{"meta":{"query_hash":"38b768d4d788","filters":{"venue":"Computational Management Science"},"cohort_total":29,"direct_labels_cover":0,"predictions_cover":29,"exported":29,"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/38b768d4d788","api":"https://metacan.xera.ac/api/v1/cohort?venue=Computational+Management+Science"},"results":[{"id":"W1979517372","doi":"10.1007/s10287-014-0204-z","title":"The impact of customer behavior models on revenue management systems","year":2014,"lang":"en","type":"article","venue":"Computational Management Science","topic":"Supply Chain and Inventory Management","field":"Business, Management and Accounting","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":"Revenue management; Revenue; Consumer behaviour; Business; Yield management; Computer science; Operations research; Marketing; Process management; Finance; Engineering","score_opus":0.026862843458537372,"score_gpt":0.27047905963204705,"score_spread":0.24361621617350968,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1979517372","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14903535,0.00010176733,0.039203584,0.0006814948,0.002878025,0.0032668167,0.000012231041,0.00024840256,0.80457234],"genre_scores_gemma":[0.9967499,0.000013982769,0.0003968972,0.00036821415,0.00022456661,0.00016431103,0.000012747878,0.000020271134,0.0020491148],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9973959,0.000024523793,0.00042111232,0.00049617887,0.001208183,0.00045412726],"domain_scores_gemma":[0.9988342,0.00007174772,0.00032195894,0.00054247415,0.00019843865,0.00003116575],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015677495,0.00023051954,0.00018535816,0.00064482016,0.0007014627,0.0005621464,0.0011319922,0.000022622558,0.0000317124],"category_scores_gemma":[0.000021411519,0.00016561804,0.00014012394,0.0012449294,0.00040248514,0.0008814438,0.0005562037,0.000085864456,0.00036782768],"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":[0.000016786353,0.00010643771,0.0006478873,0.000081567836,0.00003783396,0.0000038412786,0.000010409495,0.36550677,0.000003537804,0.6150645,0.009771055,0.008749374],"study_design_scores_gemma":[0.0008287594,0.000058774425,0.080064215,0.00015037275,0.0001033394,0.0000010646132,0.00031457836,0.83078325,0.0000025001184,0.047117997,0.04018998,0.00038518634],"about_ca_topic_score_codex":0.00014183306,"about_ca_topic_score_gemma":0.0000022588058,"teacher_disagreement_score":0.84771454,"about_ca_system_score_codex":0.00016277409,"about_ca_system_score_gemma":0.000012859444,"threshold_uncertainty_score":0.6753708},"labels":[],"label_agreement":null},{"id":"W1985476692","doi":"10.1007/s10287-007-0048-x","title":"The secondary benefits of climate change mitigation: an overlapping generations approach","year":2007,"lang":"en","type":"article","venue":"Computational Management Science","topic":"Climate Change Policy and Economics","field":"Economics, Econometrics and Finance","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":"University of Alberta; HEC Montréal","funders":"HEC Montréal","keywords":"Climate change; Natural resource economics; Welfare; Political economy of climate change; Greenhouse gas; Economics; Environmental science; Term (time); Climate change mitigation; Ecology","score_opus":0.1034868502983564,"score_gpt":0.2772514031355133,"score_spread":0.17376455283715686,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1985476692","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.62748545,0.0018654926,0.07646664,0.0017949326,0.0015622121,0.0009048259,0.00033720915,0.000080425445,0.28950283],"genre_scores_gemma":[0.98383427,0.00037947134,0.014782409,0.0005796657,0.000258589,0.000033283566,0.00006274955,0.000009435443,0.00006011693],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.9988079,0.0000050357303,0.00046462912,0.00032699623,0.00007698796,0.00031844253],"domain_scores_gemma":[0.9993136,0.00006200926,0.00027519494,0.00022880021,0.000047936184,0.0000724917],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017386783,0.00008744002,0.00012141599,0.00027190358,0.0007030015,0.00014848402,0.00038682052,0.000023050688,0.00002730712],"category_scores_gemma":[0.000017997385,0.000091514754,0.000040674266,0.0004828773,0.00026813822,0.00067486835,0.00015206532,0.000055257555,0.000049923514],"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":[0.0000035982425,0.00003790704,0.003071699,0.00002409846,0.000007810834,2.0008258e-7,0.0004845183,0.0063654548,0.0000018770801,0.976867,0.000031650245,0.013104192],"study_design_scores_gemma":[0.00042140798,0.000049221155,0.6855146,0.000018928447,0.0000064425726,0.0000048347156,0.0009581033,0.20077889,0.000027210759,0.10630113,0.0056267064,0.00029253023],"about_ca_topic_score_codex":0.00001900542,"about_ca_topic_score_gemma":0.000020040054,"teacher_disagreement_score":0.8705659,"about_ca_system_score_codex":0.00008080533,"about_ca_system_score_gemma":0.000009817462,"threshold_uncertainty_score":0.540699},"labels":[],"label_agreement":null},{"id":"W1993513623","doi":"10.1007/s10287-009-0106-7","title":"Mean-variance versus expected utility in dynamic investment analysis","year":2009,"lang":"en","type":"article","venue":"Computational Management Science","topic":"Stochastic processes and financial applications","field":"Economics, Econometrics and Finance","cited_by":29,"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; Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Expected utility hypothesis; Econometrics; Portfolio; Risk aversion (psychology); Modern portfolio theory; Portfolio optimization; Economics; Isoelastic utility; Variance (accounting); Mathematics; Merton's portfolio problem; Probability density function; Expected return; Statistics; Replicating portfolio; Financial economics","score_opus":0.029506584608912183,"score_gpt":0.26577200807857365,"score_spread":0.23626542346966148,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1993513623","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023090951,0.00021824962,0.95115465,0.00061967736,0.00012649255,0.00022685513,0.000018253568,0.000034763823,0.024510138],"genre_scores_gemma":[0.973721,0.000009321992,0.025792217,0.00035345665,0.000009082538,0.00003453979,0.000020305586,0.0000025320537,0.000057514408],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9987312,0.0000023630676,0.00037102433,0.0005474861,0.000112887545,0.00023504025],"domain_scores_gemma":[0.99946845,0.000033289853,0.00015154241,0.00024433067,0.00004263444,0.00005976453],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038475817,0.00009396232,0.00018134456,0.00064360816,0.00016600375,0.000076179065,0.0004088785,0.000019956526,0.000036583766],"category_scores_gemma":[0.00003712754,0.0001156479,0.0000520831,0.003912623,0.00013332342,0.00023577879,0.000068490575,0.000054711076,0.000104005114],"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":[0.000010558381,0.000096188305,0.00068230124,0.0000034502573,0.000019163106,0.0000014012924,0.00013681824,0.018349882,0.0000010261912,0.9788851,0.000009671285,0.0018044472],"study_design_scores_gemma":[0.00024476185,0.000016304279,0.373326,0.0000023084467,0.000007945215,9.232111e-8,0.000037711612,0.20022087,4.2918717e-7,0.4258825,0.00018094725,0.00008012029],"about_ca_topic_score_codex":0.00003909575,"about_ca_topic_score_gemma":0.000027049733,"teacher_disagreement_score":0.95063007,"about_ca_system_score_codex":0.00018962525,"about_ca_system_score_gemma":0.00002411745,"threshold_uncertainty_score":0.47159845},"labels":[],"label_agreement":null},{"id":"W1996662920","doi":"10.1007/s10287-014-0205-y","title":"Imperfect production process with learning and forgetting effects","year":2014,"lang":"en","type":"article","venue":"Computational Management Science","topic":"Supply Chain and Inventory Management","field":"Business, Management and Accounting","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":"Toronto Metropolitan University","funders":"","keywords":"Rework; Production (economics); Forgetting; Computer science; Process (computing); Quality (philosophy); Economic production quantity; Imperfect; Economics; Microeconomics; Psychology; Cognitive psychology","score_opus":0.00530146012676214,"score_gpt":0.2188435595053607,"score_spread":0.21354209937859855,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1996662920","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.85236555,0.000032607673,0.049485147,0.0014283895,0.0005909458,0.0010041314,6.7133165e-8,0.0003823443,0.0947108],"genre_scores_gemma":[0.99625003,0.000001904343,0.0021293997,0.00082827196,0.00030609214,0.00005142863,0.0000056779973,0.000014928169,0.00041223966],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99840856,0.000011820899,0.00014437376,0.0005352541,0.0006002271,0.00029978258],"domain_scores_gemma":[0.9995564,0.000040624665,0.00013570549,0.0001120928,0.0001347675,0.000020358448],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009325186,0.0001530397,0.00011020036,0.00044376758,0.00070759864,0.0004582913,0.00022552005,0.0000127278845,0.000012965884],"category_scores_gemma":[0.00012867292,0.00012998674,0.000017774048,0.0009722884,0.00025166606,0.0014253991,0.00023140339,0.00008340487,0.00005026977],"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":[0.00007009213,0.00017397848,0.1495614,0.0030559967,0.00007068127,0.000015258709,0.00027476257,0.18319191,0.00025821856,0.5137764,0.0010249277,0.14852634],"study_design_scores_gemma":[0.0015155125,0.0001434803,0.20276114,0.00042094948,0.00012706737,0.000007632509,0.0010211016,0.7102814,0.00018498118,0.04556063,0.037146468,0.000829657],"about_ca_topic_score_codex":0.000009065774,"about_ca_topic_score_gemma":0.0000023273624,"teacher_disagreement_score":0.5270895,"about_ca_system_score_codex":0.000033973483,"about_ca_system_score_gemma":0.000008180245,"threshold_uncertainty_score":0.5442348},"labels":[],"label_agreement":null},{"id":"W2002741233","doi":"10.1007/s10287-006-0032-x","title":"Developments in differential game theory and numerical methods: economic and management applications","year":2006,"lang":"en","type":"article","venue":"Computational Management Science","topic":"Economic theories and models","field":"Economics, Econometrics and Finance","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":"Group for Research in Decision Analysis; HEC Montréal","funders":"","keywords":"Differential (mechanical device); Mathematical economics; Class (philosophy); Computer science; Game theory; Differential game; Management science; Mathematical optimization; Economics; Mathematics; Artificial intelligence","score_opus":0.015800361405911727,"score_gpt":0.2623299849482956,"score_spread":0.2465296235423839,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2002741233","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.059944026,0.0003025348,0.88381237,0.00014838866,0.0001404164,0.00045409097,0.000015197669,0.00002355007,0.055159435],"genre_scores_gemma":[0.9266266,0.00008249332,0.07247201,0.0001031114,0.00002146777,0.00010308764,0.000009677325,0.0000075231574,0.00057399494],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9988165,0.000016608523,0.00038237977,0.0005327018,0.000032829084,0.00021898892],"domain_scores_gemma":[0.9996212,0.00006977963,0.00012026916,0.00013072803,0.000006544973,0.00005148882],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00090383494,0.00010826436,0.00016756443,0.0003512241,0.00014011942,0.00016237218,0.00021362395,0.000019198142,0.000029490444],"category_scores_gemma":[0.0000033300475,0.00012932664,0.000018659232,0.00016739292,0.00023654825,0.00024571142,0.00029537937,0.00004149482,0.00005819959],"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":[0.000006311975,0.000029070587,0.0047682044,0.000021472488,0.000012134449,8.325668e-7,0.000047115274,0.006442464,0.0000010488525,0.97712487,0.000016893187,0.011529588],"study_design_scores_gemma":[0.000361915,0.0000057642783,0.2504724,0.0000058522564,0.000003384189,0.0000016209116,0.00005064201,0.020891761,0.0000024144647,0.72104627,0.007014056,0.00014394066],"about_ca_topic_score_codex":0.00002662918,"about_ca_topic_score_gemma":0.000001980642,"teacher_disagreement_score":0.8666826,"about_ca_system_score_codex":0.0001356624,"about_ca_system_score_gemma":0.000007953669,"threshold_uncertainty_score":0.52737874},"labels":[],"label_agreement":null},{"id":"W2014528122","doi":"10.1007/s10287-007-0058-8","title":"Airline network revenue management by multistage stochastic programming","year":2007,"lang":"en","type":"article","venue":"Computational Management Science","topic":"Supply Chain and Inventory Management","field":"Business, Management and Accounting","cited_by":36,"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":"McGill University","keywords":"Revenue; Revenue management; Computer science; Tree (set theory); Operations research; Stochastic programming; Process (computing); Stochastic process; Dynamic programming; Mathematical optimization; Scheme (mathematics); Stochastic modelling; Control (management); Stability (learning theory); Time horizon; Stochastic control; Tree network; Optimal control; Business; Mathematics; Algorithm; Finance; Time complexity; Artificial intelligence","score_opus":0.012220720840706516,"score_gpt":0.24492051456907987,"score_spread":0.23269979372837335,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2014528122","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011982118,0.00028995358,0.8983128,0.0011551104,0.002473417,0.002467997,0.000008040382,0.000570811,0.082739726],"genre_scores_gemma":[0.95544016,0.000008882563,0.03302612,0.0047562625,0.00096447754,0.0001009192,0.00013159518,0.000049518407,0.0055220816],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99605954,0.000009909278,0.00057217205,0.0008906315,0.001416182,0.001051581],"domain_scores_gemma":[0.9989781,0.000055848763,0.0003072106,0.00042471418,0.00016523228,0.000068911286],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00256221,0.00033279453,0.00021799687,0.0007179241,0.0009123025,0.0006691385,0.0010586486,0.000038024704,0.00015187901],"category_scores_gemma":[0.00003162395,0.0003508518,0.000093425086,0.0025361574,0.0004298168,0.0012857552,0.0010093475,0.00014586681,0.0005572541],"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":[0.000069807844,0.00039203363,0.0018799449,0.00046237026,0.00011067576,0.0001529475,0.000051280844,0.15116498,0.00001849673,0.6246209,0.10159919,0.11947735],"study_design_scores_gemma":[0.00225191,0.000042871106,0.041308627,0.00030277163,0.00018430695,0.0000041328476,0.0010097258,0.15145488,0.0000065559175,0.041703258,0.760516,0.0012149723],"about_ca_topic_score_codex":0.000049386614,"about_ca_topic_score_gemma":0.000020115975,"teacher_disagreement_score":0.943458,"about_ca_system_score_codex":0.00021210487,"about_ca_system_score_gemma":0.000011291897,"threshold_uncertainty_score":0.9998943},"labels":[],"label_agreement":null},{"id":"W2016068788","doi":"10.1007/s10287-010-0129-0","title":"Single source single-commodity stochastic network design","year":2011,"lang":"en","type":"article","venue":"Computational Management Science","topic":"Supply Chain and Inventory Management","field":"Business, Management and Accounting","cited_by":27,"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":"Engineering and Physical Sciences Research Council; Natural Sciences and Engineering Research Council of Canada; Norges Forskningsråd; Université de Montréal; Université du Québec à Montréal","keywords":"Computer science; Network planning and design; Commodity; Mathematical optimization; Optimal design; Basis (linear algebra); Econometrics; Economics; Machine learning; Mathematics; Computer network","score_opus":0.08426733483619872,"score_gpt":0.2267178309486608,"score_spread":0.14245049611246208,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2016068788","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0059202258,0.000029076346,0.8978573,0.00024289888,0.0011192738,0.0006489956,5.638982e-7,0.0003332174,0.09384846],"genre_scores_gemma":[0.9601747,5.7037647e-7,0.034972306,0.0034137722,0.00058425247,0.000049073045,0.000015307185,0.000028965442,0.00076110056],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99733526,0.000021822785,0.00036318446,0.00067620596,0.00093585846,0.0006676479],"domain_scores_gemma":[0.9990822,0.000063991836,0.00024643683,0.00038032452,0.00018096894,0.000046051773],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0011675296,0.0002637259,0.0001845668,0.0005349513,0.0007360278,0.0005224044,0.0010630364,0.00003099233,0.0005234703],"category_scores_gemma":[0.000060616334,0.00026848915,0.00007723572,0.0017133354,0.0005045008,0.0016599416,0.000885109,0.00009980309,0.00086574315],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000060380928,0.0006271398,0.000913215,0.00009896124,0.00005130251,0.000023350882,0.00015110728,0.66361684,0.000035807425,0.29072553,0.02225482,0.021441516],"study_design_scores_gemma":[0.001060224,0.00010128731,0.02263778,0.00013772053,0.000136395,0.0000035429237,0.000517913,0.71402544,0.000031246673,0.21435067,0.046038385,0.0009594173],"about_ca_topic_score_codex":0.000043614105,"about_ca_topic_score_gemma":0.00000475424,"teacher_disagreement_score":0.95425445,"about_ca_system_score_codex":0.00014887626,"about_ca_system_score_gemma":0.000017980983,"threshold_uncertainty_score":0.99997675},"labels":[],"label_agreement":null},{"id":"W2023445305","doi":"10.1007/s10287-014-0203-0","title":"Risk and reward of home equity borrowing for investment in Canada, a stochastic analysis","year":2014,"lang":"en","type":"article","venue":"Computational Management Science","topic":"Housing Market and Economics","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Western University","funders":"","keywords":"Economics; Volatility (finance); Equity (law); Stochastic game; Interest rate; Monetary economics; Actuarial science; Business; Financial economics; Microeconomics","score_opus":0.02193808457273074,"score_gpt":0.22853055006289535,"score_spread":0.2065924654901646,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2023445305","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6006488,0.000032291773,0.39213327,0.00010616159,0.00011906077,0.00016116693,0.000029864985,0.0000042500506,0.0067651067],"genre_scores_gemma":[0.99046296,0.000009468488,0.009337109,0.00014675893,0.000007906569,0.0000116485135,0.00000566264,0.000003413205,0.000015088918],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99911785,0.000006882267,0.00033895476,0.00030116897,0.00005601001,0.0001791153],"domain_scores_gemma":[0.9994905,0.00011356397,0.00020557584,0.0001181152,0.000019828192,0.0000524326],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013567269,0.000062658575,0.00021002803,0.00048068888,0.000095147145,0.000042146516,0.00018963478,0.00000942378,0.00000729802],"category_scores_gemma":[0.00008585537,0.000077733246,0.000032109056,0.00059108256,0.00007622612,0.00013644272,0.00014235909,0.000027645647,0.0000019165977],"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":[0.0000065001273,0.000015236203,0.1455774,0.000032228694,0.000038908707,1.867526e-7,0.00008616316,0.5679391,3.0555358e-7,0.28228578,0.000028905499,0.003989278],"study_design_scores_gemma":[0.00017836996,0.000009705592,0.34994382,0.0000040572154,0.000013146089,4.764347e-8,0.000029711062,0.54235214,3.5573134e-7,0.1073174,0.0000889113,0.000062363855],"about_ca_topic_score_codex":0.04793181,"about_ca_topic_score_gemma":0.05201878,"teacher_disagreement_score":0.3898141,"about_ca_system_score_codex":0.00030620967,"about_ca_system_score_gemma":0.00006335691,"threshold_uncertainty_score":0.9652794},"labels":[],"label_agreement":null},{"id":"W2049450365","doi":"10.1007/s10287-007-0041-4","title":"How Crucial is Cooperation in Mitigating World Climate? Analysis with World-MARKAL","year":2007,"lang":"en","type":"article","venue":"Computational Management Science","topic":"Climate Change Policy and Economics","field":"Economics, Econometrics and Finance","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":"McGill University; Group for Research in Decision Analysis; Université du Québec à Montréal","funders":"","keywords":"Damages; Computer science; Work (physics); Payment; Nash equilibrium; Stability (learning theory); Operations research; Economics; sort; Greenhouse gas; Mathematical optimization; Mathematical economics; Mathematics; Engineering","score_opus":0.04873821126700567,"score_gpt":0.26531161308326817,"score_spread":0.2165734018162625,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2049450365","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.77562433,0.00010697356,0.11270885,0.004309594,0.0003323424,0.0004728661,0.00010052463,0.00005594785,0.10628856],"genre_scores_gemma":[0.9840413,0.000015918453,0.013987624,0.0010433294,0.00006941805,0.000013061067,0.00003867901,0.000008783805,0.00078189175],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.99845725,0.0000052319574,0.00044778886,0.00054100115,0.00009706465,0.0004516419],"domain_scores_gemma":[0.9993808,0.00006899771,0.000250369,0.00017478794,0.000042889194,0.00008210806],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014529173,0.00012835217,0.0002384369,0.0024415096,0.00028697992,0.00043576545,0.0002912185,0.000017548897,0.00014633786],"category_scores_gemma":[0.000023909777,0.00014732484,0.00005556659,0.004308574,0.0001901037,0.0007251776,0.00013211434,0.00008042748,0.00008111557],"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":[0.000024133358,0.00005981097,0.31567058,0.00002863821,0.00006320317,0.000008898086,0.00047650834,0.0493639,0.0000022286304,0.6327925,0.00010938094,0.0014002204],"study_design_scores_gemma":[0.0006914551,0.00002757145,0.72942454,0.00003000835,0.000027964938,0.0000011396245,0.00038587427,0.24534564,0.000033746343,0.021177609,0.0024674397,0.00038701846],"about_ca_topic_score_codex":0.00006398535,"about_ca_topic_score_gemma":0.0020355498,"teacher_disagreement_score":0.6116149,"about_ca_system_score_codex":0.00026514655,"about_ca_system_score_gemma":0.000012303376,"threshold_uncertainty_score":0.6007733},"labels":[],"label_agreement":null},{"id":"W2067370571","doi":"10.1007/s10287-008-0071-6","title":"Self-adaptive support vector machines: modelling and experiments","year":2008,"lang":"en","type":"article","venue":"Computational Management Science","topic":"Advanced Algorithms and Applications","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":"Lake Simcoe Region Conservation Authority","funders":"","keywords":"Support vector machine; Mathematical optimization; Kernel (algebra); Computer science; Selection (genetic algorithm); Feature selection; Range (aeronautics); Feature (linguistics); Quadratic equation; Quadratic programming; Optimization problem; Artificial intelligence; Mathematics; Engineering","score_opus":0.021587746583657055,"score_gpt":0.25195398266632035,"score_spread":0.2303662360826633,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2067370571","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.036499333,0.00005556369,0.95667446,0.000022410257,0.00007179501,0.00015284275,0.0000030294611,0.00017791732,0.00634267],"genre_scores_gemma":[0.6803605,0.000036302336,0.31947473,0.000026981044,0.000014978839,0.000023539576,0.000003104445,0.0000053601666,0.00005449745],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993679,0.000001834565,0.00009326194,0.00018547277,0.0002164397,0.00013510906],"domain_scores_gemma":[0.99981374,0.000013244265,0.000014216711,0.000074419506,0.000027130118,0.000057255394],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00004761609,0.00007369656,0.00005171642,0.000080472404,0.00025692457,0.000021944536,0.00012304973,0.000007439044,0.000008239937],"category_scores_gemma":[6.514383e-7,0.00007590711,0.0000102562135,0.00026650538,0.00010614623,0.00024010969,0.000057190748,0.00003344648,0.000023187444],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[5.286951e-7,0.00001575785,0.00003862724,0.000008309449,0.000007076638,0.0000038272055,0.00020771989,0.9736283,0.000019180257,0.024167942,0.000059637616,0.0018431218],"study_design_scores_gemma":[0.000104697654,0.000008499643,0.0025402266,0.0000032730925,0.0000026521273,0.000006435896,0.00003972198,0.992396,0.000058640617,0.004004987,0.0007449415,0.0000899404],"about_ca_topic_score_codex":9.898317e-7,"about_ca_topic_score_gemma":5.2519376e-8,"teacher_disagreement_score":0.6438612,"about_ca_system_score_codex":0.000044366498,"about_ca_system_score_gemma":0.000008816039,"threshold_uncertainty_score":0.30954024},"labels":[],"label_agreement":null},{"id":"W2069223201","doi":"10.1007/s10287-009-0101-z","title":"DrAmpl: a meta solver for optimization problem analysis","year":2009,"lang":"en","type":"article","venue":"Computational Management Science","topic":"Advanced Optimization Algorithms Research","field":"Mathematics","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; Group for Research in Decision Analysis","funders":"","keywords":"Solver; Computer science; Mathematical optimization; Problem solver; Mathematics; Software engineering; Programming language","score_opus":0.08396393811345566,"score_gpt":0.4034731677846484,"score_spread":0.3195092296711928,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2069223201","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000040779913,0.000015459435,0.9924688,0.00094662793,0.000028401671,0.0008620134,0.000007591265,0.00010943544,0.005520928],"genre_scores_gemma":[0.0327841,0.0000059262234,0.96554625,0.00027049298,0.000016878292,0.00009829465,0.000040780054,0.000008701251,0.0012285777],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99796957,0.000024149995,0.00029877658,0.00048052875,0.0009064688,0.00032048445],"domain_scores_gemma":[0.99879104,0.00022336678,0.00013693815,0.00025136425,0.00050218386,0.000095103154],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008666416,0.00013086223,0.00021976802,0.0007914351,0.0004022891,0.00018915888,0.00046368726,0.000020837024,0.00013746454],"category_scores_gemma":[0.00016187209,0.00011848873,0.00014819921,0.0033276903,0.00015231829,0.00054644246,0.00009611344,0.000049275306,0.000007389985],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000005513232,0.00007009064,0.000005855098,0.000013285455,0.000256577,7.184857e-7,0.000036237805,0.74516034,0.0000014198631,0.2521416,0.00026659222,0.0020417415],"study_design_scores_gemma":[0.00021568162,0.000028611788,0.000174065,0.0000025877923,0.00049516856,4.0530819e-7,0.00002084896,0.6896598,0.000006375611,0.30916187,0.00013860341,0.00009602537],"about_ca_topic_score_codex":5.7482714e-7,"about_ca_topic_score_gemma":5.4271993e-7,"teacher_disagreement_score":0.05702027,"about_ca_system_score_codex":0.00011060653,"about_ca_system_score_gemma":0.000046167897,"threshold_uncertainty_score":0.48318303},"labels":[],"label_agreement":null},{"id":"W2085116453","doi":"10.1007/s10287-014-0222-x","title":"The natural hedge of a gas-fired power plant","year":2014,"lang":"en","type":"article","venue":"Computational Management Science","topic":"Electric Power System Optimization","field":"Engineering","cited_by":10,"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":"Lawrence Berkeley National Laboratory; HEC Montréal","keywords":"Hedge; Natural gas prices; Spot contract; Natural gas; Electricity; Economics; Electricity generation; Stochastic programming; Volatility (finance); Electricity price; Microeconomics; Econometrics; Futures contract; Business; Financial economics; Power (physics); Engineering; Waste management; Mathematics","score_opus":0.0033074452193659967,"score_gpt":0.18609909155247228,"score_spread":0.1827916463331063,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2085116453","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.051913403,0.0003262778,0.8888871,0.00028092033,0.0017873439,0.00042433012,0.000003011742,0.00023633355,0.056141295],"genre_scores_gemma":[0.9952259,0.0000064952537,0.0046314034,0.000028850403,0.0000110154315,0.000007187832,0.0000036980618,0.000005180057,0.000080283666],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99915445,0.000014399439,0.00015771811,0.00012140462,0.00038935733,0.0001626579],"domain_scores_gemma":[0.9996444,0.00009843848,0.000038692197,0.00013420051,0.000057330555,0.00002691849],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004486651,0.00006175107,0.00005834695,0.00010869998,0.00014761,0.000055182118,0.00034058522,0.000008286484,0.000002816583],"category_scores_gemma":[0.000029543153,0.00004754059,0.000017482342,0.00056145573,0.000108105916,0.00013098799,0.000051450435,0.00003617379,0.00001577783],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000032953806,0.000008748449,0.00013759265,0.000030202977,0.00001594056,7.239881e-7,0.00009966659,0.8163668,0.00015139165,0.17586614,0.0016646697,0.0056548403],"study_design_scores_gemma":[0.000115205745,0.000013215026,0.009484699,0.00001612747,0.0000031126883,0.0000023844555,0.000016819307,0.9862289,0.000103403014,0.0022454357,0.0017076366,0.00006305537],"about_ca_topic_score_codex":8.4702776e-7,"about_ca_topic_score_gemma":6.4749315e-7,"teacher_disagreement_score":0.94331247,"about_ca_system_score_codex":0.00005105082,"about_ca_system_score_gemma":0.000010569884,"threshold_uncertainty_score":0.1938649},"labels":[],"label_agreement":null},{"id":"W2093165291","doi":"10.1007/s10287-014-0207-9","title":"An integrated approach based on DEA and AHP","year":2014,"lang":"en","type":"article","venue":"Computational Management Science","topic":"Efficiency Analysis Using DEA","field":"Decision Sciences","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":"Laurentian University","funders":"","keywords":"Data envelopment analysis; Analytic hierarchy process; Ranking (information retrieval); Computer science; Set (abstract data type); Measure (data warehouse); Closeness; Parametric statistics; Range (aeronautics); Mathematical optimization; Operations research; Data mining; Mathematics; Statistics; Engineering; Artificial intelligence","score_opus":0.05031956571346831,"score_gpt":0.3570613784667813,"score_spread":0.306741812753313,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2093165291","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06686097,0.0000039216043,0.90532064,0.0006161273,0.00011271982,0.00013318923,0.0000017827389,0.00005815165,0.026892474],"genre_scores_gemma":[0.87529147,2.8074524e-7,0.1228095,0.0017007851,0.000017293014,0.0000058127175,0.0000078518115,0.000004508698,0.00016248602],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9954649,0.00019363766,0.00036998853,0.00096844794,0.00274308,0.0002599267],"domain_scores_gemma":[0.99808747,0.0005823799,0.00014729003,0.0005867812,0.00043582136,0.00016028098],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0069294167,0.00013552711,0.00016008309,0.0010766798,0.0005749599,0.00084068853,0.001324271,0.000021947038,0.000034975328],"category_scores_gemma":[0.0009664479,0.00010039873,0.000040023988,0.0033971206,0.00073604635,0.00047551424,0.00014011227,0.00008287847,0.00012309037],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000066298235,0.000121123776,0.0015242931,0.0000022166944,0.0000022537995,0.0000010454477,0.00004553903,0.8681193,0.000017576467,0.0671813,0.00030908128,0.06266966],"study_design_scores_gemma":[0.00017569278,0.000075531774,0.06302428,0.000007136041,0.0000072788666,8.745748e-7,0.00014566808,0.90843827,0.000010771761,0.025894526,0.0021018002,0.0001181603],"about_ca_topic_score_codex":0.000005007106,"about_ca_topic_score_gemma":0.0000011760089,"teacher_disagreement_score":0.8084305,"about_ca_system_score_codex":0.00005137772,"about_ca_system_score_gemma":0.0000560946,"threshold_uncertainty_score":0.8106779},"labels":[],"label_agreement":null},{"id":"W2139693430","doi":"10.1007/s10287-013-0179-1","title":"An orienteering model for the search and rescue problem","year":2013,"lang":"en","type":"article","venue":"Computational Management Science","topic":"Facility Location and Emergency Management","field":"Business, Management and Accounting","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":"Defence Research and Development Canada","funders":"","keywords":"Orienteering; Search and rescue; Computer science; Operations research; Mathematical optimization; Artificial intelligence; Mathematics","score_opus":0.038186054223472196,"score_gpt":0.272883824737134,"score_spread":0.2346977705136618,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2139693430","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13155323,0.000025818686,0.85677856,0.0050754123,0.00025627238,0.00179771,0.0000015887382,0.00012811531,0.0043832893],"genre_scores_gemma":[0.9794474,0.0000062556455,0.01792745,0.0016072928,0.00008189443,0.00024630746,0.000012440928,0.000008387082,0.00066255825],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986157,0.000003860789,0.000191301,0.0004021324,0.0004992546,0.00028776738],"domain_scores_gemma":[0.9994163,0.000019027291,0.000018538365,0.00023963705,0.00028327387,0.00002321381],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00087553955,0.00010823745,0.00006610653,0.00023650218,0.00081867084,0.0007529925,0.00054400356,0.000011266096,0.000058891328],"category_scores_gemma":[0.000020365836,0.000083258055,0.000023959288,0.0006042708,0.0002404637,0.0019678099,0.00033571714,0.000045001816,0.00011212342],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000002952105,0.000026462623,0.00021381289,0.00008153385,0.000006117808,8.920418e-8,0.000058367295,0.69073117,0.000017736787,0.29653916,0.000620797,0.011701817],"study_design_scores_gemma":[0.00017472805,0.0000058041837,0.027317617,0.000008501063,0.000012495632,1.3262499e-7,0.00038177046,0.9496127,0.0000012947826,0.020949516,0.0014260382,0.00010941235],"about_ca_topic_score_codex":0.00036449294,"about_ca_topic_score_gemma":0.000054557608,"teacher_disagreement_score":0.8478942,"about_ca_system_score_codex":0.00003090311,"about_ca_system_score_gemma":0.000014441087,"threshold_uncertainty_score":0.7261124},"labels":[],"label_agreement":null},{"id":"W2298490962","doi":"10.1007/s10287-016-0252-7","title":"Computational management science special issue on “Robust Optimization and Applications”","year":2016,"lang":"en","type":"article","venue":"Computational Management Science","topic":"Scheduling and Optimization Algorithms","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; Robust optimization; Management science; Mathematical optimization; Operations research; Engineering; Mathematics","score_opus":0.01011924166497953,"score_gpt":0.23185884088266523,"score_spread":0.2217395992176857,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2298490962","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00066379947,0.000015994407,0.9588554,0.00038909167,0.0004442516,0.000436462,0.0000074493764,0.00023290323,0.038954668],"genre_scores_gemma":[0.1499124,0.000081793376,0.8481551,0.00033137348,0.0007124626,0.000107690445,0.000021713016,0.000025089117,0.00065236865],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979295,0.000010301461,0.00025022842,0.00054886803,0.00095139735,0.00030969252],"domain_scores_gemma":[0.99935865,0.000073375086,0.000053469037,0.00020477544,0.00016227538,0.00014742157],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004826199,0.00016405048,0.000098861005,0.0006750429,0.0005630107,0.00025266493,0.00045536342,0.000022486493,0.00014662773],"category_scores_gemma":[0.000014497862,0.0001436926,0.000020323827,0.0015196999,0.0007334496,0.00054616283,0.00015958174,0.00005106309,0.0001870416],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000029470348,0.000026012514,0.000049477032,0.00002132842,0.000009210178,0.0000017606203,0.000025335052,0.869933,0.0000033242584,0.066490635,0.00036012792,0.06307685],"study_design_scores_gemma":[0.000493822,0.000019371166,0.0051080743,0.000048946276,0.00001062054,0.0000037127802,0.00006772059,0.9867517,0.00002628219,0.0050558965,0.002194161,0.00021972867],"about_ca_topic_score_codex":3.183723e-7,"about_ca_topic_score_gemma":9.209915e-8,"teacher_disagreement_score":0.14924861,"about_ca_system_score_codex":0.00021297712,"about_ca_system_score_gemma":0.000027613021,"threshold_uncertainty_score":0.5859614},"labels":[],"label_agreement":null},{"id":"W2616838035","doi":"10.1007/s10287-017-0279-4","title":"Quality evaluation of scenario-tree generation methods for solving stochastic programming problems","year":2017,"lang":"en","type":"article","venue":"Computational Management Science","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Mathematical optimization; Stochastic programming; Tree (set theory); Extension (predicate logic); Quality (philosophy); Focus (optics); Decision maker; Decision tree; Mathematics; Operations research; Data mining","score_opus":0.382108710654722,"score_gpt":0.5502521118101507,"score_spread":0.16814340115542864,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2616838035","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.024542106,0.000048670125,0.9712686,0.00035549718,0.0005889337,0.0012864075,0.0000031623067,0.000019275449,0.0018873548],"genre_scores_gemma":[0.5999206,0.0000030592305,0.39981416,0.000018318993,0.000042217966,0.00008410666,0.000011993101,0.0000038843596,0.00010170802],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99542123,0.00018669729,0.00075183134,0.00063071935,0.0027716814,0.00023782396],"domain_scores_gemma":[0.9952352,0.00049214467,0.0010963924,0.0006450752,0.0024582306,0.00007294532],"candidate_categories":["metaresearch","sts"],"consensus_categories":[],"category_scores_codex":[0.03250589,0.000112951195,0.00019108588,0.00043510066,0.0014780159,0.0010102331,0.001183287,0.000029485642,0.000018889232],"category_scores_gemma":[0.005632904,0.00009643335,0.00007710637,0.00059027894,0.000445728,0.0013117355,0.0002701349,0.000041371786,0.00000812772],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000037610241,0.000025606463,0.00067999115,0.0000050337367,0.000004754318,3.104717e-8,0.00013091305,0.5022354,0.00017328274,0.018012233,0.00005284846,0.47867614],"study_design_scores_gemma":[0.00038870683,0.00002829095,0.0612309,0.000014098079,0.000026101367,4.2584549e-7,0.00009270763,0.8127547,0.000094015995,0.12504812,0.00022591947,0.00009601513],"about_ca_topic_score_codex":0.000017740944,"about_ca_topic_score_gemma":0.00001700633,"teacher_disagreement_score":0.5753784,"about_ca_system_score_codex":0.00009734854,"about_ca_system_score_gemma":0.00019565686,"threshold_uncertainty_score":0.9998219},"labels":[],"label_agreement":null},{"id":"W2727815168","doi":"10.1007/s10287-017-0295-4","title":"A successive linear programming algorithm with non-linear time series for the reservoir management problem","year":2017,"lang":"en","type":"article","venue":"Computational Management Science","topic":"Water resources management and optimization","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":"HEC Montréal; Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Linear programming; Computer science; Schedule; Mathematical optimization; Representation (politics); Variable (mathematics); Convexity; Inflow; Dynamic programming; Stochastic programming; Affine transformation; Series (stratigraphy); Algorithm; Mathematics","score_opus":0.010420526684452527,"score_gpt":0.2390319256512206,"score_spread":0.22861139896676808,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2727815168","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0013176792,0.000022276807,0.98573524,0.0007844515,0.0001549215,0.0019802295,0.000005860398,0.00020032514,0.009799004],"genre_scores_gemma":[0.119999096,0.000029142515,0.8729548,0.00007082673,0.00014737424,0.0005981509,0.00005584767,0.000045122044,0.006099633],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998457,0.0000068187264,0.00019923477,0.00037327353,0.0005786464,0.00038502074],"domain_scores_gemma":[0.9992153,0.00003734624,0.00011473713,0.0004439057,0.00012950959,0.0000592197],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.00046770513,0.00018901854,0.000119683165,0.00018040136,0.0015254983,0.00072931685,0.0011728992,0.000018680515,0.000009597837],"category_scores_gemma":[0.0000075748653,0.00013608187,0.000039372382,0.0003351899,0.0003863973,0.00094291766,0.00041629968,0.000066908266,0.00003840882],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000181029,0.000021047601,0.00008501615,0.00018312603,0.00010068053,0.000011209377,0.00014953296,0.9614013,0.0000015189698,0.0033123922,0.0004359968,0.034280088],"study_design_scores_gemma":[0.00057142955,0.00006114146,0.0026219976,0.00008268293,0.00006273014,0.0000012607655,0.0001427733,0.98080647,0.000029442515,0.0009862978,0.014426383,0.00020741895],"about_ca_topic_score_codex":0.0000044163503,"about_ca_topic_score_gemma":0.000002556881,"teacher_disagreement_score":0.118681416,"about_ca_system_score_codex":0.00006350964,"about_ca_system_score_gemma":0.000008887909,"threshold_uncertainty_score":0.9997744},"labels":[],"label_agreement":null},{"id":"W2757057995","doi":"10.1007/s10287-017-0291-8","title":"Putting a price tag on temperature","year":2017,"lang":"en","type":"article","venue":"Computational Management Science","topic":"Climate Change Policy and Economics","field":"Economics, Econometrics and Finance","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":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mean reversion; Sensitivity (control systems); Seasonality; Hidden Markov model; Ornstein–Uhlenbeck process; Econometrics; Computer science; Set (abstract data type); Process (computing); Component (thermodynamics); Statistics; Stochastic process; Mathematics; Artificial intelligence; Machine learning; Engineering","score_opus":0.08832412362067149,"score_gpt":0.2898558268297608,"score_spread":0.2015317032090893,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2757057995","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.38065177,0.000067357236,0.002546945,0.0075355023,0.0011383714,0.00031272552,0.00007342102,0.000061574814,0.6076123],"genre_scores_gemma":[0.9911099,0.0000254139,0.006307954,0.0013128364,0.00011376989,0.000014101246,0.0000068683225,0.000007922485,0.0011012523],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.998988,0.0000022210254,0.00023367205,0.0004425144,0.00006319265,0.00027043666],"domain_scores_gemma":[0.99918956,0.000029409295,0.00028885974,0.00040026105,0.000021475338,0.00007044587],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0006627374,0.000094276205,0.0001255168,0.00024400988,0.001148521,0.0006730621,0.0007563481,0.000023300123,0.000106717394],"category_scores_gemma":[0.00009320847,0.00011056293,0.000040376453,0.00013156045,0.00021798685,0.0005328249,0.000284829,0.0000714494,0.0011373218],"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":[0.0000032135988,0.000031989017,0.004092262,0.000018514907,0.000008659111,0.0000027599158,0.00016932476,0.004813833,0.0000040149334,0.9887623,0.00052095245,0.0015721607],"study_design_scores_gemma":[0.00050075655,0.000037718382,0.3923893,0.00003916409,0.0000024530655,0.0000022914599,0.00008809825,0.048009023,0.000023937611,0.54366344,0.014962732,0.00028110138],"about_ca_topic_score_codex":0.00001772888,"about_ca_topic_score_gemma":0.000001079002,"teacher_disagreement_score":0.6104581,"about_ca_system_score_codex":0.000118904114,"about_ca_system_score_gemma":0.000008678934,"threshold_uncertainty_score":0.9996404},"labels":[],"label_agreement":null},{"id":"W2921769576","doi":"10.1007/s10287-019-00348-2","title":"Optimized operating rules for short-term hydropower planning in a stochastic environment","year":2019,"lang":"en","type":"article","venue":"Computational Management Science","topic":"Water resources management and optimization","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":"Hydro-Québec; Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Computer science; Hydropower; Term (time); Mathematical optimization; Tabu search; Scale (ratio); Operations research; Algorithm; Mathematics; Engineering","score_opus":0.01125644155609847,"score_gpt":0.22989045528371352,"score_spread":0.21863401372761504,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2921769576","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.27514702,0.000028172728,0.7205721,0.000023106888,0.00014834547,0.0006949322,0.0000017221097,0.00007240279,0.0033122096],"genre_scores_gemma":[0.92702913,0.000002429146,0.07260725,0.000030851956,0.000016440026,0.000066278604,0.000040793388,0.00001452239,0.0001922916],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989542,0.0000064262485,0.00020091745,0.00028171824,0.0003012405,0.00025552197],"domain_scores_gemma":[0.9997809,0.000033869328,0.000020550751,0.000116496165,0.000011443432,0.000036726185],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002705241,0.00011780714,0.000104805586,0.00030443896,0.00009232885,0.00014087617,0.00026178538,0.000014972027,0.0000423763],"category_scores_gemma":[0.0000038465014,0.00012210132,0.000024683039,0.00019970337,0.000050828796,0.00031681525,0.00011979865,0.00004445738,0.000053771833],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000045382103,0.000014431386,0.0010815123,0.000043787124,0.000008122111,0.000001922793,0.00018117536,0.9963298,0.000050935527,0.0014589278,0.000028851207,0.00079604],"study_design_scores_gemma":[0.0005008928,0.00001583196,0.01196438,0.00005442871,0.0000066558027,3.7447174e-7,0.000084877014,0.9865767,0.000013729095,0.0005309161,0.00009935212,0.00015186313],"about_ca_topic_score_codex":4.3907662e-7,"about_ca_topic_score_gemma":6.0884176e-8,"teacher_disagreement_score":0.6518821,"about_ca_system_score_codex":0.00011350357,"about_ca_system_score_gemma":0.0000037907232,"threshold_uncertainty_score":0.49791476},"labels":[],"label_agreement":null},{"id":"W3136517660","doi":"10.1007/s10287-021-00389-6","title":"Some new perspectives for solving 0–1 integer programming problems using Balas method","year":2021,"lang":"en","type":"article","venue":"Computational Management Science","topic":"Optimization and Packing Problems","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":"The Scarborough Hospital; University of Toronto; Toronto Metropolitan University","funders":"","keywords":"Integer programming; Integer (computer science); Simple (philosophy); Point (geometry); Enumeration; Branch and bound; Computer science; Mathematical optimization; State (computer science); Search algorithm; Algorithm; Linear programming; Mathematics; Combinatorics","score_opus":0.03257221656774705,"score_gpt":0.3042496675554734,"score_spread":0.27167745098772633,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3136517660","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00058557035,0.00055076927,0.9966154,0.00016718177,0.00043919735,0.00031912237,0.0000014118765,0.00018450627,0.0011368481],"genre_scores_gemma":[0.106131025,0.000024203915,0.8932328,0.0000788019,0.00011243004,0.000019909952,0.000009695504,0.000018268509,0.00037283963],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989475,0.00001023195,0.00017060852,0.00028133907,0.00029134826,0.00029896217],"domain_scores_gemma":[0.9995504,0.00006675153,0.00003559027,0.00010558448,0.00014960651,0.000092045724],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004249588,0.00010946911,0.00009670138,0.00018563519,0.0002303121,0.0002670387,0.0001700073,0.000019002635,0.00001928324],"category_scores_gemma":[0.00004880256,0.0001198107,0.000044688197,0.0007398344,0.00006186345,0.0006035124,0.00008993689,0.00005764444,0.000005968161],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[6.354397e-7,0.000017881963,0.000059536775,0.00008780434,0.00002080927,8.0555657e-7,0.00074611866,0.9063566,0.00020952924,0.085821375,0.00022302508,0.006455917],"study_design_scores_gemma":[0.00020954818,0.000007844213,0.0002533027,0.000070899856,0.000014206811,0.000005803133,0.00067938113,0.98672915,0.00021071566,0.008829213,0.0028326588,0.0001572789],"about_ca_topic_score_codex":0.0000019839285,"about_ca_topic_score_gemma":4.6886439e-7,"teacher_disagreement_score":0.105545454,"about_ca_system_score_codex":0.00014180609,"about_ca_system_score_gemma":0.00006990793,"threshold_uncertainty_score":0.48857385},"labels":[],"label_agreement":null},{"id":"W3167044988","doi":"10.1007/s10287-021-00402-y","title":"A hybrid dynamic programming - Tabu Search approach for the long-term hydropower scheduling problem","year":2021,"lang":"en","type":"article","venue":"Computational Management Science","topic":"Water resources management and optimization","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":"Hydro-Québec; Polytechnique Montréal","funders":"Mitacs","keywords":"Dynamic programming; Mathematical optimization; Computer science; Stochastic programming; Markov decision process; Hydropower; Hydroelectricity; Scheduling (production processes); Tabu search; Markov process; Engineering; Mathematics","score_opus":0.013508564643246318,"score_gpt":0.24667317466841965,"score_spread":0.23316461002517333,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3167044988","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.026050163,0.0001942823,0.96993685,0.00015141185,0.00013542228,0.00085421815,0.000001911001,0.00017026196,0.0025054761],"genre_scores_gemma":[0.7406344,0.00001934528,0.2585878,0.000044057084,0.000027644426,0.00011416191,0.00009440897,0.000017382818,0.0004607944],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986066,0.0000119145,0.00017965284,0.0003516615,0.0004976768,0.00035248653],"domain_scores_gemma":[0.9995805,0.0000406517,0.000027691967,0.00019498309,0.00010708285,0.000049068418],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000453152,0.00012559854,0.00008291072,0.00016030975,0.00043194453,0.0004916809,0.0004415746,0.000011939543,0.000012943357],"category_scores_gemma":[0.0000072043176,0.00010659892,0.00004804785,0.00065443595,0.00015206027,0.0003077711,0.00023423464,0.00007757995,0.000010839044],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000002634643,0.000033136774,0.0003274132,0.00022818905,0.000041020798,0.000006262451,0.00010054407,0.97372085,0.00002026713,0.003048115,0.000035879304,0.022435693],"study_design_scores_gemma":[0.0002463589,0.000009606983,0.0035414218,0.000026409392,0.000026909915,0.0000042355127,0.00009468782,0.9949683,0.00007523202,0.00049786316,0.00036898744,0.00013997663],"about_ca_topic_score_codex":5.8548807e-7,"about_ca_topic_score_gemma":5.076414e-7,"teacher_disagreement_score":0.71458423,"about_ca_system_score_codex":0.00009473089,"about_ca_system_score_gemma":0.00001716863,"threshold_uncertainty_score":0.47412905},"labels":[],"label_agreement":null},{"id":"W3176704067","doi":"10.1007/s10287-023-00446-2","title":"Problem-driven scenario clustering in stochastic optimization","year":2023,"lang":"en","type":"article","venue":"Computational Management Science","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"National Circus School; Group for Research in Decision Analysis; Université du Québec à Montréal; McGill University","funders":"","keywords":"Cluster analysis; Computer science; Mathematical optimization; Reduction (mathematics); Stochastic programming; Partition (number theory); Stochastic optimization; Optimization problem; Monte Carlo method; Set (abstract data type); Constrained clustering; Algorithm; Mathematics; Fuzzy clustering; Machine learning; Canopy clustering algorithm","score_opus":0.0645203776832574,"score_gpt":0.36194446728550067,"score_spread":0.29742408960224326,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3176704067","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011904576,0.0000059652943,0.97776884,0.0009775191,0.0003400545,0.00041912447,0.0000022082295,0.00011285108,0.008468837],"genre_scores_gemma":[0.903107,0.00002095472,0.09549755,0.00010902756,0.000020211104,0.00003177613,0.000023227196,0.000008202099,0.0011820795],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99663097,0.00005267913,0.0005039535,0.0006075343,0.0018770559,0.00032780768],"domain_scores_gemma":[0.9990736,0.0001895398,0.00015788079,0.0002577047,0.0002362424,0.00008499558],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025222872,0.00010741265,0.000127564,0.0017951991,0.00036046634,0.00046021576,0.0008313148,0.000023542394,0.000063282605],"category_scores_gemma":[0.00025823675,0.00009679911,0.000031804135,0.0075555793,0.00019817424,0.0008843657,0.0004320559,0.000064795706,0.00049279426],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000005300029,0.0000180992,0.0021316148,0.000002085154,0.000001538919,0.000008118505,0.00031178925,0.97493565,0.000002350048,0.0063303635,0.00049232185,0.015760787],"study_design_scores_gemma":[0.0002329604,0.000013704861,0.03883224,0.000019608628,0.0000021295111,0.0000017559196,0.00028745408,0.9260705,5.0997863e-7,0.034247283,0.000185497,0.00010633009],"about_ca_topic_score_codex":0.0000069534653,"about_ca_topic_score_gemma":0.000010356734,"teacher_disagreement_score":0.8912024,"about_ca_system_score_codex":0.00009774999,"about_ca_system_score_gemma":0.00007496695,"threshold_uncertainty_score":0.6334038},"labels":[],"label_agreement":null},{"id":"W4238940982","doi":"10.1007/s10287-013-0195-1","title":"Preface","year":2013,"lang":"en","type":"article","venue":"Computational Management Science","topic":"History and Theory of Mathematics","field":"Mathematics","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":"Group for Research in Decision Analysis; HEC Montréal","funders":"","keywords":"Computer science","score_opus":0.03815913685972984,"score_gpt":0.29787589496678407,"score_spread":0.25971675810705425,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4238940982","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.30120152,0.000015026267,0.33657286,0.0005558887,0.00031320393,0.00070984254,9.882344e-7,0.00023170172,0.36039898],"genre_scores_gemma":[0.77750254,5.4434616e-7,0.21643242,0.00016687924,0.000016315966,0.000030512905,6.8766946e-7,0.0000061516944,0.0058439285],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99899185,0.000016602495,0.00015367572,0.00019818016,0.00046689974,0.00017278448],"domain_scores_gemma":[0.99944085,0.00015148206,0.00006269837,0.00019317218,0.00008447002,0.000067354726],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0005122948,0.000074111755,0.00007281453,0.00012274037,0.0002740289,0.00007516049,0.00039838644,0.0000105078425,0.00055377645],"category_scores_gemma":[0.00008434156,0.000066495326,0.000024367242,0.0003064553,0.00030339055,0.0003559782,0.00012485024,0.000040110743,0.0011332397],"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":[5.9592486e-7,0.00005546291,0.000017896431,0.0000471861,0.0000057031984,0.0000013430601,0.00023943579,0.0007651878,0.000025177585,0.9901552,0.0057900217,0.002896769],"study_design_scores_gemma":[0.00009833206,0.000009920597,0.0017364182,0.000017370277,0.0000058621927,0.0000027397875,0.000118274904,0.012236538,0.000026949012,0.9835492,0.0021107728,0.00008764207],"about_ca_topic_score_codex":7.975472e-7,"about_ca_topic_score_gemma":1.1713523e-7,"teacher_disagreement_score":0.47630104,"about_ca_system_score_codex":0.000053523923,"about_ca_system_score_gemma":0.000017171955,"threshold_uncertainty_score":0.9996445},"labels":[],"label_agreement":null},{"id":"W4322735896","doi":"10.1007/s10287-023-00445-3","title":"Using Lagrangian relaxation to locate hydrogen production facilities under uncertain demand: a case study from Norway","year":2023,"lang":"en","type":"article","venue":"Computational Management Science","topic":"Facility Location and Emergency Management","field":"Business, Management and Accounting","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","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Lagrangian relaxation; Production (economics); Lagrangian; Relaxation (psychology); Hydrogen production; Mathematical optimization; Computer science; Operations research; Hydrogen; Operations management; Economics; Mathematics; Microeconomics; Applied mathematics; Physics; Psychology","score_opus":0.09276055454817002,"score_gpt":0.3033178572490533,"score_spread":0.2105573027008833,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4322735896","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.95824426,0.0000081389635,0.036016382,0.0017418216,0.00082491426,0.0013583925,0.0000061518294,0.00030943952,0.0014905093],"genre_scores_gemma":[0.9961143,0.0000017498008,0.001265764,0.0007345705,0.00020599186,0.000095205236,0.00006354466,0.000016002856,0.0015028348],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99720806,0.000029436138,0.0004298336,0.0008726933,0.0010410206,0.0004189755],"domain_scores_gemma":[0.9991476,0.000024111132,0.000104202445,0.00038925256,0.0002909928,0.000043828833],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0013989036,0.00022412378,0.00015879894,0.0012542611,0.0010068001,0.00039586646,0.00043482686,0.00002344088,0.00015394809],"category_scores_gemma":[0.00009259017,0.00023213423,0.000051720624,0.0043965518,0.00014071667,0.0015486364,0.00055853504,0.000070361755,0.0019980571],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014527182,0.00013289871,0.0028665753,0.000077680284,0.00004418801,0.00009752174,0.00084136165,0.9856118,0.00009566015,0.006385413,0.00078334956,0.0030490125],"study_design_scores_gemma":[0.00056365243,0.000026625878,0.05983284,0.000046648467,0.00010721568,0.00000822686,0.03303245,0.88787085,0.000017532308,0.014763178,0.0032003003,0.0005304593],"about_ca_topic_score_codex":0.005565571,"about_ca_topic_score_gemma":0.0011570101,"teacher_disagreement_score":0.09774094,"about_ca_system_score_codex":0.00019967815,"about_ca_system_score_gemma":0.000032762895,"threshold_uncertainty_score":0.998779},"labels":[],"label_agreement":null},{"id":"W4380791774","doi":"10.1007/s10287-023-00464-0","title":"Robust selective maintenance optimization of series–parallel mission-critical systems subject to maintenance quality uncertainty","year":2023,"lang":"en","type":"article","venue":"Computational Management Science","topic":"Reliability and Maintenance Optimization","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":"Dalhousie University","funders":"","keywords":"Preventive maintenance; Computer science; Mathematical optimization; Quality (philosophy); Benchmark (surveying); Robust optimization; Reliability (semiconductor); Operations research; Decision maker; Reliability engineering; Optimal maintenance; Mathematics; Engineering","score_opus":0.025675144523067066,"score_gpt":0.27676620739907565,"score_spread":0.2510910628760086,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4380791774","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005593803,0.000019841467,0.9863519,0.00073928165,0.0007129565,0.0006776132,0.000025660966,0.00037876682,0.0055001886],"genre_scores_gemma":[0.89822257,0.00006173747,0.10082086,0.000083012084,0.00003158933,0.00007051749,0.00004189786,0.000021417974,0.00064641755],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977158,0.00005930185,0.00050903234,0.000502496,0.00071576115,0.0004976472],"domain_scores_gemma":[0.99868757,0.00018979484,0.00007578813,0.00028112368,0.0006107536,0.00015500275],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012706446,0.0001849996,0.00024348906,0.0003841356,0.00023976572,0.00010401196,0.000443657,0.000045175926,0.000014652258],"category_scores_gemma":[0.0005469023,0.00018001646,0.000052924435,0.0029617324,0.00037257827,0.0005559553,0.00015257025,0.00009976355,0.000046004443],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003464446,0.000023896286,0.00008080337,0.0002406195,0.000012477081,0.0000037993136,0.0001640108,0.92542875,0.00006232584,0.07075943,0.0028900676,0.00029920248],"study_design_scores_gemma":[0.00023528244,0.000046468966,0.0048321583,0.00016850024,0.000007942322,0.000003603712,0.00046662136,0.99008423,0.000032274045,0.0035556138,0.00034925426,0.00021803463],"about_ca_topic_score_codex":0.000026230404,"about_ca_topic_score_gemma":0.0000028427257,"teacher_disagreement_score":0.8926287,"about_ca_system_score_codex":0.00033394154,"about_ca_system_score_gemma":0.00006859181,"threshold_uncertainty_score":0.73408586},"labels":[],"label_agreement":null},{"id":"W4382312876","doi":"10.1007/s10287-023-00455-1","title":"Flexible supply meets flexible demand: prosumer impact on strategic hydro operations","year":2023,"lang":"en","type":"article","venue":"Computational Management Science","topic":"Electric Power System Optimization","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":"Division of Civil, Mechanical and Manufacturing Innovation; HEC Montréal; Energimyndigheten; Stockholms Universitet; National Science Foundation","keywords":"Arbitrage; Computer science; Cournot competition; Electricity; Flexibility (engineering); Environmental economics; Economics; Microeconomics; Finance; Electrical engineering","score_opus":0.01823420784807351,"score_gpt":0.27793774082819955,"score_spread":0.25970353298012605,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4382312876","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.39261478,0.00016990815,0.48443767,0.0008084843,0.0015599472,0.00214088,0.00003866921,0.004000491,0.11422916],"genre_scores_gemma":[0.9942435,0.000021505659,0.0044524865,0.00005781663,0.000032948214,0.00007770177,0.0000786403,0.000021507485,0.0010139205],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99831516,0.000020470832,0.00023977808,0.00034802817,0.00067089166,0.000405693],"domain_scores_gemma":[0.99952954,0.00004605909,0.000024936124,0.00021899489,0.00007807388,0.000102369886],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004889149,0.0001680046,0.000118323616,0.00072269235,0.00032538248,0.00028445723,0.0003735221,0.000026548189,0.00005936256],"category_scores_gemma":[0.000010079342,0.00015588185,0.000041773434,0.002997412,0.000072694886,0.0004915812,0.000065723776,0.000075901546,0.0007142454],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000027287156,0.000018453016,0.00015071403,0.0000285999,0.000021601929,0.0000055441765,0.000059951282,0.96586883,0.00019799372,0.031244703,0.0019339147,0.0004669622],"study_design_scores_gemma":[0.00021654644,0.000053988573,0.012196283,0.000036445952,0.000008559067,0.0000034547338,0.000030861367,0.98299706,0.00044783312,0.003706417,0.00012452624,0.00017802193],"about_ca_topic_score_codex":0.000006429401,"about_ca_topic_score_gemma":0.00000137815,"teacher_disagreement_score":0.6016287,"about_ca_system_score_codex":0.00023670887,"about_ca_system_score_gemma":0.00006509335,"threshold_uncertainty_score":0.9180418},"labels":[],"label_agreement":null},{"id":"W4382812750","doi":"10.1007/s10287-023-00456-0","title":"Optimal allocation of demand response considering transmission system congestion","year":2023,"lang":"en","type":"article","venue":"Computational Management Science","topic":"Smart Grid Energy Management","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","keywords":"Demand response; Computer science; Scheduling (production processes); News aggregator; Grid; Operations research; Mathematical optimization; Smart grid; Electricity","score_opus":0.013457165162797562,"score_gpt":0.23107656561281498,"score_spread":0.2176194004500174,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4382812750","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.46567336,0.00002514148,0.5307769,0.00012852234,0.00035967777,0.00020182696,0.0000013450627,0.0005146651,0.0023185587],"genre_scores_gemma":[0.9827035,0.00001427436,0.017127283,0.00001054732,0.000015532205,0.000026616706,0.000011006533,0.000011524442,0.000079719815],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99879104,0.000030211682,0.00024120176,0.00022118069,0.00051961123,0.00019678388],"domain_scores_gemma":[0.99959165,0.000100930796,0.000038135393,0.00014689188,0.00006531738,0.000057102876],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010464452,0.00009670139,0.00009472794,0.00053550774,0.00012676141,0.000041992487,0.00021728704,0.000017872302,0.0000066594],"category_scores_gemma":[0.000017034787,0.00010556206,0.000024770141,0.0011740602,0.00012671325,0.00023358442,0.000079291865,0.000035519526,0.000051166913],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019894795,0.000005902318,0.000072204806,0.00019006775,0.000016600521,0.00000942685,0.00009524918,0.979173,0.0019972704,0.016198851,0.0003381702,0.0018833448],"study_design_scores_gemma":[0.00021942909,0.000016846581,0.067987114,0.00010444559,0.000012276785,0.0000025307181,0.00021377958,0.92928946,0.0011681003,0.00018580038,0.0007000944,0.000100114645],"about_ca_topic_score_codex":0.0000012751347,"about_ca_topic_score_gemma":1.1900435e-7,"teacher_disagreement_score":0.5170301,"about_ca_system_score_codex":0.00013660274,"about_ca_system_score_gemma":0.000017172004,"threshold_uncertainty_score":0.43046957},"labels":[],"label_agreement":null},{"id":"W4396803172","doi":"10.1007/s10287-024-00517-y","title":"Hybrid simplicial-randomized approximate stochastic dynamic programming for multireservoir optimization","year":2024,"lang":"en","type":"article","venue":"Computational Management Science","topic":"Water resources management and optimization","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; Laurentian University","funders":"","keywords":"Mathematical optimization; Dynamic programming; Stochastic programming; Stochastic optimization; Computer science; Mathematics","score_opus":0.00790951913908501,"score_gpt":0.24033955756559336,"score_spread":0.23243003842650836,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396803172","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0020411804,0.0001313424,0.99362475,0.00013099155,0.00049435336,0.0017618316,0.0000070812184,0.00077987113,0.0010285744],"genre_scores_gemma":[0.7672343,0.000012760492,0.2318605,0.000029151583,0.00004124944,0.0003853356,0.00016205861,0.000033277203,0.00024133717],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986445,0.0000137385805,0.000277389,0.00036731415,0.00038334238,0.00031376773],"domain_scores_gemma":[0.99960417,0.00011648102,0.000032178024,0.00012607721,0.000065246226,0.00005583245],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000610124,0.00016302738,0.00016299562,0.0005026178,0.00022552846,0.0005989162,0.00030624607,0.000015596932,0.000011630763],"category_scores_gemma":[0.00003060424,0.00015526405,0.000076069235,0.0006110066,0.00017549373,0.000514089,0.00010399841,0.000050053335,0.000015995316],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008510063,0.000012359547,6.346224e-7,0.00033303682,0.000045356715,0.0000025475085,0.00009836913,0.9723298,0.0000036488893,0.012488509,0.00011773865,0.0144829275],"study_design_scores_gemma":[0.0031943442,0.0000107315345,0.000013055439,0.00007565084,0.00005388153,0.0000011659782,0.000023956882,0.989008,0.0000062513673,0.0070107584,0.0004214199,0.00018076613],"about_ca_topic_score_codex":0.0000010455029,"about_ca_topic_score_gemma":2.9514274e-7,"teacher_disagreement_score":0.76519316,"about_ca_system_score_codex":0.00012793308,"about_ca_system_score_gemma":0.0000106185225,"threshold_uncertainty_score":0.6331484},"labels":[],"label_agreement":null},{"id":"W4412060508","doi":"10.1007/s10287-025-00536-3","title":"A novel regime-switching commodity pricing model with stochastic convenience yield","year":2025,"lang":"en","type":"article","venue":"Computational Management Science","topic":"Market Dynamics and Volatility","field":"Economics, Econometrics and Finance","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 Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Convenience yield; Yield (engineering); Commodity; Econometrics; Economics; Mathematical economics; Microeconomics; Computer science; Mathematics; Financial economics; Spot contract; Futures contract; Finance","score_opus":0.03215042535505997,"score_gpt":0.2417158921781468,"score_spread":0.20956546682308683,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412060508","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.048817147,0.000028019986,0.9143968,0.00063978916,0.0001813233,0.00025722026,0.00002009332,0.000035149933,0.035624444],"genre_scores_gemma":[0.9480245,0.0000016708947,0.050808396,0.0004966378,0.000009256489,0.000017961565,0.0000045875113,0.0000047761887,0.00063217885],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987049,0.000003801903,0.00033014276,0.0005634499,0.00013386054,0.00026385294],"domain_scores_gemma":[0.99929374,0.00010962131,0.00018239854,0.00028802166,0.00006757924,0.0000586519],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010361781,0.00011939191,0.00017949096,0.00037094072,0.000421788,0.00015103145,0.00048432202,0.000021022739,0.000014781086],"category_scores_gemma":[0.00008617942,0.000126395,0.0000324961,0.0009304708,0.00021316392,0.0003649375,0.00026966995,0.00011395436,0.000010030712],"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":[0.0000097126285,0.00004881709,0.004796696,0.000029803683,0.000010994309,5.641237e-7,0.0000620614,0.405722,0.0000041469943,0.58882755,0.000051911135,0.00043577346],"study_design_scores_gemma":[0.00023018706,0.000011898394,0.029585244,0.000053324053,0.0000042575457,8.8281735e-7,0.000028114902,0.84960526,5.947165e-7,0.12028726,0.000071198636,0.00012178856],"about_ca_topic_score_codex":0.000050007166,"about_ca_topic_score_gemma":0.000009919166,"teacher_disagreement_score":0.8992074,"about_ca_system_score_codex":0.00015358956,"about_ca_system_score_gemma":0.00006230586,"threshold_uncertainty_score":0.51542383},"labels":[],"label_agreement":null}]}