathematics; Widest path problem; Computer science; Motion planning; Dijkstra's algorithm; Combinatorics; Artificial intelligence; Graph","score_opus":0.00833214409765692,"score_gpt":0.22956270176309485,"score_spread":0.22123055766543792,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4225279627","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18879448,0.0013188708,0.80171555,0.00063692813,0.00009581579,0.00011759596,0.0010689992,0.0005143803,0.005737391],"genre_scores_gemma":[0.87946934,0.0010348527,0.113453425,0.000113713475,0.000034331897,0.00037105614,0.0021507784,0.00017963529,0.003192895],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9987822,0.00035783078,0.00007497901,0.0003708447,0.00024805486,0.00016610234],"domain_scores_gemma":[0.9954555,0.0032733537,0.0004968986,0.00014158206,0.00049090636,0.00014175067],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016168971,0.0010274985,0.0011316739,0.0012901293,0.0006551635,0.0015042789,0.0015630308,0.0011978197,0.0026149089],"category_scores_gemma":[0.0078107505,0.0005656543,0.0013268049,0.0014486581,0.0007529613,0.0020207316,0.00095058646,0.0014335683,0.000249713],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003611482,0.000020735793,0.0014242346,0.00006898169,0.000025438756,0.000040384588,0.000024525976,0.9891194,0.00020655435,0.0026306878,0.000387722,0.0060151606],"study_design_scores_gemma":[0.000003566802,0.000025437565,0.00047082038,0.000008715325,0.000008895274,0.000023005507,0.00001725136,0.9960228,0.00016081576,0.0030506093,0.00020001728,0.000007929069],"about_ca_topic_score_codex":0.019407874,"about_ca_topic_score_gemma":0.011735484,"teacher_disagreement_score":0.019407874,"about_ca_system_score_codex":0.0021667897,"about_ca_system_score_gemma":0.0022528428,"threshold_uncertainty_score":0.038589776},"labels":[],"label_agreement":null},{"id":"W4225691621","doi":"10.1007/s10479-022-04658-8","title":"Optimization of the technician routing and scheduling problem for a telecommunication industry","year":2022,"lang":"en","type":"article","venue":"Annals of Operations Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Technician; Computer science; Scheduling (production processes); Operations research; Job shop scheduling; Metaheuristic; Service provider; Time horizon; Routing (electronic design automation); Service (business); Mathematical optimization; Computer network; Operations management; Artificial intelligence; Engineering; Business","score_opus":0.1611433904965729,"score_gpt":0.4261114328978416,"score_spread":0.2649680424012687,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4225691621","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16322182,0.0020040986,0.80113894,0.0029817696,0.00033530276,0.0003934604,0.00104227,0.00026239187,0.028619898],"genre_scores_gemma":[0.8058213,0.0020718838,0.1606789,0.00029416094,0.00029871866,0.00043639413,0.00072987156,0.00023788938,0.029430842],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987997,0.0007299722,0.000030226136,0.00015117695,0.00013368677,0.00015516931],"domain_scores_gemma":[0.9978434,0.0017179373,0.00014494777,0.00005065028,0.000112555754,0.00013050495],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027321177,0.0014583861,0.0017939875,0.0015229174,0.00067124143,0.00229578,0.0014433579,0.0028516352,0.007743686],"category_scores_gemma":[0.004293586,0.0011829817,0.0016280536,0.0020036679,0.0013543112,0.0017288177,0.0011935743,0.001919579,0.000498357],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000118396674,0.00009617167,0.00033520028,0.000082921615,0.000052313568,0.000045418492,0.000026419557,0.9811229,0.0003942593,0.008888588,0.0013172234,0.007520072],"study_design_scores_gemma":[0.000032376633,0.000058859172,0.0002530023,0.000007687382,0.000019420026,0.000015483187,0.00003067999,0.9924056,0.00015820465,0.0063071777,0.00070120994,0.000010353751],"about_ca_topic_score_codex":0.019992013,"about_ca_topic_score_gemma":0.011582695,"teacher_disagreement_score":0.019992013,"about_ca_system_score_codex":0.0028780142,"about_ca_system_score_gemma":0.003067032,"threshold_uncertainty_score":0.03975129},"labels":[],"label_agreement":null},{"id":"W4226110099","doi":"10.1109/tits.2022.3156685","title":"Stochastic Multi-Objective Vehicle Routing Model in Green Environment With Customer Satisfaction","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Transportation Systems","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":50,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University; Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Customer satisfaction; Vehicle routing problem; Computer science; Routing (electronic design automation); Transport engineering; Business; Engineering; Marketing; Computer network","score_opus":0.024506810086741965,"score_gpt":0.24317703609207894,"score_spread":0.21867022600533698,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4226110099","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10308328,0.0007039695,0.8766033,0.0010919167,0.00016589947,0.00015389972,0.0007102229,0.00029856068,0.017189078],"genre_scores_gemma":[0.9603837,0.00048722044,0.026887652,0.00016165219,0.00005083591,0.00024883242,0.00033258263,0.00004887797,0.011398656],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990096,0.00035549054,0.000032713062,0.00017041616,0.0001993566,0.00023228972],"domain_scores_gemma":[0.99910444,0.00041451585,0.00016589579,0.000029168768,0.00020744669,0.00007850603],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012622084,0.0011840966,0.0015495081,0.0006846994,0.000523761,0.0015112846,0.0019935274,0.0019232907,0.0031556545],"category_scores_gemma":[0.0014727532,0.0006424044,0.0011861287,0.0014115685,0.00086177804,0.0008421495,0.0010143883,0.0013365815,0.0003167143],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014622704,0.000008251756,0.00010344846,0.000011610105,0.000009148221,0.000043489486,0.0000074022605,0.9959698,0.00014005775,0.0029792504,0.0001252499,0.0005877446],"study_design_scores_gemma":[0.0000037638385,0.000010380406,0.000045790403,0.000001201815,0.0000030823733,0.0000050943872,0.0000047602025,0.9991098,0.000021696731,0.0007109496,0.00008111285,0.0000023507898],"about_ca_topic_score_codex":0.015206376,"about_ca_topic_score_gemma":0.008582385,"teacher_disagreement_score":0.015206376,"about_ca_system_score_codex":0.0017166351,"about_ca_system_score_gemma":0.001635726,"threshold_uncertainty_score":0.030235708},"labels":[],"label_agreement":null},{"id":"W4226200202","doi":"10.1609/aaai.v36i4.20294","title":"Learning to Search in Local Branching","year":2022,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Leverage (statistics); Local search (optimization); Mathematical optimization; Heuristic; Computer science; Linear programming; Mathematics; Integer programming; Algorithm; Constraint (computer-aided design); Local optimum; Artificial intelligence","score_opus":0.05860159410969267,"score_gpt":0.3077845184515558,"score_spread":0.24918292434186312,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4226200202","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.059144333,0.0003464857,0.93661815,0.00030468855,0.000028041652,0.0000618009,0.000031060066,0.00044603203,0.003019456],"genre_scores_gemma":[0.8036363,0.0003237264,0.1925968,0.00033608155,0.0000585989,0.0002859716,0.0001510236,0.00015641283,0.0024551044],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990926,0.0003682306,0.000044703316,0.00021800447,0.000170901,0.0001056434],"domain_scores_gemma":[0.99509877,0.0034778889,0.000470869,0.0003861466,0.00038023683,0.00018621054],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026242253,0.0008850309,0.0013465643,0.00061098963,0.0004952814,0.00093849597,0.0015509702,0.0010142355,0.0019394903],"category_scores_gemma":[0.012266936,0.00060907647,0.00064849993,0.000524942,0.0017557441,0.0016791917,0.002175935,0.0016626519,0.00034491942],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007335099,0.00004862211,0.0012553418,0.00006536147,0.00003236621,0.00005698385,0.000086745014,0.94645655,0.0011584051,0.026587931,0.00060668454,0.023571776],"study_design_scores_gemma":[0.000010404938,0.000026249521,0.000048187245,0.0000070243905,0.0000042499128,0.0000069115845,0.0000063349003,0.9905098,0.00021184058,0.009004281,0.00016232347,0.000002318175],"about_ca_topic_score_codex":0.0024885705,"about_ca_topic_score_gemma":0.0030009826,"teacher_disagreement_score":0.0026242253,"about_ca_system_score_codex":0.00091568707,"about_ca_system_score_gemma":0.001411619,"threshold_uncertainty_score":0.013878405},"labels":[],"label_agreement":null},{"id":"W4229363931","doi":"10.1016/j.eswa.2022.117292","title":"Formulation and exact algorithms for electric vehicle production routing problem","year":2022,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"European Regional Development Fund; Ministerio de Ciencia e Innovación; Agencia Estatal de Investigación; Generalitat Valenciana; European Commission","keywords":"Computer science; Solver; Vehicle routing problem; Mathematical optimization; Electric vehicle; Algorithm; Production (economics); Benchmark (surveying); Supply chain; Decomposition; Routing (electronic design automation); Mathematics","score_opus":0.01764929647510787,"score_gpt":0.2651405152044407,"score_spread":0.24749121872933283,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4229363931","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0028585845,0.00034768108,0.9910528,0.00024212172,0.000074850825,0.000042472395,0.00006683184,0.00011745315,0.0051971045],"genre_scores_gemma":[0.1795075,0.0013427626,0.8059321,0.0002610149,0.0002811516,0.00046593897,0.00036114577,0.00021419943,0.0116342325],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9995486,0.0001523025,0.000020855327,0.00007935256,0.00014703386,0.00005185549],"domain_scores_gemma":[0.9989059,0.0007086167,0.00007573459,0.00008853325,0.00019213671,0.000029039722],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010095258,0.0010055691,0.0011690747,0.0007173287,0.00046095572,0.0015609658,0.0014471352,0.0014896366,0.0061171027],"category_scores_gemma":[0.0036625217,0.0006627925,0.0008170269,0.0010651052,0.00069251435,0.0016215445,0.0011339627,0.0021090254,0.00090964994],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000316285,0.00006468022,0.00012669414,0.00011933146,0.000015410691,0.00003043064,0.000043109987,0.8761389,0.0004541299,0.0497692,0.0038480428,0.06935847],"study_design_scores_gemma":[0.000013316855,0.000010360205,0.00003817605,0.000009962304,0.0000040675745,0.000011209039,0.000011837209,0.97402805,0.00012257275,0.024369782,0.0013770575,0.0000036481501],"about_ca_topic_score_codex":0.007686568,"about_ca_topic_score_gemma":0.006871251,"teacher_disagreement_score":0.007686568,"about_ca_system_score_codex":0.0011091866,"about_ca_system_score_gemma":0.0017972566,"threshold_uncertainty_score":0.020463765},"labels":[],"label_agreement":null},{"id":"W4229698747","doi":"10.1007/978-0-387-74759-0_667","title":"Stochastic Vehicle Routing Problems","year":2008,"lang":"en","type":"book-chapter","venue":"Encyclopedia of Optimization","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"","keywords":"Vehicle routing problem; Computer science; Environmental science; Routing (electronic design automation); Mathematical optimization; Mathematics; Computer network","score_opus":0.014853531005721492,"score_gpt":0.22130532858053298,"score_spread":0.2064517975748115,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4229698747","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004116026,0.014102429,0.6293879,0.0025365904,0.0016647497,0.00010039284,0.0009380709,0.00078854547,0.3463653],"genre_scores_gemma":[0.1385804,0.036631174,0.21713123,0.0015668683,0.0022354324,0.0005521658,0.004302634,0.00073647767,0.5982636],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996668,0.00006157404,0.000014799124,0.0000681426,0.00016645079,0.000022182494],"domain_scores_gemma":[0.99985623,0.000053328346,0.000012617897,0.000020232088,0.000045825753,0.000011759468],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033314186,0.001316978,0.0009920658,0.0005543642,0.00035479944,0.0014368972,0.00092038995,0.00089408393,0.015387744],"category_scores_gemma":[0.00072512357,0.00042764793,0.000633031,0.0012593912,0.00059583597,0.00089889334,0.00073100335,0.0015301417,0.0060705612],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026022975,0.00007839478,0.00020194086,0.0003129216,0.000048623348,0.00010162681,0.00006325673,0.10788722,0.0020120298,0.56208515,0.11534425,0.2118385],"study_design_scores_gemma":[0.000018777386,0.000040360857,0.00038101288,0.00013477558,0.000021731583,0.00024121231,0.000036525264,0.17990796,0.0010681812,0.46186757,0.35625356,0.000028332894],"about_ca_topic_score_codex":0.0010659178,"about_ca_topic_score_gemma":0.0017645941,"teacher_disagreement_score":0.015387744,"about_ca_system_score_codex":0.00079752674,"about_ca_system_score_gemma":0.0010505305,"threshold_uncertainty_score":0.051477134},"labels":[],"label_agreement":null},{"id":"W4230415688","doi":"10.1002/net.20208","title":"Cycle‐based algorithms for multicommodity network flow problems with separable piecewise convex costs","year":2007,"lang":"en","type":"article","venue":"Networks","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Tabu search; Mathematical optimization; Maxima and minima; Guided Local Search; Local optimum; Local search (optimization); Multi-commodity flow problem; Convergence (economics); Flow network; Heuristic; Mathematics; Minimum-cost flow problem; Computer science; Algorithm","score_opus":0.01783978272861802,"score_gpt":0.2649947361305507,"score_spread":0.24715495340193266,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4230415688","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013760759,0.0003434384,0.9827823,0.00014032665,0.000021108352,0.000083440566,0.000038160077,0.00020645006,0.0026239695],"genre_scores_gemma":[0.37359762,0.0005546988,0.61934096,0.000114029855,0.000041448027,0.00058213546,0.00021155512,0.00023348804,0.0053240648],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999546,0.00022681653,0.00001486653,0.00005061144,0.000101228245,0.000060376606],"domain_scores_gemma":[0.99859077,0.0010553144,0.000095353265,0.00006979495,0.0001360528,0.00005264933],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013379852,0.0011112875,0.0012053335,0.0014208637,0.00069294113,0.00091016357,0.0015725819,0.001176055,0.0041423035],"category_scores_gemma":[0.003927854,0.0007367602,0.0007692013,0.00158281,0.0010017047,0.0013469157,0.0013532025,0.0013259035,0.00040607536],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000027888053,0.000026590062,0.00012403175,0.00004201624,0.00001631982,0.000010643538,0.000037114896,0.95207983,0.00021175068,0.019279072,0.00065682153,0.027487902],"study_design_scores_gemma":[0.0000072830153,0.000009823424,0.000016920136,0.0000046311543,0.000001987588,0.0000027690091,0.000004151942,0.99264294,0.000092513,0.00689119,0.0003232284,0.0000025495908],"about_ca_topic_score_codex":0.008718255,"about_ca_topic_score_gemma":0.0068731457,"teacher_disagreement_score":0.008718255,"about_ca_system_score_codex":0.0015728718,"about_ca_system_score_gemma":0.0015535199,"threshold_uncertainty_score":0.017334998},"labels":[],"label_agreement":null},{"id":"W4233980123","doi":"10.1287/opre.1110.0994","title":"Contributors","year":2011,"lang":"en","type":"article","venue":"Operations Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Computer science; Mathematical economics; Mathematics","score_opus":0.16710939077760506,"score_gpt":0.38864849971291304,"score_spread":0.22153910893530798,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4233980123","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0017051586,0.0074040885,0.010424847,0.03046728,0.057806425,0.00049206894,0.008930324,0.0028132985,0.8799566],"genre_scores_gemma":[0.006845827,0.004098723,0.003979408,0.0044206623,0.0050505362,0.00018116184,0.0068379254,0.0008555468,0.9677302],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9985669,0.00019087202,0.000089039786,0.00028860668,0.00069186214,0.00017270773],"domain_scores_gemma":[0.9936318,0.0006161541,0.00014354983,0.00060107093,0.00394342,0.0010640188],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0014520759,0.000948277,0.00078636024,0.0025385646,0.0020252678,0.005289351,0.0018787017,0.0020939738,0.5699813],"category_scores_gemma":[0.009105851,0.00036671155,0.0005989179,0.0024947266,0.00048778296,0.0039829058,0.0029478173,0.0017748871,0.37769523],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000024331857,0.000016257616,0.00016916756,0.0000720525,0.0000027364908,0.000045066045,0.000053475876,0.00011427306,0.00010954208,0.0058069853,0.9225567,0.071029425],"study_design_scores_gemma":[0.0000037845994,0.0000058579185,0.00013449116,0.000062692605,0.0000017682913,0.000048086564,0.00007218517,0.000080534286,0.00006510214,0.0016572011,0.9978642,0.0000040337864],"about_ca_topic_score_codex":0.0029447875,"about_ca_topic_score_gemma":0.0038085608,"teacher_disagreement_score":0.43001872,"about_ca_system_score_codex":0.0021596642,"about_ca_system_score_gemma":0.0027411073,"threshold_uncertainty_score":0.61336946},"labels":[],"label_agreement":null},{"id":"W4235662733","doi":"10.4018/978-1-5225-2237-9.ch010","title":"Modeling and Simulation Analyses of Healthcare Delivery Operations for Inter-Hospital Patient Transfers","year":2017,"lang":"en","type":"book-chapter","venue":"IGI Global eBooks","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Queen's University","funders":"","keywords":"Sizing; Operations research; Health care; Computer science; Transfer (computing); Operations management; Healthcare service; Healthcare delivery; Medical emergency; Engineering; Medicine; Economics","score_opus":0.04929670372028253,"score_gpt":0.3279334174875206,"score_spread":0.27863671376723803,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4235662733","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21722645,0.0024305852,0.6675451,0.0025984051,0.00028813543,0.00020853728,0.0012441819,0.00043614997,0.10802248],"genre_scores_gemma":[0.90030724,0.0025052465,0.0610246,0.00015387498,0.000060777445,0.00019444444,0.0007482345,0.00013616665,0.034869425],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996656,0.00013698472,0.000010622954,0.000048630118,0.000066480265,0.00007166429],"domain_scores_gemma":[0.99955386,0.00029716318,0.000041971896,0.000018146857,0.000064816806,0.000024004758],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00062761264,0.0008796628,0.00056054915,0.00054520153,0.00045174302,0.0012457642,0.0011509829,0.0014499457,0.0043436317],"category_scores_gemma":[0.0010223873,0.00046647576,0.0010322885,0.0009583384,0.00058369554,0.0007487415,0.0005693704,0.0007367468,0.00036500933],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000005835282,0.000007454996,0.0001300123,0.000010108575,0.0000048843985,0.000021083997,0.0000093504905,0.995135,0.0001223927,0.0028796336,0.0002714826,0.001402809],"study_design_scores_gemma":[0.000002570736,0.0000074695095,0.00012825806,0.0000051868824,0.000004128305,0.0000068366435,0.000025502066,0.9970638,0.00009839908,0.0018932973,0.0007611572,0.0000033550598],"about_ca_topic_score_codex":0.055715844,"about_ca_topic_score_gemma":0.04034528,"teacher_disagreement_score":0.055715844,"about_ca_system_score_codex":0.002970873,"about_ca_system_score_gemma":0.0019542796,"threshold_uncertainty_score":0.1107831},"labels":[],"label_agreement":null},{"id":"W4239618018","doi":"10.1002/net.20182","title":"A branch‐and‐regret heuristic for stochastic and dynamic vehicle routing problems","year":2007,"lang":"en","type":"article","venue":"Networks","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":78,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"","keywords":"Heuristics; Regret; Heuristic; Mathematical optimization; Computer science; Vehicle routing problem; Routing (electronic design automation); Class (philosophy); Process (computing); Operations research; Mathematics; Artificial intelligence; Machine learning","score_opus":0.00953183509274726,"score_gpt":0.24791674975548805,"score_spread":0.23838491466274078,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4239618018","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018231422,0.0005118325,0.97537124,0.000280035,0.00007745531,0.00010220982,0.000075892895,0.00028362876,0.005066223],"genre_scores_gemma":[0.4923445,0.0005271597,0.50087106,0.00026101564,0.00015517377,0.00035640254,0.00033966033,0.00017430108,0.004970715],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998447,0.0008248719,0.000047862894,0.00015282202,0.00030786137,0.00021961106],"domain_scores_gemma":[0.99793005,0.00149274,0.00015225004,0.00010694265,0.00018503195,0.0001329753],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025083881,0.00079875387,0.0015542752,0.00081478077,0.00060641835,0.0011834145,0.0014229945,0.0013119782,0.0028905445],"category_scores_gemma":[0.003688862,0.0006420004,0.0008127263,0.0009558061,0.00094654254,0.0012269615,0.0010259076,0.0014602559,0.00040129203],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008738473,0.000055384153,0.00020277142,0.000041617364,0.00003848569,0.00005378434,0.000031737636,0.9557068,0.00035959802,0.0147895105,0.0017837972,0.026849193],"study_design_scores_gemma":[0.000019571315,0.000018461873,0.000036270627,0.0000054950365,0.000005096386,0.000011090206,0.0000045613574,0.99433076,0.0001177722,0.00493154,0.00051554304,0.0000037830202],"about_ca_topic_score_codex":0.004649879,"about_ca_topic_score_gemma":0.0040027564,"teacher_disagreement_score":0.004649879,"about_ca_system_score_codex":0.0014761996,"about_ca_system_score_gemma":0.0021977904,"threshold_uncertainty_score":0.013265789},"labels":[],"label_agreement":null},{"id":"W4242959047","doi":"10.1504/ijor.2019.096942","title":"An integrated production and distribution problem with direct shipment: a case from Moroccan bottled-water market","year":2018,"lang":"en","type":"article","venue":"International Journal of Operational Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Production (economics); Context (archaeology); Bottled water; Product (mathematics); Operations research; Computer science; Routing (electronic design automation); Vehicle routing problem; Distribution (mathematics); Integer programming; Business; Operations management; Mathematics; Economics; Environmental science; Microeconomics; Algorithm","score_opus":0.03279020252808257,"score_gpt":0.35582834798141716,"score_spread":0.3230381454533346,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4242959047","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9731895,0.00058833277,0.010994235,0.00064181694,0.000048758433,0.00023052015,0.00074066274,0.000065570915,0.013500744],"genre_scores_gemma":[0.98724127,0.00022180579,0.0089437105,0.000042972653,0.000021913294,0.00008841962,0.00032211884,0.000017788456,0.003100067],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993895,0.0001929477,0.000021027361,0.000078240046,0.00006652126,0.0002518368],"domain_scores_gemma":[0.99867105,0.00087195553,0.00010343713,0.000055516484,0.00012292567,0.0001751118],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012128819,0.0014740573,0.0011075621,0.0011418734,0.0019980664,0.0021513237,0.0018153846,0.00348193,0.0059043146],"category_scores_gemma":[0.0020959836,0.0005225891,0.0011954899,0.0016035646,0.001055467,0.0012438175,0.0012877544,0.0010927343,0.0002929578],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007484366,0.00067113683,0.004666452,0.00038967584,0.000081337115,0.0075354637,0.00026599254,0.9612158,0.0023222463,0.010873942,0.0023963156,0.008833238],"study_design_scores_gemma":[0.00031948093,0.00029482614,0.0040295897,0.00003813873,0.00008496322,0.0005565728,0.0011144878,0.9847274,0.0015132825,0.0038282543,0.003440822,0.000052082552],"about_ca_topic_score_codex":0.08072841,"about_ca_topic_score_gemma":0.058191072,"teacher_disagreement_score":0.08072841,"about_ca_system_score_codex":0.00402463,"about_ca_system_score_gemma":0.0015945663,"threshold_uncertainty_score":0.16051704},"labels":[],"label_agreement":null},{"id":"W4243772339","doi":"10.1002/9780470400531.eorms1045.pub2","title":"Tabu Search","year":2011,"lang":"en","type":"other","venue":"Wiley Encyclopedia of Operations Research and Management Science","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; Polytechnique Montréal","funders":"","keywords":"Tabu search; Guided Local Search; Metaheuristic; Mathematical optimization; Local optimum; Hill climbing; Computer science; Beam search; Local search (optimization); Function (biology); Algorithm; Search algorithm; Mathematics","score_opus":0.042940432336543086,"score_gpt":0.33719461061731015,"score_spread":0.29425417828076705,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4243772339","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007805541,0.0071305158,0.8319939,0.00088570546,0.00058235886,0.001125923,0.0027764975,0.006633052,0.14106658],"genre_scores_gemma":[0.09683592,0.0048865834,0.8337384,0.0008213005,0.00019627718,0.0013941132,0.007068315,0.0022710033,0.052788112],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99803203,0.0008253173,0.00011370304,0.000271927,0.0005889317,0.00016801881],"domain_scores_gemma":[0.9980325,0.00092985464,0.00014795255,0.00039961556,0.000437237,0.000052725587],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018574515,0.0013681393,0.0013671799,0.0030567832,0.0012540078,0.0033231685,0.0028686218,0.0013071527,0.053801343],"category_scores_gemma":[0.0073057683,0.0005792638,0.0012240922,0.005950683,0.00077472994,0.0017963996,0.0016942235,0.0012046712,0.017574042],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015414988,0.00014547787,0.00095615175,0.0010019428,0.00015974358,0.00012152674,0.00022260501,0.156539,0.001867785,0.13755801,0.0719364,0.6293372],"study_design_scores_gemma":[0.00012428344,0.00020139228,0.00055424357,0.00058182277,0.000108076994,0.0004004564,0.0002470863,0.41737318,0.0047593215,0.12346952,0.4521082,0.0000724022],"about_ca_topic_score_codex":0.0032054514,"about_ca_topic_score_gemma":0.0037396336,"teacher_disagreement_score":0.053801343,"about_ca_system_score_codex":0.0013833031,"about_ca_system_score_gemma":0.0025365092,"threshold_uncertainty_score":0.17998344},"labels":[],"label_agreement":null},{"id":"W4245026044","doi":"10.1002/net.20178","title":"Path inequalities for the vehicle routing problem with time windows","year":2007,"lang":"en","type":"article","venue":"Networks","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":34,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"HEC Montréal; Deakin University","keywords":"Vehicle routing problem; Context (archaeology); Polytope; Path (computing); Travelling salesman problem; Mathematical optimization; Node (physics); Mathematics; Computer science; Dimension (graph theory); Routing (electronic design automation); Combinatorics","score_opus":0.014139643962934432,"score_gpt":0.2414425082797293,"score_spread":0.22730286431679486,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4245026044","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02228858,0.00071452203,0.96676,0.0004651395,0.00009285922,0.00012724382,0.00029565388,0.00007884897,0.009177327],"genre_scores_gemma":[0.53880775,0.0028451502,0.4412655,0.00030400464,0.00034594676,0.0009219249,0.0011812123,0.0001772802,0.014151278],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.998955,0.0003746054,0.000046847537,0.00017181494,0.0003120634,0.0001396587],"domain_scores_gemma":[0.99853814,0.0010358796,0.00017373258,0.00004701974,0.00015100928,0.000054109172],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009992522,0.0013249076,0.000786669,0.0006504569,0.0004045563,0.0012844953,0.000960962,0.00089065987,0.006891374],"category_scores_gemma":[0.0031932876,0.00043205902,0.0008975352,0.001180958,0.0007965037,0.0023867404,0.0011803668,0.002769144,0.00048009882],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008910927,0.00007512768,0.000390515,0.00020435537,0.00004059817,0.00018252191,0.00011484175,0.73768103,0.0026214737,0.21534611,0.0032565363,0.039997716],"study_design_scores_gemma":[0.000027644775,0.000053250224,0.00019123455,0.000028991375,0.000013121284,0.000057622266,0.000036510282,0.90760696,0.00072542584,0.08585183,0.005396609,0.000010856764],"about_ca_topic_score_codex":0.0036246616,"about_ca_topic_score_gemma":0.0023890827,"teacher_disagreement_score":0.006891374,"about_ca_system_score_codex":0.0012624736,"about_ca_system_score_gemma":0.0011551925,"threshold_uncertainty_score":0.023053944},"labels":[],"label_agreement":null},{"id":"W4245400092","doi":"10.1007/978-1-4939-7131-2_101379","title":"Trip Planning Problem","year":2018,"lang":"en","type":"book-chapter","venue":"","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science","score_opus":0.03160166859799997,"score_gpt":0.2646704322950444,"score_spread":0.23306876369704443,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4245400092","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0043634945,0.0018143303,0.24854745,0.0028699634,0.0009190524,0.00019859106,0.0032799183,0.00040758617,0.7375997],"genre_scores_gemma":[0.14056075,0.0081015965,0.2093831,0.001251277,0.00081158103,0.0006039619,0.007158698,0.00045020264,0.6316789],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997738,0.000046274028,0.000008258097,0.00007474018,0.000070015645,0.000027044636],"domain_scores_gemma":[0.99991214,0.000023484514,0.0000071987133,0.000015799298,0.000027439975,0.000013942379],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021688727,0.0009797888,0.00053333666,0.00043202235,0.0005086925,0.00149713,0.0010618172,0.000774977,0.055468325],"category_scores_gemma":[0.00057721493,0.00026541724,0.000557048,0.0009930582,0.00041915453,0.0012517716,0.000780195,0.0016564534,0.012277906],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000043561784,0.000088912144,0.00015131707,0.00034321027,0.000036932837,0.00011743446,0.00007810216,0.064945735,0.00089058914,0.50222486,0.1944824,0.2365969],"study_design_scores_gemma":[0.000024872877,0.00005013489,0.00025144083,0.0001374593,0.000026213185,0.00024082448,0.00015464562,0.08668375,0.0011487938,0.33145028,0.5798106,0.000020933461],"about_ca_topic_score_codex":0.0031643135,"about_ca_topic_score_gemma":0.0034512514,"teacher_disagreement_score":0.055468325,"about_ca_system_score_codex":0.0009385604,"about_ca_system_score_gemma":0.0011841857,"threshold_uncertainty_score":0.18556005},"labels":[],"label_agreement":null},{"id":"W4245527820","doi":"10.1002/net.20063","title":"Efficient neighborhood search for the Probabilistic Pickup and Delivery Travelling Salesman Problem","year":2005,"lang":"en","type":"article","venue":"Networks","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"","keywords":"Travelling salesman problem; Pickup; Probabilistic logic; Computation; Computer science; Mathematical optimization; Traveling purchaser problem; Mathematics; Bottleneck traveling salesman problem; Algorithm; Artificial intelligence","score_opus":0.01835366463530712,"score_gpt":0.24281943364682573,"score_spread":0.2244657690115186,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4245527820","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02684224,0.00034064907,0.9699116,0.00016069086,0.000019161154,0.00006269677,0.00004912877,0.00011083509,0.0025029972],"genre_scores_gemma":[0.5420506,0.0006398527,0.4506961,0.000066407396,0.00006635814,0.00034071118,0.00031027076,0.000116495044,0.0057132193],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994185,0.00025944776,0.000017586624,0.000082340935,0.00016881671,0.00005328081],"domain_scores_gemma":[0.99917275,0.00059378624,0.000084239124,0.00003714769,0.00008100007,0.000031120333],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010933403,0.0003880258,0.0010957427,0.0006810572,0.00051329384,0.00068189244,0.0011171098,0.0007492975,0.0019500878],"category_scores_gemma":[0.00327999,0.00039742523,0.0006106616,0.0008264517,0.0004958341,0.001167877,0.0009618721,0.0007046108,0.0002761221],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000073348085,0.000044788238,0.00034124329,0.00006354004,0.000028017075,0.00005095104,0.00004745872,0.9071597,0.0007948659,0.051000215,0.0013550284,0.039040837],"study_design_scores_gemma":[0.000006905626,0.000011734929,0.000034777142,0.000002371797,0.0000026690343,0.0000105407835,0.000006483023,0.9898739,0.00010873149,0.009587029,0.0003525606,0.0000022939562],"about_ca_topic_score_codex":0.004012366,"about_ca_topic_score_gemma":0.0027727871,"teacher_disagreement_score":0.004012366,"about_ca_system_score_codex":0.0008138217,"about_ca_system_score_gemma":0.0009909407,"threshold_uncertainty_score":0.007978022},"labels":[],"label_agreement":null},{"id":"W4245993692","doi":"10.1007/978-3-030-32177-2_15","title":"Location-Routing and Location-Arc Routing","year":2019,"lang":"en","type":"book-chapter","venue":"Location Science","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"","keywords":"Static routing; Policy-based routing; Equal-cost multi-path routing; Computer science; Link-state routing protocol; Multipath routing; Destination-Sequenced Distance Vector routing; Dynamic Source Routing; Geographic routing; Computer network; Arc routing; Routing (electronic design automation); Routing protocol","score_opus":0.02579907249741598,"score_gpt":0.27499470653268526,"score_spread":0.2491956340352693,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4245993692","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0008586962,0.016702164,0.34354094,0.001752187,0.0030767962,0.00006361592,0.00044331877,0.00075206015,0.6328102],"genre_scores_gemma":[0.018566415,0.01931551,0.10203974,0.0006279535,0.000973969,0.00011291328,0.0005889822,0.0007343512,0.85704017],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997482,0.00004613453,0.000009233006,0.00005820638,0.000121442514,0.000016909615],"domain_scores_gemma":[0.9998697,0.00005121016,0.0000074968543,0.000025947122,0.000038328602,0.00000720852],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024334954,0.0012024456,0.0006820016,0.00082366425,0.0004654439,0.0021172352,0.0010794208,0.0011006047,0.046867914],"category_scores_gemma":[0.0005989096,0.00057708594,0.0004656454,0.0022454304,0.0007819165,0.0028613408,0.00082831236,0.001962231,0.023485737],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013759807,0.00003756472,0.00006715393,0.00029498202,0.00001461714,0.00004851001,0.0000672282,0.019491991,0.0010430278,0.49514467,0.14415151,0.33962497],"study_design_scores_gemma":[0.000004173735,0.0000144425485,0.00010933007,0.00012835054,0.00000974925,0.00014402819,0.000045043893,0.016131552,0.00078669837,0.2266943,0.7559191,0.000013204077],"about_ca_topic_score_codex":0.0016754653,"about_ca_topic_score_gemma":0.0027847374,"teacher_disagreement_score":0.046867914,"about_ca_system_score_codex":0.001193398,"about_ca_system_score_gemma":0.00072985067,"threshold_uncertainty_score":0.15678877},"labels":[],"label_agreement":null},{"id":"W4246068167","doi":"10.1007/978-0-387-74759-0_702","title":"Vehicle Routing","year":2008,"lang":"en","type":"book-chapter","venue":"Encyclopedia of Optimization","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Computer science","score_opus":0.01355077143882855,"score_gpt":0.22493575977178323,"score_spread":0.2113849883329547,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4246068167","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00097548804,0.009027905,0.14998911,0.00093066716,0.0017620162,0.00010421511,0.001150621,0.0025876977,0.83347225],"genre_scores_gemma":[0.014129881,0.010295095,0.04871321,0.00032577338,0.0003924845,0.00010733404,0.002051556,0.0005838826,0.9234007],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99970883,0.000023105953,0.000009619317,0.000060607817,0.00017644854,0.000021250624],"domain_scores_gemma":[0.9998716,0.000017320943,0.000005077441,0.0000339836,0.00006152572,0.000010397091],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020918524,0.0010838726,0.00070564833,0.0011447427,0.0005987381,0.0018582189,0.0011536953,0.0009288152,0.08671086],"category_scores_gemma":[0.0004478835,0.0004796578,0.00042610793,0.0016550148,0.00038472883,0.0014085989,0.00100713,0.0011661873,0.064231604],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000020967955,0.00004718078,0.00009940316,0.00024576864,0.000015210452,0.00006322904,0.00006691534,0.0100714015,0.0028968859,0.10266606,0.28888908,0.59491795],"study_design_scores_gemma":[0.0000038200205,0.000014497678,0.00013239402,0.000077241675,0.000005978639,0.0001545334,0.000022029,0.005628082,0.0010839847,0.023961812,0.96890557,0.000010006608],"about_ca_topic_score_codex":0.0015632422,"about_ca_topic_score_gemma":0.0032263633,"teacher_disagreement_score":0.08671086,"about_ca_system_score_codex":0.00074922864,"about_ca_system_score_gemma":0.00081130565,"threshold_uncertainty_score":0.29007673},"labels":[],"label_agreement":null},{"id":"W4247344008","doi":"10.1002/net.20312","title":"A branch‐and‐cut algorithm for the pickup and delivery traveling salesman problem with LIFO loading","year":2009,"lang":"en","type":"article","venue":"Networks","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":82,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"FIFO and LIFO accounting; Travelling salesman problem; Pickup; Computer science; Traveling purchaser problem; Mathematical optimization; Branch and cut; Algorithm; Mathematics; Set (abstract data type); Bottleneck traveling salesman problem; Integer programming; FIFO (computing and electronics); Artificial intelligence","score_opus":0.010209518403106783,"score_gpt":0.22035149513800273,"score_spread":0.21014197673489596,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4247344008","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.028052201,0.00025154793,0.96413255,0.00041502717,0.00004564988,0.00021888736,0.00019458316,0.0006695523,0.0060199313],"genre_scores_gemma":[0.20883101,0.00023900768,0.7855435,0.00014556591,0.000052345036,0.00052181457,0.0006678876,0.00020109428,0.003797714],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993411,0.00024368812,0.000029552295,0.000104123625,0.00013528459,0.0001462824],"domain_scores_gemma":[0.99895203,0.000723233,0.00009948893,0.000049141323,0.00010774865,0.00006830696],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013804599,0.0010973519,0.0012209412,0.0010460169,0.0008360187,0.0015258684,0.0015583038,0.001489082,0.0060593914],"category_scores_gemma":[0.002920298,0.000639069,0.00064453733,0.0014378964,0.00065298635,0.0016193088,0.0010526543,0.0015656824,0.0008019453],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00036846558,0.00035516795,0.0007446134,0.00014468329,0.000048856116,0.00013552894,0.0001652581,0.77739686,0.0014613541,0.033030383,0.007186927,0.17896183],"study_design_scores_gemma":[0.000060497667,0.000050637533,0.00007033947,0.000009622498,0.000008021456,0.000017823691,0.000023327022,0.9898554,0.0004220642,0.0083903,0.0010861484,0.0000057842594],"about_ca_topic_score_codex":0.006840292,"about_ca_topic_score_gemma":0.0048251585,"teacher_disagreement_score":0.006840292,"about_ca_system_score_codex":0.0016239724,"about_ca_system_score_gemma":0.0019884724,"threshold_uncertainty_score":0.020270646},"labels":[],"label_agreement":null},{"id":"W4247553243","doi":"10.1287/inte.1110.0609","title":"Contributors","year":2011,"lang":"en","type":"article","venue":"INFORMS Journal on Applied Analytics","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Business; Computer science","score_opus":0.0281790540709295,"score_gpt":0.24338214228324614,"score_spread":0.21520308821231665,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4247553243","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0017306071,0.006998526,0.002770638,0.0379441,0.07000699,0.0004327021,0.0053665265,0.0016414172,0.8731085],"genre_scores_gemma":[0.0040478446,0.0033940314,0.0012179076,0.0061590546,0.0051794574,0.00015142922,0.0034526272,0.0004975554,0.9759001],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99810827,0.00023423252,0.0001016467,0.0003458548,0.0009120901,0.00029793993],"domain_scores_gemma":[0.99284995,0.0005795095,0.00016195767,0.00051595015,0.004146757,0.0017459847],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0016005704,0.00090182124,0.0006728137,0.0024172075,0.0029339248,0.007100529,0.0019070684,0.002407575,0.58924687],"category_scores_gemma":[0.010068467,0.0003222887,0.0005120238,0.0023218973,0.0005785851,0.0043962854,0.0035783895,0.0021400524,0.40015095],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016143313,0.000012948712,0.00014403478,0.00006684187,0.0000014920862,0.000039385144,0.0000965616,0.000025261119,0.00007818693,0.0037505557,0.9528731,0.04289548],"study_design_scores_gemma":[0.0000019151207,0.000003839416,0.00013131004,0.00005577372,8.8954016e-7,0.00003776438,0.00011632935,0.000011744836,0.000027877964,0.0005654607,0.9990447,0.0000022588592],"about_ca_topic_score_codex":0.0029542937,"about_ca_topic_score_gemma":0.0041177906,"teacher_disagreement_score":0.41075313,"about_ca_system_score_codex":0.002570709,"about_ca_system_score_gemma":0.0040029804,"threshold_uncertainty_score":0.58588946},"labels":[],"label_agreement":null},{"id":"W4248514134","doi":"10.1002/atr.5670420402","title":"Multiple objective optimization of the fleet sizing problem for road freight transportation","year":2008,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Sizing; Operations research; Computer science; Set (abstract data type); Mathematical optimization; Queueing theory; Sample (material); Transportation theory; Decision maker; Pareto principle; Fleet management; Mathematical model; Engineering; Mathematics","score_opus":0.012733425649113827,"score_gpt":0.24238161638303124,"score_spread":0.2296481907339174,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4248514134","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20145313,0.0006712514,0.7870187,0.00037609987,0.000037554415,0.00022220747,0.00027347787,0.00014347752,0.009804052],"genre_scores_gemma":[0.87694126,0.0003502319,0.11729393,0.000049188042,0.000025894655,0.00025089024,0.00018401057,0.00005793541,0.0048466017],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99922097,0.0004415602,0.000022683738,0.000084557905,0.00013145938,0.000098809105],"domain_scores_gemma":[0.99912006,0.0006483748,0.000091447044,0.000019308256,0.000070371985,0.00005045636],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016462879,0.0011579202,0.0011414232,0.0012109529,0.0004021048,0.0012121894,0.0006643669,0.00095117936,0.0031292245],"category_scores_gemma":[0.0019569779,0.00055752817,0.0007508694,0.0012907849,0.00056953536,0.00087894185,0.0006861152,0.0006968241,0.00018809616],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003085497,0.000027220434,0.00015416984,0.000039079365,0.00002892611,0.000037235633,0.000017762286,0.990378,0.00080227456,0.0030125892,0.000190441,0.0052813655],"study_design_scores_gemma":[0.000009908518,0.000028499802,0.000104743835,0.000004430818,0.0000083534815,0.000007659419,0.000014429946,0.99755305,0.00029921092,0.0017546586,0.0002118951,0.0000032008597],"about_ca_topic_score_codex":0.0063262475,"about_ca_topic_score_gemma":0.0052331095,"teacher_disagreement_score":0.0063262475,"about_ca_system_score_codex":0.0017062179,"about_ca_system_score_gemma":0.0011771208,"threshold_uncertainty_score":0.012578845},"labels":[],"label_agreement":null},{"id":"W4249802611","doi":"10.1007/0-306-48213-4_16","title":"TSP Software","year":2006,"lang":"en","type":"book-chapter","venue":"Combinatorial optimization","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Fortran; Travelling salesman problem; Java; Software; Pascal (unit); Heuristic; Programming language; Theoretical computer science; Focus (optics); Source code; Mathematical optimization; Algorithm; Artificial intelligence; Mathematics","score_opus":0.011471762972186987,"score_gpt":0.2210551803163071,"score_spread":0.20958341734412012,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4249802611","genre_codex":"methods","genre_gemma":"software","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"software","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0031958064,0.0011048603,0.531707,0.0008326708,0.00047087643,0.00025293967,0.0064546163,0.02414399,0.4318372],"genre_scores_gemma":[0.03857471,0.0026940648,0.5038901,0.00081883505,0.0002137632,0.00070998986,0.021924417,0.01096715,0.42020702],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99950707,0.00006693575,0.000025598443,0.000093219736,0.0002591772,0.00004796592],"domain_scores_gemma":[0.9994967,0.00012323832,0.000022381168,0.00013786751,0.00018789487,0.000031873093],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039706504,0.0012078321,0.00066981855,0.0015865482,0.0007361104,0.001657598,0.0018572251,0.0008367016,0.11248984],"category_scores_gemma":[0.0015282051,0.0006548189,0.00090137246,0.0024659764,0.00035294346,0.001752526,0.0011772969,0.0014595628,0.054661434],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000041082963,0.00011251511,0.00021879582,0.00035335723,0.00003726177,0.00008856935,0.00007421037,0.031448636,0.002278883,0.10239676,0.40317324,0.45977673],"study_design_scores_gemma":[0.00003490623,0.00002719215,0.00020731322,0.00010606725,0.00002369926,0.00020950889,0.000028941835,0.100794576,0.003769754,0.07393397,0.82083845,0.000025523403],"about_ca_topic_score_codex":0.0032488543,"about_ca_topic_score_gemma":0.0054730317,"teacher_disagreement_score":0.11248984,"about_ca_system_score_codex":0.0009083045,"about_ca_system_score_gemma":0.0013064581,"threshold_uncertainty_score":0.376316},"labels":[],"label_agreement":null},{"id":"W4250150436","doi":"10.1002/net.20170","title":"Locating a cycle in a transportation or a telecommunications network","year":2007,"lang":"en","type":"article","venue":"Networks","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":33,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"","keywords":"Travelling salesman problem; Hamiltonian path; Computer science; Graph; Mathematical optimization; Hamiltonian path problem; Hamiltonian (control theory); Mathematics; Theoretical computer science","score_opus":0.014452696560842734,"score_gpt":0.27393892333761466,"score_spread":0.2594862267767719,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4250150436","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18750733,0.0023940182,0.7697917,0.0015441725,0.00017733079,0.00017293177,0.0005799166,0.0003548138,0.03747792],"genre_scores_gemma":[0.778904,0.001721229,0.20302115,0.00014816648,0.00009273586,0.00012576202,0.00043393957,0.00006346324,0.015489519],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99972993,0.0000688565,0.000012860279,0.00009225519,0.000047821166,0.000048299436],"domain_scores_gemma":[0.9997694,0.00010600167,0.00004713181,0.000026503072,0.000021787988,0.000029260784],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024939724,0.0004199022,0.0005074353,0.00068949984,0.00060804957,0.0013827243,0.000670647,0.001072669,0.008638737],"category_scores_gemma":[0.001021042,0.0001750956,0.00040459313,0.0013679359,0.0007174853,0.0017063111,0.00082452927,0.0004646761,0.00057142053],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019121949,0.00007259839,0.0017909313,0.00032270508,0.00006943287,0.0005791486,0.00022447926,0.38523751,0.005301274,0.4770287,0.009971303,0.11921077],"study_design_scores_gemma":[0.000037654667,0.000098013654,0.00071524427,0.00008876228,0.000043230706,0.00044507577,0.00037781397,0.567344,0.0036280607,0.38782397,0.039368507,0.000029709336],"about_ca_topic_score_codex":0.0027054674,"about_ca_topic_score_gemma":0.0025562989,"teacher_disagreement_score":0.008638737,"about_ca_system_score_codex":0.001018416,"about_ca_system_score_gemma":0.0005892206,"threshold_uncertainty_score":0.02889949},"labels":[],"label_agreement":null},{"id":"W4250280589","doi":"10.1002/net.20307","title":"A branch‐and‐cut algorithm for the undirected prize collecting traveling salesman problem","year":2009,"lang":"en","type":"article","venue":"Networks","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":30,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; Université du Québec à Montréal","funders":"","keywords":"Travelling salesman problem; Undirected graph; Orienteering; Vertex (graph theory); Bottleneck traveling salesman problem; Computer science; Branch and cut; Combinatorics; Enhanced Data Rates for GSM Evolution; Graph; 2-opt; Mathematics; Algorithm; Traveling purchaser problem; Mathematical optimization; Linear programming; Artificial intelligence","score_opus":0.015512761032151857,"score_gpt":0.2532832045046128,"score_spread":0.23777044347246093,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4250280589","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.024273776,0.00017563156,0.9692197,0.00026235747,0.000051629526,0.0002092959,0.00017230486,0.00039712235,0.005238245],"genre_scores_gemma":[0.15584159,0.00019250612,0.8395499,0.00010327554,0.00003929749,0.0004068004,0.00058619806,0.00015322889,0.0031272243],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994369,0.00018113518,0.00003136693,0.00011255331,0.00012633028,0.000111747075],"domain_scores_gemma":[0.9990527,0.00058754825,0.00008029121,0.000055342607,0.0001427436,0.00008133246],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011193239,0.00094165554,0.0010497371,0.0009679649,0.0008110815,0.001065903,0.0013857756,0.0010913954,0.0054387385],"category_scores_gemma":[0.0025260013,0.0005060973,0.0007057495,0.0016900146,0.0005670173,0.0012526016,0.0011316577,0.0015587696,0.00052960165],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00029701952,0.00031647098,0.0007018002,0.00022080135,0.00007595202,0.00015046303,0.00016936321,0.6436868,0.0021868404,0.04562884,0.009459858,0.29710576],"study_design_scores_gemma":[0.00009060756,0.00007557243,0.000136599,0.000015656426,0.00002034658,0.000045045526,0.000034241617,0.9669284,0.0009973655,0.028971072,0.002673961,0.0000110287165],"about_ca_topic_score_codex":0.0047595617,"about_ca_topic_score_gemma":0.003523512,"teacher_disagreement_score":0.0054387385,"about_ca_system_score_codex":0.0010493307,"about_ca_system_score_gemma":0.0017079617,"threshold_uncertainty_score":0.018194437},"labels":[],"label_agreement":null},{"id":"W4255492803","doi":"10.1002/net.20061","title":"Exact solution of the centralized network design problem on directed graphs","year":2005,"lang":"en","type":"article","venue":"Networks","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; University of Alberta","funders":"","keywords":"Column generation; Cutting-plane method; Lagrangian relaxation; Spanning tree; Steiner tree problem; Mathematical optimization; Branch and bound; Directed graph; Linear programming relaxation; Interior point method; Branch and cut; Mathematics; Relaxation (psychology); Constraint (computer-aided design); Upper and lower bounds; Tree (set theory); Point (geometry); Computer science; Linear programming; Integer programming; Combinatorics","score_opus":0.01629736232607209,"score_gpt":0.23421502231816474,"score_spread":0.21791765999209264,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4255492803","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011769807,0.000091671514,0.9854035,0.000087875414,0.000013708612,0.0000278799,0.000040892763,0.00014404689,0.0024206105],"genre_scores_gemma":[0.49300525,0.0002500353,0.5016335,0.000073141644,0.000039777195,0.0002221848,0.00019707154,0.0001294567,0.004449463],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9994442,0.00018923204,0.000015631877,0.00010095437,0.00016676911,0.00008321348],"domain_scores_gemma":[0.9988142,0.00070259295,0.00012235847,0.00011881724,0.00018284464,0.00005911685],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012975142,0.0006955482,0.0010523996,0.0005482623,0.00046052862,0.0008695534,0.000954127,0.0007963436,0.0041470705],"category_scores_gemma":[0.002829977,0.00047716039,0.00048661916,0.0007033619,0.0007259709,0.0008755198,0.0008928435,0.0009914208,0.00041800257],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019459485,0.000017377319,0.00009361929,0.000034166107,0.000007086661,0.000021515973,0.00001919935,0.9692348,0.0007685004,0.010620588,0.0006716747,0.018492062],"study_design_scores_gemma":[0.000009765103,0.000011463388,0.000024009498,0.000003421557,0.000002338405,0.000007579663,0.0000075295115,0.99233353,0.00036943817,0.0068636504,0.00036534725,0.0000019889744],"about_ca_topic_score_codex":0.003479596,"about_ca_topic_score_gemma":0.003965465,"teacher_disagreement_score":0.0041470705,"about_ca_system_score_codex":0.0013393972,"about_ca_system_score_gemma":0.0017539387,"threshold_uncertainty_score":0.013873398},"labels":[],"label_agreement":null},{"id":"W4280493947","doi":"10.1016/j.ejor.2023.01.007","title":"Small and large neighborhood search for the park-and-loop routing problem with parking selection","year":2023,"lang":"en","type":"article","venue":"European Journal of Operational Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":26,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"Agence Nationale de la Recherche","keywords":"Vehicle routing problem; Computer science; City logistics; Benchmark (surveying); Routing (electronic design automation); Selection (genetic algorithm); Metaheuristic; Parking lot; Set (abstract data type); Iterated local search; Transport engineering; Operations research; Mathematical optimization; Computer network; Artificial intelligence; Engineering; Geography; Mathematics","score_opus":0.10177030843987613,"score_gpt":0.34285156255309185,"score_spread":0.24108125411321574,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4280493947","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1365489,0.0015777124,0.8536655,0.0009881054,0.00012541727,0.00013993321,0.00020327994,0.00017568488,0.006575407],"genre_scores_gemma":[0.8460508,0.00053216354,0.14325252,0.000135182,0.00008818021,0.00024372312,0.00029765253,0.0000854976,0.009314363],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994555,0.00034875487,0.000016886072,0.00006660773,0.0000652561,0.00004687025],"domain_scores_gemma":[0.99636286,0.003070619,0.00018024271,0.0000624641,0.0001570455,0.00016674068],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018274271,0.00069231103,0.001538125,0.0008809894,0.00059426326,0.0007887566,0.0016262989,0.0011711625,0.002931326],"category_scores_gemma":[0.005301641,0.0005639999,0.00063614605,0.00083872234,0.00088125595,0.0015920291,0.0011204429,0.00082670414,0.00016209944],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002420604,0.00013741599,0.0005762948,0.00009602784,0.000046009896,0.000050437953,0.00003690989,0.9670158,0.0003765279,0.015596768,0.0017601949,0.014065594],"study_design_scores_gemma":[0.000014994878,0.00002728624,0.000049356673,0.0000026563243,0.0000040576797,0.000005283487,0.000007908613,0.99569356,0.000031316544,0.004043555,0.0001175848,0.0000024150565],"about_ca_topic_score_codex":0.0058090524,"about_ca_topic_score_gemma":0.0060740006,"teacher_disagreement_score":0.0058090524,"about_ca_system_score_codex":0.00081122486,"about_ca_system_score_gemma":0.0010628722,"threshold_uncertainty_score":0.0115504265},"labels":[],"label_agreement":null},{"id":"W4280615951","doi":"10.1016/j.cor.2022.105870","title":"Branch-and-cut-and-price for the Electric Vehicle Routing Problem with Time Windows, Piecewise-Linear Recharging and Capacitated Recharging Stations","year":2022,"lang":"en","type":"article","venue":"Computers & Operations Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":87,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Group for Research in Decision Analysis","funders":"","keywords":"Vehicle routing problem; Piecewise linear function; Computer science; Routing (electronic design automation); Mathematical optimization; Piecewise; Operations research; Mathematics; Computer network","score_opus":0.041906010411408796,"score_gpt":0.3142522808123648,"score_spread":0.272346270400956,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4280615951","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.056111455,0.0026828735,0.91974664,0.0019717722,0.00022223474,0.0004305147,0.000793746,0.00047269667,0.017568115],"genre_scores_gemma":[0.58111304,0.0045600985,0.37619635,0.0003566019,0.0004423936,0.0008601264,0.0015760617,0.0005309095,0.034364425],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999116,0.00042714903,0.000033635934,0.00013295366,0.0001383437,0.00015186291],"domain_scores_gemma":[0.99609,0.003321701,0.00016698045,0.00008219501,0.00015165715,0.00018752286],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003022406,0.0021601326,0.0029362652,0.0014774096,0.0008883338,0.002653968,0.0026070015,0.003020783,0.012022514],"category_scores_gemma":[0.007209696,0.0015485524,0.0015136062,0.00275834,0.0016154613,0.0038666388,0.0013930697,0.0032285384,0.0007758773],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00027672117,0.00017367095,0.00038202893,0.00027035398,0.00006265238,0.00009910347,0.000054143038,0.93528587,0.00040217815,0.033663545,0.0041387384,0.025190959],"study_design_scores_gemma":[0.00003896323,0.000050434042,0.0001185264,0.000015224454,0.000023865014,0.00001868749,0.000018571174,0.97822964,0.0001079716,0.02078193,0.00058814714,0.000007988413],"about_ca_topic_score_codex":0.016440094,"about_ca_topic_score_gemma":0.012339552,"teacher_disagreement_score":0.016440094,"about_ca_system_score_codex":0.0030872636,"about_ca_system_score_gemma":0.0031911738,"threshold_uncertainty_score":0.040219367},"labels":[],"label_agreement":null},{"id":"W4280622413","doi":"10.1155/2022/4825018","title":"The Optimization of Path Planning for Express Delivery Based on Clone Adaptive Ant Colony Optimization","year":2022,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Shihezi University; China Postdoctoral Science Foundation","keywords":"Ant colony optimization algorithms; Computer science; Simulated annealing; Mathematical optimization; Operations research; Path (computing); Ant colony; Genetic algorithm; Convergence (economics); Metaheuristic; Context (archaeology); Engineering; Economics; Artificial intelligence; Mathematics; Computer network","score_opus":0.014371953632756907,"score_gpt":0.2557198655593098,"score_spread":0.24134791192655292,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4280622413","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07466265,0.00047466814,0.9180275,0.00021668916,0.00006363394,0.00006696189,0.00004553545,0.00029097046,0.0061513386],"genre_scores_gemma":[0.83710575,0.0004321522,0.1570324,0.00009333708,0.000022581351,0.0001837472,0.000119964745,0.00007300682,0.0049371314],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997565,0.000060750575,0.000010287878,0.000059949547,0.00007325368,0.000039182996],"domain_scores_gemma":[0.9996669,0.0001589982,0.000045379784,0.000017772369,0.00008669093,0.000024255394],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003566939,0.00071213004,0.00064944255,0.0004724388,0.00047122285,0.0007025198,0.0007475505,0.000703738,0.001416549],"category_scores_gemma":[0.0010561413,0.0003285918,0.0006282365,0.00064192613,0.0004891752,0.00058398215,0.00058953854,0.0006526986,0.00012908771],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026288471,0.000019462646,0.00060831534,0.000041667594,0.00002288888,0.000074810574,0.00004885854,0.9712143,0.002271572,0.003908998,0.0006657142,0.021097206],"study_design_scores_gemma":[0.0000041367953,0.000014574218,0.00008018717,0.0000014807787,0.000004675179,0.000011379351,0.000006968899,0.9988875,0.00018053906,0.0005594549,0.00024687662,0.0000023655127],"about_ca_topic_score_codex":0.010910645,"about_ca_topic_score_gemma":0.0058427076,"teacher_disagreement_score":0.010910645,"about_ca_system_score_codex":0.0006325286,"about_ca_system_score_gemma":0.0010993272,"threshold_uncertainty_score":0.021694243},"labels":[],"label_agreement":null},{"id":"W4280625240","doi":"10.1155/2022/6599089","title":"Truck and Unmanned Vehicle Routing Problem with Time Windows: A Satellite Synchronization Perspective","year":2022,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Truck; Synchronization (alternating current); Real-time computing; Computer science; Satellite; Benchmark (surveying); Simulation; Engineering; Computer network; Automotive engineering; Aerospace engineering","score_opus":0.004842788618287535,"score_gpt":0.22255379647548207,"score_spread":0.21771100785719452,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4280625240","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.34049422,0.0044802944,0.6168025,0.0020328853,0.00042759933,0.00048205064,0.0019899965,0.00041516183,0.03287536],"genre_scores_gemma":[0.8292048,0.0026942038,0.15622419,0.00019023885,0.00017065655,0.00044624112,0.0014845101,0.00013663374,0.009448552],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994766,0.00018652229,0.000021375228,0.00012273784,0.00008152767,0.0001113564],"domain_scores_gemma":[0.9994766,0.00030823643,0.00008513989,0.000026996533,0.00003826143,0.00006473692],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007124959,0.001499433,0.0012704126,0.00065909023,0.00047760337,0.0013046787,0.0013086026,0.0014216169,0.0041761515],"category_scores_gemma":[0.0015326133,0.00039059663,0.0011408998,0.0018971511,0.0004913805,0.0016637904,0.0007761739,0.0011309512,0.000235469],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006376458,0.00006789962,0.0003654909,0.00012238695,0.000028749497,0.00015447613,0.000026173318,0.9787587,0.00044728536,0.0095853275,0.0009822533,0.009397497],"study_design_scores_gemma":[0.000025949957,0.00009016651,0.00024198713,0.000015438136,0.000020457299,0.000045896286,0.000055978984,0.9920901,0.00034504317,0.0055817254,0.0014797647,0.0000074902837],"about_ca_topic_score_codex":0.010045804,"about_ca_topic_score_gemma":0.0067943353,"teacher_disagreement_score":0.010045804,"about_ca_system_score_codex":0.0011473343,"about_ca_system_score_gemma":0.0015386648,"threshold_uncertainty_score":0.019974649},"labels":[],"label_agreement":null},{"id":"W4281386812","doi":"10.1016/j.trb.2022.05.005","title":"Robust ship fleet deployment with shipping revenue management","year":2022,"lang":"en","type":"article","venue":"Transportation Research Part B Methodological","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":50,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Group for Research in Decision Analysis; HEC Montréal","funders":"","keywords":"Software deployment; Revenue; Operations research; Revenue management; Profit (economics); Fleet management; Computer science; Randomness; Constraint (computer-aided design); Service (business); Mathematical optimization; Business; Engineering; Economics; Finance; Mathematics","score_opus":0.42309109817170615,"score_gpt":0.41881113404199244,"score_spread":0.004279964129713709,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4281386812","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.030652435,0.00013007241,0.96493083,0.00021145259,0.000051368035,0.00004772696,0.000073995936,0.00021528368,0.0036868034],"genre_scores_gemma":[0.8747764,0.00018018635,0.11937274,0.00006640099,0.00007838968,0.00009677388,0.00014661849,0.00014933514,0.005133002],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99939954,0.00025627966,0.000023116148,0.00013771483,0.00011417352,0.000069221016],"domain_scores_gemma":[0.99937576,0.00030400808,0.000099270255,0.00008753087,0.00010365622,0.000029841605],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010256539,0.0009073002,0.0007589517,0.000387521,0.00017859763,0.00082150137,0.00082847534,0.00076029776,0.002168996],"category_scores_gemma":[0.0032846218,0.00048341803,0.0006432097,0.00047917047,0.00043209508,0.0011487511,0.0011207258,0.0008369265,0.00042308966],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005121832,0.00001955667,0.00013558925,0.000026997523,0.000019300242,0.00003724102,0.0000079680785,0.97681034,0.002422066,0.0074303118,0.00051222404,0.0125273205],"study_design_scores_gemma":[0.0000029387681,0.000018114752,0.00006855643,0.0000016509932,0.000003441914,0.0000059283675,0.0000032421171,0.9977673,0.0005362059,0.0014343292,0.00015620934,0.0000020202383],"about_ca_topic_score_codex":0.0018543369,"about_ca_topic_score_gemma":0.0011447241,"teacher_disagreement_score":0.002168996,"about_ca_system_score_codex":0.0006073146,"about_ca_system_score_gemma":0.000584439,"threshold_uncertainty_score":0.0072559714},"labels":[],"label_agreement":null},{"id":"W4283797864","doi":"10.1609/aaai.v36i9.21262","title":"MIP-GNN: A Data-Driven Framework for Guiding Combinatorial Solvers","year":2022,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":33,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Polytechnique Montréal; Mila - Quebec Artificial Intelligence Institute; University of Toronto","funders":"Deutscher Akademischer Austauschdienst","keywords":"Solver; Heuristics; Computer science; Integer programming; Bipartite graph; Leverage (statistics); Variable (mathematics); Graph; Theoretical computer science; Heuristic; Mathematical optimization; Algorithm; Artificial intelligence; Mathematics; Programming language","score_opus":0.16559871495142212,"score_gpt":0.3409592441482524,"score_spread":0.1753605291968303,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4283797864","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0030705584,0.00019928238,0.98593545,0.0004375201,0.00008391762,0.00011493151,0.0006992905,0.0055975714,0.0038614532],"genre_scores_gemma":[0.07785539,0.00024652356,0.91416824,0.00061871135,0.00008679553,0.0006677448,0.0022816972,0.0019063859,0.0021685543],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987563,0.00048785133,0.00006442749,0.0002658657,0.00029889404,0.00012678308],"domain_scores_gemma":[0.99675107,0.0020176882,0.00021841789,0.0003746806,0.00049520726,0.00014294991],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024836455,0.0022324251,0.0012416621,0.0012358439,0.00068147824,0.0022421821,0.0041967365,0.0021067115,0.01037633],"category_scores_gemma":[0.013376579,0.001413867,0.0015359299,0.0011607833,0.0013521983,0.002327127,0.0029044775,0.004453408,0.0031181474],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008139798,0.00007400988,0.00089751295,0.00020953883,0.00004862684,0.00007214357,0.000053973763,0.9066914,0.00086956064,0.030512968,0.011530261,0.048958562],"study_design_scores_gemma":[0.000013638773,0.0000069555754,0.000022344593,0.00001354843,0.0000029195512,0.0000067872284,0.000005647906,0.98466647,0.0002786063,0.0131440675,0.0018352066,0.0000037121563],"about_ca_topic_score_codex":0.006842284,"about_ca_topic_score_gemma":0.017033665,"teacher_disagreement_score":0.01037633,"about_ca_system_score_codex":0.0016794876,"about_ca_system_score_gemma":0.0041774227,"threshold_uncertainty_score":0.034712315},"labels":[],"label_agreement":null},{"id":"W4283810538","doi":"10.1609/aaai.v36i4.20294","title":"Learning to Search in Local Branching","year":2022,"lang":"en","type":"article","venue":"","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Leverage (statistics); Local search (optimization); Mathematical optimization; Heuristic; Computer science; Mathematics; Linear programming; Integer programming; Constraint (computer-aided design); Algorithm; Artificial intelligence","score_opus":0.014469335138050234,"score_gpt":0.2739315645870722,"score_spread":0.259462229449022,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4283810538","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.053558595,0.00033502583,0.9425646,0.0002719785,0.000026154605,0.000057997153,0.000030260868,0.0004133958,0.002742072],"genre_scores_gemma":[0.8080092,0.0003237302,0.18832608,0.00031125496,0.000057825382,0.00027850285,0.00014880025,0.00015127835,0.0023932974],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991697,0.00033617613,0.000040880263,0.00020029,0.0001547119,0.00009832999],"domain_scores_gemma":[0.99555296,0.003196397,0.0004206759,0.0003249033,0.00033955436,0.00016548195],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024603948,0.0008847214,0.0013230717,0.00057814876,0.00046169717,0.0008718939,0.0014568307,0.0009818148,0.0019666015],"category_scores_gemma":[0.011173584,0.0005839318,0.00062134484,0.0005088596,0.0016607053,0.0015156924,0.0020185325,0.0015689229,0.00033442045],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000065274515,0.000042255186,0.0011069172,0.000060788017,0.00002915273,0.000049947386,0.00007357723,0.9531178,0.0010160346,0.022119308,0.00054736045,0.021771526],"study_design_scores_gemma":[0.00000952795,0.000024640198,0.000045381505,0.0000067071082,0.000003864707,0.000006320298,0.0000056732583,0.9921767,0.00019563339,0.0073733353,0.00015021862,0.0000020924324],"about_ca_topic_score_codex":0.0025471468,"about_ca_topic_score_gemma":0.0030429387,"teacher_disagreement_score":0.0025471468,"about_ca_system_score_codex":0.0008610893,"about_ca_system_score_gemma":0.0013282765,"threshold_uncertainty_score":0.013011932},"labels":[],"label_agreement":null},{"id":"W4284896812","doi":"10.3390/technologies10040081","title":"Distribution Path Optimization by an Improved Genetic Algorithm Combined with a Divide-and-Conquer Strategy","year":2022,"lang":"en","type":"article","venue":"Technologies","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"National Natural Science Foundation of China","keywords":"Mathematical optimization; Crossover; Divide and conquer algorithms; Computer science; Path (computing); Genetic algorithm; Metaheuristic; Scheduling (production processes); Operator (biology); Vehicle routing problem; Routing (electronic design automation); Algorithm; Mathematics; Artificial intelligence","score_opus":0.007743080449083273,"score_gpt":0.2171983023533931,"score_spread":0.20945522190430982,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4284896812","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021368334,0.00022032323,0.9737602,0.00017083273,0.000052413616,0.00008918476,0.000024820049,0.0005045072,0.003809455],"genre_scores_gemma":[0.28011584,0.00026311792,0.71462774,0.00013581257,0.0000534025,0.00037613453,0.00011126217,0.000108677516,0.0042079478],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999632,0.00009395634,0.00001413249,0.00007826425,0.00012390592,0.00005762243],"domain_scores_gemma":[0.99972695,0.00013144907,0.000032847198,0.00001974667,0.00007375165,0.000015124278],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005718194,0.0009874047,0.0010386459,0.0011610022,0.00048845226,0.0006344682,0.0012593379,0.0011957422,0.002053042],"category_scores_gemma":[0.0011053439,0.00046989048,0.0008096409,0.0011100782,0.0006534461,0.0006689752,0.0006120151,0.00082362036,0.0002547809],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000046877594,0.00007080081,0.00035159374,0.00003818718,0.000036672034,0.000071715025,0.000051306353,0.91506416,0.0036304696,0.007083502,0.0009581941,0.07259651],"study_design_scores_gemma":[0.000018981103,0.000025369673,0.00003957236,0.0000024759295,0.000008382154,0.000015886566,0.0000048922006,0.99813914,0.00041398805,0.00079327123,0.00053497986,0.0000029541686],"about_ca_topic_score_codex":0.009367941,"about_ca_topic_score_gemma":0.0068850387,"teacher_disagreement_score":0.009367941,"about_ca_system_score_codex":0.0009854945,"about_ca_system_score_gemma":0.0016353646,"threshold_uncertainty_score":0.01862681},"labels":[],"label_agreement":null},{"id":"W4285164761","doi":"10.5267/j.ijiec.2022.1.001","title":"Multi-depot heterogeneous fleet vehicle routing problem with time windows: Airline and roadway integrated routing","year":2022,"lang":"en","type":"article","venue":"International Journal of Industrial Engineering Computations","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Anadolu Üniversitesi","keywords":"Vehicle routing problem; Genetic algorithm; Computer science; Variable neighborhood search; Aviation; Variable (mathematics); Routing (electronic design automation); Range (aeronautics); Mathematical optimization; Node (physics); Operations research; Engineering; Metaheuristic; Computer network; Algorithm; Mathematics","score_opus":0.021445372266146498,"score_gpt":0.25305417389850543,"score_spread":0.23160880163235892,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285164761","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13212566,0.001506138,0.8575933,0.00034449153,0.00012842973,0.000110745124,0.00040482814,0.00017257618,0.0076138116],"genre_scores_gemma":[0.8486445,0.0011615241,0.1416262,0.00006189547,0.00008007334,0.0001466533,0.0006056662,0.000065300745,0.007608172],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996321,0.00010385699,0.000015349075,0.00010869876,0.00006377663,0.00007621286],"domain_scores_gemma":[0.9997811,0.00011008545,0.00003668992,0.000013605067,0.000024161538,0.00003428955],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005181849,0.00088431267,0.0009402618,0.00045825305,0.00035540492,0.0009195388,0.0011066293,0.0009924325,0.0016836486],"category_scores_gemma":[0.00075336185,0.00033834775,0.00085145276,0.001347531,0.00030026404,0.0010749947,0.00062655716,0.00063023204,0.00012645709],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004975949,0.000036678208,0.00030276552,0.000053159685,0.000029511593,0.00008395453,0.0000123679265,0.97650313,0.0010694566,0.0074062734,0.00055101095,0.013901822],"study_design_scores_gemma":[0.00000710388,0.000031045372,0.00019998946,0.0000035328844,0.000011787338,0.000026229505,0.000014754899,0.9961132,0.00042441784,0.0024862296,0.00067694864,0.000004685483],"about_ca_topic_score_codex":0.008459766,"about_ca_topic_score_gemma":0.0061255647,"teacher_disagreement_score":0.008459766,"about_ca_system_score_codex":0.0009003978,"about_ca_system_score_gemma":0.001202965,"threshold_uncertainty_score":0.016821086},"labels":[],"label_agreement":null},{"id":"W4285267076","doi":"10.1109/access.2022.3174081","title":"A Vehicle Routing Problem With Option for Outsourcing and Time-Dependent Travel Time","year":2022,"lang":"en","type":"article","venue":"IEEE Access","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"University of Science and Technology of China; National University of Singapore","keywords":"Vehicle routing problem; Computer science; Mathematical optimization; Tabu search; Integer programming; Benchmark (surveying); Routing (electronic design automation); Outsourcing; Operations research; Engineering; Mathematics; Algorithm; Computer network","score_opus":0.017026437850412232,"score_gpt":0.2602245517578018,"score_spread":0.24319811390738955,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285267076","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2902115,0.0007461098,0.68471503,0.0010096569,0.00012925568,0.00022983106,0.0010282617,0.00023829551,0.021692162],"genre_scores_gemma":[0.8300555,0.0005250709,0.15752782,0.000113145434,0.000052364663,0.00020782051,0.00071046315,0.00010204705,0.010705807],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99944335,0.0002239412,0.00001819382,0.00012918198,0.00008512073,0.00010018125],"domain_scores_gemma":[0.9993926,0.00034700002,0.00007678108,0.00006318996,0.000052217132,0.000068308414],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00072768214,0.00086611346,0.000912342,0.00042151965,0.00044228532,0.0010112397,0.0012138174,0.0011318031,0.0036613457],"category_scores_gemma":[0.0019250811,0.00037731408,0.00097372185,0.0011974655,0.00063262956,0.0011429765,0.00078753644,0.0009133605,0.0002128971],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006815873,0.000050887007,0.0004935757,0.00007576692,0.000031317944,0.00017454676,0.000032850487,0.96290344,0.00083678355,0.01782751,0.001171642,0.016333457],"study_design_scores_gemma":[0.000033544296,0.0000711093,0.0003578833,0.000010792298,0.000022336395,0.00014400246,0.000055542732,0.98521703,0.0006280665,0.010750711,0.0026986396,0.000010331203],"about_ca_topic_score_codex":0.0078061554,"about_ca_topic_score_gemma":0.006313081,"teacher_disagreement_score":0.0078061554,"about_ca_system_score_codex":0.0011991377,"about_ca_system_score_gemma":0.0013717061,"threshold_uncertainty_score":0.015521407},"labels":[],"label_agreement":null},{"id":"W4285580373","doi":"","title":"Tactical capacity planning in an integrated multi-stakeholder freight transportation system","year":2021,"lang":"en","type":"report","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Stakeholder; Business; Transport engineering; Traffic management; Transportation planning; Engineering; Economics; Management","score_opus":0.07525117030348033,"score_gpt":0.283816822004861,"score_spread":0.2085656517013807,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285580373","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.19369158,0.00038061355,0.79205096,0.0005652858,0.000037587746,0.00016797261,0.00023148584,0.00015391881,0.012720545],"genre_scores_gemma":[0.9564003,0.00019446232,0.039919265,0.00004776114,0.000011058789,0.00015814455,0.000102899256,0.000028260807,0.0031378733],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99901736,0.0004347745,0.000023182489,0.0001757079,0.0001387064,0.0002102383],"domain_scores_gemma":[0.99897146,0.0006344984,0.00013524087,0.000029783909,0.000120356985,0.00010861459],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013651482,0.0009255604,0.00094658585,0.00070548494,0.0008707021,0.0019904957,0.0013544591,0.0017028004,0.0029437502],"category_scores_gemma":[0.002165598,0.00076433824,0.0007487943,0.0011678222,0.0011654128,0.0017610556,0.001732971,0.0012052056,0.00018816344],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019789728,0.0000149018015,0.00012706844,0.000011590338,0.000010978074,0.000060071445,0.000020352794,0.99452436,0.00022024581,0.0035428773,0.00006879892,0.001378927],"study_design_scores_gemma":[0.0000063583416,0.000021035152,0.000077180914,0.000002496982,0.0000050928957,0.000009771374,0.000039877905,0.99761885,0.00008443916,0.001957258,0.00017388743,0.0000037260565],"about_ca_topic_score_codex":0.018266873,"about_ca_topic_score_gemma":0.011806365,"teacher_disagreement_score":0.018266873,"about_ca_system_score_codex":0.0019427468,"about_ca_system_score_gemma":0.0024016188,"threshold_uncertainty_score":0.036321104},"labels":[],"label_agreement":null},{"id":"W4285600159","doi":"10.24963/ijcai.2022/253","title":"Large Neighbourhood Search for Anytime MaxSAT Solving","year":2022,"lang":"en","type":"article","venue":"Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Maximum satisfiability problem; Solver; Neighbourhood (mathematics); Computer science; Boolean satisfiability problem; Limit (mathematics); Mathematical optimization; Satisfiability; Algorithm; Mathematics; Boolean function","score_opus":0.06885179694586264,"score_gpt":0.3000225798449502,"score_spread":0.23117078289908755,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285600159","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017890284,0.0005980623,0.96749187,0.00047779802,0.00013850011,0.000083389474,0.00014187665,0.0020241798,0.011154064],"genre_scores_gemma":[0.29323545,0.00038053552,0.69884855,0.00027976124,0.00009321333,0.0002935962,0.00051504426,0.000743754,0.0056101084],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99882525,0.0005413343,0.00006136494,0.00018283655,0.0002864184,0.00010286028],"domain_scores_gemma":[0.99743575,0.0017197184,0.00015710699,0.0003640189,0.00023533082,0.00008820186],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018033956,0.000774102,0.0008644761,0.00056156755,0.0006611172,0.0011339162,0.0016524909,0.0012300343,0.007936391],"category_scores_gemma":[0.008211076,0.00043309093,0.0011023843,0.00092279934,0.001074447,0.001983709,0.0019088654,0.001824038,0.0014173749],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020270099,0.00008479662,0.0008417522,0.0003185466,0.0000594129,0.00012916778,0.0001605316,0.7825508,0.0033942387,0.082908176,0.008643262,0.12070657],"study_design_scores_gemma":[0.000043199543,0.000038427967,0.000078707,0.000027577553,0.000011443814,0.000027906435,0.000023647164,0.9532536,0.0017440409,0.03937575,0.005365857,0.000009866238],"about_ca_topic_score_codex":0.0027847202,"about_ca_topic_score_gemma":0.0071449503,"teacher_disagreement_score":0.007936391,"about_ca_system_score_codex":0.0011172737,"about_ca_system_score_gemma":0.0015227891,"threshold_uncertainty_score":0.026549876},"labels":[],"label_agreement":null},{"id":"W4285804020","doi":"10.3390/su14148661","title":"Solving the Green Open Vehicle Routing Problem Using a Membrane-Inspired Hybrid Algorithm","year":2022,"lang":"en","type":"article","venue":"Sustainability","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Tabu search; Crossover; Neighbourhood (mathematics); Vehicle routing problem; Genetic algorithm; Heuristics; Mathematical optimization; Algorithm; Travelling salesman problem; Computer science; Guided Local Search; Local search (optimization); Routing (electronic design automation); Mathematics; Artificial intelligence","score_opus":0.019340144163954943,"score_gpt":0.2829914870382855,"score_spread":0.26365134287433056,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285804020","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06957731,0.00045601022,0.92294943,0.00027360144,0.000059294904,0.000093291,0.000074318516,0.00055491517,0.0059618936],"genre_scores_gemma":[0.5774781,0.0003285756,0.41772383,0.0001701589,0.000033046257,0.00034330128,0.00020270368,0.00008592728,0.003634227],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99972147,0.000075290256,0.00001656477,0.000060455313,0.000067287816,0.00005891913],"domain_scores_gemma":[0.99969995,0.00015999321,0.00003798056,0.00002361539,0.00004807511,0.000030369123],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004877932,0.00070838525,0.0008383532,0.00068690325,0.00046497674,0.0008120698,0.0013476041,0.0015429945,0.001892451],"category_scores_gemma":[0.0008826067,0.00032803183,0.0008825996,0.00082490256,0.00042144067,0.0008708938,0.0010285738,0.00062489806,0.0002606112],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004936529,0.000050251158,0.00054099015,0.00005657017,0.000043767646,0.00006470302,0.000035265286,0.9485783,0.0030031744,0.0054617417,0.00059334777,0.0415225],"study_design_scores_gemma":[0.000008072569,0.000016705817,0.000038575443,0.0000028409493,0.0000045774423,0.000011877013,0.000009834246,0.9983216,0.00036414072,0.0009232237,0.0002955901,0.0000028755314],"about_ca_topic_score_codex":0.004025316,"about_ca_topic_score_gemma":0.0025631897,"teacher_disagreement_score":0.004025316,"about_ca_system_score_codex":0.0006571534,"about_ca_system_score_gemma":0.0012317742,"threshold_uncertainty_score":0.008003771},"labels":[],"label_agreement":null},{"id":"W4286750933","doi":"10.48550/arxiv.2207.10254","title":"The Two-Stripe Symmetric Circulant TSP is in P","year":2022,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; National Science Foundation","keywords":"Circulant matrix; Hamiltonian path; Travelling salesman problem; Combinatorics; Mathematics; Parameterized complexity; Time complexity; Hamiltonian (control theory); Symmetric function; Circulant graph; Discrete mathematics; Mathematical optimization; Graph","score_opus":0.06163703261836798,"score_gpt":0.20852244558304925,"score_spread":0.14688541296468127,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4286750933","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.46340284,0.0006707706,0.40731505,0.015355069,0.0005372206,0.00092182955,0.005850359,0.003832228,0.1021147],"genre_scores_gemma":[0.831806,0.00083192805,0.13722433,0.0015043603,0.00034480746,0.00034316033,0.005971898,0.000568241,0.021405399],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99898773,0.00016100876,0.000047323538,0.00038415779,0.000177149,0.00024255924],"domain_scores_gemma":[0.9978654,0.0009698371,0.00030435246,0.0003892118,0.00023681436,0.00023433547],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041448936,0.0008184002,0.0011401497,0.00027441647,0.0013066852,0.0024858853,0.0012433094,0.0016385901,0.009139307],"category_scores_gemma":[0.0036265394,0.00045084025,0.0011028296,0.0011115246,0.0010834623,0.0032449514,0.0012960032,0.0023156835,0.001599347],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0019619733,0.0009198125,0.005785954,0.0016512936,0.0002904855,0.004558744,0.00076894334,0.15520991,0.042255763,0.49517113,0.12706012,0.16436595],"study_design_scores_gemma":[0.00038584493,0.00037947518,0.0025002286,0.000047360467,0.00006251528,0.0023766002,0.00043339655,0.3630846,0.012332389,0.5690302,0.04930541,0.00006194732],"about_ca_topic_score_codex":0.003977135,"about_ca_topic_score_gemma":0.004237537,"teacher_disagreement_score":0.009139307,"about_ca_system_score_codex":0.0012897176,"about_ca_system_score_gemma":0.0023520812,"threshold_uncertainty_score":0.030574024},"labels":[],"label_agreement":null},{"id":"W4287327884","doi":"","title":"The electric vehicle routing problem with capacitated charging stations","year":2019,"lang":"en","type":"other","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"","keywords":"Vehicle routing problem; Electric vehicle; Computer science; Routing (electronic design automation); Business; Computer network; Physics","score_opus":0.008264213883118479,"score_gpt":0.20940731867852835,"score_spread":0.20114310479540987,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4287327884","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10630491,0.001650268,0.8449397,0.0026193259,0.00037454456,0.0004831179,0.0022342522,0.0005638619,0.04083004],"genre_scores_gemma":[0.7266062,0.001956936,0.23257719,0.0004597837,0.00030357306,0.000571248,0.0019931186,0.00029390104,0.03523798],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985642,0.0005602881,0.000075485914,0.0004039682,0.00017820776,0.000217906],"domain_scores_gemma":[0.9986198,0.00095352397,0.00015648855,0.000079936435,0.00010087687,0.00008941705],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011568117,0.0018947896,0.0014909755,0.0009175762,0.0007952966,0.0028532327,0.0023380003,0.0029013765,0.008297765],"category_scores_gemma":[0.0034892987,0.0011339653,0.0016720656,0.0023988239,0.00090348086,0.0030490998,0.0013687517,0.0018176741,0.00075909274],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005202941,0.000050992912,0.0002520105,0.00010771978,0.00004570391,0.00017138648,0.000031668285,0.96040154,0.00031172516,0.026459496,0.0018377558,0.010277998],"study_design_scores_gemma":[0.000050714076,0.000049363378,0.00015272161,0.00001609826,0.000028096378,0.00014301868,0.000068702306,0.95647806,0.0003694133,0.037929185,0.004695106,0.00001950983],"about_ca_topic_score_codex":0.006455423,"about_ca_topic_score_gemma":0.0058297873,"teacher_disagreement_score":0.008297765,"about_ca_system_score_codex":0.0020611628,"about_ca_system_score_gemma":0.001903735,"threshold_uncertainty_score":0.027758837},"labels":[],"label_agreement":null},{"id":"W4288061053","doi":"10.1016/j.cor.2022.105974","title":"Trip planning for visitors in a service system with capacity constraints","year":2022,"lang":"en","type":"article","venue":"Computers & Operations Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Science Foundation of Zhejiang Province; National Natural Science Foundation of China; Zhejiang Office of Philosophy and Social Science","keywords":"Computer science; Capacity planning; Operations research; Service (business); Operations management; Business; Mathematical optimization; Mathematics; Marketing; Economics","score_opus":0.09284608348410901,"score_gpt":0.3549047532957981,"score_spread":0.2620586698116891,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4288061053","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.50895077,0.00051944016,0.46612892,0.0010442447,0.00018024526,0.00058944145,0.0017338087,0.00069732463,0.020155799],"genre_scores_gemma":[0.95147383,0.0001923392,0.03633999,0.000056086166,0.00004365804,0.00019010037,0.0005453632,0.00015209785,0.01100659],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993876,0.00016790241,0.000015517828,0.00009056377,0.000043517666,0.000294909],"domain_scores_gemma":[0.9990933,0.00035655338,0.00006805326,0.000040642117,0.00011050265,0.00033099417],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006712276,0.00080504804,0.0013204846,0.0011772576,0.0015746573,0.0021194627,0.0019173586,0.0016410173,0.008691864],"category_scores_gemma":[0.0015947887,0.000988523,0.001532598,0.0019457513,0.0010076007,0.0012109511,0.0014097616,0.001226573,0.0007467242],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002428575,0.000071797505,0.0012866338,0.00008086033,0.00005952938,0.00025028028,0.000100418234,0.97765476,0.00096784584,0.005911836,0.0023062474,0.011066905],"study_design_scores_gemma":[0.000014477171,0.000074305965,0.00038188673,0.000008760121,0.000024295714,0.000041383893,0.00017023282,0.9949974,0.00024874823,0.0034224517,0.00060260063,0.000013513087],"about_ca_topic_score_codex":0.05249898,"about_ca_topic_score_gemma":0.043704234,"teacher_disagreement_score":0.05249898,"about_ca_system_score_codex":0.0019929924,"about_ca_system_score_gemma":0.0028623356,"threshold_uncertainty_score":0.10438681},"labels":[],"label_agreement":null},{"id":"W4288380286","doi":"","title":"Improved formulations and algorithmic components for the electric vehicle routing problem with nonlinear charging functions","year":2019,"lang":"en","type":"article","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"","keywords":"Nonlinear system; Electric vehicle; Mathematical optimization; Vehicle routing problem; Routing (electronic design automation); Computer science; Mathematics; Physics; Computer network","score_opus":0.00980659822219835,"score_gpt":0.2129487585893551,"score_spread":0.20314216036715677,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4288380286","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0037792013,0.00019061417,0.9882675,0.00028949277,0.00012079945,0.000056670502,0.000062264,0.00006532515,0.00716803],"genre_scores_gemma":[0.13730963,0.0010895146,0.8377062,0.0003357836,0.000561264,0.00062415114,0.00042513508,0.00038708583,0.02156122],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993136,0.0002831972,0.000033917473,0.00008668336,0.00022005504,0.000062565974],"domain_scores_gemma":[0.99837387,0.0009646403,0.00010794149,0.00016105735,0.00032787307,0.0000645488],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001756437,0.0016051136,0.00093473686,0.00096705783,0.00059573574,0.0019843462,0.0022057698,0.001708496,0.008530563],"category_scores_gemma":[0.005811237,0.0007531942,0.0013096419,0.0010545825,0.0010328267,0.0026454788,0.0020926602,0.0037765235,0.0015033073],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004751311,0.00012491658,0.0002680486,0.00015621484,0.000023621897,0.0000827863,0.00009442877,0.73309153,0.0011545874,0.2085298,0.0047293426,0.051697258],"study_design_scores_gemma":[0.000008196077,0.0000093762965,0.000039348448,0.000010808293,0.000005224087,0.000013017088,0.0000139908125,0.9609619,0.00015231216,0.036982927,0.0017971081,0.0000057861785],"about_ca_topic_score_codex":0.0037887893,"about_ca_topic_score_gemma":0.0058171544,"teacher_disagreement_score":0.008530563,"about_ca_system_score_codex":0.0015409514,"about_ca_system_score_gemma":0.0018010725,"threshold_uncertainty_score":0.028537571},"labels":[],"label_agreement":null},{"id":"W4288468789","doi":"10.2139/ssrn.4174007","title":"Intermodal Hub Network Design with Generalized Capacity Constraints and Non-Synchronized Train-Truck Operations","year":2022,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal; Concordia University","funders":"","keywords":"Truck; Computer science; Transport engineering; Engineering; Automotive engineering","score_opus":0.013553896276201065,"score_gpt":0.22559449900493858,"score_spread":0.2120406027287375,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4288468789","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.041194607,0.00025133617,0.94485164,0.00021565499,0.00008655837,0.00010304344,0.0003242933,0.00015408624,0.012818871],"genre_scores_gemma":[0.9188936,0.0004900958,0.064106695,0.000097012104,0.00006940267,0.0003308488,0.0004120693,0.00015693996,0.015443266],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999421,0.00020975937,0.00001729045,0.00014505765,0.000086791355,0.00012014239],"domain_scores_gemma":[0.99942493,0.00025734742,0.000116194635,0.000028737899,0.00011949713,0.000053318206],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010886384,0.002073948,0.0017117091,0.00083011115,0.0005192549,0.0014477914,0.0020823216,0.0019002397,0.0058540995],"category_scores_gemma":[0.0017840374,0.0012355166,0.0011150037,0.0016757988,0.00065122044,0.0016758649,0.001440532,0.0009914993,0.0006112625],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003009395,0.000010635742,0.000065173255,0.000029100373,0.000014667109,0.000033846056,0.000010796495,0.99447435,0.00044044215,0.0027062004,0.00022551185,0.001959109],"study_design_scores_gemma":[0.000006923885,0.000036374804,0.00007615413,0.000004084099,0.000011086877,0.0000069298303,0.000012825311,0.99770784,0.00016358572,0.0017448529,0.00022494885,0.0000043221344],"about_ca_topic_score_codex":0.006921115,"about_ca_topic_score_gemma":0.0075140684,"teacher_disagreement_score":0.006921115,"about_ca_system_score_codex":0.0014677234,"about_ca_system_score_gemma":0.0016547912,"threshold_uncertainty_score":0.01958394},"labels":[],"label_agreement":null},{"id":"W4293227094","doi":"10.1287/opre.2021.2241","title":"A Convex Reformulation and an Outer Approximation for a Large Class of Binary Quadratic Programs","year":2022,"lang":"en","type":"article","venue":"Operations Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure; Group for Research in Decision Analysis; Wilfrid Laurier University; Polytechnique Montréal","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada","keywords":"Binary number; Quadratic equation; Class (philosophy); Mathematical optimization; Quadratic programming; Regular polygon; Bilinear interpolation; Variable (mathematics); Mathematics; Quadratic unconstrained binary optimization; Convex optimization; Computer science; Applied mathematics; Artificial intelligence; Arithmetic; Mathematical analysis","score_opus":0.09093636409137897,"score_gpt":0.403254417085231,"score_spread":0.31231805299385207,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4293227094","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0042804168,0.0001949762,0.99117297,0.00023850353,0.00003684886,0.000049778115,0.0000528814,0.000081140475,0.0038923994],"genre_scores_gemma":[0.1958263,0.0008144313,0.792295,0.00032670976,0.00024201968,0.00041397798,0.0005976111,0.0003610062,0.009122955],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9984584,0.00061293365,0.000046213412,0.00021147462,0.0005131171,0.00015792578],"domain_scores_gemma":[0.9978542,0.0013530591,0.00016911892,0.00017832135,0.0003512558,0.000093950206],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002644241,0.0015306692,0.0014511379,0.00078735565,0.0005134473,0.0016694154,0.0012158436,0.0011921285,0.0049762595],"category_scores_gemma":[0.0071382187,0.00068134314,0.0012379799,0.0009575822,0.0012261402,0.0020651764,0.0017085489,0.005147046,0.00090169796],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013372998,0.00017711578,0.00032267228,0.00023175476,0.000032660926,0.00011624779,0.0001587495,0.7306251,0.004188716,0.1851322,0.0076271524,0.07125392],"study_design_scores_gemma":[0.000012235802,0.000032383286,0.000056194476,0.000015027432,0.0000043155815,0.000023701316,0.0000122484935,0.9794018,0.0005379129,0.01791534,0.0019840433,0.000004748439],"about_ca_topic_score_codex":0.0029050508,"about_ca_topic_score_gemma":0.0017945973,"teacher_disagreement_score":0.0049762595,"about_ca_system_score_codex":0.0011778573,"about_ca_system_score_gemma":0.001213344,"threshold_uncertainty_score":0.01664728},"labels":[],"label_agreement":null},{"id":"W4293252833","doi":"10.5267/j.ijiec.2022.5.001","title":"A new metaheuristic approach for the meat routing problem by considering heterogeneous fleet with time windows","year":2022,"lang":"en","type":"article","venue":"International Journal of Industrial Engineering Computations","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Simulated annealing; Vehicle routing problem; Metaheuristic; Travelling salesman problem; Computer science; Mathematical optimization; Heuristic; Routing (electronic design automation); Operations research; Engineering; Mathematics; Algorithm; Computer network; Artificial intelligence","score_opus":0.02869746294506607,"score_gpt":0.25335996152448875,"score_spread":0.2246624985794227,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4293252833","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01141873,0.00028549097,0.9851376,0.00009403976,0.000042012365,0.000052905292,0.000041140724,0.00009925912,0.0028289563],"genre_scores_gemma":[0.29050913,0.0005852234,0.70505375,0.000078954654,0.000054082568,0.00020698502,0.00018432431,0.000087684595,0.0032399355],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996898,0.00013189613,0.00001621514,0.000058126156,0.00006792902,0.00003600568],"domain_scores_gemma":[0.99977857,0.00011194573,0.00003386827,0.00002667389,0.000031960808,0.000016984102],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005920948,0.0007892393,0.00057312555,0.0009986745,0.00043436355,0.0007978315,0.0011338665,0.00086932105,0.0015213592],"category_scores_gemma":[0.00081926864,0.00047564387,0.0015172081,0.0007944154,0.0004173468,0.00071018527,0.0004908382,0.00071676634,0.0002138631],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000024285528,0.000035873785,0.0003606599,0.000065910106,0.00006825396,0.00007915144,0.000039126717,0.95670295,0.0025525168,0.01322264,0.00047524637,0.026373392],"study_design_scores_gemma":[0.0000074495624,0.000034865636,0.000102358405,0.000007888133,0.000015722038,0.000045755118,0.000015639596,0.9940959,0.00047291315,0.0033021884,0.0018937815,0.000005535792],"about_ca_topic_score_codex":0.004171151,"about_ca_topic_score_gemma":0.005005219,"teacher_disagreement_score":0.004171151,"about_ca_system_score_codex":0.0007229087,"about_ca_system_score_gemma":0.00091840664,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W4293252951","doi":"10.5267/j.ijiec.2022.7.004","title":"Two-stage stochastic programming for the inventory routing problem with stochastic demands in fuel delivery","year":2022,"lang":"en","type":"article","venue":"International Journal of Industrial Engineering Computations","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Vehicle routing problem; Mathematical optimization; Computer science; Solver; Stochastic programming; Routing (electronic design automation); Context (archaeology); Operations research; Heuristic; Mathematics","score_opus":0.0322007951797287,"score_gpt":0.2770254553558977,"score_spread":0.24482466017616897,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4293252951","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015785959,0.00056226825,0.9802372,0.00031872067,0.000059753824,0.0000878074,0.00011495509,0.00008306558,0.002750235],"genre_scores_gemma":[0.65552455,0.0018574706,0.3343103,0.00027517873,0.00016385602,0.0006880849,0.000514309,0.00015064822,0.0065156016],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988532,0.0005950816,0.000039226445,0.0001580045,0.00019001341,0.00016446077],"domain_scores_gemma":[0.99871373,0.000981545,0.0001069954,0.00002885705,0.00010189049,0.00006693003],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022540467,0.0015268516,0.0013870187,0.0006176067,0.00054535305,0.0013233621,0.0012125209,0.00132188,0.002802299],"category_scores_gemma":[0.0026888535,0.0009091864,0.0018306656,0.0011899695,0.0007469765,0.0010435515,0.001002606,0.0020442253,0.000229961],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000030274117,0.000026399784,0.00016645191,0.00006109751,0.00001693009,0.00004060872,0.000018549044,0.98594505,0.0003025156,0.009548827,0.00033235172,0.003510977],"study_design_scores_gemma":[0.0000054286156,0.000019909461,0.000044928922,0.000004957374,0.0000048670104,0.000006479437,0.000007469887,0.9966484,0.000088596564,0.0029277673,0.00023779052,0.0000034065781],"about_ca_topic_score_codex":0.008378508,"about_ca_topic_score_gemma":0.008431642,"teacher_disagreement_score":0.008378508,"about_ca_system_score_codex":0.0015546903,"about_ca_system_score_gemma":0.0023994902,"threshold_uncertainty_score":0.016659498},"labels":[],"label_agreement":null},{"id":"W4293253022","doi":"10.5267/j.ijiec.2022.6.001","title":"Memetic algorithm for the dynamic vehicle routing problem with simultaneous delivery and pickup","year":2022,"lang":"en","type":"article","venue":"International Journal of Industrial Engineering Computations","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Pickup; Memetic algorithm; Vehicle routing problem; Routing (electronic design automation); Mathematical optimization; Genetic algorithm; Computer science; Set (abstract data type); Local search (optimization); Algorithm; Mathematics; Artificial intelligence; Computer network","score_opus":0.015429245592927108,"score_gpt":0.2521010675512609,"score_spread":0.23667182195833378,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4293253022","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02395039,0.0016483082,0.96130455,0.0006969652,0.00021321795,0.00013782321,0.00008201906,0.00020623129,0.0117604695],"genre_scores_gemma":[0.6200713,0.0017845195,0.36370793,0.00040618732,0.00020199234,0.0008122774,0.0001943179,0.000088548084,0.012732854],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99966764,0.00014125239,0.000015973626,0.000052098327,0.000077214136,0.0000458272],"domain_scores_gemma":[0.99958175,0.00027998877,0.00004527071,0.000020802327,0.00005400382,0.000018185096],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009916438,0.00093784294,0.0009984281,0.00093229883,0.0007308463,0.00091411016,0.0011905823,0.0014475369,0.002059064],"category_scores_gemma":[0.0016096836,0.00045523973,0.0009548923,0.000992094,0.00068621885,0.00062993204,0.0007579001,0.0009802212,0.00025112694],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000038706963,0.00003151322,0.00022206128,0.00008262045,0.00006617956,0.00009690588,0.00005149352,0.96055174,0.0007392593,0.012079815,0.0014663476,0.024573317],"study_design_scores_gemma":[0.000018003355,0.000034409073,0.00007463506,0.000011111957,0.000014182663,0.000040941442,0.000016795653,0.9927112,0.00027715485,0.0052142777,0.0015812146,0.000006200433],"about_ca_topic_score_codex":0.0034353554,"about_ca_topic_score_gemma":0.0023518563,"teacher_disagreement_score":0.0034353554,"about_ca_system_score_codex":0.0008001743,"about_ca_system_score_gemma":0.0011675329,"threshold_uncertainty_score":0.0068882704},"labels":[],"label_agreement":null},{"id":"W4293255076","doi":"10.1139/cjfr-2021-0203","title":"A goal programming model for the optimization of log logistics considering sorting decisions and social objective","year":2022,"lang":"en","type":"article","venue":"Canadian Journal of Forest Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"Western Forest Products; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Truck; Operations research; Workload; Sorting; Time horizon; Procurement; Goal programming; Total cost; Computer science; sort; Programming paradigm; Operations management; Transport engineering; Mathematical optimization; Engineering; Business; Mathematics; Marketing","score_opus":0.12002311095704515,"score_gpt":0.3643242071465311,"score_spread":0.24430109618948592,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4293255076","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013123844,0.00041868407,0.9699359,0.000617967,0.00008867189,0.0001860094,0.0005821618,0.0002454266,0.01480126],"genre_scores_gemma":[0.47528502,0.0017154409,0.49719903,0.00042300211,0.00013154667,0.0019076414,0.0011971336,0.00016795892,0.021973215],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987828,0.000573937,0.00004384619,0.00018308648,0.00021024547,0.00020602801],"domain_scores_gemma":[0.998871,0.0007396718,0.000112653615,0.00002408165,0.00017813846,0.000074440744],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018914874,0.0020020125,0.0014013288,0.00095744536,0.00079479103,0.0023394357,0.0023359493,0.0021925338,0.004838641],"category_scores_gemma":[0.0024311927,0.0009097028,0.0014815017,0.0019785375,0.0009545516,0.0012156114,0.0011865331,0.002423971,0.0007478772],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000022740369,0.000037351314,0.0001327742,0.0000695708,0.000022261731,0.00006912986,0.000032359316,0.9805978,0.00021535644,0.014383402,0.00071860955,0.0036986514],"study_design_scores_gemma":[0.000014937134,0.000029493453,0.00006827071,0.000012584892,0.000011577382,0.000010549514,0.000020668147,0.9929979,0.000078723264,0.005665117,0.0010830903,0.000007049225],"about_ca_topic_score_codex":0.028451372,"about_ca_topic_score_gemma":0.022611152,"teacher_disagreement_score":0.9715486,"about_ca_system_score_codex":0.0025127276,"about_ca_system_score_gemma":0.0045242077,"threshold_uncertainty_score":0.056571543},"labels":[],"label_agreement":null},{"id":"W4294724286","doi":"10.1016/j.cor.2022.106015","title":"A last-mile drone-assisted one-to-one pickup and delivery problem with multi-visit drone trips","year":2022,"lang":"en","type":"article","venue":"Computers & Operations Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":89,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"China Scholarship Council; National Natural Science Foundation of China","keywords":"Drone; Computer science; Pickup; Payload (computing); Truck; Last mile (transportation); TRIPS architecture; Mile; Operations research; Real-time computing; Engineering; Artificial intelligence; Computer security; Network packet; Automotive engineering; Geography","score_opus":0.08399262146980647,"score_gpt":0.3299644542793425,"score_spread":0.24597183280953605,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4294724286","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3438534,0.0023059202,0.6262406,0.0024855747,0.00068400684,0.0010143548,0.002981322,0.00072204403,0.019712877],"genre_scores_gemma":[0.8908247,0.0008197665,0.08213355,0.00022739085,0.0002154023,0.00039630977,0.0013073415,0.00020973875,0.02386586],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99852544,0.00044136101,0.00006845207,0.0004273487,0.00016418933,0.00037325028],"domain_scores_gemma":[0.99728787,0.0015784508,0.00028617398,0.0001616496,0.00017515902,0.0005108146],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021046014,0.002515167,0.0057202983,0.0015951685,0.0020571318,0.0038060432,0.0058209337,0.0068308585,0.010281176],"category_scores_gemma":[0.003934895,0.0024564725,0.0032255044,0.0025581112,0.0017283126,0.0038459457,0.0033183533,0.0032003454,0.0011402648],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00047716734,0.0002396343,0.00069407176,0.00028534484,0.00012790311,0.0006385953,0.00010098092,0.9769876,0.0008131242,0.0071190507,0.0027203243,0.009796138],"study_design_scores_gemma":[0.00008635322,0.00020209854,0.0002590269,0.000018091918,0.000049042876,0.000121208075,0.00013225157,0.99407697,0.00031869576,0.0039039883,0.0008032763,0.000029069775],"about_ca_topic_score_codex":0.014601674,"about_ca_topic_score_gemma":0.010449073,"teacher_disagreement_score":0.014601674,"about_ca_system_score_codex":0.0020677862,"about_ca_system_score_gemma":0.0022257334,"threshold_uncertainty_score":0.034393966},"labels":[],"label_agreement":null},{"id":"W4296912568","doi":"10.1109/access.2022.3208899","title":"Green Vehicle Routing Problem: State of the Art and Future Directions","year":2022,"lang":"en","type":"article","venue":"IEEE Access","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":50,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Vehicle routing problem; Routing (electronic design automation); State (computer science); Computer network; Algorithm","score_opus":0.01676633303061775,"score_gpt":0.26835497069416964,"score_spread":0.2515886376635519,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4296912568","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0075724553,0.7393013,0.20041668,0.012937831,0.0021016821,0.00008332886,0.00020805263,0.00033471768,0.037043918],"genre_scores_gemma":[0.10112538,0.7609567,0.11179995,0.003139808,0.0068467907,0.00017844107,0.00088967127,0.00025032792,0.0148129435],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.998776,0.00037843388,0.00006498854,0.00037899296,0.0002807477,0.00012084415],"domain_scores_gemma":[0.9958639,0.003113876,0.00017866581,0.00018061297,0.00053990877,0.00012301757],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025090922,0.0014286995,0.0016505168,0.0012485001,0.0006597017,0.004213785,0.0023884824,0.0027598212,0.005378697],"category_scores_gemma":[0.0048017637,0.0006642203,0.0014388607,0.0034685836,0.0013456187,0.005558358,0.0013434477,0.003688193,0.0019336776],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011155037,0.00023467824,0.0010609899,0.0038652625,0.00015002157,0.00014763276,0.00023137924,0.08847515,0.00055091805,0.12620015,0.028748035,0.75022423],"study_design_scores_gemma":[0.00004358601,0.00023330568,0.001121962,0.0027044555,0.00019620104,0.0005251726,0.00090105546,0.33799785,0.0007876786,0.3242163,0.3311498,0.00012262752],"about_ca_topic_score_codex":0.0036759546,"about_ca_topic_score_gemma":0.0020101059,"teacher_disagreement_score":0.005378697,"about_ca_system_score_codex":0.0014329385,"about_ca_system_score_gemma":0.0017866852,"threshold_uncertainty_score":0.01799351},"labels":[],"label_agreement":null},{"id":"W4297145754","doi":"10.1155/2022/5052897","title":"The Value of Preemptive Pick-Up Services in Dynamic Vehicle Routing for Last-Mile Delivery: Space-Time Network-Based Formulation and Solution Algorithms","year":2022,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Hohai University; Arizona State University","keywords":"Computer science; Service (business); Lagrangian relaxation; Process (computing); Last mile (transportation); Task (project management); Vehicle routing problem; Dynamic programming; Flow network; Routing (electronic design automation); Service delivery framework; Operations research; Distributed computing; Algorithm; Mathematical optimization; Computer network; Engineering; Mile","score_opus":0.00563360387497039,"score_gpt":0.2381974020183943,"score_spread":0.2325637981434239,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4297145754","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009670503,0.0013194636,0.9818766,0.00062651146,0.0000914242,0.00008266604,0.00007488045,0.00008827172,0.006169557],"genre_scores_gemma":[0.67456573,0.004142561,0.30720893,0.00033164848,0.00032889092,0.00065777684,0.0002972166,0.0002029045,0.012264297],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99945277,0.00022144451,0.000015355397,0.00008447434,0.00012960633,0.000096262636],"domain_scores_gemma":[0.9987692,0.0008966122,0.00010629732,0.000023260363,0.00014028676,0.000064289685],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016944588,0.001565501,0.0014016691,0.0010591581,0.0007174436,0.0021019992,0.0017628822,0.0021465009,0.002490437],"category_scores_gemma":[0.0037003027,0.00086651254,0.0010408711,0.001493657,0.0012325346,0.0015859893,0.0013719042,0.0020044567,0.00026148584],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009764122,0.000011032874,0.000069449976,0.000028690523,0.000006003328,0.00001770629,0.000011562981,0.9891934,0.00008592948,0.007345794,0.00026925805,0.002951382],"study_design_scores_gemma":[0.0000016944249,0.0000042722086,0.000011331777,0.000003945089,0.0000021192352,0.00000284623,0.000005709327,0.997707,0.000028469889,0.0020561551,0.00017502203,0.0000014377325],"about_ca_topic_score_codex":0.016271828,"about_ca_topic_score_gemma":0.011232423,"teacher_disagreement_score":0.016271828,"about_ca_system_score_codex":0.0029539173,"about_ca_system_score_gemma":0.0029502288,"threshold_uncertainty_score":0.032354236},"labels":[],"label_agreement":null},{"id":"W4297415396","doi":"","title":"A metaheuristic for the Capacitated Location Routing Problem Combining Lagrangean Relaxation with Granular Tabu Search heuristic.","year":2005,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal; Université Laval","funders":"","keywords":"Tabu search; Metaheuristic; Relaxation (psychology); Mathematical optimization; Computer science; Routing (electronic design automation); Heuristic; Guided Local Search; Vehicle routing problem; Mathematics","score_opus":0.019780005110038437,"score_gpt":0.24022277752694915,"score_spread":0.2204427724169107,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4297415396","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04262438,0.0011394819,0.93405366,0.00049272284,0.0001791228,0.00023218025,0.00028748845,0.0012779416,0.019712998],"genre_scores_gemma":[0.37271145,0.0004754772,0.6199852,0.00027201616,0.00007356027,0.0004674204,0.00040775197,0.00029133286,0.0053157792],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994848,0.00023170508,0.000019063385,0.00004875551,0.00013599097,0.000079683276],"domain_scores_gemma":[0.9994832,0.00029599923,0.000049822607,0.00006379462,0.00006544008,0.00004178319],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001004501,0.0006617309,0.0008886288,0.0010711178,0.0005209002,0.001163111,0.0015454781,0.0015933501,0.0036468303],"category_scores_gemma":[0.0022887243,0.0006236551,0.0007139988,0.0018660263,0.0005223854,0.0010701178,0.0012218416,0.0013741435,0.00062400114],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009307618,0.00010567089,0.0001983897,0.000114241135,0.000046789995,0.000053992826,0.000054421384,0.9067279,0.0012248794,0.014873717,0.003748994,0.07275777],"study_design_scores_gemma":[0.000031601834,0.000035560934,0.00007357969,0.000020042324,0.000012189857,0.000016958162,0.000017092827,0.9920598,0.00029449846,0.005862789,0.0015703035,0.000005576295],"about_ca_topic_score_codex":0.0054164655,"about_ca_topic_score_gemma":0.0057874,"teacher_disagreement_score":0.0054164655,"about_ca_system_score_codex":0.0010116574,"about_ca_system_score_gemma":0.001258691,"threshold_uncertainty_score":0.012199819},"labels":[],"label_agreement":null},{"id":"W4297628801","doi":"10.1287/trsc.2022.0342","title":"Solving a Continent-Scale Inventory Routing Problem at Renault","year":2023,"lang":"en","type":"article","venue":"Transportation Science","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Computer science; Scale (ratio); Context (archaeology); Routing (electronic design automation); Benchmark (surveying); Heuristic; Commodity; Morse code; Relaxation (psychology); Operations research; Mathematical optimization; Artificial intelligence; Mathematics; Computer network; Business; Telecommunications; Geography","score_opus":0.02124898207473498,"score_gpt":0.2729537527922278,"score_spread":0.2517047707174928,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4297628801","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5993973,0.0029791929,0.26777002,0.00943143,0.0012058507,0.0005298929,0.0059226486,0.0025951138,0.1101685],"genre_scores_gemma":[0.7694151,0.0011644001,0.20071214,0.0003887042,0.00017661903,0.00025018476,0.0037903006,0.00045843175,0.023644052],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99951744,0.00017335734,0.000015518228,0.00013092702,0.00008713123,0.0000756722],"domain_scores_gemma":[0.99916494,0.00052662706,0.000040384224,0.0000878938,0.0000846131,0.00009556536],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011679122,0.00072874426,0.00068013417,0.00065923814,0.0007930845,0.0013210236,0.0013575442,0.0011833102,0.011448235],"category_scores_gemma":[0.003148729,0.00031377794,0.0007363721,0.0010916882,0.0005469773,0.0012376587,0.00082475325,0.0014537015,0.0009238964],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021768858,0.0002029251,0.0020357145,0.00017891997,0.000053010237,0.00038265594,0.000071723116,0.89712167,0.00088313327,0.028479042,0.028866515,0.041507035],"study_design_scores_gemma":[0.000099865436,0.00009951178,0.001182237,0.00003697101,0.000014260392,0.00013463775,0.00012214258,0.9658333,0.0016370857,0.014971669,0.01584227,0.000025962043],"about_ca_topic_score_codex":0.027527405,"about_ca_topic_score_gemma":0.031912986,"teacher_disagreement_score":0.027527405,"about_ca_system_score_codex":0.001972073,"about_ca_system_score_gemma":0.0019038245,"threshold_uncertainty_score":0.05473435},"labels":[],"label_agreement":null},{"id":"W4298006032","doi":"10.1155/2022/8253175","title":"Metaheuristics for a Large-Scale Vehicle Routing Problem of Same-Day Delivery in E-Commerce Logistics System","year":2022,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Basic and Applied Basic Research Foundation of Guangdong Province; National Natural Science Foundation of China","keywords":"Vehicle routing problem; Metaheuristic; Computer science; Variable neighborhood search; Routing (electronic design automation); Mathematical optimization; Scale (ratio); City logistics; Algorithm; Operations research; Mathematics; Transport engineering; Engineering; Computer network","score_opus":0.014224420468865984,"score_gpt":0.2574567881279074,"score_spread":0.2432323676590414,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4298006032","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.047938164,0.0011365179,0.94355404,0.0005728456,0.00013051464,0.00016774594,0.00016815534,0.00020385836,0.0061280727],"genre_scores_gemma":[0.59478253,0.0008734285,0.39950845,0.0002158456,0.00010242624,0.00038233394,0.00030755033,0.00008783819,0.003739591],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995814,0.00021461069,0.000018966302,0.00006874445,0.00006555226,0.000050798873],"domain_scores_gemma":[0.9993187,0.0004753791,0.00008023549,0.00003306761,0.000058127247,0.000034405606],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011457861,0.00084066903,0.0008862232,0.0008291917,0.0004896851,0.00094076054,0.0012065622,0.0012342464,0.0018788709],"category_scores_gemma":[0.0017311111,0.0003815801,0.0009943462,0.0010838143,0.00044264403,0.00077196193,0.00060051517,0.0009889374,0.00016110002],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019067467,0.000040496696,0.00021508956,0.000039408274,0.000032612767,0.00002910002,0.000012675582,0.9816259,0.00021070398,0.0059142215,0.00058991736,0.011270871],"study_design_scores_gemma":[0.00000859779,0.000017988388,0.00005477687,0.000004621404,0.0000063505595,0.000008751427,0.000012102185,0.9973029,0.000069036745,0.002075878,0.00043704113,0.000001858541],"about_ca_topic_score_codex":0.006324033,"about_ca_topic_score_gemma":0.006892794,"teacher_disagreement_score":0.006324033,"about_ca_system_score_codex":0.0011074719,"about_ca_system_score_gemma":0.001429772,"threshold_uncertainty_score":0.012574434},"labels":[],"label_agreement":null},{"id":"W4298328583","doi":"","title":"A cooperative lagrangean relaxation-granular tabu search heuristic for the Capacitated Location Routing Problem","year":2005,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"","keywords":"Tabu search; Mathematical optimization; Relaxation (psychology); Heuristic; Routing (electronic design automation); Computer science; Vehicle routing problem; Mathematics; Computer network","score_opus":0.020525277868537493,"score_gpt":0.25124643415415765,"score_spread":0.23072115628562015,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4298328583","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08111226,0.00051831646,0.8997951,0.00045456324,0.000148067,0.00022532437,0.00020531911,0.0010703264,0.016470794],"genre_scores_gemma":[0.54653597,0.00022476907,0.44712523,0.00020255634,0.000056392506,0.00037795227,0.00031535633,0.00019659265,0.0049652206],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994025,0.0002266538,0.000017257842,0.00007501757,0.00015452705,0.00012399355],"domain_scores_gemma":[0.99922967,0.00039286353,0.00007901149,0.00010850381,0.00011688116,0.00007317284],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010507966,0.00074813276,0.0012018736,0.0009631192,0.0007069642,0.001411385,0.0024051173,0.0018388656,0.005972302],"category_scores_gemma":[0.0025695893,0.0007467591,0.0007062484,0.0018852046,0.00069475255,0.0010835634,0.0015554064,0.0011223782,0.0007667979],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014655094,0.00009424468,0.00019849249,0.000056129367,0.000020450942,0.00004952941,0.000054649085,0.9494529,0.00090201996,0.005666884,0.0020296506,0.041328542],"study_design_scores_gemma":[0.000030659216,0.00002656742,0.000044298442,0.000005797672,0.0000054788225,0.000007660493,0.000012815093,0.9977481,0.00014746771,0.0015731928,0.00039463746,0.0000033866],"about_ca_topic_score_codex":0.008314485,"about_ca_topic_score_gemma":0.008893809,"teacher_disagreement_score":0.008314485,"about_ca_system_score_codex":0.0010061432,"about_ca_system_score_gemma":0.0016370041,"threshold_uncertainty_score":0.019979298},"labels":[],"label_agreement":null},{"id":"W4299395974","doi":"","title":"A Heuristic for the Time-Dependent Vehicle Routing Problem with Time Windows","year":2016,"lang":"en","type":"book","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"","keywords":"Heuristic; Computer science; Vehicle routing problem; Routing (electronic design automation); Mathematical optimization; Computer network; Mathematics; Artificial intelligence","score_opus":0.008809374254177509,"score_gpt":0.20878557429573735,"score_spread":0.19997620004155983,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4299395974","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.050711066,0.00081722485,0.9393208,0.0002682158,0.000106271815,0.00021357063,0.00016298563,0.00031881285,0.008080995],"genre_scores_gemma":[0.44220454,0.00055457355,0.55045635,0.00013070523,0.000056107754,0.0003676916,0.00030516108,0.000112201225,0.0058126906],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997484,0.00009546362,0.000008784625,0.000043212316,0.00004912912,0.000055016968],"domain_scores_gemma":[0.9996625,0.00019569376,0.000038257636,0.00002689903,0.000040258332,0.00003634437],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005281566,0.0005927823,0.0007152573,0.0005077176,0.00035343575,0.00074211846,0.0012211476,0.00084918656,0.00345896],"category_scores_gemma":[0.0009990952,0.00029752046,0.00048563493,0.0006975483,0.00037451132,0.0006061528,0.0006082063,0.00064358994,0.00029700663],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000050723545,0.00006267758,0.00019222131,0.00006558758,0.000016344391,0.00006382272,0.000029741215,0.9549129,0.0013043835,0.009186047,0.0015740013,0.032541454],"study_design_scores_gemma":[0.000018743856,0.00003905404,0.000056781362,0.0000064647365,0.000004467005,0.000016965167,0.000013088989,0.9962268,0.00035225987,0.0022808933,0.0009811291,0.0000033756817],"about_ca_topic_score_codex":0.0048203296,"about_ca_topic_score_gemma":0.0046715415,"teacher_disagreement_score":0.0048203296,"about_ca_system_score_codex":0.0008222328,"about_ca_system_score_gemma":0.0010932222,"threshold_uncertainty_score":0.011571348},"labels":[],"label_agreement":null},{"id":"W4301154667","doi":"","title":"The Generalized Minimum Spanning Tree Problem: Polyhedral Analysis and Branch-and-Cut Algorithm","year":2002,"lang":"en","type":"article","venue":"","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Minimum spanning tree; Spanning tree; Distributed minimum spanning tree; Computer science; Tree (set theory); Combinatorics; Algorithm; Steiner tree problem; Mathematics","score_opus":0.01486572632310597,"score_gpt":0.23942728725460607,"score_spread":0.2245615609315001,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4301154667","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0047920514,0.00062883634,0.9891991,0.00022567082,0.000042613454,0.00007353731,0.00009036794,0.0001352682,0.004812587],"genre_scores_gemma":[0.1667164,0.0018827606,0.8258023,0.00014817972,0.00014147947,0.0003834955,0.0006327169,0.00019906624,0.004093647],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99905473,0.00037616113,0.00003155806,0.00011486016,0.00030701756,0.000115697556],"domain_scores_gemma":[0.9993099,0.00042319307,0.00006668657,0.000055715893,0.00010945615,0.000035055327],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011046233,0.0010531046,0.0015431688,0.00091553794,0.0006817192,0.0014756037,0.0013274577,0.0013447233,0.004005044],"category_scores_gemma":[0.0033163226,0.0005285028,0.00081648055,0.0023699969,0.0009026531,0.0017953961,0.0010194465,0.0014732378,0.00070510886],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004840989,0.000046845093,0.0002073374,0.00013895529,0.00002969873,0.000056828052,0.000053558553,0.82414323,0.0007595451,0.08108983,0.0050521437,0.08837354],"study_design_scores_gemma":[0.000015560381,0.000016500308,0.000060596518,0.000021203914,0.000008714809,0.00003705767,0.000016018932,0.939029,0.00029391577,0.05712464,0.0033708252,0.000006045397],"about_ca_topic_score_codex":0.0049569174,"about_ca_topic_score_gemma":0.0040922966,"teacher_disagreement_score":0.0049569174,"about_ca_system_score_codex":0.0011777921,"about_ca_system_score_gemma":0.0014081233,"threshold_uncertainty_score":0.01339823},"labels":[],"label_agreement":null},{"id":"W4302011011","doi":"10.1155/2022/3017196","title":"Optimal Method for Allocation of Tractors and Trailers in Daily Dispatches of Road Drops and Pull Transport","year":2022,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Wuyi University; National Natural Science Foundation of China","keywords":"Tractor; Scheduling (production processes); Skid (aerodynamics); Transport engineering; Vehicle routing problem; Drop (telecommunication); Automotive engineering; Computer science; Engineering; Routing (electronic design automation); Operations management; Computer network","score_opus":0.010162741559574823,"score_gpt":0.27090387508600416,"score_spread":0.26074113352642936,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4302011011","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.028815905,0.0003513225,0.96463645,0.00019718254,0.00007235631,0.00016393264,0.000092612405,0.00017762216,0.005492622],"genre_scores_gemma":[0.6611671,0.00049903337,0.3281066,0.00011316008,0.000054541153,0.00048038916,0.00020901521,0.00018014696,0.009189958],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995753,0.000184037,0.000016169737,0.000074672695,0.00006919603,0.000080535254],"domain_scores_gemma":[0.99943477,0.000316166,0.000072312854,0.000017374095,0.0001146475,0.000044760825],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012171286,0.0010999218,0.001260571,0.0009820473,0.0006337563,0.0011527676,0.0009950864,0.0010675564,0.0053562876],"category_scores_gemma":[0.0017043914,0.0007086955,0.0009312557,0.000762554,0.00060476875,0.0011226166,0.0007086857,0.0009134585,0.00038608437],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004814123,0.000024366049,0.00020080438,0.00004413569,0.000017696408,0.000021051494,0.00002332885,0.9868008,0.0004013945,0.0031809597,0.0004930913,0.008744221],"study_design_scores_gemma":[0.0000067389315,0.000011046974,0.00003769062,0.0000023125083,0.000003185004,0.0000019097918,0.000007588564,0.9990695,0.000058679572,0.00064771465,0.0001515591,0.0000020739847],"about_ca_topic_score_codex":0.01855754,"about_ca_topic_score_gemma":0.017539047,"teacher_disagreement_score":0.01855754,"about_ca_system_score_codex":0.0018252947,"about_ca_system_score_gemma":0.002708624,"threshold_uncertainty_score":0.03689909},"labels":[],"label_agreement":null},{"id":"W4304609420","doi":"10.1287/ijoo.2022.0082","title":"Machine-Learning–Based Arc Selection for Constrained Shortest Path Problems in Column Generation","year":2022,"lang":"en","type":"article","venue":"INFORMS Journal on Optimization","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":43,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Polytechnique Montréal; Group for Research in Decision Analysis","funders":"","keywords":"Column generation; Computer science; Mathematical optimization; Shortest path problem; Scheduling (production processes); Heuristic; Operations research; Selection (genetic algorithm); Crew scheduling; Artificial intelligence; Graph; Mathematics; Theoretical computer science","score_opus":0.019399511709611655,"score_gpt":0.24546731220356632,"score_spread":0.22606780049395467,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4304609420","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010603294,0.00038803215,0.9855273,0.00020039246,0.000054940378,0.00014239161,0.000118022355,0.00054593914,0.0024196892],"genre_scores_gemma":[0.2398162,0.00043191202,0.75476766,0.00028768482,0.00011755648,0.0005187194,0.00073111785,0.00027700633,0.0030520544],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99894494,0.0005711227,0.000039592145,0.00013537548,0.00019630106,0.00011274515],"domain_scores_gemma":[0.99479395,0.004059643,0.00027753564,0.00022936348,0.0005138292,0.00012575228],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018144543,0.0011752795,0.0014442613,0.0015175424,0.0007313347,0.0010052325,0.0014601308,0.0010492491,0.00602558],"category_scores_gemma":[0.004892913,0.0006886294,0.0009344883,0.0021688703,0.0007678208,0.0012477053,0.00085532304,0.0018741764,0.0010195874],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000084852174,0.00015855089,0.0006159787,0.00015891965,0.000046739806,0.000068849535,0.000049005557,0.87863445,0.0012347957,0.009001076,0.004089175,0.10585764],"study_design_scores_gemma":[0.000012039933,0.000019865454,0.000051786046,0.000007034078,0.00000414602,0.00000986179,0.000005532939,0.9954977,0.0003032856,0.003663878,0.0004211967,0.000003723237],"about_ca_topic_score_codex":0.006328958,"about_ca_topic_score_gemma":0.0088302735,"teacher_disagreement_score":0.006328958,"about_ca_system_score_codex":0.0010976708,"about_ca_system_score_gemma":0.0020904755,"threshold_uncertainty_score":0.020157576},"labels":[],"label_agreement":null},{"id":"W4306160204","doi":"10.3390/su142013130","title":"Optimization of Snowplow Routes for Real-World Conditions","year":2022,"lang":"en","type":"article","venue":"Sustainability","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta; Ontario Tech University","funders":"","keywords":"Dijkstra's algorithm; Tabu search; Truck; Metaheuristic; Process (computing); Computer science; Mathematical optimization; Route planning; Transport engineering; Shortest path problem; Engineering; Automotive engineering; Algorithm; Mathematics","score_opus":0.013033691881806453,"score_gpt":0.29862575106111067,"score_spread":0.2855920591793042,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4306160204","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.70425797,0.0002917267,0.28486052,0.00020017888,0.00004572015,0.00017504611,0.0005005811,0.00042583226,0.009242391],"genre_scores_gemma":[0.9377515,0.000105969884,0.059167497,0.000017572738,0.0000049856885,0.00011742938,0.00032122654,0.00005300574,0.0024608597],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99984276,0.00005719414,0.000005481677,0.000029617477,0.000020623356,0.00004429604],"domain_scores_gemma":[0.99964416,0.00019540914,0.000052082545,0.000019786108,0.000057169796,0.00003136323],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005434378,0.0007198735,0.0004672993,0.00076065,0.0004071578,0.0006389143,0.00056448905,0.0009051654,0.0024237828],"category_scores_gemma":[0.0012374572,0.0003764248,0.0005708585,0.00057727075,0.00035064144,0.00047806636,0.0003386566,0.00033951792,0.00023097468],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002180848,0.000013942581,0.00032873204,0.000019399697,0.00000764715,0.000049206297,0.000015121876,0.9951747,0.0006336634,0.0004859566,0.00011226564,0.0031375312],"study_design_scores_gemma":[0.000009860989,0.000052563584,0.00039712622,0.0000033657425,0.0000068070644,0.000014247041,0.000052758744,0.9978871,0.0005368441,0.00068062986,0.00035412962,0.0000045809074],"about_ca_topic_score_codex":0.008257802,"about_ca_topic_score_gemma":0.01035925,"teacher_disagreement_score":0.008257802,"about_ca_system_score_codex":0.00076379074,"about_ca_system_score_gemma":0.0009978057,"threshold_uncertainty_score":0.01641947},"labels":[],"label_agreement":null},{"id":"W4307865551","doi":"10.5267/j.jpm.2022.10.001","title":"Development of a robust multi-objective model for green capacitated location-routing under crisis conditions","year":2022,"lang":"en","type":"article","venue":"Journal of Project Management","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Sorting; Computer science; Mathematical optimization; Genetic algorithm; Operations research; Supply chain network; Supply chain; Set (abstract data type); Reliability (semiconductor); Routing (electronic design automation); Integer programming; Vehicle routing problem; Robust optimization; Facility location problem; Linear programming; Network planning and design; Supply chain management; Engineering; Business; Mathematics; Algorithm","score_opus":0.07756697463210986,"score_gpt":0.3183241950681378,"score_spread":0.24075722043602793,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4307865551","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012997223,0.0003446035,0.97590387,0.00034275727,0.00005445113,0.00009770137,0.00026509046,0.00021063242,0.009783605],"genre_scores_gemma":[0.8208396,0.0007575638,0.16383412,0.0001761628,0.00006512556,0.0007985764,0.00052613317,0.00016079488,0.0128418645],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99939334,0.00021229894,0.0000251003,0.00012377491,0.00013401115,0.000111403635],"domain_scores_gemma":[0.9992901,0.00036221786,0.00012498406,0.000028899462,0.00014785105,0.00004598827],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013617954,0.001213492,0.0013329985,0.0008471645,0.00052598125,0.0018667262,0.0020839125,0.002232748,0.0037637649],"category_scores_gemma":[0.002081427,0.0009234687,0.0015675095,0.00083395396,0.0008110938,0.0011310467,0.0014484974,0.0018253503,0.0005015212],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000064799087,0.0000059413555,0.00005012061,0.00001192438,0.0000074844793,0.000025943971,0.000006882779,0.9966582,0.00013567261,0.0022111014,0.00010597093,0.0007742599],"study_design_scores_gemma":[0.0000020785087,0.0000066698717,0.000019842324,0.0000024324252,0.0000031339414,0.0000034072768,0.0000038848584,0.999057,0.00004538472,0.0007016702,0.00015233026,0.0000022486472],"about_ca_topic_score_codex":0.014498681,"about_ca_topic_score_gemma":0.009151782,"teacher_disagreement_score":0.014498681,"about_ca_system_score_codex":0.0016320741,"about_ca_system_score_gemma":0.0021577945,"threshold_uncertainty_score":0.028828561},"labels":[],"label_agreement":null},{"id":"W4308195387","doi":"10.1016/j.ejor.2022.10.045","title":"Recent advances in vehicle routing with stochastic demands: Bayesian learning for correlated demands and elementary branch-price-and-cut","year":2022,"lang":"en","type":"article","venue":"European Journal of Operational Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":23,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Computer science; Vehicle routing problem; Bayesian probability; Benchmark (surveying); Iterated function; Mathematical optimization; Operations research; Feature (linguistics); Constraint (computer-aided design); Cover (algebra); Key (lock); Routing (electronic design automation); Artificial intelligence; Mathematics; Engineering","score_opus":0.029701638751559324,"score_gpt":0.31939307167857384,"score_spread":0.2896914329270145,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4308195387","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0062942184,0.0061910064,0.9841314,0.00085623364,0.00009953729,0.000019918873,0.00007716435,0.00008450183,0.002245975],"genre_scores_gemma":[0.36822295,0.036917392,0.58056295,0.00078306725,0.0020977182,0.00019053686,0.0009961894,0.0004065511,0.009822624],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99785197,0.00088210445,0.00010466675,0.0004865333,0.00054346066,0.0001311949],"domain_scores_gemma":[0.9872162,0.009860986,0.000684587,0.0006696766,0.0012458531,0.00032273793],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005983418,0.001670216,0.0036001357,0.0012950099,0.00054237334,0.002846697,0.0044260873,0.0023997996,0.0031932388],"category_scores_gemma":[0.016146615,0.0020567295,0.0018478183,0.0033682787,0.0020683617,0.0061136913,0.0023147287,0.005013875,0.00062047405],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000099937344,0.000117345124,0.0010249593,0.00032297202,0.00012098948,0.000038683105,0.00006731048,0.7559469,0.00026282505,0.120905474,0.0025272553,0.11856539],"study_design_scores_gemma":[0.0000077486075,0.000013004621,0.00017167044,0.000020538511,0.000014695678,0.00001138537,0.0000057457796,0.9308911,0.0000816557,0.06772292,0.0010500955,0.000009469666],"about_ca_topic_score_codex":0.005871002,"about_ca_topic_score_gemma":0.0051514376,"teacher_disagreement_score":0.005983418,"about_ca_system_score_codex":0.0021967795,"about_ca_system_score_gemma":0.0020989608,"threshold_uncertainty_score":0.03164369},"labels":[],"label_agreement":null},{"id":"W4308232794","doi":"10.1145/3557991.3567776","title":"Solving a multi-trip VRP with real heterogeneous fleet and time windows based on ant colony optimization","year":2022,"lang":"en","type":"article","venue":"","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"National Research Council Canada; University of Calgary","funders":"","keywords":"Vehicle routing problem; Ant colony optimization algorithms; Computer science; Heuristics; TRIPS architecture; Constructive; Heuristic; Mathematical optimization; Metaheuristic; Routing (electronic design automation); Operations research; Algorithm; Artificial intelligence; Engineering; Process (computing); Mathematics; Parallel computing","score_opus":0.011954159391129465,"score_gpt":0.23061564973959228,"score_spread":0.21866149034846283,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4308232794","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11230251,0.00028274074,0.8812062,0.00022899595,0.000055092114,0.00011596199,0.00013006698,0.00025814,0.0054203006],"genre_scores_gemma":[0.7262916,0.00020426617,0.27065763,0.000053602424,0.00002183161,0.00015006072,0.00016774106,0.00006678014,0.0023864151],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996928,0.0000938153,0.000014039153,0.000087154665,0.000056360863,0.00005584264],"domain_scores_gemma":[0.99956626,0.00026715003,0.00006179055,0.0000293866,0.00004276475,0.000032561704],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048584375,0.00067662203,0.00064814405,0.00040370275,0.0003694771,0.00073754665,0.00094902,0.00078907865,0.0011440504],"category_scores_gemma":[0.0010413988,0.00036277488,0.0005883667,0.0006099339,0.00034632953,0.00077355886,0.0005792069,0.0005719447,0.00009495518],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001739221,0.000016793889,0.0002515234,0.000025600004,0.000017172972,0.000059463186,0.000013155075,0.9892643,0.00082277355,0.0014263341,0.00018499167,0.007900502],"study_design_scores_gemma":[0.000004959555,0.000015211986,0.000080493584,0.0000012744383,0.000003997699,0.00001614912,0.000011783499,0.9989466,0.00018078597,0.0005442852,0.00019264378,0.0000018594891],"about_ca_topic_score_codex":0.008297337,"about_ca_topic_score_gemma":0.0071612126,"teacher_disagreement_score":0.008297337,"about_ca_system_score_codex":0.0004777716,"about_ca_system_score_gemma":0.0009848736,"threshold_uncertainty_score":0.01649803},"labels":[],"label_agreement":null},{"id":"W4308272905","doi":"10.17771/pucrio.acad.61096","title":"EXPLORING THE FRONTIER OF COMBINATORIAL OPTIMIZATION AND MACHINE LEARNING: APPLICATIONS TO VEHICLE ROUTING AND SUPPORT VECTOR MACHINES","year":2022,"lang":"en","type":"dissertation","venue":"","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Pontifícia Universidade Católica do Rio de Janeiro; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior; Compute Canada; Polytechnique Montréal","keywords":"Vehicle routing problem; Computer science; Crossover; Metaheuristic; Context (archaeology); Support vector machine; Artificial intelligence; Machine learning; Scheduling (production processes); Integer programming; Mathematical optimization; Routing (electronic design automation); Algorithm; Mathematics","score_opus":0.02842710578212716,"score_gpt":0.2698469731593704,"score_spread":0.24141986737724325,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4308272905","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013766079,0.04769038,0.8989192,0.01008356,0.00038286106,0.000057319096,0.00009129696,0.0002183839,0.028790947],"genre_scores_gemma":[0.3290545,0.071775295,0.5770742,0.0015538719,0.0020428512,0.00030747565,0.00027786297,0.00020945763,0.017704468],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994435,0.00025667594,0.00002440181,0.000094640905,0.00014919565,0.0000315863],"domain_scores_gemma":[0.99696785,0.0025394335,0.00013247537,0.00009079451,0.00019607217,0.00007326822],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001148668,0.0011755029,0.0007883688,0.0011315817,0.0005131038,0.0018188467,0.0005996973,0.0014118771,0.0032892458],"category_scores_gemma":[0.004032838,0.0004522387,0.00080737623,0.0025576586,0.0016092262,0.0016934613,0.0012599467,0.0030164733,0.0005766626],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003670571,0.0001949528,0.0008459222,0.0005911614,0.00010044583,0.00009337738,0.00019549551,0.35443246,0.000992819,0.31730133,0.013042994,0.3121723],"study_design_scores_gemma":[0.000016134303,0.000066808156,0.00048106088,0.00015811264,0.000016199892,0.000058410922,0.00010241792,0.60849833,0.000643186,0.3686302,0.021304568,0.000024592538],"about_ca_topic_score_codex":0.0018475447,"about_ca_topic_score_gemma":0.0024437227,"teacher_disagreement_score":0.0032892458,"about_ca_system_score_codex":0.0009446906,"about_ca_system_score_gemma":0.00095417013,"threshold_uncertainty_score":0.011003673},"labels":[],"label_agreement":null},{"id":"W4308453111","doi":"10.3390/a15110412","title":"Branch and Price Algorithm for Multi-Trip Vehicle Routing with a Variable Number of Wagons and Time Windows","year":2022,"lang":"en","type":"article","venue":"Algorithms","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Lethbridge","funders":"","keywords":"Column generation; Vehicle routing problem; Computer science; Variable (mathematics); TRIPS architecture; Truck; Routing (electronic design automation); Column (typography); Mathematical optimization; Branch and price; Variable neighborhood search; Algorithm; Integer programming; Mathematics; Metaheuristic; Parallel computing; Engineering; Computer network","score_opus":0.013957518895598283,"score_gpt":0.2573606026811846,"score_spread":0.2434030837855863,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4308453111","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007995248,0.0004993325,0.9835285,0.00035427962,0.00008255926,0.00019809035,0.00013290471,0.00050146715,0.006707586],"genre_scores_gemma":[0.1234836,0.0005760119,0.8664885,0.00016633299,0.00007130917,0.0006569419,0.00044600744,0.00027887698,0.007832301],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999132,0.00032164884,0.000040018684,0.00012404112,0.0002050537,0.00017725874],"domain_scores_gemma":[0.9986713,0.0009097527,0.00010539642,0.00006544375,0.00014724396,0.000100913916],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020580844,0.0015307355,0.0020700456,0.0012602342,0.0011396181,0.00168906,0.002440225,0.0018983524,0.011632215],"category_scores_gemma":[0.0029274346,0.000967085,0.0011442231,0.002461467,0.0007951137,0.0022069425,0.0013281445,0.0024448608,0.0015695207],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001263252,0.00014921615,0.0003742143,0.00013974369,0.000060578266,0.000107614,0.000079379635,0.86941624,0.00054438406,0.04748207,0.007159975,0.07436026],"study_design_scores_gemma":[0.000030370245,0.000030173358,0.000040884937,0.000009875905,0.0000074246977,0.000014828303,0.000009774503,0.9870884,0.00014305577,0.011250004,0.0013701266,0.0000050402286],"about_ca_topic_score_codex":0.009795895,"about_ca_topic_score_gemma":0.0107114995,"teacher_disagreement_score":0.011632215,"about_ca_system_score_codex":0.0020141504,"about_ca_system_score_gemma":0.0032092794,"threshold_uncertainty_score":0.038913608},"labels":[],"label_agreement":null},{"id":"W4308600751","doi":"10.1016/j.eswa.2022.119228","title":"A bi-objective green vehicle routing problem with a mixed fleet of conventional and electric trucks: Considering charging power and density of stations","year":2022,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":74,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Truck; Greenhouse gas; Computer science; Vehicle routing problem; Minification; Constraint (computer-aided design); Routing (electronic design automation); Mathematical optimization; Total cost; Automotive engineering; Function (biology); Operations research; Mathematics; Engineering; Business; Computer network","score_opus":0.009149907028323509,"score_gpt":0.23046276061699422,"score_spread":0.2213128535886707,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4308600751","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3892077,0.0013211648,0.5903082,0.0017441022,0.0003646891,0.00038328485,0.001390233,0.00033397396,0.0149465855],"genre_scores_gemma":[0.89443064,0.0004107102,0.08590294,0.00024195244,0.00013343482,0.00030534767,0.00069047057,0.00013424187,0.017750308],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986551,0.0005054668,0.00004521046,0.00033125753,0.0001828851,0.00028004657],"domain_scores_gemma":[0.9982691,0.0009689811,0.000223035,0.00007175944,0.00016705618,0.0003000383],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027811697,0.0030276494,0.0034474193,0.0020140144,0.0011482276,0.0032224183,0.0039384607,0.0048603434,0.0042685405],"category_scores_gemma":[0.0029859333,0.0026326466,0.0023200214,0.0029251627,0.0016664356,0.0033566596,0.0021256518,0.0018044285,0.000436885],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009857173,0.00006612689,0.0003985276,0.000057388952,0.00008934127,0.00019332972,0.000019558558,0.9930252,0.00047008053,0.0026047644,0.00035727612,0.0026197864],"study_design_scores_gemma":[0.000020305717,0.00004788965,0.00018993451,0.000006001431,0.000028858152,0.000028201646,0.000029776877,0.9979911,0.000101170284,0.0013780722,0.0001705401,0.0000082042825],"about_ca_topic_score_codex":0.014172927,"about_ca_topic_score_gemma":0.011948651,"teacher_disagreement_score":0.014172927,"about_ca_system_score_codex":0.0024004094,"about_ca_system_score_gemma":0.0016911711,"threshold_uncertainty_score":0.028180838},"labels":[],"label_agreement":null},{"id":"W4309241540","doi":"10.1155/2022/5714991","title":"A Review of Heuristics and Hybrid Methods for Green Vehicle Routing Problems considering Emissions","year":2022,"lang":"en","type":"review","venue":"Journal of Advanced Transportation","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Agencia Nacional de Investigación y Desarrollo; Universidad Técnica Federico Santa María","keywords":"Greenhouse gas; Vehicle routing problem; Heuristics; Green logistics; Sustainability; Carbon footprint; Heuristic; Fuel efficiency; Computer science; Routing (electronic design automation); Environmental economics; Operations research; Transport engineering; Environmental science; Engineering; Automotive engineering; Economics","score_opus":0.057185492030025785,"score_gpt":0.3793666147285397,"score_spread":0.32218112269851396,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4309241540","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0014414836,0.9327414,0.051801275,0.0005943028,0.0005741492,0.000093136434,0.00020484692,0.00016146168,0.01238784],"genre_scores_gemma":[0.013536397,0.9158968,0.06511533,0.00038283507,0.0005480464,0.00015174567,0.00048274072,0.00009394289,0.00379213],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9993954,0.00013222019,0.0000737113,0.00014248105,0.00020237337,0.00005375899],"domain_scores_gemma":[0.99891245,0.00071323133,0.00007385662,0.00004692343,0.00022643272,0.00002710232],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008857986,0.001981056,0.0013336297,0.0020994463,0.00045568298,0.0014452264,0.0016892988,0.0012293871,0.004333152],"category_scores_gemma":[0.0021128624,0.0008706105,0.0015964031,0.004723164,0.00043755962,0.0015646301,0.0006175669,0.0014686314,0.0020065764],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005562405,0.00019541089,0.00045406367,0.017628353,0.00024626064,0.00010831331,0.00010509425,0.0401339,0.0016005815,0.025786247,0.025395801,0.88829035],"study_design_scores_gemma":[0.0000535745,0.0003640395,0.001529665,0.008424839,0.00055122183,0.000842574,0.00022796309,0.04056968,0.0024256993,0.02674543,0.91813576,0.00012960758],"about_ca_topic_score_codex":0.0043940134,"about_ca_topic_score_gemma":0.004691071,"teacher_disagreement_score":0.0043940134,"about_ca_system_score_codex":0.00091075787,"about_ca_system_score_gemma":0.0018609981,"threshold_uncertainty_score":0.01449579},"labels":[],"label_agreement":null},{"id":"W4309400251","doi":"10.1016/j.omega.2022.102805","title":"A novel and efficient exact technique for integrated staffing, assignment, routing, and scheduling of home care services under uncertainty","year":2022,"lang":"en","type":"article","venue":"Omega","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":47,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; Western University; University of Windsor","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mathematical optimization; Computer science; Staffing; Benders' decomposition; Scheduling (production processes); Robustness (evolution); Schedule; Integer programming; Job shop scheduling; Operations research; Mathematics","score_opus":0.013367532368724823,"score_gpt":0.25770738352579703,"score_spread":0.2443398511570722,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4309400251","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0023279998,0.000038509013,0.9964371,0.00005308302,0.000021923826,0.000017379865,0.000024584731,0.00011424728,0.00096521946],"genre_scores_gemma":[0.24103779,0.00022561735,0.7539592,0.0001342964,0.000117453856,0.00022750683,0.00018555006,0.00016154142,0.0039510224],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99934477,0.00017628809,0.000021351996,0.0000874222,0.0002843281,0.00008580884],"domain_scores_gemma":[0.99893874,0.0006436087,0.00010213256,0.00010147915,0.0001672885,0.00004668955],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014052684,0.00078329473,0.0012331659,0.0009373369,0.000651892,0.00084410334,0.0016344718,0.0010771095,0.0028172764],"category_scores_gemma":[0.004259442,0.00075868773,0.00094854855,0.0012470774,0.0006743556,0.0013486575,0.0015242731,0.0014648688,0.00043925276],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000039910592,0.000036668403,0.00021537204,0.000041832962,0.000022558585,0.000027193231,0.00004719456,0.9340418,0.00090583146,0.019202357,0.0010860258,0.04433335],"study_design_scores_gemma":[0.000004264647,0.000008008989,0.000029812054,0.0000023675927,0.000002352679,0.0000060882653,0.0000046155656,0.9949615,0.000094720024,0.0046396772,0.00024447922,0.0000021090425],"about_ca_topic_score_codex":0.009493431,"about_ca_topic_score_gemma":0.007816691,"teacher_disagreement_score":0.009493431,"about_ca_system_score_codex":0.0012566593,"about_ca_system_score_gemma":0.0027905179,"threshold_uncertainty_score":0.018876374},"labels":[],"label_agreement":null},{"id":"W4309462577","doi":"10.3390/math10224308","title":"Comparison of Genetic Operators for the Multiobjective Pickup and Delivery Problem","year":2022,"lang":"en","type":"article","venue":"Mathematics","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Pickup; Crossover; Benchmark (surveying); Mathematical optimization; Genetic algorithm; Computer science; Multi-objective optimization; Pareto principle; Operations research; Mathematics; Artificial intelligence","score_opus":0.03248189451337303,"score_gpt":0.2975622854662641,"score_spread":0.2650803909528911,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4309462577","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.76068306,0.0038623444,0.20278473,0.00077279354,0.00030681872,0.0003822544,0.0002911344,0.00054287555,0.030374017],"genre_scores_gemma":[0.87826043,0.0015441675,0.11718687,0.00015463505,0.00003558408,0.00022139639,0.0003309723,0.00012811851,0.0021378023],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989544,0.00043399795,0.000059823353,0.00008498876,0.0003363878,0.00013033143],"domain_scores_gemma":[0.9976821,0.0017185247,0.00010557538,0.00011914037,0.00029784712,0.00007674936],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021289294,0.0010667177,0.0006680517,0.001535899,0.0005051743,0.0009619325,0.0012511946,0.0011862935,0.0013274532],"category_scores_gemma":[0.004232407,0.0001993391,0.00086329196,0.0013426719,0.0005548764,0.00080141093,0.0005907794,0.00088636635,0.00013260283],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022908625,0.00025169633,0.0015526592,0.00018308751,0.00010682475,0.000093082905,0.000074301824,0.9206044,0.0019515044,0.007194087,0.00069213996,0.067067094],"study_design_scores_gemma":[0.00008880668,0.00043360968,0.0011881024,0.000037543272,0.00006457306,0.000053231415,0.000097849304,0.99153453,0.0020877763,0.0029521175,0.0014465543,0.000015381918],"about_ca_topic_score_codex":0.0047601853,"about_ca_topic_score_gemma":0.0043784063,"teacher_disagreement_score":0.0047601853,"about_ca_system_score_codex":0.0013427353,"about_ca_system_score_gemma":0.0012445783,"threshold_uncertainty_score":0.01125896},"labels":[],"label_agreement":null},{"id":"W4309716785","doi":"10.5267/j.ijiec.2022.8.004","title":"Metaheuristic algorithm for the location, routing and packing problem in the collection of recyclable waste","year":2022,"lang":"en","type":"article","venue":"International Journal of Industrial Engineering Computations","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Tabu search; Vehicle routing problem; GRASP; Metaheuristic; Mathematical optimization; Greedy randomized adaptive search procedure; Routing (electronic design automation); Guided Local Search; Computer science; Set (abstract data type); Packing problems; Greedy algorithm; Local search (optimization); Combinatorial optimization; Scheme (mathematics); Algorithm; Mathematics","score_opus":0.030770653385703135,"score_gpt":0.275353779444464,"score_spread":0.24458312605876084,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4309716785","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.043635797,0.0013374923,0.94577736,0.0003558938,0.0000664535,0.00018310272,0.0001212622,0.00029342363,0.008229202],"genre_scores_gemma":[0.33554843,0.0011418669,0.6567377,0.00013981076,0.000061625455,0.0006040568,0.0002724491,0.00010506072,0.0053890417],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996644,0.00018705151,0.000013232131,0.00004032142,0.000050877035,0.000044055272],"domain_scores_gemma":[0.9995802,0.00030330828,0.000051636933,0.000014923377,0.000033375258,0.000016564547],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008715641,0.00088873046,0.0008110652,0.0008887572,0.0005589843,0.00088255253,0.000947457,0.0013735592,0.0016854375],"category_scores_gemma":[0.0013450205,0.0004720712,0.00091550604,0.0013985931,0.0005091153,0.00056513184,0.00054955174,0.0008545756,0.00025270108],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003343723,0.000044953194,0.0001521931,0.000047599955,0.000030140745,0.00003573592,0.000026596575,0.97319657,0.00046669206,0.0069046137,0.00053383136,0.018527685],"study_design_scores_gemma":[0.000013916616,0.000024957628,0.00004371003,0.000007367189,0.000007496805,0.000010032457,0.000011336292,0.99718446,0.00016729007,0.001985751,0.000540669,0.0000029372306],"about_ca_topic_score_codex":0.00862831,"about_ca_topic_score_gemma":0.007910109,"teacher_disagreement_score":0.00862831,"about_ca_system_score_codex":0.0010877603,"about_ca_system_score_gemma":0.0016183902,"threshold_uncertainty_score":0.017156184},"labels":[],"label_agreement":null},{"id":"W4310033065","doi":"10.1016/j.ejor.2022.11.040","title":"An integer L-shaped algorithm for the vehicle routing problem with time windows and stochastic demands","year":2022,"lang":"en","type":"article","venue":"European Journal of Operational Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":23,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Vehicle routing problem; Computer science; Benchmark (surveying); Integer (computer science); Mathematical optimization; TRIPS architecture; Branch and cut; Routing (electronic design automation); Operations research; Integer programming; Algorithm; Mathematics","score_opus":0.04175022239280719,"score_gpt":0.3241135125620981,"score_spread":0.2823632901692909,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4310033065","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012064746,0.00018078151,0.9828773,0.00028388624,0.00009279196,0.00008235861,0.00009390314,0.0005450722,0.0037791447],"genre_scores_gemma":[0.16738985,0.00017807522,0.8268295,0.00041234287,0.000059731407,0.00035462916,0.0003270242,0.00023981479,0.0042090425],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992867,0.0002483852,0.000031698542,0.00014300365,0.00014206828,0.00014811911],"domain_scores_gemma":[0.99837744,0.0010312246,0.00014770574,0.000084446896,0.00021740221,0.00014179033],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014278425,0.0011250579,0.0014424537,0.0009049761,0.0006023739,0.0014593765,0.0025309885,0.0022884281,0.0057407725],"category_scores_gemma":[0.0044920403,0.00082629104,0.00096545275,0.001148348,0.00072233885,0.0013953488,0.001845365,0.0016310426,0.0009982807],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00042290692,0.00023602467,0.00050840044,0.00012021026,0.000046084177,0.00008902328,0.00009471247,0.8375799,0.0023906617,0.017625706,0.004560146,0.13632618],"study_design_scores_gemma":[0.000045086766,0.00005036426,0.000032772303,0.000008397927,0.000005361045,0.000012555476,0.000012088828,0.9963038,0.00023121912,0.0027516172,0.00054092996,0.000005717502],"about_ca_topic_score_codex":0.005921108,"about_ca_topic_score_gemma":0.0055168266,"teacher_disagreement_score":0.005921108,"about_ca_system_score_codex":0.0015188971,"about_ca_system_score_gemma":0.002874013,"threshold_uncertainty_score":0.019204736},"labels":[],"label_agreement":null},{"id":"W4310251926","doi":"10.1287/trsc.2022.1185","title":"Constrained Local Search for Last-Mile Routing","year":2022,"lang":"en","type":"article","venue":"Transportation Science","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Mile; Routing (electronic design automation); Last mile (transportation); Transport engineering; Computer science; Local search (optimization); Mathematical optimization; Operations research; Engineering; Computer network; Mathematics; Geography; Algorithm","score_opus":0.028058050547799737,"score_gpt":0.30090013077915523,"score_spread":0.2728420802313555,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4310251926","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005892464,0.0005661361,0.98812056,0.00029812285,0.000058437316,0.000049596616,0.00013551678,0.00057549635,0.0043037557],"genre_scores_gemma":[0.32504758,0.0005513263,0.6608996,0.00046100502,0.00010227234,0.00063484296,0.000863581,0.0005812968,0.010858598],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992453,0.0003263242,0.000029738761,0.00014849385,0.0001710305,0.0000791373],"domain_scores_gemma":[0.9973628,0.0019278232,0.00015673786,0.00014878897,0.00030103844,0.00010280571],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013273648,0.00087705115,0.0015010737,0.0010442656,0.0006416209,0.0009812013,0.0019442469,0.0016635703,0.0073406408],"category_scores_gemma":[0.006418751,0.00067054824,0.0007469001,0.0014164688,0.00092988677,0.0013664138,0.0015168859,0.0017764745,0.001515655],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000037668236,0.000026509333,0.00020029512,0.000080896294,0.00002539228,0.00004531375,0.000031975353,0.96731055,0.00030665804,0.011279739,0.003312141,0.017342864],"study_design_scores_gemma":[0.000008000516,0.0000066281013,0.0000236144,0.000006278377,0.000002506356,0.0000071813747,0.0000052021087,0.99383956,0.00007014252,0.0054622423,0.0005654806,0.0000032250612],"about_ca_topic_score_codex":0.005580576,"about_ca_topic_score_gemma":0.006613867,"teacher_disagreement_score":0.0073406408,"about_ca_system_score_codex":0.0011845079,"about_ca_system_score_gemma":0.001332621,"threshold_uncertainty_score":0.024556875},"labels":[],"label_agreement":null},{"id":"W4311356531","doi":"10.18280/mmep.090505","title":"Hotel Capacity Planning Using Queuing Systems and Meta-Heuristic Algorithms","year":2022,"lang":"en","type":"article","venue":"Mathematical Modelling and Engineering Problems","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Meta heuristic; Queueing theory; Heuristic; Computer science; Algorithm; Operations research; Mathematical optimization; Artificial intelligence; Engineering; Mathematics; Computer network","score_opus":0.09723651375150587,"score_gpt":0.2553161065363599,"score_spread":0.158079592784854,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4311356531","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023622055,0.0013060145,0.9690203,0.00043598315,0.00011245011,0.00016318729,0.0001781835,0.00030250807,0.004859337],"genre_scores_gemma":[0.6085555,0.0014311231,0.38550103,0.00022203993,0.000090986854,0.00046848235,0.0003769545,0.000101835336,0.0032521351],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993075,0.00031438738,0.000043956206,0.00011377043,0.00009522389,0.0001251513],"domain_scores_gemma":[0.99875855,0.0008751941,0.00012752415,0.00004923462,0.00011376281,0.00007581845],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015795345,0.0013887923,0.0016044802,0.0021277799,0.0008050335,0.002132839,0.0020540026,0.0014931841,0.0026224821],"category_scores_gemma":[0.0018893372,0.0011915189,0.0019945295,0.0022112369,0.0008522353,0.001420399,0.0011248796,0.0013455593,0.0003056527],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016966147,0.000027770888,0.00023718896,0.00004843213,0.000043841792,0.000021469914,0.000018576746,0.9860728,0.00013073435,0.005157632,0.0002717886,0.007952882],"study_design_scores_gemma":[0.0000053422646,0.000011748191,0.000028843484,0.000007778006,0.000009183805,0.0000041018534,0.000014173079,0.9966813,0.00006607107,0.0029561555,0.00021089739,0.0000044141593],"about_ca_topic_score_codex":0.014010606,"about_ca_topic_score_gemma":0.015583121,"teacher_disagreement_score":0.014010606,"about_ca_system_score_codex":0.002232129,"about_ca_system_score_gemma":0.003105561,"threshold_uncertainty_score":0.027858138},"labels":[],"label_agreement":null},{"id":"W4312068629","doi":"10.1080/14942119.2022.2142367","title":"Planning methods and decision support systems in vehicle routing problems for timber transportation: a review","year":2022,"lang":"en","type":"review","venue":"International Journal of Forest Engineering","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":46,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval; Université du Québec à Trois-Rivières","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Vehicle routing problem; Software deployment; Routing (electronic design automation); Context (archaeology); Transport engineering; Transportation planning; Decision support system; Operations research; Computer science; Engineering","score_opus":0.050798096494515416,"score_gpt":0.3840646319962774,"score_spread":0.333266535501762,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312068629","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00038484763,0.98105985,0.013293797,0.00040853265,0.00029403777,0.000036058107,0.000060387058,0.000044106313,0.0044184667],"genre_scores_gemma":[0.0034438309,0.9830755,0.011898912,0.00013773602,0.00030775924,0.000048342325,0.0001121468,0.000017250637,0.000958377],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.999218,0.00020067804,0.0000929572,0.00014840698,0.00029884608,0.000041157677],"domain_scores_gemma":[0.9972824,0.0020475376,0.00015363291,0.000069927744,0.0004052926,0.000041312454],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015655935,0.0015981004,0.0014718986,0.0029302114,0.00043975908,0.0017886746,0.0017128941,0.0015828474,0.005462382],"category_scores_gemma":[0.0032074784,0.0007616299,0.0011798757,0.0072721727,0.00075269694,0.0024181204,0.0007795417,0.0017147093,0.0024913242],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000029813038,0.00013369894,0.000357551,0.021943256,0.00013493105,0.0001103101,0.00015161594,0.008907192,0.0006040355,0.023597017,0.016897239,0.92713344],"study_design_scores_gemma":[0.000021854888,0.00016704574,0.0014261334,0.012636265,0.00027311384,0.00076436566,0.00028849568,0.009972918,0.0010290601,0.027819622,0.94549966,0.00010147018],"about_ca_topic_score_codex":0.0033824232,"about_ca_topic_score_gemma":0.0030326722,"teacher_disagreement_score":0.005462382,"about_ca_system_score_codex":0.0007494303,"about_ca_system_score_gemma":0.0020593894,"threshold_uncertainty_score":0.018273473},"labels":[],"label_agreement":null},{"id":"W4312179191","doi":"10.1287/msom.2022.1171","title":"On-Demand Delivery from Stores: Dynamic Dispatching and Routing with Random Demand","year":2022,"lang":"en","type":"article","venue":"Manufacturing & Service Operations Management","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":55,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Routing (electronic design automation); Operations research; Dynamic pricing; Mathematical optimization; Set (abstract data type); Markov decision process; Curse of dimensionality; Economics; Microeconomics; Markov process; Engineering; Mathematics; Computer network","score_opus":0.005321924987864247,"score_gpt":0.205691340433367,"score_spread":0.20036941544550277,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312179191","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07248926,0.0006437163,0.9131721,0.002361456,0.0002054823,0.0003420405,0.0013272256,0.00040536196,0.009053423],"genre_scores_gemma":[0.8133398,0.00083936553,0.17224102,0.00038937308,0.00014401764,0.000498174,0.0011996498,0.00024425137,0.011104405],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982002,0.0007280897,0.000064682616,0.00046115555,0.000256636,0.00028918104],"domain_scores_gemma":[0.9974821,0.0015903327,0.0003277493,0.00017970314,0.00021439171,0.00020574086],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023024597,0.0015752323,0.0019285813,0.00058002037,0.0007842952,0.0021229782,0.0028661063,0.0028071525,0.0056369673],"category_scores_gemma":[0.0057409466,0.0010961896,0.0014606543,0.0014529501,0.0012951036,0.0022549115,0.0015973564,0.0023931034,0.0005372833],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000053501146,0.000034770626,0.00032643878,0.000044761477,0.000016691341,0.00006148021,0.000027142478,0.9858695,0.00018283619,0.008405939,0.0013214974,0.0036554304],"study_design_scores_gemma":[0.000011716428,0.000016452697,0.00007560126,0.000004687834,0.0000050252843,0.000018110348,0.000019583711,0.995175,0.000088346795,0.004199852,0.00038025525,0.0000053693507],"about_ca_topic_score_codex":0.0173492,"about_ca_topic_score_gemma":0.009599079,"teacher_disagreement_score":0.0173492,"about_ca_system_score_codex":0.0033038696,"about_ca_system_score_gemma":0.0023019416,"threshold_uncertainty_score":0.034496486},"labels":[],"label_agreement":null},{"id":"W4312579180","doi":"10.1016/j.ifacol.2022.10.218","title":"A simulation-optimization approach for solving the forestry logistics problem","year":2022,"lang":"en","type":"article","venue":"IFAC-PapersOnLine","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"General Motors (Canada)","funders":"","keywords":"Truck; Computer science; Vehicle routing problem; Scheduling (production processes); Operations research; Mathematical optimization; Routing (electronic design automation); Optimization problem; Engineering; Mathematics; Algorithm","score_opus":0.030288823830019792,"score_gpt":0.2753424155240616,"score_spread":0.24505359169404178,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312579180","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008686744,0.00017994313,0.9854789,0.00031510784,0.00004006743,0.000057301735,0.00004728468,0.000081773884,0.0051129255],"genre_scores_gemma":[0.5331923,0.0007012139,0.45921633,0.00017426383,0.00008530061,0.0006300711,0.00020917304,0.00008508522,0.005706265],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99940217,0.00037267702,0.000019530435,0.00005554553,0.000102036014,0.000048066657],"domain_scores_gemma":[0.9987888,0.0008961058,0.0000948906,0.000036139034,0.00012601068,0.000058117872],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011476728,0.000921586,0.0006664676,0.00079829124,0.0005424385,0.00085235544,0.00096178503,0.0013622034,0.0027053528],"category_scores_gemma":[0.0024594995,0.0005368264,0.0009880211,0.0008125114,0.000856457,0.000653757,0.00096923474,0.0013536647,0.00022029843],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000006055013,0.000008942604,0.00006989462,0.000008420891,0.000008131927,0.000010332468,0.000006668586,0.9932799,0.00009685508,0.0049872585,0.00006459881,0.0014529343],"study_design_scores_gemma":[0.0000034574775,0.0000047982044,0.000014193676,0.0000027094686,0.0000018012137,0.0000032450366,0.0000032124656,0.99783,0.00004865214,0.001839819,0.00024628948,0.0000018303815],"about_ca_topic_score_codex":0.012636212,"about_ca_topic_score_gemma":0.008441367,"teacher_disagreement_score":0.012636212,"about_ca_system_score_codex":0.0011159431,"about_ca_system_score_gemma":0.002482351,"threshold_uncertainty_score":0.025125325},"labels":[],"label_agreement":null},{"id":"W4312638287","doi":"10.1109/tsmc.2022.3199096","title":"Group Role Assignment With Constraints (GRA+): A New Category of Assignment Problems","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Systems Man and Cybernetics Systems","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":40,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Nipissing University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Assignment problem; Generalized assignment problem; Group (periodic table); Computer science; Series (stratigraphy); Weapon target assignment problem; Mathematics; Mathematical optimization; Theoretical computer science","score_opus":0.012316112574734762,"score_gpt":0.20077963983724864,"score_spread":0.1884635272625139,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312638287","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0071360897,0.0008318595,0.9773676,0.0022876703,0.000276238,0.00019201844,0.00023068422,0.00012034833,0.0115575995],"genre_scores_gemma":[0.19767003,0.0026673384,0.7822776,0.0010637912,0.0012078679,0.0010820332,0.00087516976,0.00029715232,0.01285901],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9876559,0.006576063,0.00068692886,0.002431399,0.001909268,0.0007404773],"domain_scores_gemma":[0.99123365,0.0052686427,0.00087870314,0.0012940908,0.00081865955,0.0005062798],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006443113,0.0018728469,0.0014218619,0.001836622,0.0025931292,0.0045819017,0.0034965368,0.0035486862,0.010032619],"category_scores_gemma":[0.013559732,0.00086018397,0.0021826748,0.0032613596,0.004690396,0.012980262,0.005516398,0.0058884053,0.0014809234],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000056608027,0.00009021314,0.00043052775,0.00042443798,0.000043938544,0.00010072454,0.00031741677,0.020962756,0.0006165585,0.9122836,0.0066764327,0.05799679],"study_design_scores_gemma":[0.00003601717,0.00011790006,0.00034215706,0.00017191272,0.000032683045,0.0003178836,0.0005000654,0.06808951,0.0008751279,0.8737434,0.05571828,0.000054938166],"about_ca_topic_score_codex":0.0019014765,"about_ca_topic_score_gemma":0.0014169519,"teacher_disagreement_score":0.010032619,"about_ca_system_score_codex":0.002211481,"about_ca_system_score_gemma":0.0028985732,"threshold_uncertainty_score":0.034074843},"labels":[],"label_agreement":null},{"id":"W4312688152","doi":"10.1016/j.ifacol.2022.10.175","title":"A Vehicle Routing Problem with Time Windows and Workload Balancing for COVID-19 Testers: A Case Study","year":2022,"lang":"en","type":"article","venue":"IFAC-PapersOnLine","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"","keywords":"Workload; Solver; Computer science; Vehicle routing problem; Integer programming; Coronavirus disease 2019 (COVID-19); Routing (electronic design automation); Operations research; Engineering; Embedded system; Operating system; Algorithm; Medicine","score_opus":0.019501941498570194,"score_gpt":0.27644497070991386,"score_spread":0.25694302921134365,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312688152","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8009903,0.0023441894,0.17012174,0.0036461893,0.00051471783,0.0006757565,0.0051333667,0.00052618486,0.016047526],"genre_scores_gemma":[0.90330976,0.0008820071,0.08491212,0.00022792233,0.00011756662,0.00039338955,0.0031643778,0.00011298974,0.006879762],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99863225,0.00058956083,0.000052373725,0.00029747328,0.00012299196,0.0003052933],"domain_scores_gemma":[0.9972633,0.001944539,0.00022319716,0.00011304006,0.00020198364,0.00025401192],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017837757,0.0016187917,0.0012268751,0.00089324435,0.0009691183,0.0016441842,0.002640987,0.0032721316,0.004185653],"category_scores_gemma":[0.0038442547,0.0006452755,0.0014413628,0.001961656,0.00076749315,0.0015561426,0.0008131829,0.0014991716,0.00037136473],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001662515,0.00031825676,0.0018301806,0.00018877663,0.000060968272,0.0006775351,0.0000614126,0.9787648,0.0005350776,0.0038480456,0.004747623,0.008801089],"study_design_scores_gemma":[0.00006572927,0.0001126303,0.0011295452,0.0000174987,0.000027492613,0.00014887306,0.00029157908,0.99274945,0.0004309484,0.0028351112,0.0021711448,0.00002004922],"about_ca_topic_score_codex":0.021434236,"about_ca_topic_score_gemma":0.019200237,"teacher_disagreement_score":0.021434236,"about_ca_system_score_codex":0.0024066544,"about_ca_system_score_gemma":0.0017759037,"threshold_uncertainty_score":0.04261899},"labels":[],"label_agreement":null},{"id":"W4313328297","doi":"10.32907/ro-133-37011672744","title":"A divide-and-conquer strategy for the vehicle routing problem","year":2022,"lang":"en","type":"article","venue":"Research outreach","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"National Natural Science Foundation of China","keywords":"Divide and conquer algorithms; Computer science; Routing (electronic design automation); Computer network; Algorithm","score_opus":0.11168350302747315,"score_gpt":0.37870891292929976,"score_spread":0.2670254099018266,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313328297","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01956784,0.00052740163,0.9688101,0.0008920415,0.000087666696,0.00014781859,0.00005758491,0.00023744616,0.009672105],"genre_scores_gemma":[0.36861357,0.0006528549,0.614729,0.00042598796,0.00013746685,0.00037441205,0.00021545279,0.00014277794,0.014708398],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994079,0.00019975204,0.000022808515,0.00011737465,0.00013659711,0.00011552617],"domain_scores_gemma":[0.9993044,0.00045008698,0.00004038147,0.000041405372,0.00012064501,0.00004300865],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011377931,0.001108227,0.001247997,0.0010708194,0.0009379976,0.001032928,0.001659812,0.0017355351,0.0058637895],"category_scores_gemma":[0.0022813387,0.00055256294,0.000666425,0.0014615977,0.0010789355,0.001887943,0.0011125045,0.001506825,0.00067816075],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00043734768,0.00034300212,0.0005918482,0.00021076188,0.00008940575,0.00010720467,0.00019234447,0.67802477,0.003978025,0.07274392,0.011611301,0.23167008],"study_design_scores_gemma":[0.00004865732,0.0000489723,0.00004368537,0.000005932589,0.000008797842,0.000014930609,0.000029458366,0.9859196,0.00038424964,0.012466292,0.0010246896,0.00000477833],"about_ca_topic_score_codex":0.007077471,"about_ca_topic_score_gemma":0.0069225146,"teacher_disagreement_score":0.007077471,"about_ca_system_score_codex":0.0012057114,"about_ca_system_score_gemma":0.0013962295,"threshold_uncertainty_score":0.019616306},"labels":[],"label_agreement":null},{"id":"W4313593040","doi":"10.1016/j.trb.2023.01.002","title":"An exact algorithm for the two-echelon vehicle routing problem with drones","year":2023,"lang":"en","type":"article","venue":"Transportation Research Part B Methodological","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":122,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Vehicle routing problem; Drone; Computer science; Routing (electronic design automation); Mathematical optimization; Algorithm; Mathematics; Computer network","score_opus":0.2997001689468235,"score_gpt":0.46398993750047557,"score_spread":0.16428976855365207,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313593040","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015368446,0.00027028852,0.97521925,0.00021199172,0.00011948417,0.000116810304,0.00015434113,0.0007746707,0.0077646426],"genre_scores_gemma":[0.2205248,0.00029118283,0.7721563,0.0001290851,0.00006249331,0.00025929324,0.00032978522,0.00016718417,0.006079801],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99947613,0.00009847437,0.000027122736,0.00013238165,0.00015959553,0.00010628454],"domain_scores_gemma":[0.99936885,0.00032227358,0.000041736796,0.00011223909,0.00010671481,0.000048250924],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00072348246,0.00084485376,0.001378557,0.00071138015,0.00079996814,0.0014814234,0.0018519758,0.0014888693,0.008754901],"category_scores_gemma":[0.0022641143,0.0007051411,0.0007595479,0.0011915228,0.00068244955,0.0021183537,0.0019239499,0.001230435,0.0012301158],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001454092,0.00011121034,0.0003452092,0.00011856266,0.00003558186,0.00006632767,0.00007802635,0.81327325,0.0013938843,0.023520662,0.003747884,0.15716393],"study_design_scores_gemma":[0.00006309726,0.000031300442,0.00007763116,0.000008866733,0.000007019938,0.000026560261,0.000026472462,0.986414,0.0002545775,0.011641211,0.001441377,0.00000787063],"about_ca_topic_score_codex":0.011590368,"about_ca_topic_score_gemma":0.013753008,"teacher_disagreement_score":0.011590368,"about_ca_system_score_codex":0.0013440248,"about_ca_system_score_gemma":0.0025234888,"threshold_uncertainty_score":0.029288113},"labels":[],"label_agreement":null},{"id":"W4318820632","doi":"10.1145/3582500","title":"Approximation Schemes for Capacitated Vehicle Routing on Graphs of Bounded Treewidth, Bounded Doubling, or Highway Dimension","year":2023,"lang":"en","type":"article","venue":"ACM Transactions on Algorithms","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Mathematics; Combinatorics; Approximation algorithm; Bounded function; Vehicle routing problem; Euclidean geometry; Treewidth; Discrete mathematics; Graph; Routing (electronic design automation); Pathwidth; Computer science; Line graph","score_opus":0.05042969397683607,"score_gpt":0.3004710953604926,"score_spread":0.25004140138365655,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4318820632","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07829246,0.0029347118,0.8995742,0.002142859,0.00021446262,0.00020085873,0.0007811205,0.0025986955,0.013260616],"genre_scores_gemma":[0.59623337,0.0018071241,0.39249307,0.0007073733,0.00019166271,0.0002448665,0.0014638368,0.00048707594,0.0063715805],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9972012,0.0007025927,0.00014964536,0.00053932576,0.00070049096,0.00070664176],"domain_scores_gemma":[0.9906646,0.004724085,0.00077714585,0.0028959357,0.0005457305,0.0003925211],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027892257,0.0016477839,0.0014101859,0.0015090046,0.001151166,0.0029206143,0.005719645,0.0020663608,0.0061386335],"category_scores_gemma":[0.01261887,0.0006812282,0.00182998,0.003586311,0.0014043407,0.008124717,0.0028482745,0.003349134,0.0011782281],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011616064,0.0002448987,0.001841712,0.00047522032,0.00018988627,0.00014639951,0.00058190594,0.7137237,0.0053020343,0.1465269,0.012425323,0.11738047],"study_design_scores_gemma":[0.000049243714,0.000089033994,0.0002608699,0.000047222282,0.000044684733,0.00013555065,0.000109683424,0.91228527,0.002121442,0.080051176,0.0047824536,0.000023440996],"about_ca_topic_score_codex":0.0046025785,"about_ca_topic_score_gemma":0.004579669,"teacher_disagreement_score":0.0063065486,"about_ca_system_score_codex":0.0063065486,"about_ca_system_score_gemma":0.0016693734,"threshold_uncertainty_score":0.045757413},"labels":[],"label_agreement":null},{"id":"W4319310318","doi":"10.1080/01605682.2023.2174052","title":"Mechanisms for feasibility and improvement for inventory-routing problems","year":2023,"lang":"en","type":"article","venue":"Journal of the Operational Research Society","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval; Transport Canada","funders":"Natural Sciences and Engineering Research Council of Canada; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior","keywords":"Computer science; Mathematical optimization; Benchmark (surveying); Vehicle routing problem; Modular design; Routing (electronic design automation); Set (abstract data type); Exploit; Heuristic; Heuristics; Column generation; Scheme (mathematics); Class (philosophy); Operations research; Mathematics; Artificial intelligence","score_opus":0.1469068184216912,"score_gpt":0.4022142332776783,"score_spread":0.25530741485598707,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4319310318","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0085799545,0.00042663826,0.985494,0.00042380512,0.000056995006,0.0002028412,0.000058060243,0.00043515835,0.004322558],"genre_scores_gemma":[0.16918163,0.0004563796,0.82661957,0.00023717275,0.0001554364,0.0006662378,0.00021899694,0.00019144911,0.0022732166],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9930656,0.003377353,0.00037077084,0.0010413575,0.0015396931,0.00060516346],"domain_scores_gemma":[0.98638225,0.008753492,0.0016421305,0.0018679465,0.0010865296,0.00026774878],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.013532321,0.0021463833,0.0018255755,0.0031066076,0.0011753477,0.002261836,0.0045215157,0.0021080272,0.008155122],"category_scores_gemma":[0.029194254,0.0014168338,0.0026582489,0.0028034626,0.0028125474,0.006085921,0.0047695236,0.0040867557,0.0013062553],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00045971049,0.00043698863,0.0016968824,0.000693046,0.00017178633,0.00016483972,0.0003109664,0.46986538,0.0050552944,0.26404727,0.0060557113,0.2510421],"study_design_scores_gemma":[0.00013222596,0.00029476223,0.00035594671,0.00013526007,0.000076369775,0.00014851234,0.000068552086,0.8796717,0.004071887,0.106331326,0.008671931,0.000041540537],"about_ca_topic_score_codex":0.0009366923,"about_ca_topic_score_gemma":0.0008132236,"teacher_disagreement_score":0.013532321,"about_ca_system_score_codex":0.0014341958,"about_ca_system_score_gemma":0.0025158764,"threshold_uncertainty_score":0.07156664},"labels":[],"label_agreement":null},{"id":"W4319441355","doi":"10.1016/j.ejor.2023.01.066","title":"Multiple allocation hub location with service level constraints for two shipment classes","year":2023,"lang":"en","type":"article","venue":"European Journal of Operational Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada; Indian Institute of Management Ahmedabad","keywords":"Computer science; Service (business); Context (archaeology); Constraint (computer-aided design); Network planning and design; Operations research; Service provider; Service level; Queue; Class (philosophy); Location-allocation; Total cost; Variable (mathematics); Computer network; Mathematical optimization; Business; Mathematics","score_opus":0.19997675261540043,"score_gpt":0.39961275720255024,"score_spread":0.1996360045871498,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4319441355","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17198554,0.000335848,0.8110988,0.0005803578,0.00013127655,0.00018335953,0.0005812335,0.00032204148,0.014781493],"genre_scores_gemma":[0.8881926,0.00021008492,0.0968225,0.00007690463,0.000069149406,0.00017890209,0.00028458473,0.00012349724,0.014041711],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99934727,0.00019785769,0.000019709223,0.00015345792,0.00009254444,0.00018919836],"domain_scores_gemma":[0.9986051,0.00085909857,0.00018462919,0.00008864935,0.00016283855,0.00009971507],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011359915,0.0010318588,0.0015037518,0.0007319747,0.0005536368,0.0019063124,0.001964108,0.0020450982,0.009720394],"category_scores_gemma":[0.0031856496,0.00089132204,0.0011908092,0.0015440851,0.00079257984,0.0012976268,0.0012155495,0.0009838585,0.0006944977],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020616243,0.000054519096,0.000497211,0.00009641146,0.000030473266,0.00018983701,0.000039054667,0.97513163,0.0013967108,0.010893976,0.0010660348,0.0103979055],"study_design_scores_gemma":[0.000017162567,0.00002568034,0.00026454532,0.0000059566078,0.000015175537,0.000022725997,0.000022970959,0.99571425,0.00032629623,0.0032678905,0.00030867517,0.000008613658],"about_ca_topic_score_codex":0.010418958,"about_ca_topic_score_gemma":0.007710718,"teacher_disagreement_score":0.010418958,"about_ca_system_score_codex":0.0018779268,"about_ca_system_score_gemma":0.0011752767,"threshold_uncertainty_score":0.03251791},"labels":[],"label_agreement":null},{"id":"W4320033621","doi":"10.1016/j.cor.2023.106184","title":"The road train optimization problem with load assignment","year":2023,"lang":"en","type":"article","venue":"Computers & Operations Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Truck; Solver; Computer science; Train; Iterated local search; Computation; Vehicle routing problem; Routing (electronic design automation); Set (abstract data type); Mathematical optimization; Iterated function; Trailer; Optimization problem; Algorithm; Local search (optimization); Mathematics; Automotive engineering; Computer network; Engineering","score_opus":0.055466430265227865,"score_gpt":0.34737811217694564,"score_spread":0.2919116819117178,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4320033621","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.037174597,0.00064858154,0.9282886,0.0013838932,0.00018603793,0.00014112223,0.00050024065,0.00021162438,0.031465385],"genre_scores_gemma":[0.6850583,0.0012469604,0.24398993,0.00034763393,0.00043772635,0.0003737465,0.000754736,0.00037873856,0.06741223],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993124,0.00034117562,0.000015761281,0.00013399235,0.00010392164,0.00009283682],"domain_scores_gemma":[0.9994875,0.0003394289,0.000047620408,0.00003061946,0.000048247864,0.000046484463],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00088402367,0.0009009328,0.0011949104,0.0008351321,0.0005038511,0.0015427447,0.0016182561,0.0018809545,0.010097033],"category_scores_gemma":[0.0028499828,0.0006265834,0.00087387505,0.0015947501,0.00079211214,0.0020904914,0.0012230352,0.0012354561,0.00082263525],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006877409,0.000074976066,0.0002126084,0.00009232791,0.000034428635,0.00005864489,0.0000271265,0.9453545,0.00035105855,0.027078195,0.0039606267,0.022686731],"study_design_scores_gemma":[0.000016726362,0.000021790049,0.00011546575,0.000006055294,0.000009533904,0.00001734104,0.000018162234,0.98121053,0.00014427726,0.016412823,0.0020215455,0.000005805758],"about_ca_topic_score_codex":0.008757152,"about_ca_topic_score_gemma":0.0068689543,"teacher_disagreement_score":0.010097033,"about_ca_system_score_codex":0.0012229215,"about_ca_system_score_gemma":0.0014932952,"threshold_uncertainty_score":0.033777893},"labels":[],"label_agreement":null},{"id":"W4320149362","doi":"10.1007/978-3-031-24866-5_8","title":"Learning to Solve a Stochastic Orienteering Problem with Time Windows","year":2022,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Orienteering; Vehicle routing problem; Computer science; Reinforcement learning; Mathematical optimization; Combinatorial optimization; Variety (cybernetics); Travelling salesman problem; Routing (electronic design automation); Optimization problem; Artificial intelligence; Algorithm; Mathematics","score_opus":0.008960571633318136,"score_gpt":0.222514940281767,"score_spread":0.21355436864844884,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4320149362","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.039672244,0.00035272047,0.9563624,0.00029259745,0.000062954976,0.00003798114,0.000062671694,0.0001781138,0.002978179],"genre_scores_gemma":[0.7088235,0.0006902025,0.27486736,0.00023243706,0.00022175217,0.00025159202,0.00037817558,0.00016915365,0.014365899],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996996,0.00008915787,0.000017998922,0.000096976095,0.00004961668,0.000046703284],"domain_scores_gemma":[0.99835217,0.0013477482,0.00008919373,0.000057550576,0.000085111686,0.000068349655],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010837916,0.0008414544,0.0011354138,0.00032512387,0.00023846484,0.00073421345,0.0011197105,0.0017903723,0.0035427983],"category_scores_gemma":[0.0039524403,0.0006246164,0.00073610264,0.0006590723,0.0007340134,0.0015304871,0.0012028201,0.001478813,0.00029737668],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000070432114,0.000052785334,0.0003054627,0.00005753662,0.000026890317,0.000029225039,0.000023898412,0.94985807,0.0006081665,0.01137138,0.0009808344,0.036615327],"study_design_scores_gemma":[0.000007284827,0.000022577558,0.000029888675,0.000002634588,0.0000030620508,0.0000037660063,0.000002966602,0.99524766,0.00011597655,0.0044247606,0.00013749552,0.0000018720621],"about_ca_topic_score_codex":0.0056297495,"about_ca_topic_score_gemma":0.0034348038,"teacher_disagreement_score":0.0056297495,"about_ca_system_score_codex":0.00063014764,"about_ca_system_score_gemma":0.00093849417,"threshold_uncertainty_score":0.011851907},"labels":[],"label_agreement":null},{"id":"W4320341229","doi":"","title":"A railroad maintenance problem solved with a cut and column generation matheuristic","year":2015,"lang":"en","type":"article","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Column generation; Column (typography); Computer science; Mathematics; Environmental science; Mathematical optimization; Geometry","score_opus":0.01708505729145844,"score_gpt":0.2164264915894708,"score_spread":0.19934143429801238,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4320341229","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.33776537,0.0007265146,0.621365,0.0017026225,0.00027065145,0.00027137477,0.00085734087,0.0004188475,0.036622293],"genre_scores_gemma":[0.80857617,0.0002567114,0.17687194,0.00020409124,0.0001215233,0.00016325244,0.0004200818,0.00010914001,0.013277084],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998066,0.0000744024,0.000007475414,0.000038857183,0.000038927283,0.000033721797],"domain_scores_gemma":[0.99884427,0.00088795664,0.00006507777,0.000041082334,0.00009912637,0.000062482344],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005551499,0.0007403743,0.00074830034,0.00072558806,0.00055642717,0.00097165664,0.0007995406,0.0018159904,0.0061273873],"category_scores_gemma":[0.0022076617,0.0003525606,0.000731373,0.0007249449,0.0006273743,0.00068480015,0.00081293035,0.0011401143,0.00020148385],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017326217,0.00013431825,0.00071232935,0.00016314877,0.00003102485,0.00014586785,0.00006240949,0.96103054,0.0014372264,0.009464026,0.0027931836,0.023852674],"study_design_scores_gemma":[0.000013364575,0.000038851333,0.00012951951,0.0000050688777,0.00000840821,0.000016698154,0.000017188493,0.9968496,0.00025004093,0.002384921,0.0002826178,0.0000036823556],"about_ca_topic_score_codex":0.013965576,"about_ca_topic_score_gemma":0.010523261,"teacher_disagreement_score":0.013965576,"about_ca_system_score_codex":0.00073690974,"about_ca_system_score_gemma":0.0010190532,"threshold_uncertainty_score":0.027768552},"labels":[],"label_agreement":null},{"id":"W4321460198","doi":"10.1016/j.cie.2023.109108","title":"A robust multi-objective routing problem for heavy-duty electric trucks with uncertain energy consumption","year":2023,"lang":"en","type":"article","venue":"Computers & Industrial Engineering","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":35,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Truck; Heavy duty; Energy consumption; Automotive engineering; Routing (electronic design automation); Electric energy consumption; Computer science; Consumption (sociology); Energy (signal processing); Electric energy; Mathematical optimization; Engineering; Electrical engineering; Computer network; Mathematics; Physics","score_opus":0.0625244726374785,"score_gpt":0.25376819391579636,"score_spread":0.19124372127831785,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4321460198","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07767003,0.00075017026,0.9148748,0.0005737267,0.0001059267,0.000118820906,0.0004549724,0.00017617474,0.0052752537],"genre_scores_gemma":[0.87825567,0.000564939,0.11326669,0.000114005226,0.00008923413,0.00018033561,0.0005623489,0.00014432202,0.006822509],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993432,0.00019750831,0.00002975518,0.00019138034,0.00014552285,0.00009252854],"domain_scores_gemma":[0.9989849,0.00060144416,0.0001915874,0.000044566037,0.000113651404,0.000063840576],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015667112,0.0015439491,0.0018683695,0.00092638703,0.00052245386,0.0019053773,0.0015193353,0.0023634543,0.0026093954],"category_scores_gemma":[0.00325091,0.0010518313,0.0014514308,0.0011507433,0.0007360353,0.0016011114,0.0011622697,0.0010514741,0.00024776664],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003276975,0.000013434988,0.000114014365,0.000046283414,0.000024821591,0.000052716463,0.000009605217,0.99447966,0.0005053344,0.0016338184,0.00022042124,0.002867074],"study_design_scores_gemma":[0.0000062096683,0.000024857134,0.00009049411,0.000004119237,0.000008552172,0.000011248032,0.000008694682,0.99846554,0.00014171186,0.0010894588,0.00014444144,0.000004759674],"about_ca_topic_score_codex":0.009169085,"about_ca_topic_score_gemma":0.004886006,"teacher_disagreement_score":0.009169085,"about_ca_system_score_codex":0.0013642072,"about_ca_system_score_gemma":0.0012925054,"threshold_uncertainty_score":0.018231392},"labels":[],"label_agreement":null},{"id":"W4323342234","doi":"10.5267/j.ijiec.2022.12.003","title":"Airline operational crew-aircraft planning considering revenue management: A robust optimization model under disruption","year":2023,"lang":"en","type":"article","venue":"International Journal of Industrial Engineering Computations","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Crew; Crew scheduling; Schedule; Operations research; Cockpit; Scheduling (production processes); Revenue; Operational planning; Flight planning; Computer science; Operations management; Aeronautics; Engineering; Business","score_opus":0.06599283233639251,"score_gpt":0.30826771150392507,"score_spread":0.24227487916753254,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4323342234","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10486972,0.0014147522,0.8667273,0.0013469702,0.00014249497,0.00026853243,0.0009915279,0.0004471972,0.023791667],"genre_scores_gemma":[0.9655411,0.00060261885,0.024942165,0.00011481489,0.00005597899,0.0002653282,0.0004006954,0.00005881185,0.008018529],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99899167,0.000332017,0.000035008386,0.00027050384,0.00015166932,0.00021903834],"domain_scores_gemma":[0.9992009,0.00032997702,0.00022437698,0.00003398082,0.00011776718,0.00009298221],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001125861,0.0017208428,0.0015178149,0.0006788892,0.00047775003,0.0020453162,0.00187466,0.0023392644,0.0031470712],"category_scores_gemma":[0.002003194,0.0008391956,0.0014959716,0.0010164982,0.0009865105,0.0014395453,0.0014727175,0.001801029,0.00038101463],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000025184978,0.000015228305,0.0001406963,0.000021007125,0.000015878028,0.000066874825,0.000011353329,0.99546623,0.00020067184,0.0028428875,0.00017586553,0.0010181797],"study_design_scores_gemma":[0.0000073208844,0.000021263631,0.00009119895,0.000003061827,0.000007641414,0.000007107585,0.000007768154,0.99874026,0.00005721553,0.0009135111,0.00013942018,0.0000042844745],"about_ca_topic_score_codex":0.019580983,"about_ca_topic_score_gemma":0.006312692,"teacher_disagreement_score":0.019580983,"about_ca_system_score_codex":0.0018715684,"about_ca_system_score_gemma":0.0018715763,"threshold_uncertainty_score":0.038934052},"labels":[],"label_agreement":null},{"id":"W4323342349","doi":"10.5267/j.ijiec.2023.2.001","title":"General variable neighborhood search for electric vehicle routing problem with time-dependent speeds and soft time windows","year":2023,"lang":"en","type":"article","venue":"International Journal of Industrial Engineering Computations","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Science Fund of the Republic of Serbia; Ministarstvo Prosvete, Nauke i Tehnološkog Razvoja","keywords":"Vehicle routing problem; Benchmark (surveying); Metaheuristic; Mathematical optimization; Computer science; Electric vehicle; Variable (mathematics); Variable neighborhood search; Set (abstract data type); Routing (electronic design automation); Quality (philosophy); Integer (computer science); Local search (optimization); Operations research; Mathematics; Power (physics)","score_opus":0.020586449861546043,"score_gpt":0.26197481995658256,"score_spread":0.24138837009503653,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4323342349","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.108865246,0.0009995091,0.88291603,0.00039181078,0.000062025996,0.00011191629,0.00013035654,0.00011717188,0.0064058816],"genre_scores_gemma":[0.82229185,0.0006831662,0.17152902,0.000104195344,0.000042664,0.0002639746,0.0002158177,0.000049690778,0.0048195818],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996131,0.00018784436,0.000013485652,0.00007721743,0.00005907122,0.000049326867],"domain_scores_gemma":[0.9995454,0.00032301858,0.00005548344,0.0000135507435,0.00004071157,0.000021674687],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008096633,0.00068046415,0.0008364226,0.0005091211,0.00032602556,0.00063899014,0.00076266483,0.00079774717,0.0014093112],"category_scores_gemma":[0.001362245,0.00035655365,0.0005939487,0.000704213,0.00038811253,0.0007506064,0.00047432067,0.00064448116,0.00009345823],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003473338,0.00002646025,0.00033411,0.00003604536,0.000019195155,0.000040001138,0.000018418423,0.9862242,0.00037076403,0.0050243344,0.000371782,0.007499978],"study_design_scores_gemma":[0.000010237179,0.000021756017,0.00008804719,0.0000035125156,0.0000054366187,0.00001016893,0.0000111915,0.9973232,0.00010919678,0.0021220003,0.00029313457,0.0000021117817],"about_ca_topic_score_codex":0.00656992,"about_ca_topic_score_gemma":0.005239647,"teacher_disagreement_score":0.00656992,"about_ca_system_score_codex":0.0006234614,"about_ca_system_score_gemma":0.0008645493,"threshold_uncertainty_score":0.013063371},"labels":[],"label_agreement":null},{"id":"W4323357131","doi":"10.1287/trsc.2023.1199","title":"Electric Vehicle Fleets: Scalable Route and Recharge Scheduling Through Column Generation","year":2023,"lang":"en","type":"article","venue":"Transportation Science","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Column generation; Computer science; Scalability; Scheduling (production processes); Context (archaeology); Operations research; Heuristic; Vehicle routing problem; Mathematical optimization; Routing (electronic design automation); Mathematics","score_opus":0.03936776487769003,"score_gpt":0.2945584043615546,"score_spread":0.25519063948386456,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4323357131","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.053750023,0.000319176,0.9372464,0.00042514494,0.00007654017,0.00018676733,0.00067888066,0.0010024655,0.0063145985],"genre_scores_gemma":[0.5493032,0.0003113976,0.44215667,0.00018689925,0.00005615866,0.00024980228,0.0016133025,0.00029195435,0.005830574],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997422,0.00007921963,0.00000902523,0.000056351397,0.000051368064,0.00006174959],"domain_scores_gemma":[0.99934906,0.0003323823,0.00008135216,0.000085192776,0.000083829524,0.00006814395],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00052756415,0.000870183,0.00076816115,0.00054043304,0.00047190522,0.00090297137,0.0012120728,0.00065973843,0.0045565725],"category_scores_gemma":[0.001198359,0.00051087915,0.00066887034,0.001016548,0.0004268389,0.00093849725,0.0009300621,0.001020421,0.0006361658],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006239719,0.000047445563,0.00030195058,0.000041690942,0.000017429495,0.000052420975,0.00003263048,0.9532493,0.0012915651,0.005398047,0.0026144881,0.036890637],"study_design_scores_gemma":[0.0000098875435,0.000014675171,0.000048333597,0.0000028497207,0.0000027221104,0.000008362761,0.00001195696,0.99503714,0.00028609563,0.0039859917,0.0005891223,0.0000028297832],"about_ca_topic_score_codex":0.009145241,"about_ca_topic_score_gemma":0.012479799,"teacher_disagreement_score":0.009145241,"about_ca_system_score_codex":0.0010000793,"about_ca_system_score_gemma":0.0012916027,"threshold_uncertainty_score":0.018184006},"labels":[],"label_agreement":null},{"id":"W4360592163","doi":"10.5267/j.dsl.2022.11.004","title":"Design of a hybridization between Tabu search and PAES algorithms to solve a multi-depot, multi-product green vehicle routing problem","year":2023,"lang":"en","type":"article","venue":"Decision Science Letters","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Tabu search; Metaheuristic; Mathematical optimization; Vehicle routing problem; Pareto principle; Computer science; Genetic algorithm; Algorithm; Heuristic; Routing (electronic design automation); Mathematics","score_opus":0.08550765019336246,"score_gpt":0.34109869896779027,"score_spread":0.25559104877442784,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4360592163","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0865726,0.00042136674,0.90043235,0.0002415369,0.00010120752,0.00033873433,0.000073110146,0.000922455,0.01089669],"genre_scores_gemma":[0.49780756,0.0002809592,0.49722522,0.00022049273,0.000047232206,0.0009338609,0.00017573063,0.00010087232,0.003208074],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99933463,0.0002609438,0.000027933247,0.00012324867,0.00015697647,0.00009622739],"domain_scores_gemma":[0.9994543,0.0002671894,0.00005717039,0.000039715942,0.00014161108,0.00003989272],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010643232,0.0009434902,0.001001993,0.0010546001,0.00047972667,0.0009509954,0.0018276551,0.00141055,0.0032947343],"category_scores_gemma":[0.0019075068,0.0005611088,0.0009424207,0.0008588736,0.00046466614,0.00082288927,0.000987512,0.0008804095,0.0005306921],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012509222,0.00022727375,0.00072766666,0.000106824635,0.00008020582,0.00007208676,0.000060542356,0.92266446,0.0053910716,0.006397347,0.0007157692,0.06343159],"study_design_scores_gemma":[0.000017849094,0.00011294917,0.00007957683,0.0000070067986,0.0000126999485,0.000016192425,0.000020127849,0.9976623,0.00076399,0.00068338617,0.00062029134,0.0000035251553],"about_ca_topic_score_codex":0.0018674753,"about_ca_topic_score_gemma":0.0013091934,"teacher_disagreement_score":0.0032947343,"about_ca_system_score_codex":0.0006013388,"about_ca_system_score_gemma":0.0012063214,"threshold_uncertainty_score":0.011021972},"labels":[],"label_agreement":null},{"id":"W4361003883","doi":"10.1287/ijoc.2023.1280","title":"Network Migration Problem: A Hybrid Logic-Based Benders Decomposition Approach","year":2023,"lang":"en","type":"article","venue":"INFORMS journal on computing","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Ciena (Canada); HEC Montréal","funders":"","keywords":"Computer science; Constraint programming; Column generation; Upgrade; Purchasing; Mathematical optimization; Integer programming; Node (physics); Decomposition; Process (computing); Stochastic programming; Engineering; Mathematics; Algorithm; Operations management","score_opus":0.028400209408477067,"score_gpt":0.2788438215552102,"score_spread":0.25044361214673316,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4361003883","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0055072294,0.00022300675,0.9876972,0.00034975997,0.000047953414,0.00011717855,0.00017780616,0.0001354503,0.0057445182],"genre_scores_gemma":[0.12672363,0.00055658276,0.8634573,0.00032940073,0.000101397796,0.00043742007,0.00061434926,0.00018881774,0.0075911735],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999252,0.0002822537,0.00003289192,0.00013867511,0.0001832071,0.0001109405],"domain_scores_gemma":[0.9993617,0.0004106567,0.000058193033,0.00003421136,0.0000940445,0.000041299172],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014256142,0.0014865452,0.0010058609,0.001174692,0.00075732253,0.0017363077,0.0015640223,0.0014370423,0.007594728],"category_scores_gemma":[0.0019455502,0.0008263423,0.0018606766,0.0012375042,0.0006676236,0.0015369861,0.0012892527,0.0021216322,0.00089236256],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000040174018,0.000066480796,0.00035557552,0.000111691166,0.00003291901,0.000077976125,0.000058642247,0.9377523,0.0012312926,0.02672974,0.002497692,0.031045401],"study_design_scores_gemma":[0.000012073262,0.00002145756,0.000060466336,0.00001704959,0.0000098778355,0.000024024774,0.000028256933,0.9830132,0.0003191314,0.014684461,0.0018037484,0.000006275484],"about_ca_topic_score_codex":0.0061303736,"about_ca_topic_score_gemma":0.0076910565,"teacher_disagreement_score":0.007594728,"about_ca_system_score_codex":0.0015658867,"about_ca_system_score_gemma":0.0020344655,"threshold_uncertainty_score":0.025406897},"labels":[],"label_agreement":null},{"id":"W4361221305","doi":"10.1287/ijoc.2023.1288","title":"Decomposition Strategies for Vehicle Routing Heuristics","year":2023,"lang":"en","type":"article","venue":"INFORMS journal on computing","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":37,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Heuristics; Merge (version control); Decomposition; Vehicle routing problem; Computer science; Mathematical optimization; Routing (electronic design automation); Local search (optimization); Decomposition method (queueing theory); Heuristic; Mathematics; Algorithm; Discrete mathematics; Information retrieval","score_opus":0.026847903887107456,"score_gpt":0.3186934770919792,"score_spread":0.29184557320487176,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4361221305","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013698385,0.0021752683,0.9677652,0.00036435676,0.000105619074,0.00022730867,0.00011202965,0.00025687978,0.0152950445],"genre_scores_gemma":[0.2612577,0.0026996862,0.72733426,0.00037372689,0.00010510042,0.0006299041,0.00043736582,0.00031407952,0.0068481946],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987361,0.00062129565,0.000062890074,0.00013211116,0.00028676618,0.00016079651],"domain_scores_gemma":[0.9982072,0.0011274879,0.000180794,0.00017347006,0.00022671804,0.00008435233],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017591086,0.0013663566,0.00081037846,0.0015837952,0.0006014489,0.001579782,0.0009922505,0.0010185029,0.005849528],"category_scores_gemma":[0.006143182,0.00058928516,0.0011789231,0.001651912,0.0009540766,0.0014566047,0.0014174451,0.0017676969,0.0011718236],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010258131,0.000091698545,0.000753263,0.0004331593,0.00009569968,0.000081529375,0.00022714848,0.623009,0.003322619,0.17440456,0.008585936,0.18889274],"study_design_scores_gemma":[0.000059116555,0.00010351084,0.00025421457,0.00016117585,0.00004438677,0.0001113776,0.00011596689,0.868461,0.0012252046,0.11489753,0.014547229,0.000019279361],"about_ca_topic_score_codex":0.0021833791,"about_ca_topic_score_gemma":0.0025386962,"teacher_disagreement_score":0.005849528,"about_ca_system_score_codex":0.0017508443,"about_ca_system_score_gemma":0.001445914,"threshold_uncertainty_score":0.019568682},"labels":[],"label_agreement":null},{"id":"W4361247967","doi":"10.1016/j.cie.2023.109215","title":"Optimal distribution of perishable foods with storage temperature control and quality requirements: An integrated vehicle routing problem","year":2023,"lang":"en","type":"article","venue":"Computers & Industrial Engineering","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":33,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Truck; Warehouse; Vehicle routing problem; Product (mathematics); Quality (philosophy); Control (management); Heuristic; Routing (electronic design automation); Integer programming; Energy storage; Distribution (mathematics); Operations research; Computer science; Mathematical optimization; Automotive engineering; Engineering; Mathematics; Business; Algorithm","score_opus":0.03291222639858158,"score_gpt":0.2625058560046067,"score_spread":0.2295936296060251,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4361247967","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2700994,0.0012345813,0.71283644,0.0012577078,0.00015296291,0.00048525407,0.0013827162,0.0006733071,0.011877564],"genre_scores_gemma":[0.8414103,0.00088355836,0.14020194,0.00020048744,0.00009229888,0.00034850312,0.0011092001,0.00031219932,0.015441499],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99914896,0.00021467134,0.000029561663,0.0003030384,0.00009388784,0.00020989655],"domain_scores_gemma":[0.99865973,0.00073327,0.00022198698,0.00005896358,0.00017493455,0.00015113443],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014757344,0.0022773047,0.0032049716,0.0017894973,0.00075909164,0.0030250957,0.0030416835,0.0033078622,0.0063527194],"category_scores_gemma":[0.003129958,0.0023712846,0.0015604538,0.002796845,0.0014169423,0.0031131108,0.0012923146,0.0015238328,0.0007756375],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020744497,0.000095974414,0.00035394487,0.0000986433,0.000045615176,0.0000806801,0.000048374164,0.9845019,0.0016582,0.0032717336,0.00076327694,0.008874148],"study_design_scores_gemma":[0.00006389475,0.00014525323,0.00032086732,0.000011559615,0.00003598456,0.000024669098,0.00005955981,0.9927816,0.0008614513,0.005185962,0.0004914755,0.000017828148],"about_ca_topic_score_codex":0.014864423,"about_ca_topic_score_gemma":0.009449987,"teacher_disagreement_score":0.014864423,"about_ca_system_score_codex":0.0028619599,"about_ca_system_score_gemma":0.0021141577,"threshold_uncertainty_score":0.029555798},"labels":[],"label_agreement":null},{"id":"W4361267542","doi":"10.48550/arxiv.2303.15573","title":"On the integration of Dantzig-Wolfe and Fenchel decompositions via directional normalizations","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada; Centre National d’Etudes Spatiales","keywords":"Linear programming; Integer programming; Decomposition; Mathematical optimization; Mathematics; Integer (computer science); Branch and price; Space (punctuation); Dual (grammatical number); Geometric programming; Computer science","score_opus":0.09003054661395556,"score_gpt":0.21807598194548547,"score_spread":0.1280454353315299,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4361267542","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013485826,0.0003407438,0.97446716,0.00022797313,0.000067105386,0.000035440586,0.000041480973,0.00011845511,0.011215741],"genre_scores_gemma":[0.2571111,0.0012665358,0.73090845,0.00036858398,0.00015390648,0.0002723776,0.00021869058,0.00040088856,0.009299425],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99868006,0.0006045637,0.00004184811,0.00016351635,0.00036748717,0.00014247117],"domain_scores_gemma":[0.9982609,0.00072838407,0.00015941227,0.00037887914,0.00030696733,0.00016529794],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004539239,0.0012474628,0.0011178614,0.0018768079,0.0007731539,0.0019375371,0.0009447757,0.00074538763,0.0043663625],"category_scores_gemma":[0.008518565,0.00057771715,0.0012438388,0.00151661,0.0025672426,0.0038905535,0.0033647139,0.0033209692,0.000946125],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000061435436,0.000052504078,0.00044149812,0.000059363654,0.000025528045,0.0000624026,0.0001443608,0.09375935,0.0022541713,0.84704137,0.0016596104,0.05443845],"study_design_scores_gemma":[0.000018912355,0.000054688506,0.00022770389,0.000056949753,0.000018839915,0.00007330438,0.000075083975,0.44504634,0.0020040064,0.54387784,0.0085263485,0.000020110278],"about_ca_topic_score_codex":0.0014880344,"about_ca_topic_score_gemma":0.0020222247,"teacher_disagreement_score":0.004539239,"about_ca_system_score_codex":0.0011593407,"about_ca_system_score_gemma":0.0012820069,"threshold_uncertainty_score":0.024006069},"labels":[],"label_agreement":null},{"id":"W4362665816","doi":"10.1002/net.22147","title":"The probabilistic uncapacitated open vehicle routing location problem","year":2023,"lang":"en","type":"article","venue":"Networks","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"The Scarborough Hospital; University of Toronto","funders":"China Scholarship Council; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Heuristics; Computer science; Service (business); Probabilistic logic; Simple (philosophy); Vehicle routing problem; Mathematical optimization; Routing (electronic design automation); Tree (set theory); Computer network; Mathematics; Artificial intelligence; Combinatorics","score_opus":0.02512845140514344,"score_gpt":0.27067178824854166,"score_spread":0.2455433368433982,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4362665816","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.030682404,0.0008157097,0.9581544,0.001233681,0.00013883943,0.000100812664,0.00061152334,0.00032498315,0.007937668],"genre_scores_gemma":[0.78355193,0.0021278264,0.19580637,0.00031254345,0.00031893922,0.00038398433,0.0013722763,0.0001790618,0.01594706],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985399,0.00055066694,0.000057013378,0.00033455776,0.00027972044,0.00023815845],"domain_scores_gemma":[0.9975684,0.0016669623,0.00031153724,0.00015481848,0.00017737015,0.00012075228],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012064363,0.0011268562,0.0013431732,0.0006479788,0.00075764337,0.0017886796,0.0028009242,0.0017402902,0.0038130626],"category_scores_gemma":[0.0062476476,0.0010647872,0.0008544278,0.0017921815,0.0012693403,0.0032260027,0.0017691052,0.0014545213,0.0005419308],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008098527,0.000038602084,0.0003303898,0.00010625079,0.000037733116,0.00013608098,0.00005719356,0.93170303,0.00034933843,0.047533773,0.0026006866,0.017025966],"study_design_scores_gemma":[0.000020049722,0.00002789331,0.00012691843,0.000012049692,0.000014444758,0.00009565927,0.000032941945,0.9466319,0.00019342048,0.0505058,0.0023246077,0.000014231802],"about_ca_topic_score_codex":0.004368726,"about_ca_topic_score_gemma":0.004098851,"teacher_disagreement_score":0.004368726,"about_ca_system_score_codex":0.0013850697,"about_ca_system_score_gemma":0.0015640031,"threshold_uncertainty_score":0.01275593},"labels":[],"label_agreement":null},{"id":"W4366813960","doi":"10.1016/j.ejor.2023.04.016","title":"Solving a real-world multi-depot multi-period petrol replenishment problem with complex loading constraints","year":2023,"lang":"en","type":"article","venue":"European Journal of Operational Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Hydro-Québec; Polytechnique Montréal; Group for Research in Decision Analysis","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Column generation; Heuristics; Bottleneck; Computer science; Mathematical optimization; Truck; Context (archaeology); Operations research; Shortest path problem; Simulated annealing; Mathematics; Theoretical computer science; Graph; Engineering","score_opus":0.1523891100965274,"score_gpt":0.3881574141298987,"score_spread":0.2357683040333713,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4366813960","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.79314286,0.0012139302,0.19267064,0.0013409121,0.00018367368,0.00023844223,0.001228639,0.00028412972,0.009696764],"genre_scores_gemma":[0.95354074,0.0002453447,0.04267789,0.00007167692,0.00003594774,0.00008838847,0.0003287571,0.00003989395,0.0029714843],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995461,0.00019859793,0.00002091066,0.00008920022,0.00004863924,0.00009654414],"domain_scores_gemma":[0.9974692,0.002081715,0.0001454264,0.00006820778,0.000115245995,0.00012031898],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013703177,0.0011913553,0.0015898334,0.0007283558,0.00073754275,0.001593334,0.0013577951,0.0034040583,0.0038974965],"category_scores_gemma":[0.0027136663,0.0010270077,0.0012147814,0.0011110589,0.0006915666,0.0010393834,0.00083809247,0.0013501613,0.00016256285],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000073867755,0.000054737913,0.00044124064,0.00008219485,0.000029138175,0.0001859731,0.000018641336,0.99532473,0.00023857065,0.0006976792,0.00029486246,0.0025582449],"study_design_scores_gemma":[0.000020624087,0.00003141845,0.00020141712,0.000003839509,0.00000967053,0.000020375743,0.000030314404,0.99895084,0.00012471306,0.00048054164,0.00012113059,0.000005098519],"about_ca_topic_score_codex":0.026022445,"about_ca_topic_score_gemma":0.019618656,"teacher_disagreement_score":0.026022445,"about_ca_system_score_codex":0.0010577454,"about_ca_system_score_gemma":0.0017834888,"threshold_uncertainty_score":0.051741958},"labels":[],"label_agreement":null},{"id":"W4367396193","doi":"10.1016/j.dib.2023.109189","title":"Real operational data for the concrete delivery problem","year":2023,"lang":"en","type":"article","venue":"Data in Brief","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Université de Sherbrooke","funders":"Mitacs","keywords":"Benchmarking; Raw data; Computer science; Data mining; Production (economics); Operations research; Database; Engineering","score_opus":0.0870573047467152,"score_gpt":0.330298184589255,"score_spread":0.24324087984253978,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4367396193","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1750289,0.0012695672,0.009189167,0.0022710483,0.00039820257,0.0004866034,0.7952926,0.0024229642,0.01364097],"genre_scores_gemma":[0.17165603,0.00042614818,0.013490716,0.00030607794,0.000063525535,0.00037515027,0.81057245,0.00016496753,0.0029450292],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9984092,0.0002703763,0.000114058865,0.00037378533,0.0005518433,0.00028069384],"domain_scores_gemma":[0.9969848,0.00073197595,0.00018906925,0.0005636819,0.0012174832,0.0003129306],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010746308,0.001294132,0.000570813,0.0014980108,0.00091986393,0.0010140782,0.0020323803,0.0015240787,0.0058016134],"category_scores_gemma":[0.0044100215,0.00028122103,0.0007008828,0.00506796,0.00081107987,0.0007477717,0.00066276727,0.0017221532,0.0020974153],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00067602965,0.0013461966,0.022394847,0.0012878753,0.00017723304,0.00078964676,0.0002531457,0.17530373,0.0025762296,0.00883779,0.73758966,0.04876756],"study_design_scores_gemma":[0.00069223635,0.00044740405,0.10273606,0.00030962104,0.000101286365,0.0008533842,0.0021826248,0.31221437,0.0071364073,0.01021959,0.5628787,0.00022822688],"about_ca_topic_score_codex":0.27791703,"about_ca_topic_score_gemma":0.32537192,"teacher_disagreement_score":0.27791703,"about_ca_system_score_codex":0.005566569,"about_ca_system_score_gemma":0.004008254,"threshold_uncertainty_score":0.5525987},"labels":[],"label_agreement":null},{"id":"W4376456745","doi":"10.1109/tits.2023.3271430","title":"Logistics in the Sky: A Two-Phase Optimization Approach for the Drone Package Pickup and Delivery System","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Transportation Systems","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":54,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"China Scholarship Council; Natural Science Foundation for Distinguished Young Scholars of Hunan Province; National Natural Science Foundation of China","keywords":"Drone; Notation; Pickup; Simulated annealing; Computer science; Mathematics; Algorithm; Artificial intelligence; Arithmetic","score_opus":0.05128302992592292,"score_gpt":0.2945357512435078,"score_spread":0.24325272131758485,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4376456745","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020691657,0.00059523614,0.9630944,0.00054479105,0.000096611795,0.00014936611,0.0002162404,0.00024502145,0.014366734],"genre_scores_gemma":[0.67477274,0.0010015727,0.3002679,0.00039677427,0.00012992597,0.0005717043,0.00045933793,0.0002345176,0.022165518],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99964535,0.0001380937,0.000011196898,0.000058572736,0.00007783566,0.00006894489],"domain_scores_gemma":[0.999762,0.00010631685,0.000032817385,0.000012976883,0.0000588891,0.00002702565],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00079371245,0.0011409064,0.0012448041,0.0005776563,0.0006200664,0.0017089067,0.0013786783,0.0014460945,0.0057802685],"category_scores_gemma":[0.00088321796,0.00075461454,0.0011171625,0.0007563892,0.00050656445,0.0011079451,0.0013838323,0.0010646502,0.00057865377],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000043332773,0.000024759216,0.00020463458,0.000057586505,0.000027484342,0.00007355234,0.0000328128,0.9827976,0.00042913688,0.0074238735,0.000918785,0.007966507],"study_design_scores_gemma":[0.000007847374,0.000015732458,0.00004599468,0.000003286488,0.00000537141,0.000005791717,0.000014839668,0.99763334,0.00006868852,0.0015275109,0.000668385,0.0000032836617],"about_ca_topic_score_codex":0.013169583,"about_ca_topic_score_gemma":0.010233271,"teacher_disagreement_score":0.013169583,"about_ca_system_score_codex":0.0011226786,"about_ca_system_score_gemma":0.0019771145,"threshold_uncertainty_score":0.02618587},"labels":[],"label_agreement":null},{"id":"W4376564152","doi":"10.1111/tgis.13057","title":"Trailer allocation and truck routing using bipartite graph assignment and deep reinforcement learning","year":2023,"lang":"en","type":"article","venue":"Transactions in GIS","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Northern Digital (Canada); National Research Council Canada; University of Calgary","funders":"National Research Council Canada; Natural Sciences and Engineering Research Council of Canada","keywords":"Truck; Bipartite graph; Trailer; Computer science; Routing (electronic design automation); Heuristics; Vehicle routing problem; Graph; Mathematical optimization; Transport engineering; Operations research; Engineering; Computer network; Mathematics; Automotive engineering; Theoretical computer science","score_opus":0.027206449989939884,"score_gpt":0.2731889428976228,"score_spread":0.24598249290768293,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4376564152","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13943166,0.00053891406,0.8513713,0.0009471048,0.000096071766,0.00010366419,0.00016452244,0.0023115072,0.0050352],"genre_scores_gemma":[0.8979689,0.00014786235,0.09699289,0.00034784075,0.000047113215,0.00009904181,0.0003726587,0.0001275103,0.0038963002],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999572,0.00013930933,0.000017837103,0.00012858842,0.000058966354,0.00008324666],"domain_scores_gemma":[0.9985782,0.0008255737,0.00015150681,0.00012715724,0.00020167173,0.00011587431],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010204639,0.0012274162,0.00096937374,0.0007171572,0.00045272004,0.0008320747,0.0016850894,0.0015620905,0.0026091994],"category_scores_gemma":[0.0029525948,0.000674888,0.00076049013,0.0006758097,0.000884713,0.0014944104,0.001049713,0.0020874974,0.00040288808],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005192619,0.000097338954,0.00057855056,0.00002577492,0.000019251209,0.00002644432,0.00002101496,0.9624027,0.0006478866,0.0021967397,0.00090384955,0.033028524],"study_design_scores_gemma":[0.0000024563635,0.000006073317,0.00002404695,9.369315e-7,0.0000011458917,0.0000011745333,0.0000013669935,0.99890447,0.00007806638,0.00094014884,0.000039254468,8.841441e-7],"about_ca_topic_score_codex":0.02149905,"about_ca_topic_score_gemma":0.018556675,"teacher_disagreement_score":0.02149905,"about_ca_system_score_codex":0.0019592722,"about_ca_system_score_gemma":0.0017323147,"threshold_uncertainty_score":0.042747796},"labels":[],"label_agreement":null},{"id":"W4377030783","doi":"10.1016/j.cor.2023.106282","title":"The synchronized multi-commodity multi-service Transshipment-Hub Location Problem with cyclic schedules","year":2023,"lang":"en","type":"article","venue":"Computers & Operations Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Université de Montréal; Università degli studi di Bergamo; Université du Québec à Montréal","keywords":"Transshipment (information security); Computer science; Context (archaeology); Operations research; Service (business); Port (circuit theory); Flow network; Supply chain; Heuristic; Service provider; Truck; Flexibility (engineering); Mathematical optimization; Business; Mathematics; Computer security","score_opus":0.10521001024260995,"score_gpt":0.37416070598910345,"score_spread":0.2689506957464935,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4377030783","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16202566,0.0005619956,0.82204425,0.0007535329,0.00016573566,0.00031464754,0.0012576123,0.00032224559,0.012554338],"genre_scores_gemma":[0.88342434,0.0006571882,0.10101005,0.00013249266,0.00010612267,0.00032187902,0.0010416604,0.00018321302,0.013123023],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990932,0.00038765426,0.00004082485,0.0002462497,0.00009195828,0.00014014958],"domain_scores_gemma":[0.9983626,0.0009330986,0.00026809372,0.000119817196,0.00012974368,0.0001866003],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015945475,0.001296202,0.0019469663,0.00079845253,0.00072950055,0.0015666493,0.0023591365,0.0018397814,0.0072901053],"category_scores_gemma":[0.0037569623,0.0011477984,0.0010717446,0.0025652545,0.001015746,0.0025435924,0.001706957,0.00097652944,0.0005824865],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003187868,0.000062405445,0.00039301335,0.00015327711,0.00006517403,0.00023741924,0.000054578108,0.96230644,0.0010202243,0.023331255,0.0021700936,0.0098873],"study_design_scores_gemma":[0.000063040265,0.00010979015,0.00022058177,0.00001082429,0.00002823118,0.00005129949,0.000059677925,0.9809996,0.0005045555,0.016936496,0.0010018569,0.000014073699],"about_ca_topic_score_codex":0.007522797,"about_ca_topic_score_gemma":0.005965029,"teacher_disagreement_score":0.007522797,"about_ca_system_score_codex":0.0015423446,"about_ca_system_score_gemma":0.0019923304,"threshold_uncertainty_score":0.024387777},"labels":[],"label_agreement":null},{"id":"W4377157592","doi":"10.1155/2023/8390619","title":"Improved Ant Colony Optimization for the Operational Aircraft Maintenance Routing Problem with Cruise Speed Control","year":2023,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Macau University of Science and Technology","keywords":"Cruise; Ant colony optimization algorithms; Vehicle routing problem; Computer science; Routing (electronic design automation); Cruise control; Operations research; Preprocessor; Control (management); Mathematical optimization; Engineering; Aerospace engineering; Artificial intelligence; Mathematics","score_opus":0.00921352009571012,"score_gpt":0.24818142244670333,"score_spread":0.23896790235099322,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4377157592","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02965796,0.00034906308,0.96479946,0.00026328684,0.00005968859,0.000100123485,0.000070461065,0.0002434882,0.0044564432],"genre_scores_gemma":[0.58512866,0.00036485173,0.40886143,0.00014329139,0.00006358068,0.0003737259,0.00021981455,0.000108835615,0.0047358144],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996123,0.00015849687,0.000016879976,0.00008331468,0.00008573169,0.000043288383],"domain_scores_gemma":[0.9988432,0.000799351,0.00011435617,0.000047589438,0.00015270665,0.00004290737],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007206337,0.0010001813,0.0009275506,0.0004832197,0.0003526887,0.0007221489,0.0009900758,0.0010166827,0.0018498923],"category_scores_gemma":[0.002669472,0.00041741304,0.00054167514,0.00057527394,0.0005206684,0.0007575174,0.0006577809,0.0013713855,0.00029135644],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000023226476,0.000019196752,0.00011480151,0.000024993007,0.000008358223,0.000024669765,0.000013344116,0.9892435,0.00053596724,0.0020721927,0.00030757775,0.007612089],"study_design_scores_gemma":[0.000006693796,0.000010258463,0.000025339623,0.000001119178,0.0000018414928,0.0000033771153,0.0000016791738,0.9990441,0.00007416628,0.00070171966,0.00012856392,0.0000010787671],"about_ca_topic_score_codex":0.008694008,"about_ca_topic_score_gemma":0.006767706,"teacher_disagreement_score":0.008694008,"about_ca_system_score_codex":0.00064319686,"about_ca_system_score_gemma":0.0011710994,"threshold_uncertainty_score":0.017286777},"labels":[],"label_agreement":null},{"id":"W4377249550","doi":"10.1007/978-3-031-33271-5_13","title":"Neural Networks for Local Search and Crossover in Vehicle Routing: A Possible Overkill?","year":2023,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Crossover; Computer science; Vehicle routing problem; Heuristic; Local search (optimization); Greedy algorithm; Task (project management); Artificial intelligence; Machine learning; Graph; Mathematical optimization; Routing (electronic design automation); Theoretical computer science; Algorithm; Mathematics","score_opus":0.025084770520473625,"score_gpt":0.2802680108674099,"score_spread":0.25518324034693624,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4377249550","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020199927,0.028228845,0.8765271,0.01215507,0.0016002187,0.00004919886,0.00014318948,0.0015127374,0.059583776],"genre_scores_gemma":[0.47655857,0.01685749,0.36098674,0.0028373536,0.0015961294,0.00019315719,0.0002113531,0.00090367015,0.13985562],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99951434,0.00021478505,0.000022730987,0.00008779994,0.00011869162,0.000041644733],"domain_scores_gemma":[0.99865377,0.00092159095,0.00004859693,0.00017856028,0.00017064471,0.000026851802],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016751313,0.0005033603,0.00072495115,0.00036016453,0.0002463688,0.001639592,0.0018450245,0.002070146,0.010513945],"category_scores_gemma":[0.0066556362,0.00040424385,0.00031684776,0.0007914086,0.0010767417,0.004285218,0.0008344778,0.0023592028,0.0017580816],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021327731,0.000070632595,0.0004184781,0.00034014482,0.00009688318,0.00009405028,0.00017550474,0.17635956,0.0023342057,0.28588635,0.024893476,0.5091174],"study_design_scores_gemma":[0.000034215052,0.000075952055,0.00022480813,0.00016694408,0.000036244113,0.000116096475,0.000054270393,0.78741735,0.0024278145,0.18037064,0.029038737,0.000036896596],"about_ca_topic_score_codex":0.0025661367,"about_ca_topic_score_gemma":0.0040464955,"teacher_disagreement_score":0.010513945,"about_ca_system_score_codex":0.0009248366,"about_ca_system_score_gemma":0.0003932656,"threshold_uncertainty_score":0.03517258},"labels":[],"label_agreement":null},{"id":"W4379644131","doi":"10.1016/j.cor.2023.106246","title":"Heterogeneous instant delivery orders scheduling and routing problem","year":2023,"lang":"en","type":"article","venue":"Computers & Operations Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"National Natural Science Foundation of China","keywords":"Instant; Computer science; Mathematical optimization; Column generation; Scheduling (production processes); Job shop scheduling; Integer programming; Vehicle routing problem; Heuristic; Routing (electronic design automation); Algorithm; Mathematics; Computer network","score_opus":0.0665305166915655,"score_gpt":0.3491616430742612,"score_spread":0.28263112638269566,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4379644131","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17049995,0.00094635686,0.80510795,0.0013011234,0.0003640717,0.00020034001,0.0009141368,0.00029067934,0.02037543],"genre_scores_gemma":[0.9147976,0.00071556837,0.0641691,0.0001878982,0.00036109274,0.00013492188,0.00063479424,0.00011397178,0.018884981],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988807,0.0003460096,0.000036322734,0.0002909594,0.00021820051,0.0002278678],"domain_scores_gemma":[0.998516,0.00089481584,0.00017146172,0.00011409391,0.00010980113,0.00019397037],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016916848,0.00090960704,0.0016953448,0.0008533168,0.0007866589,0.0022433056,0.0020409303,0.0014354392,0.0063058017],"category_scores_gemma":[0.0032553973,0.0008373561,0.00091827015,0.0018020056,0.00074119325,0.002311811,0.0011349461,0.0014225933,0.0003802434],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00045293698,0.0001992977,0.0006245641,0.00013044845,0.00007933914,0.00034375166,0.00006617385,0.9219737,0.0022788672,0.05223585,0.0032336165,0.018381396],"study_design_scores_gemma":[0.00005280996,0.00008399888,0.0004822539,0.000006797738,0.000035142297,0.000060211325,0.000036852492,0.9803052,0.00070778554,0.017162716,0.0010541301,0.000012009132],"about_ca_topic_score_codex":0.004678313,"about_ca_topic_score_gemma":0.0030856952,"teacher_disagreement_score":0.0063058017,"about_ca_system_score_codex":0.0020709038,"about_ca_system_score_gemma":0.0013560789,"threshold_uncertainty_score":0.021095037},"labels":[],"label_agreement":null},{"id":"W4380086371","doi":"10.1051/ro/2023083","title":"A robust optimization approach for the production-routing problem with lateral transshipment and outsourcing","year":2023,"lang":"en","type":"article","venue":"RAIRO. Operations research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Simulated annealing; Mathematical optimization; Transshipment (information security); Computer science; Outsourcing; Solver; Integer programming; Routing (electronic design automation); Production (economics); Vehicle routing problem; Linear programming; Operations research; Algorithm; Mathematics; Economics; Business; Microeconomics","score_opus":0.11166082914835454,"score_gpt":0.33306997018856654,"score_spread":0.221409141040212,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4380086371","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0051706634,0.00026957286,0.99074674,0.00017974268,0.00003485411,0.000058079026,0.00006543178,0.00011557941,0.003359334],"genre_scores_gemma":[0.47230083,0.0010118154,0.51775444,0.00019048345,0.00014471481,0.0005388329,0.00033086163,0.00023152039,0.0074965158],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991086,0.00034374613,0.000041236613,0.00020407932,0.00019698346,0.00010529948],"domain_scores_gemma":[0.99929297,0.00039077803,0.000126059,0.000042819407,0.00009755337,0.00004983147],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017416949,0.0015945194,0.0013908467,0.0010436529,0.0005341053,0.0015801145,0.0016177015,0.0015513314,0.004034514],"category_scores_gemma":[0.0024486622,0.000829773,0.0018746341,0.0011344932,0.000750122,0.0012379816,0.0014181662,0.0016408914,0.0004412988],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012730183,0.000015706879,0.000066771834,0.00004280254,0.000020084617,0.000032394946,0.000012494606,0.9870151,0.00042364234,0.006292502,0.00031442143,0.0057512666],"study_design_scores_gemma":[0.0000038384565,0.000019098745,0.000027222559,0.000004677466,0.0000068198883,0.000011406697,0.000007224558,0.99684995,0.00014863616,0.00248021,0.00043708936,0.0000037781613],"about_ca_topic_score_codex":0.0075459983,"about_ca_topic_score_gemma":0.005109689,"teacher_disagreement_score":0.0075459983,"about_ca_system_score_codex":0.0015834747,"about_ca_system_score_gemma":0.002289785,"threshold_uncertainty_score":0.015004158},"labels":[],"label_agreement":null},{"id":"W4380741948","doi":"10.1016/j.ejor.2023.05.038","title":"Fifty years of operational research: 1972–2022","year":2023,"lang":"en","type":"article","venue":"European Journal of Operational Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Group for Research in Decision Analysis; HEC Montréal","funders":"HEC Montréal; Canada Research Chairs","keywords":"Medal; Operations research; Ceremony; Vehicle routing problem; Gold medal; Routing (electronic design automation); Work (physics); Presentation (obstetrics); Engineering management; Computer science; Seriation (archaeology); Operations management; Engineering; History; Computer network","score_opus":0.21609100974847759,"score_gpt":0.43141629121043973,"score_spread":0.21532528146196214,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4380741948","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01557644,0.6144159,0.012068281,0.061730485,0.101211354,0.00013804277,0.009204362,0.00040310246,0.18525197],"genre_scores_gemma":[0.14129725,0.44878575,0.0078962855,0.010530429,0.032865956,0.00022064339,0.010276848,0.0004784879,0.34764844],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9981793,0.0003544145,0.00016366912,0.00023704187,0.00086332206,0.00020238194],"domain_scores_gemma":[0.99557024,0.00085565256,0.00033616181,0.00040788445,0.0023660464,0.0004640783],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00365366,0.0011443719,0.0008020228,0.0048259487,0.00089295796,0.0038082877,0.0006381454,0.0012972307,0.0411052],"category_scores_gemma":[0.0072208103,0.00044419445,0.00063736463,0.00788645,0.0015243734,0.0027829926,0.0021271533,0.0016022332,0.014781255],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00040030817,0.00010566291,0.0030389477,0.0009829506,0.00013349314,0.00011741483,0.00016446877,0.0028839787,0.0008881483,0.02774525,0.50718814,0.4563513],"study_design_scores_gemma":[0.00000966858,0.000039770148,0.0034965407,0.0005819273,0.000028374207,0.00004971529,0.00012560032,0.000306011,0.00029218354,0.0025957767,0.99246395,0.000010440698],"about_ca_topic_score_codex":0.008914749,"about_ca_topic_score_gemma":0.010616418,"teacher_disagreement_score":0.0411052,"about_ca_system_score_codex":0.0043834727,"about_ca_system_score_gemma":0.003409647,"threshold_uncertainty_score":0.13751066},"labels":[],"label_agreement":null},{"id":"W4380882365","doi":"10.1287/trsc.2022.0112","title":"Hub Network Design Problem with Capacity, Congestion, and Stochastic Demand Considerations","year":2023,"lang":"en","type":"article","venue":"Transportation Science","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Column generation; Mathematical optimization; Computer science; Routing (electronic design automation); Exploit; Network planning and design; Operations research; Engineering; Mathematics; Computer network","score_opus":0.043050717474767894,"score_gpt":0.2641178654701955,"score_spread":0.2210671479954276,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4380882365","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.055007912,0.00059185206,0.9226395,0.0012572755,0.0001611518,0.0003923171,0.0017049883,0.0003403495,0.01790456],"genre_scores_gemma":[0.7425953,0.0008483328,0.23882794,0.0003091327,0.0001521357,0.0005275675,0.0015628292,0.0002551986,0.014921355],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984036,0.00066417333,0.000049186343,0.00037031737,0.00021527396,0.000297448],"domain_scores_gemma":[0.9981135,0.0011277008,0.00017763299,0.00011505834,0.0002443903,0.00022165895],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019477385,0.0015500496,0.001593834,0.0009266315,0.0008962214,0.0019437021,0.0017187619,0.001974259,0.009239362],"category_scores_gemma":[0.0030968192,0.0008548187,0.0011216518,0.0015714855,0.00090264186,0.0019405215,0.0012888083,0.0018583075,0.0005360219],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001254121,0.00008678796,0.0008238787,0.0002036121,0.00005922823,0.00027498737,0.000061907434,0.92221236,0.0012712675,0.049644616,0.0055152737,0.019720653],"study_design_scores_gemma":[0.000038058184,0.000056898294,0.00028885034,0.000019407064,0.00002721211,0.000093219576,0.00007157952,0.9623816,0.0006904417,0.033154503,0.003160076,0.000018124409],"about_ca_topic_score_codex":0.007467263,"about_ca_topic_score_gemma":0.008569376,"teacher_disagreement_score":0.009239362,"about_ca_system_score_codex":0.002254729,"about_ca_system_score_gemma":0.002792768,"threshold_uncertainty_score":0.030908763},"labels":[],"label_agreement":null},{"id":"W4381740798","doi":"10.5267/j.ijiec.2023.3.002","title":"A new matheheuristic approach based on Chu-Beasley genetic approach for the multi-depot electric vehicle routing problem","year":2023,"lang":"en","type":"article","venue":"International Journal of Industrial Engineering Computations","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Genetic algorithm; Routing (electronic design automation); Vehicle routing problem; Electric vehicle; Mathematical optimization; Sensitivity (control systems); Range (aeronautics); Electric power; Computer science; Power (physics); Operations research; Engineering; Reliability engineering; Computer network; Mathematics; Electronic engineering","score_opus":0.04788489507255627,"score_gpt":0.28168451647574455,"score_spread":0.23379962140318827,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4381740798","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005534315,0.00013876951,0.98964465,0.00014842373,0.00003644026,0.00004898441,0.000030379744,0.000054360386,0.0043636914],"genre_scores_gemma":[0.3077502,0.0005841133,0.6845706,0.00025570297,0.000072619616,0.00038558812,0.00016454772,0.00009800568,0.006118627],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99960667,0.000141759,0.000014503057,0.000048661488,0.00015253201,0.00003587009],"domain_scores_gemma":[0.9996524,0.00018959392,0.000032056407,0.000019682253,0.000085149826,0.000021018413],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00075136684,0.0008038902,0.0005993519,0.0010751504,0.00045506016,0.0010097062,0.001301429,0.0010765571,0.0021826748],"category_scores_gemma":[0.0012700509,0.00034341196,0.0007667526,0.0008524156,0.000644514,0.0007330585,0.00066216773,0.0009955026,0.00031402396],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008901365,0.000027773973,0.00020293758,0.000045101086,0.000017623865,0.000038156268,0.00002564152,0.95753586,0.0010460698,0.026649961,0.00039438653,0.014007522],"study_design_scores_gemma":[0.0000031823417,0.000012987043,0.00002871618,0.000004874551,0.0000028947932,0.000011163558,0.000006723192,0.99497825,0.000134695,0.004192993,0.0006206009,0.0000027758626],"about_ca_topic_score_codex":0.0063830507,"about_ca_topic_score_gemma":0.0067586605,"teacher_disagreement_score":0.0063830507,"about_ca_system_score_codex":0.0011008941,"about_ca_system_score_gemma":0.0015697761,"threshold_uncertainty_score":0.012691796},"labels":[],"label_agreement":null},{"id":"W4382866766","doi":"10.1609/socs.v16i1.27296","title":"Comparing Front-to-Front and Front-to-End Heuristics in Bidirectional Search","year":2023,"lang":"en","type":"article","venue":"Proceedings of the International Symposium on Combinatorial Search","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institute for Advanced Research","keywords":"Heuristics; Heuristic; Computer science; Front and back ends; Front (military); Task (project management); Mathematical optimization; Mathematics; Artificial intelligence; Engineering; Systems engineering","score_opus":0.02638776788821962,"score_gpt":0.28570968329370366,"score_spread":0.25932191540548405,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4382866766","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1324311,0.0037535604,0.83436656,0.00063159346,0.000265156,0.00042644315,0.00027287754,0.0010835277,0.026769198],"genre_scores_gemma":[0.5787412,0.0011827421,0.41469875,0.00037111397,0.000060450817,0.00035907768,0.00048610184,0.00037129558,0.0037291683],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9971027,0.0016157359,0.00012257215,0.00026518086,0.00054720166,0.00034659076],"domain_scores_gemma":[0.98483396,0.012302565,0.00047222344,0.0009991761,0.0010163608,0.00037579774],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0054691606,0.0014665778,0.001875049,0.0021676433,0.0010076128,0.001956529,0.0017330502,0.0022189165,0.0050582243],"category_scores_gemma":[0.020600451,0.000658677,0.0010945976,0.0018353803,0.0010529362,0.002722439,0.0019782656,0.0016392564,0.00088673754],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00072193035,0.0003282899,0.0019053521,0.0003465216,0.00013511439,0.000050926978,0.00015376678,0.83283186,0.00072860287,0.0314461,0.0016679907,0.12968357],"study_design_scores_gemma":[0.00007706627,0.00024449578,0.00030015188,0.00006430505,0.000041515294,0.00003012647,0.00012518863,0.97893745,0.0006437144,0.01815079,0.0013660133,0.00001926245],"about_ca_topic_score_codex":0.008433413,"about_ca_topic_score_gemma":0.008987548,"teacher_disagreement_score":0.008433413,"about_ca_system_score_codex":0.0018052506,"about_ca_system_score_gemma":0.0030234393,"threshold_uncertainty_score":0.028924048},"labels":[],"label_agreement":null},{"id":"W4382866847","doi":"10.1609/icaps.v33i1.27201","title":"Solving Domain-Independent Dynamic Programming Problems with Anytime Heuristic Search","year":2023,"lang":"en","type":"article","venue":"Proceedings of the International Conference on Automated Planning and Scheduling","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Solver; Travelling salesman problem; Mathematical optimization; Heuristic; Domain (mathematical analysis); Computer science; Constraint programming; Dynamic programming; Constraint satisfaction problem; Mathematics; Stochastic programming; Artificial intelligence","score_opus":0.029136682222907113,"score_gpt":0.28917392073227355,"score_spread":0.26003723850936644,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4382866847","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014729698,0.00037321492,0.9770608,0.0002053335,0.000051569343,0.00012855227,0.00009847745,0.00086319784,0.0064892247],"genre_scores_gemma":[0.19568548,0.00041360065,0.8009113,0.00021827922,0.000041740444,0.00041107333,0.0003065316,0.00024147233,0.0017704562],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988134,0.00049588515,0.000069639274,0.00020919506,0.00026669484,0.00014518072],"domain_scores_gemma":[0.9975304,0.0015501884,0.0002462475,0.000383011,0.00019592236,0.00009419295],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016930753,0.0014295003,0.0010749154,0.00072417886,0.00047615715,0.0016564984,0.0020388975,0.001343936,0.0043782922],"category_scores_gemma":[0.0047125733,0.00067101594,0.0012110318,0.001250765,0.00095375656,0.0017522307,0.0015998983,0.0018678735,0.0006449193],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000118288306,0.00019079635,0.00056820473,0.00023849792,0.000101544,0.0000648316,0.000066576016,0.8768296,0.0015352936,0.028583627,0.002703385,0.08899946],"study_design_scores_gemma":[0.000058244168,0.000046652287,0.000054212956,0.000016794871,0.000015569318,0.000019356346,0.00002353663,0.9881982,0.00083632657,0.008728747,0.0019955572,0.000006764252],"about_ca_topic_score_codex":0.0040898346,"about_ca_topic_score_gemma":0.005839742,"teacher_disagreement_score":0.0043782922,"about_ca_system_score_codex":0.0010030967,"about_ca_system_score_gemma":0.0025375844,"threshold_uncertainty_score":0.014646828},"labels":[],"label_agreement":null},{"id":"W4382930395","doi":"10.1016/j.orl.2023.06.006","title":"Unconstrained traveling tournament problem is APX-complete","year":2023,"lang":"en","type":"article","venue":"Operations Research Letters","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Tournament; Hamiltonian path; Combinatorics; Complete graph; Mathematics; Travelling salesman problem; APX; Graph; Mathematical optimization; Enhanced Data Rates for GSM Evolution; Discrete mathematics; Computer science; Artificial intelligence; Physics","score_opus":0.09799900504041466,"score_gpt":0.3645096889024662,"score_spread":0.26651068386205157,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4382930395","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21468644,0.0013507414,0.5764596,0.0076558506,0.00087787106,0.0005583085,0.0052832398,0.00083263364,0.19229539],"genre_scores_gemma":[0.7979107,0.0011460785,0.09496639,0.001046605,0.0005898106,0.0006337878,0.0052096387,0.00033435586,0.0981627],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99906725,0.00028707774,0.000042378062,0.00021900646,0.00019464354,0.00018970681],"domain_scores_gemma":[0.99794644,0.0012928352,0.00017828171,0.00014077485,0.0002294693,0.00021211797],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011065677,0.0009945328,0.0016002747,0.00060832576,0.0009847024,0.0037199531,0.0013681444,0.0018158668,0.021035166],"category_scores_gemma":[0.004943867,0.00045654562,0.0008611907,0.001338465,0.0009033362,0.0025735425,0.0013823623,0.0027558082,0.0016574243],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008601894,0.0005958628,0.0024052176,0.0009475638,0.00027537806,0.00074001105,0.00033085732,0.2697637,0.0033088587,0.45048577,0.10156287,0.16872369],"study_design_scores_gemma":[0.00025476716,0.0003463865,0.0015845479,0.00006381558,0.00003656584,0.00049077976,0.00020879353,0.44692698,0.0009850832,0.5253665,0.023703182,0.000032513963],"about_ca_topic_score_codex":0.0022657854,"about_ca_topic_score_gemma":0.0014536773,"teacher_disagreement_score":0.021035166,"about_ca_system_score_codex":0.000842436,"about_ca_system_score_gemma":0.001334739,"threshold_uncertainty_score":0.07036966},"labels":[],"label_agreement":null},{"id":"W4385226489","doi":"10.1016/j.engappai.2023.106802","title":"An efficient adaptive large neighborhood search algorithm based on heuristics and reformulations for the generalized quadratic assignment problem","year":2023,"lang":"en","type":"article","venue":"Engineering Applications of Artificial Intelligence","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":62,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal","funders":"Universiti Teknologi Malaysia; King Saud University","keywords":"Computer science; Mathematical optimization; Tabu search; Metaheuristic; Quadratic assignment problem; Heuristics; Optimization problem; Algorithm; Mathematics","score_opus":0.034130435469824166,"score_gpt":0.3130402453982657,"score_spread":0.2789098099284415,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385226489","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012563967,0.0002262706,0.98459494,0.00010885341,0.000069700465,0.000062395084,0.000028227358,0.00019878741,0.0021469288],"genre_scores_gemma":[0.26270717,0.00022852942,0.73230517,0.00015246573,0.000077566656,0.00033070197,0.00022110745,0.0001350192,0.003842218],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996437,0.00015630576,0.000011470935,0.000055675588,0.00009819704,0.00003467886],"domain_scores_gemma":[0.9995147,0.00026395955,0.000043707885,0.00004158038,0.000108553395,0.000027431108],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00065155816,0.00056972593,0.001079176,0.0005700546,0.00044441575,0.0005010042,0.0012594182,0.0009092349,0.0020154754],"category_scores_gemma":[0.0023822247,0.0004004866,0.000439014,0.00077922276,0.00051224104,0.00093918695,0.00090636924,0.00088028365,0.00035583615],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001476841,0.000113966,0.00026112894,0.00007359321,0.000033577053,0.000045828263,0.000059888287,0.83242357,0.0023970283,0.023860143,0.0043914476,0.13619217],"study_design_scores_gemma":[0.000018990786,0.00003237736,0.000036122514,0.0000023724529,0.0000030509243,0.000008694906,0.000004727275,0.99708384,0.00013499304,0.00218102,0.0004904552,0.0000033658184],"about_ca_topic_score_codex":0.006220169,"about_ca_topic_score_gemma":0.0071521522,"teacher_disagreement_score":0.006220169,"about_ca_system_score_codex":0.0005592041,"about_ca_system_score_gemma":0.001096625,"threshold_uncertainty_score":0.012367904},"labels":[],"label_agreement":null},{"id":"W4385318392","doi":"10.3389/fams.2023.1128181","title":"A machine learning framework for neighbor generation in metaheuristic search","year":2023,"lang":"en","type":"article","venue":"Frontiers in Applied Mathematics and Statistics","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"Polytechnique Montréal","keywords":"Metaheuristic; Tabu search; Computer science; Mathematical optimization; Guided Local Search; Artificial intelligence; Heuristic; Local search (optimization); Machine learning; Hyper-heuristic; Mathematics","score_opus":0.030151435582191192,"score_gpt":0.28724854583237924,"score_spread":0.25709711025018805,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385318392","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00078401377,0.00016332825,0.997908,0.00007896419,0.000021613978,0.000039731938,0.00001279237,0.00007847293,0.00091308326],"genre_scores_gemma":[0.108770624,0.00045669163,0.88773674,0.00024908208,0.00014236162,0.00071752165,0.00011310078,0.0001246894,0.0016891026],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99851876,0.0007799484,0.00007778317,0.00018826914,0.00035810057,0.0000773005],"domain_scores_gemma":[0.9979711,0.0013143508,0.00020038483,0.00019689942,0.00026941564,0.000047863577],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028443849,0.0011102469,0.001606768,0.0014533651,0.0007811892,0.0013332963,0.0030270535,0.0017698448,0.0024713546],"category_scores_gemma":[0.0058332016,0.00055563136,0.0014065304,0.00176051,0.0015812184,0.0013478012,0.0014973255,0.0023800533,0.0005866172],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000020598342,0.00005469968,0.00032330374,0.00012820226,0.00006832109,0.00004461755,0.00007318942,0.81468034,0.0007393995,0.12466816,0.0010437312,0.05815534],"study_design_scores_gemma":[0.0000098902965,0.00002077456,0.00003835685,0.000015765014,0.000007225035,0.000017592005,0.0000069987645,0.9664964,0.00021901992,0.03147866,0.0016824331,0.000006852899],"about_ca_topic_score_codex":0.0025315266,"about_ca_topic_score_gemma":0.002504845,"teacher_disagreement_score":0.0030270535,"about_ca_system_score_codex":0.0014560131,"about_ca_system_score_gemma":0.0012770017,"threshold_uncertainty_score":0.015042663},"labels":[],"label_agreement":null},{"id":"W4385438103","doi":"10.1016/j.tre.2023.103226","title":"A branch-and-regret algorithm for the same-day delivery problem","year":2023,"lang":"en","type":"article","venue":"Transportation Research Part E Logistics and Transportation Review","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Regret; Computer science; Vehicle routing problem; Routing (electronic design automation); Variety (cybernetics); Mathematical optimization; Operations research; Scheme (mathematics); Look-ahead; Algorithm; Mathematics; Computer network; Artificial intelligence","score_opus":0.10634184700762042,"score_gpt":0.3734209020231644,"score_spread":0.26707905501554396,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385438103","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0063116965,0.0009099826,0.98578334,0.0006026494,0.00023441516,0.000117758646,0.0001505369,0.00053189107,0.005357814],"genre_scores_gemma":[0.13710655,0.0011375546,0.8482821,0.000500796,0.00040692766,0.00045015538,0.0006759418,0.00044246056,0.010997551],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984463,0.0006317608,0.000060784707,0.0002776441,0.00031226163,0.00027120966],"domain_scores_gemma":[0.99724615,0.0019795783,0.00010041956,0.0001466988,0.00033265952,0.0001945472],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0030276133,0.0018010967,0.0036531913,0.001120292,0.0012824312,0.001996347,0.0042510107,0.0039634523,0.00860918],"category_scores_gemma":[0.0045380397,0.001191091,0.001986889,0.002404607,0.0012657443,0.0028171637,0.0023417918,0.0045388476,0.0017773194],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002728152,0.00028478942,0.00040954183,0.00019261017,0.000104811545,0.00006764458,0.00007889575,0.8057347,0.0005989601,0.02146094,0.01634019,0.15445416],"study_design_scores_gemma":[0.000057663183,0.00005419342,0.000076055534,0.000012688853,0.000017055985,0.000022277914,0.000014353597,0.9882246,0.00012876577,0.0103250155,0.0010594596,0.000007783988],"about_ca_topic_score_codex":0.010349751,"about_ca_topic_score_gemma":0.008443347,"teacher_disagreement_score":0.010349751,"about_ca_system_score_codex":0.0021517158,"about_ca_system_score_gemma":0.0040203794,"threshold_uncertainty_score":0.028800547},"labels":[],"label_agreement":null},{"id":"W4385622316","doi":"10.1155/2023/1200526","title":"An Adaptive Large Neighborhood Search Heuristic for the Electric Vehicle Routing Problems with Time Windows and Recharging Strategies","year":2023,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"","funders":"Humanities and Social Science Fund of Ministry of Education of China; Natural Science Foundation of Fujian Province","keywords":"Vehicle routing problem; Benchmark (surveying); Solver; Heuristic; Computer science; Mathematical optimization; Integer programming; Routing (electronic design automation); Set (abstract data type); Linear programming; Series (stratigraphy); Operations research; Algorithm; Mathematics; Computer network","score_opus":0.015029798025990642,"score_gpt":0.2708631476375044,"score_spread":0.25583334961151377,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385622316","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.060534358,0.0011384332,0.92765576,0.00036753927,0.0000958118,0.0002684291,0.0001244517,0.0002434235,0.009571829],"genre_scores_gemma":[0.67420757,0.0006922641,0.3189113,0.00014943816,0.0000612671,0.00046226723,0.00026537548,0.00008390647,0.005166654],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999503,0.0002480441,0.000018576287,0.00008430587,0.00008391555,0.00006218489],"domain_scores_gemma":[0.99940264,0.00040438917,0.000080635924,0.000020261228,0.00005221462,0.000039836672],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008798799,0.00079718407,0.0009280192,0.0006182115,0.0003794303,0.0005941375,0.0013713313,0.0008303148,0.0016885656],"category_scores_gemma":[0.0017341833,0.0004094002,0.00067272584,0.00067224837,0.0003787379,0.0010585518,0.0006265273,0.0006557132,0.00018172868],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000042075324,0.000049159527,0.0002777019,0.000042101416,0.00002160235,0.00004844536,0.000022477505,0.9791181,0.00030749154,0.0055322805,0.0006434438,0.013895066],"study_design_scores_gemma":[0.000010736859,0.000028494169,0.00004943662,0.000004734995,0.0000056594863,0.000010119615,0.000012014692,0.9979563,0.00008228801,0.0013977881,0.0004395641,0.0000028870122],"about_ca_topic_score_codex":0.007949978,"about_ca_topic_score_gemma":0.010545119,"teacher_disagreement_score":0.007949978,"about_ca_system_score_codex":0.00095832825,"about_ca_system_score_gemma":0.0012642979,"threshold_uncertainty_score":0.01580739},"labels":[],"label_agreement":null},{"id":"W4385763708","doi":"10.24963/ijcai.2023/739","title":"Machine Learning for Cutting Planes in Integer Programming: A Survey","year":2023,"lang":"en","type":"article","venue":"","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Integer programming; Computer science; Linear programming; Linear programming relaxation; Set (abstract data type); Node (physics); Heuristic; Task (project management); Selection (genetic algorithm); Mathematical optimization; Tree (set theory); Machine learning; Process (computing); Integer (computer science); Decision tree; Branch and bound; Branch and price; Artificial intelligence; Algorithm; Mathematics; Programming language; Engineering","score_opus":0.045671819724117234,"score_gpt":0.3069942294338229,"score_spread":0.26132240970970566,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385763708","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0039054034,0.1540213,0.8250581,0.00304531,0.00047471654,0.00017627676,0.00046396808,0.0007854463,0.012069507],"genre_scores_gemma":[0.076285385,0.2615136,0.6497504,0.0017988159,0.0029759982,0.00068064523,0.0025079902,0.0006493065,0.0038378763],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99590504,0.0014015014,0.00037588854,0.0006778232,0.0014772745,0.00016250716],"domain_scores_gemma":[0.9838074,0.013061251,0.00064486713,0.0008702186,0.001436108,0.00018007564],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006168168,0.0026699938,0.0025938435,0.0033482555,0.0006986554,0.0038644553,0.0034643093,0.002038244,0.005617852],"category_scores_gemma":[0.017572455,0.0012493576,0.0020259907,0.0086055705,0.0013947071,0.0048583373,0.002015937,0.0046819425,0.003233514],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000095431096,0.00032960673,0.002522325,0.0039143204,0.00015898987,0.00007409004,0.00014532753,0.11565735,0.000529135,0.06774556,0.016555687,0.7922722],"study_design_scores_gemma":[0.000058990718,0.0002721088,0.0014315823,0.0027893465,0.000100246725,0.0003375518,0.00023821827,0.6353394,0.001853798,0.2109751,0.14650238,0.00010136477],"about_ca_topic_score_codex":0.0022307765,"about_ca_topic_score_gemma":0.0021471218,"teacher_disagreement_score":0.006168168,"about_ca_system_score_codex":0.0013254383,"about_ca_system_score_gemma":0.0020637969,"threshold_uncertainty_score":0.032620788},"labels":[],"label_agreement":null},{"id":"W4385802534","doi":"10.1007/s10009-023-00714-1","title":"Publisher Correction: Algorithm selection for SMT","year":2023,"lang":"en","type":"article","venue":"International Journal on Software Tools for Technology Transfer","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Theory of computation; Computer science; Selection (genetic algorithm); Algorithm; Artificial intelligence","score_opus":0.024416113692210247,"score_gpt":0.29348725613433285,"score_spread":0.2690711424421226,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385802534","genre_codex":"editorial","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0007591739,0.0013803345,0.03320641,0.025461026,0.91150326,0.00009187239,0.0043403944,0.0063375058,0.016920026],"genre_scores_gemma":[0.05834415,0.004461487,0.12619877,0.02825408,0.2340557,0.00064328697,0.01524243,0.02449556,0.50830454],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9929842,0.0016138516,0.00097009615,0.0010890844,0.002947772,0.0003951447],"domain_scores_gemma":[0.9325325,0.012648082,0.0016703837,0.006933466,0.04504501,0.0011706095],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004689871,0.0025138885,0.0025846544,0.005576381,0.0034552896,0.005082667,0.003926994,0.0043419963,0.1920172],"category_scores_gemma":[0.09478725,0.0011784997,0.002421728,0.005390828,0.0016811731,0.0035103322,0.0023148982,0.00723762,0.07946075],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000052042156,0.000009859346,0.00009959495,0.00017471908,0.00003032418,0.00011596798,0.00001843527,0.0005451475,0.00013209145,0.00328648,0.9750744,0.02046086],"study_design_scores_gemma":[0.00013269525,0.00007570913,0.0012935667,0.0004318257,0.00011621766,0.00073839125,0.00007416595,0.010353406,0.0021402922,0.01428259,0.970265,0.00009627232],"about_ca_topic_score_codex":0.010105072,"about_ca_topic_score_gemma":0.012185457,"teacher_disagreement_score":0.1920172,"about_ca_system_score_codex":0.0032917603,"about_ca_system_score_gemma":0.0050375396,"threshold_uncertainty_score":0.6423615},"labels":[],"label_agreement":null},{"id":"W4385840258","doi":"10.3390/math11163521","title":"Optimization Models for the Vehicle Routing Problem under Disruptions","year":2023,"lang":"en","type":"article","venue":"Mathematics","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"Social Sciences and Humanities Research Council of Canada; Natural Sciences and Engineering Research Council of Canada","keywords":"Vehicle routing problem; Routing (electronic design automation); Computer science; Mathematical optimization; Operations research; Heuristic; Generalization; Plan (archaeology); Property (philosophy); Mathematics; Artificial intelligence; Computer network","score_opus":0.056174691931972394,"score_gpt":0.2983257588552173,"score_spread":0.2421510669232449,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385840258","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014989351,0.003119702,0.9662144,0.0020429343,0.00022047242,0.00011342314,0.0004603301,0.00018078099,0.012658518],"genre_scores_gemma":[0.7373466,0.01089021,0.21754016,0.00083667756,0.0007918577,0.0009075556,0.0012713654,0.0003439622,0.030071571],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99817336,0.00086751784,0.00007419647,0.00037189198,0.00026522647,0.00024771882],"domain_scores_gemma":[0.99701166,0.0021159062,0.00045074226,0.00008305562,0.00022598913,0.00011269835],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024522138,0.0024555868,0.0015061966,0.0013376959,0.0007163484,0.0025346628,0.002331901,0.0029815899,0.0047726077],"category_scores_gemma":[0.0076658023,0.0008953307,0.001560456,0.0020057084,0.0014487478,0.0030793091,0.0015906888,0.003073429,0.0007541954],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002895156,0.000030436839,0.00023130972,0.000105402454,0.000032717395,0.00007167944,0.00005444806,0.9222935,0.0002084687,0.07099342,0.0016623674,0.004287238],"study_design_scores_gemma":[0.000013063423,0.000021675134,0.000103565464,0.000016535147,0.0000129716045,0.00003090828,0.00003838832,0.96179956,0.00006271698,0.035906784,0.001985092,0.000008711905],"about_ca_topic_score_codex":0.0075387666,"about_ca_topic_score_gemma":0.004901429,"teacher_disagreement_score":0.0075387666,"about_ca_system_score_codex":0.0034223462,"about_ca_system_score_gemma":0.0015550746,"threshold_uncertainty_score":0.024830997},"labels":[],"label_agreement":null},{"id":"W4385840825","doi":"10.1016/j.cor.2023.106385","title":"Enhanced iterated local search for the technician routing and scheduling problem","year":2023,"lang":"en","type":"article","venue":"Computers & Operations Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"European Regional Development Fund; Région Hauts-de-France","keywords":"Iterated local search; Computer science; Scheduling (production processes); Operations research; Job shop scheduling; Mathematical optimization; Local optimum; Local search (optimization); Routing (electronic design automation); Engineering; Mathematics; Artificial intelligence; Computer network","score_opus":0.07122862884273742,"score_gpt":0.3804832934992185,"score_spread":0.3092546646564811,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385840825","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.044729378,0.0003142135,0.94745624,0.00023669137,0.000044586293,0.000047810667,0.000045883404,0.00019833421,0.00692687],"genre_scores_gemma":[0.7561206,0.00024429898,0.23351802,0.00014334203,0.0000693916,0.00027616924,0.0001369246,0.00017035734,0.009320873],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99938464,0.00034251466,0.000016692506,0.000054778724,0.00013758912,0.000063789914],"domain_scores_gemma":[0.9981623,0.0013468328,0.00014859585,0.00007883352,0.00019219832,0.00007125492],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017147844,0.00055627467,0.0011597483,0.00063027284,0.0003341129,0.0006891247,0.0014131509,0.0013768951,0.0030395915],"category_scores_gemma":[0.004236724,0.00044755152,0.0006878295,0.00060493045,0.00073822273,0.00095422013,0.0011158978,0.0010309033,0.0003182534],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006534787,0.000057748868,0.00020064335,0.000042587075,0.000029132736,0.000039271155,0.000029958546,0.9760224,0.00062895904,0.010568439,0.00065975846,0.011655768],"study_design_scores_gemma":[0.000011932286,0.00001569135,0.000027031852,0.0000020339733,0.000003100265,0.000004328975,0.0000028395607,0.9979736,0.00006023847,0.0018009363,0.00009644678,0.0000018427943],"about_ca_topic_score_codex":0.00481527,"about_ca_topic_score_gemma":0.0039774273,"teacher_disagreement_score":0.00481527,"about_ca_system_score_codex":0.00095789385,"about_ca_system_score_gemma":0.0011356523,"threshold_uncertainty_score":0.010168493},"labels":[],"label_agreement":null},{"id":"W4385877360","doi":"10.1287/trsc.2022.0261","title":"A Three-Front Parallel Branch-and-Cut Algorithm for Production and Inventory Routing Problems","year":2023,"lang":"en","type":"article","venue":"Transportation Science","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Université Laval; Transport Canada","funders":"","keywords":"Branch and cut; Benchmark (surveying); Mathematical optimization; Computer science; Branch and bound; Routing (electronic design automation); Production (economics); Heuristic; Time horizon; Algorithm; Vendor-managed inventory; Integer programming; Supply chain; Mathematics; Supply chain management","score_opus":0.03466089813080044,"score_gpt":0.2828480735673259,"score_spread":0.24818717543652544,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385877360","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010857877,0.00030963233,0.98012435,0.00031165633,0.00006503543,0.0002266918,0.00018314658,0.0008659928,0.0070557],"genre_scores_gemma":[0.06853582,0.00021262695,0.92664593,0.0001562821,0.000051659383,0.00039273823,0.00052685133,0.00018951677,0.0032884842],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991658,0.00018417981,0.00003799971,0.00016643226,0.00031497868,0.00013066993],"domain_scores_gemma":[0.9991009,0.00046675318,0.000083735846,0.00008195977,0.00019132881,0.00007542241],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012588535,0.0017643657,0.0015901315,0.0014581153,0.001138431,0.001578997,0.0022043912,0.002202348,0.006175032],"category_scores_gemma":[0.0027216943,0.00094763614,0.001380911,0.0022398492,0.0005985549,0.0018390346,0.0014487237,0.0023669659,0.0013183213],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015367607,0.00021672397,0.0004124033,0.0001223273,0.00004765132,0.000087887835,0.000049664017,0.7725138,0.0017146493,0.009344431,0.006164184,0.20917253],"study_design_scores_gemma":[0.0000479162,0.00003488183,0.000054966175,0.000006644387,0.000007684592,0.00002635993,0.000008810848,0.99375546,0.0004452757,0.004414555,0.0011912617,0.0000061300693],"about_ca_topic_score_codex":0.009073329,"about_ca_topic_score_gemma":0.0077394117,"teacher_disagreement_score":0.009073329,"about_ca_system_score_codex":0.0014755833,"about_ca_system_score_gemma":0.0029476578,"threshold_uncertainty_score":0.020657599},"labels":[],"label_agreement":null},{"id":"W4385945676","doi":"10.2139/ssrn.4544139","title":"Multi-Objective Particle Swarm Optimization Algorithm Based on Multiple Adaptive Methods for Fire Trucks Dispatching in Mixed Uncertain Forest Fire Environments","year":2023,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Truck; Particle swarm optimization; Computer science; Swarm behaviour; Mathematical optimization; Algorithm; Environmental science; Engineering; Automotive engineering; Mathematics; Artificial intelligence","score_opus":0.029614203628022667,"score_gpt":0.31016849617341763,"score_spread":0.280554292545395,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385945676","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.030487906,0.0003034973,0.9667822,0.00015589943,0.0000990634,0.000045580528,0.000020539192,0.00008200122,0.002023326],"genre_scores_gemma":[0.7447454,0.00039054098,0.2506607,0.000078993646,0.00010011937,0.00027305319,0.00008517636,0.000053741525,0.003612212],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997769,0.0000952898,0.000012822881,0.000037275186,0.00005354329,0.000024105466],"domain_scores_gemma":[0.9993555,0.00043287646,0.00006687066,0.000021718735,0.00009856122,0.000024497216],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009561782,0.00082118274,0.0009788027,0.00046623836,0.00044923683,0.0007635367,0.00087190844,0.0011156608,0.0011689083],"category_scores_gemma":[0.0021777838,0.000523898,0.0007317153,0.000587969,0.00046278487,0.0007031066,0.00069260277,0.0011338303,0.00012607577],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000033170796,0.00001825,0.00020864082,0.000024359282,0.000025000834,0.000021468759,0.000018773198,0.9862248,0.00034982842,0.0013312693,0.00018034967,0.011564107],"study_design_scores_gemma":[0.000002839982,0.0000072057924,0.000030220399,0.0000012162884,0.0000018592493,0.0000013675593,0.0000017226348,0.9996973,0.000032394433,0.00018376586,0.000039183327,9.610052e-7],"about_ca_topic_score_codex":0.008545324,"about_ca_topic_score_gemma":0.005048079,"teacher_disagreement_score":0.008545324,"about_ca_system_score_codex":0.00045586983,"about_ca_system_score_gemma":0.00084006763,"threshold_uncertainty_score":0.016991198},"labels":[],"label_agreement":null},{"id":"W4386003478","doi":"10.1016/j.cie.2023.109552","title":"Last mile delivery routing problem using autonomous electric vehicles","year":2023,"lang":"en","type":"article","venue":"Computers & Industrial Engineering","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":38,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Vehicle routing problem; Metaheuristic; Simulated annealing; Computer science; Solver; Integer programming; Ant colony optimization algorithms; Routing (electronic design automation); Variable neighborhood search; Mathematical optimization; Operations research; Engineering; Algorithm; Mathematics; Computer network","score_opus":0.0374162422301799,"score_gpt":0.23784974006442802,"score_spread":0.20043349783424813,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386003478","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13416065,0.0008050367,0.84559935,0.0014562492,0.00026548814,0.00016933585,0.0008221463,0.00040040587,0.016321275],"genre_scores_gemma":[0.86594826,0.00055374444,0.10314955,0.00017098077,0.00013486561,0.00017236237,0.00067552755,0.00018799528,0.029006867],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99964666,0.0001198062,0.000011702436,0.00009138066,0.00005875257,0.0000716624],"domain_scores_gemma":[0.9994097,0.00037010736,0.000066640954,0.000030169234,0.00006088596,0.00006260863],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008368827,0.00092779374,0.0010771608,0.0008284762,0.00065055606,0.0013380112,0.0013831399,0.0014491963,0.0040555587],"category_scores_gemma":[0.0017241957,0.0006979899,0.0007079644,0.00094685913,0.00049814326,0.0012957483,0.0008789549,0.0008698107,0.00037236264],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009709251,0.00004997746,0.0003791512,0.00006801804,0.00003204309,0.00011188983,0.00003057874,0.9708642,0.00090008526,0.010291588,0.0022680273,0.014907331],"study_design_scores_gemma":[0.000012853778,0.000037584723,0.0001316012,0.00000407269,0.000009879998,0.000025737543,0.000029041365,0.9936587,0.00030336378,0.0049428833,0.0008395163,0.0000048625184],"about_ca_topic_score_codex":0.007323728,"about_ca_topic_score_gemma":0.004549646,"teacher_disagreement_score":0.007323728,"about_ca_system_score_codex":0.0013682344,"about_ca_system_score_gemma":0.0010341106,"threshold_uncertainty_score":0.014562249},"labels":[],"label_agreement":null},{"id":"W4386326396","doi":"10.1287/ijoc.2022.0175","title":"Fast Continuous and Integer L-Shaped Heuristics Through Supervised Learning","year":2023,"lang":"en","type":"article","venue":"INFORMS journal on computing","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Polytechnique Montréal; Université de Montréal","funders":"","keywords":"Heuristics; Computer science; Operations research; Integer (computer science); Knapsack problem; Integer programming; Mathematical optimization; Container (type theory); Mathematics; Algorithm; Engineering","score_opus":0.02120533820114971,"score_gpt":0.2735105454951502,"score_spread":0.25230520729400047,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386326396","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019998647,0.00024040545,0.97587013,0.00031532787,0.000043180164,0.00009138304,0.00009473179,0.0009630453,0.0023831385],"genre_scores_gemma":[0.44451097,0.00015887362,0.55173904,0.0004178431,0.000087826156,0.0003549853,0.0004660279,0.00022089813,0.0020436482],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99840933,0.00061412406,0.00007257661,0.0003970941,0.0003210872,0.00018575181],"domain_scores_gemma":[0.9905094,0.0066166646,0.0011447545,0.0008562383,0.00061395083,0.0002589158],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002585461,0.0013184362,0.0016967996,0.0011183114,0.00057909184,0.0015835847,0.0026554756,0.0017581542,0.0037845334],"category_scores_gemma":[0.01182792,0.0009137958,0.0012163683,0.0011803692,0.0016045145,0.0023786123,0.0016357306,0.002929221,0.0008062468],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012839113,0.00013989353,0.0010251009,0.00010906083,0.000038195187,0.00004087753,0.00006218295,0.917222,0.0006165952,0.013400148,0.0018734375,0.06534414],"study_design_scores_gemma":[0.000015024207,0.00002844936,0.000043980817,0.000008934044,0.000002985577,0.0000055986625,0.000007263146,0.9931433,0.00015127817,0.0063549886,0.00023534648,0.0000028405154],"about_ca_topic_score_codex":0.0049043535,"about_ca_topic_score_gemma":0.0067956876,"teacher_disagreement_score":0.0049043535,"about_ca_system_score_codex":0.0016783391,"about_ca_system_score_gemma":0.0028683436,"threshold_uncertainty_score":0.013673365},"labels":[],"label_agreement":null},{"id":"W4386365073","doi":"10.1016/j.tre.2023.103267","title":"Scheduling trucks and drones for cooperative deliveries","year":2023,"lang":"en","type":"article","venue":"Transportation Research Part E Logistics and Transportation Review","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":48,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"National Natural Science Foundation of China","keywords":"Truck; Drone; Computer science; Scheduling (production processes); Job shop scheduling; Integer programming; Operations research; Heuristic; Simulation; Transport engineering; Engineering; Automotive engineering; Operations management; Algorithm; Schedule","score_opus":0.13029055133490883,"score_gpt":0.3975847856348976,"score_spread":0.2672942342999888,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386365073","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1046251,0.009300096,0.8649049,0.00061558967,0.00097293337,0.000439074,0.00025689605,0.0005543473,0.018331],"genre_scores_gemma":[0.82138294,0.008753235,0.14772026,0.00013292038,0.00032644585,0.00026574126,0.0004303976,0.00021525678,0.020772802],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99941003,0.00019341148,0.000022693028,0.00013963442,0.00012244044,0.000111840876],"domain_scores_gemma":[0.99938405,0.00036116494,0.000054055425,0.000050067767,0.00008291459,0.00006775041],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010646716,0.0012493726,0.0014748725,0.0004921111,0.00052693114,0.001052394,0.0020092605,0.00085301476,0.0038469557],"category_scores_gemma":[0.001563249,0.0010130401,0.0011250344,0.0011662847,0.0004996198,0.001379157,0.00074058323,0.0015111277,0.0006469446],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002260252,0.00014610983,0.00024819135,0.00024203012,0.000076828925,0.000041781994,0.000055004744,0.8891242,0.002288024,0.009765103,0.0025170306,0.095269695],"study_design_scores_gemma":[0.0000864347,0.00034882617,0.0003966178,0.00002757812,0.000044504526,0.000037928636,0.00010758377,0.97670346,0.001905201,0.010163365,0.0101563195,0.0000221781],"about_ca_topic_score_codex":0.00897991,"about_ca_topic_score_gemma":0.009168843,"teacher_disagreement_score":0.00897991,"about_ca_system_score_codex":0.0013379736,"about_ca_system_score_gemma":0.0020826457,"threshold_uncertainty_score":0.017855287},"labels":[],"label_agreement":null},{"id":"W4386773677","doi":"10.5267/j.ijiec.2023.6.001","title":"Inventory routing problem with backhaul considering returnable transport items collection","year":2023,"lang":"en","type":"article","venue":"International Journal of Industrial Engineering Computations","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Backhaul (telecommunications); Vehicle routing problem; Supply chain; Operations research; Economic order quantity; Routing (electronic design automation); Environmental economics; Business; Operations management; Computer network; Economics; Engineering","score_opus":0.03594425694504643,"score_gpt":0.26429579503772316,"score_spread":0.22835153809267672,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386773677","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09337698,0.0009721645,0.89250416,0.0007634548,0.00013686765,0.0002621892,0.0005559475,0.00027033634,0.011157992],"genre_scores_gemma":[0.84292114,0.0010854925,0.13982183,0.0001740937,0.000107211934,0.00043831015,0.000627326,0.00014682183,0.014677829],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99902594,0.00034287662,0.00004578901,0.00022338532,0.00012765189,0.00023442798],"domain_scores_gemma":[0.9991823,0.00049087015,0.00012757973,0.000038097118,0.00009435486,0.00006683026],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012651429,0.0014052204,0.002083519,0.0009055367,0.00076298794,0.0025957453,0.00175062,0.0021743712,0.0037488863],"category_scores_gemma":[0.0019745936,0.0009438633,0.0016528213,0.0016027183,0.00079006853,0.0018118859,0.0011773384,0.0012703496,0.0003544529],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000044169094,0.00002689766,0.00024523097,0.000056797595,0.000025997411,0.0001366763,0.00002187294,0.98963684,0.00040330988,0.004058723,0.00035799784,0.0049855304],"study_design_scores_gemma":[0.000014581661,0.000044253775,0.0000976158,0.000006145496,0.000015849027,0.000034796223,0.00002181385,0.99563235,0.00024891458,0.0034426278,0.00043283874,0.000008094991],"about_ca_topic_score_codex":0.01054824,"about_ca_topic_score_gemma":0.004548683,"teacher_disagreement_score":0.01054824,"about_ca_system_score_codex":0.0018206639,"about_ca_system_score_gemma":0.0020769187,"threshold_uncertainty_score":0.020973682},"labels":[],"label_agreement":null},{"id":"W4386773874","doi":"10.5267/j.ijiec.2023.9.004","title":"A modified clustering search based genetic algorithm for the proactive electric vehicle routing problem","year":2023,"lang":"en","type":"article","venue":"International Journal of Industrial Engineering Computations","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Vehicle routing problem; Cluster analysis; Genetic algorithm; Mathematical optimization; Electric vehicle; Routing (electronic design automation); Computer science; Integer programming; Plan (archaeology); Algorithm; Mathematics; Artificial intelligence; Computer network","score_opus":0.05167001046008366,"score_gpt":0.3033825025772423,"score_spread":0.2517124921171586,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386773874","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014689503,0.000558487,0.9768574,0.00031033801,0.00010252382,0.000117824056,0.000084818144,0.0003306831,0.0069484813],"genre_scores_gemma":[0.34553567,0.00073811255,0.6445668,0.0004070474,0.00008270128,0.00058593,0.00044215433,0.00014357128,0.007498059],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995388,0.00015910431,0.00001680075,0.00010058613,0.0001331583,0.00005153252],"domain_scores_gemma":[0.99962366,0.00020249528,0.000046325094,0.000018709268,0.000088640896,0.000020157497],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00063588476,0.0010376974,0.0008605861,0.0009804271,0.00043892587,0.0007570667,0.0013209865,0.0016030334,0.002309841],"category_scores_gemma":[0.0015926401,0.00041059774,0.000774189,0.0014251494,0.00046497895,0.0006212463,0.0007606655,0.0009824589,0.00044581192],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000024341005,0.000032628446,0.00022723251,0.00004507224,0.000029095194,0.000044987988,0.000027380016,0.9512947,0.00092312036,0.007046643,0.0014985959,0.03880634],"study_design_scores_gemma":[0.000010078711,0.000022160937,0.00005901265,0.0000063632356,0.0000060835505,0.000019094596,0.000007963024,0.9971982,0.00015192784,0.001679264,0.0008358499,0.0000040201226],"about_ca_topic_score_codex":0.009464576,"about_ca_topic_score_gemma":0.0073101614,"teacher_disagreement_score":0.009464576,"about_ca_system_score_codex":0.0010078615,"about_ca_system_score_gemma":0.0019462968,"threshold_uncertainty_score":0.018818915},"labels":[],"label_agreement":null},{"id":"W4386893171","doi":"10.1007/978-3-031-32338-6_13","title":"Hub Location Models Under Uncertainty","year":2023,"lang":"en","type":"book-chapter","venue":"International series in management science/operations research/International series in operations research & management science","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Robust optimization; Computer science; Mathematical optimization; Mathematics","score_opus":0.11089164277173964,"score_gpt":0.4179775263247292,"score_spread":0.30708588355298955,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386893171","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.102300465,0.0049246093,0.7874141,0.0065733916,0.0007025795,0.00010163863,0.0033051814,0.0009694575,0.093708694],"genre_scores_gemma":[0.8725138,0.002443661,0.012710487,0.0003609188,0.00037772997,0.00016067628,0.0012353791,0.0003340845,0.10986328],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9992518,0.00025580716,0.000024597439,0.00019170367,0.00012147045,0.00015461943],"domain_scores_gemma":[0.99724424,0.0017409023,0.000392986,0.00015950571,0.0003196499,0.00014263896],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015756628,0.0016263631,0.0030324028,0.0011240311,0.0008295113,0.0034039728,0.0034934033,0.0037145512,0.012118834],"category_scores_gemma":[0.0055870074,0.0016988979,0.0015646814,0.0025525265,0.0020822582,0.0048815706,0.0019122342,0.0030295353,0.0018525858],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026685106,0.0000152466355,0.00016001158,0.000023550849,0.00002137583,0.00003813388,0.000020514146,0.91603804,0.000075999655,0.07910822,0.0024127087,0.0020594937],"study_design_scores_gemma":[0.000007694836,0.00001006666,0.000092898335,0.0000065176678,0.000010661257,0.000011054936,0.000015544114,0.9502731,0.00002765022,0.048811104,0.0007228964,0.000010768346],"about_ca_topic_score_codex":0.020189606,"about_ca_topic_score_gemma":0.010181555,"teacher_disagreement_score":0.020189606,"about_ca_system_score_codex":0.0032110354,"about_ca_system_score_gemma":0.0012979357,"threshold_uncertainty_score":0.04054159},"labels":[],"label_agreement":null},{"id":"W4386968557","doi":"10.1016/j.cor.2023.106434","title":"Split demand and deliveries in an integrated three-level lot sizing and replenishment problem","year":2023,"lang":"en","type":"article","venue":"Computers & Operations Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Group for Research in Decision Analysis; HEC Montréal; Université du Québec à Montréal","funders":"","keywords":"Heuristics; Sizing; Computer science; Production (economics); Time horizon; Mathematical optimization; Heuristic; Routing (electronic design automation); Operations research; Holding cost; Mathematics; Economics; Microeconomics","score_opus":0.12825433258663296,"score_gpt":0.375426004235031,"score_spread":0.247171671648398,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386968557","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4758375,0.00051170663,0.51330227,0.00062269677,0.00007367615,0.00022381268,0.00047628256,0.0001908721,0.008761212],"genre_scores_gemma":[0.94662344,0.00018988637,0.047498517,0.00005253078,0.00003422252,0.000113747636,0.00024802465,0.00005599557,0.005183759],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991026,0.00028041718,0.000043380303,0.00016466161,0.00016851262,0.00024048316],"domain_scores_gemma":[0.99804914,0.0012924856,0.0001995014,0.00006807213,0.00015205459,0.00023877283],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020379694,0.0011077669,0.002056383,0.00077346415,0.00060773845,0.0028817013,0.00213271,0.0019650466,0.0043120235],"category_scores_gemma":[0.003632725,0.0013848935,0.0012538794,0.0015754806,0.0011717906,0.0022104618,0.0014848956,0.0013657822,0.00027845858],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002702626,0.00012066863,0.00068921037,0.00006037895,0.00004278064,0.00012309742,0.000049477978,0.98424536,0.0009885399,0.0075653926,0.00026664537,0.0055780434],"study_design_scores_gemma":[0.000026261834,0.00007611183,0.00037761833,0.0000044914227,0.000018064698,0.000021844306,0.000030491174,0.9944992,0.00032629145,0.004486786,0.00012461863,0.000008181349],"about_ca_topic_score_codex":0.007727077,"about_ca_topic_score_gemma":0.00615749,"teacher_disagreement_score":0.007727077,"about_ca_system_score_codex":0.002162079,"about_ca_system_score_gemma":0.001963395,"threshold_uncertainty_score":0.015687108},"labels":[],"label_agreement":null},{"id":"W4386989678","doi":"10.2139/ssrn.4581245","title":"The Pollution-Routing Problem with Speed Optimization and Uneven Topography","year":2023,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Routing (electronic design automation); Pollution; Computer science; Environmental science; Mathematical optimization; Computer network; Mathematics; Ecology","score_opus":0.010993315755688876,"score_gpt":0.2400125813362227,"score_spread":0.22901926558053382,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386989678","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14125313,0.0017084393,0.83370954,0.0029168457,0.00024743585,0.000105673455,0.0006906735,0.00015511614,0.01921319],"genre_scores_gemma":[0.8709239,0.0013206805,0.1034876,0.0003295452,0.00022628787,0.0001630088,0.0005927366,0.00021632218,0.02273997],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99935025,0.00026779086,0.000021076161,0.00013727874,0.00011137864,0.00011219098],"domain_scores_gemma":[0.9986688,0.0008622898,0.00018314102,0.00005529929,0.00011979224,0.000110826266],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011615084,0.0010678794,0.001386977,0.001061538,0.00068146846,0.002058344,0.0016864216,0.0026942051,0.003573372],"category_scores_gemma":[0.0059488914,0.001085724,0.0010301459,0.0018745342,0.0016232582,0.0024144999,0.0021580225,0.0013278687,0.00023032348],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003583211,0.000017833301,0.0004947291,0.00004250885,0.000027521104,0.00008336023,0.000016177835,0.9749175,0.0001790632,0.020409804,0.000761542,0.003014145],"study_design_scores_gemma":[0.000013907628,0.000013948846,0.00018021587,0.000006352977,0.000009077128,0.00002864788,0.000018133433,0.97415304,0.00008546856,0.024951844,0.0005326577,0.000006638626],"about_ca_topic_score_codex":0.013181591,"about_ca_topic_score_gemma":0.008065382,"teacher_disagreement_score":0.013181591,"about_ca_system_score_codex":0.0015956562,"about_ca_system_score_gemma":0.0015034004,"threshold_uncertainty_score":0.026209772},"labels":[],"label_agreement":null},{"id":"W4387087085","doi":"10.1016/j.ejor.2023.09.031","title":"Multi-attribute two-echelon location routing: Formulation and dynamic discretization discovery approach","year":2023,"lang":"en","type":"article","venue":"European Journal of Operational Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal; Université de Montréal; Transport Canada","funders":"","keywords":"Computer science; Scheduling (production processes); Vehicle routing problem; Transshipment (information security); Routing (electronic design automation); Discretization; Integer programming; Operations research; Synchronization (alternating current); Job shop scheduling; Mathematical optimization; Distributed computing; Computer network; Mathematics; Algorithm","score_opus":0.08653340626897581,"score_gpt":0.37327542406983916,"score_spread":0.28674201780086334,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387087085","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008876169,0.00030070063,0.98735076,0.00047356638,0.000057173445,0.00003913284,0.000108257074,0.00005419757,0.002740025],"genre_scores_gemma":[0.63231164,0.0007527728,0.358691,0.00026235415,0.0001710481,0.00023725469,0.0004066363,0.00007669861,0.00709068],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99908864,0.00033582046,0.000042642776,0.00020433892,0.00021783424,0.00011069051],"domain_scores_gemma":[0.99803966,0.0013236548,0.0001693744,0.0001186427,0.0002598906,0.000088777284],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020790256,0.0006153367,0.0017515544,0.0010091584,0.00063842244,0.0022948189,0.00308316,0.0021047466,0.0028154135],"category_scores_gemma":[0.0045728134,0.0010211787,0.0013572023,0.001863408,0.0011339854,0.0022592323,0.001943295,0.0017015468,0.0002698365],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019764533,0.0000302439,0.00033333988,0.00004400999,0.000021496224,0.000052709645,0.000029542063,0.97145957,0.0002582539,0.020246498,0.00063040445,0.006874136],"study_design_scores_gemma":[0.0000012477332,0.0000026220052,0.00001966879,0.0000020744894,0.0000019395245,0.0000051261663,0.000005054915,0.99765575,0.00003006171,0.0021593529,0.0001152603,0.0000016793383],"about_ca_topic_score_codex":0.010586429,"about_ca_topic_score_gemma":0.0075912564,"teacher_disagreement_score":0.010586429,"about_ca_system_score_codex":0.0020249584,"about_ca_system_score_gemma":0.0016962934,"threshold_uncertainty_score":0.021049619},"labels":[],"label_agreement":null},{"id":"W4387430419","doi":"10.1007/978-981-99-6441-3_1","title":"Crew Scheduling Problem: Integer Optimization Using Set-Covering Model","year":2023,"lang":"en","type":"book-chapter","venue":"Applied economics and policy studies","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Crew scheduling; Crew; Integer programming; Operations research; Scheduling (production processes); Mathematical optimization; Computer science; MATLAB; Linear programming; Job shop scheduling; Branch and price; Engineering; Schedule; Mathematics; Aeronautics; Operating system","score_opus":0.08088362128537244,"score_gpt":0.30145352918369095,"score_spread":0.2205699078983185,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387430419","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01972217,0.0021863477,0.9402712,0.00075952156,0.00020586724,0.0001054012,0.0007107973,0.00023025846,0.035808526],"genre_scores_gemma":[0.6210555,0.007425224,0.3063461,0.0004548388,0.00039655934,0.0006592486,0.0015081388,0.00053369,0.061620705],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994943,0.00019322426,0.000012942675,0.00008820353,0.0001293862,0.00008185361],"domain_scores_gemma":[0.9996425,0.00023090378,0.000036428588,0.000032033076,0.000032442553,0.000025665544],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00065356947,0.0012743974,0.0015318312,0.0007240463,0.00042271978,0.0020559055,0.0018058677,0.0018646128,0.005474044],"category_scores_gemma":[0.001627605,0.00071344717,0.0011261388,0.0025848795,0.00063215673,0.0020590238,0.0010465727,0.001418512,0.0006992182],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000033225064,0.0000355639,0.00013966575,0.000116003815,0.00003665218,0.000050540904,0.00003365466,0.9387856,0.000786934,0.033017166,0.0052657197,0.021699332],"study_design_scores_gemma":[0.000007692387,0.000019626683,0.00013510534,0.000017895316,0.000010331159,0.00003630961,0.000021826161,0.9760088,0.00027292134,0.019528836,0.0039319117,0.000008852617],"about_ca_topic_score_codex":0.008249195,"about_ca_topic_score_gemma":0.0045518875,"teacher_disagreement_score":0.008249195,"about_ca_system_score_codex":0.0015279243,"about_ca_system_score_gemma":0.0012672064,"threshold_uncertainty_score":0.018312454},"labels":[],"label_agreement":null},{"id":"W4387642517","doi":"10.1111/itor.13387","title":"A hybrid adaptive iterated local search heuristic for the maximal covering location problem","year":2023,"lang":"en","type":"article","venue":"International Transactions in Operational Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Group for Research in Decision Analysis; HEC Montréal","funders":"Conselho Nacional de Desenvolvimento Científico e Tecnológico; Fundação de Amparo à Pesquisa do Estado de São Paulo","keywords":"Metaheuristic; Iterated local search; Vehicle routing problem; Mathematical optimization; Heuristic; Iterated function; Local search (optimization); Computer science; Routing (electronic design automation); Mathematics","score_opus":0.09645886260686357,"score_gpt":0.3898434265457198,"score_spread":0.29338456393885626,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387642517","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.112150095,0.00054438587,0.8769595,0.000277761,0.00006009059,0.00017231298,0.000079683894,0.00057811465,0.009177957],"genre_scores_gemma":[0.76858765,0.00013893,0.22832309,0.00016073967,0.000037082587,0.00025501868,0.0001404851,0.0000748946,0.0022820977],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995658,0.00019250398,0.000015004805,0.000060463197,0.000096579744,0.00006959762],"domain_scores_gemma":[0.99941456,0.00034813498,0.00006798677,0.000041777646,0.00007760825,0.000050005114],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008083986,0.0005720112,0.0008082145,0.0008255993,0.00030694928,0.00055925496,0.0012846544,0.00088828936,0.0019428355],"category_scores_gemma":[0.0013335972,0.0003089737,0.0006130026,0.0006350338,0.00049196824,0.0005963387,0.0008336317,0.0004739953,0.00022011429],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000059724367,0.00007713003,0.00040542718,0.000047835892,0.00004004343,0.00006871899,0.000039172643,0.9589053,0.0015256628,0.0051626884,0.0008886604,0.032779552],"study_design_scores_gemma":[0.000016184322,0.000033338332,0.000044453885,0.0000036563265,0.000004909256,0.000012883446,0.0000068808895,0.99860674,0.00021815088,0.0007780947,0.0002719098,0.0000027955778],"about_ca_topic_score_codex":0.0030730504,"about_ca_topic_score_gemma":0.0027486964,"teacher_disagreement_score":0.0030730504,"about_ca_system_score_codex":0.00076809234,"about_ca_system_score_gemma":0.0008521882,"threshold_uncertainty_score":0.0064994693},"labels":[],"label_agreement":null},{"id":"W4388740479","doi":"10.1007/978-3-031-38310-6_8","title":"Variable Neighborhood Search","year":2023,"lang":"en","type":"book-chapter","venue":"Springer optimization and its applications","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Military College of Canada","funders":"","keywords":"Heuristics; Variable neighborhood search; Diversity (politics); Variable (mathematics); Mathematical optimization; Descent (aeronautics); Local search (optimization); Computer science; Gradient descent; Metaheuristic; Mathematics; Artificial intelligence; Geography; Sociology","score_opus":0.02533726015697855,"score_gpt":0.252314370603558,"score_spread":0.22697711044657945,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388740479","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0051681297,0.012202168,0.67035043,0.0010111588,0.0014107486,0.00008547574,0.0004214807,0.0010524876,0.30829787],"genre_scores_gemma":[0.13677134,0.008493413,0.34722963,0.00075815944,0.0006607451,0.0002768745,0.0014358232,0.0013223336,0.50305164],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9997731,0.00004719162,0.000007012522,0.00005621047,0.00009774704,0.000018767378],"domain_scores_gemma":[0.9998696,0.000047816033,0.000007641647,0.000031363725,0.000034704928,0.000008936069],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029978293,0.0007205898,0.0008512642,0.00062977266,0.000512949,0.0011540672,0.0008897866,0.00091265584,0.022994023],"category_scores_gemma":[0.0009470544,0.00037140652,0.0004775604,0.0012246298,0.0005982897,0.0011825846,0.00094333116,0.0012736098,0.006448658],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000057738223,0.00006115424,0.00019893933,0.00019388128,0.000044026914,0.000051235937,0.00007549719,0.092539616,0.0015235887,0.27512118,0.10561937,0.5245137],"study_design_scores_gemma":[0.000032197135,0.00008603591,0.00054733065,0.00015821875,0.000039592844,0.00022066526,0.00006161441,0.36767906,0.0025969893,0.2813059,0.34723824,0.0000341594],"about_ca_topic_score_codex":0.00206891,"about_ca_topic_score_gemma":0.003353646,"teacher_disagreement_score":0.022994023,"about_ca_system_score_codex":0.00061738124,"about_ca_system_score_gemma":0.0005433469,"threshold_uncertainty_score":0.076922655},"labels":[],"label_agreement":null},{"id":"W4388820886","doi":"10.23919/iccas59377.2023.10316850","title":"FS-ACO: An Algorithm for Unsafe U-Turn Detours in Service Vehicle Route Optimization Applications","year":2023,"lang":"en","type":"article","venue":"","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Intersection (aeronautics); Ant colony optimization algorithms; Vehicle routing problem; Computer science; Routing (electronic design automation); Operator (biology); Mathematical optimization; Algorithm; Service (business); Engineering; Mathematics; Computer network; Transport engineering","score_opus":0.023729412389067395,"score_gpt":0.2939906766645964,"score_spread":0.270261264275529,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388820886","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015888663,0.00031548965,0.9747121,0.00025829498,0.00013264717,0.00016860539,0.00009365438,0.0010128791,0.007417696],"genre_scores_gemma":[0.18486544,0.00033077106,0.80512106,0.00017828078,0.000044762182,0.00025732437,0.00029600516,0.00020979042,0.008696546],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997639,0.000044183027,0.000013414747,0.000048436283,0.0000928405,0.000037153644],"domain_scores_gemma":[0.9997551,0.00008232383,0.00003029735,0.000023583698,0.00009147838,0.00001720289],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034221433,0.0006736629,0.00053868064,0.0008132891,0.0007537955,0.00055368955,0.0012490953,0.00085227063,0.0026611863],"category_scores_gemma":[0.0008515238,0.0003037378,0.0005430606,0.00081764685,0.000390363,0.0006634561,0.00060720375,0.00065897434,0.0005505355],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014793554,0.00015277526,0.0011037523,0.0001654903,0.000071308496,0.00015170567,0.000093276605,0.6629959,0.006818486,0.015332895,0.008040011,0.30492648],"study_design_scores_gemma":[0.000018810557,0.00003257117,0.0001311701,0.000007438247,0.000007661639,0.00005199143,0.00001615895,0.9928169,0.0012069386,0.0018395061,0.0038637198,0.0000071486006],"about_ca_topic_score_codex":0.011043053,"about_ca_topic_score_gemma":0.013267557,"teacher_disagreement_score":0.011043053,"about_ca_system_score_codex":0.0004536495,"about_ca_system_score_gemma":0.0014515428,"threshold_uncertainty_score":0.021957517},"labels":[],"label_agreement":null},{"id":"W4388853132","doi":"10.47749/t/unicamp.2022.1254700","title":"Geometric decomposition problems","year":2022,"lang":"en","type":"dissertation","venue":"","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Conselho Nacional de Desenvolvimento Científico e Tecnológico; Natural Sciences and Engineering Research Council of Canada; Knut och Alice Wallenbergs Stiftelse; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior; Fundação de Amparo à Pesquisa do Estado de São Paulo","keywords":"Convex hull; Combinatorics; Heuristics; Mathematics; Partition (number theory); Vertex (graph theory); Integer programming; Graph partition; Regular polygon; Algorithm; Discrete mathematics; Mathematical optimization; Graph; Geometry","score_opus":0.012351702519640803,"score_gpt":0.2905012375673073,"score_spread":0.2781495350476665,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388853132","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011610459,0.0056378366,0.79767704,0.0038828112,0.0012766895,0.0011884336,0.0097834505,0.0017161871,0.16722707],"genre_scores_gemma":[0.15243457,0.009551293,0.73664606,0.0019934701,0.0013474518,0.0018061291,0.02678398,0.0012920126,0.06814507],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99710876,0.0007066501,0.0002533284,0.0008408207,0.00071197,0.00037848807],"domain_scores_gemma":[0.99801457,0.00096956827,0.00019561987,0.00037446836,0.0003124542,0.00013336707],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013799506,0.0033559261,0.0018840142,0.0019027411,0.0016450931,0.0057178284,0.0025058745,0.0024820748,0.05180268],"category_scores_gemma":[0.005256191,0.00090053806,0.0031199297,0.0034480614,0.0014535541,0.004241607,0.003923245,0.003896466,0.012057647],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012710913,0.00024041087,0.0008259566,0.0013894031,0.00013683992,0.00032813955,0.0002845268,0.09434952,0.0012296106,0.5393383,0.14896734,0.21278292],"study_design_scores_gemma":[0.00009249225,0.00008965058,0.0004088417,0.00031833202,0.000060782688,0.0007177161,0.00042472812,0.13187987,0.0009918131,0.6122707,0.25269747,0.000047552352],"about_ca_topic_score_codex":0.0025440983,"about_ca_topic_score_gemma":0.0028211733,"teacher_disagreement_score":0.05180268,"about_ca_system_score_codex":0.0025363043,"about_ca_system_score_gemma":0.0019184165,"threshold_uncertainty_score":0.17329723},"labels":[],"label_agreement":null},{"id":"W4389219889","doi":"10.1016/j.ejor.2023.11.047","title":"A multiphase dynamic programming algorithm for the shortest path problem with resource constraints","year":2023,"lang":"en","type":"article","venue":"European Journal of Operational Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Group for Research in Decision Analysis","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Mathematical optimization; Column generation; Linear subspace; Scheduling (production processes); Shortest path problem; Crew scheduling; Dynamic programming; Disjoint sets; Algorithm; Mathematics; Theoretical computer science; Graph","score_opus":0.06122002784236803,"score_gpt":0.36021484830249323,"score_spread":0.2989948204601252,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389219889","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0039892974,0.00010037901,0.9930821,0.00013605287,0.000047276837,0.00006899718,0.00004609029,0.0002045292,0.002325253],"genre_scores_gemma":[0.0957781,0.00016792223,0.89927924,0.00011028794,0.000039583465,0.00036471637,0.00016819534,0.00013753412,0.0039544175],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999548,0.0001237571,0.000020423693,0.000109332505,0.00013363647,0.00006483151],"domain_scores_gemma":[0.9993845,0.00040459246,0.00004505506,0.0000327746,0.00009212513,0.000040980325],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00092974544,0.0009861713,0.0012624069,0.0010447511,0.0007065097,0.0012267599,0.001603828,0.0015960733,0.006621716],"category_scores_gemma":[0.0020703578,0.0009949333,0.00085981726,0.0012761676,0.00052030646,0.0015904383,0.0017456083,0.0016833089,0.00087322743],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012738763,0.0001776278,0.00023436698,0.00012872867,0.000039838676,0.00005557961,0.00006622057,0.8145652,0.0026455573,0.027977701,0.003203415,0.1507784],"study_design_scores_gemma":[0.000035122277,0.000036328904,0.000031868287,0.0000080462605,0.000005864709,0.000016923357,0.0000076260276,0.99348843,0.00031121552,0.0048568733,0.0011957659,0.0000059796103],"about_ca_topic_score_codex":0.003338587,"about_ca_topic_score_gemma":0.003957822,"teacher_disagreement_score":0.006621716,"about_ca_system_score_codex":0.00087120704,"about_ca_system_score_gemma":0.0018507139,"threshold_uncertainty_score":0.022151828},"labels":[],"label_agreement":null},{"id":"W4389304834","doi":"10.1016/j.cor.2023.106502","title":"A heuristic approach for the integrated production–transportation problem with process flexibility","year":2023,"lang":"en","type":"article","venue":"Computers & Operations Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Group for Research in Decision Analysis; HEC Montréal","funders":"Fundo de Apoio ao Ensino, à Pesquisa e Extensão, Universidade Estadual de Campinas; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Fundação de Amparo à Pesquisa do Estado de São Paulo","keywords":"Flexibility (engineering); Computer science; Mathematical optimization; Sizing; Production (economics); Heuristic; Process (computing); Integer programming; Linear programming; Investment (military); Operations research; Constraint (computer-aided design); Mathematics; Algorithm; Economics; Artificial intelligence","score_opus":0.09606306208765346,"score_gpt":0.3827119897256506,"score_spread":0.28664892763799715,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389304834","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011820072,0.00026490842,0.9800167,0.00020948632,0.00006733422,0.0001249139,0.00006441636,0.00011018527,0.0073220166],"genre_scores_gemma":[0.3197178,0.000429794,0.67406046,0.00016294776,0.000110661436,0.0005115801,0.00018172436,0.000097616285,0.0047273734],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992865,0.00031885132,0.000023368933,0.0000971537,0.00016697113,0.00010724396],"domain_scores_gemma":[0.9993179,0.00045791798,0.00006175965,0.000043515545,0.00007607412,0.000042812677],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013651308,0.0011040568,0.0012253748,0.0015646174,0.0007119702,0.0014658167,0.0020239903,0.0021954014,0.0051715616],"category_scores_gemma":[0.0021536928,0.0009008599,0.0016076441,0.001917825,0.00092638884,0.001206973,0.0012267343,0.001451247,0.000404553],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000029019173,0.000051630435,0.000090738075,0.00004417963,0.000026233593,0.000058734495,0.000020094934,0.9703966,0.00051827525,0.015295246,0.00047560086,0.012993726],"study_design_scores_gemma":[0.000016453985,0.000028255434,0.00003225432,0.0000087742455,0.000010198571,0.000015345844,0.000010042047,0.9936283,0.000120820725,0.005542988,0.0005803589,0.000006115645],"about_ca_topic_score_codex":0.005949715,"about_ca_topic_score_gemma":0.005992463,"teacher_disagreement_score":0.005949715,"about_ca_system_score_codex":0.0014980321,"about_ca_system_score_gemma":0.0023363794,"threshold_uncertainty_score":0.017300606},"labels":[],"label_agreement":null},{"id":"W4389738699","doi":"10.1002/net.22200","title":"Learning to repeatedly solve routing problems","year":2023,"lang":"en","type":"article","venue":"Networks","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Group for Research in Decision Analysis","funders":"","keywords":"Heuristics; Benchmark (surveying); Mathematical optimization; Computer science; Vehicle routing problem; Heuristic; Focus (optics); Routing (electronic design automation); Optimization problem; Resolution (logic); Combinatorial optimization; Algorithm; Artificial intelligence; Mathematics","score_opus":0.01633280587669093,"score_gpt":0.2509699629027951,"score_spread":0.2346371570261042,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389738699","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.042576972,0.0011615705,0.9466876,0.001165072,0.00022824013,0.00023825812,0.00015685367,0.0011086664,0.006676722],"genre_scores_gemma":[0.45035392,0.0007413889,0.54095066,0.0007442938,0.00040493172,0.00093345525,0.0007809086,0.0003891877,0.0047011888],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99854374,0.0006364006,0.00009191893,0.0003303562,0.00021506447,0.0001825603],"domain_scores_gemma":[0.9929941,0.0057099084,0.00035126234,0.00037569203,0.00043986106,0.0001291683],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024549447,0.002143799,0.0018663538,0.00083433825,0.0006107169,0.0010075927,0.0020150898,0.0020387706,0.0046733175],"category_scores_gemma":[0.011461507,0.00088948774,0.0013031134,0.0009466332,0.0011199351,0.0015032135,0.0015679896,0.0031479895,0.00076329627],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006264238,0.00011324878,0.0009282597,0.0002024384,0.000069714944,0.00006675755,0.00006671719,0.93973,0.0004947449,0.008708715,0.0020710842,0.047485743],"study_design_scores_gemma":[0.000021519876,0.000032165375,0.000039483122,0.000012539032,0.0000076208366,0.000012604626,0.000013618119,0.99001265,0.00017079555,0.009094565,0.0005793438,0.0000030969104],"about_ca_topic_score_codex":0.0038826466,"about_ca_topic_score_gemma":0.005386961,"teacher_disagreement_score":0.0046733175,"about_ca_system_score_codex":0.001178333,"about_ca_system_score_gemma":0.0016661288,"threshold_uncertainty_score":0.015633881},"labels":[],"label_agreement":null},{"id":"W4389829004","doi":"10.5267/j.jpm.2023.8.003","title":"A constraint programming approach for multi-objective tourist trip design problem with mandatory visits: A case study for İzmir Turkey","year":2023,"lang":"en","type":"article","venue":"Journal of Project Management","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Orienteering; Operations research; Mathematical optimization; Profit (economics); Integer programming; Vehicle routing problem; Computer science; Interval (graph theory); Profit maximization; Constraint programming; Linear programming; Tourism; Programming paradigm; Routing (electronic design automation); Stochastic programming; Mathematics; Geography; Economics; Computer network","score_opus":0.0754302478022522,"score_gpt":0.33967537219242133,"score_spread":0.2642451243901691,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389829004","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6965844,0.0033268852,0.23756349,0.0015189335,0.00017027649,0.0008264045,0.001345683,0.00033125037,0.058332633],"genre_scores_gemma":[0.88982725,0.0010414682,0.102887966,0.00007865933,0.00002538572,0.00028155613,0.00042012698,0.00004747084,0.005390159],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993673,0.00028749087,0.000028674456,0.00007961846,0.000100162746,0.00013667782],"domain_scores_gemma":[0.9992662,0.00042581078,0.000081691214,0.000033619657,0.00010418983,0.00008843623],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008834802,0.0011188036,0.00078174815,0.000842436,0.0010612033,0.0014308644,0.001320476,0.0018943498,0.0032601198],"category_scores_gemma":[0.0009801736,0.00044252718,0.0013272334,0.002172696,0.0005511837,0.00078386854,0.0008398556,0.0012023207,0.00018449812],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015725684,0.00025252407,0.0020393147,0.00041964444,0.000073209725,0.0029622316,0.00016516974,0.95893794,0.0018633796,0.012027046,0.002406319,0.018695982],"study_design_scores_gemma":[0.000049581835,0.00020691357,0.0016351774,0.000043078962,0.00006029308,0.000462758,0.00048577396,0.98895335,0.0007904728,0.0023413321,0.0049376297,0.000033687316],"about_ca_topic_score_codex":0.030434256,"about_ca_topic_score_gemma":0.040107775,"teacher_disagreement_score":0.030434256,"about_ca_system_score_codex":0.0021336582,"about_ca_system_score_gemma":0.0024990435,"threshold_uncertainty_score":0.06051421},"labels":[],"label_agreement":null},{"id":"W4390103296","doi":"10.5267/j.ijiec.2023.9.011","title":"Heterogeneous multi-drone and helicopter routing problem for reconnaissance","year":2023,"lang":"en","type":"article","venue":"International Journal of Industrial Engineering Computations","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Natural Science Foundation of Shandong Province; National Natural Science Foundation of China","keywords":"Drone; Heuristics; Computer science; Orienteering; Routing (electronic design automation); Simulated annealing; Integer programming; Linear programming; Sorting; Mathematical optimization; Real-time computing; Operations research; Algorithm; Engineering; Computer network; Mathematics","score_opus":0.06545099294135076,"score_gpt":0.3112549915261021,"score_spread":0.24580399858475133,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390103296","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.093644865,0.0007716534,0.893653,0.0007099752,0.0001252511,0.0001718726,0.0003986147,0.00020893358,0.010315882],"genre_scores_gemma":[0.8395601,0.0008368393,0.14890984,0.00016632341,0.00008118102,0.0003112062,0.00075800484,0.0000698911,0.009306634],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99953234,0.00015110646,0.000020085465,0.00014764268,0.00006207095,0.000086728964],"domain_scores_gemma":[0.99970716,0.00014494968,0.000052893596,0.000019356154,0.0000383724,0.000037159545],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00050057226,0.0009482454,0.00092723966,0.0005497191,0.00071891234,0.0010627072,0.00095080014,0.0012213512,0.0032644428],"category_scores_gemma":[0.0008927755,0.00034276285,0.0008100866,0.00096531986,0.00038434356,0.0010377716,0.0007244387,0.000595093,0.00023322279],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000057573838,0.00004262551,0.0006205037,0.00009506707,0.00004069896,0.00017702358,0.00004596819,0.9744116,0.0014455279,0.0076167695,0.0014227561,0.01402388],"study_design_scores_gemma":[0.000013836307,0.000046495123,0.00027037974,0.0000066779558,0.000017982586,0.000053861066,0.00007046977,0.9934383,0.00049252936,0.004116546,0.0014651872,0.000007680505],"about_ca_topic_score_codex":0.007358229,"about_ca_topic_score_gemma":0.0065135015,"teacher_disagreement_score":0.007358229,"about_ca_system_score_codex":0.00094202295,"about_ca_system_score_gemma":0.0012060755,"threshold_uncertainty_score":0.0146307945},"labels":[],"label_agreement":null},{"id":"W4390103319","doi":"10.5267/j.ijiec.2023.9.006","title":"A hybrid heuristic approach for the multi-objective multi depot vehicle routing problem","year":2023,"lang":"en","type":"article","venue":"International Journal of Industrial Engineering Computations","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Vehicle routing problem; Standard deviation; Mathematical optimization; Heuristic; Routing (electronic design automation); Function (biology); Computer science; Variable (mathematics); Genetic algorithm; Operations research; Value (mathematics); Mathematics; Statistics","score_opus":0.05862155019185327,"score_gpt":0.30389646599607206,"score_spread":0.24527491580421878,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390103319","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.024258679,0.0006603626,0.96776754,0.00015668049,0.000072567906,0.00015713478,0.00008945828,0.00023073233,0.006606914],"genre_scores_gemma":[0.41489387,0.0006049033,0.5778272,0.00017011668,0.00007180484,0.00059131975,0.0002238625,0.00008606036,0.005530849],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99943346,0.00025609534,0.000020838352,0.000063428255,0.00015808664,0.000068021145],"domain_scores_gemma":[0.99956447,0.00027228665,0.00004606956,0.000027704693,0.00006182793,0.000027601236],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00093286036,0.00090247934,0.0008949937,0.0013326945,0.00040475032,0.0010989305,0.0014982675,0.0012025314,0.0027023673],"category_scores_gemma":[0.0011358621,0.00046090366,0.0009303919,0.001339854,0.00041494495,0.0007108922,0.0007380265,0.00076203357,0.0003877015],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000048954724,0.000067418434,0.0002550078,0.000111538175,0.00006203347,0.00009034515,0.000042052317,0.94891363,0.0012285667,0.0093670925,0.0005749397,0.039238412],"study_design_scores_gemma":[0.000015252219,0.000055822282,0.00005638036,0.000009957525,0.000010886379,0.000029214954,0.000013286168,0.9966653,0.00023167809,0.0020124982,0.0008942888,0.0000054529546],"about_ca_topic_score_codex":0.0027787308,"about_ca_topic_score_gemma":0.0039676614,"teacher_disagreement_score":0.0027787308,"about_ca_system_score_codex":0.00071168935,"about_ca_system_score_gemma":0.0011504255,"threshold_uncertainty_score":0.009040296},"labels":[],"label_agreement":null},{"id":"W4390122151","doi":"10.5267/j.ijiec.2023.10.007","title":"The optimal design of differentiated subsidy policies for new energy vehicle firms by considering the difference in market share and endurance mileage","year":2023,"lang":"en","type":"article","venue":"International Journal of Industrial Engineering Computations","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"China Postdoctoral Science Foundation; National Natural Science Foundation of China","keywords":"Transit (satellite); Vehicle routing problem; Routing (electronic design automation); Computer science; Cash; Genetic algorithm; Mathematical optimization; Transport engineering; Engineering; Business; Public transport; Computer network; Finance; Mathematics","score_opus":0.04426574693649306,"score_gpt":0.2758473466521813,"score_spread":0.23158159971568826,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390122151","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.35202867,0.00086084876,0.62359595,0.0017688553,0.00015643847,0.00042107352,0.0001933403,0.00021763275,0.020757131],"genre_scores_gemma":[0.98393226,0.00022139485,0.0139177255,0.00008233504,0.000013742361,0.00009077261,0.000026950484,0.0000127840985,0.001702076],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99937016,0.00020363246,0.000020180696,0.00013610198,0.00006160088,0.00020836247],"domain_scores_gemma":[0.99882895,0.0005572839,0.00019942911,0.000054316875,0.00016687304,0.0001931726],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012290528,0.0006249475,0.0011082281,0.00055217306,0.00055825297,0.0015053557,0.0010532124,0.0012294428,0.0031973107],"category_scores_gemma":[0.004287817,0.000548497,0.0005445207,0.0003664486,0.00083268626,0.0018563449,0.0010923949,0.0011048124,0.00016660606],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00030656217,0.00027114403,0.0029154331,0.0002326648,0.00006127642,0.00023757068,0.00013267668,0.8397538,0.004159245,0.11399197,0.0035234296,0.034414306],"study_design_scores_gemma":[0.00007339048,0.00007271248,0.0008581681,0.000023818311,0.000027727974,0.000025102127,0.00013867371,0.9726556,0.0005381776,0.024418455,0.0011551538,0.000013013562],"about_ca_topic_score_codex":0.0063239606,"about_ca_topic_score_gemma":0.004409276,"teacher_disagreement_score":0.0063239606,"about_ca_system_score_codex":0.0026209648,"about_ca_system_score_gemma":0.0034572114,"threshold_uncertainty_score":0.019016504},"labels":[],"label_agreement":null},{"id":"W4390826113","doi":"10.1080/23302674.2023.2301610","title":"Heuristic approach for optimising reliable supply chain network using drones in last-mile delivery under uncertainty","year":2024,"lang":"en","type":"article","venue":"International Journal of Systems Science Operations & Logistics","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Drone; Last mile (transportation); Supply chain; Computer science; Supply chain network; Reliability (semiconductor); Operations research; Stochastic programming; Heuristic; Supply chain management; Reliability engineering; Engineering; Mile; Business; Mathematical optimization; Marketing","score_opus":0.04302151775393111,"score_gpt":0.3164280432353582,"score_spread":0.27340652548142713,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390826113","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.082848765,0.000897895,0.908374,0.00032595752,0.00006504695,0.0001556111,0.00011220087,0.00019792347,0.007022503],"genre_scores_gemma":[0.8337215,0.00056807784,0.16218504,0.00011144812,0.000030125204,0.00023710272,0.00015017565,0.000056551595,0.0029399586],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997116,0.00012600326,0.000011881782,0.000049222792,0.000050448885,0.00005081276],"domain_scores_gemma":[0.99926525,0.0005076829,0.000085086496,0.00002418729,0.0000781553,0.000039660925],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00081218354,0.0010216852,0.0009846485,0.00087754976,0.0003943855,0.0012120388,0.0009833111,0.0011992638,0.0021923177],"category_scores_gemma":[0.0019670527,0.00071520725,0.00072066876,0.0007070657,0.00063874235,0.00090320193,0.000956135,0.0009467053,0.00019449057],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011752376,0.0000063420957,0.00010264608,0.000016848875,0.000008260616,0.00002517603,0.000013142218,0.99613994,0.00022482571,0.0013538633,0.0000791245,0.0020181846],"study_design_scores_gemma":[0.00000483766,0.000013854083,0.000024510819,0.0000048497486,0.000004042074,0.0000055137752,0.000010957893,0.99887925,0.000093003,0.0008038824,0.00015320344,0.0000020100144],"about_ca_topic_score_codex":0.0061119096,"about_ca_topic_score_gemma":0.005289785,"teacher_disagreement_score":0.0061119096,"about_ca_system_score_codex":0.0008799382,"about_ca_system_score_gemma":0.0012137416,"threshold_uncertainty_score":0.012152672},"labels":[],"label_agreement":null},{"id":"W4390862194","doi":"10.1016/j.ejor.2024.05.031","title":"A competitive heuristic algorithm for vehicle routing problems with drones","year":2024,"lang":"en","type":"article","venue":"European Journal of Operational Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"China Scholarship Council; Northwestern Polytechnical University; Northwestern University","keywords":"Vehicle routing problem; Drone; Computer science; Heuristic; Routing (electronic design automation); Mathematical optimization; Algorithm; Operations research; Artificial intelligence; Mathematics; Computer network","score_opus":0.06020974464802066,"score_gpt":0.34766038793449233,"score_spread":0.2874506432864717,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390862194","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.029194178,0.0009818844,0.95337975,0.00036074826,0.00035491496,0.00032848667,0.000121861434,0.00045369042,0.014824556],"genre_scores_gemma":[0.30533347,0.0006993801,0.6846052,0.000321862,0.00021329097,0.00063542306,0.00024541304,0.00017552031,0.0077704643],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99917054,0.00030321578,0.00002828726,0.00010295689,0.0002842338,0.00011084949],"domain_scores_gemma":[0.998505,0.0010023225,0.00007644938,0.000059783437,0.00024490576,0.00011161755],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014561164,0.0013039185,0.001621529,0.0014363424,0.00095549604,0.0015270822,0.00245685,0.0025752466,0.0047580083],"category_scores_gemma":[0.0033017069,0.0008047386,0.00092397036,0.0018701339,0.0010498171,0.0011537666,0.0013708945,0.001413155,0.00075548736],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017276978,0.00014915234,0.00026351307,0.00012843692,0.000058185957,0.000086422755,0.00006694149,0.9012002,0.0013616391,0.021178693,0.0034627966,0.07187119],"study_design_scores_gemma":[0.000044032215,0.00005687502,0.00003398424,0.000006979051,0.0000074369295,0.000018604804,0.000008782564,0.9966954,0.00012768513,0.0021088943,0.00088526297,0.000005994303],"about_ca_topic_score_codex":0.012632691,"about_ca_topic_score_gemma":0.010620281,"teacher_disagreement_score":0.012632691,"about_ca_system_score_codex":0.0013173493,"about_ca_system_score_gemma":0.0020241444,"threshold_uncertainty_score":0.025118351},"labels":[],"label_agreement":null},{"id":"W4391407522","doi":"10.1007/978-3-031-53025-8_6","title":"A Pattern Mining Heuristic for the Extension of Multi-trip Vehicle Routing","year":2024,"lang":"en","type":"book-chapter","venue":"Communications in computer and information science","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Extension (predicate logic); Vehicle routing problem; Computer science; Heuristic; Routing (electronic design automation); Information retrieval; Artificial intelligence; Computer network; Programming language","score_opus":0.06408659692197742,"score_gpt":0.32001787985576213,"score_spread":0.2559312829337847,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391407522","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019190094,0.00031104562,0.9766266,0.00022962817,0.00008787714,0.00018547435,0.00032566508,0.0004919052,0.0025518015],"genre_scores_gemma":[0.14535044,0.0002434873,0.8495574,0.00015549488,0.000058867932,0.00026709327,0.0008119213,0.00015156323,0.0034037929],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992847,0.00023422876,0.000054516167,0.00016644016,0.00017124567,0.000088818386],"domain_scores_gemma":[0.99764746,0.0015200058,0.0001456452,0.00024398911,0.00036115941,0.000081636535],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012899283,0.00057789613,0.001284176,0.0016512335,0.00064933364,0.0007839462,0.002449841,0.0010930363,0.004323559],"category_scores_gemma":[0.0043822783,0.00056026655,0.0014784223,0.0021466918,0.0005104265,0.0016574007,0.0010699553,0.0011378049,0.0005423899],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020581325,0.00029099028,0.00217752,0.00021104913,0.00014138837,0.00022224008,0.000110501554,0.5965845,0.0016818629,0.017313147,0.0071056387,0.37395528],"study_design_scores_gemma":[0.00001701484,0.00004341181,0.00018882443,0.000018169989,0.000018369607,0.000060168757,0.000022943883,0.9878444,0.00041385673,0.010071001,0.0012958525,0.0000059770673],"about_ca_topic_score_codex":0.005194762,"about_ca_topic_score_gemma":0.0065015834,"teacher_disagreement_score":0.005194762,"about_ca_system_score_codex":0.000754267,"about_ca_system_score_gemma":0.001448041,"threshold_uncertainty_score":0.014463782},"labels":[],"label_agreement":null},{"id":"W4391486815","doi":"10.1016/j.eng.2023.10.014","title":"Unmanned Aerial Vehicle Inspection Routing and Scheduling for Engineering Management","year":2024,"lang":"en","type":"article","venue":"Engineering","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"National Natural Science Foundation of China","keywords":"Scalability; Computer science; Scheduling (production processes); Vehicle routing problem; Integer programming; Metaheuristic; Routing (electronic design automation); Variable neighborhood search; Real-time computing; Engineering; Artificial intelligence; Algorithm; Embedded system","score_opus":0.008557602085211521,"score_gpt":0.23182048571791994,"score_spread":0.2232628836327084,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391486815","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014931573,0.0007279705,0.97684926,0.0003801323,0.0000954122,0.00009396756,0.00012945244,0.00022014577,0.0065720226],"genre_scores_gemma":[0.61049044,0.0017791392,0.3796223,0.00016109792,0.000114585324,0.00034419278,0.0004359457,0.00013154725,0.006920774],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99960464,0.00018677759,0.000012714594,0.00006454984,0.00008002824,0.000051208302],"domain_scores_gemma":[0.9996456,0.00018028675,0.00007431909,0.000025150395,0.0000448285,0.000029825333],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005868806,0.0007527938,0.00048525177,0.0004195965,0.00041388386,0.0008232547,0.0007444474,0.00066148216,0.0023361626],"category_scores_gemma":[0.0011995079,0.00034078863,0.0005109096,0.0006981336,0.0003712447,0.00060450105,0.0005886506,0.000711863,0.000265559],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016317956,0.000027092123,0.00021415953,0.000063615975,0.000013453297,0.000025358582,0.000022029943,0.9655976,0.0007229346,0.00915027,0.0013898737,0.0227573],"study_design_scores_gemma":[0.000004132059,0.000017975895,0.00007922024,0.0000070852197,0.0000038721455,0.000008663906,0.00001918483,0.9942721,0.00018991175,0.0036664952,0.0017287984,0.0000025363943],"about_ca_topic_score_codex":0.0071858475,"about_ca_topic_score_gemma":0.009220232,"teacher_disagreement_score":0.0071858475,"about_ca_system_score_codex":0.0010811806,"about_ca_system_score_gemma":0.0021450203,"threshold_uncertainty_score":0.014288068},"labels":[],"label_agreement":null},{"id":"W4391608031","doi":"10.4236/wjet.2024.121009","title":"Optimizing a Transportation System Using Metaheuristics Approaches (EGD/GA/ACO): A Forest Vehicle Routing Case Study","year":2024,"lang":"en","type":"article","venue":"World Journal of Engineering and Technology","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Metaheuristic; Vehicle routing problem; Ant colony optimization algorithms; Routing (electronic design automation); Computer science; Transport engineering; Mathematical optimization; Operations research; Engineering; Algorithm; Computer network; Mathematics","score_opus":0.02713596508855586,"score_gpt":0.24900663179514645,"score_spread":0.22187066670659059,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391608031","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.87574756,0.00085765606,0.10011881,0.0006663571,0.00007748857,0.00030638877,0.00056376425,0.00023565826,0.02142636],"genre_scores_gemma":[0.94097775,0.00026416153,0.0557489,0.000034753837,0.000008403998,0.000087961555,0.00016060667,0.000023801465,0.002693633],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996991,0.00011135613,0.000012198086,0.00003388496,0.00005999632,0.00008352646],"domain_scores_gemma":[0.99964094,0.00020057449,0.00003234186,0.000027924703,0.00006386801,0.00003442783],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000600353,0.00061788515,0.00042091677,0.00077551074,0.0008750336,0.0005982638,0.00057633786,0.001357296,0.0017274249],"category_scores_gemma":[0.00070468325,0.00019196521,0.0007553516,0.0012025274,0.00048375243,0.0005738709,0.00033383144,0.00046500022,0.00012720014],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008247864,0.00019105575,0.0024502408,0.00009697121,0.00003005515,0.0010771379,0.0000499455,0.9699019,0.0015973882,0.005416141,0.001350501,0.017756242],"study_design_scores_gemma":[0.000035825975,0.00019856368,0.0014654809,0.000013984396,0.000029304574,0.0002993371,0.0002049385,0.99058807,0.0022889976,0.0024670337,0.0023927165,0.000015820213],"about_ca_topic_score_codex":0.01947929,"about_ca_topic_score_gemma":0.0201108,"teacher_disagreement_score":0.01947929,"about_ca_system_score_codex":0.0010143656,"about_ca_system_score_gemma":0.00088213047,"threshold_uncertainty_score":0.038731813},"labels":[],"label_agreement":null},{"id":"W4391677279","doi":"10.1016/j.swevo.2024.101507","title":"A customized adaptive large neighborhood search algorithm for solving a multi-objective home health care problem in a pandemic environment","year":2024,"lang":"en","type":"article","venue":"Swarm and Evolutionary Computation","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal","funders":"Humanities and Social Sciences Youth Foundation, Ministry of Education of the People's Republic of China; Ministry of Education of the People's Republic of China","keywords":"Computer science; Heuristics; Workload; Pareto principle; Simulated annealing; Mathematical optimization; Heuristic; Multi-objective optimization; Constraint programming; Vehicle routing problem; Context (archaeology); Scheduling (production processes); Solver; Operations research; Algorithm; Routing (electronic design automation); Machine learning; Artificial intelligence; Stochastic programming; Mathematics","score_opus":0.017681700088664726,"score_gpt":0.28390980542097294,"score_spread":0.26622810533230823,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391677279","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.046222247,0.0002780051,0.947942,0.00021863116,0.000112788155,0.00011776519,0.000057461923,0.00024095987,0.004810251],"genre_scores_gemma":[0.5822699,0.00018724531,0.41255462,0.00016356594,0.000057629182,0.00041753292,0.0001824428,0.00007945026,0.004087609],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997788,0.00008528437,0.000011408257,0.000040098283,0.00005524209,0.000029130255],"domain_scores_gemma":[0.99958724,0.00024290926,0.000035868128,0.000021780237,0.00008342548,0.00002885088],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00078098266,0.00059176417,0.00089668506,0.00057146,0.00046085272,0.00050405745,0.0013067015,0.0013652191,0.001996707],"category_scores_gemma":[0.0017017672,0.00035421798,0.00058618153,0.000539894,0.00041122606,0.000540079,0.0009043192,0.00059427996,0.00016718231],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000041197232,0.000040839852,0.0003270366,0.000027311684,0.00002222759,0.000050241975,0.000022531034,0.97672325,0.0006016125,0.002447123,0.0006856129,0.019011093],"study_design_scores_gemma":[0.000008219634,0.000015125287,0.000038341037,0.0000015196081,0.0000025305928,0.0000056042286,0.000004268737,0.9994666,0.00005191685,0.00026980165,0.00013468953,0.0000014241214],"about_ca_topic_score_codex":0.0075142286,"about_ca_topic_score_gemma":0.007131267,"teacher_disagreement_score":0.0075142286,"about_ca_system_score_codex":0.00051419344,"about_ca_system_score_gemma":0.0010368095,"threshold_uncertainty_score":0.014940977},"labels":[],"label_agreement":null},{"id":"W4392135127","doi":"10.1016/j.eswa.2024.123561","title":"An efficient hybrid adaptive large neighborhood search method for the capacitated team orienteering problem","year":2024,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":18,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval; Air Liquide (Canada)","funders":"","keywords":"Orienteering; Computer science; Mathematical optimization; Artificial intelligence; Mathematics","score_opus":0.017482284406608904,"score_gpt":0.3035203629416793,"score_spread":0.28603807853507035,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392135127","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013091513,0.00023952837,0.98303014,0.00010633467,0.00007301567,0.000040497158,0.000022912469,0.00012097473,0.0032750438],"genre_scores_gemma":[0.4767528,0.0003159608,0.5113813,0.0001859795,0.0001073692,0.00040932564,0.00016373488,0.00016469863,0.010518827],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99976236,0.000087228254,0.000008467551,0.00003719379,0.00007696633,0.000027722546],"domain_scores_gemma":[0.999546,0.00026551803,0.000030143065,0.000020649368,0.00010591817,0.000031779844],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007157121,0.00060512015,0.0011173625,0.00049059634,0.00038262134,0.0005869522,0.0014651421,0.0013021004,0.0030888019],"category_scores_gemma":[0.0013699392,0.00039958567,0.00051625085,0.00053588214,0.00042770317,0.0007319308,0.00092734344,0.0007004334,0.00040371547],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000095092466,0.000059617596,0.00021540205,0.00006150285,0.000032057964,0.000043776356,0.000041594754,0.930845,0.0015709947,0.007779963,0.0017165064,0.057538524],"study_design_scores_gemma":[0.0000061591168,0.000011340985,0.000014781435,0.0000015790805,0.0000016311602,0.000003375526,0.0000024455505,0.9993161,0.000052980115,0.00040736815,0.0001807206,0.000001467047],"about_ca_topic_score_codex":0.006193301,"about_ca_topic_score_gemma":0.005477173,"teacher_disagreement_score":0.006193301,"about_ca_system_score_codex":0.00049388176,"about_ca_system_score_gemma":0.0009120383,"threshold_uncertainty_score":0.012314498},"labels":[],"label_agreement":null},{"id":"W4392587943","doi":"10.5194/egusphere-egu24-1959","title":"SWOT Level-3 Overview algorithms and examples","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"SWOT analysis; Computer science; Algorithm; Economics; Management","score_opus":0.10961080565806787,"score_gpt":0.3271106569669713,"score_spread":0.21749985130890342,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392587943","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0037417256,0.004054006,0.84383726,0.00077197386,0.00058499543,0.00091641815,0.023180218,0.07386331,0.04905016],"genre_scores_gemma":[0.040187966,0.0047414848,0.8016426,0.0013599144,0.0002271969,0.0016508644,0.09810062,0.018165233,0.033924058],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989323,0.0001342387,0.00011454407,0.00015183674,0.00055017136,0.00011685996],"domain_scores_gemma":[0.99927443,0.000115227645,0.000032961678,0.00014428644,0.00039317104,0.000039865383],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011165624,0.0020787844,0.0006685532,0.0018022174,0.0006198983,0.00270547,0.0027365296,0.0013719805,0.0463194],"category_scores_gemma":[0.0036945553,0.0010540547,0.0011477635,0.0018865939,0.00037227216,0.0020614483,0.0017615394,0.0014602141,0.044584576],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006320278,0.00011914065,0.0015910967,0.0013207106,0.00010544159,0.0002336686,0.00015165629,0.06304669,0.008446734,0.020786632,0.41193283,0.49163342],"study_design_scores_gemma":[0.00014321503,0.000109634435,0.00084967614,0.00034011443,0.00004335975,0.0003643342,0.00008465528,0.205999,0.013452717,0.028487919,0.7500416,0.00008375013],"about_ca_topic_score_codex":0.007749804,"about_ca_topic_score_gemma":0.008359003,"teacher_disagreement_score":0.0463194,"about_ca_system_score_codex":0.000878704,"about_ca_system_score_gemma":0.0013911208,"threshold_uncertainty_score":0.15495384},"labels":[],"label_agreement":null},{"id":"W4392784336","doi":"10.1142/s1793830924300017","title":"Proposed theoretical value for TSP constant","year":2024,"lang":"en","type":"article","venue":"Discrete Mathematics Algorithms and Applications","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Constant (computer programming); Value (mathematics); Mathematics; Computer science; Statistics","score_opus":0.012324863491558058,"score_gpt":0.28830243177306103,"score_spread":0.275977568281503,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392784336","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01953814,0.006113616,0.78956175,0.010560289,0.001507663,0.00017332817,0.00094962557,0.0018127387,0.16978289],"genre_scores_gemma":[0.72902566,0.010197871,0.21887334,0.0048990315,0.0039027603,0.0012897496,0.0015821314,0.0012728486,0.028956635],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99567956,0.0009724132,0.00010572501,0.0011143016,0.0016396944,0.000488301],"domain_scores_gemma":[0.97528857,0.01709594,0.00083140674,0.0029117332,0.0032352048,0.0006372422],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0040974068,0.0015626969,0.0015266387,0.0034726192,0.0023184502,0.0053252806,0.0049961885,0.0035620942,0.026969884],"category_scores_gemma":[0.05513583,0.0007853979,0.001258993,0.0038177767,0.004037489,0.008785821,0.0031819209,0.005870206,0.0066887094],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005050079,0.00005477341,0.00061138574,0.00017370122,0.000018340445,0.00010259766,0.00011776393,0.02966526,0.00085544086,0.92867297,0.012293832,0.027383456],"study_design_scores_gemma":[0.00002466194,0.000057124314,0.0004649211,0.00019434672,0.00003796676,0.0005743933,0.00010524891,0.23169045,0.0013932245,0.73803145,0.027368594,0.000057625137],"about_ca_topic_score_codex":0.0023138213,"about_ca_topic_score_gemma":0.0013072572,"teacher_disagreement_score":0.026969884,"about_ca_system_score_codex":0.005820669,"about_ca_system_score_gemma":0.0028150654,"threshold_uncertainty_score":0.09022325},"labels":[],"label_agreement":null},{"id":"W4393121399","doi":"10.5267/j.ijiec.2024.1.003","title":"A GRASP algorithm for the bus crew scheduling problem","year":2024,"lang":"en","type":"article","venue":"International Journal of Industrial Engineering Computations","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"GRASP; Crew scheduling; Crew; Computer science; Scheduling (production processes); Mathematical optimization; Job shop scheduling; Algorithm; Engineering; Mathematics; Embedded system; Aeronautics; Programming language","score_opus":0.03539057958593704,"score_gpt":0.3015435518242727,"score_spread":0.26615297223833567,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393121399","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00494582,0.00020698403,0.9905911,0.000113527836,0.00003659504,0.00006673881,0.000060546674,0.0004969626,0.0034815886],"genre_scores_gemma":[0.108439654,0.00059610035,0.88576996,0.00010938568,0.0000675638,0.0003993035,0.00040330898,0.00024561674,0.003969082],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99955016,0.000115223615,0.000023757473,0.00009293607,0.00014493853,0.00007303705],"domain_scores_gemma":[0.999703,0.00015330726,0.000035259996,0.000026909254,0.000056406017,0.000025138504],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00055093184,0.0011403256,0.001187052,0.0010729106,0.0007731476,0.00094484503,0.0011100395,0.0014154238,0.005858433],"category_scores_gemma":[0.0015938643,0.00051604497,0.0011808027,0.0013487866,0.00064711506,0.0012668764,0.0012656914,0.0014731054,0.00104438],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000078863064,0.000086965156,0.0002501793,0.0002187818,0.000047277495,0.00010946734,0.00011503193,0.78251904,0.003330666,0.030519567,0.0054545486,0.17726961],"study_design_scores_gemma":[0.00003705253,0.000066058,0.000097137316,0.000026156,0.000013266775,0.00006754962,0.00004476047,0.9749474,0.0007295961,0.018145647,0.0058115968,0.0000138062],"about_ca_topic_score_codex":0.00332254,"about_ca_topic_score_gemma":0.002233984,"teacher_disagreement_score":0.005858433,"about_ca_system_score_codex":0.0007785944,"about_ca_system_score_gemma":0.0014792962,"threshold_uncertainty_score":0.019598365},"labels":[],"label_agreement":null},{"id":"W4393128897","doi":"10.1016/j.ejor.2024.03.031","title":"Vehicle routing with stochastic demand, service and waiting times — The case of food bank collection problems","year":2024,"lang":"en","type":"article","venue":"European Journal of Operational Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"HEC Montréal","funders":"HORIZON EUROPE Framework Programme","keywords":"Vehicle routing problem; Computer science; Service (business); Operations research; Routing (electronic design automation); Limit (mathematics); Set (abstract data type); Plan (archaeology); Variety (cybernetics); Mathematical optimization; Business; Marketing; Computer network; Mathematics","score_opus":0.050957089695265,"score_gpt":0.31425548329844916,"score_spread":0.26329839360318413,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393128897","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4213201,0.0016038285,0.5494054,0.005164982,0.00031057466,0.0003139256,0.0019292746,0.00041491401,0.019536989],"genre_scores_gemma":[0.91499335,0.00080160366,0.07462291,0.00034613843,0.00020237034,0.00022395655,0.0007103515,0.00014328983,0.007956144],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99855083,0.0006575292,0.000068336725,0.00026480577,0.00016504708,0.0002934075],"domain_scores_gemma":[0.9947502,0.0038535497,0.0006448492,0.00017019943,0.00024170754,0.00033948675],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026242363,0.0016425397,0.0017769691,0.0009107787,0.0010025607,0.0023458716,0.002257814,0.0030535606,0.0035974379],"category_scores_gemma":[0.0076917373,0.0009667579,0.0016462958,0.0015212079,0.0015446807,0.0018365487,0.0012408616,0.002340893,0.00024325699],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000044770433,0.000028378598,0.0003441415,0.00004047461,0.000022237473,0.00013141328,0.000022928974,0.9907374,0.00013912519,0.006540746,0.00050439005,0.0014439323],"study_design_scores_gemma":[0.00002403799,0.000021021768,0.00018008046,0.0000057382085,0.000009566505,0.00004330628,0.000033508088,0.9919008,0.00011107382,0.0072156424,0.00044640232,0.000008963156],"about_ca_topic_score_codex":0.020809134,"about_ca_topic_score_gemma":0.012685799,"teacher_disagreement_score":0.020809134,"about_ca_system_score_codex":0.0031728563,"about_ca_system_score_gemma":0.0018627545,"threshold_uncertainty_score":0.041376054},"labels":[],"label_agreement":null},{"id":"W4393236743","doi":"10.1016/j.cor.2024.106632","title":"Arrival and service time dependencies in the single- and multi-visit selective traveling salesman problem","year":2024,"lang":"en","type":"article","venue":"Computers & Operations Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"Ministerio de Ciencia e Innovación; European Regional Development Fund; Ministerio de Ciencia, Innovación y Universidades","keywords":"Travelling salesman problem; Computer science; Service (business); Traveling purchaser problem; Operations research; Mathematical optimization; Bottleneck traveling salesman problem; Mathematics; Algorithm; Business; Marketing","score_opus":0.07149047988899913,"score_gpt":0.347697424587011,"score_spread":0.2762069446980118,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393236743","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5285387,0.00082140416,0.4483061,0.0017913573,0.00007826364,0.00019748528,0.0007228715,0.00019289297,0.019350937],"genre_scores_gemma":[0.9598981,0.00054372224,0.033664085,0.00012267848,0.000063593056,0.00013436614,0.00031159475,0.00009457915,0.0051672603],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99926144,0.00023335312,0.000031401793,0.00012831441,0.0001443575,0.00020108162],"domain_scores_gemma":[0.99636096,0.002691416,0.0004956706,0.00009738343,0.00016217785,0.00019242756],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001746935,0.00072674855,0.000911633,0.0006173683,0.00063097547,0.0012810566,0.0019978685,0.0012295212,0.004680765],"category_scores_gemma":[0.0059654275,0.000775996,0.0011235243,0.0011620086,0.00096916594,0.002183399,0.00079529476,0.0015041785,0.00031843278],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000108218155,0.000074843236,0.0014829345,0.000076452256,0.000031432875,0.0002837243,0.00008423298,0.9453948,0.00082301383,0.044713672,0.0010067518,0.005919803],"study_design_scores_gemma":[0.000011855073,0.000033219276,0.00069312187,0.000008218617,0.000015384474,0.00006543045,0.000048115544,0.98451126,0.0003501225,0.013714531,0.00053882715,0.000009949896],"about_ca_topic_score_codex":0.011048679,"about_ca_topic_score_gemma":0.008700763,"teacher_disagreement_score":0.011048679,"about_ca_system_score_codex":0.0023397328,"about_ca_system_score_gemma":0.0020242212,"threshold_uncertainty_score":0.021968722},"labels":[],"label_agreement":null},{"id":"W4393302837","doi":"10.1155/2024/8753106","title":"Modification of the Clarke and Wright Algorithm with a Dynamic Savings Matrix","year":2024,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Agentúra na Podporu Výskumu a Vývoja","keywords":"Wright; Algorithm; Matrix (chemical analysis); Computer science; Mathematics; Materials science","score_opus":0.005119081544223749,"score_gpt":0.25660713447021577,"score_spread":0.251488052925992,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393302837","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0050689806,0.000110708694,0.9904908,0.0001321437,0.000098930104,0.00011391304,0.00006756698,0.00054877135,0.0033682124],"genre_scores_gemma":[0.07654995,0.00017489104,0.9154688,0.000109110944,0.000058084934,0.00024064198,0.00030897598,0.00026757395,0.0068220757],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99874437,0.00031911448,0.000098340446,0.00024913243,0.00042453062,0.00016440522],"domain_scores_gemma":[0.9988427,0.00032370738,0.00007577106,0.00022095421,0.00046589866,0.00007089957],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012528314,0.001119599,0.0010444381,0.0015412971,0.0007741038,0.0012850252,0.0024794256,0.0012612146,0.007875082],"category_scores_gemma":[0.003600642,0.00073731545,0.001185145,0.0015814541,0.00069382496,0.0018592187,0.0020593775,0.001400603,0.0024095282],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023161591,0.00013219,0.0009730859,0.00015401652,0.000086896194,0.0002797742,0.00015074789,0.46499327,0.006033132,0.061069436,0.010975347,0.4549205],"study_design_scores_gemma":[0.00007547288,0.00007980615,0.00015245883,0.000017087423,0.000023427883,0.00016737703,0.000034461038,0.968113,0.0025759295,0.016581804,0.012150512,0.000028573644],"about_ca_topic_score_codex":0.007982306,"about_ca_topic_score_gemma":0.0077454816,"teacher_disagreement_score":0.007982306,"about_ca_system_score_codex":0.0010159275,"about_ca_system_score_gemma":0.002805276,"threshold_uncertainty_score":0.026344776},"labels":[],"label_agreement":null},{"id":"W4393352772","doi":"10.3390/systems12040117","title":"Resilient Network Design: Disjoint Shortest Path Problem for Power Transmission Application","year":2024,"lang":"en","type":"article","venue":"Systems","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Shortest path problem; Computer science; Disjoint sets; Redundancy (engineering); Constrained Shortest Path First; Mathematical optimization; Integer programming; Routing (electronic design automation); Path (computing); Distributed computing; K shortest path routing; Graph; Algorithm; Theoretical computer science; Computer network; Mathematics","score_opus":0.016956475143771035,"score_gpt":0.260923422866855,"score_spread":0.24396694772308397,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393352772","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.051951654,0.0024302688,0.9124707,0.0035549656,0.00037772587,0.00041891742,0.009768327,0.002025964,0.017001368],"genre_scores_gemma":[0.367078,0.0019992832,0.60800415,0.00041784102,0.00021564354,0.00087745464,0.013459374,0.0005853196,0.007362896],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990007,0.00033866023,0.000044891785,0.0003258274,0.00020786478,0.00008204375],"domain_scores_gemma":[0.9987929,0.00067230675,0.00015730801,0.00016574161,0.00015087605,0.000060900227],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001291356,0.0015146789,0.00086457934,0.0015166077,0.0008781943,0.001327318,0.0017870945,0.001923311,0.0074410434],"category_scores_gemma":[0.0056504863,0.0005553666,0.0012777738,0.00268501,0.00069014117,0.0022911963,0.0009871296,0.0013868832,0.0011961072],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000928999,0.00008223814,0.0007294478,0.00046476666,0.000059229955,0.00016867444,0.000078003155,0.8845951,0.0009920807,0.019592805,0.02958153,0.0635632],"study_design_scores_gemma":[0.000029978522,0.000034060686,0.00027413343,0.000040377152,0.000020480635,0.00011651893,0.00007392704,0.9335599,0.00080482755,0.05003973,0.014991556,0.000014522762],"about_ca_topic_score_codex":0.00598453,"about_ca_topic_score_gemma":0.008332054,"teacher_disagreement_score":0.0074410434,"about_ca_system_score_codex":0.0016192965,"about_ca_system_score_gemma":0.0016357681,"threshold_uncertainty_score":0.024892747},"labels":[],"label_agreement":null},{"id":"W4394693707","doi":"10.1016/j.ejor.2024.04.007","title":"An exact method for a last-mile delivery routing problem with multiple deliverymen","year":2024,"lang":"en","type":"article","venue":"European Journal of Operational Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"Alliance de recherche numérique du Canada; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Natural Sciences and Engineering Research Council of Canada; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior; Fundação de Amparo à Pesquisa do Estado de São Paulo","keywords":"Mile; Computer science; Last mile (transportation); Routing (electronic design automation); Mathematical optimization; Operations research; Mathematics; Computer network; Geography","score_opus":0.06314009654853942,"score_gpt":0.3729746432483227,"score_spread":0.3098345466997833,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394693707","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0019680136,0.00021399146,0.99093574,0.00024079469,0.00007449434,0.00012442221,0.000112192545,0.00016894941,0.00616142],"genre_scores_gemma":[0.08229217,0.00047586317,0.9076745,0.00019327243,0.00012510373,0.0005176897,0.00034210348,0.00022717593,0.0081520295],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993787,0.00017189092,0.000026258285,0.000114512884,0.00021791659,0.000090816546],"domain_scores_gemma":[0.9987405,0.0008552098,0.00007575347,0.000083943385,0.00019055032,0.000054099288],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015371836,0.0013076994,0.001029802,0.00096785097,0.00080211414,0.001456787,0.0016789592,0.0016296206,0.012241632],"category_scores_gemma":[0.0039362446,0.00074180483,0.0012779527,0.0014100075,0.0006546046,0.0014714397,0.00122858,0.0021660754,0.0015525536],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000039240484,0.00008062143,0.00029513348,0.00021570043,0.00003294245,0.000088993664,0.00007519905,0.8703136,0.00093208364,0.054633483,0.0051041567,0.06818888],"study_design_scores_gemma":[0.000016597027,0.000015935062,0.00004387886,0.000018115106,0.000008174062,0.0000222645,0.000019786901,0.9805103,0.00016071035,0.016094964,0.0030842898,0.000004862854],"about_ca_topic_score_codex":0.010211328,"about_ca_topic_score_gemma":0.013285759,"teacher_disagreement_score":0.012241632,"about_ca_system_score_codex":0.0016959542,"about_ca_system_score_gemma":0.0032220478,"threshold_uncertainty_score":0.040952325},"labels":[],"label_agreement":null},{"id":"W4394744190","doi":"10.2139/ssrn.4763354","title":"A Mathheuristic Approach for the Vehicle Routing Problem with Queuing Considerations","year":2024,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Trois-Rivières; Université Laval","funders":"","keywords":"Download; Computer science; Queueing theory; Routing (electronic design automation); Computer network; World Wide Web","score_opus":0.01250580493827293,"score_gpt":0.2464209292636225,"score_spread":0.23391512432534955,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394744190","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0019707326,0.00023646321,0.99147755,0.00035208833,0.00012301488,0.00004008277,0.000048577273,0.000057640194,0.0056939013],"genre_scores_gemma":[0.17370187,0.0015801258,0.80079365,0.0006925299,0.00059389696,0.00052803935,0.00022236927,0.00031989516,0.021567762],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99960774,0.00016627404,0.00001591489,0.0000516755,0.00010542747,0.000052864187],"domain_scores_gemma":[0.9991381,0.0005854109,0.000069741356,0.000034994424,0.00012529487,0.000046371002],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010386929,0.0010607126,0.00077629526,0.0012980489,0.00075899257,0.0014227403,0.0018572959,0.0018285216,0.0059159184],"category_scores_gemma":[0.0023869595,0.00069750944,0.0014166017,0.0013794985,0.0009516475,0.0014026493,0.0014616067,0.0020332846,0.0007559035],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000018384015,0.00005155669,0.00015163343,0.00006738505,0.00003354864,0.000046455505,0.000027835358,0.85421956,0.0005580647,0.12625124,0.0018492315,0.016724996],"study_design_scores_gemma":[0.000007120611,0.000011214799,0.000031327763,0.000008866688,0.000005292926,0.000008201043,0.000007251392,0.9691622,0.00006123683,0.029291863,0.0014003615,0.0000050433305],"about_ca_topic_score_codex":0.007767747,"about_ca_topic_score_gemma":0.0076409117,"teacher_disagreement_score":0.007767747,"about_ca_system_score_codex":0.0015360657,"about_ca_system_score_gemma":0.0021604246,"threshold_uncertainty_score":0.019790709},"labels":[],"label_agreement":null},{"id":"W4394910141","doi":"10.1016/j.ejor.2024.04.011","title":"A hybrid genetic search and dynamic programming-based split algorithm for the multi-trip time-dependent vehicle routing problem","year":2024,"lang":"en","type":"article","venue":"European Journal of Operational Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":26,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal; Polytechnique Montréal","funders":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China; National University's Basic Research Foundation of China; Ministry of Education - Singapore","keywords":"Vehicle routing problem; Solver; Computer science; Algorithm; Monotone polygon; Queue; Mathematical optimization; Genetic algorithm; Computation; Routing (electronic design automation); Dynamic programming; Mathematics","score_opus":0.046085922399229394,"score_gpt":0.34650779066864007,"score_spread":0.3004218682694107,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394910141","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.047766857,0.00031219737,0.9464052,0.0001778691,0.000075551616,0.00010639357,0.00006082281,0.0003725639,0.00472254],"genre_scores_gemma":[0.51752657,0.00023065679,0.4765815,0.00020637429,0.000052248957,0.00041142027,0.0002739965,0.00013170672,0.0045856205],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99970835,0.00008241683,0.0000106003035,0.0000526431,0.000096025535,0.000049865826],"domain_scores_gemma":[0.99966025,0.00016804258,0.000029231236,0.000024040486,0.000081846425,0.00003658821],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00071619725,0.000763213,0.0011918696,0.00092773925,0.0004742095,0.0007583112,0.001571995,0.0013279135,0.0030584317],"category_scores_gemma":[0.0011259527,0.0004927985,0.00074293604,0.00091649144,0.00056022167,0.00086578465,0.001209035,0.00087299163,0.00037446723],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012367008,0.0001395588,0.0005196476,0.00004410282,0.00005346416,0.000044390265,0.0000451685,0.9038722,0.002500371,0.006625545,0.0010547092,0.08497715],"study_design_scores_gemma":[0.000020679743,0.000036575075,0.00006415735,0.0000032542348,0.0000067496676,0.000009387604,0.000006271111,0.99848014,0.00019186352,0.00093539275,0.00024305456,0.0000024430224],"about_ca_topic_score_codex":0.005137276,"about_ca_topic_score_gemma":0.0042708092,"teacher_disagreement_score":0.005137276,"about_ca_system_score_codex":0.00074273575,"about_ca_system_score_gemma":0.0014473674,"threshold_uncertainty_score":0.010231495},"labels":[],"label_agreement":null},{"id":"W4395678464","doi":"10.1016/j.omega.2024.103100","title":"Increasing schedule reliability in the multiple depot vehicle scheduling problem with stochastic travel time","year":2024,"lang":"en","type":"article","venue":"Omega","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Université de Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Schedule; Scheduling (production processes); Computer science; Reliability (semiconductor); Mathematical optimization; Operations research; Stochastic dominance; Public transport; Service quality; Service (business); Transport engineering; Engineering; Mathematics; Economics","score_opus":0.010892364363572445,"score_gpt":0.23617127959774442,"score_spread":0.22527891523417198,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4395678464","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12559964,0.00032089432,0.87027466,0.0003646914,0.000031442814,0.00011303657,0.00016642499,0.0001625186,0.0029667967],"genre_scores_gemma":[0.809638,0.0004723461,0.18678588,0.00006592939,0.000054496133,0.0002892749,0.00021697076,0.00011292779,0.0023640313],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991374,0.0004473763,0.000027588101,0.00012085112,0.00014105151,0.00012568313],"domain_scores_gemma":[0.9977302,0.0016607984,0.000255816,0.000075694676,0.00016879081,0.00010872956],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018091575,0.0010757861,0.0010168623,0.0009168552,0.0005114236,0.0008745831,0.0008278023,0.0008664263,0.0013447796],"category_scores_gemma":[0.0044573634,0.0007432122,0.0007807416,0.0009400007,0.00066537724,0.000945284,0.00088745315,0.00089744193,0.00015506204],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000045074823,0.00002405261,0.00035218673,0.000052319436,0.000014675355,0.000051621006,0.000040980274,0.9827875,0.0007165059,0.0077618454,0.00029495894,0.007858373],"study_design_scores_gemma":[0.000011227058,0.000031714993,0.00010859127,0.0000048136126,0.000007706376,0.000018300554,0.0000148058025,0.9943699,0.0003210817,0.0048262374,0.00028215846,0.0000033073568],"about_ca_topic_score_codex":0.0044962834,"about_ca_topic_score_gemma":0.0036220485,"teacher_disagreement_score":0.0044962834,"about_ca_system_score_codex":0.001273938,"about_ca_system_score_gemma":0.0014576002,"threshold_uncertainty_score":0.009567916},"labels":[],"label_agreement":null},{"id":"W4396609541","doi":"10.1287/msom.2022.0339","title":"Robust Drone Delivery with Weather Information","year":2024,"lang":"en","type":"article","venue":"Manufacturing & Service Operations Management","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":71,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Group for Research in Decision Analysis; HEC Montréal","funders":"","keywords":"Computer science; Drone; Scheduling (production processes); Operations research; Scalability; Cluster analysis; Mathematical optimization; Engineering; Mathematics","score_opus":0.011017275093000661,"score_gpt":0.2025417523027919,"score_spread":0.19152447720979124,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396609541","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.052611437,0.0005588403,0.9367535,0.00084151764,0.00013269128,0.00037910484,0.0011505072,0.00038704587,0.0071853],"genre_scores_gemma":[0.88844264,0.00051814114,0.09988013,0.00017735267,0.00012155155,0.00041280984,0.00090247986,0.00014377669,0.009401052],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99870574,0.00034525647,0.000054333694,0.00046929406,0.00022670584,0.00019872279],"domain_scores_gemma":[0.9980482,0.0011518535,0.00032399726,0.00014257165,0.00021023153,0.00012312428],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014365496,0.001628849,0.0019217702,0.000689511,0.0005530234,0.0018691185,0.0022095144,0.0021505747,0.006331954],"category_scores_gemma":[0.004931677,0.0008926462,0.0012695584,0.0010172995,0.0010870814,0.0019037178,0.00152651,0.0021929552,0.00050035614],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005003869,0.000023446219,0.0003837179,0.000063956344,0.000017684284,0.00008101125,0.000019567438,0.9883409,0.0003538579,0.0043240087,0.00060558855,0.0057361764],"study_design_scores_gemma":[0.000015561842,0.000038284044,0.00022214963,0.000008911529,0.000010478279,0.000026962301,0.00001947441,0.99464107,0.00030300757,0.004216353,0.00048840744,0.000009351252],"about_ca_topic_score_codex":0.013942988,"about_ca_topic_score_gemma":0.004915336,"teacher_disagreement_score":0.013942988,"about_ca_system_score_codex":0.0019562384,"about_ca_system_score_gemma":0.0018599099,"threshold_uncertainty_score":0.02772367},"labels":[],"label_agreement":null},{"id":"W4396768746","doi":"10.1016/j.ejor.2024.05.012","title":"A disaggregated integer L-shaped method for stochastic vehicle routing problems with monotonic recourse","year":2024,"lang":"en","type":"article","venue":"European Journal of Operational Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval; Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Vehicle routing problem; Monotonic function; Integer programming; Integer (computer science); Mathematical optimization; Computer science; Routing (electronic design automation); Mathematics; Computer network","score_opus":0.0710932116016096,"score_gpt":0.3777479419120455,"score_spread":0.30665473031043594,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396768746","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004009811,0.00011444773,0.9929899,0.00010143801,0.00006754606,0.000043478376,0.000071141825,0.0001497202,0.0024524918],"genre_scores_gemma":[0.20563485,0.00027206205,0.7843036,0.00047255366,0.00012239371,0.0005131322,0.0003875786,0.00042191354,0.0078719],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994404,0.00023436088,0.000025551564,0.00007661614,0.00014060547,0.000082608516],"domain_scores_gemma":[0.99825877,0.0009813579,0.00011584023,0.00013052518,0.0003563257,0.00015730098],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019199747,0.0010066858,0.0014487837,0.0008148918,0.00049597706,0.001288207,0.002028358,0.0018637202,0.0075027365],"category_scores_gemma":[0.0042269505,0.0009364122,0.0013214779,0.00089875335,0.0007203992,0.0010199279,0.0022753207,0.0021824974,0.0010625538],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008200378,0.00007264612,0.00025924758,0.000085042964,0.00003351806,0.000059354043,0.000046705594,0.9510548,0.0011417391,0.012832151,0.0014798406,0.03285286],"study_design_scores_gemma":[0.000005151159,0.0000113896,0.000014828457,0.0000051803286,0.0000022974723,0.000003301288,0.0000033386098,0.99812764,0.000059729562,0.0014281592,0.00033704026,0.0000019932102],"about_ca_topic_score_codex":0.0057814717,"about_ca_topic_score_gemma":0.0060474356,"teacher_disagreement_score":0.0075027365,"about_ca_system_score_codex":0.00094588305,"about_ca_system_score_gemma":0.0019018946,"threshold_uncertainty_score":0.025099158},"labels":[],"label_agreement":null},{"id":"W4398230519","doi":"10.48550/arxiv.2405.12876","title":"Approximating Traveling Salesman Problems Using a Bridge Lemma","year":2024,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Deutsche Forschungsgemeinschaft; European Commission","keywords":"Lemma (botany); Bridge (graph theory); Mathematics; Combinatorics; Computer science; Biology; Botany; Anatomy","score_opus":0.1344195853920871,"score_gpt":0.2186557351095713,"score_spread":0.0842361497174842,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4398230519","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.029158385,0.0010599574,0.94924104,0.00090561004,0.0002589282,0.00016061278,0.00026807436,0.0026771862,0.016270313],"genre_scores_gemma":[0.24651422,0.000949944,0.73845017,0.00059229275,0.00019586683,0.0005461996,0.0015585194,0.0007809777,0.010411869],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997936,0.000490733,0.00009390272,0.00033518038,0.0007455951,0.00039865312],"domain_scores_gemma":[0.99677306,0.0018706272,0.00015532465,0.0006664379,0.00037438722,0.00016018597],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022463514,0.0017465367,0.0015274493,0.001774899,0.0008653137,0.0018122256,0.0033258148,0.0018524693,0.011941205],"category_scores_gemma":[0.009180534,0.0008326585,0.0022055558,0.0024781725,0.0010844376,0.0057416274,0.0032266637,0.004288363,0.0033391376],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004441496,0.00058557006,0.0012096543,0.00030584278,0.0001456914,0.00022643531,0.00030080124,0.57650965,0.0048531815,0.16069636,0.026694592,0.22802806],"study_design_scores_gemma":[0.000049552513,0.000069141504,0.000082883926,0.000020087697,0.000015828662,0.00004918387,0.00003396583,0.964514,0.00081852655,0.029708544,0.004628823,0.000009505494],"about_ca_topic_score_codex":0.0073617194,"about_ca_topic_score_gemma":0.007979539,"teacher_disagreement_score":0.011941205,"about_ca_system_score_codex":0.0021249552,"about_ca_system_score_gemma":0.0021796778,"threshold_uncertainty_score":0.03994727},"labels":[],"label_agreement":null},{"id":"W4399058371","doi":"10.1007/978-3-031-60597-0_18","title":"A Benders Decomposition Approach for a Capacitated Multi-vehicle Covering Tour Problem with Intermediate Facilities","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Group for Research in Decision Analysis","funders":"","keywords":"Benders' decomposition; Computer science; Decomposition; Operations research; Mathematical optimization; Engineering; Mathematics","score_opus":0.02216477098698155,"score_gpt":0.2545022546696616,"score_spread":0.23233748368268003,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399058371","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00394806,0.00039257618,0.9852791,0.00017434407,0.00008533841,0.00008536853,0.00019668881,0.00014868133,0.009689825],"genre_scores_gemma":[0.108025186,0.0017385934,0.8644202,0.00020112786,0.0002557094,0.00044381103,0.00087189756,0.00043402083,0.023609607],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99942845,0.00018975187,0.00001933954,0.00010188214,0.00016872051,0.00009194047],"domain_scores_gemma":[0.9996427,0.00018196121,0.000034350196,0.00003282079,0.0000699436,0.000038182377],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007135451,0.002021449,0.0014251221,0.0012014341,0.0006986714,0.0017714022,0.0019496749,0.0015984898,0.010882251],"category_scores_gemma":[0.0012915147,0.0013626842,0.0027300268,0.0020219474,0.00058166654,0.0018693997,0.001404086,0.0034108027,0.0018114754],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006707293,0.00014993887,0.00017463921,0.0002369087,0.000075270866,0.00011519545,0.000088865505,0.8585161,0.0029789149,0.0604829,0.009049836,0.06806442],"study_design_scores_gemma":[0.000010541451,0.000039023893,0.000096490236,0.000033033033,0.00001968561,0.00004071985,0.000037067603,0.96076685,0.00043731477,0.034639385,0.003867463,0.000012399799],"about_ca_topic_score_codex":0.0065040076,"about_ca_topic_score_gemma":0.0068129436,"teacher_disagreement_score":0.010882251,"about_ca_system_score_codex":0.0013860698,"about_ca_system_score_gemma":0.0012842452,"threshold_uncertainty_score":0.03640479},"labels":[],"label_agreement":null},{"id":"W4399261791","doi":"10.1609/socs.v17i1.31550","title":"On the Properties of All-Pair Heuristics","year":2024,"lang":"en","type":"article","venue":"Proceedings of the International Symposium on Combinatorial Search","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Israel Science Foundation; United States-Israel Binational Science Foundation; Natural Sciences and Engineering Research Council of Canada; Canadian Institute for Advanced Research","keywords":"Heuristics; Computer science; Mathematics; Mathematical optimization","score_opus":0.026655059834424417,"score_gpt":0.2642059223824091,"score_spread":0.23755086254798466,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399261791","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.085401855,0.0008722718,0.89496374,0.00078736333,0.0000791515,0.00018712471,0.00026968637,0.00044095784,0.016997846],"genre_scores_gemma":[0.65644026,0.0005069189,0.33926928,0.00047182557,0.000062843545,0.00027375366,0.00034543092,0.0003644294,0.002265226],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99517065,0.0023121242,0.00026079372,0.0008239398,0.0009439033,0.00048862747],"domain_scores_gemma":[0.9608838,0.0304921,0.0021070484,0.004252727,0.0016571062,0.0006071576],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0063551217,0.00094408507,0.0014667412,0.0012826853,0.0013081478,0.0026911036,0.0016528992,0.0013880143,0.0044695893],"category_scores_gemma":[0.04607035,0.0007383111,0.0013480495,0.0017694521,0.002131086,0.004816623,0.0020632902,0.0027458223,0.000487949],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00046411177,0.00017458452,0.004989639,0.000348703,0.00015916201,0.00017682163,0.0004212062,0.5037284,0.002398445,0.39167425,0.003272137,0.09219246],"study_design_scores_gemma":[0.000085207605,0.000386761,0.0008509643,0.00013534549,0.00007631167,0.00022851827,0.00024229387,0.6177817,0.0026081933,0.37366632,0.0038951535,0.000043222928],"about_ca_topic_score_codex":0.0023859127,"about_ca_topic_score_gemma":0.0022829259,"teacher_disagreement_score":0.0063551217,"about_ca_system_score_codex":0.0014222263,"about_ca_system_score_gemma":0.0026253946,"threshold_uncertainty_score":0.03360951},"labels":[],"label_agreement":null},{"id":"W4399348317","doi":"10.1016/j.trb.2024.102968","title":"The consistent vehicle routing problem with stochastic customers and demands","year":2024,"lang":"en","type":"article","venue":"Transportation Research Part B Methodological","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Group for Research in Decision Analysis; HEC Montréal","funders":"","keywords":"Vehicle routing problem; Routing (electronic design automation); Computer science; Transport engineering; Operations research; Mathematical optimization; Economics; Engineering; Mathematics; Computer network","score_opus":0.18988650726679812,"score_gpt":0.4088746423061573,"score_spread":0.21898813503935916,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399348317","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.043216377,0.0003921067,0.9503194,0.0018004832,0.00009152868,0.00006106138,0.00031476867,0.000059171543,0.0037450127],"genre_scores_gemma":[0.68069375,0.001019521,0.3050613,0.0006213709,0.00022650439,0.0003693031,0.0008723058,0.00017648576,0.010959531],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9967114,0.0019932734,0.00009333511,0.0006214427,0.00039641233,0.00018417226],"domain_scores_gemma":[0.9927356,0.005936097,0.0005125116,0.00026120697,0.00038281758,0.00017166053],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0042572278,0.0008776873,0.001770566,0.0008653912,0.0006169472,0.0021050582,0.0033814972,0.003107157,0.002656026],"category_scores_gemma":[0.015874328,0.0017474723,0.001322108,0.0017452777,0.0022229424,0.004184501,0.0014177366,0.0025703765,0.0002114831],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008649127,0.000067195724,0.0007677704,0.00011091526,0.00008121043,0.00020410518,0.00007728297,0.64661705,0.00041932418,0.34191623,0.0017290658,0.007923268],"study_design_scores_gemma":[0.000052081392,0.00003830384,0.00024205355,0.000017733377,0.000029929457,0.00004520118,0.00005777389,0.8004225,0.00015772821,0.19777647,0.0011411404,0.000019094372],"about_ca_topic_score_codex":0.004836127,"about_ca_topic_score_gemma":0.0038993703,"teacher_disagreement_score":0.004836127,"about_ca_system_score_codex":0.0017391996,"about_ca_system_score_gemma":0.002947603,"threshold_uncertainty_score":0.022514641},"labels":[],"label_agreement":null},{"id":"W4399534518","doi":"10.1016/j.ejor.2024.06.016","title":"A review of recent advances in time-dependent vehicle routing","year":2024,"lang":"en","type":"review","venue":"European Journal of Operational Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":34,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"Ministero dell'Istruzione e del Merito; Ministero dell’Istruzione, dell’Università e della Ricerca","keywords":"Vehicle routing problem; Computer science; Discretization; Routing (electronic design automation); Field (mathematics); Travel time; Operations research; Graph; Data science; Machine learning; Artificial intelligence; Theoretical computer science; Transport engineering; Computer network; Mathematics; Engineering","score_opus":0.13910451194393095,"score_gpt":0.45000431415672326,"score_spread":0.31089980221279234,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399534518","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00024967475,0.99462765,0.0015905005,0.00037342004,0.00048273895,0.000008229572,0.000052862415,0.000024911707,0.0025901757],"genre_scores_gemma":[0.0015703301,0.9954999,0.0011577038,0.0002027626,0.00043973234,0.000009593098,0.00009046441,0.000007221492,0.001022324],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9997309,0.000041582196,0.000040345727,0.00006890334,0.00009322639,0.000025024008],"domain_scores_gemma":[0.9992649,0.00043418395,0.00007456198,0.00002452061,0.00016584253,0.00003596553],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00067022594,0.0011248543,0.0012179943,0.0023136572,0.0002967621,0.0011826915,0.0010594357,0.0010853482,0.007297715],"category_scores_gemma":[0.0015321027,0.00056113285,0.00071672193,0.0037147931,0.00032946476,0.0020347985,0.00063236256,0.0013786573,0.003730462],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000065868284,0.000102385275,0.0002538559,0.023786059,0.000107630905,0.00015447027,0.000067827605,0.0036666712,0.0015912339,0.015112202,0.04521503,0.9098767],"study_design_scores_gemma":[0.00000954519,0.00011649967,0.00048667635,0.0034830002,0.00012755876,0.0005225534,0.000059669448,0.00084901904,0.00050791056,0.0051288884,0.98867387,0.00003486687],"about_ca_topic_score_codex":0.0012137272,"about_ca_topic_score_gemma":0.0014987792,"teacher_disagreement_score":0.007297715,"about_ca_system_score_codex":0.0005192984,"about_ca_system_score_gemma":0.0012255996,"threshold_uncertainty_score":0.024413347},"labels":[],"label_agreement":null},{"id":"W4399635305","doi":"10.5267/j.dsl.2024.5.008","title":"Stas crossover with K-mean clustering for vehicle routing problem with time window","year":2024,"lang":"en","type":"article","venue":"Decision Science Letters","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Crossover; Cluster analysis; Vehicle routing problem; Window (computing); Computer science; Path (computing); Routing (electronic design automation); Mathematical optimization; Operations research; Mathematics; Artificial intelligence; Computer network","score_opus":0.013378693451587877,"score_gpt":0.2747596204419801,"score_spread":0.2613809269903922,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399635305","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2622063,0.0010276827,0.727561,0.000727868,0.00020127758,0.0002724236,0.0002023091,0.0006505535,0.00715058],"genre_scores_gemma":[0.7833006,0.0005608935,0.21125674,0.00012317758,0.000053319847,0.00026777186,0.00037103283,0.000091598755,0.0039748712],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993375,0.00022260993,0.000038012768,0.00013051303,0.00016748538,0.00010390849],"domain_scores_gemma":[0.99934834,0.0003309836,0.00007795277,0.00007060455,0.00012143319,0.000050775943],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010883103,0.00069124764,0.00096982176,0.0011316787,0.0009995383,0.00088314014,0.001145909,0.0010219476,0.0014907846],"category_scores_gemma":[0.0018972704,0.00029254972,0.0013934611,0.0017504492,0.0005557895,0.00093782553,0.0006904066,0.0011145842,0.00015162071],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019666713,0.00018348725,0.002174863,0.00014606547,0.00012048937,0.00018601523,0.00019880614,0.89162284,0.006490468,0.019177364,0.0023467774,0.077156104],"study_design_scores_gemma":[0.00004358714,0.00016072944,0.0006681058,0.000010695127,0.000030624917,0.00008702646,0.000058449823,0.9904418,0.0019823213,0.0051320554,0.0013698062,0.000014818538],"about_ca_topic_score_codex":0.0057584364,"about_ca_topic_score_gemma":0.003976241,"teacher_disagreement_score":0.0057584364,"about_ca_system_score_codex":0.0012314111,"about_ca_system_score_gemma":0.0015345212,"threshold_uncertainty_score":0.011449814},"labels":[],"label_agreement":null},{"id":"W4399640490","doi":"10.1007/s11067-024-09633-3","title":"The Classical p-median Problem as a Surrogate Model in Hub Location","year":2024,"lang":"en","type":"article","venue":"Networks and Spatial Economics","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Military College of Canada","funders":"","keywords":"Solver; Mathematical optimization; Heuristic; Spoke-hub distribution paradigm; Mathematics; Limit (mathematics); Fraction (chemistry); Flow network; Facility location problem; Variable (mathematics); Computer science; Engineering","score_opus":0.00905182457387664,"score_gpt":0.22379274917860567,"score_spread":0.21474092460472904,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399640490","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010650881,0.00037396417,0.9828681,0.00094266183,0.000099113124,0.000026368145,0.00017973637,0.000053008396,0.004806207],"genre_scores_gemma":[0.679147,0.001293564,0.29028565,0.00059965637,0.00043732897,0.00034527094,0.0005967872,0.00026218928,0.027032413],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99804235,0.0013374195,0.00004513698,0.00026758146,0.00020841352,0.000099073244],"domain_scores_gemma":[0.9960901,0.002901005,0.0003478115,0.00019962926,0.00027644055,0.00018496948],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0051013166,0.0009315379,0.0020820224,0.0011948837,0.0007695998,0.0025735607,0.0027978602,0.0038727762,0.0037481347],"category_scores_gemma":[0.010636613,0.0010961159,0.0013676201,0.0022501799,0.0023551346,0.0037777422,0.002397138,0.0024643992,0.00048211712],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000028262348,0.00001711759,0.00017752494,0.00003715253,0.000020949421,0.000033717937,0.000017798002,0.9120442,0.000082128325,0.083772615,0.0009141683,0.0028542546],"study_design_scores_gemma":[0.000004551292,0.000007495501,0.000027822009,0.000006648034,0.0000033842139,0.000008751896,0.000007668673,0.9639118,0.00003674201,0.03556937,0.0004120661,0.0000036911729],"about_ca_topic_score_codex":0.004737529,"about_ca_topic_score_gemma":0.0041015297,"teacher_disagreement_score":0.0051013166,"about_ca_system_score_codex":0.0018660607,"about_ca_system_score_gemma":0.0016727374,"threshold_uncertainty_score":0.026978672},"labels":[],"label_agreement":null},{"id":"W4399715246","doi":"10.1007/978-3-031-62912-9_8","title":"A Memetic Algorithm for Large-Scale Real-World Vehicle Routing Problems with Simultaneous Pickup and Delivery with Time Windows","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brock University","funders":"","keywords":"Pickup; Vehicle routing problem; Computer science; Memetic algorithm; Routing (electronic design automation); Scale (ratio); Algorithm; Local search (optimization); Embedded system; Artificial intelligence","score_opus":0.00935294862045187,"score_gpt":0.22789409507101396,"score_spread":0.2185411464505621,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399715246","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011011463,0.0008321474,0.9794622,0.0003899564,0.00032128618,0.00009288477,0.00006426986,0.0004479194,0.007377967],"genre_scores_gemma":[0.22952151,0.0007631486,0.7558606,0.0003985446,0.0002816309,0.00051711133,0.00014835417,0.00020814681,0.012300893],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99973243,0.000096643766,0.000016038714,0.000045608896,0.0000737975,0.00003538757],"domain_scores_gemma":[0.9991874,0.00056146743,0.000048348695,0.00006165731,0.00010844461,0.00003272235],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012309157,0.0009489938,0.0010960783,0.00078799366,0.0007713039,0.0010134348,0.0020988209,0.0019720795,0.0036939026],"category_scores_gemma":[0.002252801,0.0006390939,0.0010466479,0.001107213,0.00085826044,0.00097438676,0.0012286257,0.0014740209,0.0004670034],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006453799,0.000065990316,0.00017147318,0.00008038342,0.0000801433,0.0000623567,0.000039351144,0.9195446,0.00092295726,0.012290357,0.0035500026,0.063127965],"study_design_scores_gemma":[0.000012670117,0.00001615704,0.000025287449,0.000005416774,0.000007231101,0.0000135414775,0.000005169723,0.99506736,0.00014168068,0.003942808,0.0007594212,0.0000032697453],"about_ca_topic_score_codex":0.0029731817,"about_ca_topic_score_gemma":0.0031126754,"teacher_disagreement_score":0.0036939026,"about_ca_system_score_codex":0.0007701904,"about_ca_system_score_gemma":0.0010635529,"threshold_uncertainty_score":0.012357354},"labels":[],"label_agreement":null},{"id":"W4399828009","doi":"10.32920/26052727","title":"Electric Vehicle Routing Problem and Solution Approaches","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Vehicle routing problem; Routing (electronic design automation); Computer science; Computer network","score_opus":0.03867867903395701,"score_gpt":0.2503435447999927,"score_spread":0.21166486576603571,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399828009","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009628385,0.008124319,0.85194653,0.0028545463,0.00050491944,0.0003729935,0.000562413,0.00020727435,0.1257986],"genre_scores_gemma":[0.3155529,0.023835845,0.5362229,0.0012838602,0.0010667221,0.0015564857,0.0016008953,0.0001793057,0.11870121],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993356,0.00020951772,0.000028752793,0.00017913552,0.00015881716,0.00008816782],"domain_scores_gemma":[0.99975306,0.00011203868,0.00003301523,0.000012361339,0.000073903124,0.000015646865],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007367191,0.0014748084,0.00077050575,0.0009939636,0.00069270935,0.00226481,0.0016762209,0.0019722204,0.0108872205],"category_scores_gemma":[0.0014087711,0.0005760792,0.0010425413,0.0019613479,0.00059282914,0.0012534359,0.0012737293,0.0018676921,0.0011382202],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000029710502,0.000101399026,0.00029199146,0.00041709552,0.00004278116,0.00018828543,0.00012545002,0.64682776,0.00059973495,0.27525055,0.011665728,0.064459436],"study_design_scores_gemma":[0.000039954666,0.00006510032,0.0002159315,0.0001286632,0.000025566304,0.00017067991,0.000187171,0.7665823,0.0003872312,0.1750228,0.05715025,0.000024449524],"about_ca_topic_score_codex":0.0053774216,"about_ca_topic_score_gemma":0.0040779174,"teacher_disagreement_score":0.0108872205,"about_ca_system_score_codex":0.0019705503,"about_ca_system_score_gemma":0.001984947,"threshold_uncertainty_score":0.03642142},"labels":[],"label_agreement":null},{"id":"W4399828222","doi":"10.32920/26052727.v1","title":"Electric Vehicle Routing Problem and Solution Approaches","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Vehicle routing problem; Computer science; Electric vehicle; Routing (electronic design automation); Mathematical optimization; Mathematics; Computer network; Physics","score_opus":0.03867867903395701,"score_gpt":0.2503435447999927,"score_spread":0.21166486576603571,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399828222","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009628385,0.008124319,0.85194653,0.0028545463,0.00050491944,0.0003729935,0.000562413,0.00020727435,0.1257986],"genre_scores_gemma":[0.3155529,0.023835845,0.5362229,0.0012838602,0.0010667221,0.0015564857,0.0016008953,0.0001793057,0.11870121],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993356,0.00020951772,0.000028752793,0.00017913552,0.00015881716,0.00008816782],"domain_scores_gemma":[0.99975306,0.00011203868,0.00003301523,0.000012361339,0.000073903124,0.000015646865],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007367191,0.0014748084,0.00077050575,0.0009939636,0.00069270935,0.00226481,0.0016762209,0.0019722204,0.0108872205],"category_scores_gemma":[0.0014087711,0.0005760792,0.0010425413,0.0019613479,0.00059282914,0.0012534359,0.0012737293,0.0018676921,0.0011382202],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000029710502,0.000101399026,0.00029199146,0.00041709552,0.00004278116,0.00018828543,0.00012545002,0.64682776,0.00059973495,0.27525055,0.011665728,0.064459436],"study_design_scores_gemma":[0.000039954666,0.00006510032,0.0002159315,0.0001286632,0.000025566304,0.00017067991,0.000187171,0.7665823,0.0003872312,0.1750228,0.05715025,0.000024449524],"about_ca_topic_score_codex":0.0053774216,"about_ca_topic_score_gemma":0.0040779174,"teacher_disagreement_score":0.0108872205,"about_ca_system_score_codex":0.0019705503,"about_ca_system_score_gemma":0.001984947,"threshold_uncertainty_score":0.03642142},"labels":[],"label_agreement":null},{"id":"W4399924090","doi":"10.2139/ssrn.4872999","title":"A Contextual Framework for Learning Routing Experiences in Last-Mile Delivery","year":2024,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"","keywords":"Mile; Last mile (transportation); Routing (electronic design automation); Computer science; Business; Geography; Computer network","score_opus":0.015909885999673502,"score_gpt":0.2856285855236928,"score_spread":0.2697186995240193,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399924090","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.031544935,0.00047114317,0.96389294,0.0005411782,0.000054906704,0.000053580807,0.0003840631,0.0003310815,0.0027261411],"genre_scores_gemma":[0.8167204,0.00056249154,0.17841288,0.00017991243,0.00011642924,0.00021139669,0.0008784104,0.00010150434,0.0028165688],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9988417,0.00042189658,0.000060560036,0.00043576185,0.00012860163,0.000111417554],"domain_scores_gemma":[0.99418855,0.004007553,0.00044261955,0.0005642062,0.0004420113,0.0003549209],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019898934,0.0006799284,0.00081730133,0.0013203564,0.0006240306,0.0018318633,0.0022168346,0.0015056868,0.006532905],"category_scores_gemma":[0.014121378,0.0005972525,0.00093727576,0.0013853922,0.0016566095,0.0036493717,0.0028015024,0.0020656725,0.00042949026],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00046003057,0.0003440994,0.010014075,0.00032163385,0.00021016148,0.0003186815,0.001342096,0.5396581,0.0015529705,0.31212467,0.0032958542,0.13035761],"study_design_scores_gemma":[0.000029253906,0.000094392875,0.0012011696,0.000057943016,0.000038374917,0.000034538494,0.00022827431,0.8400981,0.00040318046,0.15547252,0.0023185154,0.000023680415],"about_ca_topic_score_codex":0.008910899,"about_ca_topic_score_gemma":0.011712782,"teacher_disagreement_score":0.008910899,"about_ca_system_score_codex":0.0015431297,"about_ca_system_score_gemma":0.0010417957,"threshold_uncertainty_score":0.021854758},"labels":[],"label_agreement":null},{"id":"W4400083685","doi":"10.1007/978-3-031-57603-4_17","title":"Express Package Delivery Optimization Using Walkers, Cargo Tricycles and Delivery Trucks","year":2024,"lang":"en","type":"book-chapter","venue":"International series in management science/operations research/International series in operations research & management science","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Truck; Transport engineering; Computer science; Business; Engineering; Automotive engineering","score_opus":0.05921402736840863,"score_gpt":0.3765023370451528,"score_spread":0.31728830967674415,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400083685","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03650075,0.0008812919,0.9363724,0.00027629567,0.0001805824,0.000038728856,0.00017895272,0.0001992839,0.025371704],"genre_scores_gemma":[0.54463226,0.002677466,0.31818196,0.00019873427,0.00020218815,0.00025026474,0.0007335837,0.0009048612,0.13221867],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998036,0.00006994145,0.000008080467,0.000039231578,0.000054651846,0.000024470037],"domain_scores_gemma":[0.9998584,0.000062331834,0.000020168174,0.000014679068,0.00003202377,0.000012348714],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047217592,0.0009220317,0.0011989281,0.00048297126,0.00029099,0.0009937169,0.00087841775,0.0008170712,0.006483673],"category_scores_gemma":[0.0009997283,0.00059095566,0.0011457977,0.0011917753,0.00048217984,0.0012929677,0.0008270284,0.0009524712,0.0010305562],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000065260436,0.000083928106,0.000275576,0.00009461847,0.000029110552,0.00003654744,0.000027725899,0.8849887,0.0019048593,0.054064162,0.0047309143,0.053698637],"study_design_scores_gemma":[0.0000029735938,0.000029147908,0.000084173196,0.000007111844,0.000008153372,0.0000114556415,0.000011424885,0.98701525,0.00026302785,0.010528899,0.0020339976,0.000004368899],"about_ca_topic_score_codex":0.0031638548,"about_ca_topic_score_gemma":0.0037420306,"teacher_disagreement_score":0.006483673,"about_ca_system_score_codex":0.00062863005,"about_ca_system_score_gemma":0.00078646635,"threshold_uncertainty_score":0.02169007},"labels":[],"label_agreement":null},{"id":"W4400083698","doi":"10.1007/978-3-031-57603-4_16","title":"Production Inventory Technician Routing Problem: A Bi-objective Post-sales Application","year":2024,"lang":"en","type":"book-chapter","venue":"International series in management science/operations research/International series in operations research & management science","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal; Polytechnique Montréal","funders":"","keywords":"Technician; Routing (electronic design automation); Production (economics); Operations research; Business; Manufacturing engineering; Computer science; Operations management; Engineering; Computer network; Economics; Electrical engineering; Microeconomics","score_opus":0.04092022036264643,"score_gpt":0.3819230771249259,"score_spread":0.3410028567622795,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400083698","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07576657,0.0021621042,0.8772489,0.0011660052,0.00040769833,0.00033386814,0.0012617866,0.00047695686,0.041176103],"genre_scores_gemma":[0.54855615,0.0027220212,0.38074934,0.00035269218,0.00049685943,0.0006116414,0.0014229107,0.00045238572,0.06463615],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993031,0.00026053746,0.000029068728,0.00012370684,0.00018829378,0.00009522465],"domain_scores_gemma":[0.9992073,0.0004903386,0.00006836639,0.000044740267,0.000121768106,0.00006751772],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016620727,0.002392843,0.0021718235,0.0012199008,0.0006552147,0.0022090056,0.0024492727,0.0028503311,0.0082183555],"category_scores_gemma":[0.0020796668,0.0008561222,0.0014626469,0.0028800622,0.00061157864,0.0017752049,0.0015871925,0.0020602352,0.00093479815],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018752534,0.00027327795,0.00048316648,0.0003259707,0.00008331352,0.00019340201,0.000043567754,0.9317877,0.0016576553,0.008981926,0.0041064317,0.05187605],"study_design_scores_gemma":[0.000016653357,0.0000731136,0.00024296567,0.000013003458,0.000017927618,0.000036379824,0.000022409586,0.99546754,0.00036552042,0.002799676,0.00093556894,0.000009196419],"about_ca_topic_score_codex":0.0054105604,"about_ca_topic_score_gemma":0.004382526,"teacher_disagreement_score":0.0082183555,"about_ca_system_score_codex":0.0010377716,"about_ca_system_score_gemma":0.0013019932,"threshold_uncertainty_score":0.02749312},"labels":[],"label_agreement":null},{"id":"W4400278253","doi":"10.2139/ssrn.4884036","title":"An Analytical Approach to Planning for and Managing Random Disruption in Rail Intermodal Networks","year":2024,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Computer science; Business; Operations research; Transport engineering; Engineering","score_opus":0.01748252304989184,"score_gpt":0.3081381141617294,"score_spread":0.2906555911118376,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400278253","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0060864077,0.00046610044,0.9845479,0.0007725696,0.00008532292,0.000049237693,0.00007028627,0.000085110645,0.007837052],"genre_scores_gemma":[0.7553565,0.0026797373,0.22626874,0.00035442633,0.000312891,0.0003497312,0.00015481858,0.00017934451,0.014343671],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99926275,0.00032480512,0.000025728614,0.00009344614,0.00017869897,0.000114614755],"domain_scores_gemma":[0.99860674,0.0010054008,0.00013112371,0.00004761026,0.00014842092,0.000060746253],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001845591,0.001199994,0.0010079291,0.0015011021,0.0007979315,0.0020950653,0.0021766329,0.0016305681,0.003701943],"category_scores_gemma":[0.0053804987,0.001237889,0.0012842146,0.0018908206,0.0019788775,0.0019081442,0.0014550823,0.0016974969,0.00029477777],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000054466323,0.000011565434,0.00007104145,0.000020304742,0.000010455715,0.000024191113,0.000019382867,0.96065944,0.00010133625,0.03563145,0.00039245782,0.003052912],"study_design_scores_gemma":[0.0000017973839,0.000005124975,0.000023126491,0.0000056230074,0.0000042571614,0.000005071324,0.000016535814,0.97684735,0.000044200762,0.022593247,0.00045063454,0.0000031360269],"about_ca_topic_score_codex":0.016836805,"about_ca_topic_score_gemma":0.013312434,"teacher_disagreement_score":0.016836805,"about_ca_system_score_codex":0.0035519758,"about_ca_system_score_gemma":0.002858326,"threshold_uncertainty_score":0.033477604},"labels":[],"label_agreement":null},{"id":"W4400338818","doi":"10.1016/j.cor.2024.106761","title":"An exact algorithm for simultaneous pickup and delivery problem with split demand and time windows","year":2024,"lang":"en","type":"article","venue":"Computers & Operations Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University; University of Toronto","funders":"National Natural Science Foundation of China","keywords":"Column generation; Pickup; Computer science; Vehicle routing problem; Mathematical optimization; Solver; Algorithm; Heuristic; Routing (electronic design automation); Mathematics; Artificial intelligence","score_opus":0.024986264957067815,"score_gpt":0.32485885482482557,"score_spread":0.2998725898677578,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400338818","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015274098,0.000313394,0.9785059,0.0001797617,0.00013225312,0.00013501008,0.000112815585,0.00074614247,0.0046005985],"genre_scores_gemma":[0.19917893,0.00030118885,0.79413986,0.00014826666,0.00010004336,0.0003561162,0.0003011479,0.00018521707,0.005289236],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991014,0.00013731238,0.00004342421,0.00021090505,0.00029201846,0.0002149102],"domain_scores_gemma":[0.99891233,0.00063406676,0.00007142118,0.00012439846,0.00017788082,0.000079857884],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00116493,0.0011962007,0.0020584976,0.0008810632,0.00089870155,0.001538341,0.0025377797,0.0018281597,0.008139267],"category_scores_gemma":[0.002677958,0.0011626048,0.001181085,0.0015741782,0.00071967836,0.0023782118,0.001904382,0.0017135114,0.0009493071],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00034914652,0.00022657146,0.00035054915,0.00018333239,0.00005847541,0.000089631896,0.00008521878,0.81542486,0.002903967,0.01863766,0.004792965,0.15689763],"study_design_scores_gemma":[0.000063084895,0.00004104535,0.000067212466,0.0000055557844,0.000011048017,0.000022914608,0.000012471936,0.9933001,0.00033018488,0.0054670167,0.00067250716,0.0000069149055],"about_ca_topic_score_codex":0.011103478,"about_ca_topic_score_gemma":0.010915571,"teacher_disagreement_score":0.011103478,"about_ca_system_score_codex":0.0017313979,"about_ca_system_score_gemma":0.0041690622,"threshold_uncertainty_score":0.027228534},"labels":[],"label_agreement":null},{"id":"W4400684999","doi":"10.1287/opre.2023.0569","title":"Hardness of Pricing Routes for Two-Stage Stochastic Vehicle Routing Problems with Scenarios","year":2024,"lang":"en","type":"article","venue":"Operations Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Vehicle routing problem; Computer science; Mathematical optimization; Routing (electronic design automation); Operations research; Stage (stratigraphy); Mathematics; Computer network; Geology","score_opus":0.06734017771732487,"score_gpt":0.37508820165387585,"score_spread":0.307748023936551,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400684999","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1322932,0.0025005033,0.8330431,0.0060854782,0.00019044607,0.00028537135,0.0011279867,0.00026864064,0.024205271],"genre_scores_gemma":[0.78402054,0.004251527,0.19835083,0.00085986545,0.0005984931,0.00059299293,0.0015366522,0.00034330174,0.00944578],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99787354,0.0010480762,0.00010119475,0.00041278225,0.00029270197,0.0002715721],"domain_scores_gemma":[0.9811715,0.016977495,0.000598685,0.00047865944,0.0004162502,0.00035730438],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00328137,0.0013959197,0.002247334,0.0011482489,0.00096017995,0.0028774752,0.002333116,0.0025722538,0.006367669],"category_scores_gemma":[0.01679499,0.0011374658,0.0026446322,0.0016002136,0.002359361,0.006504821,0.002067838,0.004990039,0.00042795055],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015129037,0.00017653526,0.0011211045,0.00039087515,0.00011045937,0.00018230698,0.00022457034,0.7539611,0.0006322296,0.21700403,0.0060671885,0.019978367],"study_design_scores_gemma":[0.00004612632,0.00004997834,0.0004232555,0.00004439717,0.000018958712,0.00007481559,0.000094997995,0.7236496,0.00027470512,0.27331197,0.0019864428,0.000024739347],"about_ca_topic_score_codex":0.0031440267,"about_ca_topic_score_gemma":0.0017865245,"teacher_disagreement_score":0.006367669,"about_ca_system_score_codex":0.0019171668,"about_ca_system_score_gemma":0.00159042,"threshold_uncertainty_score":0.021302044},"labels":[],"label_agreement":null},{"id":"W4400765328","doi":"10.1007/s00291-024-00781-z","title":"New formulations for the robust vehicle routing problem with time windows under demand and travel time uncertainty","year":2024,"lang":"en","type":"article","venue":"OR Spectrum","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Transport Canada; Université Laval","funders":"Fonds de recherche du Québec – Nature et technologies; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Natural Sciences and Engineering Research Council of Canada; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior; Fundação de Amparo à Pesquisa do Estado de São Paulo","keywords":"Time travel; Vehicle routing problem; Travel time; Computer science; Routing (electronic design automation); Operations research; Transport engineering; Engineering; Computer network; Artificial intelligence","score_opus":0.016687559788272018,"score_gpt":0.24562073554589928,"score_spread":0.22893317575762726,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400765328","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.001147303,0.00027564383,0.99550855,0.00012750657,0.00006316236,0.00001966646,0.000058042184,0.000032013213,0.0027680967],"genre_scores_gemma":[0.23006417,0.0031199786,0.7367485,0.00051313173,0.0006684845,0.0005927199,0.00066702423,0.00058581197,0.027040198],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991227,0.00034166907,0.000043079966,0.00016187753,0.0002544324,0.000076281394],"domain_scores_gemma":[0.99845207,0.00094295124,0.00021771183,0.00008075245,0.0002505332,0.00005602121],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022459188,0.0018587905,0.0010966047,0.00089014706,0.00029452803,0.0018584101,0.0020669086,0.001856245,0.0065469705],"category_scores_gemma":[0.005068684,0.0008464459,0.0012573118,0.0010782146,0.00078817725,0.0027414516,0.0015087395,0.0024006744,0.0008767942],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002947751,0.00003743746,0.00011137011,0.00013466057,0.000042551088,0.000061022285,0.00004856285,0.8583754,0.0011502493,0.11835354,0.0024514487,0.01920431],"study_design_scores_gemma":[0.0000048235393,0.000009148398,0.000026441114,0.000011894167,0.0000069163693,0.000011064317,0.000009128119,0.98291093,0.00011511145,0.015476893,0.0014122492,0.000005449204],"about_ca_topic_score_codex":0.003395447,"about_ca_topic_score_gemma":0.0032696535,"teacher_disagreement_score":0.0065469705,"about_ca_system_score_codex":0.0014302772,"about_ca_system_score_gemma":0.0013572824,"threshold_uncertainty_score":0.021901786},"labels":[],"label_agreement":null},{"id":"W4401084216","doi":"10.1287/ijoc.2023.0106","title":"AILS-II: An Adaptive Iterated Local Search Heuristic for the Large-Scale Capacitated Vehicle Routing Problem","year":2024,"lang":"en","type":"article","venue":"INFORMS journal on computing","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Group for Research in Decision Analysis; HEC Montréal","funders":"","keywords":"Vehicle routing problem; Iterated local search; Mathematical optimization; Scale (ratio); Heuristic; Computer science; Local search (optimization); Routing (electronic design automation); Iterated function; Mathematics; Geography","score_opus":0.02699017078605199,"score_gpt":0.2916248504667616,"score_spread":0.26463467968070964,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401084216","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.053137343,0.0014412833,0.92972034,0.00048232396,0.00017724866,0.00039084454,0.00026188183,0.002087465,0.012301355],"genre_scores_gemma":[0.48451284,0.00063720037,0.5060305,0.0004644012,0.000099009914,0.0008396058,0.00085420976,0.00041944892,0.006142925],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994074,0.00022939971,0.000027615195,0.00009507188,0.00014095541,0.00009951369],"domain_scores_gemma":[0.9991091,0.0005499524,0.00008962242,0.000054383014,0.000116556286,0.00008043285],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010235718,0.0013342096,0.0012903658,0.0011857711,0.00048704632,0.0009232246,0.0022784478,0.0013651092,0.003173657],"category_scores_gemma":[0.0017826164,0.0005684315,0.0013252124,0.0012210017,0.0006253264,0.0009833304,0.001273975,0.0012337168,0.0005495918],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008003585,0.00011065991,0.0003915587,0.00016885388,0.00007653661,0.000116236995,0.00006838534,0.9355017,0.001308245,0.004839508,0.0032362367,0.054102175],"study_design_scores_gemma":[0.000048344653,0.000067555746,0.00007223261,0.000013729667,0.000018738223,0.000026551108,0.000024183637,0.9958477,0.00034661024,0.0021368298,0.0013882729,0.000009254445],"about_ca_topic_score_codex":0.004852561,"about_ca_topic_score_gemma":0.0053120162,"teacher_disagreement_score":0.004852561,"about_ca_system_score_codex":0.00086554134,"about_ca_system_score_gemma":0.0017460337,"threshold_uncertainty_score":0.010616899},"labels":[],"label_agreement":null},{"id":"W4401328212","doi":"10.1016/j.trb.2024.103040","title":"Sustainable hub location under uncertainty","year":2024,"lang":"en","type":"article","venue":"Transportation Research Part B Methodological","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Transport engineering; Computer science; Operations research; Engineering; Environmental science","score_opus":0.30516005378843936,"score_gpt":0.4689050529878287,"score_spread":0.16374499919938934,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401328212","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07689321,0.0006907317,0.90004987,0.0015617483,0.000086882195,0.000029320494,0.00043070168,0.00013242912,0.020125093],"genre_scores_gemma":[0.9574222,0.0006208128,0.03130121,0.00010075907,0.00006993435,0.000065971486,0.00023187816,0.00010775328,0.010079526],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987845,0.0005035341,0.00003144025,0.00032925906,0.00020090688,0.00015049541],"domain_scores_gemma":[0.99584204,0.0030652215,0.00037384816,0.00025326753,0.00037011024,0.00009543584],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002355781,0.00062533864,0.0013883838,0.0011386148,0.00065053813,0.0021003895,0.0015105157,0.0015464402,0.0038450377],"category_scores_gemma":[0.00903448,0.00092189596,0.0009412949,0.002093126,0.0017543413,0.0033020629,0.001763673,0.0013101422,0.00036369127],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026656327,0.000009534347,0.00044559472,0.000037686714,0.000034127333,0.000050559644,0.000037991387,0.8391008,0.00017770418,0.15404676,0.0009041017,0.005128478],"study_design_scores_gemma":[0.000005025222,0.00001437571,0.0002563421,0.000012748434,0.000015783253,0.000022007749,0.00005262374,0.7933267,0.00019762896,0.20519312,0.00089254475,0.000011095145],"about_ca_topic_score_codex":0.004827962,"about_ca_topic_score_gemma":0.0032692822,"teacher_disagreement_score":0.004827962,"about_ca_system_score_codex":0.0028844296,"about_ca_system_score_gemma":0.0017096341,"threshold_uncertainty_score":0.020928144},"labels":[],"label_agreement":null},{"id":"W4401515398","doi":"10.1287/trsc.2023.0252","title":"A Unified Branch-Price-and-Cut Algorithm for Multicompartment Pickup and Delivery Problems","year":2024,"lang":"en","type":"article","venue":"Transportation Science","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Group for Research in Decision Analysis; Université du Québec à Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Nederlandse Organisatie voor Wetenschappelijk Onderzoek","keywords":"Compartment (ship); Benchmark (surveying); Computer science; Flexibility (engineering); Branch and cut; Pickup; Mathematical optimization; Running time; Algorithm; Operations research; Mathematics; Integer programming; Artificial intelligence; Statistics","score_opus":0.026585570824390187,"score_gpt":0.2830538029403578,"score_spread":0.25646823211596764,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401515398","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014599229,0.0006426356,0.9787636,0.0003870719,0.00009081723,0.00022268086,0.00025568594,0.000517199,0.004521125],"genre_scores_gemma":[0.13905202,0.00052511226,0.85439116,0.00019301467,0.00009420771,0.00047201707,0.00096578785,0.0002614289,0.0040452713],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99895525,0.00034690308,0.000044343116,0.00023062051,0.0002193299,0.0002035914],"domain_scores_gemma":[0.998406,0.0010545966,0.00010524108,0.000118820564,0.00019232408,0.00012303308],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018035107,0.0016865998,0.0023404441,0.0010478632,0.00084013207,0.0021148117,0.0025709034,0.00279792,0.008191031],"category_scores_gemma":[0.004000808,0.00091004,0.0012278991,0.0023133394,0.00061195676,0.0023170412,0.0016742838,0.0026481596,0.0013710747],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024611916,0.00025426646,0.0005629733,0.00032028023,0.00006310352,0.0001339563,0.00009924398,0.80846083,0.0013447035,0.030242983,0.006975188,0.15129642],"study_design_scores_gemma":[0.000043577762,0.000040188697,0.00006390195,0.000013799749,0.000012341463,0.000025255846,0.000016883909,0.9868198,0.00025909385,0.011402908,0.0012965216,0.00000574376],"about_ca_topic_score_codex":0.005094344,"about_ca_topic_score_gemma":0.004925487,"teacher_disagreement_score":0.008191031,"about_ca_system_score_codex":0.0016836261,"about_ca_system_score_gemma":0.0028847381,"threshold_uncertainty_score":0.027401686},"labels":[],"label_agreement":null},{"id":"W4401541814","doi":"10.1007/978-981-97-3682-9_7","title":"Waste Collection Route Optimization for the City of Oshawa","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes in electrical engineering","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Lakeridge Health; Ontario Tech University","funders":"","keywords":"Computer science; Business","score_opus":0.011512280624056128,"score_gpt":0.2306307699567984,"score_spread":0.21911848933274228,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401541814","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.847536,0.001954133,0.10330198,0.0010963553,0.00021942816,0.00016978422,0.0049706316,0.0006238776,0.040127706],"genre_scores_gemma":[0.9177617,0.000821879,0.0540303,0.000062591804,0.00004722087,0.00013060938,0.0031667391,0.0002794548,0.023699587],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998004,0.00005927649,0.0000050414915,0.000037339574,0.000023272945,0.00007474458],"domain_scores_gemma":[0.99982613,0.00008519452,0.000016725793,0.0000096941985,0.000042709915,0.000019627712],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003281995,0.0009453051,0.0017301752,0.0007784717,0.0011520672,0.0013656737,0.00094205793,0.0013963092,0.003932311],"category_scores_gemma":[0.00057482644,0.00079677283,0.0011765984,0.0021056822,0.00043959668,0.000693165,0.0005146283,0.0007239018,0.00041009448],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000058867965,0.000040783296,0.000684942,0.000040641888,0.000026727164,0.000063242594,0.000027417898,0.9884448,0.00029979242,0.0015484904,0.0017797459,0.00698466],"study_design_scores_gemma":[0.000015498972,0.000033082895,0.0012922495,0.0000069058037,0.000022026341,0.000017820095,0.0001753025,0.99531025,0.00021886351,0.0016597197,0.0012369704,0.000011395924],"about_ca_topic_score_codex":0.15426134,"about_ca_topic_score_gemma":0.17288095,"teacher_disagreement_score":0.84573865,"about_ca_system_score_codex":0.0022554486,"about_ca_system_score_gemma":0.0028578097,"threshold_uncertainty_score":0.30672687},"labels":[],"label_agreement":null},{"id":"W4401665365","doi":"10.1016/j.cor.2024.106807","title":"A branch-and-cut algorithm for the time-dependent vehicle routing problem with time windows and combinatorial auctions","year":2024,"lang":"en","type":"article","venue":"Computers & Operations Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"Fundamental Research Funds for the Central Universities; National University's Basic Research Foundation of China; National Natural Science Foundation of China","keywords":"Vehicle routing problem; Computer science; Combinatorial auction; Branch and cut; Routing (electronic design automation); Combinatorial optimization; Algorithm; Common value auction; Mathematical optimization; Integer programming; Mathematics; Computer network","score_opus":0.023290804997226442,"score_gpt":0.3124205499573705,"score_spread":0.28912974496014404,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401665365","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010123187,0.0004208383,0.983373,0.00033178515,0.00011100012,0.00016116818,0.00011224281,0.0004463414,0.0049205185],"genre_scores_gemma":[0.100506805,0.00043895218,0.8925191,0.00016638264,0.000104624545,0.00030532735,0.00036026823,0.00023807111,0.005360446],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99914896,0.00028622174,0.000039989147,0.00014725511,0.00023198359,0.00014554203],"domain_scores_gemma":[0.99826884,0.0012501383,0.00007795371,0.00008724959,0.00018596389,0.00012983844],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016268898,0.0013019764,0.0023457343,0.0013091417,0.0009917435,0.0020519032,0.0024913433,0.002543988,0.008270007],"category_scores_gemma":[0.0039216946,0.0012565833,0.001323579,0.002306401,0.0007409157,0.0025300605,0.0015978501,0.0027959144,0.0011307029],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003911928,0.00047821505,0.00041398616,0.0001791768,0.00010398122,0.0001181011,0.000078126046,0.7274806,0.0018437713,0.03699422,0.009205908,0.2227127],"study_design_scores_gemma":[0.00006670131,0.000045455945,0.00006437903,0.000009763733,0.000017736747,0.000025306486,0.000012351758,0.9854527,0.000250486,0.013107154,0.00094054197,0.000007464762],"about_ca_topic_score_codex":0.007149213,"about_ca_topic_score_gemma":0.0064731855,"teacher_disagreement_score":0.008270007,"about_ca_system_score_codex":0.0014642494,"about_ca_system_score_gemma":0.0030994257,"threshold_uncertainty_score":0.027665913},"labels":[],"label_agreement":null},{"id":"W4402125184","doi":"10.1287/ijoc.2023.0404","title":"The Electric Vehicle Routing and Overnight Charging Scheduling Problem on a Multigraph","year":2024,"lang":"en","type":"article","venue":"INFORMS journal on computing","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"HEC Montréal; Polytechnique Montréal; Group for Research in Decision Analysis","funders":"","keywords":"Multigraph; Vehicle routing problem; Scheduling (production processes); Computer science; Parallel computing; Operations research; Mathematical optimization; Routing (electronic design automation); Mathematics; Computer network; Theoretical computer science","score_opus":0.010682196198372724,"score_gpt":0.25409578791987375,"score_spread":0.24341359172150104,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402125184","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17187728,0.00081232254,0.7940673,0.0028129623,0.00021248151,0.0004717067,0.0039556245,0.0007372295,0.025053095],"genre_scores_gemma":[0.56275827,0.0011631139,0.41122898,0.00048696646,0.00017369054,0.00041335597,0.0038337049,0.00037761062,0.019564372],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992217,0.0002757371,0.00003197513,0.00024102893,0.00009863048,0.00013095133],"domain_scores_gemma":[0.99863094,0.0009878114,0.00013149607,0.00007372984,0.00007396436,0.000101994716],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00077046826,0.0011205161,0.00091341144,0.0007417536,0.0009110521,0.0013820832,0.0012166081,0.0016746847,0.009378132],"category_scores_gemma":[0.0022907625,0.0005688418,0.0010660025,0.001912462,0.00066148624,0.0022629953,0.0010693916,0.0012024111,0.0009472932],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021262812,0.00018115278,0.0011633378,0.00037282574,0.00008262736,0.00046740397,0.00018945853,0.90465003,0.0026259962,0.04046879,0.009551987,0.040033653],"study_design_scores_gemma":[0.00005201863,0.00008218711,0.0008918638,0.000031266398,0.00003997786,0.00019517975,0.0001770374,0.94473284,0.0011122546,0.042809475,0.009856436,0.000019373003],"about_ca_topic_score_codex":0.0072148885,"about_ca_topic_score_gemma":0.009311055,"teacher_disagreement_score":0.009378132,"about_ca_system_score_codex":0.0017400675,"about_ca_system_score_gemma":0.0013872854,"threshold_uncertainty_score":0.031372964},"labels":[],"label_agreement":null},{"id":"W4402130536","doi":"10.1016/j.eswa.2024.125183","title":"Prize-collecting Electric Vehicle routing model for parcel delivery problem","year":2024,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":30,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Vehicle routing problem; Operations research; Service (business); Last mile (transportation); Electric vehicle; Outsourcing; Metaheuristic; Total cost; Loyalty; Routing (electronic design automation); Total cost of ownership; Integer programming; Quality of service; Work (physics); Business; Computer network; Engineering; Marketing","score_opus":0.022918078238572306,"score_gpt":0.2738865281985426,"score_spread":0.2509684499599703,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402130536","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08732235,0.000986186,0.87158996,0.0024010907,0.00035959543,0.00026109483,0.0015856747,0.0003531097,0.035141017],"genre_scores_gemma":[0.86521983,0.0009419727,0.047062285,0.0002392612,0.000174799,0.00029213866,0.00089073164,0.00011601859,0.085062906],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990858,0.00032466772,0.000030533567,0.0002097668,0.00016128476,0.00018797441],"domain_scores_gemma":[0.9991654,0.00036169303,0.00011964442,0.000050880455,0.00017711513,0.00012531466],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001557582,0.0010259412,0.0022543587,0.0010073383,0.00070792256,0.0019882785,0.004128226,0.0028063662,0.009843189],"category_scores_gemma":[0.0024651743,0.000666974,0.0010166803,0.0018911753,0.001027517,0.0020693708,0.0012819604,0.001791414,0.0007528806],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006051377,0.000052431726,0.0003355232,0.00007473729,0.000020153058,0.00011752893,0.000025638128,0.9622597,0.00032225344,0.028818144,0.002394476,0.0055188793],"study_design_scores_gemma":[0.000010609901,0.000019131929,0.00011103118,0.0000047608337,0.0000077443565,0.000022164239,0.000012814349,0.99156064,0.00006439083,0.007434034,0.00074520597,0.0000075167623],"about_ca_topic_score_codex":0.010455915,"about_ca_topic_score_gemma":0.008906407,"teacher_disagreement_score":0.010455915,"about_ca_system_score_codex":0.0019898147,"about_ca_system_score_gemma":0.0016280247,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W4402615264","doi":"10.1016/j.ejtl.2024.100143","title":"A metaheuristic for a time-dependent vehicle routing problem with time windows, two vehicle fleets and synchronization on a road network","year":2024,"lang":"en","type":"article","venue":"EURO Journal on Transportation and Logistics","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Université de Montréal; Transport Canada","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Vehicle routing problem; Metaheuristic; Synchronization (alternating current); Computer science; Arrival time; Travel time; Time synchronization; Routing (electronic design automation); Fleet management; Real-time computing; Engineering; Transport engineering; Computer network; Artificial intelligence","score_opus":0.013122357018105244,"score_gpt":0.24962707871012074,"score_spread":0.2365047216920155,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402615264","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05507526,0.0009379903,0.93298155,0.0006694417,0.00019940174,0.00024427066,0.00037344158,0.0005431607,0.00897539],"genre_scores_gemma":[0.42718828,0.00071543857,0.5628966,0.00037618988,0.0001153078,0.0006475783,0.0007560628,0.00020680038,0.0070977593],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99952507,0.00019491815,0.000025839494,0.00009193067,0.00008241698,0.0000797251],"domain_scores_gemma":[0.99913824,0.00054927677,0.000107801636,0.000054596625,0.00007677266,0.000073194904],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009808373,0.0013582653,0.0009947527,0.0011351488,0.00049723336,0.0012008202,0.0015833947,0.0020128712,0.0027186961],"category_scores_gemma":[0.0016443728,0.00057503604,0.0016530905,0.0013152086,0.0005506481,0.0011069482,0.00092910556,0.0016117631,0.00030513183],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000044757,0.00006692288,0.00020395531,0.000059161954,0.00005536981,0.000058757112,0.00002125267,0.9768197,0.0006253496,0.007388079,0.0008342707,0.013822409],"study_design_scores_gemma":[0.000025497682,0.00004696679,0.00006731411,0.000012256786,0.000018582485,0.000019376943,0.000014682932,0.9954921,0.00024470154,0.002980036,0.0010735928,0.0000048083575],"about_ca_topic_score_codex":0.00505666,"about_ca_topic_score_gemma":0.004206041,"teacher_disagreement_score":0.00505666,"about_ca_system_score_codex":0.0013120577,"about_ca_system_score_gemma":0.0019232286,"threshold_uncertainty_score":0.0100544095},"labels":[],"label_agreement":null},{"id":"W4402669414","doi":"10.1016/j.omega.2024.103196","title":"Strategic expansion of freight transportation hub networks under demand uncertainty","year":2024,"lang":"en","type":"article","venue":"Omega","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Traffic management; Business; Transport engineering; Industrial organization; Operations research; Engineering","score_opus":0.020982257667322944,"score_gpt":0.2583771455770698,"score_spread":0.23739488790974686,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402669414","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.52763695,0.00038442283,0.45303872,0.0008144637,0.00003562286,0.00015508967,0.00026122484,0.00016293951,0.0175106],"genre_scores_gemma":[0.97338766,0.00020261537,0.023387928,0.000032987653,0.000010846734,0.000052674124,0.000058080732,0.00001906588,0.0028481283],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994124,0.00028439017,0.000011625619,0.000107200525,0.000063786145,0.00012064332],"domain_scores_gemma":[0.998281,0.001064878,0.0003014458,0.00007578985,0.00014367883,0.00013323358],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009798678,0.0006387046,0.00051210314,0.0004825265,0.0004887911,0.0010833534,0.0010429493,0.0012594325,0.0024481635],"category_scores_gemma":[0.0038875071,0.000539763,0.00054209994,0.00072229485,0.0007797167,0.002104399,0.0011587853,0.00089607143,0.00013894065],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000023319255,0.000020960417,0.0004314657,0.000012099981,0.0000074693985,0.000056639994,0.000031681022,0.9868804,0.0003720212,0.00911104,0.00018787355,0.0028648947],"study_design_scores_gemma":[0.0000043686873,0.000017086364,0.00016515762,0.0000030653803,0.000004907881,0.000010050429,0.000050872684,0.9945469,0.00014797263,0.004725302,0.00032065465,0.0000036853944],"about_ca_topic_score_codex":0.008722888,"about_ca_topic_score_gemma":0.009457667,"teacher_disagreement_score":0.008722888,"about_ca_system_score_codex":0.0024628197,"about_ca_system_score_gemma":0.0009703272,"threshold_uncertainty_score":0.017869115},"labels":[],"label_agreement":null},{"id":"W4402827990","doi":"10.1016/j.ejtl.2024.100146","title":"Stability metrics for a maritime inventory routing problem under sailing time uncertainty","year":2024,"lang":"en","type":"article","venue":"EURO Journal on Transportation and Logistics","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"","keywords":"Routing (electronic design automation); Stability (learning theory); Vehicle routing problem; Operations research; Computer science; Marine engineering; Environmental science; Engineering; Computer network; Machine learning","score_opus":0.04209613403359662,"score_gpt":0.28176092578484135,"score_spread":0.23966479175124472,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402827990","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.28870597,0.002638377,0.701681,0.0009064518,0.000074310854,0.00016389423,0.00036324435,0.00018503057,0.0052816626],"genre_scores_gemma":[0.9464513,0.00090398634,0.05111794,0.000039262708,0.00004751033,0.00012482126,0.00023740105,0.0000659849,0.0010118737],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99884343,0.0006020062,0.000059914677,0.00014673898,0.00020558632,0.00014226702],"domain_scores_gemma":[0.99215126,0.0060483385,0.0009359071,0.000114764254,0.000500472,0.00024937867],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0036578246,0.0017129366,0.0011156678,0.0019883665,0.0006622757,0.0016915738,0.0009564538,0.0011152369,0.001212574],"category_scores_gemma":[0.009415747,0.0004620494,0.0011589222,0.0013995597,0.0011692067,0.001773546,0.0011077953,0.0012710412,0.00008812081],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000056730187,0.000025451112,0.0005560529,0.00006045483,0.000043506705,0.00004620831,0.000035479145,0.98779416,0.00068787765,0.0066603585,0.00018894576,0.0038447354],"study_design_scores_gemma":[0.000005107804,0.0000535388,0.0002183984,0.0000072765874,0.000012607972,0.000014981864,0.00001881033,0.9948171,0.00022615997,0.0045241164,0.00009560302,0.0000062275035],"about_ca_topic_score_codex":0.004171205,"about_ca_topic_score_gemma":0.0014101214,"teacher_disagreement_score":0.004171205,"about_ca_system_score_codex":0.0019465078,"about_ca_system_score_gemma":0.0011757911,"threshold_uncertainty_score":0.019344628},"labels":[],"label_agreement":null},{"id":"W4403210609","doi":"10.1109/icecet61485.2024.10698631","title":"Electric-vehicle routing problem with time windows and energy minimization: green logistics with same-day delivery approaches","year":2024,"lang":"en","type":"article","venue":"","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Vehicle routing problem; Minification; Computer science; Energy minimization; Routing (electronic design automation); Energy (signal processing); Mathematical optimization; Automotive engineering; Engineering; Embedded system; Mathematics; World Wide Web; Physics","score_opus":0.01817089141925737,"score_gpt":0.20366100383655444,"score_spread":0.18549011241729707,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403210609","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06529711,0.0012867608,0.92103946,0.0008195053,0.0001455988,0.00014687037,0.00025143495,0.00016531705,0.010847963],"genre_scores_gemma":[0.73676795,0.0013388785,0.24519578,0.00021880273,0.000100332276,0.00027020514,0.0004604803,0.0001477909,0.01549981],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994698,0.00020185027,0.000017604947,0.00010752748,0.000082265884,0.000120942525],"domain_scores_gemma":[0.9996815,0.00015969407,0.00004749502,0.000017515153,0.000045110977,0.00004861911],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00084999163,0.0011608447,0.0010833091,0.0005407221,0.00040939142,0.0010159203,0.001388547,0.0010610371,0.0033402166],"category_scores_gemma":[0.0010389684,0.00047219705,0.0010937128,0.0010956388,0.00040460704,0.0015463739,0.0007984267,0.0010737302,0.00023075014],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010666686,0.00010561082,0.0004462652,0.00014008237,0.000047438563,0.000095655596,0.000046634686,0.950994,0.0010097454,0.017341064,0.0018124729,0.027854245],"study_design_scores_gemma":[0.0000144584965,0.000059187587,0.00017137885,0.000010445805,0.000018828467,0.000044103726,0.000048995003,0.99030626,0.00048554325,0.0072079254,0.0016251337,0.000007735275],"about_ca_topic_score_codex":0.0046292148,"about_ca_topic_score_gemma":0.0048426073,"teacher_disagreement_score":0.0046292148,"about_ca_system_score_codex":0.0010338558,"about_ca_system_score_gemma":0.0014088731,"threshold_uncertainty_score":0.011174142},"labels":[],"label_agreement":null},{"id":"W4403238270","doi":"10.1007/s12532-024-00262-y","title":"Local elimination in the traveling salesman problem","year":2024,"lang":"en","type":"article","venue":"Mathematical Programming Computation","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Rheinische Friedrich-Wilhelms-Universität Bonn; Deutsche Forschungsgemeinschaft","keywords":"Travelling salesman problem; Theory of computation; Mathematical optimization; Computer science; Mathematics; Algorithm","score_opus":0.020716518062968307,"score_gpt":0.2932313392360823,"score_spread":0.272514821173114,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403238270","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2471818,0.000575062,0.7326786,0.00070414954,0.000044987177,0.00021301824,0.00022792337,0.00066878245,0.017705647],"genre_scores_gemma":[0.77634126,0.00038322312,0.21294537,0.00016551884,0.000049591672,0.00023113163,0.00061301904,0.0001711932,0.009099566],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9990214,0.00052539416,0.000019535604,0.0001026625,0.0001867281,0.00014425059],"domain_scores_gemma":[0.9984842,0.0011172257,0.00009074799,0.00012026673,0.00013619587,0.000051411567],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011151423,0.00050145574,0.0010397418,0.0007503068,0.00059872813,0.00085910934,0.0012737238,0.0004523042,0.004011905],"category_scores_gemma":[0.0030698222,0.0003099207,0.0010270497,0.00094248354,0.0008776656,0.0009961777,0.001045867,0.0013455458,0.0005504408],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00035357822,0.0003886374,0.0017696624,0.00037642423,0.00014793861,0.00044884038,0.00026196163,0.81259435,0.0042630727,0.085857086,0.0077489754,0.08578947],"study_design_scores_gemma":[0.00012463682,0.00013350567,0.00038658013,0.000019577636,0.00004466488,0.00008451021,0.000082975916,0.94974095,0.0026085735,0.04325055,0.0035118167,0.000011619361],"about_ca_topic_score_codex":0.004497722,"about_ca_topic_score_gemma":0.0049103773,"teacher_disagreement_score":0.004497722,"about_ca_system_score_codex":0.0007150239,"about_ca_system_score_gemma":0.0010436872,"threshold_uncertainty_score":0.013421118},"labels":[],"label_agreement":null},{"id":"W4403268005","doi":"10.1016/j.tcs.2024.114900","title":"Algorithms for the thief orienteering problem on directed acyclic graphs","year":2024,"lang":"en","type":"article","venue":"Theoretical Computer Science","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Hewlett-Packard (Canada); Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Orienteering; Directed acyclic graph; Computer science; Combinatorics; Mathematics; Directed graph; Theoretical computer science; Algorithm; Mathematical optimization","score_opus":0.014807567598796405,"score_gpt":0.2794228516752325,"score_spread":0.2646152840764361,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403268005","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02034362,0.00045684265,0.9702138,0.00050321757,0.000091029855,0.00018086708,0.00027361966,0.00079557387,0.007141439],"genre_scores_gemma":[0.2079108,0.0007981289,0.7783681,0.00024586477,0.00015341298,0.00043111865,0.0014887125,0.00046428348,0.010139558],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9991818,0.00024165338,0.000041258125,0.00025292693,0.00013311823,0.0001492629],"domain_scores_gemma":[0.9958411,0.0031086984,0.00025952273,0.0003567934,0.00026461156,0.0001692216],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015419627,0.0016111579,0.0016556501,0.0016265877,0.00095943786,0.0024371417,0.0034729822,0.0027184025,0.009199099],"category_scores_gemma":[0.0065142727,0.0011008562,0.0013397768,0.00247502,0.0011027718,0.0045716763,0.002576804,0.0028787341,0.0013323221],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000236686,0.00027721503,0.00082485576,0.00037958397,0.00007313704,0.00008973671,0.00021163067,0.6954941,0.0012366112,0.07868919,0.012556376,0.20993075],"study_design_scores_gemma":[0.0000523918,0.000030519495,0.00009540305,0.000021969166,0.000015573065,0.000022796177,0.00005003596,0.9474309,0.00031415478,0.05041807,0.0015397168,0.000008465701],"about_ca_topic_score_codex":0.008754856,"about_ca_topic_score_gemma":0.0095395325,"teacher_disagreement_score":0.009199099,"about_ca_system_score_codex":0.0016964434,"about_ca_system_score_gemma":0.0019831841,"threshold_uncertainty_score":0.030774117},"labels":[],"label_agreement":null},{"id":"W4403306216","doi":"10.1080/01605682.2024.2412214","title":"An exact branch-and-price-and-cut algorithm for a practical and large-scale dial-a-ride problem","year":2024,"lang":"en","type":"article","venue":"Journal of the Operational Research Society","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Group for Research in Decision Analysis; Giro (Canada); Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Dial; Scale (ratio); Purchasing; Project management; Algorithm; Operations research; Mathematical optimization; Economics; Mathematics; Operations management; Engineering; Management; Electrical engineering","score_opus":0.04458444319968899,"score_gpt":0.40219240256818095,"score_spread":0.357607959368492,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403306216","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.046026483,0.0006560731,0.93953824,0.0010021098,0.00011229152,0.00044842562,0.0006881183,0.0016218644,0.009906418],"genre_scores_gemma":[0.158428,0.00027186944,0.8350903,0.00025734535,0.00007615774,0.0005013839,0.0013677803,0.00029860483,0.003708536],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992861,0.00022973002,0.000028656626,0.00018174977,0.00013047,0.00014322916],"domain_scores_gemma":[0.9985428,0.0010557364,0.00009998842,0.000097753466,0.00011445421,0.00008920676],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013681706,0.001430694,0.0013957003,0.0008284051,0.00091238815,0.0014507559,0.0016618496,0.0017224696,0.0070320005],"category_scores_gemma":[0.0029140357,0.00081467594,0.00086194725,0.0015106903,0.0006841218,0.0014973263,0.0011478923,0.0018930293,0.001065461],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021116924,0.00025454824,0.0006315409,0.00016544004,0.00004491441,0.0001846374,0.00007918106,0.8463079,0.0012066425,0.011894736,0.010110583,0.12890872],"study_design_scores_gemma":[0.00006994163,0.00005166757,0.00010694004,0.000009038513,0.000008821291,0.000038962215,0.000027914166,0.99018526,0.00026610104,0.008010145,0.0012194704,0.0000056765507],"about_ca_topic_score_codex":0.008112897,"about_ca_topic_score_gemma":0.0088114,"teacher_disagreement_score":0.008112897,"about_ca_system_score_codex":0.0012297385,"about_ca_system_score_gemma":0.0029713828,"threshold_uncertainty_score":0.023524404},"labels":[],"label_agreement":null},{"id":"W4403496846","doi":"10.3390/engproc2024076017","title":"Formulation and Solution of the Stochastic Truck and Trailer Routing Problem","year":2024,"lang":"en","type":"article","venue":"","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"","keywords":"Trailer; Truck; Routing (electronic design automation); Computer science; Vehicle routing problem; Mathematical optimization; Operations research; Automotive engineering; Engineering; Computer network; Mathematics","score_opus":0.013600623187200154,"score_gpt":0.24183447012161718,"score_spread":0.22823384693441703,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403496846","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.027244663,0.00057988346,0.96077126,0.0005100094,0.000076219694,0.00013018934,0.0003552208,0.00012855974,0.010204124],"genre_scores_gemma":[0.613269,0.0013320285,0.3761768,0.00013747769,0.00013945061,0.00052234245,0.00071434915,0.00010610199,0.0076024337],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99936455,0.00027171941,0.000030372748,0.00011140659,0.00013420974,0.00008769147],"domain_scores_gemma":[0.99941015,0.00036513203,0.00008280217,0.000026017715,0.00008727872,0.000028518298],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010426239,0.0007591405,0.00091125135,0.0005479993,0.00034607953,0.0011100349,0.00096425775,0.0011828723,0.0028272443],"category_scores_gemma":[0.0018591195,0.00043025677,0.0008793887,0.0008641314,0.00046704727,0.0006800781,0.00087345677,0.0010428777,0.00026561317],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012858145,0.000013674742,0.00012085419,0.00004568405,0.000009070662,0.00003155861,0.000012322953,0.98428875,0.0002514397,0.0091253985,0.00042229588,0.0056660348],"study_design_scores_gemma":[0.000005967287,0.000019118368,0.000071252434,0.0000066171892,0.0000034739062,0.000016910142,0.000013927584,0.9940806,0.00015090415,0.004832542,0.00079555745,0.00000310242],"about_ca_topic_score_codex":0.0071769836,"about_ca_topic_score_gemma":0.0057313526,"teacher_disagreement_score":0.0071769836,"about_ca_system_score_codex":0.00082401955,"about_ca_system_score_gemma":0.0024945308,"threshold_uncertainty_score":0.014270425},"labels":[],"label_agreement":null},{"id":"W4403556658","doi":"10.1002/net.22251","title":"Layered Graph Models for the Electric Vehicle Routing Problem With Nonlinear Charging Functions","year":2024,"lang":"en","type":"article","venue":"Networks","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"","keywords":"Vehicle routing problem; Nonlinear system; Electric vehicle; Computer science; Graph; Mathematical optimization; Routing (electronic design automation); Mathematics; Computer network; Theoretical computer science; Physics; Power (physics)","score_opus":0.015748927321926454,"score_gpt":0.23598880879591413,"score_spread":0.22023988147398768,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403556658","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03696146,0.00048161592,0.9523437,0.0007269496,0.00007872784,0.00013573689,0.000625396,0.00044676263,0.008199715],"genre_scores_gemma":[0.5902729,0.0013054097,0.39589703,0.00033830004,0.00010109598,0.00044523698,0.0015545158,0.00031344758,0.00977206],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992743,0.0003234562,0.000030867126,0.00011047336,0.00014320685,0.00011777494],"domain_scores_gemma":[0.9982462,0.0011746183,0.00018636267,0.00009772927,0.00019263837,0.00010250069],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001037142,0.0015567325,0.0008358534,0.00094540056,0.00038963114,0.0019561874,0.001494436,0.0014679625,0.006285474],"category_scores_gemma":[0.0040135514,0.00071886205,0.0013197528,0.001387797,0.00074808183,0.002500136,0.0011696094,0.0018478631,0.00069257105],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017364977,0.000023190285,0.000115265764,0.000034933768,0.000008650482,0.000036942372,0.0000215719,0.97715807,0.00021495127,0.017213168,0.00070375495,0.004452102],"study_design_scores_gemma":[0.0000064110754,0.000011007986,0.000039613322,0.000008222552,0.0000043205114,0.000010172038,0.000015215394,0.9874808,0.00007457102,0.01164785,0.0006982364,0.0000035319865],"about_ca_topic_score_codex":0.008750491,"about_ca_topic_score_gemma":0.010027889,"teacher_disagreement_score":0.008750491,"about_ca_system_score_codex":0.0023351603,"about_ca_system_score_gemma":0.001370204,"threshold_uncertainty_score":0.021027029},"labels":[],"label_agreement":null},{"id":"W4403616496","doi":"10.1007/s10479-024-06351-4","title":"Migratory beekeeping routing: a combinatorial optimization problem in apiculture","year":2024,"lang":"en","type":"article","venue":"Annals of Operations Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Beekeeping; Theory of computation; Varroa; Computer science; Combinatorial optimization; Routing (electronic design automation); Mathematical optimization; Mathematics; Biology; Honey bee; Computer network; Ecology; Algorithm","score_opus":0.11323461520207793,"score_gpt":0.417617561499046,"score_spread":0.30438294629696805,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403616496","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05579621,0.005616798,0.9129041,0.0023232396,0.00038435165,0.00014149233,0.000566461,0.00011734209,0.022150012],"genre_scores_gemma":[0.6233419,0.010684441,0.3278271,0.0006060775,0.0006479773,0.00043053305,0.00083335745,0.0003829565,0.03524568],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992168,0.00038413302,0.000033116372,0.00018439026,0.000094202755,0.000087285895],"domain_scores_gemma":[0.9979493,0.0016024483,0.00019451455,0.000059872233,0.00010498148,0.00008894373],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014717235,0.0015529823,0.0016540273,0.0012701645,0.0009866924,0.0044186194,0.0021022821,0.0027924362,0.0043344805],"category_scores_gemma":[0.0038097205,0.0010052918,0.0013821399,0.0029321078,0.0017643765,0.002578302,0.0011769034,0.0023025058,0.00043289032],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000062269144,0.00011803518,0.0012902878,0.00032680476,0.00012958239,0.00019327544,0.00009991584,0.87925535,0.000850442,0.074321955,0.006130942,0.03722108],"study_design_scores_gemma":[0.000015597034,0.000038615362,0.00040703555,0.00005053212,0.000041226245,0.00006263852,0.00013045532,0.95437515,0.00024405449,0.04061814,0.0039978265,0.000018714029],"about_ca_topic_score_codex":0.015752569,"about_ca_topic_score_gemma":0.015518728,"teacher_disagreement_score":0.015752569,"about_ca_system_score_codex":0.0020534396,"about_ca_system_score_gemma":0.0016157259,"threshold_uncertainty_score":0.031321704},"labels":[],"label_agreement":null},{"id":"W4404094938","doi":"10.1016/j.cor.2024.106890","title":"Optimizing task assignment and routing operations with a heterogeneous fleet of unmanned aerial vehicles for emergency healthcare services","year":2024,"lang":"en","type":"article","venue":"Computers & Operations Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"","keywords":"Task (project management); Routing (electronic design automation); Computer science; Vehicle routing problem; Health care; Operations research; Emergency vehicle; Medical emergency; Aeronautics; Computer network; Transport engineering; Real-time computing; Systems engineering; Engineering; Medicine","score_opus":0.047266171689287956,"score_gpt":0.36209067222948693,"score_spread":0.31482450054019895,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404094938","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.24976136,0.0011940805,0.7396655,0.00081159803,0.00014748462,0.00023982624,0.0002936998,0.00032317484,0.0075632194],"genre_scores_gemma":[0.92052895,0.00030642503,0.07605556,0.000117968506,0.000027744021,0.00013204716,0.00021016847,0.00003788934,0.0025832853],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996024,0.00015452507,0.00001538279,0.00008474538,0.000046550587,0.000096396114],"domain_scores_gemma":[0.9992028,0.0004893983,0.00011034837,0.00003633812,0.0000762993,0.00008479613],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009171902,0.0010687725,0.00083421386,0.00045627588,0.0004633576,0.0007712561,0.0007827646,0.0009159222,0.0023132667],"category_scores_gemma":[0.0021014793,0.0003845431,0.0006009578,0.0005700616,0.0005127051,0.0010280311,0.00068183587,0.00073307945,0.00022070293],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003418535,0.000029681238,0.0003140591,0.000025444355,0.0000142090075,0.000033696462,0.0000095393125,0.9912656,0.00033607407,0.00080675277,0.00026516384,0.0068656174],"study_design_scores_gemma":[0.000010406368,0.00004064765,0.00012448517,0.000003415589,0.0000053122653,0.000009530297,0.000019475596,0.99835676,0.00015130705,0.0010262028,0.00025063613,0.0000018684227],"about_ca_topic_score_codex":0.009672495,"about_ca_topic_score_gemma":0.006748799,"teacher_disagreement_score":0.009672495,"about_ca_system_score_codex":0.0009290016,"about_ca_system_score_gemma":0.001468003,"threshold_uncertainty_score":0.019232392},"labels":[],"label_agreement":null},{"id":"W4404202501","doi":"10.1016/j.trc.2024.104892","title":"Online algorithms for the multi-vehicle inventory-routing problem with real-time demands","year":2024,"lang":"en","type":"article","venue":"Transportation Research Part C Emerging Technologies","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"GLS Industries (Canada); Université Laval","funders":"","keywords":"Vehicle routing problem; Computer science; Routing algorithm; Routing (electronic design automation); Transport engineering; Algorithm; Real-time computing; Operations research; Engineering; Computer network; Routing protocol","score_opus":0.08574787846066453,"score_gpt":0.3780584446576243,"score_spread":0.2923105661969597,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404202501","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.038915228,0.000773825,0.94961923,0.0007377311,0.00011423109,0.00020713241,0.0001756472,0.0008187364,0.008638167],"genre_scores_gemma":[0.42210618,0.00061837304,0.57058907,0.00033451593,0.00019053527,0.00046541233,0.00058861804,0.0002794837,0.0048277746],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988759,0.0004062973,0.00004865124,0.00024412353,0.00017853612,0.00024642755],"domain_scores_gemma":[0.9942984,0.00444546,0.0004916923,0.00026035722,0.00029058248,0.00021356011],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002195769,0.001815999,0.0014621323,0.00087489234,0.0007178088,0.0018314412,0.0028787367,0.0026384012,0.0054063257],"category_scores_gemma":[0.005674052,0.0007238307,0.001166369,0.0010600726,0.00086250546,0.0025908242,0.001338646,0.0024141807,0.00072867196],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023869374,0.00043249974,0.0006871033,0.00020974284,0.00006589844,0.00008054258,0.000082629,0.9244388,0.00094213133,0.015028206,0.002657463,0.055136435],"study_design_scores_gemma":[0.000033713353,0.000034275894,0.000052824973,0.0000071799113,0.000007786974,0.000016046812,0.000013653929,0.9944665,0.00018925307,0.0048319884,0.00034296507,0.0000038847393],"about_ca_topic_score_codex":0.0059165186,"about_ca_topic_score_gemma":0.006357516,"teacher_disagreement_score":0.0059165186,"about_ca_system_score_codex":0.002007755,"about_ca_system_score_gemma":0.0026259744,"threshold_uncertainty_score":0.018085957},"labels":[],"label_agreement":null},{"id":"W4404299638","doi":"10.1287/ijoc.2023.0061","title":"An Iterative Exact Algorithm over a Time-Expanded Network for the Transportation of Biomedical Samples","year":2024,"lang":"en","type":"article","venue":"INFORMS journal on computing","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Concordia University; Group for Research in Decision Analysis; Université du Québec à Montréal","funders":"","keywords":"Algorithm; Flow network; Computer science; Mathematical optimization; Mathematics","score_opus":0.01660145908052911,"score_gpt":0.30073011913337855,"score_spread":0.2841286600528494,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404299638","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0061524743,0.00016357204,0.9903093,0.00018795973,0.000041543324,0.00010549298,0.00004474025,0.00026602854,0.0027288657],"genre_scores_gemma":[0.12552424,0.00019714017,0.869604,0.00012811992,0.00003962571,0.00038981007,0.0002111422,0.000121691286,0.0037842137],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99884844,0.00040784557,0.000057134534,0.00023970289,0.00026591306,0.00018093153],"domain_scores_gemma":[0.99714345,0.0020488715,0.00021079507,0.0001769326,0.00031245436,0.00010742492],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023746882,0.0013630119,0.0013234271,0.0009858974,0.0009320599,0.0012313307,0.0018906181,0.0013346195,0.0059401514],"category_scores_gemma":[0.005943159,0.0007002612,0.0011371515,0.0013989779,0.0011393955,0.0018552205,0.001912713,0.001673405,0.0008795387],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001057883,0.00006505045,0.0006009042,0.00012631774,0.000041892803,0.0000855848,0.00011505452,0.9175591,0.00088693755,0.019785818,0.0017274761,0.058900177],"study_design_scores_gemma":[0.000025471598,0.000027911972,0.000037630904,0.0000079208,0.000007779215,0.000018608638,0.000023187022,0.99064386,0.00021575789,0.008099583,0.0008881551,0.0000040709456],"about_ca_topic_score_codex":0.010228249,"about_ca_topic_score_gemma":0.012103943,"teacher_disagreement_score":0.010228249,"about_ca_system_score_codex":0.0018686845,"about_ca_system_score_gemma":0.003754395,"threshold_uncertainty_score":0.020337403},"labels":[],"label_agreement":null},{"id":"W4404443165","doi":"10.1016/j.cie.2024.110730","title":"Set Covering Routing Problems: A review and classification scheme","year":2024,"lang":"en","type":"review","venue":"Computers & Industrial Engineering","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École Nationale d'Administration Publique; Université du Québec à Montréal; Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada; Concordia University; Gina Cody School of Engineering and Computer Science, Concordia University","keywords":"Classification scheme; Scheme (mathematics); Routing (electronic design automation); Set (abstract data type); Computer science; Operations research; Engineering; Computer network; Mathematics; Information retrieval","score_opus":0.12311227486092223,"score_gpt":0.32317628869671466,"score_spread":0.20006401383579242,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404443165","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00041142723,0.9911385,0.0050697015,0.00040265176,0.00032646066,0.000042523,0.00012659142,0.000032115102,0.0024498962],"genre_scores_gemma":[0.0021032968,0.98965293,0.0065365722,0.00021527972,0.0004085085,0.000040257404,0.0002058471,0.000010246413,0.0008270889],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99939823,0.00009089388,0.00010046769,0.00014885904,0.00020904526,0.000052540665],"domain_scores_gemma":[0.99888796,0.0006318459,0.0001429883,0.00004644338,0.0002441714,0.00004655966],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011760192,0.0020225036,0.0024227316,0.005612825,0.00040675278,0.0024073182,0.0020784452,0.0013816423,0.0041553695],"category_scores_gemma":[0.002198605,0.0008195911,0.0014136591,0.0116202915,0.0007591875,0.0031134377,0.0012696347,0.002058965,0.0017592685],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007901989,0.00015522567,0.000367261,0.024567176,0.00017885119,0.00010363064,0.00008749725,0.004285869,0.00091701886,0.01657182,0.034639843,0.9180467],"study_design_scores_gemma":[0.000060754726,0.00034465158,0.0017224331,0.009492149,0.00056261674,0.001737472,0.00017370313,0.005914837,0.0013425618,0.017900696,0.9606129,0.00013514886],"about_ca_topic_score_codex":0.0021503565,"about_ca_topic_score_gemma":0.0024540038,"teacher_disagreement_score":0.005612825,"about_ca_system_score_codex":0.001188361,"about_ca_system_score_gemma":0.0020388481,"threshold_uncertainty_score":0.013901114},"labels":[],"label_agreement":null},{"id":"W4404536325","doi":"10.1287/inte.2023.0069","title":"Freight Gateway Consolidation for Purolator International Using Integer Programming","year":2024,"lang":"en","type":"article","venue":"INFORMS Journal on Applied Analytics","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"McMaster University; DuPont (Canada); Saint Mary's University","funders":"","keywords":"Consolidation (business); Integer programming; Gateway (web page); Computer science; Business; Operations research; Computer network; Engineering; World Wide Web; Finance; Algorithm","score_opus":0.028474612331637006,"score_gpt":0.300931678086638,"score_spread":0.272457065755001,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404536325","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13714215,0.0013475477,0.73233724,0.0020453397,0.00023105416,0.0007798878,0.0013412563,0.000970251,0.12380527],"genre_scores_gemma":[0.71462697,0.0019182246,0.24656568,0.00025040764,0.00009700451,0.00044125348,0.0014426039,0.00033416468,0.034323644],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99951065,0.00014218445,0.000013537563,0.00008257376,0.000111787944,0.00013926477],"domain_scores_gemma":[0.9996691,0.00014204177,0.000036527694,0.000020371308,0.00009138994,0.00004056584],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010719019,0.0010299643,0.00066866796,0.000975613,0.0010291763,0.0037799033,0.0009821932,0.0007691852,0.007931501],"category_scores_gemma":[0.0017548567,0.0006064738,0.00091460714,0.0014113548,0.00042845242,0.0019013381,0.00095859496,0.0014232872,0.00064551114],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002623938,0.000053440755,0.0008961638,0.00003466516,0.000010921065,0.00005341691,0.0000373215,0.9519245,0.00031025874,0.019435436,0.003266008,0.02395152],"study_design_scores_gemma":[0.0000051473403,0.000017310009,0.0001413091,0.00002065885,0.0000076003435,0.0000065911627,0.000071392256,0.99269897,0.00019469268,0.0036187684,0.0032120144,0.0000054431393],"about_ca_topic_score_codex":0.15208362,"about_ca_topic_score_gemma":0.20590422,"teacher_disagreement_score":0.15208362,"about_ca_system_score_codex":0.0064750994,"about_ca_system_score_gemma":0.007497633,"threshold_uncertainty_score":0.30239683},"labels":[],"label_agreement":null},{"id":"W4404595747","doi":"10.1155/2024/5754231","title":"An Interval Integrated Optimization to Air‐Cargo Hub Network Design and Airline Fleet Planning","year":2024,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Natural Science Foundation of China; Fundamental Research Funds for the Central Universities; Civil Aviation Administration of China; National Science Foundation","keywords":"Interval (graph theory); Network planning and design; Air cargo; Transport engineering; Operations research; Air travel; Aviation; Computer science; Marine engineering; Engineering; Telecommunications; Aerospace engineering; Mathematics","score_opus":0.014839273126343483,"score_gpt":0.29033313804278993,"score_spread":0.27549386491644645,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404595747","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.024220059,0.0005567288,0.97071505,0.00018162894,0.000045719662,0.00004535846,0.00012776806,0.00015454215,0.0039531956],"genre_scores_gemma":[0.70165443,0.00082009286,0.2919518,0.00011705089,0.00007490513,0.00024278826,0.0003815285,0.00010987444,0.004647501],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99939656,0.00026335585,0.000019852703,0.00012829126,0.00011675194,0.00007514028],"domain_scores_gemma":[0.99939597,0.00037359173,0.000082987404,0.000026055435,0.0000854214,0.00003589623],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010681638,0.001056947,0.0011045575,0.00089852,0.00031793254,0.00124007,0.0011016154,0.001098394,0.002645344],"category_scores_gemma":[0.0021324991,0.00065115496,0.0011130997,0.0012970837,0.0005064061,0.0012283145,0.00070104474,0.0011985438,0.00021685811],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014954722,0.00001605439,0.0001387397,0.000024155974,0.000016400349,0.00001548554,0.0000080343,0.99074835,0.00016491514,0.0023111335,0.00024720127,0.0062945616],"study_design_scores_gemma":[0.0000028499046,0.0000141877445,0.000045280143,0.00000286791,0.000004615919,0.0000032405205,0.0000045338365,0.9987017,0.00006763612,0.0009790594,0.00017209686,0.0000018736281],"about_ca_topic_score_codex":0.011137143,"about_ca_topic_score_gemma":0.007064809,"teacher_disagreement_score":0.011137143,"about_ca_system_score_codex":0.0012243231,"about_ca_system_score_gemma":0.0016557367,"threshold_uncertainty_score":0.022144616},"labels":[],"label_agreement":null},{"id":"W4404612065","doi":"10.1145/3678717.3691208","title":"EFECTIW-ROTER: Deep Reinforcement Learning Approach for Solving Heterogeneous Fleet and Demand Vehicle Routing Problem With Time-Window Constraints","year":2024,"lang":"en","type":"article","venue":"","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"National Research Council Canada; University of Calgary","funders":"","keywords":"Reinforcement learning; Vehicle routing problem; Computer science; Window (computing); Routing (electronic design automation); Artificial intelligence; Operations research; Real-time computing; Computer network; Engineering; World Wide Web","score_opus":0.010073285972453673,"score_gpt":0.22795512371466814,"score_spread":0.21788183774221448,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404612065","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020580126,0.0002639018,0.97544485,0.00026026324,0.00004326289,0.000041447343,0.00011633788,0.0009941511,0.0022556912],"genre_scores_gemma":[0.75668746,0.00026049247,0.23366974,0.00052229327,0.000057279325,0.00021627404,0.0006918618,0.00021427544,0.0076803667],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99973565,0.000057419835,0.000012995713,0.00007783289,0.000056013905,0.000060042134],"domain_scores_gemma":[0.9995701,0.00024221391,0.00005184187,0.000033987577,0.000063795786,0.000038193873],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00068679557,0.0009916935,0.00088244065,0.00030654337,0.00020795625,0.00049137516,0.001517528,0.0010916975,0.0026717656],"category_scores_gemma":[0.0014644642,0.00043461146,0.0005224058,0.00034678102,0.00046866416,0.0011366983,0.0010332095,0.00183019,0.000391058],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000068587826,0.00009393943,0.0005327185,0.00006126762,0.000041943254,0.00008763595,0.00003735295,0.91497433,0.0022745358,0.0097473245,0.0025239792,0.06955645],"study_design_scores_gemma":[0.0000048035145,0.00001243571,0.000021431622,0.0000016509233,0.0000025268846,0.0000039637634,0.0000017312867,0.9980191,0.00023337037,0.0015570753,0.00014056575,0.0000013983117],"about_ca_topic_score_codex":0.0076434356,"about_ca_topic_score_gemma":0.008454741,"teacher_disagreement_score":0.0076434356,"about_ca_system_score_codex":0.00095572695,"about_ca_system_score_gemma":0.0014123679,"threshold_uncertainty_score":0.015197873},"labels":[],"label_agreement":null},{"id":"W4404632811","doi":"10.1016/j.trc.2024.104932","title":"The Heterogeneous-Fleet Electric Vehicle Routing Problem with Nonlinear Charging Functions","year":2024,"lang":"en","type":"article","venue":"Transportation Research Part C Emerging Technologies","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":28,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Group for Research in Decision Analysis; HEC Montréal","funders":"Fundamental Research Funds for the Central Universities","keywords":"Electric vehicle; Vehicle routing problem; Nonlinear system; Routing (electronic design automation); Engineering; Automotive engineering; Computer science; Transport engineering; Computer network; Physics; Power (physics)","score_opus":0.03463670212720022,"score_gpt":0.320661306216311,"score_spread":0.2860246040891108,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404632811","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16869825,0.00082250783,0.8092555,0.00193669,0.000189028,0.0001507054,0.0010886652,0.00015523609,0.017703352],"genre_scores_gemma":[0.92871624,0.0006494502,0.045301996,0.00022844615,0.0001709412,0.00012985275,0.0006681356,0.00010891527,0.02402609],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993711,0.00023350159,0.000017841514,0.00017858001,0.00007855224,0.00012041546],"domain_scores_gemma":[0.9991211,0.00053495134,0.00012048343,0.000055332355,0.00006684166,0.000101262056],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014141222,0.0011958332,0.0014844121,0.00068523525,0.00062838563,0.0020851386,0.0021535186,0.0020726416,0.0049662115],"category_scores_gemma":[0.0032033466,0.0008141595,0.0009797338,0.001523193,0.0010683994,0.0029521005,0.0015517983,0.0011168852,0.00032343742],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007993313,0.000039079205,0.00036498482,0.000042482057,0.00003681733,0.00020436045,0.00001587623,0.9750401,0.00038337125,0.017307103,0.0010896652,0.005396287],"study_design_scores_gemma":[0.000019980967,0.000021309505,0.00026797544,0.0000052983946,0.000011430723,0.00004987995,0.000034936373,0.98395014,0.00016788453,0.01488321,0.0005799929,0.000007946925],"about_ca_topic_score_codex":0.0067377295,"about_ca_topic_score_gemma":0.005948748,"teacher_disagreement_score":0.0067377295,"about_ca_system_score_codex":0.0018886216,"about_ca_system_score_gemma":0.0010730348,"threshold_uncertainty_score":0.016613603},"labels":[],"label_agreement":null},{"id":"W4404864841","doi":"10.1080/01605682.2024.2432605","title":"Adaptive large neighbourhood search for the multi-depot arc routing problem with flexible assignment of end depot and different arc types","year":2024,"lang":"en","type":"article","venue":"Journal of the Operational Research Society","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Group for Research in Decision Analysis; Université du Québec à Trois-Rivières","funders":"Mitacs","keywords":"Depot; Arc (geometry); Arc routing; Neighbourhood (mathematics); Computer science; Operations research; Routing (electronic design automation); Mathematical optimization; Computer network; Engineering; Mathematics; Geography; Mechanical engineering","score_opus":0.07232362554463277,"score_gpt":0.3580735006696667,"score_spread":0.2857498751250339,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404864841","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023563588,0.00045717318,0.9714732,0.00015804681,0.00004825486,0.00007636386,0.00007629805,0.00012411212,0.004022953],"genre_scores_gemma":[0.4627613,0.0004674884,0.52898973,0.00010183597,0.00006706745,0.0003535309,0.00028084932,0.00010484278,0.0068732766],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999666,0.00014948782,0.000014074031,0.00006468648,0.00006198796,0.000043883872],"domain_scores_gemma":[0.9995016,0.0003524913,0.000051352126,0.000024086881,0.000039387574,0.000031057774],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007542709,0.0007834365,0.0009116946,0.0007714914,0.00037402447,0.00062994135,0.00092465733,0.0010699333,0.0020675864],"category_scores_gemma":[0.0016486775,0.0004305971,0.00082681957,0.000878928,0.00047459337,0.0008041583,0.00086828007,0.000875509,0.0002616205],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000034474346,0.000028879042,0.00023204177,0.00005160658,0.000022888142,0.000045978264,0.000026819012,0.97602785,0.000688519,0.0068499055,0.00047695657,0.015514055],"study_design_scores_gemma":[0.000008262201,0.000026621286,0.00005846273,0.0000059713075,0.000005099071,0.0000130065255,0.000010533628,0.9954716,0.0001545302,0.0036338472,0.0006088166,0.0000031211166],"about_ca_topic_score_codex":0.0038735708,"about_ca_topic_score_gemma":0.0055695074,"teacher_disagreement_score":0.0038735708,"about_ca_system_score_codex":0.00073716545,"about_ca_system_score_gemma":0.00086816086,"threshold_uncertainty_score":0.007701993},"labels":[],"label_agreement":null},{"id":"W4405026148","doi":"10.1007/s10878-024-01237-4","title":"Approximation algorithms for the airport and railway problem","year":2024,"lang":"en","type":"article","venue":"Journal of Combinatorial Optimization","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Theory of computation; Computer science; Approximation algorithm; Algorithm; Mathematical optimization; Mathematics","score_opus":0.015325515364176581,"score_gpt":0.26801951362225956,"score_spread":0.252693998258083,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405026148","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.024749864,0.0038392488,0.9496282,0.002519926,0.00034555214,0.00012660927,0.00036814684,0.00043427595,0.017988222],"genre_scores_gemma":[0.4436969,0.0043492555,0.52919215,0.0009490588,0.0007431473,0.00052007457,0.0014780561,0.00047215653,0.01859916],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99832517,0.0008759898,0.00005565064,0.00022103483,0.00027450366,0.0002475652],"domain_scores_gemma":[0.99205375,0.006560893,0.00032653034,0.0003762103,0.00040443352,0.00027819644],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0032994463,0.0016872301,0.0022856274,0.001577558,0.0009540133,0.0028155528,0.00330433,0.0031514436,0.007363305],"category_scores_gemma":[0.014893087,0.0010155544,0.0016118676,0.003077951,0.0015371273,0.0038543125,0.0019458993,0.0044189845,0.00092044874],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00027786056,0.00024196884,0.00071570644,0.00018382842,0.000093160874,0.000053605345,0.000086968575,0.832495,0.00024091425,0.09185269,0.011713442,0.0620449],"study_design_scores_gemma":[0.00004350757,0.00002045803,0.000081683276,0.000020014731,0.000015973,0.000017907261,0.000022970913,0.9510164,0.000063463085,0.047508504,0.0011834251,0.0000055862456],"about_ca_topic_score_codex":0.012428267,"about_ca_topic_score_gemma":0.011791432,"teacher_disagreement_score":0.012428267,"about_ca_system_score_codex":0.003256855,"about_ca_system_score_gemma":0.0029908605,"threshold_uncertainty_score":0.024711847},"labels":[],"label_agreement":null},{"id":"W4405036614","doi":"10.4236/ajor.2024.146009","title":"An Elementary Approach to the Vehicle Routing Problem via Python and Google API","year":2024,"lang":"en","type":"article","venue":"American Journal of Operations Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Queen's University; Queen's University Belfast","keywords":"Python (programming language); Computer science; Vehicle routing problem; Programming language; World Wide Web; Information retrieval; Routing (electronic design automation); Computer network","score_opus":0.03696489952496746,"score_gpt":0.37009940208506703,"score_spread":0.3331345025600996,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405036614","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0034663628,0.00011667354,0.9348076,0.0007342697,0.00012527754,0.0003184718,0.00431329,0.034129895,0.021988105],"genre_scores_gemma":[0.07619423,0.00045978505,0.8895805,0.000656099,0.00006554401,0.001650213,0.00693495,0.006995333,0.017463326],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994018,0.00012375801,0.000049208247,0.00010428926,0.00022185681,0.000099070916],"domain_scores_gemma":[0.99942183,0.00026145283,0.000047024565,0.00009868421,0.00013134231,0.000039648796],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007288174,0.0011699059,0.00052826054,0.00057959073,0.0006303327,0.0014306859,0.0028209197,0.0010082283,0.03243108],"category_scores_gemma":[0.0027055058,0.0006197716,0.0017530108,0.00071986153,0.00055450795,0.0017115339,0.001986854,0.002259815,0.01035949],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023717679,0.0003111203,0.0028202992,0.0015146518,0.00013720202,0.0007185685,0.00046993108,0.4007361,0.006838954,0.21429235,0.17671214,0.19521147],"study_design_scores_gemma":[0.000082021295,0.00004131293,0.0005157952,0.00009287905,0.000020739439,0.0002372247,0.000077998,0.76885563,0.0031096402,0.082150884,0.14476292,0.00005296693],"about_ca_topic_score_codex":0.007863419,"about_ca_topic_score_gemma":0.013118092,"teacher_disagreement_score":0.03243108,"about_ca_system_score_codex":0.00077483186,"about_ca_system_score_gemma":0.0028743506,"threshold_uncertainty_score":0.10849285},"labels":[],"label_agreement":null},{"id":"W4405045402","doi":"10.1016/j.eswa.2024.125996","title":"A new branch-and-Benders-cut algorithm for the time-dependent vehicle routing problem","year":2024,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada; Conselho Nacional de Desenvolvimento Científico e Tecnológico","keywords":"Vehicle routing problem; Branch and cut; Computer science; Algorithm; Mathematical optimization; Routing (electronic design automation); Integer programming; Mathematics; Computer network","score_opus":0.012920647537111862,"score_gpt":0.2621486258310455,"score_spread":0.24922797829393362,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405045402","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002970395,0.00019340862,0.9938066,0.0001314517,0.000112653186,0.00006915533,0.000081209255,0.00038697422,0.0022483063],"genre_scores_gemma":[0.03389503,0.0002698551,0.9594429,0.00015522256,0.0000983727,0.00023517234,0.0004317169,0.00023749925,0.005234215],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993067,0.00013588661,0.00003468853,0.00013319532,0.0003244683,0.00006504025],"domain_scores_gemma":[0.99911124,0.00043811786,0.000057882484,0.0000652248,0.00025652503,0.00007098189],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00088685297,0.0012151662,0.0015637134,0.0012933773,0.00062497164,0.001408374,0.0021018023,0.0022503296,0.0097501],"category_scores_gemma":[0.0025316593,0.00087750214,0.00095073535,0.0015779389,0.00052700326,0.0018737075,0.0014247485,0.0026735757,0.0017510576],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002616235,0.0002629971,0.00037353789,0.0002314752,0.000090453825,0.00009316649,0.00007076777,0.47951514,0.0055113286,0.021671467,0.013392604,0.47852552],"study_design_scores_gemma":[0.000059863632,0.000052270687,0.000079530386,0.000014193456,0.000015900314,0.000032259744,0.000009993718,0.98842865,0.0007777873,0.006904339,0.0036155954,0.000009588434],"about_ca_topic_score_codex":0.0041342843,"about_ca_topic_score_gemma":0.005794489,"teacher_disagreement_score":0.0097501,"about_ca_system_score_codex":0.0009652596,"about_ca_system_score_gemma":0.0020892792,"threshold_uncertainty_score":0.03261733},"labels":[],"label_agreement":null},{"id":"W4405402617","doi":"10.1002/net.22250","title":"The workforce scheduling and routing problem with park‐and‐loop","year":2024,"lang":"en","type":"article","venue":"Networks","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"HEC Montréal; Institut de Valorisation des Données","keywords":"Vehicle routing problem; Loop (graph theory); Workforce; Scheduling (production processes); Mathematical optimization; Operations research; Computer science; Job shop scheduling; Routing (electronic design automation); Mathematics; Economics; Computer network; Combinatorics; Economic growth","score_opus":0.010000771375888149,"score_gpt":0.23543693567133323,"score_spread":0.22543616429544508,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405402617","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.097588114,0.00022660634,0.88452476,0.0008233381,0.00010724705,0.00024474104,0.0005376378,0.00053260673,0.015414899],"genre_scores_gemma":[0.61019844,0.00020918835,0.37665147,0.00020577025,0.00007671227,0.00033076442,0.0006792414,0.0001777955,0.011470521],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993363,0.00024198042,0.000026840138,0.00014464669,0.00011364976,0.00013663096],"domain_scores_gemma":[0.9991055,0.0005333638,0.000104249,0.00010051012,0.00006830573,0.00008796395],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010256843,0.0005685998,0.00078709493,0.0004481778,0.0004597652,0.0010706104,0.0011395855,0.001030194,0.0072024977],"category_scores_gemma":[0.0025868667,0.00035805398,0.0007480219,0.00071031053,0.00052680785,0.0014323334,0.001102419,0.0010480159,0.0004864798],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016617379,0.00023702245,0.0010258364,0.00022302836,0.000038592832,0.00025722853,0.00009634099,0.847695,0.0019943356,0.058641035,0.00803749,0.08158798],"study_design_scores_gemma":[0.00004875374,0.00012327624,0.00025385796,0.000015017633,0.000010930541,0.000115000585,0.0000772344,0.9570068,0.0011897079,0.03623064,0.0049183033,0.0000104097435],"about_ca_topic_score_codex":0.0027946609,"about_ca_topic_score_gemma":0.003014459,"teacher_disagreement_score":0.0072024977,"about_ca_system_score_codex":0.0007061024,"about_ca_system_score_gemma":0.0016778997,"threshold_uncertainty_score":0.02409476},"labels":[],"label_agreement":null},{"id":"W4405676638","doi":"10.1145/3704657.3704673","title":"Research on solving time-varying vehicle routing with modular operation genetic algorithm based on ALNS","year":2024,"lang":"en","type":"article","venue":"","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Coquitlam College","funders":"","keywords":"Modular design; Computer science; Genetic algorithm; Vehicle routing problem; Routing (electronic design automation); Algorithm design; Algorithm; Embedded system; Machine learning; Operating system","score_opus":0.02904260432139993,"score_gpt":0.3114693240944435,"score_spread":0.28242671977304357,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405676638","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03809134,0.000895969,0.9558763,0.00020973444,0.00010273104,0.000059633843,0.000038551112,0.00041378464,0.004311973],"genre_scores_gemma":[0.55690324,0.0013796709,0.43701887,0.00023851047,0.0001018297,0.00020635792,0.00024744665,0.00010816574,0.003795948],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996424,0.00009084709,0.00002044437,0.00009628301,0.00011023111,0.000039710874],"domain_scores_gemma":[0.9996964,0.00015675857,0.000037898113,0.000024237865,0.00006851312,0.000016239357],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006460918,0.0008249615,0.00071074284,0.00089627726,0.00043373735,0.00076338573,0.0011376484,0.0006774289,0.001504276],"category_scores_gemma":[0.0012665916,0.00030010263,0.00082662574,0.0012280921,0.0005488631,0.0010867061,0.0005114234,0.0007761746,0.000175557],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000044398363,0.00005364185,0.0008730254,0.000090785565,0.000058432943,0.000033615204,0.000050975985,0.8818949,0.0029183712,0.009779538,0.000752918,0.10344938],"study_design_scores_gemma":[0.000009844665,0.00003123509,0.000119248834,0.0000054426487,0.000011123437,0.000016479133,0.000012514445,0.99618953,0.0005744911,0.0021432054,0.00088292424,0.000003958214],"about_ca_topic_score_codex":0.008584232,"about_ca_topic_score_gemma":0.005711586,"teacher_disagreement_score":0.008584232,"about_ca_system_score_codex":0.00081937516,"about_ca_system_score_gemma":0.0014915294,"threshold_uncertainty_score":0.017068505},"labels":[],"label_agreement":null},{"id":"W4406106880","doi":"10.1007/s10479-024-06442-2","title":"Sustainable vehicle route planning under uncertainty for modular integrated construction: multi-trip time-dependent VRP with time windows and data analytics","year":2025,"lang":"en","type":"article","venue":"Annals of Operations Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Vehicle routing problem; Modular design; Truck; Computer science; Transport engineering; Integer programming; Operations research; Ant colony optimization algorithms; Routing (electronic design automation); Engineering; Computer network","score_opus":0.15333913247327524,"score_gpt":0.4288628080374323,"score_spread":0.2755236755641571,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406106880","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.059022006,0.00050892803,0.9377375,0.00046065278,0.000060446793,0.00006000557,0.00048923906,0.00017851152,0.0014826487],"genre_scores_gemma":[0.9261141,0.0004587485,0.070378646,0.00005562364,0.0000661282,0.00012363026,0.00062075176,0.00012302783,0.002059478],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987116,0.00049695466,0.0000656269,0.00034089654,0.00016985738,0.00021506351],"domain_scores_gemma":[0.99412525,0.0044734627,0.000613601,0.00021330963,0.00040271127,0.00017156295],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0035320427,0.0014573253,0.0026145556,0.001173099,0.00048453378,0.0018125492,0.0023690704,0.0016436724,0.0016891862],"category_scores_gemma":[0.009426738,0.0019686362,0.0016413939,0.0022170965,0.0011185787,0.0034743007,0.0019795368,0.0022357234,0.00016586346],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013745668,0.000008042467,0.00012241815,0.000015504982,0.00001389077,0.000015115577,0.0000081877815,0.99598,0.000055077027,0.0017375473,0.00010569078,0.0019247923],"study_design_scores_gemma":[9.726746e-7,0.000004383989,0.00003610798,0.0000014298903,0.0000026263767,0.0000015629388,0.0000038179387,0.9984712,0.000025269797,0.0014170584,0.00003339535,0.0000020991724],"about_ca_topic_score_codex":0.0263032,"about_ca_topic_score_gemma":0.015354149,"teacher_disagreement_score":0.0263032,"about_ca_system_score_codex":0.0016848528,"about_ca_system_score_gemma":0.002064845,"threshold_uncertainty_score":0.052300215},"labels":[],"label_agreement":null},{"id":"W4406226854","doi":"10.1016/j.trpro.2024.12.115","title":"Mixed integer linear programming model for a multi-depot arc routing problem with different arc types and flexible assignment of end depot","year":2025,"lang":"en","type":"article","venue":"Transportation research procedia","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Group for Research in Decision Analysis; Université du Québec à Trois-Rivières","funders":"Mitacs","keywords":"Depot; Integer programming; Arc (geometry); Arc routing; Mathematical optimization; Linear programming; Routing (electronic design automation); Integer (computer science); Computer science; Mathematics; Computer network; Geography","score_opus":0.09595747101197241,"score_gpt":0.366136574342881,"score_spread":0.27017910333090855,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406226854","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.030758977,0.0018832519,0.9383366,0.0014693205,0.00034822623,0.0003785963,0.0013425387,0.00051388924,0.024968656],"genre_scores_gemma":[0.51274955,0.0022720918,0.44418108,0.00069736684,0.0003511453,0.001866156,0.0018112365,0.00023693312,0.03583449],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982679,0.00083219726,0.00006966261,0.00034814168,0.00023797098,0.00024407127],"domain_scores_gemma":[0.9982742,0.0012807993,0.0001669024,0.000042859054,0.00015628929,0.000079013436],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019919095,0.002464547,0.0015794331,0.0009013136,0.0006638036,0.0030048585,0.0021790722,0.0026381186,0.007876681],"category_scores_gemma":[0.0024315002,0.0010938147,0.0016565092,0.001867343,0.00083216827,0.0015134448,0.0013169792,0.002906355,0.001289386],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008954502,0.000120746314,0.0003296759,0.00019529725,0.000055669272,0.00019747541,0.000054848497,0.9713109,0.00050332415,0.0148581695,0.0020566257,0.010227693],"study_design_scores_gemma":[0.000022607572,0.000053384618,0.00008087402,0.000015200792,0.0000159613,0.000024221537,0.000024561454,0.994724,0.0001155534,0.003381751,0.0015356379,0.0000063195844],"about_ca_topic_score_codex":0.009212303,"about_ca_topic_score_gemma":0.011430423,"teacher_disagreement_score":0.009212303,"about_ca_system_score_codex":0.0022297907,"about_ca_system_score_gemma":0.0021502636,"threshold_uncertainty_score":0.02635014},"labels":[],"label_agreement":null},{"id":"W4406570280","doi":"10.1016/j.tre.2025.103967","title":"A learning-based robust optimization framework for synchromodal freight transportation under uncertainty","year":2025,"lang":"en","type":"article","venue":"Transportation Research Part E Logistics and Transportation Review","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Transport engineering; Robust optimization; Computer science; Traffic management; Operations research; Engineering; Business; Mathematical optimization; Mathematics","score_opus":0.07722718735032036,"score_gpt":0.3717103736926973,"score_spread":0.29448318634237697,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406570280","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0012943221,0.00058720406,0.99676824,0.00014810755,0.000033235727,0.0000126203095,0.00003383563,0.00009036432,0.0010319948],"genre_scores_gemma":[0.55707383,0.004235949,0.42807692,0.00032351358,0.0006614761,0.0003229747,0.0005417497,0.00028268556,0.008480947],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990823,0.0002995879,0.000057141046,0.00023964426,0.00023923324,0.00008199437],"domain_scores_gemma":[0.99889994,0.0006032768,0.00014716001,0.0000661744,0.00024465285,0.000038707378],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023950338,0.0014804708,0.0020703194,0.00088952726,0.00040304463,0.0017034375,0.0022776383,0.0017152515,0.0023062825],"category_scores_gemma":[0.0034179937,0.0007812916,0.0015623991,0.0013318653,0.0014908349,0.0023499953,0.00181754,0.002145498,0.00040566444],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017095364,0.0000145372205,0.000062397725,0.00008304189,0.00003926663,0.00002222525,0.000015262041,0.94943523,0.00035850427,0.029677488,0.0006774977,0.0195974],"study_design_scores_gemma":[0.0000032521289,0.000013056028,0.000030072011,0.000007396832,0.0000064667233,0.000005194226,0.000002031033,0.9895146,0.000097747616,0.009876327,0.00043859633,0.000005284226],"about_ca_topic_score_codex":0.008908622,"about_ca_topic_score_gemma":0.0037551872,"teacher_disagreement_score":0.008908622,"about_ca_system_score_codex":0.0015428874,"about_ca_system_score_gemma":0.001823815,"threshold_uncertainty_score":0.017713547},"labels":[],"label_agreement":null},{"id":"W4406959045","doi":"10.1145/3681772.3698216","title":"Clustering-Based Enhanced Ant Colony Optimization for Multi-Trip Vehicle Routing Problem with Heterogeneous Fleet and Time Windows: An Industrial Case Study","year":2024,"lang":"en","type":"article","venue":"","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"National Research Council Canada; University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Vehicle routing problem; Ant colony optimization algorithms; Cluster analysis; Computer science; ANT; Routing (electronic design automation); Ant colony; Travelling salesman problem; Artificial intelligence; Computer network; Algorithm","score_opus":0.04106927700715379,"score_gpt":0.2969030575333903,"score_spread":0.2558337805262365,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406959045","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.41606218,0.00076969963,0.5682873,0.0006746342,0.00007390405,0.0002914107,0.000301404,0.00031537432,0.013224031],"genre_scores_gemma":[0.92212105,0.0001942864,0.075357355,0.000031785814,0.000013860037,0.000078184334,0.00012904427,0.000026917907,0.0020474396],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995148,0.00019268885,0.000019612919,0.000088731635,0.00009591607,0.00008822123],"domain_scores_gemma":[0.9993506,0.0003899817,0.00007668381,0.000043034433,0.0000891122,0.0000505974],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00077181164,0.0007325585,0.0006320342,0.0005690939,0.0006252069,0.00082973903,0.0010556625,0.001203329,0.0009802842],"category_scores_gemma":[0.0012084334,0.0002874416,0.0006113065,0.0010107668,0.00045170143,0.0006590965,0.000525214,0.0006241183,0.00009532343],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000025414418,0.00003217638,0.0005033053,0.00002690851,0.000014704865,0.00015564357,0.000016591805,0.99147886,0.00039739182,0.0016184449,0.0002716402,0.005458922],"study_design_scores_gemma":[0.000005626714,0.00002259294,0.00019456737,0.0000014127013,0.0000051928887,0.000035236622,0.00002758534,0.9985097,0.00021560957,0.0006932319,0.0002862624,0.0000028869333],"about_ca_topic_score_codex":0.017272199,"about_ca_topic_score_gemma":0.01572177,"teacher_disagreement_score":0.017272199,"about_ca_system_score_codex":0.0009706749,"about_ca_system_score_gemma":0.0009942173,"threshold_uncertainty_score":0.03434336},"labels":[],"label_agreement":null},{"id":"W4407011375","doi":"10.1016/j.tre.2025.103991","title":"Designing hub-based regional transportation networks with service level constraints","year":2025,"lang":"en","type":"article","venue":"Transportation Research Part E Logistics and Transportation Review","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Université du Québec à Montréal; Université Laval; Center for Interuniversity Research and Analysis on Organizations; Centre for Interdisciplinary Research in Rehabilitation","funders":"","keywords":"Transport engineering; Service (business); Computer science; Level of service; Flow network; Business; Computer network; Engineering; Marketing","score_opus":0.13894294849498845,"score_gpt":0.3687718858812177,"score_spread":0.22982893738622923,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407011375","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05865064,0.00033366733,0.92516273,0.00043552698,0.000063430685,0.00029418996,0.0006139707,0.0002756804,0.014170094],"genre_scores_gemma":[0.68623334,0.0006549101,0.3033189,0.00011157568,0.000032603708,0.00034939664,0.0007513599,0.00014930815,0.00839859],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993622,0.00025537497,0.000020252526,0.000119449054,0.0000922802,0.00015047709],"domain_scores_gemma":[0.99934167,0.00023996516,0.00009384238,0.000059211055,0.000171101,0.00009424094],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010573948,0.0012787064,0.00082064065,0.0008290897,0.0007322664,0.0019960525,0.0017451074,0.0014238491,0.005932981],"category_scores_gemma":[0.0019178878,0.00064147875,0.0012349237,0.0016189204,0.0006841707,0.001505256,0.0011494489,0.0010890975,0.000682888],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002720083,0.000021666176,0.0003030066,0.000060672566,0.000016185299,0.000104446895,0.00003424896,0.97133124,0.0009724809,0.018101856,0.0010931519,0.007933866],"study_design_scores_gemma":[0.000012405187,0.000041850144,0.00017615351,0.000014965467,0.000015085303,0.000037989823,0.000089014706,0.9891832,0.00060868205,0.007450968,0.0023580806,0.000011576996],"about_ca_topic_score_codex":0.023142312,"about_ca_topic_score_gemma":0.028638424,"teacher_disagreement_score":0.023142312,"about_ca_system_score_codex":0.003235311,"about_ca_system_score_gemma":0.0028589321,"threshold_uncertainty_score":0.046015203},"labels":[],"label_agreement":null},{"id":"W4407182114","doi":"10.1111/itor.13620","title":"Smart selective navigator (SSN): enhancing urban winter road maintenance through optimized arc routing with hard turn restrictions","year":2025,"lang":"en","type":"article","venue":"International Transactions in Operational Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Ontario Tech University","funders":"Mitacs","keywords":"Arc (geometry); Routing (electronic design automation); Turn (biochemistry); Computer science; Arc routing; Environmental science; Business; Transport engineering; Engineering; Computer network","score_opus":0.03379930696044189,"score_gpt":0.3572425382447277,"score_spread":0.3234432312842858,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407182114","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.109768674,0.0001527043,0.88458264,0.00014191183,0.00004869509,0.00006499775,0.00007320631,0.00059613277,0.0045710574],"genre_scores_gemma":[0.8052473,0.000096555406,0.19154783,0.00007647571,0.000020228346,0.000072737195,0.00014669822,0.000102509584,0.0026897357],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997656,0.00008993063,0.000009468988,0.000043380118,0.000052056144,0.00003960366],"domain_scores_gemma":[0.99960047,0.0001649221,0.00006264926,0.00006273524,0.00006890342,0.000040381798],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00055337144,0.0005107608,0.0005708527,0.00042154276,0.00026642147,0.00045282405,0.000995139,0.0003532375,0.0017889384],"category_scores_gemma":[0.001017066,0.00023101474,0.00040335962,0.00034103403,0.00034764994,0.00068775675,0.00069339725,0.00038022964,0.00021830907],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000079472986,0.00006787631,0.001391913,0.00006087377,0.00004206064,0.00008099657,0.00008055563,0.9132469,0.0048256167,0.0063825413,0.0012702068,0.07247092],"study_design_scores_gemma":[0.000007636578,0.000043353994,0.00012681566,0.0000029891469,0.000008362014,0.00002243597,0.000024040848,0.9966995,0.0007465118,0.0014468535,0.00086783024,0.0000036804952],"about_ca_topic_score_codex":0.004226138,"about_ca_topic_score_gemma":0.008805555,"teacher_disagreement_score":0.004226138,"about_ca_system_score_codex":0.00039406214,"about_ca_system_score_gemma":0.000963905,"threshold_uncertainty_score":0.008403122},"labels":[],"label_agreement":null},{"id":"W4407538937","doi":"10.1504/ijise.2025.144406","title":"Vehicle routing decision-support system development using integer programming and heuristics: a model-driven structured approach","year":2025,"lang":"en","type":"article","venue":"International Journal of Industrial and Systems Engineering","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Heuristics; Integer programming; Computer science; Decision support system; Vehicle routing problem; Development (topology); Routing (electronic design automation); Mathematical optimization; Operations research; Artificial intelligence; Engineering; Algorithm; Mathematics; Computer network","score_opus":0.0334414799586438,"score_gpt":0.26796601452145824,"score_spread":0.23452453456281444,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407538937","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006049835,0.000054541564,0.98916245,0.00019322801,0.000009262622,0.0003064394,0.00009964869,0.00034331603,0.0037813832],"genre_scores_gemma":[0.05447856,0.00013685977,0.9436291,0.00005345662,0.0000057394163,0.00059789926,0.00022457709,0.000066291475,0.00080755935],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99782205,0.0012011668,0.0001535578,0.0001992484,0.00048668485,0.0001372674],"domain_scores_gemma":[0.9967159,0.0023671307,0.00022883122,0.00021607819,0.00038604796,0.0000860149],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0035538545,0.0010135416,0.00071143743,0.0015227492,0.0008001587,0.003315683,0.0020090807,0.0010940691,0.0030250885],"category_scores_gemma":[0.0050581573,0.0011589692,0.0014184626,0.0011541916,0.0010046196,0.0016722328,0.001590221,0.0013252691,0.00060064887],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000067461006,0.00027370683,0.0008927965,0.000569731,0.0000751706,0.00027801195,0.00093179935,0.82076055,0.0047564683,0.092101246,0.0013093562,0.07798377],"study_design_scores_gemma":[0.000048442616,0.00010549444,0.00013811643,0.0001597356,0.000031170835,0.00006575798,0.0003257225,0.95208555,0.0032652617,0.03537827,0.008373703,0.0000227902],"about_ca_topic_score_codex":0.0030060888,"about_ca_topic_score_gemma":0.0047105937,"teacher_disagreement_score":0.0035538545,"about_ca_system_score_codex":0.0018046373,"about_ca_system_score_gemma":0.00457581,"threshold_uncertainty_score":0.018794775},"labels":[],"label_agreement":null},{"id":"W4407658241","doi":"10.1016/j.trb.2025.103172","title":"A contextual framework for learning routing experiences in last-mile delivery","year":2025,"lang":"en","type":"article","venue":"Transportation Research Part B Methodological","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mile; Routing (electronic design automation); Last mile (transportation); Computer science; Transport engineering; Environmental science; Engineering; Geology; Computer network","score_opus":0.29881428823957445,"score_gpt":0.47494993983353617,"score_spread":0.17613565159396172,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407658241","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017300926,0.00038284741,0.9789578,0.000424574,0.000043392614,0.0000792168,0.00039345075,0.0005186346,0.0018991883],"genre_scores_gemma":[0.60729253,0.0005441022,0.3880821,0.0002702154,0.00014199861,0.00029486278,0.001550865,0.0001390004,0.0016844084],"study_design_codex":"simulation_or_modeling","study_design_gemma":"qualitative","domain_scores_codex":[0.99874604,0.00039889532,0.0000734737,0.00045997833,0.00019857129,0.0001229553],"domain_scores_gemma":[0.9973459,0.0014554072,0.00027909805,0.00033188504,0.0003703974,0.00021731494],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014895678,0.0010703471,0.0010304806,0.0017359739,0.0007988742,0.0017490543,0.0025875473,0.0014197194,0.0037904382],"category_scores_gemma":[0.008182429,0.0006790351,0.0014638843,0.0016365967,0.0014495475,0.0027404088,0.0020683873,0.0021753025,0.00043506225],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012693553,0.00022159793,0.005184018,0.0002083287,0.000118621756,0.00022534445,0.00043336095,0.8470276,0.0011308098,0.06404739,0.0025682203,0.07870771],"study_design_scores_gemma":[0.000013647376,0.00004041032,0.00037687703,0.000025420588,0.000017551793,0.000023191114,0.00006806641,0.9686254,0.00033686092,0.028890349,0.0015705016,0.000011628464],"about_ca_topic_score_codex":0.012359699,"about_ca_topic_score_gemma":0.019606298,"teacher_disagreement_score":0.012359699,"about_ca_system_score_codex":0.0015944791,"about_ca_system_score_gemma":0.0015103754,"threshold_uncertainty_score":0.024575531},"labels":[],"label_agreement":null},{"id":"W4407722540","doi":"10.1016/j.seps.2025.102185","title":"The time-definite hub line location problem","year":2025,"lang":"en","type":"article","venue":"Socio-Economic Planning Sciences","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal; Université Laval","funders":"","keywords":"Line (geometry); Computer science; Mathematics; Geometry","score_opus":0.01842435965363123,"score_gpt":0.2911151290782471,"score_spread":0.2726907694246159,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407722540","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0151393255,0.00026241134,0.97594553,0.0006286462,0.0000926516,0.00010787741,0.00054414914,0.00017868399,0.007100702],"genre_scores_gemma":[0.5759209,0.001344254,0.39429298,0.00042473007,0.00025325993,0.0005455296,0.0021660828,0.00035796055,0.024694387],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982924,0.0007921516,0.00008588173,0.0003889818,0.00023675669,0.00020386593],"domain_scores_gemma":[0.99754167,0.0014871187,0.00030672218,0.00018073445,0.0003087563,0.00017516951],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018803647,0.0013514367,0.0010878101,0.0006878538,0.0007159028,0.002005004,0.0017793915,0.0020485383,0.007879413],"category_scores_gemma":[0.004466571,0.00067062944,0.0010042604,0.001254919,0.0010484089,0.0032026889,0.001438684,0.0018981646,0.0012859098],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001605544,0.000119312535,0.0006972702,0.00025033357,0.000054022952,0.0002762051,0.00012231623,0.81011343,0.001406856,0.14629106,0.0067088827,0.03379966],"study_design_scores_gemma":[0.00003398231,0.00008884521,0.0001679304,0.000023188068,0.000015537356,0.00014063838,0.00009459148,0.91615844,0.00074819924,0.07659268,0.005913533,0.000022363745],"about_ca_topic_score_codex":0.0037592207,"about_ca_topic_score_gemma":0.002780897,"teacher_disagreement_score":0.007879413,"about_ca_system_score_codex":0.0014297816,"about_ca_system_score_gemma":0.0018070689,"threshold_uncertainty_score":0.02635926},"labels":[],"label_agreement":null},{"id":"W4407871667","doi":"10.2139/ssrn.5117161","title":"Tactical Routing and Fleet Planning in Drone-Assisted Last-Mile Delivery","year":2025,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; York University","funders":"","keywords":"Drone; Mile; Last mile (transportation); Routing (electronic design automation); Aeronautics; Computer science; Transport engineering; Engineering; Operations management; Computer network; Geography","score_opus":0.015104458336358543,"score_gpt":0.2822653026075462,"score_spread":0.26716084427118764,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407871667","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.032022383,0.001009126,0.95857096,0.0005627355,0.00010689673,0.0000541197,0.00016370718,0.00007917411,0.0074309],"genre_scores_gemma":[0.7995191,0.0024176827,0.17630845,0.00012509176,0.00016943857,0.00020324215,0.0003469519,0.00016619731,0.020743746],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995745,0.00023331383,0.0000121944595,0.00007620891,0.00006309108,0.000040806357],"domain_scores_gemma":[0.9988142,0.0009043654,0.00010255423,0.00004041401,0.00008538137,0.000053184496],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010867686,0.0007021945,0.0009953552,0.0007488157,0.000535648,0.0015629081,0.0010677625,0.0013232366,0.0031571533],"category_scores_gemma":[0.0046785483,0.0008660037,0.0006921276,0.001390979,0.0009047497,0.0018257118,0.0012144343,0.0013652329,0.00028262087],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000023267652,0.000012745829,0.00015629546,0.000028605458,0.000012757844,0.000023267077,0.000024260844,0.9735903,0.00014464979,0.019123595,0.00044380254,0.0064164572],"study_design_scores_gemma":[0.0000021706892,0.00000843556,0.000043993867,0.000003790402,0.0000028382005,0.0000047696653,0.000010634988,0.99129534,0.000043792686,0.008259227,0.00032261817,0.0000023618536],"about_ca_topic_score_codex":0.01749252,"about_ca_topic_score_gemma":0.011126404,"teacher_disagreement_score":0.01749252,"about_ca_system_score_codex":0.0012246416,"about_ca_system_score_gemma":0.0012217736,"threshold_uncertainty_score":0.034781456},"labels":[],"label_agreement":null},{"id":"W4408247425","doi":"10.1111/anzs.12439","title":"A seminal contribution of Ailsa Land and Alison Doig Harcourt to the field of mathematical programming","year":2025,"lang":"en","type":"article","venue":"Australian & New Zealand Journal of Statistics","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Group for Research in Decision Analysis; HEC Montréal","funders":"","keywords":"Mathematics","score_opus":0.012317149472729097,"score_gpt":0.3102088968377304,"score_spread":0.2978917473650013,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408247425","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011876151,0.13972335,0.5487547,0.10530814,0.024349956,0.0001448192,0.00080480095,0.0005008139,0.16853721],"genre_scores_gemma":[0.27746692,0.15537386,0.32534033,0.026750065,0.041056395,0.0004248175,0.0007772046,0.0011033638,0.17170702],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9979113,0.00059269444,0.000106934305,0.000516911,0.0007429115,0.00012921813],"domain_scores_gemma":[0.9932769,0.00513844,0.00020539798,0.00028442458,0.0008559498,0.000238861],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026944678,0.0009504171,0.00083023333,0.0017305425,0.0012861051,0.0036549116,0.0006681997,0.001494552,0.006252105],"category_scores_gemma":[0.011979325,0.00074731297,0.001019701,0.0021765025,0.0040968894,0.0038834878,0.0018387694,0.006206328,0.0027255374],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006580675,0.000046212957,0.00049571635,0.00036620878,0.00004247276,0.00011938905,0.00030494534,0.008743061,0.0005548044,0.8375768,0.0712419,0.08044263],"study_design_scores_gemma":[0.000027432261,0.000044665176,0.0003224716,0.00038524985,0.00002359591,0.0001991983,0.00006423975,0.012156531,0.00087915064,0.52097523,0.46486938,0.00005293893],"about_ca_topic_score_codex":0.0036185859,"about_ca_topic_score_gemma":0.0018630667,"teacher_disagreement_score":0.006252105,"about_ca_system_score_codex":0.0029314293,"about_ca_system_score_gemma":0.0029601,"threshold_uncertainty_score":0.021269143},"labels":[],"label_agreement":null},{"id":"W4408382245","doi":"10.1111/itor.70012","title":"Workload equity in multiperiod vehicle routing problems","year":2025,"lang":"en","type":"article","venue":"International Transactions in Operational Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Centre interuniversitaire de recherche sur les reseaux d'entreprise, la logistique et le transport; Institut de Valorisation des Données; Compute Canada","keywords":"Workload; Equity (law); Time horizon; Computer science; Context (archaeology); Operations research; Routing (electronic design automation); Vehicle routing problem; Mathematical optimization; Computer network; Mathematics; Geography","score_opus":0.09813934638325195,"score_gpt":0.448750739662477,"score_spread":0.3506113932792251,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408382245","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.23279423,0.0011251104,0.7574541,0.00072678976,0.00011211838,0.000258129,0.00021466055,0.000120547404,0.0071943374],"genre_scores_gemma":[0.9549155,0.00035385383,0.042204667,0.00010068617,0.00005778508,0.00015028659,0.00014967384,0.000060839517,0.0020067317],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99864715,0.00065963174,0.000060663842,0.00019159206,0.00017844196,0.0002624994],"domain_scores_gemma":[0.9966001,0.002440491,0.0003309973,0.00012422394,0.00029047733,0.00021376215],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0031178584,0.0011901191,0.0012472271,0.00076117855,0.00059024163,0.001395923,0.0010729597,0.0011762421,0.0021481703],"category_scores_gemma":[0.0061755967,0.00064635323,0.0006234412,0.00075367774,0.0007364868,0.0015257216,0.0012118691,0.0008812089,0.00013469318],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000065045875,0.00004318086,0.00060282915,0.000060278548,0.000021278403,0.000068814465,0.0000307681,0.9852614,0.0008568563,0.005759916,0.0002948347,0.0069348565],"study_design_scores_gemma":[0.00001078414,0.000050825685,0.00031077402,0.0000096473705,0.0000060801876,0.00002327915,0.00003309414,0.99253994,0.00043177058,0.0063152695,0.000263753,0.0000047932663],"about_ca_topic_score_codex":0.0035174284,"about_ca_topic_score_gemma":0.0019843045,"teacher_disagreement_score":0.0035174284,"about_ca_system_score_codex":0.0014856304,"about_ca_system_score_gemma":0.00091955543,"threshold_uncertainty_score":0.016489029},"labels":[],"label_agreement":null},{"id":"W4408741510","doi":"10.1016/j.trc.2025.105100","title":"The container drayage problem for electric trucks with charging resource constraints","year":2025,"lang":"en","type":"article","venue":"Transportation Research Part C Emerging Technologies","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"Shanghai Education Development Foundation; Shanghai Municipal Education Commission; National Natural Science Foundation of China","keywords":"Truck; Container (type theory); Resource (disambiguation); Transport engineering; Engineering; Computer science; Operations research; Automotive engineering; Computer network","score_opus":0.027236116427368538,"score_gpt":0.33034308501846327,"score_spread":0.3031069685910947,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408741510","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.25002745,0.0034759063,0.69908965,0.0049294257,0.00083359325,0.00078487606,0.0027196442,0.0005480927,0.037591353],"genre_scores_gemma":[0.8561015,0.0022785359,0.09780846,0.00050954346,0.00034930746,0.00042048743,0.0019605972,0.000576497,0.039995153],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983942,0.00062668946,0.00006606846,0.00036218346,0.00015329693,0.00039759526],"domain_scores_gemma":[0.9964194,0.0023478854,0.00033059757,0.00017008878,0.00027773765,0.00045434415],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003046466,0.0025182373,0.0045392285,0.0015115105,0.0017769819,0.0053723543,0.00431628,0.005925879,0.012288962],"category_scores_gemma":[0.0070333756,0.0026135447,0.0029109977,0.0028451844,0.0026172616,0.006206057,0.0032800806,0.0036361753,0.0009667667],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003968244,0.00014557465,0.0007523667,0.0003890107,0.00013519837,0.000685314,0.00012351273,0.9527848,0.000995049,0.027475324,0.0055995192,0.0105174575],"study_design_scores_gemma":[0.00006968009,0.00011805298,0.00033279578,0.00004873198,0.000056260793,0.00015226238,0.0002326971,0.97378176,0.00047049386,0.022487516,0.0022056326,0.00004397259],"about_ca_topic_score_codex":0.019406382,"about_ca_topic_score_gemma":0.011162436,"teacher_disagreement_score":0.019406382,"about_ca_system_score_codex":0.0031889265,"about_ca_system_score_gemma":0.002730889,"threshold_uncertainty_score":0.041110635},"labels":[],"label_agreement":null},{"id":"W4409175666","doi":"10.1080/14942119.2025.2480017","title":"Dynamic cost allocation in horizontal collaboration – a case study in forest transportation in Québec","year":2025,"lang":"en","type":"article","venue":"International Journal of Forest Engineering","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Université TÉLUQ; Université Laval","funders":"Université Laval","keywords":"Transport engineering; Business; Environmental resource management; Operations research; Environmental science; Engineering","score_opus":0.006037188396702747,"score_gpt":0.28148067825615053,"score_spread":0.2754434898594478,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409175666","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9741489,0.00012604882,0.007999321,0.0009019186,0.000014779463,0.00019612696,0.0002656763,0.0000480898,0.016299095],"genre_scores_gemma":[0.9887195,0.00007157073,0.005777942,0.000058405025,0.0000043934847,0.000046708516,0.00013857937,0.000013155005,0.0051696342],"study_design_codex":"simulation_or_modeling","study_design_gemma":"case_report","domain_scores_codex":[0.99877137,0.0004882581,0.000027793762,0.00011354468,0.00019436702,0.0004047134],"domain_scores_gemma":[0.9976502,0.0011651298,0.00014054624,0.0001364418,0.0005497451,0.00035799525],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016691786,0.00040283168,0.00022400291,0.0007317938,0.004053183,0.0017407243,0.001254953,0.0017002724,0.0043700826],"category_scores_gemma":[0.0028351923,0.00015454157,0.0003351558,0.0017189254,0.0011225524,0.00095230923,0.0010304168,0.0008479939,0.00025043258],"study_design_candidate":"case_report","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006819711,0.0022566363,0.09514666,0.00024829738,0.00021445139,0.0121168345,0.0076082028,0.7004041,0.0076077245,0.05617479,0.014720479,0.10281993],"study_design_scores_gemma":[0.00021940806,0.0005480535,0.06505431,0.00009755711,0.00008662746,0.0005794845,0.034917284,0.8503957,0.0028712596,0.008640764,0.0364537,0.00013588685],"about_ca_topic_score_codex":0.8467741,"about_ca_topic_score_gemma":0.9140185,"teacher_disagreement_score":0.1532259,"about_ca_system_score_codex":0.017610831,"about_ca_system_score_gemma":0.010907022,"threshold_uncertainty_score":0.3082565},"labels":[],"label_agreement":null},{"id":"W4409430417","doi":"10.1287/trsc.2024.0725","title":"Branch-Price-and-Cut for the Electric Vehicle Routing Problem with Heterogeneous Recharging Technologies and Nonlinear Recharging Functions","year":2025,"lang":"en","type":"article","venue":"Transportation Science","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Group for Research in Decision Analysis; HEC Montréal","funders":"","keywords":"Nonlinear system; Routing (electronic design automation); Vehicle routing problem; Electric vehicle; Mathematical optimization; Computer science; Operations research; Engineering; Computer network; Mathematics; Power (physics); Physics","score_opus":0.013599852196323897,"score_gpt":0.26021661817717856,"score_spread":0.24661676598085466,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409430417","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.022010686,0.00096815504,0.9539706,0.00092100055,0.00016124718,0.0004567387,0.0011132492,0.00042640546,0.019971937],"genre_scores_gemma":[0.23704077,0.0012968077,0.744468,0.00026481974,0.00016331213,0.001125537,0.0028552471,0.0004062794,0.012379236],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993438,0.00025679354,0.00002852217,0.00012147191,0.00014904198,0.0001003655],"domain_scores_gemma":[0.998723,0.00091666356,0.000093614944,0.000046030247,0.00014773643,0.000072927076],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014086532,0.0016165276,0.0012872227,0.0009920715,0.00073568115,0.0017644631,0.0013350995,0.0017311771,0.015645111],"category_scores_gemma":[0.0037450453,0.00064077607,0.0011843612,0.0018508654,0.00054952555,0.001168365,0.0009562976,0.002417704,0.0014840106],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010456817,0.00013735137,0.0007091836,0.00036206425,0.000041746513,0.0002369865,0.00006180169,0.8975902,0.00071697135,0.034063283,0.011053337,0.054922372],"study_design_scores_gemma":[0.00003835387,0.00004160388,0.00016606563,0.0000340695,0.000014167433,0.000058556623,0.00003322998,0.9739546,0.00031065647,0.022064434,0.0032758943,0.00000829225],"about_ca_topic_score_codex":0.0070564914,"about_ca_topic_score_gemma":0.009776658,"teacher_disagreement_score":0.015645111,"about_ca_system_score_codex":0.0015701678,"about_ca_system_score_gemma":0.002738365,"threshold_uncertainty_score":0.052338123},"labels":[],"label_agreement":null},{"id":"W4409687729","doi":"10.1155/atr/5584617","title":"Applying a Hybrid Gray Wolf‐Enhanced Whale Optimization Algorithm to the Capacitated Vehicle Routing Problem","year":2025,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Viet Nam National University Ho Chi Minh City; Ho Chi Minh City University of Technology and Education","keywords":"Vehicle routing problem; Whale; Gray (unit); Computer science; Optimization algorithm; Routing (electronic design automation); Algorithm; Gray wolf; Mathematical optimization; Mathematics; Fishery; Computer network; Biology; Ecology","score_opus":0.006983923984710725,"score_gpt":0.24657330889182913,"score_spread":0.2395893849071184,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409687729","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.045943897,0.0005030724,0.94721884,0.00018896764,0.00007478952,0.000069066955,0.00003083809,0.0002877812,0.0056827106],"genre_scores_gemma":[0.7295208,0.0003663081,0.26293507,0.00020976148,0.00004685127,0.00019661344,0.00012965732,0.0000897841,0.0065051024],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996445,0.00010879428,0.00001775735,0.000069540736,0.00010293286,0.00005640621],"domain_scores_gemma":[0.99973375,0.00012717527,0.000030392895,0.000026895712,0.000057832003,0.000024001201],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005209903,0.0008280393,0.0008085226,0.00069607905,0.00038133698,0.00077350286,0.00093322794,0.00094608,0.0013192062],"category_scores_gemma":[0.0010316574,0.00033145113,0.0007946857,0.0007631021,0.00044131879,0.00071460806,0.0009058405,0.0006041238,0.00019639227],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003665858,0.00004207282,0.00058999984,0.0000560227,0.000068703775,0.00008542507,0.000039855484,0.93980867,0.0022286482,0.0052843587,0.0007201962,0.05103944],"study_design_scores_gemma":[0.0000065604345,0.00002846591,0.00007454247,0.0000030730578,0.000006836674,0.00001817251,0.0000073672645,0.99778455,0.00037405692,0.0011543183,0.0005379705,0.0000040214118],"about_ca_topic_score_codex":0.004995795,"about_ca_topic_score_gemma":0.0039636577,"teacher_disagreement_score":0.004995795,"about_ca_system_score_codex":0.00042062614,"about_ca_system_score_gemma":0.00096664054,"threshold_uncertainty_score":0.009933412},"labels":[],"label_agreement":null},{"id":"W4409728692","doi":"10.1287/ijoc.2023.0367","title":"Machine Learning-Empowered Benders Decomposition for Flow Hub Location in E-Commerce","year":2025,"lang":"en","type":"article","venue":"INFORMS journal on computing","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"","keywords":"Benders' decomposition; Flow (mathematics); Decomposition; Computer science; Mathematical optimization; Operations research; Industrial engineering; Mathematics; Engineering; Chemistry","score_opus":0.013507336622891448,"score_gpt":0.3022088723143746,"score_spread":0.28870153569148316,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409728692","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010040712,0.00044217485,0.9864271,0.00021006257,0.000059906666,0.00007490454,0.00012741194,0.00035575827,0.0022619034],"genre_scores_gemma":[0.3231638,0.0011071191,0.6686329,0.00026924504,0.0001540494,0.0004783434,0.0011325007,0.00037245182,0.0046895077],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992606,0.0002915101,0.00003747698,0.00015699987,0.00014624678,0.00010704623],"domain_scores_gemma":[0.9990013,0.00058556465,0.00010671027,0.00006503973,0.00018364842,0.000057685455],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015036844,0.0019148755,0.0018421648,0.0014247017,0.00066639675,0.0014238274,0.0011940115,0.0016119343,0.0042417706],"category_scores_gemma":[0.0031886112,0.0009839198,0.0016838136,0.001736864,0.0008432098,0.0016646492,0.0010650525,0.0026069654,0.0007360935],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000051250994,0.000047697522,0.0003740832,0.00009350529,0.000024847779,0.00004324418,0.00003122116,0.9638346,0.0006125502,0.0077622333,0.0013609015,0.025763854],"study_design_scores_gemma":[0.000008728506,0.000016575914,0.000055554217,0.000008666529,0.0000056510576,0.0000083202885,0.000010756415,0.99384683,0.00021395227,0.0053346497,0.0004862435,0.0000041609237],"about_ca_topic_score_codex":0.006318279,"about_ca_topic_score_gemma":0.005308382,"teacher_disagreement_score":0.006318279,"about_ca_system_score_codex":0.0011942522,"about_ca_system_score_gemma":0.00208043,"threshold_uncertainty_score":0.014190137},"labels":[],"label_agreement":null},{"id":"W4410252793","doi":"10.1016/j.ejor.2025.04.019","title":"First-improvement or best-improvement? An in-depth local search computational study to elucidate a dominance claim","year":2025,"lang":"en","type":"article","venue":"European Journal of Operational Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Natural Sciences and Engineering Research Council of Canada; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior","keywords":"Dominance (genetics); Stochastic dominance; Computer science; Mathematical optimization; Operations research; Mathematics; Biology","score_opus":0.07134054237777429,"score_gpt":0.39701539868102026,"score_spread":0.325674856303246,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410252793","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07969751,0.0061521656,0.80583084,0.007003139,0.00043494135,0.00013896232,0.00020552895,0.00021553221,0.100321405],"genre_scores_gemma":[0.7384208,0.003015159,0.23520772,0.0014211865,0.00047006458,0.00014831072,0.00016085824,0.00037914386,0.020776717],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982913,0.00093125756,0.00005307287,0.00014058975,0.00036722067,0.0002164893],"domain_scores_gemma":[0.98377335,0.0138281975,0.0003395236,0.0007464453,0.00096007215,0.00035230708],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005102178,0.00090031663,0.0015422256,0.0010822476,0.0013771285,0.002044959,0.0019388803,0.001930655,0.014616172],"category_scores_gemma":[0.03243265,0.00048067258,0.0013409774,0.001244752,0.0021243992,0.00527928,0.0019937807,0.0034007374,0.00090731017],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024308184,0.00027266625,0.0013086689,0.0005060258,0.00008135399,0.00024245972,0.0005486681,0.18441081,0.0014898521,0.72291946,0.014616232,0.07336062],"study_design_scores_gemma":[0.00004927689,0.00021181027,0.00035246043,0.00013678396,0.000064035106,0.00018048167,0.00019450246,0.69538903,0.0007673635,0.29747018,0.0051620468,0.00002194148],"about_ca_topic_score_codex":0.003525197,"about_ca_topic_score_gemma":0.005919927,"teacher_disagreement_score":0.014616172,"about_ca_system_score_codex":0.0009960851,"about_ca_system_score_gemma":0.0018062012,"threshold_uncertainty_score":0.048895955},"labels":[],"label_agreement":null},{"id":"W4410815503","doi":"10.1016/j.cor.2025.107152","title":"Learning-based column generation approach for the vehicle routing problem with release dates and incompatible loading constraints","year":2025,"lang":"en","type":"article","venue":"Computers & Operations Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"National Natural Science Foundation of China","keywords":"Column generation; Vehicle routing problem; Column (typography); Computer science; Routing (electronic design automation); Mathematical optimization; Operations research; Mathematics; Computer network","score_opus":0.047957797486706706,"score_gpt":0.3309234114648892,"score_spread":0.2829656139781825,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410815503","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014400559,0.00041929918,0.9824057,0.00023471133,0.00007003696,0.00009104206,0.00016746204,0.0006807979,0.0015304496],"genre_scores_gemma":[0.47135285,0.00048189962,0.51953924,0.0005050106,0.00023509588,0.00042170557,0.0011862635,0.00031851546,0.0059594093],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995029,0.00017428378,0.00002680046,0.00010448499,0.00010017424,0.00009131839],"domain_scores_gemma":[0.9979728,0.0013819366,0.00012860278,0.00009278064,0.00034405192,0.00007985783],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010107601,0.0010685414,0.00184083,0.0010189858,0.0005427426,0.00094698183,0.0018136227,0.0013140129,0.005927944],"category_scores_gemma":[0.0024631605,0.00087353744,0.0011073733,0.0012374269,0.00061877124,0.0011061007,0.00095233234,0.0016796069,0.0007542414],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009894343,0.00010979113,0.00026528805,0.00010546283,0.00004511211,0.000059935977,0.00003824743,0.91450936,0.00092187204,0.002996329,0.0026137612,0.0782359],"study_design_scores_gemma":[0.000008625139,0.00001453788,0.000026097006,0.000002588766,0.0000052997198,0.0000048406414,0.0000035818816,0.99854493,0.00010535787,0.0011700782,0.00011124942,0.0000028635163],"about_ca_topic_score_codex":0.013377208,"about_ca_topic_score_gemma":0.014394954,"teacher_disagreement_score":0.013377208,"about_ca_system_score_codex":0.000844898,"about_ca_system_score_gemma":0.0018280108,"threshold_uncertainty_score":0.026598692},"labels":[],"label_agreement":null},{"id":"W4411297645","doi":"10.1016/j.cie.2025.111279","title":"Holding inventory for shipment consolidation in hub location modeling","year":2025,"lang":"en","type":"article","venue":"Computers & Industrial Engineering","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Consolidation (business); Operations management; Business; Operations research; Transport engineering; Computer science; Engineering; Finance","score_opus":0.05197102613057943,"score_gpt":0.27309306049818466,"score_spread":0.22112203436760522,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411297645","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13176371,0.0010046319,0.85427284,0.0006710714,0.00012666939,0.00013781681,0.0006637065,0.00025119414,0.011108328],"genre_scores_gemma":[0.94603044,0.0006447162,0.03844136,0.000110452456,0.000065705695,0.00012454845,0.00041871483,0.00013131097,0.014032723],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994667,0.00021673963,0.00002884509,0.000095373674,0.00006392743,0.00012831116],"domain_scores_gemma":[0.998906,0.00064254744,0.000118505624,0.00007791098,0.00014911444,0.00010588973],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016477086,0.0009844501,0.0017134666,0.000880871,0.0008208945,0.0020624746,0.003078374,0.0015832636,0.0058986526],"category_scores_gemma":[0.0035013403,0.0013686958,0.0013934558,0.001664482,0.0010315542,0.0026792218,0.0013355542,0.0016188301,0.00055039255],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013701272,0.00001526196,0.00023300103,0.000010264294,0.000008538362,0.00002548673,0.000008314934,0.99452764,0.00009896455,0.0035009254,0.00017858692,0.001379292],"study_design_scores_gemma":[0.0000013322336,0.000004188964,0.000034215536,0.0000019216043,0.0000038690628,0.0000021370745,0.0000047617345,0.99893016,0.000033241107,0.0009293845,0.000053429616,0.0000012627283],"about_ca_topic_score_codex":0.04850341,"about_ca_topic_score_gemma":0.03124037,"teacher_disagreement_score":0.04850341,"about_ca_system_score_codex":0.0021098745,"about_ca_system_score_gemma":0.002149066,"threshold_uncertainty_score":0.09644216},"labels":[],"label_agreement":null},{"id":"W4411690650","doi":"10.1016/j.ejor.2025.06.014","title":"Beyond fifty years of vehicle routing: Insights into the history and the future","year":2025,"lang":"en","type":"article","venue":"European Journal of Operational Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Vehicle routing problem; Computer science; Operations research; Routing (electronic design automation); History; Engineering; Computer network","score_opus":0.02854984090072116,"score_gpt":0.31013046177736303,"score_spread":0.2815806208766419,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411690650","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0100290645,0.76299584,0.037634306,0.09130045,0.0079149995,0.00003680476,0.00040204765,0.00013176369,0.089554764],"genre_scores_gemma":[0.1346109,0.80488795,0.014545877,0.014240558,0.008238829,0.00007286265,0.00046441058,0.00021119628,0.02272754],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9986143,0.0005682912,0.00006648691,0.00024047591,0.000396274,0.0001140054],"domain_scores_gemma":[0.99618727,0.0023065112,0.00025073535,0.0003102878,0.0006623521,0.00028298027],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0030455668,0.0007966499,0.00086338236,0.0015695705,0.0012995277,0.0049469834,0.0013847735,0.002547543,0.007724598],"category_scores_gemma":[0.008263294,0.00042252662,0.00044983145,0.00370536,0.0049934965,0.012566025,0.0020257006,0.00431013,0.0020152233],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013948689,0.0000793662,0.0012756648,0.001137882,0.00004039674,0.00014813,0.0007384361,0.0048750644,0.00031811275,0.67406327,0.041656137,0.27552816],"study_design_scores_gemma":[0.000008195112,0.000056927143,0.0007617127,0.0013928715,0.00001580855,0.00021395992,0.0007963287,0.0019495594,0.00015291202,0.3595442,0.6350683,0.00003922876],"about_ca_topic_score_codex":0.0038732453,"about_ca_topic_score_gemma":0.003779634,"teacher_disagreement_score":0.007724598,"about_ca_system_score_codex":0.002716034,"about_ca_system_score_gemma":0.0023877441,"threshold_uncertainty_score":0.025841415},"labels":[],"label_agreement":null},{"id":"W4412448016","doi":"10.1016/j.tre.2025.104278","title":"Learning for routing: A guided review of recent developments and future directions","year":2025,"lang":"en","type":"review","venue":"Transportation Research Part E Logistics and Transportation Review","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"Chalmers Tekniska Högskola; Energimyndigheten; VINNOVA; European Commission","keywords":"Computer science; Engineering","score_opus":0.16195595960732687,"score_gpt":0.44384761012884455,"score_spread":0.28189165052151766,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412448016","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00018231753,0.99221474,0.0037396431,0.0008109379,0.0003308243,0.000013549508,0.000028589626,0.000032096945,0.0026472646],"genre_scores_gemma":[0.001250203,0.99543613,0.00203072,0.00024273522,0.00039465775,0.000013313666,0.00004734549,0.000008783698,0.0005760886],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99933404,0.00015117103,0.000073324256,0.00015163074,0.00024316082,0.000046699704],"domain_scores_gemma":[0.9969373,0.0021998591,0.00016470638,0.00008684881,0.0005320079,0.00007919327],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018510188,0.0014670178,0.0015656399,0.0027574177,0.00043696185,0.0020961282,0.0018506505,0.0018507703,0.006515615],"category_scores_gemma":[0.004184401,0.0007837905,0.0009919084,0.0050009014,0.000907551,0.003915201,0.0010207585,0.0024203877,0.003921646],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000032027874,0.00010469078,0.00028144283,0.016538814,0.00010015925,0.00008232993,0.00007602614,0.0035775779,0.00047271542,0.024191216,0.025176218,0.9293667],"study_design_scores_gemma":[0.000014578408,0.00014077991,0.00050766417,0.007898651,0.00016430502,0.0004515698,0.00014388758,0.0025952514,0.0005355466,0.016171105,0.971321,0.000055696135],"about_ca_topic_score_codex":0.002275535,"about_ca_topic_score_gemma":0.0024076654,"teacher_disagreement_score":0.006515615,"about_ca_system_score_codex":0.0009898801,"about_ca_system_score_gemma":0.0021453092,"threshold_uncertainty_score":0.021796942},"labels":[],"label_agreement":null},{"id":"W4412456327","doi":"10.1016/j.ejor.2025.07.007","title":"Robot-aided electric vehicle routing problem with lockers and prime customers prioritization","year":2025,"lang":"en","type":"article","venue":"European Journal of Operational Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École Nationale d'Administration Publique; Université du Québec à Montréal; Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada; Gina Cody School of Engineering and Computer Science, Concordia University; Qatar National Research Fund; Qatar Research, Development and Innovation Council","keywords":"Prioritization; Vehicle routing problem; Computer science; Prime (order theory); Routing (electronic design automation); Robot; Electric vehicle; Operations research; City logistics; Artificial intelligence; Business; Computer network; Transport engineering; Engineering; Mathematics; Combinatorics","score_opus":0.026315521792389274,"score_gpt":0.3180905560652358,"score_spread":0.2917750342728465,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412456327","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2852029,0.0012799344,0.6836497,0.0016472553,0.00027930926,0.0005148056,0.0014273009,0.0013407561,0.024658088],"genre_scores_gemma":[0.8155072,0.00047726074,0.16851158,0.0002540274,0.00007453428,0.00026725067,0.0009966481,0.00016208582,0.013749403],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99932694,0.00022041608,0.000025778849,0.00018315077,0.00007396517,0.00016984715],"domain_scores_gemma":[0.9993358,0.0003768415,0.000089296336,0.000041402334,0.00007185585,0.00008467189],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000648495,0.0012039153,0.0012217672,0.0005058077,0.000612582,0.0013438833,0.0012202994,0.0014621217,0.005201538],"category_scores_gemma":[0.0014025925,0.0006611834,0.00089396216,0.0009271703,0.00047188837,0.0010820822,0.0008117561,0.0011196006,0.00047782378],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018293038,0.000097883,0.00066540693,0.00014797591,0.000041307212,0.00033824248,0.000059223254,0.9649331,0.0015447757,0.007584431,0.0034485848,0.020956129],"study_design_scores_gemma":[0.000052733725,0.00011116718,0.00031639813,0.000011057532,0.000026276432,0.0001384472,0.00008734778,0.99049604,0.00079590967,0.0052399626,0.0027113678,0.000013235219],"about_ca_topic_score_codex":0.0073338095,"about_ca_topic_score_gemma":0.0073374896,"teacher_disagreement_score":0.0073338095,"about_ca_system_score_codex":0.001077632,"about_ca_system_score_gemma":0.001741981,"threshold_uncertainty_score":0.01740092},"labels":[],"label_agreement":null},{"id":"W4412694328","doi":"10.1007/978-3-030-54621-2_667-1","title":"Stochastic Vehicle RoutingProblems","year":2025,"lang":"en","type":"book-chapter","venue":"Encyclopedia of Optimization","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"","keywords":"Computer science","score_opus":0.00927828317183463,"score_gpt":0.22586276670093255,"score_spread":0.21658448352909793,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412694328","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003178547,0.01208325,0.57313055,0.0019295131,0.0015634871,0.00010347414,0.0015037514,0.0010983507,0.40540907],"genre_scores_gemma":[0.092952564,0.027581923,0.19688775,0.0012311224,0.0018018524,0.00056744344,0.005944174,0.0010499052,0.6719833],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9997327,0.000050691815,0.00000984134,0.00005354495,0.00013411444,0.000019098175],"domain_scores_gemma":[0.99987435,0.00004429685,0.000010190616,0.000022284647,0.000038543363,0.000010428411],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000265638,0.0013522131,0.00090980687,0.00048888964,0.00034906002,0.0014184862,0.0007856765,0.00073691644,0.028409248],"category_scores_gemma":[0.0006213363,0.00044535406,0.00061212445,0.0011655422,0.00051057973,0.0008315409,0.0009362047,0.0016785109,0.011034142],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000031715907,0.000093120136,0.00017656256,0.00033690213,0.000043002237,0.000060654962,0.000052885836,0.12365618,0.0025040975,0.42097175,0.17261143,0.27946165],"study_design_scores_gemma":[0.000021298032,0.00004054705,0.00041910927,0.00013624196,0.00001978772,0.0001706175,0.0000306029,0.19986288,0.0014588081,0.34679437,0.45102307,0.000022654347],"about_ca_topic_score_codex":0.0012824391,"about_ca_topic_score_gemma":0.0023406213,"teacher_disagreement_score":0.028409248,"about_ca_system_score_codex":0.000807332,"about_ca_system_score_gemma":0.00092796347,"threshold_uncertainty_score":0.095038414},"labels":[],"label_agreement":null},{"id":"W4412845438","doi":"10.1155/atr/6668589","title":"Logistics Distribution Path Optimization Considering Carbon Emissions and Multifuel‐Type Vehicles","year":2025,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Path (computing); Distribution (mathematics); Carbon fibers; Environmental science; Transport engineering; Automotive engineering; Computer science; Engineering; Mathematics; Algorithm","score_opus":0.011898958981986042,"score_gpt":0.2693034358198958,"score_spread":0.25740447683790973,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412845438","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2540631,0.0009181655,0.7329409,0.00064703607,0.00008269015,0.00010499773,0.00032141377,0.00021502077,0.010706526],"genre_scores_gemma":[0.93625677,0.00056197017,0.0571919,0.000045896813,0.000012867839,0.0000902943,0.00019515367,0.000051653595,0.0055935076],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997695,0.00008265984,0.0000056198232,0.000045139004,0.000033262597,0.00006379914],"domain_scores_gemma":[0.99975616,0.00013463506,0.00003646833,0.000008550905,0.00004409849,0.000020121332],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004917882,0.0010018593,0.0007061219,0.0007177845,0.00053259725,0.00096517784,0.0006769,0.00095356995,0.0018766855],"category_scores_gemma":[0.0007822776,0.0004621875,0.0008697971,0.0010255885,0.0003882748,0.0010031,0.00063352677,0.0006044492,0.00010662065],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000136965855,0.000009601662,0.0002128101,0.000017279583,0.000009896405,0.00002803373,0.0000069108737,0.9953107,0.00032430733,0.0015714255,0.00011958965,0.0023757287],"study_design_scores_gemma":[0.000005435703,0.000016594875,0.00011231199,0.0000021450242,0.0000065252825,0.000008860219,0.000017696804,0.99768984,0.00020902253,0.001675257,0.0002525425,0.0000036780368],"about_ca_topic_score_codex":0.017922144,"about_ca_topic_score_gemma":0.009635586,"teacher_disagreement_score":0.017922144,"about_ca_system_score_codex":0.0016358326,"about_ca_system_score_gemma":0.0018692672,"threshold_uncertainty_score":0.03563565},"labels":[],"label_agreement":null},{"id":"W4413940061","doi":"10.1016/j.omega.2025.103419","title":"Column generation and local search for the profit-oriented hub-line location problem with elastic demands","year":2025,"lang":"en","type":"article","venue":"Omega","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Concordia University; Group for Research in Decision Analysis; Transport Canada","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Column generation; Column (typography); Profit (economics); Mathematical optimization; Computer science; Mathematics; Structural engineering; Engineering; Economics; Microeconomics","score_opus":0.016857956068773276,"score_gpt":0.2726278391212295,"score_spread":0.25576988305245624,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413940061","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09771415,0.0021859447,0.88834226,0.0010690041,0.00017992711,0.0002984214,0.00073367805,0.0009597334,0.008516863],"genre_scores_gemma":[0.6136749,0.00070109597,0.3792557,0.000454648,0.000107228756,0.00036642954,0.0012540504,0.0002812947,0.0039046549],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993856,0.00033974904,0.00001865171,0.000090513946,0.00007145583,0.00009391348],"domain_scores_gemma":[0.9961092,0.0031184924,0.00025266764,0.00011012585,0.00027022077,0.00013923536],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014098919,0.0011975664,0.0010792773,0.00092539104,0.0005014335,0.0010187251,0.0010322094,0.0009989262,0.004532421],"category_scores_gemma":[0.003996279,0.00053607556,0.0008474628,0.0012817883,0.0006930804,0.00089435186,0.00093825243,0.0013549228,0.00044632217],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000093988034,0.00008772419,0.00060176465,0.00010704421,0.000032088956,0.000076939905,0.00003350077,0.9712186,0.00039923302,0.005202863,0.0025725965,0.01957373],"study_design_scores_gemma":[0.000016492879,0.000018182976,0.00006579535,0.0000052709584,0.0000055486216,0.000007670084,0.000013234581,0.9975522,0.00011283561,0.0019857553,0.0002139127,0.0000030281333],"about_ca_topic_score_codex":0.010493423,"about_ca_topic_score_gemma":0.013422489,"teacher_disagreement_score":0.010493423,"about_ca_system_score_codex":0.0011705802,"about_ca_system_score_gemma":0.0016025159,"threshold_uncertainty_score":0.020864725},"labels":[],"label_agreement":null},{"id":"W4414015801","doi":"10.11159/cist25.154","title":"Solving the Vehicle Routing Problem via Distance-Aware Clustering and Simulated Annealing","year":2025,"lang":"en","type":"article","venue":"Proceedings of the World Congress on Electrical Engineering and Computer Systems and Science","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Istanbul Teknik Üniversitesi","keywords":"Vehicle routing problem; Cluster analysis; Simulated annealing; Computer science; Routing (electronic design automation); Mathematical optimization; Computer network; Algorithm; Artificial intelligence; Mathematics","score_opus":0.0058794323783142345,"score_gpt":0.2164570457438098,"score_spread":0.21057761336549558,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414015801","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.028670136,0.00023711423,0.9652421,0.00020619141,0.000042675696,0.0001149534,0.000085740096,0.00046587392,0.0049352963],"genre_scores_gemma":[0.3038193,0.00025798206,0.69170445,0.00010560667,0.000028537239,0.00030618487,0.00028443543,0.00019882755,0.0032946542],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993505,0.00030018715,0.000027883385,0.00012520587,0.00012480543,0.000071410126],"domain_scores_gemma":[0.9991061,0.0005612754,0.00009515079,0.00008346067,0.000120263394,0.000033638353],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008360206,0.00093729224,0.0009792467,0.0009583269,0.00074140483,0.0008050753,0.0012428369,0.0013586204,0.0022126145],"category_scores_gemma":[0.00240976,0.00070818845,0.0012782977,0.0010351386,0.0006236768,0.0008073743,0.00071436464,0.00093544996,0.00040953935],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014447905,0.000019922692,0.00019402747,0.000030011708,0.00002332538,0.000010753783,0.00002797172,0.985407,0.0006740648,0.0030440385,0.00036367835,0.010190702],"study_design_scores_gemma":[0.0000079286,0.000015277543,0.00008017956,0.000004738484,0.000006144511,0.000011108694,0.000015948111,0.99600935,0.00043788744,0.002682854,0.0007240989,0.0000043951723],"about_ca_topic_score_codex":0.010987461,"about_ca_topic_score_gemma":0.010070658,"teacher_disagreement_score":0.010987461,"about_ca_system_score_codex":0.0011848429,"about_ca_system_score_gemma":0.001674059,"threshold_uncertainty_score":0.02184701},"labels":[],"label_agreement":null},{"id":"W4414449054","doi":"10.1080/00207543.2025.2557530","title":"An innovative framework integrating MILP and a parallel optimal algorithm for UAV-Enabled last-Mile delivery","year":2025,"lang":"en","type":"article","venue":"International Journal of Production Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Optimal design; Production (economics); Minification; Key (lock); Algorithm design; Efficient algorithm","score_opus":0.04409514489310777,"score_gpt":0.41156813893355976,"score_spread":0.367472994040452,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414449054","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0027943908,0.00009647541,0.9925768,0.000100204954,0.000043203952,0.000040012186,0.000045350473,0.00048662655,0.0038169576],"genre_scores_gemma":[0.17009616,0.0003085508,0.8248265,0.00012427181,0.000056412548,0.0002595826,0.00027093288,0.00031633678,0.0037413777],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996722,0.00007801314,0.000013451601,0.000063246705,0.00011846856,0.00005459788],"domain_scores_gemma":[0.9997955,0.0000789186,0.000024480576,0.000031266674,0.000049935945,0.000019980578],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005685399,0.001022342,0.0006850636,0.0005298109,0.0005322128,0.00092818897,0.0012297829,0.000836466,0.0032274567],"category_scores_gemma":[0.0010303358,0.00047517777,0.0009489992,0.00062678114,0.00051674194,0.0008064578,0.0010535176,0.0012750741,0.0006816942],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000023178744,0.00003379854,0.00020883423,0.0000568048,0.000024208346,0.000043004275,0.000023019385,0.9436546,0.0017057708,0.01701876,0.0015426795,0.03566543],"study_design_scores_gemma":[0.0000067190313,0.000015982363,0.000031493313,0.000005491303,0.0000055257115,0.000015038401,0.0000073762526,0.99261147,0.00046013054,0.003932096,0.0029054878,0.0000033182216],"about_ca_topic_score_codex":0.0071607744,"about_ca_topic_score_gemma":0.007565163,"teacher_disagreement_score":0.0071607744,"about_ca_system_score_codex":0.00091687916,"about_ca_system_score_gemma":0.0019900703,"threshold_uncertainty_score":0.014238179},"labels":[],"label_agreement":null},{"id":"W4414516671","doi":"10.1016/j.clet.2025.101082","title":"From cost-centering to sustainability: A review of Pollution Routing Problems","year":2025,"lang":"en","type":"article","venue":"Cleaner Engineering and Technology","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Blueprint; Sustainability; Service (business); Routing (electronic design automation); Service provider; Vehicle routing problem; Sustainable development","score_opus":0.005967532315502251,"score_gpt":0.24786932294119188,"score_spread":0.24190179062568962,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414516671","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0005712021,0.98624414,0.0071719615,0.0011573441,0.00040546013,0.000010999487,0.000031378822,0.000013986392,0.0043934886],"genre_scores_gemma":[0.0062071006,0.9871001,0.0046204105,0.00033660908,0.0008340412,0.000020145162,0.000054460404,0.000013860695,0.0008133318],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9988939,0.00032289705,0.00013826825,0.00021392747,0.00036770015,0.00006328626],"domain_scores_gemma":[0.99691737,0.0022631723,0.00020801091,0.00006264041,0.0004884937,0.00006046124],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015446587,0.0014583166,0.0013511034,0.0037596086,0.0005751569,0.0025529538,0.0013609858,0.0016474451,0.0035797309],"category_scores_gemma":[0.0044446685,0.0006228105,0.0011342749,0.009510166,0.0011878823,0.0034301647,0.0009268691,0.0019158481,0.0008252178],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000057783913,0.00014562064,0.0008422208,0.02926041,0.00021186729,0.0002509291,0.00031431505,0.020421363,0.0004850259,0.12888955,0.04651613,0.7726048],"study_design_scores_gemma":[0.000018382381,0.00016773549,0.0023331328,0.018276347,0.00025586342,0.0009212546,0.00060209044,0.011878868,0.00046237593,0.105067626,0.8599319,0.00008445788],"about_ca_topic_score_codex":0.0037184816,"about_ca_topic_score_gemma":0.0032025608,"teacher_disagreement_score":0.0037596086,"about_ca_system_score_codex":0.0015713209,"about_ca_system_score_gemma":0.0024592106,"threshold_uncertainty_score":0.011975408},"labels":[],"label_agreement":null},{"id":"W4414567829","doi":"10.1016/j.ifacol.2025.09.037","title":"A Decomposition-Based Framework for Large-Scale Multi-Period Log-Truck Routing and Scheduling: A Case Study in Canadian Forestry","year":2025,"lang":"en","type":"article","venue":"IFAC-PapersOnLine","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Polytechnique Montréal; Cégep de Lévis; Université du Québec à Rimouski; Natural Resources Canada","funders":"","keywords":"Routing (electronic design automation); Scheduling (production processes); Work (physics); Heuristic; Decomposition; Linear programming; Forest industry","score_opus":0.018065031708536825,"score_gpt":0.32959342778408235,"score_spread":0.3115283960755455,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414567829","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16902706,0.0008981406,0.79520607,0.0012827854,0.00009897564,0.0004803716,0.001087282,0.00055001234,0.031369325],"genre_scores_gemma":[0.6441683,0.0006432415,0.3459647,0.00010798049,0.000024105286,0.00017395294,0.000638514,0.00009081629,0.008188259],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995734,0.000117502066,0.000011768604,0.0000575537,0.000106641935,0.0001331102],"domain_scores_gemma":[0.99962807,0.00017554041,0.000031631353,0.000026444113,0.00007959125,0.000058745256],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00095742254,0.0008004199,0.00041750894,0.0006979084,0.0015665614,0.0013142094,0.0012952855,0.000950835,0.002246343],"category_scores_gemma":[0.0010880175,0.00031557167,0.00060807186,0.0013272876,0.00075339735,0.00059635716,0.0007086097,0.0009591564,0.00016132198],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026834838,0.000039759634,0.0006072747,0.00004786195,0.000010356512,0.00013400872,0.00007284406,0.97045296,0.000850699,0.0127591295,0.0014487144,0.013549582],"study_design_scores_gemma":[0.000007538848,0.00001385451,0.0004763758,0.000008961572,0.000006685096,0.000029995632,0.00012791711,0.9928886,0.0002961084,0.0028943408,0.003240429,0.000009113841],"about_ca_topic_score_codex":0.5840542,"about_ca_topic_score_gemma":0.6876283,"teacher_disagreement_score":0.41594583,"about_ca_system_score_codex":0.007393454,"about_ca_system_score_gemma":0.010673089,"threshold_uncertainty_score":0.8367908},"labels":[],"label_agreement":null},{"id":"W4415191847","doi":"10.48550/arxiv.2508.05877","title":"Superadditivity-based valid inequalities and asymptotic bounds for the vehicle routing problem with stochastic demands","year":2025,"lang":"en","type":"preprint","venue":"ArXiv.org","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Alliance de recherche numérique du Canada","keywords":"Superadditivity; Integer (computer science); Computation; Function (biology); Integer programming; Exploit; Bounded function","score_opus":0.04194404882473119,"score_gpt":0.28145271826055074,"score_spread":0.23950866943581955,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415191847","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010121032,0.001892658,0.97471195,0.0010248619,0.00015699345,0.000095939526,0.00029182,0.00025906268,0.011445784],"genre_scores_gemma":[0.5314914,0.006702495,0.44466057,0.0017443439,0.0010767309,0.0009412207,0.0015711557,0.00070758804,0.01110454],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9930848,0.0024971087,0.000277452,0.0009358073,0.0023591032,0.0008457907],"domain_scores_gemma":[0.9609177,0.03169548,0.0020382968,0.0015459942,0.0031004914,0.00070190086],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008197523,0.0030999177,0.0018466226,0.0025864902,0.00102086,0.0037690843,0.0040409933,0.0018339552,0.0064584604],"category_scores_gemma":[0.046513118,0.0013264208,0.0029060703,0.003234793,0.0033739898,0.0058200066,0.0036817207,0.009443227,0.0010892214],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001908245,0.00020454155,0.0013891789,0.00050195307,0.000120839475,0.00022568597,0.00025247637,0.5832315,0.002954578,0.35227427,0.0049919933,0.053662226],"study_design_scores_gemma":[0.00001384574,0.00004397541,0.00019727893,0.00006219853,0.000027684868,0.000041726307,0.000031648302,0.87729144,0.0007919539,0.119873665,0.001606858,0.00001779793],"about_ca_topic_score_codex":0.0042279903,"about_ca_topic_score_gemma":0.0040840223,"teacher_disagreement_score":0.008197523,"about_ca_system_score_codex":0.0047196457,"about_ca_system_score_gemma":0.0029242602,"threshold_uncertainty_score":0.04335314},"labels":[],"label_agreement":null},{"id":"W4415588099","doi":"10.1287/ijoc.2025.1140","title":"Exact Methods and a Two-Stage Iterative Heuristic for the Carrier-Vehicle Traveling Salesman Problem","year":2025,"lang":"en","type":"article","venue":"INFORMS journal on computing","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Travelling salesman problem; Heuristics; Heuristic; 2-opt; Benchmark (surveying); Set (abstract data type); Traveling purchaser problem; Software; Bottleneck traveling salesman problem","score_opus":0.02174006309521651,"score_gpt":0.34764696089454084,"score_spread":0.3259068977993243,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415588099","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006605584,0.00024107433,0.9883591,0.00011007512,0.000046390825,0.00016738477,0.000087047774,0.00033409407,0.004049204],"genre_scores_gemma":[0.11163218,0.0003300356,0.88478047,0.00010728307,0.00004208961,0.00049875444,0.00031700116,0.00015615133,0.0021360898],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988349,0.00040100428,0.000065533044,0.0001743679,0.00034955874,0.00017468646],"domain_scores_gemma":[0.99802065,0.0013044676,0.00017652912,0.00016998251,0.00026662627,0.00006168788],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016845529,0.0015172441,0.0010131289,0.001251197,0.00058355095,0.0011979601,0.002060143,0.0012929689,0.0063612885],"category_scores_gemma":[0.0040990124,0.0008008983,0.0013514046,0.0014531956,0.000684459,0.0013151041,0.0009811103,0.0016118883,0.0009546167],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006676292,0.00011121164,0.00041587662,0.00019269087,0.00004249975,0.00007566543,0.00008816883,0.89231473,0.0013008222,0.01976831,0.0022494658,0.083373696],"study_design_scores_gemma":[0.00003139215,0.000035506142,0.000059238875,0.0000129587115,0.000013206388,0.00001884164,0.000018642544,0.9929488,0.0003902063,0.005065864,0.0013983288,0.0000070207057],"about_ca_topic_score_codex":0.008215177,"about_ca_topic_score_gemma":0.009041868,"teacher_disagreement_score":0.008215177,"about_ca_system_score_codex":0.0013360764,"about_ca_system_score_gemma":0.0031262673,"threshold_uncertainty_score":0.021280587},"labels":[],"label_agreement":null},{"id":"W4415616276","doi":"10.1016/j.tre.2025.104494","title":"The two-echelon location-routing problem: A comparative analysis of novel and existing compact formulations","year":2025,"lang":"en","type":"article","venue":"Transportation Research Part E Logistics and Transportation Review","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"Alliance de recherche numérique du Canada; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Natural Sciences and Engineering Research Council of Canada; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior; Fundação de Amparo à Pesquisa do Estado de São Paulo","keywords":"Arc routing; Benchmark (surveying); Linear programming; Polynomial; Vehicle routing problem; Integer programming; Routing (electronic design automation)","score_opus":0.19399182872757764,"score_gpt":0.4513913791284737,"score_spread":0.25739955040089607,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415616276","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.048895422,0.017521879,0.88452643,0.0027491257,0.00044549681,0.0006105263,0.0010747492,0.00063497707,0.043541346],"genre_scores_gemma":[0.26359713,0.015217458,0.70953125,0.0008123457,0.00051339256,0.0007768015,0.0023385673,0.0004861498,0.006726925],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9966307,0.0012421622,0.00020516885,0.00041404754,0.0012001343,0.00030780496],"domain_scores_gemma":[0.9913965,0.005487313,0.0010226208,0.0008282225,0.0010362102,0.00022914827],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0044805063,0.0019410418,0.00153138,0.0022419037,0.00078171346,0.0035257516,0.0026475112,0.002035924,0.007099329],"category_scores_gemma":[0.012066483,0.000717843,0.0021001156,0.0040101255,0.0009716006,0.006762422,0.002479244,0.002956854,0.00090819807],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002591294,0.0006288164,0.0013464909,0.0017796477,0.000161243,0.00028839984,0.00022046728,0.653871,0.0018970944,0.12085284,0.013080555,0.20561427],"study_design_scores_gemma":[0.00009920419,0.000285627,0.0006580121,0.00043448497,0.00009931277,0.0004017559,0.00027209765,0.9333515,0.0013122088,0.039334815,0.023705687,0.00004534169],"about_ca_topic_score_codex":0.0026637476,"about_ca_topic_score_gemma":0.004085075,"teacher_disagreement_score":0.007099329,"about_ca_system_score_codex":0.0026317053,"about_ca_system_score_gemma":0.0032599152,"threshold_uncertainty_score":0.02374965},"labels":[],"label_agreement":null},{"id":"W4415702155","doi":"10.19139/soic-2310-5070-2916","title":"Vehicle Routing Problem with Synchronization and Scheduling Constraints of support vehicles","year":2025,"lang":"en","type":"article","venue":"Statistics Optimization & Information Computing","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Group for Research in Decision Analysis","funders":"","keywords":"Scheduling (production processes); Vehicle routing problem; Robustness (evolution); Synchronization (alternating current); Flow network; Job shop scheduling; Computation; Linear programming","score_opus":0.005838467422967215,"score_gpt":0.23433599663862803,"score_spread":0.22849752921566083,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415702155","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22855897,0.0008604611,0.7472763,0.0017393981,0.00026255526,0.00051473273,0.0018794289,0.0007130263,0.018195095],"genre_scores_gemma":[0.83137584,0.00047225,0.15477681,0.00022194388,0.0001299421,0.0004949657,0.001466293,0.00016914334,0.010892824],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987,0.00041772213,0.000058160495,0.00037590865,0.00015701177,0.0002910497],"domain_scores_gemma":[0.9987326,0.00078753027,0.00021597465,0.0000640304,0.000103199134,0.00009661969],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010695109,0.0016227454,0.0014187976,0.00067755894,0.00070362835,0.001650391,0.0016230427,0.0020906266,0.0044986787],"category_scores_gemma":[0.0024371953,0.00088184513,0.0011941184,0.0012691475,0.00080937595,0.0018767901,0.000858721,0.0012214081,0.00042684682],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009313356,0.000050018574,0.0003264477,0.000079326644,0.0000305765,0.00015744545,0.000037363883,0.9808492,0.00080726197,0.008503944,0.0013288738,0.0077363825],"study_design_scores_gemma":[0.000065729546,0.00005171487,0.00018917973,0.000006332091,0.000016110023,0.00005517165,0.00004540478,0.9905021,0.00052601076,0.0071870987,0.0013456529,0.000009479016],"about_ca_topic_score_codex":0.010640035,"about_ca_topic_score_gemma":0.00621443,"teacher_disagreement_score":0.010640035,"about_ca_system_score_codex":0.0016047057,"about_ca_system_score_gemma":0.0022106122,"threshold_uncertainty_score":0.021156192},"labels":[],"label_agreement":null},{"id":"W4415896744","doi":"10.1016/j.tre.2025.104509","title":"Consistent home health care routing and scheduling problem under time uncertainty","year":2025,"lang":"en","type":"article","venue":"Transportation Research Part E Logistics and Transportation Review","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal; Polytechnique Montréal; Université de Montréal; Group for Research in Decision Analysis","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Scheduling (production processes); Vehicle routing problem; Benchmark (surveying); Routing (electronic design automation); Consistency (knowledge bases); Set (abstract data type); Job shop scheduling; Home health","score_opus":0.06014247469206965,"score_gpt":0.3755320767420944,"score_spread":0.31538960205002475,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415896744","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0580549,0.0003988457,0.93422043,0.0014385988,0.000103610226,0.00013554792,0.0007071715,0.00022814512,0.0047127325],"genre_scores_gemma":[0.81805784,0.0005429897,0.17589772,0.00028734896,0.00013901544,0.00029178607,0.0008496684,0.00012235621,0.003811243],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980274,0.00080045545,0.0000734815,0.0004575393,0.00031041482,0.0003306161],"domain_scores_gemma":[0.99669975,0.0022282759,0.00045039415,0.0001498871,0.00025913536,0.00021247214],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023456472,0.0012875544,0.0017526271,0.0008421655,0.0007503826,0.0017071562,0.0017980379,0.002152489,0.0035828825],"category_scores_gemma":[0.0066139423,0.00079015206,0.0016704274,0.0017042082,0.00096640753,0.001758191,0.0015576339,0.0020196473,0.00022950975],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000025309439,0.00002076938,0.00038547578,0.000031151038,0.000023653867,0.000077625235,0.000019290337,0.986272,0.00014476546,0.008673028,0.00064299593,0.0036839966],"study_design_scores_gemma":[0.000010680374,0.000016300526,0.0001324337,0.0000045263714,0.0000073600286,0.000020529005,0.000015395864,0.98947746,0.00008514442,0.009961068,0.00026417203,0.000004890689],"about_ca_topic_score_codex":0.010809527,"about_ca_topic_score_gemma":0.005801726,"teacher_disagreement_score":0.010809527,"about_ca_system_score_codex":0.001990924,"about_ca_system_score_gemma":0.0027690083,"threshold_uncertainty_score":0.021493256},"labels":[],"label_agreement":null},{"id":"W4416027488","doi":"10.1016/j.tre.2025.104491","title":"Inventory routing with heterogeneous vehicles and hazardous material backhauling","year":2025,"lang":"en","type":"article","venue":"Transportation Research Part E Logistics and Transportation Review","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Hydro-Québec; HEC Montréal","funders":"Mitacs","keywords":"Routing (electronic design automation); Stockout; Vehicle routing problem; Hazardous waste; Heuristic; Decomposition; Delivery Performance","score_opus":0.0547987876728708,"score_gpt":0.34189014457411365,"score_spread":0.2870913569012429,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416027488","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.29576913,0.002422805,0.61201614,0.002593089,0.00035974945,0.0011084224,0.0041866708,0.0009736004,0.0805703],"genre_scores_gemma":[0.8983237,0.0009808013,0.081106566,0.0001600215,0.000068261776,0.0001716487,0.0011872303,0.00008089204,0.017920991],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994592,0.00014350995,0.000015587748,0.00009327886,0.0001236994,0.00016475684],"domain_scores_gemma":[0.9994764,0.00022220559,0.000091206304,0.00004346222,0.00009086043,0.00007592136],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00067702355,0.001313874,0.00067205395,0.0006975968,0.0010636445,0.0017286851,0.0018872799,0.0012361205,0.003946295],"category_scores_gemma":[0.0012139307,0.00042499785,0.0007918525,0.0017154186,0.0008896226,0.00089303206,0.00083209615,0.00085333636,0.00029581267],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000036814607,0.000043736523,0.00038278563,0.000050529798,0.000014736386,0.00016081585,0.000013423366,0.98550147,0.0005256716,0.005679192,0.001616054,0.0059747687],"study_design_scores_gemma":[0.000024387447,0.0000297197,0.00045611642,0.0000111177005,0.000012655179,0.000046817535,0.000046109548,0.99201256,0.0005345402,0.004190252,0.0026256738,0.0000100524],"about_ca_topic_score_codex":0.21508938,"about_ca_topic_score_gemma":0.20552234,"teacher_disagreement_score":0.21508938,"about_ca_system_score_codex":0.0060972846,"about_ca_system_score_gemma":0.0054511153,"threshold_uncertainty_score":0.42767483},"labels":[],"label_agreement":null},{"id":"W4416118539","doi":"10.48550/arxiv.2504.05109","title":"Inverse Mixed Integer Optimization: An Interior Point Perspective","year":2025,"lang":"en","type":"preprint","venue":"ArXiv.org","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Cutting-plane method; Linear programming; Interior point method; Integer programming; Inverse; Point (geometry); Inverse problem; Norm (philosophy)","score_opus":0.03748786668656436,"score_gpt":0.3009208295311216,"score_spread":0.26343296284455725,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416118539","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00078825647,0.00012658483,0.9973144,0.00013188462,0.000023443277,0.000019608711,0.000017155624,0.000060960872,0.0015177082],"genre_scores_gemma":[0.09386575,0.00072610634,0.90138054,0.00026099102,0.000116443436,0.00042986727,0.0001428135,0.00026691894,0.0028105322],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99837935,0.0006806297,0.000065259796,0.00023209644,0.0005172797,0.0001254098],"domain_scores_gemma":[0.9978033,0.0013839068,0.00025227273,0.00019956336,0.00027812578,0.000082857296],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0037136285,0.0023094139,0.0019594876,0.0014164492,0.0005824091,0.0027871614,0.0026291243,0.0021835112,0.0043370747],"category_scores_gemma":[0.0073052635,0.0011414684,0.002236163,0.0013746833,0.0022026917,0.0025432985,0.002949724,0.0049914075,0.0010051343],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003309436,0.000048265196,0.00027032333,0.00018043895,0.000039879844,0.00006904788,0.000084839834,0.82416934,0.0011832827,0.14851895,0.0014793517,0.023923185],"study_design_scores_gemma":[0.000008330164,0.00002559917,0.000020822024,0.00002692803,0.000006293505,0.000021140428,0.000014564591,0.96177673,0.0004458616,0.035659898,0.0019864473,0.0000073853553],"about_ca_topic_score_codex":0.0017280681,"about_ca_topic_score_gemma":0.0011504244,"teacher_disagreement_score":0.0043370747,"about_ca_system_score_codex":0.0012101821,"about_ca_system_score_gemma":0.0020323966,"threshold_uncertainty_score":0.01963973},"labels":[],"label_agreement":null},{"id":"W4416223881","doi":"10.1080/01605682.2025.2579861","title":"Snowplough service area reconfiguration using workload balancing techniques with route optimisation for large municipalities","year":2025,"lang":"en","type":"article","venue":"Journal of the Operational Research Society","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Surrey Place Centre; Ontario Tech University","funders":"","keywords":"Workload; Control reconfiguration; Scheduling (production processes); Service (business); Information technology; Information system; Information and Communications Technology","score_opus":0.07968452884789758,"score_gpt":0.38376901599018215,"score_spread":0.3040844871422846,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416223881","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.708718,0.00021851751,0.27990374,0.00024461694,0.000050604012,0.00019711217,0.00016174994,0.0010342507,0.009471379],"genre_scores_gemma":[0.9721995,0.000046420882,0.026415521,0.000013732747,0.0000060931898,0.00004389809,0.00008305659,0.000042950356,0.0011488056],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997503,0.00007605705,0.000007905066,0.000047900878,0.00004266894,0.00007524469],"domain_scores_gemma":[0.9997291,0.0000907322,0.000038705497,0.000033958964,0.000056556535,0.00005088853],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038255076,0.0006048437,0.00038058666,0.00050701346,0.0005987886,0.00074404845,0.0008760088,0.0003921941,0.0014127285],"category_scores_gemma":[0.00091399084,0.0002980164,0.00039170892,0.0007164174,0.0002768961,0.00058705965,0.0005653551,0.0003059606,0.00020598697],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006446737,0.000039468177,0.0014021598,0.000023373956,0.000016140653,0.000067588226,0.00006705343,0.97415656,0.0030976997,0.00051462883,0.000368125,0.020182848],"study_design_scores_gemma":[0.000007329205,0.0000396187,0.00063081726,0.0000020145014,0.0000061618352,0.000014227574,0.00012872367,0.99774927,0.0006217171,0.00033832274,0.00045758873,0.0000041320304],"about_ca_topic_score_codex":0.030523863,"about_ca_topic_score_gemma":0.04737521,"teacher_disagreement_score":0.030523863,"about_ca_system_score_codex":0.0009763705,"about_ca_system_score_gemma":0.0011357297,"threshold_uncertainty_score":0.06069237},"labels":[],"label_agreement":null},{"id":"W4416254644","doi":"10.1016/j.omega.2025.103461","title":"Multi-driver transportation scheduling for improving supply chain resilience","year":2025,"lang":"en","type":"article","venue":"Omega","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Laurentian University","funders":"Humanities and Social Science Fund of Ministry of Education of China; Fundamental Research Funds for the Central Universities; Higher Education Discipline Innovation Project; State Key Laboratory of Fluid Power and Mechatronic Systems; China Postdoctoral Science Foundation; National Natural Science Foundation of China","keywords":"Supply chain; Scheduling (production processes); Linear programming; Flexibility (engineering); Metaheuristic; Resilience (materials science); Job shop scheduling; Convergence (economics)","score_opus":0.012097279558888214,"score_gpt":0.2716048377932268,"score_spread":0.2595075582343386,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416254644","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08926226,0.00020834118,0.9052981,0.00027672463,0.00015542931,0.00006610907,0.00013032301,0.0003268533,0.0042757886],"genre_scores_gemma":[0.92200977,0.00012174613,0.07318034,0.00007123134,0.000043693344,0.00005760909,0.00012980266,0.00008101831,0.0043047178],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997428,0.00008270976,0.000008536808,0.000067923414,0.00003542812,0.000062545594],"domain_scores_gemma":[0.9993806,0.0003233524,0.000075150965,0.000048770096,0.00010018443,0.000071887036],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000822015,0.0008336528,0.00079288887,0.00066131196,0.00056477915,0.00067328743,0.0009306374,0.0004945349,0.003114721],"category_scores_gemma":[0.0017909193,0.0003721861,0.00052003923,0.0006268779,0.0002816211,0.00094934733,0.0010032788,0.0006768287,0.00023666257],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007951857,0.000054910917,0.00036265654,0.000024308778,0.00002237423,0.00001513203,0.000023806413,0.97499317,0.0012895333,0.0038230412,0.00064045563,0.018671134],"study_design_scores_gemma":[0.000003272952,0.000021347927,0.00006198584,0.0000013236589,0.0000043186988,0.0000019741692,0.000009217661,0.9983266,0.00018828767,0.0012144389,0.00016571669,0.0000015464577],"about_ca_topic_score_codex":0.0053414595,"about_ca_topic_score_gemma":0.0056343083,"teacher_disagreement_score":0.0053414595,"about_ca_system_score_codex":0.0009757897,"about_ca_system_score_gemma":0.0014473392,"threshold_uncertainty_score":0.010620773},"labels":[],"label_agreement":null},{"id":"W4416323349","doi":"","title":"Algorithms for solving the On-Demand Bus Routing Problem with Bus Stops Assignment","year":2024,"lang":"en","type":"article","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"","keywords":"Routing (electronic design automation); Algorithm design; Vehicle routing problem; Class (philosophy)","score_opus":0.014789497403528673,"score_gpt":0.23765099254122743,"score_spread":0.22286149513769876,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416323349","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.035476357,0.00054989883,0.9442491,0.00062098657,0.00016667946,0.0002371262,0.00034460783,0.0008704194,0.01748495],"genre_scores_gemma":[0.3572675,0.00075207395,0.62391096,0.00030376101,0.00024693806,0.000700262,0.0013356484,0.00040007228,0.0150828445],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993481,0.00021909039,0.00002746441,0.00013148463,0.00013288794,0.00014091948],"domain_scores_gemma":[0.9975199,0.0017978863,0.0001608386,0.0001626087,0.00023125927,0.00012736613],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012075141,0.0018923115,0.0016251433,0.0011070468,0.00083288184,0.0017497579,0.0027565262,0.002861859,0.008772382],"category_scores_gemma":[0.0045423675,0.0008937722,0.001092292,0.001729625,0.00069796725,0.0019454899,0.0017008408,0.0020784975,0.0010090792],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012417058,0.00017101303,0.00038646662,0.00013963177,0.00003832392,0.000038128885,0.0000532829,0.91047496,0.00039327727,0.00994346,0.0044585196,0.07377884],"study_design_scores_gemma":[0.000047273053,0.000029223074,0.00006860036,0.000008072494,0.000007931865,0.000011886852,0.0000223903,0.99034977,0.0001385172,0.008573385,0.0007392401,0.0000037316383],"about_ca_topic_score_codex":0.008922491,"about_ca_topic_score_gemma":0.011740174,"teacher_disagreement_score":0.008922491,"about_ca_system_score_codex":0.0012885033,"about_ca_system_score_gemma":0.0020219272,"threshold_uncertainty_score":0.029346526},"labels":[],"label_agreement":null},{"id":"W4416407132","doi":"10.1007/978-3-032-00563-2_1","title":"Moving Toward Sustainability: A Methodological Review of the Pollution-Routing Problem in the Optimization Literature","year":2025,"lang":"en","type":"book-chapter","venue":"","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Sustainability; Control (management); Sustainable development; Systematic review; Vehicle routing problem","score_opus":0.03936644087898629,"score_gpt":0.3091937171689351,"score_spread":0.26982727628994885,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416407132","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0006928606,0.82170284,0.11255956,0.01377142,0.0033103444,0.000049105896,0.00014028861,0.00006845929,0.04770516],"genre_scores_gemma":[0.015808837,0.905691,0.052623015,0.0034611765,0.0046740994,0.00012594314,0.00019916888,0.00014272316,0.017274052],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9981654,0.0008529633,0.00011201512,0.0002601071,0.00052020955,0.00008928367],"domain_scores_gemma":[0.9973218,0.0018195924,0.0001467428,0.000098315635,0.0005537947,0.00005978987],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0036727618,0.0013140836,0.0014119147,0.0038774367,0.0010089816,0.004916066,0.002334887,0.0026419156,0.006335511],"category_scores_gemma":[0.0056500845,0.00064833864,0.0010899688,0.010778043,0.0036123951,0.0053507118,0.0018329918,0.0033454068,0.0022205145],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000116939345,0.00006356115,0.00020115796,0.0035066388,0.00005264035,0.00007406783,0.00018907752,0.011232802,0.0002597893,0.6945686,0.069944754,0.21989523],"study_design_scores_gemma":[0.00000434043,0.000025867159,0.0004323228,0.0030657477,0.00003361045,0.00012961746,0.0003102319,0.0054182964,0.00023413972,0.39186805,0.59844816,0.000029544124],"about_ca_topic_score_codex":0.006317833,"about_ca_topic_score_gemma":0.011013799,"teacher_disagreement_score":0.006335511,"about_ca_system_score_codex":0.0036429362,"about_ca_system_score_gemma":0.005432343,"threshold_uncertainty_score":0.026431441},"labels":[{"model":"gemma","categories":[],"domain":null,"study_design":"not_applicable","genre":"review","about_ca_system":false,"about_ca_topic":false,"confidence":"medium"},{"model":"gpt","categories":[],"domain":null,"study_design":"design_other","genre":"review","about_ca_system":false,"about_ca_topic":false,"confidence":"low"}],"label_agreement":"split"},{"id":"W4416539314","doi":"10.4230/lipics.socg.2025.53","title":"A PTAS for TSP with Neighbourhoods over Parallel Line Segments","year":2025,"lang":"en","type":"article","venue":"ArXiv.org","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Travelling salesman problem; Neighbourhood (mathematics); Line segment; Approximation algorithm; Euclidean geometry; Line (geometry); Euclidean distance; Point (geometry)","score_opus":0.02501878103276981,"score_gpt":0.2937707257300304,"score_spread":0.26875194469726055,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416539314","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.047054514,0.0009244786,0.92583483,0.0018458316,0.00035359582,0.0003738344,0.0010851695,0.004199489,0.018328233],"genre_scores_gemma":[0.2623142,0.00078242074,0.72011834,0.0004932574,0.000226399,0.00038443843,0.0019274018,0.0007552325,0.012998317],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99866414,0.00020675734,0.00007830534,0.00052460306,0.00034889905,0.00017719332],"domain_scores_gemma":[0.9987657,0.0004267693,0.00014423429,0.000388959,0.00013009548,0.00014425961],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007467634,0.0012111737,0.0014387894,0.000709872,0.0012064162,0.0018764925,0.0029048836,0.0019139469,0.0130806435],"category_scores_gemma":[0.0050950157,0.00062103756,0.002144903,0.0019386082,0.00096294034,0.004650662,0.002806856,0.00360204,0.0034640178],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009519461,0.00044800257,0.0017499743,0.0006463806,0.00012022093,0.00047748652,0.00053919485,0.6205613,0.008517912,0.12751004,0.03641341,0.20206416],"study_design_scores_gemma":[0.00013849165,0.0001762045,0.00019388615,0.000039211467,0.00003642356,0.0003493787,0.00008705951,0.9035991,0.0015729023,0.07832495,0.015453396,0.000029016586],"about_ca_topic_score_codex":0.006493894,"about_ca_topic_score_gemma":0.0058103353,"teacher_disagreement_score":0.0130806435,"about_ca_system_score_codex":0.0022954054,"about_ca_system_score_gemma":0.0017771798,"threshold_uncertainty_score":0.043759167},"labels":[],"label_agreement":null},{"id":"W4416554829","doi":"","title":"An Optimal Transport Based Goal-Oriented hr-Adaptive Mesh Pursuit","year":2025,"lang":"en","type":"article","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Wilfrid Laurier University; University of Ottawa","funders":"","keywords":"Focus (optics); Measure (data warehouse); Key (lock); Object (grammar)","score_opus":0.008591729561347718,"score_gpt":0.23611269874243318,"score_spread":0.22752096918108547,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416554829","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008209067,0.00008089979,0.98759776,0.00011804179,0.00007774545,0.000025918547,0.000030275884,0.00012011378,0.0037402327],"genre_scores_gemma":[0.5504993,0.00034174704,0.4364926,0.0002539479,0.000153633,0.00020040845,0.00020169462,0.00014316864,0.011713465],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997998,0.00005059176,0.0000075877842,0.00004759451,0.00006095889,0.000033455508],"domain_scores_gemma":[0.99970955,0.00012393753,0.000026511496,0.000032416337,0.00007646062,0.000031134852],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048762438,0.0006523917,0.0008765261,0.00042259853,0.0003115753,0.00064049487,0.0011625604,0.0013789146,0.0033821387],"category_scores_gemma":[0.0012693113,0.00032237443,0.00067096855,0.0004851644,0.00059794885,0.0006903796,0.0018580873,0.0008489175,0.00051402336],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016418086,0.00006338955,0.00040346105,0.00010225653,0.000035961995,0.00006335939,0.0000587755,0.8965632,0.010807532,0.018283842,0.0020312658,0.071422815],"study_design_scores_gemma":[0.0000034677425,0.00001488798,0.000020009718,0.0000018964621,0.0000018414443,0.000006157267,0.0000030949845,0.9985777,0.00019377675,0.00094856217,0.00022672434,0.0000018879069],"about_ca_topic_score_codex":0.0027042325,"about_ca_topic_score_gemma":0.0015153664,"teacher_disagreement_score":0.0033821387,"about_ca_system_score_codex":0.00039851575,"about_ca_system_score_gemma":0.0008131802,"threshold_uncertainty_score":0.011314392},"labels":[],"label_agreement":null},{"id":"W4416731099","doi":"10.1016/j.ejor.2025.11.021","title":"Large neighborhood and hybrid genetic search for inventory routing problems","year":2025,"lang":"en","type":"article","venue":"European Journal of Operational Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"Shenzhen Research Institute of Big Data; National Natural Science Foundation of China; Scale AI","keywords":"Benchmark (surveying); Routing (electronic design automation); Vehicle routing problem; Heuristic; Genetic algorithm; Operator (biology); Local search (optimization); Preprocessor","score_opus":0.06201460042252724,"score_gpt":0.3530211286014591,"score_spread":0.2910065281789319,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416731099","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0780677,0.001631456,0.91091096,0.00043637134,0.00012137134,0.000052532923,0.000055511067,0.00014615821,0.00857807],"genre_scores_gemma":[0.75781703,0.0007862641,0.22958598,0.00018066671,0.00013074224,0.000245875,0.00014507926,0.00012030431,0.010987973],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99954516,0.0002561738,0.000014351825,0.000047741567,0.00010988433,0.00002674657],"domain_scores_gemma":[0.9985483,0.0011179787,0.000090162444,0.00005453203,0.0001423717,0.000046608056],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015788722,0.00052936823,0.0009464032,0.001013753,0.0004903046,0.0008314042,0.0013158345,0.0013713915,0.0017023878],"category_scores_gemma":[0.0036549675,0.0005268114,0.00057977805,0.0010513614,0.00083996035,0.0011658291,0.0008570371,0.0008425211,0.00021671219],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006119815,0.000047085425,0.00028745068,0.000035121135,0.000037030808,0.00002861273,0.000029938397,0.96556485,0.00048238374,0.017767414,0.000575454,0.015083504],"study_design_scores_gemma":[0.000007828188,0.000015446676,0.00004783832,0.000003267977,0.000004366411,0.00000564031,0.0000042192514,0.9947351,0.000054071916,0.004932925,0.00018681669,0.0000025220313],"about_ca_topic_score_codex":0.005620745,"about_ca_topic_score_gemma":0.0052536554,"teacher_disagreement_score":0.005620745,"about_ca_system_score_codex":0.0009325418,"about_ca_system_score_gemma":0.00057998585,"threshold_uncertainty_score":0.01117605},"labels":[],"label_agreement":null},{"id":"W4416773034","doi":"10.1016/j.cor.2025.107341","title":"A data-driven heuristic for the dynamic vehicle routing problem with multiple soft time windows","year":2025,"lang":"en","type":"article","venue":"Computers & Operations Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"King Fahd University of Petroleum and Minerals","keywords":"Vehicle routing problem; Benchmark (surveying); Flexibility (engineering); Heuristic; Adaptability; Genetic algorithm; Crossover; Service (business)","score_opus":0.047575154236137486,"score_gpt":0.35770509348541674,"score_spread":0.31012993924927923,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416773034","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.037970208,0.0003958292,0.9561217,0.00029246073,0.0002093402,0.00020726214,0.00019844332,0.00051206135,0.004092677],"genre_scores_gemma":[0.60208833,0.00023383062,0.39344937,0.00020221132,0.00009830831,0.0003878292,0.00036386066,0.00016128104,0.0030149936],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994759,0.00012999507,0.00002757256,0.000090354886,0.00014668494,0.00012960873],"domain_scores_gemma":[0.9985654,0.0009035048,0.000103498896,0.00008733645,0.00021349112,0.00012686926],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012850428,0.0007825054,0.0014270508,0.0009591906,0.00056241604,0.0012195581,0.0019661991,0.0013730655,0.0034591933],"category_scores_gemma":[0.0027397743,0.0008163579,0.0008597465,0.0011439812,0.00061496877,0.0012882395,0.0011684196,0.0011548344,0.0004369523],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015737976,0.000110532135,0.00020841042,0.00006138022,0.000031354313,0.000045954654,0.000024504334,0.94935834,0.0010397303,0.0050989296,0.0010466032,0.042816922],"study_design_scores_gemma":[0.00002889044,0.00002830224,0.00003209756,0.0000043631103,0.0000065789336,0.0000062468876,0.0000060123016,0.9978108,0.00019453578,0.0015999064,0.00027780898,0.0000044387307],"about_ca_topic_score_codex":0.006315615,"about_ca_topic_score_gemma":0.006792801,"teacher_disagreement_score":0.006315615,"about_ca_system_score_codex":0.0014389266,"about_ca_system_score_gemma":0.0027392947,"threshold_uncertainty_score":0.012557745},"labels":[],"label_agreement":null},{"id":"W4417062269","doi":"10.2316/j.2026.206-1295","title":"RESEARCH ON OPTIMISATION OF COUNTY-LEVEL URBAN EXPRESS DELIVERY USING A MULTI-STRATEGY IMPROVED GENETIC ALGORITHM","year":2025,"lang":"en","type":"article","venue":"International Journal of Robotics and Automation","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Genetic algorithm; Path (computing); Distribution (mathematics); Distribution center; Stability (learning theory)","score_opus":0.07587368645534594,"score_gpt":0.3677128739842457,"score_spread":0.29183918752889976,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417062269","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13200925,0.0007892848,0.85999465,0.0003929427,0.00006445375,0.000060548802,0.00010574865,0.00022138056,0.006361775],"genre_scores_gemma":[0.8813169,0.00086759485,0.11130083,0.00007804534,0.000027327444,0.00006814068,0.00015609687,0.00008356745,0.0061014723],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997751,0.00009329669,0.000006670396,0.000047285463,0.000041167197,0.000036484427],"domain_scores_gemma":[0.99971396,0.00016456499,0.000037047837,0.000015642641,0.000051932373,0.000016732629],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00054688944,0.0005138029,0.0006867412,0.00043701855,0.00026848156,0.0008240129,0.000784451,0.0008042242,0.0018837309],"category_scores_gemma":[0.0010476505,0.00037925583,0.0005839332,0.0009576429,0.0003456255,0.00082475913,0.00035239512,0.00072379457,0.00015196216],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000006331426,0.000011712972,0.0001681579,0.000012285138,0.000011205776,0.000007032909,0.0000072183275,0.99281716,0.0002283626,0.0018815523,0.0001147988,0.0047343043],"study_design_scores_gemma":[0.0000025805168,0.000015749018,0.000085392334,0.0000014325749,0.0000037409052,0.000003033114,0.0000070069764,0.99910825,0.00010791937,0.00042063356,0.00024285355,0.0000013941317],"about_ca_topic_score_codex":0.014207064,"about_ca_topic_score_gemma":0.009089155,"teacher_disagreement_score":0.014207064,"about_ca_system_score_codex":0.0010373781,"about_ca_system_score_gemma":0.0011854058,"threshold_uncertainty_score":0.028248727},"labels":[],"label_agreement":null},{"id":"W4417359070","doi":"10.23977/cpcs.2025.090113","title":"Research on delivery optimization of food delivery orders based on crowdsourcing platform","year":2025,"lang":"","type":"article","venue":"Computing Performance and Communication systems","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Crowdsourcing; Food delivery; Order (exchange); Key (lock); Service delivery framework; Quality (philosophy); Weighting; Constraint (computer-aided design)","score_opus":0.058035303530947616,"score_gpt":0.320393985297898,"score_spread":0.2623586817669504,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417359070","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.057496756,0.0017666009,0.9264338,0.000800922,0.00026000393,0.00021650286,0.00013773142,0.000326239,0.012561399],"genre_scores_gemma":[0.8661668,0.0018503807,0.12107736,0.00023539572,0.00014024308,0.00019527297,0.00018018631,0.00012825258,0.010026201],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99912244,0.0002789895,0.00003367534,0.0002053063,0.00021762503,0.00014190846],"domain_scores_gemma":[0.9985588,0.00083249214,0.00016288877,0.0001122806,0.00022904585,0.00010449426],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013866408,0.0011008652,0.0011243722,0.0007113269,0.0008074424,0.0015159589,0.0016536808,0.0010403864,0.003159136],"category_scores_gemma":[0.0035333252,0.0005020545,0.00094154023,0.0011153186,0.0007185133,0.0015866539,0.0010631765,0.0010576234,0.0003803908],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009338609,0.0001079138,0.00079055777,0.00026715163,0.00006391267,0.00012195138,0.0001402251,0.9262191,0.0036543587,0.015811812,0.0019416575,0.050787874],"study_design_scores_gemma":[0.000010785717,0.00006891535,0.00020120521,0.000011032062,0.000016902644,0.000021952746,0.00006667031,0.9910817,0.0006822766,0.005978999,0.0018475025,0.0000120406],"about_ca_topic_score_codex":0.0099311005,"about_ca_topic_score_gemma":0.0058323527,"teacher_disagreement_score":0.0099311005,"about_ca_system_score_codex":0.0015720378,"about_ca_system_score_gemma":0.001797717,"threshold_uncertainty_score":0.019746542},"labels":[],"label_agreement":null},{"id":"W4417437321","doi":"10.1287/trsc.2024.0556","title":"Fair Stochastic Vehicle Routing with Partial Deliveries","year":2025,"lang":"en","type":"article","venue":"Transportation Science","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal; Polytechnique Montréal","funders":"","keywords":"Bounding overwatch; Vehicle routing problem; Routing (electronic design automation); Resource allocation; Resource (disambiguation); Interdependence; Service (business); Equity (law)","score_opus":0.010843213677750973,"score_gpt":0.2616994611993252,"score_spread":0.2508562475215742,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417437321","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02546759,0.00022996466,0.96966,0.0002474645,0.00008465495,0.00010458338,0.0001768871,0.0002362435,0.0037925919],"genre_scores_gemma":[0.8559749,0.00036320687,0.13738178,0.00013728856,0.00008962534,0.0001769777,0.00028477333,0.00010852448,0.0054829163],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981799,0.0006317112,0.00006208534,0.0003060489,0.00042372965,0.00039646635],"domain_scores_gemma":[0.99778026,0.0014190237,0.00019962444,0.00020207041,0.00023596932,0.00016310549],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023268291,0.0008928431,0.0012536448,0.0006431539,0.0007941418,0.0016148554,0.0017002902,0.001014727,0.0035984614],"category_scores_gemma":[0.005688841,0.00057489274,0.000950586,0.0009467401,0.001228422,0.0019076412,0.0012462379,0.0009822325,0.00034760305],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005673452,0.000018702689,0.00019684894,0.000034203855,0.000015256205,0.000052988904,0.000021059004,0.95619667,0.0005612707,0.031660806,0.00069263804,0.010492775],"study_design_scores_gemma":[0.000011611799,0.000025669617,0.000060427737,0.0000044301014,0.0000060047632,0.00001958457,0.000011039364,0.969842,0.0003013341,0.028909646,0.0008029233,0.0000052965324],"about_ca_topic_score_codex":0.0064242026,"about_ca_topic_score_gemma":0.006522091,"teacher_disagreement_score":0.0064242026,"about_ca_system_score_codex":0.0022856342,"about_ca_system_score_gemma":0.0025211952,"threshold_uncertainty_score":0.016583502},"labels":[],"label_agreement":null},{"id":"W4417445704","doi":"10.20944/preprints202512.1327.v1","title":"MCAH-ACO: A Multi-Criteria Adaptive Hybrid Ant Colony Optimization for Last-Mile Delivery Vehicle Routing","year":2025,"lang":"","type":"preprint","venue":"Preprints.org","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Vehicle routing problem; Ant colony optimization algorithms; Travelling salesman problem; Routing (electronic design automation); Scheme (mathematics); Ant colony; Decomposition; Baseline (sea)","score_opus":0.11449119753223537,"score_gpt":0.35888011226966193,"score_spread":0.24438891473742658,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417445704","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.050149143,0.00071574305,0.93558663,0.0003969981,0.00020246129,0.00024303095,0.00020541203,0.001678119,0.010822539],"genre_scores_gemma":[0.51479423,0.0002944005,0.47719744,0.00025483491,0.000084137464,0.00029042285,0.0002831854,0.00026641626,0.0065349285],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99974006,0.000067604895,0.000010193855,0.00004374514,0.00010958695,0.000028658164],"domain_scores_gemma":[0.99948066,0.00027784958,0.000058081747,0.00004734196,0.000102529426,0.000033565677],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00050366815,0.00061466906,0.0005783858,0.0005633217,0.00038182424,0.0005712019,0.0012016429,0.00072137546,0.0017440079],"category_scores_gemma":[0.0013998249,0.00028931722,0.00046821093,0.00067334,0.00034661577,0.00041399556,0.0005805876,0.00067699485,0.00029092425],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004811885,0.00006361139,0.0006938617,0.000060223527,0.00005477958,0.000049548777,0.000019772682,0.9356774,0.0022846574,0.002570237,0.0022818483,0.056195986],"study_design_scores_gemma":[0.000008811763,0.000018607048,0.00007163924,0.0000020674122,0.000003373434,0.000009838402,0.0000025433144,0.99854726,0.00024093424,0.0004328668,0.0006598744,0.000002183289],"about_ca_topic_score_codex":0.008376642,"about_ca_topic_score_gemma":0.01284704,"teacher_disagreement_score":0.008376642,"about_ca_system_score_codex":0.00050840265,"about_ca_system_score_gemma":0.0010630818,"threshold_uncertainty_score":0.016655743},"labels":[],"label_agreement":null},{"id":"W5534414","doi":"10.1007/978-3-642-21527-8_46","title":"An Adaptive Large Neighborhood Search Heuristic for a Snow Plowing Problem with Synchronized Routes","year":2011,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; HEC Montréal","funders":"","keywords":"Computer science; Heuristic; Snow removal; Set (abstract data type); Routing (electronic design automation); Arc routing; Snow; Mathematical optimization; Algorithm; Artificial intelligence; Mathematics; Computer network","score_opus":0.022629902807734154,"score_gpt":0.25866538093853747,"score_spread":0.2360354781308033,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W5534414","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07022777,0.0006050552,0.92084855,0.00024152415,0.00016422504,0.00033475333,0.00016453637,0.00049698714,0.0069165435],"genre_scores_gemma":[0.50574017,0.00027657722,0.48684585,0.00013452182,0.00008109048,0.0004809085,0.00034485434,0.00020245014,0.0058935205],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99961483,0.00013237704,0.000019084875,0.00008532708,0.000082484294,0.00006588852],"domain_scores_gemma":[0.9990539,0.00062317954,0.00008857129,0.000058175443,0.00008601086,0.000090152374],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010047978,0.0010393952,0.0019777825,0.001113663,0.0007894205,0.00089545606,0.0029613557,0.0019591723,0.005246822],"category_scores_gemma":[0.0021877373,0.00086803763,0.0010093879,0.0012712423,0.00073655136,0.0015786489,0.0012933576,0.0009535127,0.00039455024],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012656396,0.00008716593,0.00020763546,0.00005779338,0.00002797025,0.000054316977,0.000034544813,0.96901816,0.0008075631,0.0037611625,0.0011524517,0.02466466],"study_design_scores_gemma":[0.000023889013,0.000031420477,0.00003423053,0.0000040244568,0.0000061548026,0.000006637106,0.000009163907,0.99844474,0.00009788294,0.001112547,0.00022604843,0.0000031777558],"about_ca_topic_score_codex":0.008445863,"about_ca_topic_score_gemma":0.008817961,"teacher_disagreement_score":0.008445863,"about_ca_system_score_codex":0.0010700199,"about_ca_system_score_gemma":0.0013501066,"threshold_uncertainty_score":0.017552376},"labels":[],"label_agreement":null},{"id":"W56282943","doi":"10.1007/0-387-34221-4_2","title":"Path-based formulations of a bilevel toll setting problem","year":2006,"lang":"en","type":"book-chapter","venue":"Springer optimization and its applications","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":34,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Group for Research in Decision Analysis; Université de Montréal; Computer Research Institute of Montréal","funders":"","keywords":"Toll; Path (computing); Bilevel optimization; Computer science; Mathematical optimization; Mathematics; Computer network; Medicine; Algorithm; Optimization problem; Immunology","score_opus":0.013787655185918414,"score_gpt":0.23300002949434978,"score_spread":0.21921237430843138,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W56282943","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0047418466,0.0005096159,0.9655925,0.00040485332,0.00012311472,0.00003231434,0.00012298398,0.000064324275,0.02840846],"genre_scores_gemma":[0.38010287,0.0031970593,0.5262098,0.00033889397,0.0003602275,0.00037972463,0.000623784,0.00046174796,0.08832602],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99960345,0.00014749344,0.000015099698,0.00004487691,0.0001496054,0.000039469654],"domain_scores_gemma":[0.99965155,0.0001574362,0.000031185555,0.000033781776,0.000102175814,0.000023864892],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006745816,0.0010745105,0.0009544002,0.00079581485,0.0004611214,0.0023240822,0.0019106215,0.0015930975,0.010094494],"category_scores_gemma":[0.0019841054,0.0006827062,0.0009885663,0.0017103993,0.0007984557,0.002271588,0.0015415496,0.0023974823,0.0011150939],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000018592185,0.000048146434,0.00007708518,0.00009634472,0.000018861821,0.000044958157,0.000055448185,0.5756904,0.0005482099,0.3950047,0.0037071495,0.02469016],"study_design_scores_gemma":[0.0000063979714,0.000014508051,0.00003332239,0.000016627646,0.000006623837,0.000030183615,0.00002314735,0.80800676,0.0001524148,0.18633373,0.005366529,0.00000984638],"about_ca_topic_score_codex":0.001935121,"about_ca_topic_score_gemma":0.0029248637,"teacher_disagreement_score":0.010094494,"about_ca_system_score_codex":0.0011396728,"about_ca_system_score_gemma":0.0010044441,"threshold_uncertainty_score":0.03376943},"labels":[],"label_agreement":null},{"id":"W585220962","doi":"","title":"Addressing the Challenges of Planning Snow Plowing and Salt Spreading Routes in a Hybrid Rural and Urban Network","year":2014,"lang":"en","type":"article","venue":"Transportation Research Board 93rd Annual MeetingTransportation Research Board","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Snow removal; Computer science; Metaheuristic; Truck; Operations research; Hierarchy; Snow; Order (exchange); Transport engineering; Engineering; Business; Geography; Economics; Meteorology","score_opus":0.08921472819173604,"score_gpt":0.37194492460491324,"score_spread":0.2827301964131772,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W585220962","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4892906,0.00041691415,0.49718997,0.0003916337,0.00002279438,0.00022406544,0.00025032376,0.0002155431,0.011998293],"genre_scores_gemma":[0.90188664,0.00024239164,0.09121905,0.000033976175,0.000011199496,0.00008335154,0.00014400261,0.000040603318,0.0063389046],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998129,0.00007774113,0.0000041427206,0.000038697002,0.000020745158,0.00004582709],"domain_scores_gemma":[0.9996464,0.00022780665,0.0000448296,0.000015530877,0.00003173449,0.000033675235],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00052155275,0.0005486751,0.0003586718,0.00040422054,0.0006633451,0.00091920554,0.0010124829,0.000917602,0.002064297],"category_scores_gemma":[0.00088209636,0.00035359818,0.00026682363,0.00068788754,0.00045493414,0.00062317535,0.00047610656,0.00030834778,0.0001339981],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026347458,0.00003141653,0.0010327337,0.000037484948,0.000019271176,0.00013130857,0.00005542648,0.98188907,0.001153703,0.0019420617,0.00030146167,0.013379808],"study_design_scores_gemma":[0.000009975884,0.00005857094,0.000690726,0.0000061289306,0.000014032286,0.000056941597,0.00028842414,0.9952324,0.0004871446,0.0020375475,0.0011126287,0.0000053351396],"about_ca_topic_score_codex":0.048677534,"about_ca_topic_score_gemma":0.094498105,"teacher_disagreement_score":0.048677534,"about_ca_system_score_codex":0.0012478615,"about_ca_system_score_gemma":0.001474468,"threshold_uncertainty_score":0.09678841},"labels":[],"label_agreement":null},{"id":"W593153863","doi":"10.71781/10045","title":"Algorithmes pour le problème de repositionnement","year":2008,"lang":"fr","type":"dissertation","venue":"Open MIND","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Vehicle routing problem; Vertex (graph theory); Graph; Heuristic; Computer science; Mathematical optimization; Mathematics; Theoretical computer science; Algorithm; Routing (electronic design automation)","score_opus":0.032139584593978365,"score_gpt":0.3157601848380647,"score_spread":0.2836206002440863,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W593153863","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0062948167,0.00059280376,0.98814684,0.00035421667,0.00014703516,0.00011524687,0.00008966546,0.0004967688,0.0037626084],"genre_scores_gemma":[0.09088978,0.0006198094,0.8923661,0.00021334802,0.0001700031,0.0004980342,0.00037212775,0.00050406705,0.014366774],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998987,0.00032873277,0.0000697229,0.00023205685,0.0002641457,0.00011838736],"domain_scores_gemma":[0.9971123,0.0021048766,0.00010529443,0.00017403896,0.00040052616,0.00010289678],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022893853,0.0017936827,0.0019736025,0.0015282327,0.0010421145,0.002124941,0.002457923,0.0032216464,0.010091163],"category_scores_gemma":[0.007862819,0.0009882711,0.0016695824,0.0018808839,0.0013768282,0.002383526,0.0018929644,0.0035253426,0.0016178071],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013144662,0.0001328257,0.00043460738,0.00013851392,0.00007482178,0.00005560776,0.00015163764,0.78780746,0.0010415915,0.045690697,0.00619566,0.15814508],"study_design_scores_gemma":[0.000027242124,0.000021821088,0.00006192192,0.000014660916,0.000007505121,0.000016468392,0.000019678962,0.9867524,0.00026965307,0.0110729905,0.0017286293,0.000006934678],"about_ca_topic_score_codex":0.015652103,"about_ca_topic_score_gemma":0.01691129,"teacher_disagreement_score":0.015652103,"about_ca_system_score_codex":0.0017867474,"about_ca_system_score_gemma":0.002523794,"threshold_uncertainty_score":0.033758283},"labels":[],"label_agreement":null},{"id":"W644258566","doi":"10.71781/11127","title":"Étude d'un problème de tournées de véhicules sur les arcs avec contraintes de capacité et coûts de service dépendants du temps","year":2008,"lang":"fr","type":"dissertation","venue":"Papyrus : Institutional Repository (Université de Montréal)","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Université de Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Column generation; Interval (graph theory); Variable (mathematics); Routing (electronic design automation); Piecewise linear function; Arc routing; Heuristic; Mathematical optimization; Arc (geometry); Service (business); Computer science; Mathematics; Operations research; Economics; Combinatorics; Computer network","score_opus":0.009369826602914988,"score_gpt":0.18538856244749358,"score_spread":0.17601873584457858,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W644258566","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.28349674,0.0014391121,0.70133126,0.0014279871,0.00023616306,0.00022051187,0.00032100567,0.00020586707,0.011321308],"genre_scores_gemma":[0.8079281,0.0006934152,0.17333439,0.00019654352,0.00021113125,0.00023814358,0.00046754265,0.00014375687,0.016786968],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99877995,0.0005129986,0.00005097941,0.00029223147,0.00018934232,0.00017456329],"domain_scores_gemma":[0.987673,0.010791997,0.00037009694,0.00019452447,0.0005226962,0.0004478005],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003310641,0.0012787952,0.0019317891,0.00097502384,0.0011655921,0.003499291,0.0020741345,0.003259754,0.005620807],"category_scores_gemma":[0.013250094,0.0009006453,0.001865458,0.0011929031,0.0016373551,0.0020790277,0.0012082441,0.0016754487,0.00022090082],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018258332,0.000115602896,0.0014042612,0.0001219118,0.000091660884,0.00019557976,0.000122055484,0.96414995,0.0011371218,0.01877465,0.00092249503,0.012782019],"study_design_scores_gemma":[0.000028064274,0.000068076464,0.00030119327,0.000008672525,0.000015496642,0.000042116088,0.000037165366,0.9954454,0.00031342928,0.0032434056,0.0004887248,0.000008291845],"about_ca_topic_score_codex":0.028111888,"about_ca_topic_score_gemma":0.01220132,"teacher_disagreement_score":0.028111888,"about_ca_system_score_codex":0.0017400972,"about_ca_system_score_gemma":0.0016095778,"threshold_uncertainty_score":0.05589652},"labels":[],"label_agreement":null},{"id":"W648203179","doi":"","title":"Scheduled service network design with synchronization and transshipment constraints for intermodal container transportation networks","year":2012,"lang":"en","type":"article","venue":"Data Archiving and Networked Services (DANS)","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":84,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Université de Montréal; Compute Canada; Université du Québec à Montréal","keywords":"Container (type theory); Transshipment (information security); Computer science; Service (business); Network planning and design; Routing (electronic design automation); Vehicle routing problem; Operations research; Solver; Integer programming; Flow network; Transport engineering; Computer network; Mathematical optimization; Engineering; Business","score_opus":0.021017549410984396,"score_gpt":0.241143561860551,"score_spread":0.2201260124495666,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W648203179","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025623111,0.00023776936,0.970709,0.00016377737,0.00005581951,0.00007814532,0.00013398312,0.00011967556,0.0028786606],"genre_scores_gemma":[0.61037934,0.00077149447,0.37927288,0.00013103767,0.00008026573,0.0004292341,0.0005538896,0.00018882491,0.008192995],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99911433,0.00039498848,0.000030234332,0.00014921423,0.00017522997,0.00013603756],"domain_scores_gemma":[0.99917537,0.00043109158,0.00015861657,0.000040817617,0.00012285846,0.00007123452],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013607959,0.0015364732,0.00094171515,0.0006821176,0.00053343497,0.0013156247,0.0015549324,0.0009957806,0.0033458471],"category_scores_gemma":[0.0023716255,0.0006424185,0.0010852292,0.0011777634,0.0008258255,0.0014861326,0.00081545225,0.0011557059,0.00036752544],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000020943875,0.000014213454,0.00012436871,0.000033512708,0.000011486938,0.000036467034,0.000020150748,0.9865775,0.00045016847,0.008269935,0.00027699978,0.004164329],"study_design_scores_gemma":[0.0000058880296,0.000021061527,0.00002688667,0.000004343205,0.0000055445485,0.0000073108167,0.000012734093,0.9961272,0.00020434112,0.0029391565,0.0006429035,0.0000027158303],"about_ca_topic_score_codex":0.012126269,"about_ca_topic_score_gemma":0.012993965,"teacher_disagreement_score":0.012126269,"about_ca_system_score_codex":0.0021853934,"about_ca_system_score_gemma":0.0025296104,"threshold_uncertainty_score":0.02411139},"labels":[],"label_agreement":null},{"id":"W65917153","doi":"","title":"Vehicle routing problems with multiple trips: using specific local search operators","year":2014,"lang":"en","type":"article","venue":"Open Repository and Bibliography (University of Liège)","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"","keywords":"TRIPS architecture; Vehicle routing problem; Computer science; Routing (electronic design automation); Computer network","score_opus":0.0230776868708437,"score_gpt":0.22301247665745588,"score_spread":0.1999347897866122,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W65917153","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014821996,0.1369611,0.41520634,0.072295964,0.22559415,0.0002894375,0.00113076,0.001171957,0.13252844],"genre_scores_gemma":[0.20104022,0.14247678,0.25019428,0.01074273,0.12851053,0.00036599307,0.0022069851,0.0019784907,0.26248407],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993911,0.00020646534,0.000039966653,0.000106794665,0.0002274255,0.000028358962],"domain_scores_gemma":[0.9980488,0.00088551996,0.00008809385,0.00007256451,0.0008248681,0.000080171594],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013647999,0.0006092989,0.0008873478,0.0009673932,0.0005191262,0.0020815728,0.0013533193,0.0017204055,0.012322234],"category_scores_gemma":[0.004685401,0.00031045036,0.0008266668,0.0012924242,0.00078624865,0.0018455338,0.0005225858,0.0022460162,0.0024744596],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012863165,0.00006481318,0.00018786412,0.001588535,0.00010760642,0.00022650132,0.000061232,0.035439707,0.0008753243,0.045600165,0.67476094,0.24095866],"study_design_scores_gemma":[0.00010589493,0.00011912652,0.0007412612,0.0010842034,0.00018430612,0.00047163016,0.00022394836,0.11885597,0.0018182087,0.05612536,0.82019603,0.00007415168],"about_ca_topic_score_codex":0.002067731,"about_ca_topic_score_gemma":0.004092005,"teacher_disagreement_score":0.012322234,"about_ca_system_score_codex":0.0007883414,"about_ca_system_score_gemma":0.0009822648,"threshold_uncertainty_score":0.041222036},"labels":[],"label_agreement":null},{"id":"W6891564556","doi":"10.4230/lipics.cp.2025.30","title":"Exact Methods for the Travelling Salesperson Problem with Self-Deleting Graphs","year":2025,"lang":"en","type":"article","venue":"DROPS (Schloss Dagstuhl – Leibniz Center for Informatics)","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Alliance de recherche numérique du Canada; University of Toronto; Innovation, Science and Economic Development Canada","keywords":"Vertex (graph theory); Solver; Preprocessor; Integer programming; Linear programming; Constraint programming; Dependency graph; Simple (philosophy)","score_opus":0.012088851120582863,"score_gpt":0.29377524430412555,"score_spread":0.28168639318354266,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6891564556","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008075647,0.00083996105,0.98420006,0.00027777674,0.000061031846,0.0000964941,0.00014169504,0.00033261793,0.005974737],"genre_scores_gemma":[0.20383039,0.0011118122,0.7873941,0.00022337116,0.00012199793,0.00036602432,0.00050418277,0.00044118555,0.0060069533],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9991738,0.00034374924,0.00003351284,0.0001490998,0.0001957483,0.00010416429],"domain_scores_gemma":[0.9975216,0.0018878214,0.00017816233,0.00014810389,0.00019363532,0.00007070586],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016833794,0.0013759087,0.0011868168,0.0009540107,0.0005047351,0.0014749996,0.0021434186,0.0014036141,0.0064707873],"category_scores_gemma":[0.004962747,0.00075054105,0.0011167427,0.0015429718,0.0008271726,0.0017897076,0.001158334,0.0022739766,0.0009314127],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000036969366,0.00006521459,0.00025224636,0.00017439316,0.000036114245,0.00004103821,0.000041272913,0.9296945,0.00032488967,0.026737668,0.0018619705,0.040733706],"study_design_scores_gemma":[0.000011663671,0.0000131818415,0.000033755492,0.00001510529,0.000005321889,0.000009480572,0.000015828093,0.98423946,0.00010250449,0.0146922385,0.0008583251,0.0000031954467],"about_ca_topic_score_codex":0.008659228,"about_ca_topic_score_gemma":0.010136141,"teacher_disagreement_score":0.008659228,"about_ca_system_score_codex":0.001509852,"about_ca_system_score_gemma":0.0023128903,"threshold_uncertainty_score":0.021646976},"labels":[],"label_agreement":null},{"id":"W6894072492","doi":"10.5281/zenodo.6812942","title":"Introducing a bi-objective cluster-based location-routing problem in e-commerce logistics with possible failed home deliveries","year":2022,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Wilfrid Laurier University","funders":"","keywords":"Humanitarian Logistics; Work (physics); Key (lock); Outsourcing","score_opus":0.021182481125717523,"score_gpt":0.23538224944034095,"score_spread":0.21419976831462342,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6894072492","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.050884843,0.0013186351,0.9338975,0.0021059704,0.0005450576,0.00035751256,0.0014868212,0.0002491854,0.009154507],"genre_scores_gemma":[0.4946035,0.0012294346,0.48023778,0.0006538261,0.00035788552,0.0006810075,0.0026893509,0.0004488599,0.019098334],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983955,0.00072609243,0.00006420271,0.00035839854,0.00026824654,0.00018761554],"domain_scores_gemma":[0.9974598,0.001496303,0.00024411686,0.0001282433,0.00043758375,0.0002339094],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028929766,0.002039733,0.0019069867,0.00096359965,0.0009665576,0.0023705414,0.003589335,0.003315483,0.005777863],"category_scores_gemma":[0.0057372083,0.0012490329,0.0021266483,0.0020083187,0.0011179929,0.0020850275,0.0024636257,0.0025624766,0.00052484946],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006666767,0.000045084616,0.00041826468,0.00011901969,0.000059622296,0.00007586528,0.000025638194,0.9821811,0.0002007298,0.007617798,0.0034946105,0.0056956788],"study_design_scores_gemma":[0.000017018385,0.000028398012,0.00016210688,0.00001834764,0.000017665378,0.00002395566,0.00003306253,0.9926327,0.00011839741,0.005807466,0.0011288039,0.000012073345],"about_ca_topic_score_codex":0.015644971,"about_ca_topic_score_gemma":0.011695737,"teacher_disagreement_score":0.015644971,"about_ca_system_score_codex":0.0023688618,"about_ca_system_score_gemma":0.0022996988,"threshold_uncertainty_score":0.031107783},"labels":[],"label_agreement":null},{"id":"W6901467311","doi":"10.60692/3kejg-bte52","title":"Data for a meta-analysis of the adaptive layer in adaptive large neighborhood search","year":2020,"lang":"en","type":"article","venue":"Greater South Information System","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University; Université Laval","funders":"","keywords":"Replicate; Set (abstract data type); Range (aeronautics); Variety (cybernetics); Domain (mathematical analysis); Layer (electronics); Implementation; Metaheuristic","score_opus":0.2394440626324139,"score_gpt":0.29707504850752525,"score_spread":0.05763098587511134,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6901467311","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011559436,0.017953716,0.031152723,0.0039214846,0.00051250146,0.0066954056,0.9148521,0.0013414412,0.012011221],"genre_scores_gemma":[0.16429172,0.013238582,0.14319883,0.004898138,0.00041427303,0.068634585,0.59646195,0.0016591725,0.0072028143],"study_design_codex":"not_applicable","study_design_gemma":"meta_analysis","domain_scores_codex":[0.9687942,0.015869513,0.005680877,0.002709254,0.006214116,0.00073195325],"domain_scores_gemma":[0.79579735,0.17419678,0.011515967,0.00971504,0.007985174,0.00078971835],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.028289946,0.001861613,0.0029357362,0.013775411,0.0007783403,0.0035477525,0.0027161401,0.0024211188,0.058392588],"category_scores_gemma":[0.18020266,0.0010555132,0.010739564,0.0144953,0.0005777045,0.0019840316,0.0020963696,0.0030145755,0.0060582743],"study_design_candidate":"meta_analysis","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.008115512,0.00088071753,0.026600473,0.21868391,0.044779506,0.0009335816,0.0009669885,0.036954135,0.0025118804,0.027388858,0.42670503,0.2054794],"study_design_scores_gemma":[0.007633979,0.0022794604,0.033367656,0.06060634,0.038073156,0.001057214,0.0010743313,0.011207074,0.0056659984,0.037020355,0.8015984,0.0004159809],"about_ca_topic_score_codex":0.0033665344,"about_ca_topic_score_gemma":0.0068849744,"teacher_disagreement_score":0.058392588,"about_ca_system_score_codex":0.0021470166,"about_ca_system_score_gemma":0.0041302145,"threshold_uncertainty_score":0.19534266},"labels":[],"label_agreement":null},{"id":"W6910549475","doi":"10.4230/lipics.isaac.2022.8","title":"Bi-Criteria Approximation Algorithms for Bounded-Degree Subset TSP","year":2022,"lang":"en","type":"article","venue":"DROPS (Schloss Dagstuhl – Leibniz Center for Informatics)","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Travelling salesman problem; Approximation algorithm; Vertex (graph theory); Degree (music); Steiner tree problem; Multiset; Vertex cover; Graph; Spanning tree","score_opus":0.04451651043492749,"score_gpt":0.29754264998369145,"score_spread":0.25302613954876396,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6910549475","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.053193923,0.0010153264,0.9363136,0.00062888063,0.0000881794,0.00014819668,0.00027260295,0.00088756526,0.0074517257],"genre_scores_gemma":[0.4965846,0.0005622879,0.49529618,0.00033725583,0.000072225055,0.00035334434,0.0008873288,0.0002814616,0.005625296],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.99844766,0.0004531512,0.00009669056,0.000256145,0.0005195365,0.00022680855],"domain_scores_gemma":[0.9977894,0.0012071689,0.00019922762,0.00031777573,0.00030597232,0.00018046911],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014461853,0.0011850693,0.0016253974,0.0009249688,0.00072776596,0.0014819856,0.002358608,0.0013129746,0.0038211967],"category_scores_gemma":[0.0060918573,0.0005402932,0.000776196,0.0024554643,0.00059318636,0.0023244536,0.0017397659,0.0016335796,0.0010808875],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00048146522,0.00027502127,0.0014478574,0.00024378851,0.000100619945,0.000113599046,0.00029825044,0.83467966,0.0032058952,0.046207957,0.006167435,0.10677853],"study_design_scores_gemma":[0.000023730361,0.000036434074,0.00007601606,0.00000832825,0.000006299438,0.000027910233,0.000022295078,0.9883807,0.00027202495,0.010304353,0.000838028,0.0000039807774],"about_ca_topic_score_codex":0.0053097866,"about_ca_topic_score_gemma":0.0040648547,"teacher_disagreement_score":0.0053097866,"about_ca_system_score_codex":0.0016525476,"about_ca_system_score_gemma":0.0013923323,"threshold_uncertainty_score":0.01278317},"labels":[],"label_agreement":null},{"id":"W6921793134","doi":"10.1016/j.cor.2025.107198","title":"Integrated and sequential algorithms for the robust two-echelon location-routing problem under demand uncertainty","year":2025,"lang":"en","type":"article","venue":"Computers & Operations Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"GLS Industries (Canada); Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada; Alliance de recherche numérique du Canada","keywords":"Robustness (evolution); Benchmark (surveying); Robust optimization; Heuristic; Facility location problem; Vehicle routing problem","score_opus":0.0952182887740222,"score_gpt":0.39118072464910475,"score_spread":0.29596243587508253,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6921793134","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012147662,0.00019121298,0.9846859,0.00012944944,0.000025485704,0.000087017455,0.00005726447,0.0003295814,0.002346359],"genre_scores_gemma":[0.33193058,0.0002595478,0.6641818,0.00011900748,0.00005593109,0.00036311088,0.00037997303,0.0001593131,0.0025507326],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99881184,0.0003533882,0.00005803436,0.00032364554,0.00027475037,0.00017830603],"domain_scores_gemma":[0.99782,0.0014387136,0.00024998997,0.00015826493,0.00022801496,0.000104943465],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018940559,0.0014258723,0.0015931862,0.00085017836,0.00063430105,0.0013252269,0.0019136873,0.0015516469,0.0044169105],"category_scores_gemma":[0.004178151,0.00085915806,0.001131973,0.0012907443,0.00071984436,0.0016471698,0.0017943317,0.0017217319,0.00052686164],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004753342,0.00005624496,0.0002162121,0.000044305598,0.00002526239,0.00003474964,0.000030184734,0.969362,0.00041829114,0.005596812,0.0004610763,0.023707284],"study_design_scores_gemma":[0.000018705041,0.000022570335,0.00004044379,0.0000030851327,0.0000058337255,0.000012159629,0.00000921554,0.99639916,0.00015313773,0.0030910864,0.00024159226,0.0000029599569],"about_ca_topic_score_codex":0.0068837088,"about_ca_topic_score_gemma":0.00778195,"teacher_disagreement_score":0.0068837088,"about_ca_system_score_codex":0.0012931431,"about_ca_system_score_gemma":0.0021427853,"threshold_uncertainty_score":0.014776051},"labels":[],"label_agreement":null},{"id":"W6931278592","doi":"10.5281/zenodo.4370754","title":"Kalmia angustifolia Linnaeus 1753","year":2007,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Typification; Taxonomy (biology); Nomenclature; Subspecies; Nova scotia","score_opus":0.029129550432872547,"score_gpt":0.2613930308347663,"score_spread":0.23226348040189373,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6931278592","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.23935185,0.014220991,0.018046215,0.0008787047,0.00067810284,0.00066911924,0.01121594,0.0030404446,0.7118986],"genre_scores_gemma":[0.7979993,0.0056786207,0.019738242,0.00066806347,0.00016925523,0.00033922665,0.0070265327,0.00023326457,0.16814744],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9999095,0.000008812205,0.000007133511,0.00003755257,0.000026429949,0.000010484771],"domain_scores_gemma":[0.99995124,0.0000067810292,0.000019253406,0.000003799701,0.000011195882,0.000007616921],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000106534266,0.0007034155,0.00032203714,0.0012451683,0.0009873159,0.00033140436,0.00041482315,0.00022390088,0.024882333],"category_scores_gemma":[0.00013521551,0.00015416686,0.00012880148,0.0005305834,0.00017457214,0.0009468095,0.00050913444,0.0003207062,0.012038388],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015499667,0.00017957103,0.020425284,0.00044863785,0.00006228517,0.00042710046,0.00096896663,0.0012611906,0.036583744,0.0050050747,0.053312186,0.8811709],"study_design_scores_gemma":[0.000051281288,0.00019664875,0.26495212,0.0001589747,0.0001023625,0.0025910584,0.0007307771,0.0015163521,0.0032942288,0.0024575952,0.7238943,0.000054203516],"about_ca_topic_score_codex":0.0076363613,"about_ca_topic_score_gemma":0.030810237,"teacher_disagreement_score":0.024882333,"about_ca_system_score_codex":0.00060451054,"about_ca_system_score_gemma":0.00016552504,"threshold_uncertainty_score":0.083239734},"labels":[],"label_agreement":null},{"id":"W6939168688","doi":"10.60692/tw6s4-es884","title":"Rich vehicle routing with auxiliary depots and anticipated deliveries: An application to pharmaceutical distribution","year":2019,"lang":"en","type":"article","venue":"Greater South Information System","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"","keywords":"Iterated local search; Vehicle routing problem; Routing (electronic design automation); Distribution (mathematics); Duration (music); Iterated function","score_opus":0.020793077873679948,"score_gpt":0.24499254781456617,"score_spread":0.22419946994088621,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6939168688","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.30341077,0.00061072694,0.6857496,0.0007484896,0.00007524018,0.00018930396,0.00032267964,0.00031128395,0.008581946],"genre_scores_gemma":[0.82509303,0.000380469,0.16937019,0.000058935246,0.000054391534,0.00014967626,0.00030139223,0.00006320224,0.004528634],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995877,0.00022718537,0.000011356366,0.000063012296,0.000049506816,0.00006128032],"domain_scores_gemma":[0.9985775,0.0011429038,0.000110509034,0.00004266089,0.000055197343,0.000071304006],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009873102,0.0008413085,0.0010443864,0.0008051493,0.00058622006,0.0008312521,0.0012612855,0.001361887,0.0027887325],"category_scores_gemma":[0.002078289,0.0004344174,0.00085929723,0.0011898007,0.0007137651,0.0007525976,0.0009795444,0.000967301,0.00014976783],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000024554123,0.000029307757,0.00015258185,0.00002926229,0.000011237442,0.00007297431,0.000014093512,0.99446315,0.00019245707,0.0020599982,0.0001698065,0.002780522],"study_design_scores_gemma":[0.000018196106,0.00005584542,0.00013712181,0.000004138552,0.000009166897,0.000030529554,0.000024772346,0.99684924,0.00027071076,0.0021422063,0.00045313197,0.000004916194],"about_ca_topic_score_codex":0.007023089,"about_ca_topic_score_gemma":0.006402984,"teacher_disagreement_score":0.007023089,"about_ca_system_score_codex":0.0011602405,"about_ca_system_score_gemma":0.0008915989,"threshold_uncertainty_score":0.013964415},"labels":[],"label_agreement":null},{"id":"W6939445248","doi":"10.60692/jvdfq-gva63","title":"An exact method for a last-mile delivery routing problem with multiple deliverymen","year":2024,"lang":"en","type":"article","venue":"Greater South Information System","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Minification; Vehicle routing problem; Routing (electronic design automation); Traffic congestion; City logistics","score_opus":0.021972377054120144,"score_gpt":0.2391839431468658,"score_spread":0.21721156609274567,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6939445248","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.001981971,0.00019738147,0.9904901,0.00024085955,0.00007145888,0.00011826675,0.000111628004,0.00016156115,0.0066268635],"genre_scores_gemma":[0.08566373,0.00047682837,0.903465,0.00019177345,0.00011936124,0.0004933179,0.0003412985,0.00022144424,0.009027289],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993998,0.0001696798,0.000025048666,0.00011017252,0.00020941492,0.000085896354],"domain_scores_gemma":[0.998868,0.00076231966,0.00007051665,0.00007633414,0.0001755259,0.00004742086],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001465154,0.0012345286,0.0009507762,0.00095415703,0.00080988713,0.0014317326,0.0016015688,0.0015324303,0.012812701],"category_scores_gemma":[0.003701412,0.0007016098,0.0012097285,0.0014003196,0.0006259421,0.0014578375,0.0011401442,0.0020693867,0.0015549917],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003673084,0.00008045202,0.00031161838,0.0002069031,0.00003199315,0.000083321494,0.00007694141,0.86023504,0.0008939505,0.06080675,0.005464884,0.071771376],"study_design_scores_gemma":[0.000015520145,0.000015264104,0.000046819696,0.000018025916,0.000007708769,0.000021760352,0.000020951828,0.9796799,0.00016083525,0.01666857,0.0033399542,0.0000047973413],"about_ca_topic_score_codex":0.010687539,"about_ca_topic_score_gemma":0.014545774,"teacher_disagreement_score":0.012812701,"about_ca_system_score_codex":0.0017532603,"about_ca_system_score_gemma":0.0032060363,"threshold_uncertainty_score":0.042862773},"labels":[],"label_agreement":null},{"id":"W6939501351","doi":"10.6084/m9.figshare.29663062.v1","title":"Overnight technician routing and scheduling problem with time windows and balanced workloads: a bi-objective zebra optimization algorithm","year":2025,"lang":"en","type":"dataset","venue":"Figshare","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Job shop scheduling; Solver; Scheduling (production processes); Schedule; Key (lock); Nonlinear programming; Routing (electronic design automation); Linear programming; Quadratic programming","score_opus":0.007408907945701544,"score_gpt":0.22835646116872463,"score_spread":0.2209475532230231,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6939501351","genre_codex":"methods","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.026867867,0.00052955153,0.963601,0.0005743208,0.00005022994,0.00017139716,0.00019312998,0.00044354264,0.007569057],"genre_scores_gemma":[0.37534693,0.00062860124,0.6107975,0.00041314258,0.000056940888,0.00077385746,0.00063819444,0.0003049054,0.011039864],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993451,0.0002910389,0.00002358986,0.00011121016,0.00011924898,0.00010978483],"domain_scores_gemma":[0.99908495,0.00060661766,0.00010611778,0.000039197697,0.0001022087,0.000060957493],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012529829,0.0013400684,0.001631938,0.00094142806,0.00057706365,0.0015938005,0.002033367,0.0021171486,0.006753],"category_scores_gemma":[0.0026374287,0.00091440196,0.001348752,0.0011087666,0.0007327195,0.0017205963,0.001642377,0.0021449632,0.0009686328],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000075895856,0.000043510023,0.00027187594,0.000049835846,0.000026834727,0.000029038178,0.000031605585,0.983564,0.00036078732,0.0042557237,0.0007642228,0.010526661],"study_design_scores_gemma":[0.000014355909,0.000020591227,0.000049124585,0.0000060752322,0.000003413719,0.000007039869,0.000010684932,0.99826896,0.000057992027,0.0011782352,0.0003804324,0.0000030374813],"about_ca_topic_score_codex":0.008196988,"about_ca_topic_score_gemma":0.006532252,"teacher_disagreement_score":0.008196988,"about_ca_system_score_codex":0.0011618425,"about_ca_system_score_gemma":0.001860535,"threshold_uncertainty_score":0.022591054},"labels":[],"label_agreement":null},{"id":"W6949083032","doi":"10.5281/zenodo.10260963","title":"Evaluation Files: Multilayer Graph Partitioning for Enabling a Decentralized Path Planning for Large and Heterogeneous AGV Fleets","year":2023,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Network topology; Metric (unit); Intersection (aeronautics); Bounding overwatch; Graph; Path (computing); Topology (electrical circuits)","score_opus":0.07208743064156531,"score_gpt":0.31309291176040677,"score_spread":0.24100548111884146,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6949083032","genre_codex":"dataset","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025201382,0.00061554037,0.18146631,0.0011494457,0.0009075191,0.0017643634,0.61194927,0.095523596,0.08142258],"genre_scores_gemma":[0.08680275,0.0005095372,0.10745633,0.00030144327,0.00010960386,0.002306029,0.76300013,0.023288764,0.016225437],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984112,0.00035480762,0.00010008267,0.00016330079,0.00082510663,0.00014541998],"domain_scores_gemma":[0.9957812,0.0015488438,0.00015622031,0.00069447234,0.0016088005,0.0002105083],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0032355832,0.0021348442,0.00082387624,0.0013548112,0.00054918387,0.0016488754,0.0020712495,0.0008871881,0.1158847],"category_scores_gemma":[0.011430025,0.0005786877,0.00097040675,0.0016126819,0.00028302695,0.0018714998,0.0016240174,0.0010739276,0.028360533],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00089351705,0.00023621763,0.002200488,0.0016204289,0.00011561366,0.00015521463,0.0000873452,0.07776475,0.0044183484,0.0049406574,0.83015233,0.077415004],"study_design_scores_gemma":[0.0015601718,0.0011797332,0.008332605,0.0008633207,0.0001610344,0.00045689702,0.000434172,0.4588985,0.030146668,0.01830864,0.47946745,0.00019077751],"about_ca_topic_score_codex":0.0057604704,"about_ca_topic_score_gemma":0.0062070074,"teacher_disagreement_score":0.1158847,"about_ca_system_score_codex":0.0012296436,"about_ca_system_score_gemma":0.0011282788,"threshold_uncertainty_score":0.3876729},"labels":[],"label_agreement":null},{"id":"W6955578435","doi":"10.58079/uajf","title":"Open Track \"Turning Things into Assets\", 2016 4S/EASST Conference in Barcelona, 31 Aug. - 3 Sept. 2016","year":2015,"lang":"en","type":"article","venue":"OpenEdition (OpenEdition)","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Track (disk drive); Window (computing); Product (mathematics); Presentation (obstetrics); Key (lock)","score_opus":0.03471752116318828,"score_gpt":0.28904427669509464,"score_spread":0.25432675553190637,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6955578435","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.027358642,0.03632449,0.025885874,0.12517446,0.08712609,0.0004150353,0.006609474,0.0015795116,0.6895265],"genre_scores_gemma":[0.08093003,0.008819692,0.0073270225,0.0026817743,0.007320982,0.00019372675,0.005022731,0.00075648865,0.8869476],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9986853,0.00035070672,0.00005277955,0.0002124723,0.00038216868,0.0003166661],"domain_scores_gemma":[0.99783945,0.0004213814,0.00009311375,0.0002069976,0.00044133718,0.0009976674],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0044255997,0.0007056237,0.00054896856,0.0010775303,0.0026205434,0.0077173947,0.0016891685,0.0028436182,0.10230361],"category_scores_gemma":[0.0040118173,0.00030203868,0.0005714712,0.0009910483,0.0015312181,0.0042149033,0.0059549324,0.0027910382,0.02513461],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026775268,0.000083820945,0.0010122029,0.00022765456,0.000011639245,0.0001972081,0.0009401186,0.0009724694,0.0006241306,0.015773669,0.9082797,0.07160957],"study_design_scores_gemma":[0.000017089018,0.000030564694,0.001362015,0.00023488431,0.0000042416605,0.000035634035,0.0012377474,0.0005880348,0.00028450316,0.003940255,0.9922551,0.000009879393],"about_ca_topic_score_codex":0.009631228,"about_ca_topic_score_gemma":0.034334674,"teacher_disagreement_score":0.9973795,"about_ca_system_score_codex":0.0028197153,"about_ca_system_score_gemma":0.0032962365,"threshold_uncertainty_score":0.34223968},"labels":[],"label_agreement":null},{"id":"W6958142136","doi":"10.6084/m9.figshare.21500611","title":"Additional file 1 of The association between chiropractic integration in an Ontario community health centre and continued prescription opioid use for chronic non-cancer spinal pain: a sequential explanatory mixed methods study","year":2022,"lang":"en","type":"article","venue":"Figshare","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"St. Joseph’s Healthcare Hamilton; University of Waterloo; McMaster University","funders":"","keywords":"Chiropractic; Medical prescription; Computer file; Data file; Qualitative research; Health centre; Spinal manipulation","score_opus":0.0737429905163264,"score_gpt":0.34300813695705096,"score_spread":0.2692651464407245,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6958142136","genre_codex":"dataset","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00091463,0.000028446615,0.00045327912,0.00020944947,0.000028458473,0.0017553847,0.9936633,0.00015229623,0.002794775],"genre_scores_gemma":[0.039676193,0.00043716992,0.017645348,0.0013105865,0.0002250081,0.082102664,0.8061691,0.00066228485,0.05177161],"study_design_codex":"not_applicable","study_design_gemma":"qualitative","domain_scores_codex":[0.9988997,0.00027179564,0.0002152536,0.00016536239,0.0003024648,0.00014538143],"domain_scores_gemma":[0.9718668,0.01844387,0.0020678148,0.0013677716,0.005420355,0.0008334538],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0025907613,0.00049398694,0.00088512775,0.0021512778,0.001370732,0.0010613888,0.0015436977,0.0006903948,0.80179125],"category_scores_gemma":[0.040400546,0.00049121055,0.00072336086,0.0036852905,0.00028241237,0.0011405607,0.0007430154,0.00058129383,0.057173073],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003124017,0.00011290285,0.004428598,0.0029590705,0.000028229239,0.000048087608,0.0002671749,0.00022489867,0.000051340747,0.00084059295,0.97438973,0.016336974],"study_design_scores_gemma":[0.0062268497,0.00052041846,0.120744236,0.009878317,0.00021731283,0.00026091817,0.0026839185,0.0023565397,0.00047256408,0.0062246863,0.85027766,0.00013645714],"about_ca_topic_score_codex":0.083986856,"about_ca_topic_score_gemma":0.17116301,"teacher_disagreement_score":0.80179125,"about_ca_system_score_codex":0.0032029382,"about_ca_system_score_gemma":0.006765809,"threshold_uncertainty_score":0.28272074},"labels":[],"label_agreement":null},{"id":"W6966428720","doi":"10.4230/artifacts.23555","title":"Travelling Salesperson Problem with Self Deleting Graphs","year":2024,"lang":"en","type":"other","venue":"DROPS (Schloss Dagstuhl – Leibniz Center for Informatics)","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Code (set theory); Source code; Field (mathematics); Set (abstract data type)","score_opus":0.008824150382975711,"score_gpt":0.23078614731428884,"score_spread":0.22196199693131313,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6966428720","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014265072,0.0011725771,0.5596807,0.001867716,0.0006623077,0.00064993434,0.11960832,0.038994297,0.26309907],"genre_scores_gemma":[0.211567,0.001957623,0.464809,0.0006821483,0.00018987249,0.001781359,0.11945736,0.022713378,0.17684226],"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99981517,0.000021531952,0.000010893948,0.000038496593,0.00007301003,0.000040879997],"domain_scores_gemma":[0.9996989,0.00012513467,0.000016629672,0.000049589064,0.00008186052,0.000027825674],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020982287,0.0010695502,0.00066201156,0.00078309135,0.0003594639,0.0010579948,0.001519658,0.0009738025,0.1889339],"category_scores_gemma":[0.0016153636,0.00053906895,0.00074462907,0.0013224398,0.00023866977,0.0014139256,0.00093987497,0.0010782162,0.041702345],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011888027,0.0002730291,0.00071791,0.0008741535,0.000052878422,0.00028197456,0.00009981495,0.21840526,0.0014753066,0.1144645,0.5641703,0.099065945],"study_design_scores_gemma":[0.00022886747,0.000022439635,0.00041877766,0.00011127304,0.000022450358,0.0001755268,0.000056445744,0.6968405,0.0016485852,0.13281922,0.16762677,0.00002914714],"about_ca_topic_score_codex":0.009355187,"about_ca_topic_score_gemma":0.015026502,"teacher_disagreement_score":0.1889339,"about_ca_system_score_codex":0.0009761573,"about_ca_system_score_gemma":0.001421087,"threshold_uncertainty_score":0.6320468},"labels":[],"label_agreement":null},{"id":"W6976333268","doi":"10.60692/abx1r-ymz29","title":"An exact method for a last-mile delivery routing problem with multiple deliverymen","year":2024,"lang":"en","type":"article","venue":"Greater South Information System","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Minification; Vehicle routing problem; Routing (electronic design automation); Traffic congestion; City logistics","score_opus":0.021972377054120144,"score_gpt":0.2391839431468658,"score_spread":0.21721156609274567,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6976333268","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.001981971,0.00019738147,0.9904901,0.00024085955,0.00007145888,0.00011826675,0.000111628004,0.00016156115,0.0066268635],"genre_scores_gemma":[0.08566373,0.00047682837,0.903465,0.00019177345,0.00011936124,0.0004933179,0.0003412985,0.00022144424,0.009027289],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993998,0.0001696798,0.000025048666,0.00011017252,0.00020941492,0.000085896354],"domain_scores_gemma":[0.998868,0.00076231966,0.00007051665,0.00007633414,0.0001755259,0.00004742086],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001465154,0.0012345286,0.0009507762,0.00095415703,0.00080988713,0.0014317326,0.0016015688,0.0015324303,0.012812701],"category_scores_gemma":[0.003701412,0.0007016098,0.0012097285,0.0014003196,0.0006259421,0.0014578375,0.0011401442,0.0020693867,0.0015549917],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003673084,0.00008045202,0.00031161838,0.0002069031,0.00003199315,0.000083321494,0.00007694141,0.86023504,0.0008939505,0.06080675,0.005464884,0.071771376],"study_design_scores_gemma":[0.000015520145,0.000015264104,0.000046819696,0.000018025916,0.000007708769,0.000021760352,0.000020951828,0.9796799,0.00016083525,0.01666857,0.0033399542,0.0000047973413],"about_ca_topic_score_codex":0.010687539,"about_ca_topic_score_gemma":0.014545774,"teacher_disagreement_score":0.012812701,"about_ca_system_score_codex":0.0017532603,"about_ca_system_score_gemma":0.0032060363,"threshold_uncertainty_score":0.042862773},"labels":[],"label_agreement":null},{"id":"W6976601838","doi":"10.60692/3f9kk-5h791","title":"Development and characterization of chitosan and beeswax coated biodegradable corn husk and sugarcane bagasse-based cellulose paper.","year":2022,"lang":"en","type":"article","venue":"Greater South Information System","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Chitosan; Ultimate tensile strength; Cellulose; Husk; Emulsion; Pulp (tooth); Coating; Fiber; Beeswax","score_opus":0.018298871842502437,"score_gpt":0.18727771719885097,"score_spread":0.16897884535634855,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6976601838","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98985755,0.0022923017,0.0055997446,0.00003133524,0.00003731212,0.000073462856,0.00034442384,0.000034228397,0.0017297153],"genre_scores_gemma":[0.98222846,0.0016957737,0.009628226,0.00003917638,0.000008451819,0.000058501253,0.00049251725,0.000032938515,0.005816065],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99986327,0.000010601481,0.000011450063,0.000027085931,0.00006817967,0.000019370627],"domain_scores_gemma":[0.99981016,0.000019803894,0.00006990777,0.000012347953,0.00005181616,0.000035845995],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016969319,0.00042160426,0.00015267717,0.00029488487,0.00012005368,0.00028807038,0.00013092102,0.00029066615,0.0006182182],"category_scores_gemma":[0.00024600452,0.00012626887,0.0002733493,0.00028640399,0.000114410264,0.00019727398,0.00009437708,0.00024459028,0.00018803071],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012805504,0.0000084523945,0.000119662174,0.00003465769,0.00000414851,0.000049409064,0.000011390006,0.00004654181,0.99817836,0.0000131633105,0.000007003166,0.0015143974],"study_design_scores_gemma":[0.0000026473062,0.00013030654,0.0068392595,0.0000061684755,0.000013620675,0.00012110003,0.00003115684,0.00022510442,0.9914619,0.000008225838,0.0011557789,0.000004951175],"about_ca_topic_score_codex":0.0017314052,"about_ca_topic_score_gemma":0.003259996,"teacher_disagreement_score":0.0017314052,"about_ca_system_score_codex":0.00021896533,"about_ca_system_score_gemma":0.00022891641,"threshold_uncertainty_score":0.003442645},"labels":[],"label_agreement":null},{"id":"W6977306455","doi":"10.6084/m9.figshare.29663062","title":"Overnight technician routing and scheduling problem with time windows and balanced workloads: a bi-objective zebra optimization algorithm","year":2025,"lang":"en","type":"dataset","venue":"Figshare","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Job shop scheduling; Solver; Scheduling (production processes); Schedule; Key (lock); Nonlinear programming; Routing (electronic design automation); Linear programming; Quadratic programming","score_opus":0.007408907945701544,"score_gpt":0.22835646116872463,"score_spread":0.2209475532230231,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6977306455","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.026867867,0.00052955153,0.963601,0.0005743208,0.00005022994,0.00017139716,0.00019312998,0.00044354264,0.007569057],"genre_scores_gemma":[0.37534693,0.00062860124,0.6107975,0.00041314258,0.000056940888,0.00077385746,0.00063819444,0.0003049054,0.011039864],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993451,0.0002910389,0.00002358986,0.00011121016,0.00011924898,0.00010978483],"domain_scores_gemma":[0.99908495,0.00060661766,0.00010611778,0.000039197697,0.0001022087,0.000060957493],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012529829,0.0013400684,0.001631938,0.00094142806,0.00057706365,0.0015938005,0.002033367,0.0021171486,0.006753],"category_scores_gemma":[0.0026374287,0.00091440196,0.001348752,0.0011087666,0.0007327195,0.0017205963,0.001642377,0.0021449632,0.0009686328],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000075895856,0.000043510023,0.00027187594,0.000049835846,0.000026834727,0.000029038178,0.000031605585,0.983564,0.00036078732,0.0042557237,0.0007642228,0.010526661],"study_design_scores_gemma":[0.000014355909,0.000020591227,0.000049124585,0.0000060752322,0.000003413719,0.000007039869,0.000010684932,0.99826896,0.000057992027,0.0011782352,0.0003804324,0.0000030374813],"about_ca_topic_score_codex":0.008196988,"about_ca_topic_score_gemma":0.006532252,"teacher_disagreement_score":0.008196988,"about_ca_system_score_codex":0.0011618425,"about_ca_system_score_gemma":0.001860535,"threshold_uncertainty_score":0.022591054},"labels":[],"label_agreement":null},{"id":"W6977340785","doi":"10.60692/pm804-h3043","title":"Data for a meta-analysis of the adaptive layer in adaptive large neighborhood search","year":2020,"lang":"en","type":"article","venue":"Greater South Information System","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University; Université Laval","funders":"","keywords":"Replicate; Set (abstract data type); Range (aeronautics); Variety (cybernetics); Domain (mathematical analysis); Layer (electronics); Implementation; Metaheuristic","score_opus":0.2394440626324139,"score_gpt":0.29707504850752525,"score_spread":0.05763098587511134,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6977340785","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011559436,0.017953716,0.031152723,0.0039214846,0.00051250146,0.0066954056,0.9148521,0.0013414412,0.012011221],"genre_scores_gemma":[0.16429172,0.013238582,0.14319883,0.004898138,0.00041427303,0.068634585,0.59646195,0.0016591725,0.0072028143],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9687942,0.015869513,0.005680877,0.002709254,0.006214116,0.00073195325],"domain_scores_gemma":[0.79579735,0.17419678,0.011515967,0.00971504,0.007985174,0.00078971835],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.028289946,0.001861613,0.0029357362,0.013775411,0.0007783403,0.0035477525,0.0027161401,0.0024211188,0.058392588],"category_scores_gemma":[0.18020266,0.0010555132,0.010739564,0.0144953,0.0005777045,0.0019840316,0.0020963696,0.0030145755,0.0060582743],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.008115512,0.00088071753,0.026600473,0.21868391,0.044779506,0.0009335816,0.0009669885,0.036954135,0.0025118804,0.027388858,0.42670503,0.2054794],"study_design_scores_gemma":[0.007633979,0.0022794604,0.033367656,0.06060634,0.038073156,0.001057214,0.0010743313,0.011207074,0.0056659984,0.037020355,0.8015984,0.0004159809],"about_ca_topic_score_codex":0.0033665344,"about_ca_topic_score_gemma":0.0068849744,"teacher_disagreement_score":0.058392588,"about_ca_system_score_codex":0.0021470166,"about_ca_system_score_gemma":0.0041302145,"threshold_uncertainty_score":0.19534266},"labels":[],"label_agreement":null},{"id":"W6979233929","doi":"","title":"PROPEL: Supervised and Reinforcement Learning for Large-Scale Supply Chain Planning","year":2025,"lang":"en","type":"article","venue":"ArXiv.org","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"National Science Foundation","keywords":"Reinforcement learning; Supervised learning; Supply chain; Graph; Optimization problem; Feature (linguistics); Reduction (mathematics); Component (thermodynamics)","score_opus":0.023708634442979888,"score_gpt":0.2764857893147522,"score_spread":0.2527771548717723,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6979233929","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010348821,0.0002386228,0.98480093,0.00032127573,0.00004478303,0.00004952758,0.00006914456,0.0015764604,0.002550432],"genre_scores_gemma":[0.4497982,0.00024659472,0.54493964,0.00040005625,0.00009316208,0.0002556256,0.0003647001,0.00041907528,0.0034830393],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995414,0.00017879602,0.000018138273,0.00008799646,0.00012875712,0.000044960998],"domain_scores_gemma":[0.998273,0.0011508062,0.00012902038,0.00017163347,0.00020350437,0.00007202588],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014535154,0.0009786806,0.00094585423,0.00042134308,0.0003555959,0.0007135403,0.0016218656,0.0010588216,0.0031707499],"category_scores_gemma":[0.004410082,0.0007312347,0.00055502506,0.0004714226,0.0009770632,0.0013560588,0.0016032906,0.0021341823,0.0005707178],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000028296847,0.000045630943,0.00032376416,0.00004521539,0.000021874683,0.000026402566,0.00001433417,0.9596074,0.000398788,0.0049131885,0.0012149647,0.03336017],"study_design_scores_gemma":[0.0000041542376,0.000007503072,0.000013364247,0.0000021644692,9.35007e-7,0.0000021353999,0.0000013207132,0.9975936,0.0000873432,0.002095563,0.00019113859,8.649809e-7],"about_ca_topic_score_codex":0.0052017276,"about_ca_topic_score_gemma":0.007884584,"teacher_disagreement_score":0.0052017276,"about_ca_system_score_codex":0.00090889743,"about_ca_system_score_gemma":0.0018162108,"threshold_uncertainty_score":0.010607243},"labels":[],"label_agreement":null},{"id":"W6979937257","doi":"","title":"Annual Report of the Officers &amp; Committees of the Town of Shutesbury, Massachusetts for the Year Ended June 30, 2016","year":2016,"lang":"en","type":"other","venue":"State Library's electronic repository (State Library of Massachusetts)","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Annual report; Year-ending; Work (physics); Quarter (Canadian coin)","score_opus":0.007298657495674682,"score_gpt":0.22306870206492768,"score_spread":0.215770044569253,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6979937257","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0064469296,0.006380245,0.0038236259,0.040179707,0.029534202,0.0011101862,0.0689995,0.0015294629,0.84199613],"genre_scores_gemma":[0.0025028684,0.0012322222,0.00064199115,0.00035130774,0.00060754915,0.0001457092,0.004298045,0.00021693326,0.99000335],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99870586,0.0001235351,0.000049567974,0.00013891199,0.00079868606,0.00018342015],"domain_scores_gemma":[0.995195,0.00020419798,0.00020560648,0.0002897747,0.003096294,0.0010091384],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0031026187,0.00089293375,0.00064151175,0.001682217,0.0027929803,0.0035980633,0.0010791565,0.0014696773,0.21405368],"category_scores_gemma":[0.004516807,0.0005633445,0.00038598373,0.0014853665,0.000625134,0.001638258,0.0016751065,0.0016782295,0.09027722],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015629383,0.0000143615725,0.00025012525,0.00003993277,0.0000014905621,0.000017007624,0.0000452102,0.00005732775,0.00013640469,0.0011820411,0.98775303,0.010487506],"study_design_scores_gemma":[0.0000040100786,0.0000073996525,0.0015778234,0.00003615144,0.0000018341958,0.0000068177505,0.00007875151,0.000049624963,0.00012501224,0.00014574212,0.9979621,0.0000046946816],"about_ca_topic_score_codex":0.08749698,"about_ca_topic_score_gemma":0.19168097,"teacher_disagreement_score":0.21405368,"about_ca_system_score_codex":0.0023349994,"about_ca_system_score_gemma":0.016705463,"threshold_uncertainty_score":0.7160809},"labels":[],"label_agreement":null},{"id":"W6981739641","doi":"","title":"Extraction au point trouble séquentielle de radionucléides d'origine naturelle à des fins de surveillance environnementale","year":2022,"lang":"fr","type":"other","venue":"Corpus Université Laval (Université Laval)","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Context (archaeology); Homogeneous","score_opus":0.0072156075037013555,"score_gpt":0.20011579624917009,"score_spread":0.19290018874546874,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6981739641","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.79619956,0.007510512,0.18393749,0.00030889496,0.0000959887,0.00034967918,0.0011268186,0.001207964,0.009263112],"genre_scores_gemma":[0.78297234,0.007638357,0.15897352,0.0004031282,0.000026469788,0.00039454226,0.0017222053,0.00041195707,0.04745738],"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","domain_scores_codex":[0.9995009,0.00004030752,0.00002469473,0.00016381923,0.00022086817,0.000049435665],"domain_scores_gemma":[0.99947566,0.00013622154,0.000101085774,0.000058117585,0.0002027915,0.000026148326],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005751078,0.00046090892,0.0005276979,0.0005540541,0.00036495834,0.0006852637,0.0003635939,0.0007010284,0.0018276098],"category_scores_gemma":[0.0007037199,0.0003914407,0.00035059114,0.000476886,0.00043129138,0.0004276995,0.00037450972,0.0005314689,0.001159925],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006375247,0.000008071685,0.00045758727,0.0001215738,0.000009843942,0.000049401187,0.00008719416,0.00027904075,0.99183184,0.00011287884,0.00008530576,0.0068935486],"study_design_scores_gemma":[0.0000037430148,0.000094139825,0.0016818425,0.000013099875,0.000016402977,0.00008630645,0.000037988182,0.00058138696,0.99167657,0.000051005176,0.005748816,0.000008725092],"about_ca_topic_score_codex":0.0033848607,"about_ca_topic_score_gemma":0.0082698455,"teacher_disagreement_score":0.0033848607,"about_ca_system_score_codex":0.0006164185,"about_ca_system_score_gemma":0.0007306875,"threshold_uncertainty_score":0.006730318},"labels":[],"label_agreement":null},{"id":"W6983412287","doi":"","title":"Methods for solving combinatorial pricing problems","year":2023,"lang":"fr","type":"dissertation","venue":"Papyrus : Institutional Repository (Université de Montréal)","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Alliance de recherche numérique du Canada; Institut de Valorisation des Données","keywords":"Utility maximization; Pillar; Agrégation","score_opus":0.01201693862041881,"score_gpt":0.2378600388478454,"score_spread":0.2258431002274266,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6983412287","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002489565,0.003444423,0.9738253,0.0005965154,0.00030073719,0.00012090864,0.00011353399,0.00030293813,0.018806046],"genre_scores_gemma":[0.093865536,0.00768532,0.86949044,0.00046851367,0.00063150947,0.00074032944,0.00048547224,0.0005362425,0.026096584],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99855465,0.0005013609,0.000071648385,0.00017280299,0.00058340316,0.00011608094],"domain_scores_gemma":[0.99808794,0.0013541739,0.00011438026,0.00018396147,0.00020944382,0.00005015504],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018377824,0.0019320467,0.0013243513,0.0019026225,0.000797061,0.0025440303,0.0021773735,0.0020129133,0.013032788],"category_scores_gemma":[0.0063521713,0.0010216769,0.0022672627,0.0027853781,0.0014766522,0.0029409456,0.0019367565,0.003690773,0.0028660968],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000085266,0.0001191944,0.0005539067,0.0009313276,0.00015989237,0.00010250496,0.00017767046,0.42379713,0.0014398241,0.35838136,0.011727057,0.2025249],"study_design_scores_gemma":[0.000055547134,0.000040901636,0.0001143446,0.00015400122,0.000028838174,0.00008217793,0.000054918473,0.73989046,0.0004995741,0.23290603,0.026152665,0.00002059417],"about_ca_topic_score_codex":0.0045259646,"about_ca_topic_score_gemma":0.005615271,"teacher_disagreement_score":0.013032788,"about_ca_system_score_codex":0.0015443417,"about_ca_system_score_gemma":0.002161383,"threshold_uncertainty_score":0.04359907},"labels":[],"label_agreement":null},{"id":"W6992928624","doi":"","title":"Multi-attribute deterministic and stochastic two echelon location routing problems","year":2023,"lang":"fr","type":"dissertation","venue":"Papyrus : Institutional Repository (Université de Montréal)","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Centre interuniversitaire de recherche sur les reseaux d'entreprise, la logistique et le transport; Université de Montréal; Université du Québec à Montréal","keywords":"ESPACE; Distribution (mathematics); Context (archaeology); Identity (music)","score_opus":0.0136025115567061,"score_gpt":0.2182798944064998,"score_spread":0.2046773828497937,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6992928624","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.045397744,0.0012642787,0.9461054,0.0013432775,0.00016974845,0.00010306334,0.0009228333,0.00019195474,0.0045016217],"genre_scores_gemma":[0.78002006,0.0014845408,0.19905196,0.00029673675,0.00032373072,0.00034814372,0.0013561366,0.00012333735,0.016995318],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9966191,0.0014413425,0.00018095794,0.0008292222,0.0005695352,0.0003597869],"domain_scores_gemma":[0.99201643,0.005999089,0.00078636507,0.0003370477,0.00052947534,0.00033162866],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0038213332,0.001160114,0.0022857809,0.0013165502,0.00085636525,0.0028114347,0.0021057443,0.002640358,0.0044552227],"category_scores_gemma":[0.009696738,0.00088783074,0.0019444403,0.0024484769,0.001331852,0.0025607636,0.0018748645,0.0018682088,0.00038363246],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000059995393,0.000033305518,0.0008672229,0.00009443733,0.000054038384,0.000076173135,0.000034913402,0.9724389,0.00019644269,0.017414594,0.0006837127,0.008046365],"study_design_scores_gemma":[0.000011495741,0.000022669217,0.00028799384,0.0000071213926,0.000009693253,0.00003243143,0.000020838164,0.9823528,0.000106909916,0.016402809,0.00073377596,0.000011468956],"about_ca_topic_score_codex":0.006484582,"about_ca_topic_score_gemma":0.0048376257,"teacher_disagreement_score":0.006484582,"about_ca_system_score_codex":0.0022532789,"about_ca_system_score_gemma":0.0015102581,"threshold_uncertainty_score":0.020209372},"labels":[],"label_agreement":null},{"id":"W6995510161","doi":"","title":"Optimization Model for Production-Distribution Planning in the Cosmetic Industry: The Case of Cosmetics Company Canada","year":2024,"lang":"en","type":"dissertation","venue":"Spectrum Research Repository (Concordia University)","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Colorado School of Mines","keywords":"Supply chain; Production (economics); Product (mathematics); Sorting; Matching (statistics)","score_opus":0.03893438373508366,"score_gpt":0.30421185444763715,"score_spread":0.2652774707125535,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6995510161","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.37285733,0.003331879,0.43539405,0.0047625704,0.00025004943,0.000905035,0.0038226405,0.0006149559,0.17806154],"genre_scores_gemma":[0.929742,0.0010985988,0.03381913,0.0001568098,0.00003632535,0.0003352933,0.00071575836,0.00008559858,0.03401057],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99947804,0.00014802985,0.000014324951,0.00009177542,0.00009512142,0.00017278455],"domain_scores_gemma":[0.99931073,0.00040165355,0.000051638817,0.000017745513,0.00014151215,0.0000768169],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00083975313,0.0012975995,0.0011068924,0.0011163045,0.0013990665,0.003001395,0.0019909248,0.0025860586,0.007273037],"category_scores_gemma":[0.0018144283,0.0009082902,0.0011545345,0.0017961509,0.0010865073,0.00087678543,0.0013172093,0.0016314012,0.00044960165],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026316362,0.000018156168,0.00030387263,0.000025512507,0.0000074386235,0.00013331023,0.00002536839,0.99282175,0.00011614214,0.004429027,0.0005377648,0.0015553909],"study_design_scores_gemma":[0.0000132500545,0.00001173629,0.0001997468,0.000007879199,0.000005866111,0.000011927125,0.00005525882,0.9975249,0.00005279948,0.0011974504,0.00091213884,0.0000069558096],"about_ca_topic_score_codex":0.49751574,"about_ca_topic_score_gemma":0.3369616,"teacher_disagreement_score":0.49751574,"about_ca_system_score_codex":0.01038094,"about_ca_system_score_gemma":0.0092032375,"threshold_uncertainty_score":0.98923975},"labels":[],"label_agreement":null},{"id":"W6995524898","doi":"","title":"Optimising feed-in tariff design through efficient risk allocation","year":2017,"lang":"en","type":"article","venue":"Research Portal (Queen's University Belfast)","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Tariff; Government (linguistics); Production (economics); Risk management; Resource allocation; Investment (military)","score_opus":0.0430581319641293,"score_gpt":0.3129282388942151,"score_spread":0.26987010693008584,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6995524898","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020951698,0.00012903675,0.97297543,0.00015800961,0.000042963446,0.00007313063,0.000038568458,0.0002783287,0.0053527197],"genre_scores_gemma":[0.64303833,0.00018026817,0.3460025,0.00009750286,0.000044323766,0.0001851661,0.000102067774,0.00029511921,0.010054781],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99931777,0.00028897327,0.000023611203,0.0000761974,0.00019208968,0.00010140163],"domain_scores_gemma":[0.99890184,0.0006673665,0.0001118304,0.00008731116,0.00017693252,0.000054729026],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019596852,0.0012924728,0.0012486011,0.000993696,0.00042902317,0.0016536469,0.001161343,0.0018429451,0.0059637083],"category_scores_gemma":[0.0043859747,0.0012628771,0.0010198103,0.0006293716,0.00059429783,0.0015702987,0.0015644789,0.0015569319,0.00073881145],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000041380867,0.00003255598,0.00013412708,0.000033257824,0.00002126149,0.000015867607,0.000019167004,0.97779375,0.0011197706,0.004413383,0.00031716816,0.01605838],"study_design_scores_gemma":[0.000010215518,0.00002779526,0.000031366184,0.0000060055427,0.000009207027,0.000007238445,0.0000064190262,0.9953734,0.0005433877,0.0035967203,0.0003847251,0.0000035279688],"about_ca_topic_score_codex":0.0020523798,"about_ca_topic_score_gemma":0.002087107,"teacher_disagreement_score":0.0059637083,"about_ca_system_score_codex":0.00090123934,"about_ca_system_score_gemma":0.0013152775,"threshold_uncertainty_score":0.019950628},"labels":[],"label_agreement":null},{"id":"W6996630002","doi":"","title":"Solving Traveling Salesman Problem With a non-complete Graph","year":2010,"lang":"en","type":"dissertation","venue":"UWSpace (University of Waterloo)","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"University of Waterloo","keywords":"Travelling salesman problem; Graph; Set (abstract data type); Complete graph; Obstacle; 2-opt; Christofides algorithm; Shortest path problem","score_opus":0.008591879861394194,"score_gpt":0.194236627860805,"score_spread":0.1856447479994108,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6996630002","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.29736644,0.0011212814,0.672436,0.0014133843,0.00015027048,0.00074063696,0.0026848959,0.0019845043,0.022102604],"genre_scores_gemma":[0.33741754,0.0011970385,0.64665365,0.00026046895,0.00006536992,0.0003850137,0.005155532,0.00027854467,0.008586869],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99939775,0.00019818422,0.000032430158,0.00017572923,0.0001223696,0.000073572635],"domain_scores_gemma":[0.9989255,0.00068715,0.000083849074,0.00013081891,0.00012163814,0.00005109592],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00072648475,0.00077533006,0.0007755902,0.00056293467,0.0006796317,0.0012336023,0.0010830273,0.0009327226,0.003949558],"category_scores_gemma":[0.0027061878,0.00048632605,0.001076112,0.0010922769,0.00041628888,0.0017797651,0.0006693868,0.0011645094,0.0005522565],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022811645,0.00040166863,0.0011318648,0.00080485974,0.0001643058,0.00033854967,0.0002921548,0.8037343,0.0047216574,0.029337365,0.014512035,0.14433315],"study_design_scores_gemma":[0.00010569492,0.00017504147,0.0005128505,0.000026583573,0.000048230464,0.00017011043,0.00023333196,0.9540231,0.0026581343,0.033585925,0.00844446,0.000016542375],"about_ca_topic_score_codex":0.007649818,"about_ca_topic_score_gemma":0.007959656,"teacher_disagreement_score":0.007649818,"about_ca_system_score_codex":0.00081309344,"about_ca_system_score_gemma":0.0020781443,"threshold_uncertainty_score":0.015210569},"labels":[],"label_agreement":null},{"id":"W6999968585","doi":"","title":"Efficient Routing for Disaster Scenarios in Uncertain Networks: A Computational Study of Adaptive Algorithms for the Stochastic Canadian Traveler Problem with Multiple Agents and Destinations","year":2023,"lang":"en","type":"article","venue":"Journal of the Arkansas Academy of Science","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Node (physics); Context (archaeology); Ranging; Path (computing); Routing (electronic design automation); Range (aeronautics); Delaunay triangulation","score_opus":0.05104875995564393,"score_gpt":0.3079323181188424,"score_spread":0.25688355816319847,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6999968585","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.33149984,0.0015008859,0.650025,0.0029765575,0.00018286903,0.00036195436,0.00038355074,0.0005255121,0.012543782],"genre_scores_gemma":[0.8048182,0.00058975286,0.19172029,0.00026383836,0.000089394336,0.00023269313,0.00037790378,0.00007645363,0.0018313932],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992706,0.00029142934,0.000029677185,0.00016970077,0.000117686024,0.000120948636],"domain_scores_gemma":[0.99484324,0.004083842,0.00041404975,0.0002041484,0.00024694257,0.00020784278],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020800023,0.0011912374,0.001237925,0.0009541079,0.00094760285,0.0013164631,0.0025830003,0.0016946169,0.0025921403],"category_scores_gemma":[0.007664155,0.00046576592,0.000812882,0.0012527037,0.0012768158,0.0017325314,0.0011345797,0.0016767924,0.0001484153],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004023482,0.000029486462,0.000605433,0.0000323417,0.000017887955,0.000019185303,0.000017276829,0.99013114,0.000094431394,0.003568325,0.00043930992,0.005004993],"study_design_scores_gemma":[0.000009545322,0.0000096302565,0.00006270518,0.0000024021106,0.0000031436155,0.0000061768565,0.000013520231,0.9985043,0.00003724649,0.001229453,0.000119956436,0.000001883172],"about_ca_topic_score_codex":0.04469513,"about_ca_topic_score_gemma":0.041894715,"teacher_disagreement_score":0.04469513,"about_ca_system_score_codex":0.002283622,"about_ca_system_score_gemma":0.002964132,"threshold_uncertainty_score":0.08886999},"labels":[],"label_agreement":null},{"id":"W7000750157","doi":"","title":"Heuristic solution methods for multi-attribute vehicle routing problems","year":2012,"lang":"en","type":"other","venue":"Open MIND","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Domain (mathematical analysis); Network routing; Statistical analysis; Orthogonality","score_opus":0.11660681988582546,"score_gpt":0.3955630886467599,"score_spread":0.27895626876093443,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7000750157","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011538073,0.0033203599,0.9752917,0.00038759655,0.00018938245,0.00019172503,0.00013614389,0.00038355196,0.008561389],"genre_scores_gemma":[0.29171616,0.004205385,0.69123554,0.0003076624,0.0003551395,0.00084470643,0.00047300477,0.00022862724,0.010633848],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988362,0.00056033285,0.000064112326,0.00013260094,0.0002769498,0.00012987258],"domain_scores_gemma":[0.9981717,0.0013940097,0.00013419062,0.00008027053,0.00015804011,0.00006181354],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016761611,0.0013480636,0.0011222268,0.0013638202,0.0005617871,0.0016643949,0.0016677681,0.0014359833,0.004443006],"category_scores_gemma":[0.0038751443,0.0006269221,0.0011416232,0.0022140816,0.00075414294,0.0010537464,0.0010969673,0.0014733768,0.00068075804],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000083722996,0.00008009928,0.0004287265,0.00036698033,0.00008949383,0.0000700075,0.00010656276,0.8741906,0.00074683287,0.024797382,0.0027433287,0.096296154],"study_design_scores_gemma":[0.000055705874,0.000053639455,0.0001261952,0.000052961674,0.000021579614,0.000033904005,0.000050767332,0.97363156,0.00033940448,0.019843519,0.005780266,0.000010477839],"about_ca_topic_score_codex":0.0041279714,"about_ca_topic_score_gemma":0.004818708,"teacher_disagreement_score":0.004443006,"about_ca_system_score_codex":0.001148848,"about_ca_system_score_gemma":0.0015747066,"threshold_uncertainty_score":0.014863372},"labels":[],"label_agreement":null},{"id":"W7006072923","doi":"","title":"Solving Canadian Traveller Problem","year":2017,"lang":"cs","type":"dissertation","venue":"Brno University of Technology Digital Library (Brno University of Technology)","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Term (time); Identity (music)","score_opus":0.005919188159447874,"score_gpt":0.17758284003066044,"score_spread":0.17166365187121257,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7006072923","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.27374578,0.0023293647,0.34874105,0.009390985,0.000695198,0.0011410291,0.0055556414,0.0012830812,0.35711786],"genre_scores_gemma":[0.701022,0.0015167661,0.1899586,0.00064798165,0.00011281269,0.00048768532,0.0049719666,0.00038687882,0.10089526],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991621,0.00022028896,0.000036032758,0.00021434091,0.00013981349,0.00022734908],"domain_scores_gemma":[0.9989641,0.0005877119,0.000052086092,0.00006210255,0.0001930411,0.00014097997],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00089935213,0.0010838211,0.00095564174,0.0009573961,0.0017150015,0.0029169966,0.0016887633,0.0025850085,0.035045635],"category_scores_gemma":[0.0037543508,0.0004118522,0.0010674628,0.0012837547,0.0007794237,0.0021206078,0.0015142787,0.0020819204,0.001495147],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00040448617,0.00037874808,0.0030428213,0.0007893916,0.00014808032,0.00047787145,0.0004651784,0.7020456,0.0009067723,0.13305272,0.053753763,0.10453455],"study_design_scores_gemma":[0.00013899889,0.0001640136,0.001475364,0.00011954489,0.000064448745,0.00014833754,0.0013035621,0.8906839,0.0009990722,0.05635792,0.048483375,0.00006153391],"about_ca_topic_score_codex":0.19278033,"about_ca_topic_score_gemma":0.19170783,"teacher_disagreement_score":0.19278033,"about_ca_system_score_codex":0.0051943455,"about_ca_system_score_gemma":0.008299087,"threshold_uncertainty_score":0.3833164},"labels":[],"label_agreement":null},{"id":"W7008668676","doi":"","title":"Cold Storage Panels Delivery Route Optimizations","year":2022,"lang":"en","type":"article","venue":"DigitalCommons - Kennesaw State University (Kennesaw State University)","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Truck; Integer programming; Routing (electronic design automation); Cold storage; Maximization; Automotive industry; Linear programming; Vehicle routing problem","score_opus":0.012975822780067528,"score_gpt":0.1830355766095125,"score_spread":0.17005975382944497,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7008668676","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13555919,0.0008496592,0.69168645,0.0008371724,0.00034064928,0.00066873967,0.002322817,0.0013903759,0.16634487],"genre_scores_gemma":[0.61150205,0.00074255595,0.30195382,0.0002571501,0.000059070735,0.00037264102,0.002457691,0.0010090846,0.08164584],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995665,0.00009046509,0.000011083067,0.0000893749,0.00013736488,0.00010518761],"domain_scores_gemma":[0.999652,0.00011807084,0.00003011431,0.00003350292,0.00014275391,0.000023432794],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006077266,0.0011794739,0.00069185335,0.0009785822,0.0007657577,0.001592891,0.001117789,0.00094645907,0.01655859],"category_scores_gemma":[0.0012251262,0.0005606715,0.0008860776,0.0011366485,0.00035770886,0.0012719957,0.00082131743,0.0011454789,0.0022909062],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015236234,0.0001509499,0.00064832804,0.00013692494,0.000025517775,0.00011857823,0.00006945268,0.8872771,0.0034278568,0.026890328,0.013338578,0.067764066],"study_design_scores_gemma":[0.000027859638,0.00013621534,0.0005502183,0.00003228106,0.000022402934,0.00008330513,0.00016539593,0.9659439,0.0041400734,0.010456077,0.018423527,0.000018758094],"about_ca_topic_score_codex":0.011289767,"about_ca_topic_score_gemma":0.020263119,"teacher_disagreement_score":0.01655859,"about_ca_system_score_codex":0.0020292636,"about_ca_system_score_gemma":0.0025042468,"threshold_uncertainty_score":0.055393994},"labels":[],"label_agreement":null},{"id":"W7014241079","doi":"","title":"Obtaining optimal and approximate solutions to the problem of scheduling inbound and outbound trucks in cross docking operations","year":2009,"lang":"en","type":"article","venue":"Borås Academic Digital Archive (University of Borås)","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Wilfrid Laurier University","keywords":"Truck; Heuristic; Scheduling (production processes); Job shop scheduling; Mathematical model; Integer programming; Vehicle routing problem","score_opus":0.01645139821972165,"score_gpt":0.24448502694841617,"score_spread":0.22803362872869454,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7014241079","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14739595,0.0008495586,0.8300763,0.0004467114,0.000103806924,0.00017621597,0.0003206456,0.0003784255,0.020252453],"genre_scores_gemma":[0.52406585,0.0010608708,0.46744615,0.00013122156,0.000054708282,0.0004145205,0.00065336644,0.0001762186,0.005997163],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993759,0.000193295,0.00003591818,0.00011283036,0.0001556189,0.00012655735],"domain_scores_gemma":[0.9983163,0.0011669454,0.00017293762,0.00008223303,0.00020102558,0.000060687016],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014357633,0.0012180071,0.001146664,0.0009516993,0.0006651062,0.0020251214,0.0009693626,0.0017771823,0.0041491822],"category_scores_gemma":[0.0059460164,0.00078246143,0.0009910823,0.0013909427,0.00063281774,0.0013788983,0.0008435023,0.0011374716,0.0006528974],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004368795,0.000042387408,0.00028676557,0.00008709093,0.000019183406,0.000024572106,0.000052420542,0.975938,0.00049272703,0.008191507,0.00058233493,0.014239342],"study_design_scores_gemma":[0.000008999543,0.00003102606,0.00009432686,0.000013062054,0.0000070870815,0.0000071289123,0.000058336424,0.9948719,0.00029156668,0.004130865,0.0004812223,0.0000043262307],"about_ca_topic_score_codex":0.01005768,"about_ca_topic_score_gemma":0.0098964935,"teacher_disagreement_score":0.01005768,"about_ca_system_score_codex":0.0019311154,"about_ca_system_score_gemma":0.0031117585,"threshold_uncertainty_score":0.019998312},"labels":[],"label_agreement":null},{"id":"W7017117829","doi":"","title":"An Adaptive Large Neighborhood Search heuristic for last-mile deliveries under stochastic customer availability and multiple visits","year":2023,"lang":"en","type":"article","venue":"RePEc: Research Papers in Economics","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"Nederlandse Organisatie voor Wetenschappelijk Onderzoek","keywords":"Service provider; Heuristic; Customer service; Service (business); Attendance; Plan (archaeology)","score_opus":0.036223518905132875,"score_gpt":0.321279288444918,"score_spread":0.2850557695397851,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7017117829","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.157103,0.0007051373,0.8346221,0.0005719554,0.00011054118,0.00020172585,0.00016495629,0.00059457513,0.0059260568],"genre_scores_gemma":[0.83346194,0.00016816363,0.1627433,0.0001489691,0.000036818536,0.00023288901,0.00021267445,0.00011074951,0.0028844078],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995608,0.00017972609,0.000016696751,0.00008465372,0.000066076114,0.00009206504],"domain_scores_gemma":[0.9983584,0.0012097561,0.0001461263,0.00004033932,0.00012571395,0.00011960629],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009888334,0.0006206086,0.0014915046,0.0006762223,0.00049791945,0.00064152584,0.0016584796,0.0011417783,0.0021839214],"category_scores_gemma":[0.0027511993,0.0005866429,0.00058419985,0.00058125064,0.0005231759,0.00091495353,0.00073224236,0.00080909184,0.00021964489],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006209867,0.000052594205,0.00026258358,0.000020321184,0.000020697908,0.00003542415,0.000018357678,0.9889264,0.00021093289,0.0016969556,0.00051033817,0.008183212],"study_design_scores_gemma":[0.000009142778,0.000013383315,0.00002729886,0.0000017080705,0.0000024534672,0.0000029366174,0.000005420116,0.99942565,0.000032227697,0.000416491,0.00006188015,0.0000013432242],"about_ca_topic_score_codex":0.01361064,"about_ca_topic_score_gemma":0.012908194,"teacher_disagreement_score":0.01361064,"about_ca_system_score_codex":0.0012495749,"about_ca_system_score_gemma":0.0018099999,"threshold_uncertainty_score":0.027062833},"labels":[],"label_agreement":null},{"id":"W7021234365","doi":"","title":"To name another life, already lived: blackness and queerness, in love and study","year":2019,"lang":"en","type":"dissertation","venue":"eScholarship@McGill (McGill)","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Narrative; Meaning (existential); Identity (music); Face (sociological concept); Perspective (graphical)","score_opus":0.01674008959135649,"score_gpt":0.2597700952550838,"score_spread":0.24303000566372734,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7021234365","genre_codex":"commentary","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.056556378,0.15395163,0.0026408315,0.4906563,0.017084077,0.000070606926,0.00022737976,0.000093801624,0.278719],"genre_scores_gemma":[0.5540354,0.08277484,0.0018174115,0.06320272,0.0048266943,0.00013074647,0.00010050449,0.00028145142,0.29283017],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9988612,0.00066067884,0.000017476828,0.00011680319,0.00017693595,0.00016687605],"domain_scores_gemma":[0.99740183,0.00088603573,0.00018491039,0.00013310989,0.0004424299,0.000951745],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024050921,0.0002858445,0.0005118433,0.00065269676,0.011060533,0.0075696683,0.00042516013,0.0013344695,0.017577995],"category_scores_gemma":[0.0045398604,0.00020873237,0.00016179969,0.001241248,0.011352526,0.005199197,0.002625582,0.004680703,0.0017024186],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000054503893,0.00008137143,0.0022374466,0.00022521947,0.000011947743,0.00016366964,0.27133745,0.000035220877,0.00034441872,0.20625716,0.42355365,0.09569795],"study_design_scores_gemma":[0.000007721806,0.000022635753,0.0035964008,0.0005446224,0.000008461195,0.00016839385,0.18555434,0.000032618464,0.00010803178,0.019881053,0.7900558,0.000019855452],"about_ca_topic_score_codex":0.025833918,"about_ca_topic_score_gemma":0.075942196,"teacher_disagreement_score":0.025833918,"about_ca_system_score_codex":0.0027137774,"about_ca_system_score_gemma":0.0043248297,"threshold_uncertainty_score":0.058804274},"labels":[],"label_agreement":null},{"id":"W7042525512","doi":"","title":"A proposal for a value sensitive design approach to modelling the refugee chain","year":2017,"lang":"en","type":"other","venue":"TU/e Research Portal","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Refugee; Flexibility (engineering); Process (computing); Value (mathematics); Developing country; Robustness (evolution)","score_opus":0.131159287010716,"score_gpt":0.3864106391421689,"score_spread":0.2552513521314529,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7042525512","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0018524076,0.00012774258,0.9878086,0.0012496581,0.00010022595,0.00026577414,0.0002224138,0.00018853614,0.008184671],"genre_scores_gemma":[0.080332376,0.0004888116,0.9082026,0.0005109826,0.00010589101,0.0012826519,0.00057129556,0.00017854912,0.008326786],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99441165,0.003078091,0.00033280146,0.00074649527,0.0010163249,0.00041474454],"domain_scores_gemma":[0.9934876,0.00389218,0.00045252932,0.0006779442,0.0011486345,0.00034105187],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008870596,0.0017504825,0.0010021891,0.0019186375,0.0013632105,0.006565251,0.0047341282,0.0044017383,0.018669764],"category_scores_gemma":[0.01647087,0.0015234113,0.0043391194,0.0021767614,0.0029622233,0.005620359,0.0062271357,0.0051147193,0.0026296785],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008304079,0.00016249731,0.0018105054,0.00033850395,0.000093027076,0.00035823107,0.00088663213,0.33355182,0.0011184893,0.6255392,0.003655435,0.03240266],"study_design_scores_gemma":[0.000051017734,0.000094315685,0.0001685112,0.00019587987,0.000058031284,0.00013289829,0.00039192959,0.57965916,0.0004789289,0.38756418,0.031167084,0.000038022292],"about_ca_topic_score_codex":0.008487073,"about_ca_topic_score_gemma":0.008102605,"teacher_disagreement_score":0.018669764,"about_ca_system_score_codex":0.0032500266,"about_ca_system_score_gemma":0.006090732,"threshold_uncertainty_score":0.06245655},"labels":[],"label_agreement":null},{"id":"W7042648376","doi":"","title":"Planification coopérative en temps réel d'itinéraires d'une flotte de véhicules électriques partageant des bornes de recharge","year":2022,"lang":"fr","type":"other","venue":"Archipelago (University of Quebec in Montreal)","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Context (archaeology); Coronavirus disease 2019 (COVID-19)","score_opus":0.011345941692192479,"score_gpt":0.22003840442925965,"score_spread":0.20869246273706718,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7042648376","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.094892226,0.00026495676,0.89747536,0.0002414226,0.00006146243,0.0001622403,0.00014331768,0.0011869681,0.0055719577],"genre_scores_gemma":[0.59574604,0.00027679233,0.39025205,0.00012103192,0.000028322835,0.00040394423,0.00049590867,0.00024737717,0.012428457],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996093,0.000049856084,0.00001959873,0.00012739334,0.0000914047,0.000102501064],"domain_scores_gemma":[0.9994848,0.0002163846,0.00005469343,0.000052215422,0.00013300983,0.000058949743],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00079756207,0.0010056515,0.0009262569,0.00059207727,0.00082754076,0.0013764774,0.0014907122,0.0011429508,0.0049558147],"category_scores_gemma":[0.0013052549,0.00056875695,0.0010579887,0.0005396653,0.0005522404,0.00116944,0.0012537774,0.0012232955,0.0005974253],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017976841,0.00010499568,0.0020925282,0.000115186485,0.000055353536,0.00012838222,0.00032185303,0.8797233,0.008747054,0.0057891794,0.0013239355,0.1014185],"study_design_scores_gemma":[0.000027242239,0.00010608938,0.0005257393,0.000015899732,0.000022120068,0.000031938558,0.00010736234,0.99094266,0.0033495142,0.002195911,0.0026638494,0.000011711144],"about_ca_topic_score_codex":0.019390875,"about_ca_topic_score_gemma":0.018163001,"teacher_disagreement_score":0.019390875,"about_ca_system_score_codex":0.0010264727,"about_ca_system_score_gemma":0.0018780081,"threshold_uncertainty_score":0.03855604},"labels":[],"label_agreement":null},{"id":"W7070821011","doi":"","title":"Postman Problems on Mixed Graphs","year":2006,"lang":"en","type":"dissertation","venue":"UWSpace (University of Waterloo)","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"University of Waterloo; Consejo Nacional de Ciencia y Tecnología","keywords":"Mixed graph; Conjecture; Undirected graph; Graph; Traverse; Enhanced Data Rates for GSM Evolution; Linear programming; Eulerian path","score_opus":0.008879276959484027,"score_gpt":0.19707778546265073,"score_spread":0.1881985085031667,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7070821011","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09881076,0.0021123134,0.79165626,0.0050527793,0.0006428895,0.00094599277,0.004085985,0.00143285,0.09526029],"genre_scores_gemma":[0.46673295,0.0028043538,0.4308119,0.0020100954,0.0007403416,0.0014413113,0.0072532184,0.0009598831,0.087245986],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99854255,0.0004582053,0.00009129888,0.00037115318,0.00026156558,0.00027527421],"domain_scores_gemma":[0.99611783,0.0023893125,0.0004478878,0.0004180666,0.00031498208,0.0003118393],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015214527,0.002507837,0.001246847,0.0011242208,0.0014265398,0.0030855956,0.0024920958,0.0020800587,0.027569134],"category_scores_gemma":[0.0069830664,0.0012347379,0.0017728775,0.0020826198,0.0015167407,0.009262069,0.0027568266,0.00472633,0.002257364],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00043378692,0.00039113342,0.00080844335,0.0010632524,0.00015336051,0.00050646823,0.0003628965,0.17662297,0.0037347926,0.6858263,0.03968894,0.09040769],"study_design_scores_gemma":[0.00015808275,0.00022118753,0.0004637592,0.00014164935,0.000069810245,0.0003451551,0.000299394,0.3685229,0.0025733854,0.59293264,0.034231503,0.000040630308],"about_ca_topic_score_codex":0.003441756,"about_ca_topic_score_gemma":0.005692003,"teacher_disagreement_score":0.027569134,"about_ca_system_score_codex":0.0025196332,"about_ca_system_score_gemma":0.0016969903,"threshold_uncertainty_score":0.092227995},"labels":[],"label_agreement":null},{"id":"W7071672532","doi":"","title":"A Study on Routing and Scheduling of Hazardous&#13;\\nMaterials in Railway Transportation","year":2019,"lang":"en","type":"dissertation","venue":"Spectrum Research Repository (Concordia University)","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Hazardous waste; Damages; Scheduling (production processes); Population; Integer programming; Plan (archaeology); Limiting; Heuristic; Routing (electronic design automation); Stochastic programming","score_opus":0.02897284966968524,"score_gpt":0.296109220183206,"score_spread":0.2671363705135208,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7071672532","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.60815424,0.0025163537,0.33467773,0.003399737,0.00036489076,0.0005040698,0.0006077198,0.00012649492,0.049648833],"genre_scores_gemma":[0.92591876,0.002396012,0.057760295,0.0002358122,0.00015379975,0.00010427313,0.00023940168,0.00007440752,0.013117175],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99917066,0.00040249524,0.000029320028,0.00016798015,0.00008453946,0.0001449844],"domain_scores_gemma":[0.99754274,0.0017636241,0.00029014022,0.00006843265,0.00017462696,0.00016054665],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013236079,0.0006455683,0.0005325919,0.000483304,0.0007688807,0.0019916347,0.0010417347,0.0011202733,0.005021307],"category_scores_gemma":[0.0037777687,0.00049211597,0.0010298132,0.0011087782,0.0007165775,0.0016089106,0.00040672196,0.0010298932,0.00027060942],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007339796,0.00014236818,0.0021462655,0.0001519858,0.000045504217,0.00015196306,0.00014782928,0.9415782,0.0019030633,0.037484977,0.0019939197,0.014180593],"study_design_scores_gemma":[0.000013477482,0.00017231461,0.0014380272,0.000023528492,0.00003284444,0.000052604184,0.0003412972,0.9857861,0.0005940363,0.008248321,0.0032832061,0.000014230225],"about_ca_topic_score_codex":0.032336265,"about_ca_topic_score_gemma":0.026870945,"teacher_disagreement_score":0.032336265,"about_ca_system_score_codex":0.00360665,"about_ca_system_score_gemma":0.0026922917,"threshold_uncertainty_score":0.06429607},"labels":[],"label_agreement":null},{"id":"W7084154972","doi":"10.1007/978-981-95-0673-6_63","title":"No Sedation Vs. Light Sedation in Mechanically Ventilated Patients: The NONSEDA Trial","year":2025,"lang":"en","type":"book-chapter","venue":"","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"ASTER","funders":"","keywords":"Sedation; Mechanical ventilation; Randomized controlled trial; Critically ill; Clinical trial; Artificial ventilation","score_opus":0.01004320073919106,"score_gpt":0.23203073803922009,"score_spread":0.22198753730002901,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7084154972","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.92275757,0.038120165,0.0041903914,0.008865168,0.0057942113,0.0011831796,0.0035049915,0.0001245577,0.015459749],"genre_scores_gemma":[0.969659,0.010135809,0.0024640707,0.0050880844,0.0018312938,0.00092660106,0.0014027398,0.00006616012,0.0084261885],"study_design_codex":"randomized_trial","study_design_gemma":"randomized_trial","domain_scores_codex":[0.9989637,0.00075653114,0.000046854246,0.00009157235,0.000040773644,0.00010062341],"domain_scores_gemma":[0.99806434,0.001350156,0.00015231206,0.000120476,0.000047781577,0.0002648846],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018019746,0.0013387162,0.0028165958,0.00018692364,0.00027726992,0.0011680613,0.0009911082,0.0018645737,0.009321732],"category_scores_gemma":[0.003727314,0.0003387801,0.0027981529,0.0002526635,0.0008183581,0.0015059686,0.0005077924,0.004577035,0.0009475418],"study_design_candidate":"randomized_trial","study_design_consensus":"randomized_trial","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.9298698,0.003108612,0.0008311809,0.0008150317,0.0020933982,0.0000788196,0.000045167897,0.0022268696,0.00072215934,0.0010182991,0.0054958374,0.05369492],"study_design_scores_gemma":[0.7462054,0.20345376,0.009891281,0.0008744993,0.006764236,0.00022291241,0.0005342884,0.0109752035,0.0010601316,0.010495328,0.0093713,0.00015172178],"about_ca_topic_score_codex":0.0009141045,"about_ca_topic_score_gemma":0.0020135331,"teacher_disagreement_score":0.009321732,"about_ca_system_score_codex":0.00060308195,"about_ca_system_score_gemma":0.0008078853,"threshold_uncertainty_score":0.031184256},"labels":[],"label_agreement":null},{"id":"W7095082914","doi":"","title":"A Heuristic to Solve the Weekly Log-Truck Scheduling Problem","year":2009,"lang":"en","type":"article","venue":"","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Scheduling (production processes); Job shop scheduling; Tabu search; Integer programming; Schedule; Set (abstract data type); Vehicle routing problem","score_opus":0.01557900989011249,"score_gpt":0.2631635241132104,"score_spread":0.24758451422309793,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7095082914","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.039817918,0.00023773665,0.94689304,0.00026964743,0.00006855689,0.00033279188,0.0003489484,0.0008947214,0.011136597],"genre_scores_gemma":[0.21446274,0.0001878803,0.77829945,0.00010923786,0.000024865123,0.00036712963,0.00055750506,0.0001456529,0.0058455067],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99971765,0.00010087804,0.000011407982,0.00004074135,0.000067797715,0.00006143019],"domain_scores_gemma":[0.9995377,0.00027460884,0.00003952231,0.000045443638,0.000058175872,0.000044515153],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006159851,0.00059380574,0.0005045184,0.00082012115,0.0006950238,0.0007245029,0.0010028133,0.0008987619,0.006917249],"category_scores_gemma":[0.0015474919,0.00036532385,0.0005809485,0.0011281085,0.0003424013,0.0007279508,0.0004471398,0.00071753585,0.0007086806],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012187533,0.0003337474,0.0009006621,0.00014845026,0.000037440866,0.0001609818,0.00011645129,0.810382,0.002507021,0.018531322,0.009871446,0.15688863],"study_design_scores_gemma":[0.00006695251,0.00008722873,0.00028790822,0.000013214733,0.00001643001,0.00008392026,0.000091941874,0.98539114,0.00095780875,0.0067927716,0.006199765,0.000010900955],"about_ca_topic_score_codex":0.0151060335,"about_ca_topic_score_gemma":0.02453712,"teacher_disagreement_score":0.0151060335,"about_ca_system_score_codex":0.00089156884,"about_ca_system_score_gemma":0.0026581176,"threshold_uncertainty_score":0.030036211},"labels":[],"label_agreement":null},{"id":"W7096097577","doi":"","title":"Set Covering Problem","year":2003,"lang":"en","type":"article","venue":"","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Set (abstract data type); Set cover problem; Set theory; Subject (documents); Context (archaeology); Cover (algebra)","score_opus":0.01794915712803758,"score_gpt":0.2519660683179457,"score_spread":0.23401691118990814,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7096097577","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09427319,0.009708251,0.6883663,0.010518116,0.001280494,0.0007874073,0.011797155,0.0014172453,0.18185183],"genre_scores_gemma":[0.6996118,0.008145416,0.20177844,0.0016858407,0.0012218177,0.00080129714,0.0118822865,0.0005615668,0.07431154],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980533,0.0006809881,0.00009468031,0.00039848482,0.00042179276,0.00035069807],"domain_scores_gemma":[0.99761534,0.0016628607,0.00015520911,0.00023012458,0.00015946405,0.00017702208],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013414653,0.0011837318,0.001984518,0.0010586083,0.00123832,0.0036642726,0.0014452512,0.0026021441,0.020166725],"category_scores_gemma":[0.0045566307,0.00048642902,0.0015278187,0.002906326,0.00089212286,0.00327183,0.0018446759,0.0020679033,0.0027566957],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00045326413,0.00042026513,0.001449561,0.0015050432,0.00039924576,0.0007434378,0.00033906987,0.2797506,0.0029015653,0.32680744,0.13949746,0.24573307],"study_design_scores_gemma":[0.00015465387,0.00024278283,0.0013588901,0.00018583117,0.00015387393,0.0013474886,0.0003746508,0.45241085,0.0027979652,0.4351582,0.10574308,0.00007179779],"about_ca_topic_score_codex":0.0019814756,"about_ca_topic_score_gemma":0.0015542271,"teacher_disagreement_score":0.020166725,"about_ca_system_score_codex":0.0019086148,"about_ca_system_score_gemma":0.0017375586,"threshold_uncertainty_score":0.06746441},"labels":[],"label_agreement":null},{"id":"W7097911121","doi":"","title":"GERAD and HEC Montreal, Canada,","year":2004,"lang":"en","type":"article","venue":"","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Metaheuristic; Variable neighborhood search; Local search (optimization); Local optimum; Variable (mathematics); Optimization problem","score_opus":0.006465422324205908,"score_gpt":0.20443845377095235,"score_spread":0.19797303144674644,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7097911121","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0071503296,0.014709912,0.005330692,0.014417172,0.002813579,0.00020631132,0.018996872,0.0018670013,0.9345082],"genre_scores_gemma":[0.011675723,0.0029601762,0.00255941,0.00066913635,0.00008109042,0.000038264676,0.0037223499,0.00027859883,0.9780151],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9985007,0.00007457172,0.000029044944,0.00051212887,0.0005040237,0.0003796017],"domain_scores_gemma":[0.9982493,0.00015542288,0.00005953358,0.00014701647,0.0009985428,0.00039029506],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0006658606,0.0019120396,0.0011658303,0.0016393259,0.004121048,0.0050060335,0.0025686303,0.0026275134,0.56554234],"category_scores_gemma":[0.0016678268,0.00061628985,0.0007392583,0.0024230347,0.001248547,0.0020526513,0.0016351996,0.0017632868,0.20624995],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006157232,0.0001931524,0.0032602816,0.0005282319,0.00007159423,0.0010915624,0.0004263091,0.002628648,0.0053007435,0.03895614,0.65006584,0.2968619],"study_design_scores_gemma":[0.000043893437,0.000035593544,0.0029983637,0.0001156844,0.000017513667,0.00018105225,0.00026862798,0.00090821256,0.0010230179,0.0018820135,0.99249345,0.000032672568],"about_ca_topic_score_codex":0.69304967,"about_ca_topic_score_gemma":0.8036158,"teacher_disagreement_score":0.69304967,"about_ca_system_score_codex":0.016178012,"about_ca_system_score_gemma":0.01865165,"threshold_uncertainty_score":0.61970115},"labels":[],"label_agreement":null},{"id":"W7101418124","doi":"10.1108/jm2-03-2025-0129","title":"Two metaheuristic algorithms for the technician routing and scheduling problem with time windows and balanced workloads","year":2025,"lang":"en","type":"article","venue":"Journal of Modelling in Management","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Regina","funders":"","keywords":"Metaheuristic; Technician; Job shop scheduling; Scheduling (production processes); Scalability; Simulated annealing; Variable neighborhood search; Ant colony optimization algorithms; Vehicle routing problem","score_opus":0.015050631564196943,"score_gpt":0.26189936423238075,"score_spread":0.2468487326681838,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7101418124","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06321032,0.0010288443,0.91909945,0.00092479185,0.0002157377,0.00037308925,0.00028651723,0.00042873848,0.014432459],"genre_scores_gemma":[0.410105,0.00068757037,0.5823968,0.0003642073,0.00011085621,0.00071990635,0.0003525305,0.00011736463,0.0051457514],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99935466,0.00029157536,0.00002888408,0.000107143125,0.000110588706,0.00010725987],"domain_scores_gemma":[0.9989531,0.00064484915,0.00016064015,0.00006513528,0.00011047324,0.00006567946],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013151215,0.0013339574,0.0008919605,0.0012134474,0.0005820589,0.0015377107,0.0015669038,0.002258623,0.0026160022],"category_scores_gemma":[0.003230021,0.0005914927,0.0014061522,0.0014491024,0.0006071152,0.0008914533,0.00086134666,0.0015521138,0.00034800873],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000714209,0.000089128196,0.00040916415,0.000058656355,0.00005570478,0.000044237182,0.00003866521,0.9631416,0.0005568035,0.008345483,0.0011294394,0.026059693],"study_design_scores_gemma":[0.00003946843,0.00004964156,0.00013132753,0.000014473325,0.0000176453,0.000020087458,0.000028103037,0.99478996,0.00027331358,0.003545135,0.0010849758,0.000005896799],"about_ca_topic_score_codex":0.008236756,"about_ca_topic_score_gemma":0.00803378,"teacher_disagreement_score":0.008236756,"about_ca_system_score_codex":0.001865671,"about_ca_system_score_gemma":0.0024505625,"threshold_uncertainty_score":0.016377628},"labels":[],"label_agreement":null},{"id":"W7104438572","doi":"10.71781/10756","title":"Competitive EV charging station location with queues","year":2025,"lang":"en","type":"dissertation","venue":"Papyrus : Institutional Repository (Université de Montréal)","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Context (archaeology); Operation planning; Scheduling (production processes); Queue","score_opus":0.004802782006997965,"score_gpt":0.18411429319438438,"score_spread":0.17931151118738642,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7104438572","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09035849,0.0018456494,0.83850217,0.0029551168,0.00082174817,0.00033386928,0.0012212237,0.0010755685,0.062886156],"genre_scores_gemma":[0.9012932,0.00096333085,0.052573968,0.00033630556,0.0002048449,0.00016400161,0.0004535724,0.00021637892,0.04379439],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985039,0.0004408051,0.00006301773,0.00035453512,0.0002608674,0.00037683765],"domain_scores_gemma":[0.9969072,0.0018692194,0.00026695154,0.000267334,0.0004662743,0.0002230685],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022389235,0.0012629745,0.0018147307,0.0009350204,0.0010248838,0.004541076,0.0033007385,0.0025144336,0.02632724],"category_scores_gemma":[0.0077638784,0.0009131234,0.0016038074,0.0018513023,0.0013070811,0.004299004,0.0029909215,0.0020577633,0.0021836045],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025993513,0.000063976055,0.0010460067,0.00012135361,0.00003923483,0.00014185454,0.00010352539,0.8915117,0.0005123591,0.082262054,0.0037174863,0.0202205],"study_design_scores_gemma":[0.00005008204,0.000081771854,0.00024838286,0.000020727302,0.000029204823,0.00006122122,0.00008280877,0.96865183,0.00024686838,0.026362656,0.0041408017,0.000023683491],"about_ca_topic_score_codex":0.016398987,"about_ca_topic_score_gemma":0.014152081,"teacher_disagreement_score":0.02632724,"about_ca_system_score_codex":0.003950162,"about_ca_system_score_gemma":0.0024942018,"threshold_uncertainty_score":0.08807343},"labels":[],"label_agreement":null},{"id":"W7104453159","doi":"10.71781/9707","title":"Data-driven large neighbourhood search for combinatorial optimization problems","year":2025,"lang":"en","type":"dissertation","venue":"Open MIND","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Institut de Valorisation des Données; Canada First Research Excellence Fund","keywords":"ESPACE","score_opus":0.06321108985735971,"score_gpt":0.3607349507421455,"score_spread":0.2975238608847858,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7104453159","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.032347817,0.0025926735,0.9513949,0.00084512465,0.00017380541,0.00022934483,0.00043812764,0.0006900302,0.0112881195],"genre_scores_gemma":[0.37726578,0.001632062,0.6081586,0.00029999396,0.00013235427,0.00075052516,0.0015246681,0.00036306126,0.0098729925],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989085,0.00051935064,0.000058531572,0.00016775855,0.00026864404,0.000077170516],"domain_scores_gemma":[0.9945991,0.004427164,0.00023426734,0.00023839559,0.000363779,0.00013725556],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019246881,0.00085836434,0.0017308779,0.001338896,0.000505935,0.0016436864,0.0020755562,0.001818273,0.0048587634],"category_scores_gemma":[0.0085632885,0.00074653,0.0011600532,0.0018342242,0.0008060533,0.0018366745,0.0015553368,0.0017049244,0.00079975126],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007764991,0.00006468802,0.0006174461,0.00027973155,0.000059905535,0.000053392283,0.00006605881,0.9427271,0.00054998446,0.013078884,0.0021733097,0.040251825],"study_design_scores_gemma":[0.000021872474,0.0000232909,0.00011751864,0.000022764281,0.0000062281465,0.000015241195,0.000021352052,0.9890063,0.00019570689,0.0090071,0.0015567249,0.0000058450805],"about_ca_topic_score_codex":0.0065207444,"about_ca_topic_score_gemma":0.0114715975,"teacher_disagreement_score":0.0065207444,"about_ca_system_score_codex":0.0015988362,"about_ca_system_score_gemma":0.0014450889,"threshold_uncertainty_score":0.016254187},"labels":[],"label_agreement":null},{"id":"W7104832298","doi":"10.2139/ssrn.5737083","title":"REINFORCEMENT LEARNING-ACCELERATED BRANCH AND PRICE TO SOLVE A BUS ROUTING MODEL","year":2025,"lang":"","type":"preprint","venue":"SSRN Electronic Journal","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Greenhouse gas; Robustness (evolution); Column generation; Fuel efficiency; Linear programming; Scalability; Limiting; Sensitivity (control systems)","score_opus":0.017729937573803863,"score_gpt":0.2804378688871844,"score_spread":0.26270793131338055,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7104832298","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07693369,0.00055839296,0.9119898,0.000996745,0.00021196913,0.00010213624,0.000093955205,0.00035164139,0.008761745],"genre_scores_gemma":[0.8586832,0.00029302863,0.12926175,0.00023909677,0.00014290892,0.00031815073,0.00013995288,0.00016745567,0.010754477],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996081,0.00018984445,0.0000142534,0.000047609803,0.0000756186,0.00006445422],"domain_scores_gemma":[0.9962548,0.0030695933,0.00012584233,0.00008478859,0.00031792978,0.00014701644],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001964109,0.0009235752,0.0020073939,0.00071505253,0.0005816807,0.0009220321,0.0017174656,0.0027943435,0.005677758],"category_scores_gemma":[0.0065233363,0.000853166,0.0006699734,0.0009118401,0.001244032,0.0012655653,0.0011481664,0.0025602863,0.00043055977],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000212277,0.000021639114,0.0001111586,0.000016053442,0.000008318961,0.000013168844,0.000007637294,0.9921165,0.000058774665,0.003595816,0.00031872856,0.0037109838],"study_design_scores_gemma":[0.000004924619,0.000003571479,0.0000084514295,8.9874226e-7,0.0000010498197,7.1631297e-7,6.417413e-7,0.9989957,0.00001041138,0.0009488253,0.000024145023,6.0867006e-7],"about_ca_topic_score_codex":0.018267259,"about_ca_topic_score_gemma":0.013071345,"teacher_disagreement_score":0.018267259,"about_ca_system_score_codex":0.001444156,"about_ca_system_score_gemma":0.0022919932,"threshold_uncertainty_score":0.03632188},"labels":[],"label_agreement":null},{"id":"W7109966539","doi":"10.4230/lipics.socg.2025.53","title":"A PTAS for TSP with Neighbourhoods over Parallel Line Segments","year":2025,"lang":"en","type":"article","venue":"DROPS (Schloss Dagstuhl – Leibniz Center for Informatics)","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Travelling salesman problem; Neighbourhood (mathematics); Line segment; Approximation algorithm; Euclidean geometry; Line (geometry); Euclidean distance; Point (geometry)","score_opus":0.011387866767055601,"score_gpt":0.2773204266513546,"score_spread":0.265932559884299,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7109966539","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03039192,0.0005282548,0.95772666,0.0007575265,0.00016338106,0.00016076189,0.0003720201,0.0013081477,0.008591385],"genre_scores_gemma":[0.27516487,0.0007271516,0.7119152,0.00024312908,0.0001658254,0.00027848713,0.0010065866,0.0004105964,0.010088099],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9991629,0.0001631839,0.000047343074,0.0003070947,0.00021278727,0.000106763364],"domain_scores_gemma":[0.99911803,0.00035438425,0.000106319436,0.00022300785,0.00009478846,0.00010335245],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006889221,0.0009158201,0.0010921968,0.00061295286,0.0008418101,0.0013637273,0.0021427763,0.0014313918,0.0085385805],"category_scores_gemma":[0.0041730157,0.00048028052,0.0016458852,0.0014511545,0.00080231176,0.0029862102,0.0020277752,0.0025944717,0.0018848617],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004929352,0.00022654969,0.001296992,0.0003790225,0.000074771815,0.00028028816,0.00032204454,0.7302574,0.00532399,0.09593445,0.015760483,0.1496511],"study_design_scores_gemma":[0.00005604296,0.000116744864,0.00013573338,0.000024856039,0.000019010009,0.00017847586,0.000045495046,0.9429771,0.0008554252,0.047787283,0.0077893035,0.000014546735],"about_ca_topic_score_codex":0.0049721105,"about_ca_topic_score_gemma":0.0039850585,"teacher_disagreement_score":0.0085385805,"about_ca_system_score_codex":0.001701994,"about_ca_system_score_gemma":0.0012729409,"threshold_uncertainty_score":0.028564394},"labels":[],"label_agreement":null},{"id":"W7114775904","doi":"10.5267/j.jpm.2025.11.003","title":"Route optimization for open-close multiple travelling salesman problem with load-balancing constraint: A multi-chromosome based genetic algorithm","year":2025,"lang":"en","type":"article","venue":"Journal of Project Management","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Travelling salesman problem; Crossover; 2-opt; Genetic algorithm; Benchmark (surveying); Bottleneck traveling salesman problem; Combinatorial optimization; Nearest neighbour algorithm; Lin–Kernighan heuristic","score_opus":0.01920267751821153,"score_gpt":0.28319407971233257,"score_spread":0.26399140219412104,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7114775904","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.057143737,0.00091881526,0.9317484,0.00046779402,0.00010528318,0.00015967575,0.00013041355,0.000514361,0.008811559],"genre_scores_gemma":[0.5054086,0.0007862482,0.4851207,0.00025349596,0.00006862505,0.00037564788,0.00047711062,0.00014392215,0.0073656454],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996793,0.000095209194,0.0000114919085,0.00008210018,0.000071830385,0.0000600801],"domain_scores_gemma":[0.9996954,0.00014808083,0.000051053787,0.00001692668,0.000056895005,0.000031689015],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00058939867,0.00089003803,0.0009237618,0.00089402596,0.00045440643,0.0010729593,0.0014522811,0.0014125697,0.0029792022],"category_scores_gemma":[0.0012568437,0.00037484762,0.0008434694,0.0009976038,0.00045334102,0.00071462704,0.0008048119,0.00095489086,0.00036008543],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004332861,0.000070351765,0.00074989826,0.00006823299,0.000039417533,0.0001003181,0.00005995674,0.9415194,0.0010678734,0.006805165,0.0016890639,0.047787093],"study_design_scores_gemma":[0.000015131835,0.000035262485,0.00012104966,0.000010143941,0.000012082965,0.000031611646,0.000025004489,0.9966738,0.00018582298,0.0020715545,0.0008138264,0.000004765518],"about_ca_topic_score_codex":0.008211045,"about_ca_topic_score_gemma":0.00589404,"teacher_disagreement_score":0.008211045,"about_ca_system_score_codex":0.0007979323,"about_ca_system_score_gemma":0.0016575065,"threshold_uncertainty_score":0.016326487},"labels":[],"label_agreement":null},{"id":"W7114912747","doi":"10.1145/3748636.3764603","title":"Learning Heuristics to Solve Dynamic Vehicle Routing Problems Using Large Language Models","year":2025,"lang":"en","type":"article","venue":"","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada; University of Toronto; University of Waterloo","funders":"National Research Council Canada","keywords":"Heuristics; Vehicle routing problem; Leverage (statistics); Scalability; Greedy algorithm; Executable; Reinforcement learning; Routing (electronic design automation)","score_opus":0.013812041959171724,"score_gpt":0.2859743451169398,"score_spread":0.27216230315776807,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7114912747","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.027660018,0.00041002486,0.9651764,0.0003753414,0.000066091525,0.00016562773,0.00019022664,0.002865464,0.003090743],"genre_scores_gemma":[0.3616999,0.00037275947,0.63190246,0.00049188326,0.00009638282,0.0005873853,0.0008851534,0.00053069735,0.003433412],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999385,0.0002151841,0.0000365271,0.00017400584,0.000104786974,0.0000844112],"domain_scores_gemma":[0.9975719,0.0018627942,0.00014903746,0.00015081871,0.00018973963,0.00007571638],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011267982,0.0016920898,0.0010971245,0.0007521718,0.0004827266,0.0011164816,0.002087408,0.0014144912,0.0032764503],"category_scores_gemma":[0.004728432,0.0009514504,0.0012794592,0.00075874454,0.00078641425,0.0013796863,0.0010813243,0.00216532,0.0009034309],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000038207054,0.00010754613,0.0007408728,0.00012410432,0.000040782164,0.000090864145,0.000064937434,0.91403717,0.0010731474,0.005319017,0.0024671245,0.0758963],"study_design_scores_gemma":[0.000013567069,0.00001520015,0.00003432899,0.000006946206,0.0000056725676,0.0000107892265,0.000013403037,0.99572974,0.00026293818,0.0034918413,0.00041240032,0.0000031080172],"about_ca_topic_score_codex":0.007921115,"about_ca_topic_score_gemma":0.015410546,"teacher_disagreement_score":0.007921115,"about_ca_system_score_codex":0.0011945161,"about_ca_system_score_gemma":0.002518376,"threshold_uncertainty_score":0.015749991},"labels":[],"label_agreement":null},{"id":"W7115715308","doi":"10.5267/j.dsl.2025.10.008","title":"An advanced optimization framework for cross-docking site selection in global supply chains using an enhanced k-means clustering algorithm integrated with geographic information systems","year":2025,"lang":"en","type":"article","venue":"Decision Science Letters","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Khon Kaen University","keywords":"Transshipment (information security); Cluster analysis; Supply chain; Identification (biology); Selection (genetic algorithm); Geographic information system; Facility location problem; Logistics center","score_opus":0.009999305551592777,"score_gpt":0.31891627098775155,"score_spread":0.30891696543615876,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7115715308","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0037014154,0.00017594158,0.994605,0.00009655608,0.00001819263,0.00003774219,0.0000419762,0.0000645344,0.0012585915],"genre_scores_gemma":[0.39763117,0.0011076384,0.5961462,0.00013583557,0.00008370319,0.0004952787,0.00037038667,0.00014610881,0.003883539],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99906224,0.00039019893,0.000052798212,0.00020181244,0.00019547592,0.00009755382],"domain_scores_gemma":[0.9992291,0.000349475,0.00010699039,0.000042991138,0.0002270168,0.000044429235],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019422319,0.0016218696,0.0014708877,0.0014253318,0.0008290163,0.0020034276,0.002000243,0.0015963165,0.0021308295],"category_scores_gemma":[0.0024382668,0.000969867,0.0017733064,0.0020140046,0.000979374,0.0017884286,0.0019231132,0.0015855454,0.00046294543],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000006217793,0.0000064103847,0.000110507084,0.000019789828,0.000013231232,0.000014435916,0.000015740854,0.99045837,0.00014149256,0.004980995,0.00018695928,0.0040458143],"study_design_scores_gemma":[0.0000024158358,0.000009183231,0.000041254276,0.0000043136647,0.0000041482363,0.0000044383623,0.000008367082,0.99700123,0.000059703023,0.0025687602,0.00029156168,0.0000045807797],"about_ca_topic_score_codex":0.021442387,"about_ca_topic_score_gemma":0.013102656,"teacher_disagreement_score":0.021442387,"about_ca_system_score_codex":0.0020824822,"about_ca_system_score_gemma":0.0032426312,"threshold_uncertainty_score":0.042635143},"labels":[],"label_agreement":null},{"id":"W7116650998","doi":"10.18280/mmep.121135","title":"An Accelerated Crow Search Algorithm for Multifactorial Optimization in Vehicle Routing Problem","year":2025,"lang":"","type":"article","venue":"Mathematical Modelling and Engineering Problems","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Vehicle routing problem; Routing (electronic design automation); Genetic algorithm; Search algorithm; Optimization algorithm; Optimization problem; Local search (optimization)","score_opus":0.03959600188158354,"score_gpt":0.2903268334158408,"score_spread":0.2507308315342573,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7116650998","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008028804,0.00042952882,0.9878199,0.0001581711,0.00015025292,0.000044105494,0.00003228658,0.00016056282,0.003176412],"genre_scores_gemma":[0.255583,0.00047831918,0.73386014,0.00030853198,0.00016545426,0.00033868648,0.0001862265,0.00025495567,0.008824638],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994567,0.00022210355,0.00002200172,0.000069822825,0.00017375131,0.000055636512],"domain_scores_gemma":[0.99878806,0.00073419843,0.00006638936,0.000066500375,0.00028149722,0.00006335448],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015874101,0.000890407,0.0013371112,0.0010428334,0.0006274284,0.00092017424,0.0016308428,0.0019191945,0.0047040684],"category_scores_gemma":[0.0039353794,0.0005786207,0.00087514124,0.0011825975,0.00062549807,0.0009867445,0.0013150465,0.0015694436,0.000691386],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010523354,0.00008841631,0.00035197983,0.000101189566,0.00005647376,0.00006592862,0.000046136694,0.89828044,0.0016749188,0.027108518,0.0029276754,0.06919309],"study_design_scores_gemma":[0.000009646082,0.000016138825,0.0000364476,0.000003678708,0.000004055105,0.0000072704966,0.0000035302228,0.9972801,0.00009067875,0.0020311833,0.00051418424,0.0000030904441],"about_ca_topic_score_codex":0.008583554,"about_ca_topic_score_gemma":0.008305117,"teacher_disagreement_score":0.008583554,"about_ca_system_score_codex":0.00089178886,"about_ca_system_score_gemma":0.0017996054,"threshold_uncertainty_score":0.017067194},"labels":[],"label_agreement":null},{"id":"W7116980251","doi":"10.1016/j.omega.2025.103496","title":"The stochastic production routing problem with adaptive routing and service level constraints","year":2025,"lang":"en","type":"article","venue":"Omega","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Healthcare Excellence Canada","keywords":"Routing (electronic design automation); Production (economics); Service (business); Service level; Multipath routing","score_opus":0.021645396550457763,"score_gpt":0.24635704450719642,"score_spread":0.22471164795673865,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7116980251","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.022112772,0.00017383664,0.9737154,0.0003966589,0.000037046528,0.00009105144,0.00015686481,0.00012428545,0.003192076],"genre_scores_gemma":[0.6603621,0.0005416765,0.3311011,0.00019683824,0.00009158964,0.00045661005,0.0004594495,0.00011112435,0.0066796076],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989693,0.0004206888,0.00004634177,0.00024792206,0.00016356482,0.00015230526],"domain_scores_gemma":[0.99857306,0.0010078388,0.00018729796,0.00005503274,0.000119408716,0.00005741457],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014638562,0.0010842909,0.0010029146,0.0005494022,0.00052964024,0.0012548532,0.001155723,0.001755377,0.0025698545],"category_scores_gemma":[0.0028139239,0.00078183046,0.00094465236,0.0009543661,0.00080206414,0.0010482356,0.0008782788,0.0011643093,0.00024249696],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012735088,0.000009474117,0.00010715887,0.000023465378,0.000008868828,0.000036590533,0.000009190479,0.9912411,0.0003468542,0.0044822125,0.00022375461,0.003498536],"study_design_scores_gemma":[0.000007683943,0.000017223065,0.000064440275,0.000003109065,0.0000043713285,0.00001680102,0.000008120809,0.99580705,0.00015195584,0.0035716777,0.0003434677,0.0000040908794],"about_ca_topic_score_codex":0.006851966,"about_ca_topic_score_gemma":0.004858999,"teacher_disagreement_score":0.006851966,"about_ca_system_score_codex":0.0013969084,"about_ca_system_score_gemma":0.0019455798,"threshold_uncertainty_score":0.013624191},"labels":[],"label_agreement":null},{"id":"W7117152070","doi":"10.2139/ssrn.5964219","title":"Carrier Collaborative Vehicle Routing Problem with Shared Customer Demands and Vehicle Capacities","year":2025,"lang":"","type":"preprint","venue":"SSRN Electronic Journal","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Vehicle routing problem; Heuristic; Homogeneous; Routing (electronic design automation); Quality (philosophy); Computation","score_opus":0.008061387881110445,"score_gpt":0.2444858075244021,"score_spread":0.23642441964329167,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7117152070","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06727815,0.0003039486,0.92187864,0.00074752636,0.00012981094,0.00015908077,0.00038579947,0.00021133627,0.008905687],"genre_scores_gemma":[0.85399026,0.00035168685,0.12957993,0.00021667409,0.00016204292,0.0004233155,0.00052740175,0.00019212688,0.014556566],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997652,0.00075394625,0.00007715816,0.0006600566,0.00043444807,0.00042237615],"domain_scores_gemma":[0.9943159,0.003918801,0.0004698318,0.0004594737,0.0005197681,0.00031611705],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00318797,0.0016323188,0.0032387374,0.0011837775,0.0012092129,0.0035285752,0.004142477,0.0058145025,0.006008649],"category_scores_gemma":[0.009814802,0.001565013,0.001667587,0.0030667956,0.0016524635,0.0041680094,0.0031973347,0.0020679887,0.0007448409],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017100967,0.000068581896,0.00024428233,0.00008214716,0.00007333315,0.00027507078,0.00006718181,0.9643711,0.00077900686,0.02278914,0.0019090048,0.009170225],"study_design_scores_gemma":[0.000027891405,0.000037197213,0.00006767326,0.000004550707,0.000020514177,0.00004278781,0.000027769187,0.98828775,0.0003299253,0.010745905,0.0003989792,0.000009090827],"about_ca_topic_score_codex":0.0070133964,"about_ca_topic_score_gemma":0.0036579084,"teacher_disagreement_score":0.0070133964,"about_ca_system_score_codex":0.002065491,"about_ca_system_score_gemma":0.0022811757,"threshold_uncertainty_score":0.020100951},"labels":[],"label_agreement":null},{"id":"W7117325248","doi":"10.1016/j.tre.2025.104626","title":"A comparison of cost-sharing models in horizontal cooperative routing","year":2025,"lang":"en","type":"article","venue":"Transportation Research Part E Logistics and Transportation Review","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"HEC Montréal; Université du Québec à Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Stability (learning theory); Measure (data warehouse); Gini coefficient; Routing (electronic design automation); Yield (engineering); Vehicle routing problem; Dispersion (optics)","score_opus":0.16473147736188717,"score_gpt":0.4442473126133604,"score_spread":0.2795158352514733,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7117325248","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.33093944,0.0019945097,0.6079612,0.0017678204,0.00013375928,0.00039771962,0.00039426115,0.00020838848,0.056202833],"genre_scores_gemma":[0.94782376,0.0007220932,0.045174062,0.000116899806,0.000035587884,0.00020129557,0.00012337239,0.000059045768,0.0057437695],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986022,0.000789248,0.000036988502,0.00011897682,0.00026190898,0.00019065988],"domain_scores_gemma":[0.9958734,0.0030351786,0.0002659423,0.00022506734,0.00039409264,0.00020630957],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004345851,0.0009732479,0.000956343,0.0010673658,0.00079365534,0.0017949569,0.0022161715,0.0015340742,0.003361834],"category_scores_gemma":[0.005254418,0.0004647049,0.0010832853,0.001098129,0.0010537273,0.0019281793,0.0012414812,0.00092148763,0.00020920378],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026458554,0.000032752472,0.0002461409,0.000021189524,0.00001788927,0.000014275552,0.00004345126,0.968113,0.00010232977,0.026118964,0.0002933043,0.004970244],"study_design_scores_gemma":[0.000006617433,0.00003581993,0.00011534686,0.0000099440285,0.000007814095,0.0000043616387,0.000037435577,0.9928306,0.00004577853,0.0064907745,0.0004099032,0.000005687397],"about_ca_topic_score_codex":0.04381562,"about_ca_topic_score_gemma":0.036586545,"teacher_disagreement_score":0.04381562,"about_ca_system_score_codex":0.006626928,"about_ca_system_score_gemma":0.0034916124,"threshold_uncertainty_score":0.08712113},"labels":[],"label_agreement":null},{"id":"W7117470175","doi":"10.23919/iccas66577.2025.11301391","title":"Multi-Agent Tsallis Actor–Critic for Autonomous Vehicle Fleet Coordination on Road Graph Networks","year":2025,"lang":"","type":"article","venue":"","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Korea Evaluation Institute of Industrial Technology; Ministry of Trade, Industry and Energy","keywords":"Job shop scheduling; Benchmark (surveying); Graph; Inference; Scheduling (production processes); Heuristic; Reinforcement learning","score_opus":0.02370621082766563,"score_gpt":0.30243376266814365,"score_spread":0.27872755184047804,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7117470175","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.022257527,0.00022845335,0.973265,0.00022404814,0.000044429125,0.00003503877,0.00005680442,0.00042516825,0.0034634683],"genre_scores_gemma":[0.9101415,0.0002025874,0.084371105,0.00009867598,0.000042013427,0.00014981227,0.00012870252,0.000123405,0.0047421716],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99975854,0.00007780476,0.0000095574305,0.00006329122,0.0000545207,0.000036410656],"domain_scores_gemma":[0.99919885,0.00049425423,0.000103563274,0.000040075676,0.00010877482,0.000054606076],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00071710866,0.0009532623,0.0008810188,0.0003611169,0.0003286187,0.00064656156,0.0011039448,0.00078577787,0.0017542437],"category_scores_gemma":[0.001844466,0.00050979195,0.00047413196,0.00038491614,0.00075735967,0.0006259961,0.00073844334,0.0012079898,0.00026582775],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000065610934,0.0000034271725,0.000065607834,0.000007165014,0.0000052298624,0.000012738811,0.000005381179,0.99657804,0.00016436275,0.0011304182,0.00011771002,0.0019034575],"study_design_scores_gemma":[0.0000011843088,0.0000020135997,0.000010677369,5.994406e-7,7.042534e-7,9.38373e-7,8.4460055e-7,0.99950016,0.000033755634,0.00040124264,0.00004744033,5.175274e-7],"about_ca_topic_score_codex":0.015241106,"about_ca_topic_score_gemma":0.011398726,"teacher_disagreement_score":0.015241106,"about_ca_system_score_codex":0.0012221471,"about_ca_system_score_gemma":0.0013636507,"threshold_uncertainty_score":0.03030479},"labels":[],"label_agreement":null},{"id":"W7117476672","doi":"10.3390/asi9010010","title":"An Artificial Intelligence Enhanced Transfer Graph Framework for Time-Dependent Intermodal Transport Optimization","year":2025,"lang":"en","type":"article","venue":"Applied System Innovation","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Francophone University Association","funders":"","keywords":"Modular design; Subnetwork; Scalability; Graph; Computation; Routing (electronic design automation); Bridging (networking); Schedule","score_opus":0.016496534255903514,"score_gpt":0.27893153333419624,"score_spread":0.2624349990782927,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7117476672","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00844044,0.00019220109,0.98667836,0.0001767503,0.00002748831,0.0000233931,0.00007278195,0.0001500335,0.004238546],"genre_scores_gemma":[0.626516,0.0006372478,0.36369365,0.00018556132,0.000090263726,0.00027306066,0.00040182762,0.00018037505,0.008021988],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998479,0.00006562066,0.000004516879,0.000027089698,0.000034776564,0.00002009152],"domain_scores_gemma":[0.9997528,0.00013719632,0.000025037783,0.000019476476,0.000045234006,0.0000201921],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046519135,0.00077324896,0.0005808729,0.0005903989,0.00031729334,0.0006649997,0.0011016278,0.0007888701,0.0028395066],"category_scores_gemma":[0.00088072586,0.00025622084,0.00065615517,0.00076456624,0.0007047192,0.0008845954,0.000847502,0.0010602555,0.00038140424],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000005019614,0.00000883884,0.00007374764,0.000012074039,0.000008114959,0.000014020068,0.00000754938,0.98297244,0.00020646931,0.011600323,0.00026749045,0.0048239287],"study_design_scores_gemma":[9.876943e-7,0.0000029523883,0.000011620654,9.093879e-7,8.997777e-7,0.0000014329017,0.0000016293785,0.99447614,0.00002511117,0.005254209,0.00022357344,6.8790797e-7],"about_ca_topic_score_codex":0.009163087,"about_ca_topic_score_gemma":0.009012189,"teacher_disagreement_score":0.009163087,"about_ca_system_score_codex":0.0010444273,"about_ca_system_score_gemma":0.0010622598,"threshold_uncertainty_score":0.018219471},"labels":[],"label_agreement":null},{"id":"W7117662164","doi":"10.25077/josi.v24.n2.p324-344.2025","title":"Goal Programming and Monte Carlo Simulation for Optimizing Inbound Scheduling in Resource-Constrained Warehouses","year":2025,"lang":"id","type":"article","venue":"Jurnal Optimasi Sistem Industri","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Universitas Kristen Petra","keywords":"Scheduling (production processes); Robustness (evolution); Key (lock); Monte Carlo method; Truck; Prioritization; FIFO (computing and electronics); Material handling","score_opus":0.03250396975506274,"score_gpt":0.30685081471271985,"score_spread":0.2743468449576571,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7117662164","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10955569,0.00047480204,0.8806642,0.0005395874,0.000075612785,0.00015202035,0.00022432508,0.0004243999,0.007889236],"genre_scores_gemma":[0.83530676,0.00035596252,0.16111064,0.0001504008,0.000032450946,0.0003378072,0.00027481373,0.00009572235,0.0023354199],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99939895,0.00035523588,0.00002078583,0.000059464805,0.00008626191,0.00007939169],"domain_scores_gemma":[0.9970804,0.0024132107,0.00018413416,0.000055880373,0.00017714362,0.00008925282],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019876389,0.0009779964,0.0010099034,0.0008034211,0.0004429714,0.00134622,0.0010020221,0.0012831563,0.0016472694],"category_scores_gemma":[0.0037715666,0.0006723208,0.0008746022,0.0007978407,0.000819968,0.0005484003,0.0009090872,0.0013291251,0.00017883227],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000083179575,0.000008154849,0.00012794185,0.0000071443433,0.0000048087923,0.000005504728,0.000004533112,0.99818414,0.000033010165,0.000981344,0.000033624692,0.0006014402],"study_design_scores_gemma":[0.0000016283443,0.0000027755393,0.000014650306,0.0000012568213,9.565014e-7,5.365169e-7,0.000001906218,0.99952126,0.000021345077,0.00040154168,0.0000314226,6.8406723e-7],"about_ca_topic_score_codex":0.018627113,"about_ca_topic_score_gemma":0.012694665,"teacher_disagreement_score":0.018627113,"about_ca_system_score_codex":0.001302439,"about_ca_system_score_gemma":0.0018758872,"threshold_uncertainty_score":0.037037373},"labels":[],"label_agreement":null},{"id":"W7117754593","doi":"10.2139/ssrn.5991236","title":"A New Multi-Objective Optimization Model for Carinata-based Sustainable Aviation Fuel Supply Chain Network Design","year":2025,"lang":"","type":"preprint","venue":"SSRN Electronic Journal","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Greenhouse gas; Sustainability; Minification; Supply chain; Maximization; Fuzzy logic; Aviation; Supply chain network; Production (economics); Process (computing)","score_opus":0.01790130678189506,"score_gpt":0.27372164109631203,"score_spread":0.255820334314417,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7117754593","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.022081032,0.00037993048,0.96290195,0.00029500359,0.000097061675,0.000116802825,0.00017014009,0.0001586143,0.0137995295],"genre_scores_gemma":[0.76375276,0.0007260782,0.21333887,0.00022570469,0.00009064581,0.00079340977,0.0004960015,0.00013640276,0.020440103],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99963593,0.00012357174,0.000012519738,0.00007870085,0.00009673512,0.00005251789],"domain_scores_gemma":[0.9996724,0.00013841428,0.0000421255,0.000015606174,0.00010048806,0.000031002382],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009368052,0.0012094969,0.0012682158,0.0007955589,0.00066706334,0.0016151182,0.0016933082,0.001956774,0.0040328456],"category_scores_gemma":[0.0012423599,0.0007315532,0.0012069248,0.000941033,0.0005571075,0.0010527581,0.0013580841,0.00137994,0.0004730273],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000076111764,0.000009885061,0.00006555219,0.000015621054,0.000009031766,0.0000144194455,0.0000067570395,0.9953804,0.00026495114,0.0018467392,0.00015887938,0.0022202607],"study_design_scores_gemma":[0.0000021420642,0.000007977922,0.000018841907,0.0000021619014,0.0000033505714,0.000002122425,0.0000024256983,0.9992192,0.00004426858,0.0004622435,0.00023396181,0.0000013703484],"about_ca_topic_score_codex":0.012318915,"about_ca_topic_score_gemma":0.012207972,"teacher_disagreement_score":0.012318915,"about_ca_system_score_codex":0.001361555,"about_ca_system_score_gemma":0.0019285354,"threshold_uncertainty_score":0.02449441},"labels":[],"label_agreement":null},{"id":"W7117764697","doi":"10.1002/net.70024","title":"Partial‐Outsourcing Strategy for the Vehicle Routing Problem With Stochastic Demands","year":2025,"lang":"en","type":"article","venue":"Networks","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; HEC Montréal","funders":"China Scholarship Council","keywords":"Vehicle routing problem; Routing (electronic design automation); Markov decision process; Heuristic; Stochastic programming; Markov chain; Markov process; Static routing","score_opus":0.014630117411430988,"score_gpt":0.25702623743899644,"score_spread":0.24239612002756544,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7117764697","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14734717,0.00038080945,0.8402715,0.00059343514,0.000050738992,0.00019249452,0.00021545558,0.00023994171,0.010708521],"genre_scores_gemma":[0.9347208,0.00027499232,0.061589163,0.00007963084,0.000021421314,0.0001071125,0.00013633088,0.000052582396,0.0030178993],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994174,0.00026010597,0.000023775714,0.0000635656,0.00010527368,0.00012974768],"domain_scores_gemma":[0.99897003,0.0005724676,0.00011345014,0.00009420967,0.000117905816,0.0001319313],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013975725,0.0008665175,0.0011453753,0.0003660486,0.00036814628,0.0008408869,0.0011315768,0.0007127778,0.0036689355],"category_scores_gemma":[0.0020767683,0.00039465853,0.0007258644,0.00057124026,0.0005728809,0.0009916485,0.0009268987,0.00092735403,0.00023496909],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000054459106,0.000032866712,0.00023512304,0.00004099776,0.000014511844,0.00005325573,0.000025000149,0.98144364,0.00052309287,0.00862986,0.00044469448,0.008502482],"study_design_scores_gemma":[0.0000065718,0.00002577227,0.000063045765,0.000003900706,0.0000035668054,0.000009744406,0.000014636933,0.9969627,0.00022285957,0.0024844846,0.00020039397,0.0000023135437],"about_ca_topic_score_codex":0.0077473614,"about_ca_topic_score_gemma":0.0045628077,"teacher_disagreement_score":0.0077473614,"about_ca_system_score_codex":0.0012086907,"about_ca_system_score_gemma":0.0014812901,"threshold_uncertainty_score":0.015404522},"labels":[],"label_agreement":null},{"id":"W7124158831","doi":"10.1109/codit66093.2025.11321472","title":"Transport optimization of an anaerobic digestion co-product in a closed-loop supply chain","year":2025,"lang":"","type":"article","venue":"","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Digestate; Truck; Anaerobic digestion; Supply chain; Supply chain optimization; Biogas","score_opus":0.013207253161775805,"score_gpt":0.27951011236129225,"score_spread":0.26630285919951646,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7124158831","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.49745053,0.00059689186,0.48496434,0.00037424793,0.0000888629,0.00045236677,0.0005802514,0.00040038736,0.015092183],"genre_scores_gemma":[0.9552391,0.00024946136,0.033211596,0.000041578514,0.000008112665,0.00019691867,0.00033715344,0.00005722648,0.01065887],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993957,0.00016228243,0.000020792108,0.00013995866,0.00007621098,0.0002049604],"domain_scores_gemma":[0.99918026,0.00039868377,0.00013744073,0.000035509158,0.00015115309,0.00009691426],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012100644,0.0018878693,0.0014221673,0.0011650675,0.001243137,0.0023651752,0.0012217282,0.0021127216,0.0047008432],"category_scores_gemma":[0.0014880331,0.0010426367,0.0013241804,0.0012449886,0.0009878173,0.001365567,0.0012533147,0.00093363,0.00043299928],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006331478,0.00003320171,0.00020601272,0.00002468768,0.000012994663,0.000055240987,0.000013467452,0.99646914,0.0006975182,0.0005542658,0.00007033268,0.0017997999],"study_design_scores_gemma":[0.000013385719,0.0001127376,0.00017246132,0.000004911068,0.000014009316,0.000009808792,0.000034796754,0.9983677,0.00042624012,0.00059040776,0.00024766335,0.000005870272],"about_ca_topic_score_codex":0.05401201,"about_ca_topic_score_gemma":0.024484498,"teacher_disagreement_score":0.05401201,"about_ca_system_score_codex":0.0034358583,"about_ca_system_score_gemma":0.0024155623,"threshold_uncertainty_score":0.10739523},"labels":[],"label_agreement":null},{"id":"W7124217846","doi":"10.1109/codit66093.2025.11321233","title":"A lexicographic bi-objective approach to fleet sizing and routing for service vehicles in a real-world passenger transport system","year":2025,"lang":"","type":"article","venue":"","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Lexicographical order; Software deployment; Sizing; Service (business); Public transport; Vehicle routing problem; Sustainability; Linear programming","score_opus":0.024223979383669804,"score_gpt":0.2839181024757135,"score_spread":0.25969412309204365,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7124217846","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.038988166,0.00040970038,0.94820666,0.00073170674,0.00008757877,0.0002951144,0.00045445922,0.0002832242,0.010543363],"genre_scores_gemma":[0.30148277,0.00049115287,0.690221,0.00027893484,0.00004875624,0.00046675056,0.00053133874,0.00019800324,0.0062812315],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992119,0.0004943625,0.000031925338,0.00008097467,0.00011448329,0.00006630279],"domain_scores_gemma":[0.9987954,0.00090068945,0.00009220306,0.000035483095,0.00012371878,0.000052576623],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015209304,0.0012407765,0.00091946253,0.0015685053,0.00063571293,0.0015645415,0.0010987254,0.0012599427,0.0051443945],"category_scores_gemma":[0.0024299787,0.00091186713,0.0007745115,0.0017431232,0.00082536566,0.000995084,0.0008466383,0.00095230964,0.0005535059],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000033756256,0.00003423249,0.00018604119,0.000067365654,0.00001900864,0.00004969245,0.000027961069,0.98596036,0.00046490144,0.0047873976,0.00063244806,0.0077369125],"study_design_scores_gemma":[0.00001285952,0.00002804971,0.000055305176,0.000006631076,0.00000502359,0.000010124017,0.000025874288,0.99673885,0.00016540312,0.0025062403,0.0004407201,0.0000049382083],"about_ca_topic_score_codex":0.010965135,"about_ca_topic_score_gemma":0.020333488,"teacher_disagreement_score":0.010965135,"about_ca_system_score_codex":0.001707969,"about_ca_system_score_gemma":0.0026821666,"threshold_uncertainty_score":0.021802604},"labels":[],"label_agreement":null},{"id":"W7124264926","doi":"10.65109/dhhr9122","title":"Hierarchical Learning-based Graph Partition for Large-scale Vehicle Routing Problems","year":2025,"lang":"","type":"article","venue":"","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Partition (number theory); Graph partition; Partition problem; Frequency partition of a graph; Graph","score_opus":0.014952014769874294,"score_gpt":0.2761065169035638,"score_spread":0.2611545021336895,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7124264926","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.033992637,0.00030307742,0.96252406,0.00030619357,0.000026161852,0.000087664535,0.0000666004,0.0005868091,0.0021068251],"genre_scores_gemma":[0.7566225,0.000305742,0.23831262,0.000260863,0.00007158106,0.0004377714,0.00050824793,0.00026113566,0.0032194802],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995443,0.00015796792,0.000017782733,0.000121850186,0.00007839873,0.000079643956],"domain_scores_gemma":[0.99886024,0.0007112409,0.00011753699,0.00009581497,0.00012665283,0.00008843824],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010047011,0.0011637743,0.0013024204,0.0005783367,0.000558041,0.0007943,0.0017604546,0.0013139803,0.0023442584],"category_scores_gemma":[0.003474329,0.0007886273,0.0007553246,0.0006066808,0.0011267397,0.0015753801,0.0018624194,0.0019670397,0.0003341661],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016725937,0.000020357667,0.00017745228,0.000028336774,0.000008601847,0.000017677134,0.000021247262,0.98607373,0.00037452125,0.0036545338,0.00036983137,0.009237051],"study_design_scores_gemma":[0.0000043428763,0.000008756393,0.000023912307,0.0000018267643,0.0000015550963,0.0000021704786,0.000003882922,0.9969613,0.00009473253,0.0028227733,0.00007360691,0.0000011077572],"about_ca_topic_score_codex":0.007519885,"about_ca_topic_score_gemma":0.009228391,"teacher_disagreement_score":0.007519885,"about_ca_system_score_codex":0.0013254471,"about_ca_system_score_gemma":0.0016887923,"threshold_uncertainty_score":0.014952183},"labels":[],"label_agreement":null},{"id":"W7125775515","doi":"10.21428/594757db.efbe03e5","title":"SPADE: Solving the Multi-Depot Vehicle Routing Problem with Inter-Depot Routes Using Multi-Agent Deep Reinforcement Learning","year":2025,"lang":"en","type":"article","venue":"","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Reinforcement learning; Pooling; Vehicle routing problem; Graph; ENCODE; Heuristic; Transformer; Routing (electronic design automation)","score_opus":0.026794366581056366,"score_gpt":0.2785219176892571,"score_spread":0.2517275511082007,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7125775515","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0669931,0.0007885349,0.924032,0.000804647,0.00012953275,0.00010572054,0.00031622418,0.0022532486,0.00457694],"genre_scores_gemma":[0.79042697,0.00030559857,0.2014507,0.00063830096,0.00006205818,0.0002048079,0.0009021561,0.00020725907,0.0058021215],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997429,0.00007095041,0.000012747229,0.00006843605,0.00004526195,0.00005963898],"domain_scores_gemma":[0.9992482,0.00047558386,0.00006700239,0.00004682506,0.00010257951,0.00005987246],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00068235653,0.0012306331,0.0011697626,0.00040641654,0.0003054223,0.000756048,0.0015255902,0.0014808275,0.0023799797],"category_scores_gemma":[0.0017897605,0.00065818883,0.0007508637,0.00041747393,0.0006242457,0.0010124549,0.0011242148,0.0020515039,0.00040847354],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000041399,0.00006599177,0.00068417325,0.0000499083,0.00003738729,0.00005574086,0.00002117186,0.9668596,0.0005039219,0.0023615484,0.0017962842,0.027522964],"study_design_scores_gemma":[0.0000055872288,0.000010339933,0.000022228096,0.0000016605967,0.0000023505943,0.0000034020825,0.0000027015265,0.99899715,0.00009061823,0.0007573173,0.00010562696,0.0000010001522],"about_ca_topic_score_codex":0.015853073,"about_ca_topic_score_gemma":0.018732335,"teacher_disagreement_score":0.015853073,"about_ca_system_score_codex":0.0009992718,"about_ca_system_score_gemma":0.001909666,"threshold_uncertainty_score":0.03152162},"labels":[],"label_agreement":null},{"id":"W7126372933","doi":"10.21428/594757db.52f0016b","title":"Edge-DIRECT: A Deep Reinforcement Learning-based Method for Solving Heterogeneous Electric Vehicle Routing Problem with Time Window Constraints","year":2024,"lang":"en","type":"article","venue":"","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Vehicle routing problem; Focus (optics); Electric vehicle; Routing (electronic design automation); Reinforcement learning; Key (lock)","score_opus":0.00910362427285603,"score_gpt":0.24958747370837053,"score_spread":0.2404838494355145,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7126372933","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014419357,0.00075279153,0.9788948,0.00030411466,0.00014080926,0.000049974115,0.00011918708,0.001038867,0.004280052],"genre_scores_gemma":[0.5490816,0.0006576884,0.4313286,0.0010038587,0.00014886618,0.00032271838,0.0007881866,0.00045021396,0.016218333],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99983776,0.00004683432,0.000007718232,0.00003745677,0.00003642552,0.00003367156],"domain_scores_gemma":[0.99956566,0.00026911628,0.000027457449,0.000020147487,0.000078983045,0.000038699833],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00057554955,0.00092901307,0.0009458682,0.0003315039,0.00028616135,0.0005939076,0.0015371661,0.0013755216,0.0045955624],"category_scores_gemma":[0.0012366311,0.00048055252,0.00052128336,0.0003488333,0.0004298585,0.00070163293,0.0009908718,0.0015892348,0.00069999695],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000086281616,0.00008734514,0.00047380052,0.00007564262,0.000052260675,0.00008062873,0.000024431025,0.8964985,0.0011917866,0.004458663,0.004694154,0.09227651],"study_design_scores_gemma":[0.00000596482,0.000011611041,0.000016610775,0.0000028392221,0.0000020564542,0.000004080798,0.0000017901015,0.9987972,0.000106392996,0.0007734979,0.00027660903,0.0000013141151],"about_ca_topic_score_codex":0.010466197,"about_ca_topic_score_gemma":0.014786303,"teacher_disagreement_score":0.010466197,"about_ca_system_score_codex":0.0005975019,"about_ca_system_score_gemma":0.0012546537,"threshold_uncertainty_score":0.020810544},"labels":[],"label_agreement":null},{"id":"W7126417008","doi":"10.21428/594757db.0ea536db","title":"Genetic Algorithm and Loading Strategy for the DynamicVehicle Routing Problem with Simultaneous Pickup and Delivery","year":2024,"lang":"en","type":"article","venue":"","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brock University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Pickup; Vehicle routing problem; Routing (electronic design automation); Scheduling (production processes); Genetic algorithm; Memetic algorithm; Job shop scheduling","score_opus":0.011596546851299691,"score_gpt":0.23999088580733033,"score_spread":0.22839433895603065,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7126417008","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.070792034,0.0005915369,0.9135508,0.0006368312,0.00009880673,0.00020457598,0.00009353076,0.0003314287,0.01370047],"genre_scores_gemma":[0.6955153,0.00060558046,0.2944899,0.00018181281,0.0000567886,0.00044519483,0.00024484642,0.00007576606,0.008384802],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997304,0.00009483805,0.000011147942,0.000048401504,0.00006909255,0.000046034693],"domain_scores_gemma":[0.9997142,0.0001561663,0.000041103372,0.00001500782,0.000046180372,0.00002736639],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005797841,0.0008667789,0.0007053366,0.0009116655,0.0003711973,0.0008974005,0.0010526668,0.0012612315,0.0019812954],"category_scores_gemma":[0.0013179467,0.00035066903,0.0005356797,0.00095119706,0.00051158207,0.00055994705,0.00064474565,0.00069353305,0.00025978588],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000023929364,0.00004117122,0.00027700304,0.000029788247,0.00001844769,0.00007635971,0.000034565517,0.9665413,0.00076479965,0.009122512,0.000784827,0.022285344],"study_design_scores_gemma":[0.0000118239695,0.000031270778,0.000071784125,0.000005817296,0.0000065902314,0.000020524762,0.00001589286,0.9963618,0.00023151237,0.002519216,0.000719761,0.0000040501614],"about_ca_topic_score_codex":0.0044889343,"about_ca_topic_score_gemma":0.0033297704,"teacher_disagreement_score":0.0044889343,"about_ca_system_score_codex":0.00096675014,"about_ca_system_score_gemma":0.0013329592,"threshold_uncertainty_score":0.008925617},"labels":[],"label_agreement":null},{"id":"W7132997671","doi":"","title":"Calibration of the aggregate transit assignment model of EMME/2 using genetic algorithms","year":2003,"lang":"","type":"dissertation","venue":"TSpace","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Bibliographical Society of Canada","funders":"","keywords":"Calibration; Aggregate (composite); Set (abstract data type); Genetic algorithm; Goodness of fit","score_opus":0.03995598545810222,"score_gpt":0.3179381604429164,"score_spread":0.27798217498481415,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7132997671","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14306659,0.00005641425,0.8478465,0.00017576599,0.000026781658,0.00009102719,0.00017034671,0.00089091464,0.0076756496],"genre_scores_gemma":[0.8228699,0.000050790673,0.17345555,0.000067467525,0.000009460911,0.00022616438,0.00028146734,0.00012798814,0.0029111984],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999416,0.00022492518,0.000020141206,0.0001438411,0.00012241607,0.000072674105],"domain_scores_gemma":[0.99875546,0.0006353647,0.00013652277,0.00018269705,0.0002620871,0.000027860138],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002071083,0.0005725488,0.0005782058,0.0006711163,0.00037413414,0.0011412596,0.0009765662,0.0009940881,0.0014994487],"category_scores_gemma":[0.0050368183,0.00041292387,0.0006047281,0.00058303284,0.00048518888,0.00079758186,0.0006202727,0.001202189,0.00032312027],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017458078,0.000012113079,0.00046314657,0.0000067585133,0.0000071295276,0.000007745201,0.0000142245635,0.99201113,0.0003258758,0.0013295092,0.00014322059,0.005661771],"study_design_scores_gemma":[0.0000032301427,0.000008371619,0.0002061798,0.0000025401284,0.0000029543976,0.000004005321,0.000005402614,0.9983365,0.0004175816,0.00083067885,0.00017936871,0.0000031320158],"about_ca_topic_score_codex":0.011952119,"about_ca_topic_score_gemma":0.0074875094,"teacher_disagreement_score":0.011952119,"about_ca_system_score_codex":0.0015794316,"about_ca_system_score_gemma":0.0011383047,"threshold_uncertainty_score":0.023765147},"labels":[],"label_agreement":null},{"id":"W7134867000","doi":"","title":"Group-Consistent Dial-a-Ride solved with Adaptive Large Neighborhood Search,","year":2025,"lang":"en","type":"preprint","venue":"","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Noise (video); Constraint (computer-aided design); Stability (learning theory); Identification (biology); Intersection (aeronautics)","score_opus":0.021749589117367518,"score_gpt":0.2581056141781455,"score_spread":0.23635602506077796,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7134867000","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.028903188,0.0006550114,0.95934963,0.00089519244,0.0002531503,0.00013778449,0.000250461,0.00022959757,0.009326016],"genre_scores_gemma":[0.54064447,0.0003935626,0.43508512,0.00048355662,0.00016903234,0.0006676229,0.00063542835,0.0004897064,0.021431474],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995845,0.00018624733,0.000014489589,0.00009585847,0.00007715177,0.00004178866],"domain_scores_gemma":[0.9979868,0.0014655477,0.00013036473,0.0001230146,0.00019341087,0.00010077774],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014896868,0.001137274,0.002344816,0.0009041452,0.0009562237,0.0011380819,0.0025329296,0.0032077816,0.0046535],"category_scores_gemma":[0.007266038,0.0010970796,0.0007818301,0.0009426932,0.0014763928,0.0019502243,0.002602357,0.0018473617,0.00049221027],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011692215,0.0000545052,0.00036190217,0.00009583702,0.00004694069,0.000050320545,0.00005653236,0.9670963,0.00023574501,0.018108722,0.0031644714,0.010611814],"study_design_scores_gemma":[0.000015956875,0.000009224384,0.000019044754,0.0000028911245,0.0000032573435,0.000003637428,0.000008484021,0.9961824,0.000041661486,0.0034798689,0.00023148922,0.0000020915422],"about_ca_topic_score_codex":0.0154183395,"about_ca_topic_score_gemma":0.011967405,"teacher_disagreement_score":0.0154183395,"about_ca_system_score_codex":0.0011224801,"about_ca_system_score_gemma":0.0019750316,"threshold_uncertainty_score":0.030657232},"labels":[],"label_agreement":null},{"id":"W7151055761","doi":"10.70675/4d1089f4zbdfez444bzb573ze5f15d05bc38","title":"Towards the conception of a Supply Chain efficient and sustainable in the Aeronautic industry - Airbus case study","year":2021,"lang":"","type":"dissertation","venue":"","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Supply chain; Sugar industry; Umwelt; Production system (computer science)","score_opus":0.02057533524170215,"score_gpt":0.3019620660113096,"score_spread":0.28138673076960746,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7151055761","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09209694,0.0014007614,0.8221478,0.0037287911,0.00007771209,0.00055460446,0.00044698946,0.000113844275,0.07943253],"genre_scores_gemma":[0.6020179,0.002084331,0.37524238,0.00024324842,0.000043459495,0.0006444735,0.000411647,0.000096146665,0.019216424],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"qualitative","domain_scores_codex":[0.9981457,0.0009183547,0.00009268404,0.00019921309,0.00045065756,0.00019340054],"domain_scores_gemma":[0.9984067,0.0007924585,0.0001890167,0.0001521584,0.0003454304,0.00011433128],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022593783,0.00077967806,0.00047089194,0.0017947014,0.0016133239,0.006255717,0.0013683399,0.00332314,0.005461481],"category_scores_gemma":[0.0035905442,0.0005546879,0.0013451228,0.0028726961,0.0027751748,0.005212673,0.0029203338,0.0020418707,0.00052625104],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004345584,0.00007720577,0.0015179394,0.00024961217,0.000032653497,0.00091676775,0.0015267554,0.20950706,0.0020833188,0.7685149,0.0010925807,0.014437737],"study_design_scores_gemma":[0.000059983344,0.00020561439,0.001374315,0.0005530151,0.00006585081,0.000629318,0.005920204,0.4589007,0.003955469,0.41147614,0.1167957,0.00006368446],"about_ca_topic_score_codex":0.020424407,"about_ca_topic_score_gemma":0.014881318,"teacher_disagreement_score":0.020424407,"about_ca_system_score_codex":0.0033884642,"about_ca_system_score_gemma":0.004335932,"threshold_uncertainty_score":0.04061103},"labels":[],"label_agreement":null},{"id":"W7164345423","doi":"10.5281/zenodo.20637942","title":"Graph Theory in Daily Life: Using Small-Scale Graphs to Optimize School Bus Routing","year":2015,"lang":"en","type":"article","venue":"Open MIND","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Impact","funders":"","keywords":"Graph theory; Graph; Heuristic; Scalability; Vehicle routing problem; Routing (electronic design automation); Intersection (aeronautics); School bus","score_opus":0.07304150779660866,"score_gpt":0.3158904307719233,"score_spread":0.24284892297531463,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7164345423","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13300824,0.00090705516,0.85460705,0.0014173881,0.00015754359,0.000113732785,0.0002544756,0.00036098386,0.0091735795],"genre_scores_gemma":[0.8529412,0.0007217214,0.1432632,0.00019018525,0.000079466765,0.00010428609,0.0003849085,0.00016174713,0.0021531172],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999602,0.00021176288,0.00000948849,0.000075396216,0.00006262822,0.00003866107],"domain_scores_gemma":[0.9981256,0.0012246981,0.00020147329,0.00012660713,0.00019531167,0.00012639721],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00094284955,0.00088656694,0.00064443296,0.0011136481,0.00052308297,0.0013258663,0.0010136787,0.0007575943,0.001867268],"category_scores_gemma":[0.0042910594,0.0004719986,0.00065279164,0.0011179637,0.0010627279,0.0015904247,0.0009123307,0.00084679853,0.00019280463],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013112804,0.000020824611,0.0006083911,0.000028436789,0.000020696078,0.00002833176,0.000026228397,0.97416884,0.00029993977,0.01667766,0.00088331394,0.007224179],"study_design_scores_gemma":[0.000005397392,0.000016426595,0.0002130227,0.0000060382076,0.0000068654876,0.0000064997844,0.000030928313,0.9765969,0.00010629638,0.022368837,0.00063905714,0.000003766672],"about_ca_topic_score_codex":0.011020257,"about_ca_topic_score_gemma":0.009816225,"teacher_disagreement_score":0.011020257,"about_ca_system_score_codex":0.001618206,"about_ca_system_score_gemma":0.0013253038,"threshold_uncertainty_score":0.021912217},"labels":[],"label_agreement":null},{"id":"W7164389602","doi":"10.5281/zenodo.20637943","title":"Graph Theory in Daily Life: Using Small-Scale Graphs to Optimize School Bus Routing","year":2015,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Impact","funders":"","keywords":"Graph theory; Graph; Heuristic; Scalability; Vehicle routing problem; Routing (electronic design automation); Intersection (aeronautics); School bus","score_opus":0.05789884959224535,"score_gpt":0.26472750839226294,"score_spread":0.2068286588000176,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7164389602","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13300824,0.00090705516,0.85460705,0.0014173881,0.00015754359,0.000113732785,0.0002544756,0.00036098386,0.0091735795],"genre_scores_gemma":[0.8529412,0.0007217214,0.1432632,0.00019018525,0.000079466765,0.00010428609,0.0003849085,0.00016174713,0.0021531172],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999602,0.00021176288,0.00000948849,0.000075396216,0.00006262822,0.00003866107],"domain_scores_gemma":[0.9981256,0.0012246981,0.00020147329,0.00012660713,0.00019531167,0.00012639721],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00094284955,0.00088656694,0.00064443296,0.0011136481,0.00052308297,0.0013258663,0.0010136787,0.0007575943,0.001867268],"category_scores_gemma":[0.0042910594,0.0004719986,0.00065279164,0.0011179637,0.0010627279,0.0015904247,0.0009123307,0.00084679853,0.00019280463],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013112804,0.000020824611,0.0006083911,0.000028436789,0.000020696078,0.00002833176,0.000026228397,0.97416884,0.00029993977,0.01667766,0.00088331394,0.007224179],"study_design_scores_gemma":[0.000005397392,0.000016426595,0.0002130227,0.0000060382076,0.0000068654876,0.0000064997844,0.000030928313,0.9765969,0.00010629638,0.022368837,0.00063905714,0.000003766672],"about_ca_topic_score_codex":0.011020257,"about_ca_topic_score_gemma":0.009816225,"teacher_disagreement_score":0.011020257,"about_ca_system_score_codex":0.001618206,"about_ca_system_score_gemma":0.0013253038,"threshold_uncertainty_score":0.021912217},"labels":[],"label_agreement":null},{"id":"W767970607","doi":"10.1007/s00170-015-7420-8","title":"Meta-heuristic solution approaches for robust single allocation p-hub median problem with stochastic demands and travel times","year":2015,"lang":"en","type":"article","venue":"The International Journal of Advanced Manufacturing Technology","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":23,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Mathematical optimization; Heuristic; Meta heuristic; Computer science; Operations research; Mathematics","score_opus":0.06360452788071073,"score_gpt":0.25358489125543643,"score_spread":0.18998036337472568,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W767970607","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021685397,0.001326132,0.9703208,0.00025503666,0.00011489615,0.0001218374,0.00012487642,0.00018495975,0.0058659497],"genre_scores_gemma":[0.5363111,0.0012051926,0.45664468,0.00018169859,0.00016555341,0.00052317715,0.00025662527,0.00017087006,0.004541124],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99934465,0.0002964565,0.000034578046,0.00010855595,0.00012154826,0.000094306495],"domain_scores_gemma":[0.9988136,0.0008133293,0.00013383252,0.0000529849,0.00013194953,0.000054279328],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002003138,0.0016720273,0.0020460978,0.0023240712,0.000684247,0.0018692644,0.0024528424,0.002388219,0.0033195838],"category_scores_gemma":[0.003138359,0.0013467625,0.0021332977,0.002323663,0.0006958459,0.0012853189,0.0012049593,0.0015931922,0.00034695695],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000024942854,0.000025285946,0.000075216245,0.000040280014,0.000040615338,0.000019351073,0.000011514759,0.98963296,0.00014231045,0.002909726,0.00025003363,0.0068276688],"study_design_scores_gemma":[0.0000066285183,0.000015902793,0.000024262396,0.000007205997,0.000009967043,0.0000050631284,0.0000083844925,0.9978492,0.000059244656,0.0018361928,0.00017482226,0.0000029935998],"about_ca_topic_score_codex":0.006824311,"about_ca_topic_score_gemma":0.006119822,"teacher_disagreement_score":0.006824311,"about_ca_system_score_codex":0.0015461772,"about_ca_system_score_gemma":0.0018804478,"threshold_uncertainty_score":0.013569176},"labels":[],"label_agreement":null},{"id":"W834673113","doi":"10.1016/j.asoc.2015.06.035","title":"Customer satisfaction in dynamic vehicle routing problem with time windows","year":2015,"lang":"en","type":"article","venue":"Applied Soft Computing","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":50,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Defence Research and Development Canada","funders":"","keywords":"Computer science; Customer satisfaction; Vehicle routing problem; Mathematical optimization; Genetic algorithm; Scheduling (production processes); Plan (archaeology); Routing (electronic design automation); Operations research; Machine learning; Engineering; Embedded system; Marketing; Mathematics","score_opus":0.009919678541106057,"score_gpt":0.2320866207892608,"score_spread":0.22216694224815473,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W834673113","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.23192017,0.0013913189,0.75509924,0.0019350447,0.00027838466,0.00012377524,0.00039300747,0.00011778694,0.008741366],"genre_scores_gemma":[0.9617635,0.00064374675,0.029766599,0.00017233248,0.00018939372,0.000107558815,0.00028084536,0.0000636669,0.0070123617],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986823,0.0005470472,0.00004581597,0.00023664912,0.00021318998,0.00027488248],"domain_scores_gemma":[0.99758136,0.0016899708,0.00021308416,0.00005558802,0.00025936577,0.00020052791],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002001678,0.00092014484,0.0018637966,0.00079206575,0.00051446137,0.0021841822,0.0020290038,0.001805682,0.0032882735],"category_scores_gemma":[0.0043688756,0.0007044466,0.0009889064,0.0017857731,0.00077674026,0.0021136426,0.0009884904,0.0014187733,0.00018610845],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003057882,0.00016854904,0.0020455455,0.00018905748,0.00010818582,0.00021960163,0.00006631797,0.95436525,0.00072757626,0.01978536,0.0023193175,0.019699335],"study_design_scores_gemma":[0.000008230208,0.000036987105,0.00026386985,0.0000049635714,0.000012595131,0.000018374756,0.000033978584,0.99603647,0.00011261271,0.0033078599,0.00015903033,0.0000050397075],"about_ca_topic_score_codex":0.0073844106,"about_ca_topic_score_gemma":0.0034355938,"teacher_disagreement_score":0.0073844106,"about_ca_system_score_codex":0.0015372407,"about_ca_system_score_gemma":0.001374394,"threshold_uncertainty_score":0.014682889},"labels":[],"label_agreement":null},{"id":"W913363275","doi":"10.1016/j.trc.2015.06.019","title":"An adaptive large neighborhood search heuristic for fleet deployment problems with voyage separation requirements","year":2015,"lang":"en","type":"article","venue":"Transportation Research Part C Emerging Technologies","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":26,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Luonnontieteiden ja Tekniikan Tutkimuksen Toimikunta; Norges Forskningsråd","keywords":"Heuristic; Computer science; Scheduling (production processes); Schedule; Computation; Mathematical optimization; Software deployment; Separation (statistics); Vehicle routing problem; Operations research; Routing (electronic design automation); Real-time computing; Algorithm; Artificial intelligence; Engineering; Mathematics; Machine learning; Computer network","score_opus":0.14532489720753686,"score_gpt":0.41015516414556025,"score_spread":0.26483026693802336,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W913363275","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07068131,0.0009489777,0.92106825,0.00039011115,0.00018735998,0.00023199656,0.00011755074,0.00043544892,0.0059389733],"genre_scores_gemma":[0.56455255,0.00039159798,0.42989284,0.00024323128,0.000098280696,0.0004870212,0.000278482,0.00016003013,0.003896062],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99954283,0.00021651029,0.00002372429,0.00006256911,0.000091065645,0.000063387364],"domain_scores_gemma":[0.9977441,0.0017408739,0.00014232282,0.000077907236,0.00018840063,0.0001064079],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015151565,0.00074294384,0.0014866911,0.0011797975,0.0005771049,0.00068619626,0.0020906227,0.0017175301,0.0024499926],"category_scores_gemma":[0.004139883,0.00071943563,0.00081338326,0.0010097625,0.00062697823,0.0013485684,0.0010870473,0.00091234676,0.0002735096],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000111643836,0.00009174328,0.00029502955,0.00004854161,0.00003170572,0.00004150539,0.000032539854,0.968812,0.000473794,0.0030626243,0.001082769,0.02591617],"study_design_scores_gemma":[0.000017052766,0.000025354253,0.00003660649,0.000004300908,0.000005442828,0.0000056513454,0.000006785463,0.9989423,0.00005432563,0.0007441089,0.00015560168,0.0000025061506],"about_ca_topic_score_codex":0.0074319285,"about_ca_topic_score_gemma":0.008674071,"teacher_disagreement_score":0.0074319285,"about_ca_system_score_codex":0.00097247073,"about_ca_system_score_gemma":0.0013365186,"threshold_uncertainty_score":0.014777303},"labels":[],"label_agreement":null}]}