{"meta":{"query_hash":"472b00b69566","filters":{"topic":"Risk and Portfolio Optimization"},"cohort_total":710,"direct_labels_cover":2,"predictions_cover":710,"exported":710,"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/472b00b69566","api":"https://metacan.xera.ac/api/v1/cohort?topic=Risk+and+Portfolio+Optimization"},"results":[{"id":"W1170742967","doi":"10.2139/ssrn.2436392","title":"General Extremal Dependence Concepts","year":2014,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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 Waterloo","funders":"","keywords":"Econometrics; Mathematical economics; Mathematics; Economics","score_opus":0.030817444304755956,"score_gpt":0.3515407070682912,"score_spread":0.32072326276353524,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1170742967","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":[0.40130585,0.0012014215,0.5818707,0.0008649211,0.0007638845,0.00008228365,0.0000010853472,0.0000364058,0.013873459],"genre_scores_gemma":[0.98159456,0.0025448964,0.001109574,0.00021698195,0.0007424262,0.0000017579214,0.0000015280265,0.0000132348905,0.013775062],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99600875,0.00033973626,0.0005444165,0.000307487,0.001158476,0.0016411375],"domain_scores_gemma":[0.99867374,0.0002311203,0.0003352352,0.00032529613,0.0002880034,0.00014659311],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00774085,0.00013889473,0.0002136412,0.00019333775,0.0003148997,0.00028414882,0.0007643604,0.00008506814,0.00026722768],"category_scores_gemma":[0.0009897318,0.000099196186,0.00013945291,0.00041927234,0.000066157496,0.00049786223,0.000049517712,0.00096044404,0.00040069208],"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":[0.00005198707,0.000037822872,0.025967356,2.9516318e-7,0.000032073003,0.0000048964366,0.000196051,0.006614857,0.00035823096,0.41840255,0.0022873967,0.5460465],"study_design_scores_gemma":[0.000706081,0.0002987979,0.004213888,0.000004925987,0.000017371298,0.0008969665,0.00061093783,0.020949611,0.00019625122,0.91615283,0.055698294,0.00025405438],"about_ca_topic_score_codex":0.000031877287,"about_ca_topic_score_gemma":0.00035898012,"teacher_disagreement_score":0.58076113,"about_ca_system_score_codex":0.00019682596,"about_ca_system_score_gemma":0.00104596,"threshold_uncertainty_score":0.515022},"labels":[],"label_agreement":null},{"id":"W12258319","doi":"10.1007/0-387-25118-9_12","title":"Continuous Min-Max Approach for Single Period Portfolio Selection Problem","year":2005,"lang":"en","type":"book-chapter","venue":"","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Group for Research in Decision Analysis; HEC Montréal","funders":"","keywords":"Portfolio; Selection (genetic algorithm); Benchmark (surveying); Mathematical optimization; Range (aeronautics); Portfolio optimization; Period (music); Computer science; Investment strategy; Upper and lower bounds; Mathematics; Economics; Engineering; Artificial intelligence; Financial economics; Microeconomics","score_opus":0.06665413155380374,"score_gpt":0.3098101498718629,"score_spread":0.24315601831805916,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W12258319","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000014224793,0.0003178306,0.17411824,0.00013514982,0.00026339627,0.0012854165,0.00004707798,0.00015111583,0.8236675],"genre_scores_gemma":[0.0018617184,0.00020171808,0.10385167,0.00017504944,0.000812048,0.000073971685,0.0002551381,0.0000914713,0.8926772],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99570787,0.00003284102,0.0013566862,0.0011530143,0.0013180238,0.00043154747],"domain_scores_gemma":[0.9969561,0.000201735,0.0009676068,0.0005964716,0.0010929798,0.00018512113],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0013222,0.0005124356,0.0008371867,0.0006744188,0.0002786804,0.0005785004,0.00057708495,0.00069751486,0.0036069038],"category_scores_gemma":[0.00019354139,0.00039739523,0.0004735772,0.00020811592,0.0001045372,0.00038509458,0.00007779852,0.0002688743,0.000314967],"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":[0.00020002718,0.00026138802,0.00022038053,0.000024207851,0.00015370682,0.000008545414,0.00042807986,0.007068156,0.0001325863,0.13933088,0.39588666,0.4562854],"study_design_scores_gemma":[0.00042478504,0.00027595917,0.0000086320315,0.000016962751,0.00008948262,0.0001110228,0.00008149553,0.007987204,0.00010392238,0.018334117,0.9719971,0.0005693516],"about_ca_topic_score_codex":0.000015989222,"about_ca_topic_score_gemma":0.00004058327,"teacher_disagreement_score":0.5761104,"about_ca_system_score_codex":0.00012562296,"about_ca_system_score_gemma":0.00021340884,"threshold_uncertainty_score":0.99984777},"labels":[],"label_agreement":null},{"id":"W1502977466","doi":"10.1080/10920277.2015.1085752","title":"Response to Hans U. Gerber on His Comments on Our Paper Entitled ”Empirical Approach for Optimal Reinsurance Design”","year":2015,"lang":"en","type":"article","venue":"North American Actuarial Journal","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Reinsurance; Mathematical economics; Economics; Actuarial science; Computer science","score_opus":0.1907872206343384,"score_gpt":0.4184764891522577,"score_spread":0.22768926851791932,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1502977466","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":[0.37524384,0.000007015235,0.614477,0.00712403,0.0013593722,0.0006397631,0.000057965422,0.000036388836,0.0010546263],"genre_scores_gemma":[0.8692357,0.000025868407,0.12095313,0.007190732,0.0011129235,0.000044943114,0.000016723678,0.00004404805,0.0013759321],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99518883,0.0011121505,0.00084095466,0.0005671806,0.0017430058,0.0005478962],"domain_scores_gemma":[0.99634075,0.0009807982,0.00064506737,0.00055161567,0.0006273141,0.0008544693],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004370273,0.0002936425,0.00054483063,0.0005045532,0.00035690368,0.0005182504,0.00079552893,0.000078243844,0.000093226125],"category_scores_gemma":[0.0048954487,0.0002084907,0.00023180494,0.0010322019,0.00008107664,0.00035238653,0.00006308571,0.00040330293,0.00024995577],"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":[0.03578481,0.00047955886,0.012947916,5.234103e-7,0.00006710424,0.00003072028,0.0017460531,0.32578903,0.00010264667,0.000010319462,0.53499234,0.08804897],"study_design_scores_gemma":[0.01009629,0.014692175,0.08834306,0.00003286132,0.00011004401,0.00020502134,0.0027942194,0.035801955,0.0005300868,0.00037604274,0.8456181,0.0014001391],"about_ca_topic_score_codex":0.000032492593,"about_ca_topic_score_gemma":0.000011168375,"teacher_disagreement_score":0.49399185,"about_ca_system_score_codex":0.00024303999,"about_ca_system_score_gemma":0.00046475607,"threshold_uncertainty_score":0.8502004},"labels":[],"label_agreement":null},{"id":"W1513121068","doi":"10.34989/swp-2002-11","title":"Risk, Entropy, and the Transformation of Distributions","year":2021,"lang":"en","type":"preprint","venue":"RePEc: Research Papers in Economics","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Bank of Canada; University of Waterloo","funders":"","keywords":"Transformation (genetics); Entropy (arrow of time); Statistical physics; Mathematics; Econometrics; Physics; Thermodynamics; Chemistry","score_opus":0.05051044225566792,"score_gpt":0.3687231150913928,"score_spread":0.31821267283572485,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1513121068","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":[0.95618016,0.0005820517,0.0024355352,0.0015962857,0.00044831983,0.0009939631,0.0003660558,0.000012518909,0.03738511],"genre_scores_gemma":[0.91537875,0.08358591,0.00047229978,0.000010410623,0.00004638449,0.00006305666,0.00010729871,0.000008780261,0.00032710933],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99655193,0.0011373208,0.0010149741,0.00046963303,0.0005545305,0.00027159846],"domain_scores_gemma":[0.99576753,0.0025046514,0.00041784652,0.000852568,0.00037566232,0.00008171355],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008913432,0.00013461027,0.00044452492,0.00033507356,0.00022222185,0.00036530165,0.0006110932,0.00021853283,0.00009620399],"category_scores_gemma":[0.0038820454,0.0000935835,0.00017761377,0.00030674145,0.0006367016,0.00018097083,0.0004622828,0.0009011918,0.0000030713088],"study_design_candidate":"design_other","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":[0.0002820702,0.00011190901,0.02340874,0.000029745977,0.000089885725,0.0000041400344,0.00529349,0.097355306,0.000016820868,0.013452675,0.0001336365,0.85982156],"study_design_scores_gemma":[0.0048617143,0.00012096573,0.12585057,0.00023297333,0.00008466119,0.000026772628,0.017393349,0.657492,0.000826545,0.13816394,0.0542239,0.00072264054],"about_ca_topic_score_codex":0.00023189544,"about_ca_topic_score_gemma":0.00038930192,"teacher_disagreement_score":0.8590989,"about_ca_system_score_codex":0.00012664008,"about_ca_system_score_gemma":0.00036902702,"threshold_uncertainty_score":0.46474513},"labels":[],"label_agreement":null},{"id":"W1544795972","doi":"10.66573/001c.141988","title":"Bounds for Probabilities of Extreme Events Defined by Two Random Variables","year":2009,"lang":"en","type":"article","venue":"Variance","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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 New Brunswick; University of Manitoba","funders":"","keywords":"Consistency (knowledge bases); Random variable; Mathematics; Extreme value theory; Tail risk; Volatility (finance); Event (particle physics); Econometrics; Mathematical optimization; Applied mathematics; Statistics; Discrete mathematics","score_opus":0.07222635242455985,"score_gpt":0.3448634654160929,"score_spread":0.27263711299153304,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1544795972","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":[0.033443756,0.00084249896,0.9519777,0.0013926126,0.0006169775,0.0008265751,0.00015083032,0.00005606364,0.010692956],"genre_scores_gemma":[0.921457,0.00017114816,0.0670363,0.00024837116,0.000103134866,0.00004584124,0.000030675026,0.000010562541,0.010896945],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99822736,0.00009956684,0.00061277737,0.0003259302,0.00052276603,0.00021160168],"domain_scores_gemma":[0.99803704,0.000839652,0.00031094387,0.0004177122,0.0003394606,0.000055193374],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001708278,0.00011637482,0.00030720176,0.00009041127,0.00011310918,0.00007774779,0.00039173992,0.000059128277,0.00012757287],"category_scores_gemma":[0.0019084573,0.00008858995,0.000099118464,0.00047605517,0.000044482425,0.00033051096,0.000017457452,0.00003992097,0.0000153145],"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":[0.0029644468,0.001512137,0.027120467,0.000044293374,0.00011215581,0.000002776933,0.0028352267,0.044035114,0.0133174425,0.39671838,0.30183798,0.20949958],"study_design_scores_gemma":[0.0042988104,0.00024848233,0.0030792807,0.000031790914,0.000027330158,0.0000032067655,0.000065498905,0.022697078,0.0017766211,0.85524833,0.11228529,0.000238283],"about_ca_topic_score_codex":0.000036818594,"about_ca_topic_score_gemma":0.0000072466064,"teacher_disagreement_score":0.88801324,"about_ca_system_score_codex":0.000016928543,"about_ca_system_score_gemma":0.00009104096,"threshold_uncertainty_score":0.36125934},"labels":[],"label_agreement":null},{"id":"W1554675403","doi":"10.1504/ijferm.2014.065649","title":"Computational dynamic market risk measures in discrete time setting","year":2014,"lang":"en","type":"article","venue":"International Journal of Financial Engineering and Risk Management","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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; University of Calgary","funders":"","keywords":"Recursion (computer science); Dynamic programming; Computer science; Representation (politics); Market risk; Econometrics; Mathematical optimization; Mathematical economics; Risk analysis (engineering); Economics; Mathematics; Algorithm; Business","score_opus":0.005056137826602153,"score_gpt":0.25905087121375764,"score_spread":0.2539947333871555,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1554675403","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":[0.32299238,0.0003215874,0.6732988,0.0003291357,0.0010902355,0.00009330561,0.000023335137,0.000015790127,0.0018354659],"genre_scores_gemma":[0.972826,0.0034899928,0.023014471,0.00003334426,0.00018475213,0.0000018692822,0.0000036877143,0.000009798364,0.00043606778],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980241,0.00010391787,0.0006558439,0.00015952563,0.0009337664,0.00012281559],"domain_scores_gemma":[0.9987878,0.0003372113,0.00048097468,0.00008581554,0.0002504452,0.00005771167],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0030861397,0.000106590225,0.00018434772,0.0006219811,0.000052716005,0.00014755064,0.00035145943,0.000038242313,0.000021855418],"category_scores_gemma":[0.0012349314,0.00008459637,0.00007883353,0.0002026952,0.00001984951,0.00023172655,0.00007567484,0.00019004098,0.000010405718],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000040786657,0.00001439838,0.011367929,0.0000012422678,0.000033239012,0.000017540948,0.00008702487,0.70790607,0.0000017855157,0.00083296944,0.00069964316,0.27899736],"study_design_scores_gemma":[0.0005213682,0.00003242223,0.25538963,0.000048348098,0.00001823149,0.000016870428,0.000016293156,0.7120702,0.000001758599,0.006997879,0.024800014,0.000086945576],"about_ca_topic_score_codex":0.00001818341,"about_ca_topic_score_gemma":0.000008114284,"teacher_disagreement_score":0.6502843,"about_ca_system_score_codex":0.000052474075,"about_ca_system_score_gemma":0.00001857669,"threshold_uncertainty_score":0.344974},"labels":[],"label_agreement":null},{"id":"W1559247248","doi":"10.1007/978-3-540-74450-4_35","title":"Robust Optimization Model for a Class of Uncertain Linear Programs","year":2007,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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 New Brunswick","funders":"","keywords":"Robustness (evolution); Computer science; Robust optimization; Mathematical optimization; Probabilistic logic; Class (philosophy); Optimization problem; Linear programming; Contrast (vision); Algorithm; Mathematics; Artificial intelligence","score_opus":0.15522962164068388,"score_gpt":0.35903669729226834,"score_spread":0.20380707565158446,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1559247248","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":[0.000020553878,0.00013465386,0.99583673,0.00021122182,0.0006395368,0.0008508399,0.000019674519,0.00004165023,0.0022451123],"genre_scores_gemma":[0.008672663,0.00012183715,0.988871,0.00031561634,0.00028838796,0.000012619087,0.000043564527,0.000036686135,0.0016376377],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99531543,0.000026447993,0.0011428864,0.0011645773,0.0018856005,0.00046505962],"domain_scores_gemma":[0.9958173,0.000883011,0.00080923864,0.0009048048,0.0014501758,0.00013545353],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.003637387,0.00036217488,0.0006003635,0.0012669223,0.00017329887,0.00024623607,0.001607948,0.0004422115,0.000024073384],"category_scores_gemma":[0.0006804076,0.00028402236,0.0002023683,0.0010906339,0.00067394006,0.00037306626,0.00026143657,0.00032168717,0.000006171158],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000022034665,0.000018273991,0.000040396444,0.000006320888,0.0000029906055,0.0000018698264,0.00024242468,0.68663955,0.0000018694657,0.0014232977,0.00001907614,0.31158188],"study_design_scores_gemma":[0.00023728202,0.00014775443,0.0000028054953,0.00010365089,0.000011109267,0.00000579115,5.3463975e-7,0.92922133,0.00006280853,0.06914036,0.0007839042,0.0002826699],"about_ca_topic_score_codex":0.00001428126,"about_ca_topic_score_gemma":0.000102799546,"teacher_disagreement_score":0.3112992,"about_ca_system_score_codex":0.00012992542,"about_ca_system_score_gemma":0.0006080978,"threshold_uncertainty_score":0.9999612},"labels":[],"label_agreement":null},{"id":"W1561159387","doi":"","title":"Mean-Absolute Deviation Portfolio Models with Discrete Choice Constraints","year":2011,"lang":"en","type":"article","venue":"Algorithmic operations research","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"University of Toronto","funders":"","keywords":"Portfolio; Variance (accounting); Mathematical optimization; Absolute deviation; Solver; Linear programming; Heuristic; Set (abstract data type); Portfolio optimization; Mathematics; Integer programming; Computer science; Statistics; Economics; Finance","score_opus":0.3088138248982664,"score_gpt":0.4443298865650969,"score_spread":0.1355160616668305,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1561159387","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":[0.08651361,0.00021481194,0.785979,0.0006928138,0.00030645702,0.0014881984,0.00009755445,0.00013453873,0.12457302],"genre_scores_gemma":[0.9363512,0.00038283927,0.054469265,0.00007713692,0.0001842906,0.00015213805,0.00007011098,0.00003400031,0.008279038],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99447966,0.0006377643,0.0007732821,0.00077938306,0.0026938184,0.00063608564],"domain_scores_gemma":[0.9954754,0.00047972682,0.00008907389,0.0009909967,0.0026687626,0.0002960467],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.004912045,0.00020889919,0.00028346948,0.0009649521,0.0009523029,0.0006363167,0.0008922774,0.00014123821,0.0018917532],"category_scores_gemma":[0.00085360545,0.00014786243,0.00007504993,0.0025965248,0.0005972556,0.0020235851,0.00016436186,0.0004522125,0.00071422243],"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":[0.00028466512,0.0008215372,0.013086325,0.00001152159,0.00031304779,0.00017986464,0.021021763,0.2823193,0.0005248394,0.26577517,0.018440941,0.397221],"study_design_scores_gemma":[0.00101003,0.00038256714,0.01330167,0.00003218747,0.000027169526,0.00006737224,0.0024635748,0.9597749,0.00068674935,0.01595834,0.005822242,0.00047317997],"about_ca_topic_score_codex":0.0016429506,"about_ca_topic_score_gemma":0.0014111333,"teacher_disagreement_score":0.84983754,"about_ca_system_score_codex":0.00009371855,"about_ca_system_score_gemma":0.0006314411,"threshold_uncertainty_score":0.99902064},"labels":[],"label_agreement":null},{"id":"W1576094778","doi":"","title":"Worst-Case Copulas, Mass Transportation and Wrong-Way Risk in Counterparty Credit Risk Management","year":2013,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Credit risk; Actuarial science; Credit valuation adjustment; Financial risk management; Risk management; Portfolio; Business; Valuation (finance); Counterparty; Risk analysis (engineering); Economics; Finance; Credit reference","score_opus":0.01270370945254358,"score_gpt":0.2802055445542024,"score_spread":0.2675018351016588,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1576094778","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":[0.8589609,0.0014494287,0.13780127,0.00021442816,0.0003106833,0.00034881834,0.000019695528,0.000020565763,0.00087417866],"genre_scores_gemma":[0.95722353,0.040053505,0.0012364239,0.000037913996,0.00011812902,0.00001867236,0.000009499154,0.000017641934,0.001284693],"study_design_codex":"observational","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99644667,0.00027515,0.0008553349,0.0003948419,0.00079697074,0.0012310433],"domain_scores_gemma":[0.9986089,0.00017641312,0.0006048644,0.00027958225,0.00018092585,0.000149341],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0052365786,0.00019527854,0.00026382916,0.00043590064,0.00032703485,0.00035338543,0.00029850312,0.00009939681,0.00022572327],"category_scores_gemma":[0.00014078207,0.00014883668,0.00009397835,0.0005883352,0.00006088641,0.0008968319,0.000013682454,0.0011542358,0.00013028258],"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":[0.00014854586,0.00010995068,0.5976977,0.000003959356,0.00014296385,0.00023339846,0.0009977171,0.013454347,0.000008492098,0.014332798,0.002647077,0.37022305],"study_design_scores_gemma":[0.0023705643,0.00033636915,0.19407268,0.000028237162,0.00013840465,0.0008119312,0.009160827,0.024466416,0.000011811264,0.7612769,0.0069036735,0.00042220368],"about_ca_topic_score_codex":0.0009610526,"about_ca_topic_score_gemma":0.0072761998,"teacher_disagreement_score":0.74694407,"about_ca_system_score_codex":0.00028759896,"about_ca_system_score_gemma":0.00019670857,"threshold_uncertainty_score":0.60693836},"labels":[],"label_agreement":null},{"id":"W1581988586","doi":"10.1007/3-540-44860-8_10","title":"Dynamic Mean Semi-variance Portfolio Selection","year":2003,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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 Calgary","funders":"","keywords":"Efficient frontier; Portfolio optimization; Portfolio; Variance (accounting); Downside risk; Selection (genetic algorithm); Computer science; Mathematical optimization; Modern portfolio theory; Optimization problem; Mathematics; Economics; Artificial intelligence; Finance","score_opus":0.0261651060344557,"score_gpt":0.30451822766717684,"score_spread":0.27835312163272113,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1581988586","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":[0.00020009065,0.00042646888,0.97350454,0.00046125788,0.002723341,0.00036654578,0.000009310441,0.00008910673,0.022219349],"genre_scores_gemma":[0.34857392,0.0024171802,0.5897702,0.0077115903,0.0012654691,0.000030550083,0.00006151696,0.00024028898,0.049929272],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99352753,0.00009275183,0.0010981024,0.001957224,0.0026660697,0.00065833074],"domain_scores_gemma":[0.99638265,0.0006386526,0.00076143787,0.0012257139,0.0007717448,0.00021978436],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0032469463,0.0005503479,0.0006773202,0.001679698,0.00040447654,0.00083908293,0.0019239198,0.0005009442,0.00044389995],"category_scores_gemma":[0.00058768934,0.00045790488,0.00019134568,0.002029528,0.0006127981,0.0006884995,0.0002876464,0.00081100606,0.00022439875],"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":[0.0000111430345,0.000021242722,0.00041476765,0.000003760203,0.000008418878,0.000037198595,0.0002935987,0.34249026,0.000059081915,0.0031261628,0.0005874778,0.6529469],"study_design_scores_gemma":[0.00021855552,0.00011386834,0.0002931608,0.00010398754,0.000015166937,0.0001580027,4.74776e-7,0.6209656,0.00026540333,0.34676716,0.030415254,0.0006833403],"about_ca_topic_score_codex":0.000026937214,"about_ca_topic_score_gemma":0.000292945,"teacher_disagreement_score":0.6522636,"about_ca_system_score_codex":0.00037880585,"about_ca_system_score_gemma":0.00063215493,"threshold_uncertainty_score":0.9997873},"labels":[],"label_agreement":null},{"id":"W1583855298","doi":"","title":"Heuristic Methods in Finance","year":2011,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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; Selection (genetic algorithm); Portfolio; Portfolio optimization; Econometric model; Computational finance; Computer science; Economics; Econometrics; Estimation; Finance; Mathematical optimization; Financial economics; Actuarial science; Mathematics; Artificial intelligence; Management","score_opus":0.09319381800899128,"score_gpt":0.41838131264731093,"score_spread":0.32518749463831964,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1583855298","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":[0.105122685,0.003709856,0.8659229,0.00021458021,0.0005530337,0.00009637397,7.3241875e-7,0.00001776826,0.02436204],"genre_scores_gemma":[0.94874525,0.012816078,0.03262796,0.00007426786,0.000099847864,0.0000032531018,4.3429438e-7,0.000012469722,0.0056204614],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9970561,0.00041755367,0.0005850254,0.0002294078,0.00043288586,0.0012789866],"domain_scores_gemma":[0.99906397,0.00022220446,0.00027853422,0.00025749774,0.00013736988,0.000040411716],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.011330164,0.00010131034,0.00019815947,0.00033748083,0.00010799448,0.00006502133,0.00057079893,0.00006430906,0.00017408121],"category_scores_gemma":[0.001094017,0.00007432368,0.000085684755,0.00080356706,0.000039628383,0.00031862533,0.000034515753,0.0009926308,0.00013973557],"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":[0.00007053697,0.00007680136,0.026251262,2.9924462e-7,0.000014455752,0.000015880762,0.00080210826,0.0007343387,0.000038202455,0.30294532,0.00038832382,0.6686625],"study_design_scores_gemma":[0.00026606763,0.00012347465,0.012188567,0.000004217675,0.000005437034,0.00022054322,0.0005864176,0.0018347179,0.00010138299,0.97689515,0.00766909,0.000104907274],"about_ca_topic_score_codex":0.000071175855,"about_ca_topic_score_gemma":0.0004331695,"teacher_disagreement_score":0.84362257,"about_ca_system_score_codex":0.00020497231,"about_ca_system_score_gemma":0.0010110517,"threshold_uncertainty_score":0.43125424},"labels":[],"label_agreement":null},{"id":"W1584018694","doi":"10.1002/9780470061602.eqf19011","title":"Risk Measures: Statistical Estimation","year":2010,"lang":"en","type":"other","venue":"Encyclopedia of Quantitative Finance","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Solvency; Econometrics; Value at risk; Actuarial science; Estimator; Estimation; Economics; Computer science; Mathematics; Risk management; Statistics; Finance","score_opus":0.046297555054737136,"score_gpt":0.37384423995310195,"score_spread":0.3275466848983648,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1584018694","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00016357345,0.0009844534,0.43668225,0.000038167553,0.0010303939,0.00029168342,0.0009329422,0.00005106582,0.5598255],"genre_scores_gemma":[0.0010506959,0.046221815,0.40909496,0.000019904148,0.0001861272,0.000035723686,0.00013058468,0.00022620815,0.543034],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99633,0.0003603296,0.0009437624,0.000662831,0.0014484103,0.00025462985],"domain_scores_gemma":[0.9951185,0.0017160656,0.0019275337,0.00077158475,0.00039221588,0.00007408117],"candidate_categories":["metaresearch","metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0015977839,0.0003183584,0.0007116515,0.0005709876,0.00008328647,0.000052739586,0.0006558309,0.00040730071,0.0014405494],"category_scores_gemma":[0.011410113,0.0002537036,0.00012181414,0.0007393917,0.0003790623,0.00017502475,0.0000624395,0.00048518728,0.0010704837],"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":[0.00003335647,0.00006878652,0.0019266603,0.000007157217,0.000024922527,0.000006593529,0.00043344818,0.0009869251,0.0000024348699,0.04342988,0.676529,0.27655086],"study_design_scores_gemma":[0.00021700768,0.000119284676,0.006076795,0.00006289063,0.000040812745,0.0000012057269,0.000054831027,0.005051797,0.000011733507,0.02308161,0.9649959,0.000286125],"about_ca_topic_score_codex":0.00039325227,"about_ca_topic_score_gemma":0.00074450753,"teacher_disagreement_score":0.28846693,"about_ca_system_score_codex":0.000014362316,"about_ca_system_score_gemma":0.00026492748,"threshold_uncertainty_score":0.99999154},"labels":[],"label_agreement":null},{"id":"W1587624416","doi":"10.48550/arxiv.math/0702727","title":"On Robust Utility Maximization","year":2007,"lang":"en","type":"preprint","venue":"ArXiv.org","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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 British Columbia","funders":"","keywords":"Utility maximization; Maximization; Computer science; Economics; Econometrics; Mathematical economics; Microeconomics","score_opus":0.2748289471333841,"score_gpt":0.4004051129961193,"score_spread":0.12557616586273518,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1587624416","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":[0.7511582,0.0001736374,0.20528543,0.0004558903,0.0026658557,0.00044321886,0.00003900694,0.00015549466,0.039623257],"genre_scores_gemma":[0.9902295,0.0005983854,0.0026295427,0.0005891785,0.00036423598,0.000016606558,0.00018361425,0.000035596666,0.0053533628],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99546343,0.00026507792,0.0011146906,0.0011925314,0.0015851593,0.00037910076],"domain_scores_gemma":[0.99598724,0.0006712846,0.0007498761,0.0018177234,0.0005859699,0.00018789449],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0034639826,0.00035747365,0.00049098115,0.00056751684,0.0002127706,0.00027310825,0.0010667046,0.00059879804,0.0016221118],"category_scores_gemma":[0.0029218083,0.0002895774,0.00025583603,0.00082000106,0.000113667294,0.00021951603,0.0005443636,0.0007106507,0.0019494615],"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":[0.00009254318,0.0001347976,0.72764295,0.000007430519,0.000021333643,0.000017934455,0.00022682565,0.22309439,0.000004317846,0.0005606291,0.018707978,0.029488886],"study_design_scores_gemma":[0.00035175326,0.00006266954,0.8940781,0.00006056484,0.00003912373,0.0000037953916,0.000096685384,0.0493928,0.0003331478,0.026631368,0.02840661,0.00054341165],"about_ca_topic_score_codex":0.00010597738,"about_ca_topic_score_gemma":0.000054137876,"teacher_disagreement_score":0.23907126,"about_ca_system_score_codex":0.00008531262,"about_ca_system_score_gemma":0.00016444892,"threshold_uncertainty_score":0.99995565},"labels":[],"label_agreement":null},{"id":"W1644797908","doi":"10.1007/s10107-016-0979-5","title":"On the mixing set with a knapsack constraint","year":2016,"lang":"en","type":"article","venue":"Mathematical Programming","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":27,"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":"Knapsack problem; Mathematics; Cardinality (data modeling); Continuous knapsack problem; Constraint (computer-aided design); Polytope; Mathematical optimization; Combinatorics; Set (abstract data type); Change-making problem; Discrete mathematics; Computer science","score_opus":0.09817941881720438,"score_gpt":0.351488615016236,"score_spread":0.25330919619903164,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1644797908","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":[0.13571174,0.000014347398,0.82930446,0.008035659,0.00006895806,0.00064719917,0.000004607548,0.00012175409,0.026091281],"genre_scores_gemma":[0.97711253,0.0000032796847,0.021338457,0.00021146906,0.000037509886,0.000044422974,5.1609413e-7,0.000013463611,0.0012383721],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99798846,0.000120232,0.00040194797,0.00028479716,0.0009223383,0.00028224266],"domain_scores_gemma":[0.9954414,0.0036922921,0.00015503039,0.000490386,0.00012221563,0.00009867632],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0021585515,0.00012886814,0.00019022916,0.0000714483,0.00017765579,0.0002936581,0.00036983518,0.000047808422,0.0009735709],"category_scores_gemma":[0.0030072196,0.000042691307,0.00006985508,0.00037131607,0.0002848094,0.00012825102,0.00004920207,0.000088613466,0.0010666957],"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":[0.0000339166,0.00008439665,0.00047101086,0.000004326796,0.000019743475,0.000016364027,0.00086731574,0.00004550171,0.000053152817,0.46789253,0.0021470294,0.5283647],"study_design_scores_gemma":[0.00075341295,0.00041823802,0.00014386588,0.00036212758,0.000032039356,0.00010379427,0.002158313,0.0056986767,0.0008972737,0.9431627,0.045879643,0.00038996225],"about_ca_topic_score_codex":0.0000011298679,"about_ca_topic_score_gemma":0.0000021108558,"teacher_disagreement_score":0.84140074,"about_ca_system_score_codex":0.000018295514,"about_ca_system_score_gemma":0.00003065554,"threshold_uncertainty_score":0.9999397},"labels":[],"label_agreement":null},{"id":"W164732995","doi":"10.2139/ssrn.2578733","title":"Mean-Field Game Strategies for a Major-Minor Agent Optimal Execution Problem","year":2015,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Bank of Canada; Business Development Bank of Canada; University of Toronto","funders":"","keywords":"Minor (academic); Field (mathematics); Computer science; Operations research; Engineering; Mathematics; Political science; Law","score_opus":0.05781413338350861,"score_gpt":0.3502350529430512,"score_spread":0.2924209195595426,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W164732995","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":[0.120738,0.0020551179,0.86966676,0.0015413859,0.0005097049,0.000410493,0.0000058835512,0.00004502366,0.0050276276],"genre_scores_gemma":[0.98490363,0.0015258776,0.0056647873,0.00015046512,0.0005171721,0.000029777442,0.000009429601,0.00002266727,0.007176217],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9962124,0.0001395201,0.0006921031,0.00033056297,0.0010475962,0.001577812],"domain_scores_gemma":[0.9982073,0.0001875644,0.00044459966,0.00026484262,0.0006862781,0.00020945763],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0066820295,0.00018398273,0.00025684357,0.00027910824,0.0002094908,0.00060557114,0.00056501915,0.0001258321,0.000054128534],"category_scores_gemma":[0.00044290643,0.0001342959,0.00018770606,0.0004235932,0.000037616097,0.0009953784,0.00004398965,0.00069200277,0.00008376918],"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":[0.0010477534,0.00020366968,0.0012497618,0.000004537494,0.00017960383,0.000010652514,0.0043478687,0.051852655,0.00027536607,0.6866739,0.030053385,0.2241009],"study_design_scores_gemma":[0.0014616868,0.0014578219,0.00006573785,0.000010297995,0.000047621514,0.00037952233,0.018120991,0.007887156,0.00020055404,0.91713595,0.05298254,0.00025010842],"about_ca_topic_score_codex":0.00006639988,"about_ca_topic_score_gemma":0.0008688408,"teacher_disagreement_score":0.8641656,"about_ca_system_score_codex":0.0004494145,"about_ca_system_score_gemma":0.0040636687,"threshold_uncertainty_score":0.7208774},"labels":[],"label_agreement":null},{"id":"W1669838345","doi":"","title":"The Abridged Nested Decomposition Method for Multistage Stochastic Linear Programs with Relatively Complete Recourse","year":2006,"lang":"en","type":"article","venue":"Algorithmic operations research","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":86,"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":"Decomposition; Computation; Set (abstract data type); Mathematical optimization; Computer science; Scale (ratio); Sampling (signal processing); Nested set model; Multistage sampling; Decomposition method (queueing theory); Mathematics; Algorithm; Statistics; Data mining","score_opus":0.1881139073848471,"score_gpt":0.5090869338795994,"score_spread":0.32097302649475223,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1669838345","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":[0.0100419335,0.00009366897,0.98411965,0.0025060538,0.0001386521,0.0024913277,0.00009750867,0.00005652101,0.00045468964],"genre_scores_gemma":[0.17949149,0.000050641676,0.8032867,0.00003685166,0.00049027195,0.0013096432,0.0005876157,0.00005543011,0.014691384],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9953335,0.0011725967,0.0007092558,0.0006031299,0.0016229749,0.00055853656],"domain_scores_gemma":[0.99223995,0.0036776422,0.000102496706,0.00063219893,0.0032282232,0.00011947797],"candidate_categories":["sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.007987165,0.0001758486,0.00023991519,0.0003844678,0.0029621236,0.0011368787,0.00061566563,0.00009946652,0.000028375865],"category_scores_gemma":[0.0011387673,0.00010531835,0.0000846818,0.0018473252,0.00034406356,0.00049155223,0.000074008916,0.00038352044,0.00012931191],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003134541,0.000301793,0.0003564495,0.0000032923338,0.000051667485,0.00000811123,0.00067344663,0.76752454,0.0009197649,0.01371468,0.0033908929,0.21274188],"study_design_scores_gemma":[0.0008518202,0.00037955298,0.0025636172,0.000016258293,0.000014477907,0.000024988762,0.0006333853,0.9768262,0.000079521786,0.002113936,0.0163372,0.00015906592],"about_ca_topic_score_codex":0.0011534311,"about_ca_topic_score_gemma":0.0023059063,"teacher_disagreement_score":0.21258281,"about_ca_system_score_codex":0.00010542326,"about_ca_system_score_gemma":0.00032269926,"threshold_uncertainty_score":0.99990004},"labels":[],"label_agreement":null},{"id":"W1690448612","doi":"10.1111/j.1937-5956.2010.01194.x","title":"Facility Location: A Robust Optimization Approach","year":2010,"lang":"en","type":"article","venue":"Production and Operations Management","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":217,"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":"Robust optimization; Mathematical optimization; Ellipsoid; Computer science; Set (abstract data type); Network topology; Facility location problem; Optimization problem; Network planning and design; Operations research; Mathematics","score_opus":0.05828145820881826,"score_gpt":0.31135658895330487,"score_spread":0.2530751307444866,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1690448612","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":[0.009849114,0.000045712146,0.96745455,0.0029274006,0.000967522,0.0006449928,0.0000045100805,0.00008645859,0.018019756],"genre_scores_gemma":[0.61857265,0.00044643774,0.36211586,0.00017971966,0.00024795576,0.00015030346,0.0001127884,0.000008786041,0.018165505],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998299,0.00008098971,0.00041045973,0.0005823951,0.0005016556,0.0001254889],"domain_scores_gemma":[0.99876565,0.00001648251,0.00006975566,0.00057450007,0.00049784273,0.00007579208],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014665395,0.00011459907,0.00011434414,0.0002823724,0.0005326441,0.0005083844,0.00017984863,0.000053359414,0.00030573813],"category_scores_gemma":[0.00052924984,0.00009299858,0.000027009066,0.0009977305,0.00008919192,0.0007164641,0.000073467156,0.00012423289,0.000067152185],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000006236839,0.00008832679,0.00092548435,0.000004547538,0.000008655663,2.0136022e-7,0.00023775683,0.9549666,0.000018453768,0.009714539,0.002629673,0.031399533],"study_design_scores_gemma":[0.0002116338,0.0000199234,0.004712139,0.0000030655783,0.000029233257,0.000017003578,0.0010293464,0.9595566,0.00009265758,0.00046609313,0.033652432,0.00020989781],"about_ca_topic_score_codex":0.000014830588,"about_ca_topic_score_gemma":0.00005849985,"teacher_disagreement_score":0.6087235,"about_ca_system_score_codex":0.000014106992,"about_ca_system_score_gemma":0.000022378279,"threshold_uncertainty_score":0.4902362},"labels":[],"label_agreement":null},{"id":"W1773042507","doi":"10.3968/j.sms.1923845220120202.005","title":"A Fuzzy LP Approach to Option Portfolio","year":2011,"lang":"en","type":"article","venue":"Studies in mathematical sciences","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Portfolio; Fuzzy logic; Fuzzy number; Fuzzy transportation; Volatility (finance); Mathematical optimization; Mathematics; Mathematical economics; Economics; Computer science; Econometrics; Fuzzy set; Financial economics; Artificial intelligence","score_opus":0.5148404570432799,"score_gpt":0.4783031462658013,"score_spread":0.03653731077747857,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1773042507","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":[0.12578318,0.00028921253,0.036259644,0.00025458177,0.0003822428,0.00044362515,0.0000017789332,0.000054928518,0.8365308],"genre_scores_gemma":[0.8517567,0.00020431804,0.14644113,0.00019176636,0.000057301448,0.00007033356,2.9473273e-7,0.000005932844,0.0012722252],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9967286,0.0001348479,0.00077516603,0.00059005263,0.0013755132,0.00039581946],"domain_scores_gemma":[0.9986517,0.00052244053,0.00015896474,0.0003491694,0.00018681434,0.00013093221],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006011216,0.00015572626,0.00039449424,0.00045081676,0.0002686508,0.00011075891,0.00088829896,0.000054350192,0.00014303703],"category_scores_gemma":[0.004204565,0.00009218643,0.00007394699,0.0027168505,0.000769204,0.0004129566,0.00029572975,0.00008732165,0.0005584308],"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":[0.000025514575,0.00048752932,0.011665412,0.000017180151,0.000020354995,0.0000109734865,0.03057141,0.0016288344,0.000015446949,0.9275811,0.007695004,0.020281281],"study_design_scores_gemma":[0.00010676066,0.00012216884,0.005199306,0.00003284592,0.000007306821,0.000013131001,0.015751224,0.0034308205,0.00005146759,0.97440666,0.00069213315,0.00018619608],"about_ca_topic_score_codex":0.000014761386,"about_ca_topic_score_gemma":0.000010008876,"teacher_disagreement_score":0.8352586,"about_ca_system_score_codex":0.00003561138,"about_ca_system_score_gemma":0.000040658542,"threshold_uncertainty_score":0.7177685},"labels":[],"label_agreement":null},{"id":"W1822193863","doi":"10.1111/cjag.12079","title":"Risk Management for Grain Processors and “Copulas”","year":2015,"lang":"en","type":"article","venue":"Canadian Journal of Agricultural Economics/Revue canadienne d agroeconomie","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Copula (linguistics); Futures contract; Econometrics; Price risk; Hedge; Volatility (finance); Economics; Risk management; Actuarial science; Financial economics; Finance","score_opus":0.06416113815722291,"score_gpt":0.241331726304096,"score_spread":0.1771705881468731,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1822193863","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":[0.99109995,0.0005172333,0.00023921426,0.0028701185,0.001788865,0.00040181787,0.00021159908,0.0000058293563,0.0028653725],"genre_scores_gemma":[0.99215114,0.0003713768,0.003400367,0.00025922526,0.0005970801,0.000018382469,0.00002886879,0.000020663534,0.003152893],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99763125,0.000064700864,0.0012285457,0.00042573013,0.000038669226,0.00061111705],"domain_scores_gemma":[0.995221,0.00016358055,0.0012409269,0.00025698388,0.0006553719,0.0024621252],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021357902,0.00025513765,0.00056586304,0.0006008405,0.0002636185,0.00049424305,0.0006951742,0.00011902423,0.00004914217],"category_scores_gemma":[0.0007495478,0.00019172751,0.00020680788,0.00024123696,0.00011145293,0.0009003065,0.00002785551,0.00016936602,0.000029471035],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00044619356,0.000059502163,0.1321944,0.00006713246,0.0009446557,0.00019095415,0.018488444,0.18250592,0.0000045068696,0.13216597,0.30212244,0.23080987],"study_design_scores_gemma":[0.003548649,0.000987702,0.110658646,0.0000634133,0.00026058478,0.001052664,0.024843616,0.0032822944,0.000026749827,0.15875442,0.6954406,0.0010806797],"about_ca_topic_score_codex":0.008281373,"about_ca_topic_score_gemma":0.58404356,"teacher_disagreement_score":0.57576215,"about_ca_system_score_codex":0.0008366024,"about_ca_system_score_gemma":0.00061257265,"threshold_uncertainty_score":0.99832255},"labels":[],"label_agreement":null},{"id":"W1824963387","doi":"","title":"Quantile of a Mixture","year":2014,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Quantile; Hospitality; Value (mathematics); Economics; Management; Mathematical economics; Actuarial science; Operations research; Library science; Econometrics; Mathematics; Statistics; Political science; Computer science; Law","score_opus":0.15151476753445517,"score_gpt":0.2605773188281548,"score_spread":0.10906255129369966,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1824963387","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":[0.6533107,0.00007452969,0.32800874,0.000062682695,0.0007596767,0.00019817035,0.000047818558,0.000054088097,0.017483583],"genre_scores_gemma":[0.9915103,0.00042982717,0.00058554317,0.000042616564,0.00007637519,2.0961704e-7,0.00002224279,0.000012867824,0.0073200483],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99805367,0.00026297936,0.00041694238,0.0008018793,0.00027377537,0.00019077415],"domain_scores_gemma":[0.9969359,0.00039387547,0.00074232084,0.0012956826,0.0005134254,0.00011878111],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010503599,0.00020646397,0.00047880234,0.00045539238,0.00007425118,0.0000689258,0.0013045975,0.0003653179,0.00036754025],"category_scores_gemma":[0.00048286517,0.0001876898,0.0003078049,0.0007922119,0.00013957488,0.00014697944,0.0006461654,0.00032087084,0.00023953209],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000073803494,0.00006827764,0.026322283,0.000016915797,0.000044693912,0.000037060494,0.00021472288,0.9193041,0.000024112296,0.04141581,0.010583544,0.0018947126],"study_design_scores_gemma":[0.0005625513,0.000085768836,0.007453196,0.00008249973,0.00014034109,0.0000034035024,0.00026393766,0.7060469,0.0003291154,0.25200298,0.032486778,0.0005425193],"about_ca_topic_score_codex":0.00012502272,"about_ca_topic_score_gemma":0.000042809468,"teacher_disagreement_score":0.3381996,"about_ca_system_score_codex":0.000031881726,"about_ca_system_score_gemma":0.00014267203,"threshold_uncertainty_score":0.7653768},"labels":[],"label_agreement":null},{"id":"W185892825","doi":"10.1023/a:1021806200854","title":"Portfolio Selection and Transactions Costs","year":2003,"lang":"en","type":"article","venue":"Computational Optimization and Applications","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":44,"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":"Portfolio; Mathematics; Upper and lower bounds; Selection (genetic algorithm); Asset (computer security); Mathematical optimization; Covariance matrix; Econometrics; Finance; Statistics; Economics; Computer science","score_opus":0.030158023478763972,"score_gpt":0.32938959606335677,"score_spread":0.2992315725845928,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W185892825","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":[0.0012892318,0.00017681335,0.9875532,0.00036517702,0.000051524992,0.00038722384,0.000016519132,0.00007197254,0.010088344],"genre_scores_gemma":[0.82073843,0.0006500726,0.1766783,0.0003104399,0.000047521844,0.0001626762,0.000083534265,0.000019558507,0.0013094638],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99844015,0.0001127244,0.00046659462,0.00042551613,0.0004176443,0.00013739016],"domain_scores_gemma":[0.9986651,0.00036584793,0.0001857472,0.00014617242,0.0004752374,0.0001619029],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005177751,0.0001338152,0.00015487437,0.0003107745,0.0005549037,0.0002716608,0.00009078732,0.000079115234,0.00046587456],"category_scores_gemma":[0.0001237149,0.00012631225,0.000038283975,0.0010656284,0.00008793553,0.00038466378,0.000009285953,0.00009000961,0.000031250693],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000031258685,0.0000427044,0.0013076953,0.0000010053994,0.000007805085,1.2917651e-7,0.000044979282,0.87227136,0.00000553307,0.105007626,0.00034706216,0.020960953],"study_design_scores_gemma":[0.00051402603,0.00003027865,0.0030114278,0.0000039117604,0.000029136347,0.00008753952,0.00026515394,0.9035794,0.000039212722,0.035946924,0.056259423,0.00023354417],"about_ca_topic_score_codex":0.0000059969534,"about_ca_topic_score_gemma":0.000005222949,"teacher_disagreement_score":0.8194492,"about_ca_system_score_codex":0.000031388907,"about_ca_system_score_gemma":0.00008530689,"threshold_uncertainty_score":0.5150864},"labels":[],"label_agreement":null},{"id":"W190528034","doi":"","title":"Incremental utility elicitation with minimax regret decision criterion","year":2003,"lang":"en","type":"article","venue":"","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"University of Toronto","funders":"","keywords":"Regret; Minimax; Computer science; Decision theory; Mathematical optimization; Operations research; Mathematics; Machine learning; Statistics","score_opus":0.0788822402157648,"score_gpt":0.3773851525976945,"score_spread":0.2985029123819297,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W190528034","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":[0.6994552,0.000042894448,0.23088554,0.00014413675,0.00020123021,0.00018561164,0.0000042021916,0.000042789794,0.06903841],"genre_scores_gemma":[0.9376278,0.00005681539,0.060711995,0.0002412898,0.000018737628,0.0000061202095,0.0000091404745,0.000008227863,0.001319901],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9974141,0.00021004131,0.0005224537,0.00043640015,0.001230882,0.00018613048],"domain_scores_gemma":[0.99839354,0.000481782,0.00016766103,0.0004717672,0.00038031847,0.00010494893],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.002165122,0.00012922767,0.00017359368,0.00021682936,0.00017896143,0.00023838742,0.00021623015,0.00006824514,0.0023114912],"category_scores_gemma":[0.0014310321,0.000079348676,0.000051821673,0.0008317268,0.000072069466,0.0006431573,0.00002549612,0.000066646935,0.00029935603],"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":[0.000596837,0.00024277692,0.2639313,0.000002309771,0.000017402997,0.000016732072,0.0012087537,0.0007053087,0.001030096,0.01218866,0.056321833,0.663738],"study_design_scores_gemma":[0.0038058865,0.0010930619,0.4922797,0.000060884217,0.000052675554,0.00013406076,0.006876347,0.040273517,0.023955509,0.09163698,0.33877188,0.0010594851],"about_ca_topic_score_codex":0.000045233854,"about_ca_topic_score_gemma":0.0001101328,"teacher_disagreement_score":0.6626785,"about_ca_system_score_codex":0.000028198718,"about_ca_system_score_gemma":0.000061857165,"threshold_uncertainty_score":0.99860054},"labels":[],"label_agreement":null},{"id":"W1906637006","doi":"10.1080/14697688.2015.1115891","title":"Risk minimization and portfolio diversification","year":2016,"lang":"en","type":"article","venue":"Quantitative Finance","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"Hankuk University of Foreign Studies; McMaster University","keywords":"Diversification (marketing strategy); Portfolio; Portfolio optimization; Economics; Constraint (computer-aided design); Post-modern portfolio theory; Modern portfolio theory; Econometrics; Financial economics; Replicating portfolio; Variance (accounting); Actuarial science; Mathematics; Business","score_opus":0.07489397127194174,"score_gpt":0.3576940216884543,"score_spread":0.28280005041651257,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1906637006","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":[0.6185614,0.00043231007,0.3756961,0.0009075655,0.00024544614,0.0001685703,0.00007962794,0.000036455214,0.0038725545],"genre_scores_gemma":[0.97133803,0.008881354,0.015417856,0.000049455793,0.000024996334,0.000009214553,0.0000056228364,0.000008025733,0.0042654267],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9984856,0.00016781119,0.0003617782,0.00041410455,0.0004275387,0.00014316232],"domain_scores_gemma":[0.9979859,0.00083797146,0.00045539282,0.0002976471,0.00038015487,0.0000429557],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008296742,0.00010198416,0.00015415873,0.00018620685,0.00019107223,0.000070860195,0.00019341215,0.00005458292,0.00010430688],"category_scores_gemma":[0.0026591502,0.00006231587,0.00003773204,0.0005778177,0.00015768177,0.0007111733,0.000042791686,0.00003957201,0.00037398358],"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":[0.00011549596,0.000046881007,0.3624363,0.0000010062843,0.000012900924,0.0000041485023,0.0011104963,0.0007953201,0.00040522162,0.1476706,0.025914485,0.46148717],"study_design_scores_gemma":[0.0005943098,0.0001593386,0.79560065,0.000022155251,0.000015343549,0.0000031721006,0.00027454193,0.008579773,0.00068495044,0.05006507,0.14377856,0.00022210141],"about_ca_topic_score_codex":0.000024175495,"about_ca_topic_score_gemma":0.00001209509,"teacher_disagreement_score":0.46126506,"about_ca_system_score_codex":0.000017802264,"about_ca_system_score_gemma":0.000029495897,"threshold_uncertainty_score":0.4806927},"labels":[],"label_agreement":null},{"id":"W1934208600","doi":"10.1002/cpe.3695","title":"Computing probable maximum loss in catastrophe reinsurance portfolios on multi‐core and many‐core architectures","year":2015,"lang":"en","type":"article","venue":"Concurrency and Computation Practice and Experience","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Dalhousie University","funders":"","keywords":"Reinsurance; Portfolio; Computer science; Multi-core processor; Petascale computing; Natural disaster; Natural hazard; Storm; Supercomputer; Actuarial science; Meteorology; Parallel computing; Finance; Economics","score_opus":0.13009492035308984,"score_gpt":0.4146381363978724,"score_spread":0.28454321604478255,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1934208600","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":[0.95271844,0.001735602,0.04383571,0.00043584107,0.00028072912,0.00028217683,0.0000065200907,0.00003034931,0.00067460333],"genre_scores_gemma":[0.9907684,0.00049119245,0.008247241,0.00036648865,0.000044719218,0.0000140927905,0.0000108774575,0.000007809794,0.000049230024],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99793667,0.00015430112,0.00053433294,0.0006126985,0.000518987,0.00024299357],"domain_scores_gemma":[0.9982575,0.00069730106,0.00037999236,0.00018968905,0.00028005108,0.00019544212],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010188698,0.00019081576,0.00025275178,0.00019838735,0.00023104333,0.00027839534,0.00015559164,0.000072636685,0.000005277152],"category_scores_gemma":[0.0017598298,0.00015486074,0.000018793638,0.0005081864,0.00023959516,0.0007065159,0.000110357185,0.00022748523,0.000008839081],"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":[0.0004505431,0.00023022889,0.18478815,0.00002316839,0.0000122504425,0.00014997227,0.05304559,0.048618793,0.000056623558,0.004271607,0.0007118516,0.70764124],"study_design_scores_gemma":[0.0047149113,0.00094916736,0.18222821,0.00028125878,0.000035536494,0.0009056015,0.036487326,0.72882074,0.00016323809,0.028115204,0.016254788,0.001044009],"about_ca_topic_score_codex":0.00009617676,"about_ca_topic_score_gemma":0.000016800484,"teacher_disagreement_score":0.7065972,"about_ca_system_score_codex":0.000015361558,"about_ca_system_score_gemma":0.00007930709,"threshold_uncertainty_score":0.63150376},"labels":[],"label_agreement":null},{"id":"W1958818679","doi":"10.3968/j.sms.1923845220120401.398","title":"The Optimal Portfolio Model Based on Multivariate T Distribution with Fuzzy Mathematics Method","year":2012,"lang":"en","type":"article","venue":"Studies in mathematical sciences","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"CVAR; Portfolio; Multivariate statistics; Portfolio optimization; Multivariate t-distribution; Black–Litterman model; Mathematics; Multivariate normal distribution; Fuzzy logic; Econometrics; Modern portfolio theory; Post-modern portfolio theory; Mathematical optimization; Replicating portfolio; Expected shortfall; Statistics; Computer science; Economics; Financial economics; Artificial intelligence","score_opus":0.18851170416138913,"score_gpt":0.47324527222391827,"score_spread":0.28473356806252914,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1958818679","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":[0.02308635,0.00024769505,0.9454621,0.0012755068,0.00024511755,0.0004798895,0.000014591622,0.000049850776,0.029138919],"genre_scores_gemma":[0.675577,0.000107814056,0.32356128,0.00010404555,0.000060985305,0.00007746983,0.0000017396877,0.0000100460475,0.0004996052],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9957457,0.00027286448,0.0008276622,0.00041203844,0.0020812477,0.0006604542],"domain_scores_gemma":[0.9924682,0.006312669,0.0003573341,0.00049382966,0.00023421642,0.0001337989],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.013955082,0.00023672216,0.00043208865,0.00014031437,0.00090509653,0.00023971064,0.0007853127,0.00006638673,0.000032807962],"category_scores_gemma":[0.0063757515,0.00010176778,0.00008846002,0.0015894189,0.0011421926,0.00045308287,0.00016125872,0.00017283196,0.00008886679],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006169186,0.00050102273,0.0025565529,0.000016471564,0.000028813134,0.0000037431043,0.00416928,0.51112956,0.00000865901,0.46926436,0.0023376215,0.009922199],"study_design_scores_gemma":[0.00020043382,0.00010313938,0.0004390968,0.000057864054,0.000017834189,0.000006322552,0.004691433,0.8181439,0.00006299223,0.17580141,0.000314705,0.00016088742],"about_ca_topic_score_codex":0.0000039967167,"about_ca_topic_score_gemma":0.0000064836436,"teacher_disagreement_score":0.6524907,"about_ca_system_score_codex":0.000078108285,"about_ca_system_score_gemma":0.00008503263,"threshold_uncertainty_score":0.763283},"labels":[],"label_agreement":null},{"id":"W1963541556","doi":"10.1239/jap/1189717532","title":"Characterizations of Conditional Comonotonicity","year":2007,"lang":"en","type":"article","venue":"Journal of Applied Probability","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mathematics; Generalization; Representation (politics); Conditional expectation; Conditional probability distribution; Field (mathematics); Conditional random field; Mathematical economics; Discrete mathematics; Econometrics; Pure mathematics; Artificial intelligence; Computer science; Mathematical analysis; Political science; Law","score_opus":0.07150790968494311,"score_gpt":0.36495250390048833,"score_spread":0.29344459421554525,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1963541556","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":[0.84530854,0.000011708339,0.14405255,0.00019025344,0.00018391284,0.00017706587,0.00002152546,0.0000057026687,0.010048762],"genre_scores_gemma":[0.98577553,0.000020636086,0.013995205,0.000074012394,0.0000924156,0.0000011255397,0.00000694707,0.0000039980655,0.00003013123],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9971261,0.000053960383,0.0015234239,0.00014686077,0.0010176161,0.000132061],"domain_scores_gemma":[0.9963504,0.00069009536,0.001472171,0.00027345752,0.0011023122,0.00011156567],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007045297,0.00008187617,0.00032112806,0.00022275634,0.00007465687,0.00003519456,0.00036487562,0.00007630598,0.00030702862],"category_scores_gemma":[0.0007195857,0.000059079495,0.00013899826,0.0005863464,0.00016616507,0.00024201759,0.00003738298,0.00016312943,0.000013712156],"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":[0.0032018495,0.0027064316,0.40884167,0.00003885401,0.00018678453,0.00001835733,0.003527843,0.050947122,0.03353926,0.35322085,0.0047752773,0.13899569],"study_design_scores_gemma":[0.00065782963,0.00012751014,0.5066604,0.0000072481685,0.000025147103,0.000025499932,0.0001794049,0.0006368825,0.017115615,0.46327856,0.011167744,0.00011814435],"about_ca_topic_score_codex":0.0000016497314,"about_ca_topic_score_gemma":0.000005381559,"teacher_disagreement_score":0.140467,"about_ca_system_score_codex":0.000046054367,"about_ca_system_score_gemma":0.0001735038,"threshold_uncertainty_score":0.33617482},"labels":[],"label_agreement":null},{"id":"W1964597368","doi":"10.1111/0008-4085.00156","title":"How long to eat a cake of unknown size? Optimal time horizon under uncertainty","year":2002,"lang":"en","type":"article","venue":"Canadian Journal of Economics/Revue canadienne d économique","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Mathematical economics; Economics; Horizon; Stock (firearms); Time horizon; Mathematics; Econometrics; Mathematical optimization; Geography","score_opus":0.17396007435608754,"score_gpt":0.22894331475418664,"score_spread":0.0549832403980991,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1964597368","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":[0.98597306,0.0002318912,0.0009523717,0.009152895,0.0011192353,0.0002772062,0.0001857919,0.0000050537874,0.002102502],"genre_scores_gemma":[0.9843435,0.00017028263,0.00137718,0.0004810893,0.0005138841,0.000006219216,0.0000054450047,0.000043842738,0.0130585395],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.9970382,0.0001447461,0.0014575325,0.00051568396,0.000023504417,0.00082033064],"domain_scores_gemma":[0.9936541,0.000668796,0.0011909609,0.00070803595,0.0010041331,0.0027739722],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0019511774,0.00033374722,0.00095946685,0.001285676,0.00018601048,0.0004827849,0.0012144458,0.00024038236,0.0018822671],"category_scores_gemma":[0.0021702207,0.00033790877,0.0003704217,0.0005367196,0.00019100841,0.00083173765,0.00003791521,0.0003057043,0.00014470666],"study_design_candidate":"simulation_or_modeling","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":[0.00010812902,0.00002924666,0.0050157146,0.000008745067,0.00017932,0.00021042286,0.00194721,0.892298,0.0000520671,0.04135842,0.025356831,0.033435885],"study_design_scores_gemma":[0.0045892005,0.005212367,0.008079176,0.0003721422,0.0003178775,0.0026622904,0.004328803,0.19194837,0.00096859044,0.13848434,0.63987684,0.0031600203],"about_ca_topic_score_codex":0.030615238,"about_ca_topic_score_gemma":0.8963012,"teacher_disagreement_score":0.865686,"about_ca_system_score_codex":0.0017359976,"about_ca_system_score_gemma":0.0013807004,"threshold_uncertainty_score":0.9999073},"labels":[],"label_agreement":null},{"id":"W1964713924","doi":"10.1007/s10957-010-9668-3","title":"Parametric Integer Programming Algorithm for Bilevel Mixed Integer Programs","year":2010,"lang":"en","type":"article","venue":"Journal of Optimization Theory and Applications","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":72,"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","keywords":"Integer (computer science); Mathematics; Integer programming; Infimum and supremum; Polynomial; Parametric statistics; Bilevel optimization; Mathematical optimization; Theory of computation; Logarithm; Discrete mathematics; Algorithm; Optimization problem; Computer science; Statistics","score_opus":0.040927593523935275,"score_gpt":0.3582411616101763,"score_spread":0.317313568086241,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1964713924","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":[0.0011418929,0.00013362651,0.99692774,0.00027905742,0.0003359942,0.0007782023,0.000010760643,0.000025262389,0.00036748743],"genre_scores_gemma":[0.14476797,0.00026920086,0.853192,0.000118403485,0.00051281985,0.00028323,0.000037930015,0.000029558414,0.00078891724],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979406,0.00015264748,0.0009895713,0.0002659454,0.0004620684,0.00018915177],"domain_scores_gemma":[0.9955311,0.0013721816,0.0010098154,0.00030947768,0.0015961746,0.00018129157],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004958418,0.00015107784,0.00028675832,0.0005956998,0.00029711684,0.0004173805,0.00042580595,0.00014269505,0.0000876962],"category_scores_gemma":[0.001572529,0.00010610103,0.00016023662,0.0014033073,0.00017483959,0.0005615463,0.000041726267,0.0002822468,0.0000075612493],"study_design_candidate":"design_other","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":[0.000042509713,0.00017835673,0.00028315102,0.0000022193155,0.000024723553,4.1590397e-7,0.00012988792,0.022202937,0.000026667441,0.042142764,0.00031588404,0.9346505],"study_design_scores_gemma":[0.0018764074,0.00048510247,0.00032296052,0.000039263257,0.00021411038,0.00027842793,0.0024816955,0.5033526,0.000563491,0.21349654,0.2763511,0.00053835765],"about_ca_topic_score_codex":8.8991897e-7,"about_ca_topic_score_gemma":0.0000012692004,"teacher_disagreement_score":0.93411213,"about_ca_system_score_codex":0.000013484368,"about_ca_system_score_gemma":0.00010291491,"threshold_uncertainty_score":0.43266746},"labels":[],"label_agreement":null},{"id":"W1965261290","doi":"10.1007/s11146-014-9476-x","title":"Cornish-Fisher Expansion for Commercial Real Estate Value at Risk","year":2014,"lang":"en","type":"article","venue":"The Journal of Real Estate Finance and Economics","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Université Laval","funders":"","keywords":"Real estate; Value at risk; Normality; Actuarial science; Econometrics; Basel II; Cornish; Solvency; Economics; Risk management; Mathematics; Statistics; Finance; Market liquidity; Capital requirement; Microeconomics","score_opus":0.037809792244475636,"score_gpt":0.30393649184471117,"score_spread":0.26612669960023555,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1965261290","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":[0.9884762,0.000018281766,0.0034605535,0.00046411037,0.0005428348,0.00012808628,0.000041948697,0.000004941597,0.0068630194],"genre_scores_gemma":[0.57730764,0.41923654,0.0023535045,0.000121298144,0.00028042984,0.0000021432074,0.0000054470574,0.00001908311,0.0006739317],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99842924,0.00019127205,0.0008252223,0.00016759198,0.00016345915,0.00022321021],"domain_scores_gemma":[0.99698067,0.001109718,0.0013408065,0.00028596848,0.00019546009,0.00008740321],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0042411545,0.00013690579,0.00037190958,0.00009162088,0.00037796455,0.00011801249,0.00037353756,0.00007076596,0.000009956575],"category_scores_gemma":[0.0002231485,0.00008643152,0.000114932154,0.000091622285,0.00012423604,0.0004604252,0.00007277765,0.00015136447,0.000017080689],"study_design_candidate":"design_other","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":[0.0009204021,0.000021953147,0.018129934,0.0000023537275,0.000014471107,0.0000014318698,0.002225134,0.09133375,0.000011902039,0.00025410322,0.0071832384,0.87990135],"study_design_scores_gemma":[0.0031575393,0.001460256,0.22061662,0.000036409732,0.00016569301,0.00018686311,0.00096668827,0.23002379,0.0003486653,0.020970644,0.5216219,0.00044496835],"about_ca_topic_score_codex":0.00047224807,"about_ca_topic_score_gemma":0.00050184753,"teacher_disagreement_score":0.87945634,"about_ca_system_score_codex":0.000047742124,"about_ca_system_score_gemma":0.00007554954,"threshold_uncertainty_score":0.35245752},"labels":[],"label_agreement":null},{"id":"W1965759322","doi":"10.1021/ie501898n","title":"A Comparative Theoretical and Computational Study on Robust Counterpart Optimization: III. Improving the Quality of Robust Solutions","year":2014,"lang":"en","type":"article","venue":"Industrial & Engineering Chemistry Research","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"U.S. National Library of Medicine","keywords":"A priori and a posteriori; Mathematical optimization; Probabilistic logic; Computer science; Robust optimization; Optimization problem; Upper and lower bounds; Algorithm; Mathematics; Artificial intelligence","score_opus":0.33857149884931814,"score_gpt":0.44843826420530936,"score_spread":0.10986676535599121,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1965759322","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":[0.843116,0.000032324006,0.1525142,0.0007280868,0.00017774812,0.0007299593,0.000037031743,0.0000499002,0.0026147335],"genre_scores_gemma":[0.99908274,0.000003977816,0.0004461887,0.0000061987816,0.00028717576,0.000030805597,0.000014924439,0.000010075389,0.00011792405],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9962684,0.00049164594,0.00070122984,0.0004066156,0.0018188675,0.00031327168],"domain_scores_gemma":[0.9936779,0.0048037022,0.00016487815,0.00044437664,0.00078332453,0.0001258174],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009816476,0.00014644818,0.00033604188,0.000119613345,0.0003474392,0.00025958946,0.000493643,0.00014296468,0.00013888405],"category_scores_gemma":[0.004855541,0.0001035551,0.00005383435,0.0008438939,0.00043761058,0.00012066059,0.00020444745,0.0006511151,0.000005122812],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008909216,0.00009122771,0.0007270184,0.0000032779658,0.000021593887,5.3912777e-7,0.00043933312,0.99410987,0.0009684208,0.0025972081,0.0006929302,0.00025947165],"study_design_scores_gemma":[0.0010106238,0.00012801768,0.00090380333,0.000021478134,0.000008814481,0.0000031059735,0.0016030362,0.99421835,0.0015615008,0.000241867,0.00018048107,0.000118942575],"about_ca_topic_score_codex":0.000043061988,"about_ca_topic_score_gemma":0.0000015034533,"teacher_disagreement_score":0.15596671,"about_ca_system_score_codex":0.000059555605,"about_ca_system_score_gemma":0.00015085978,"threshold_uncertainty_score":0.5812887},"labels":[],"label_agreement":null},{"id":"W1968355947","doi":"10.1287/opre.1090.0741","title":"Distributionally Robust Optimization Under Moment Uncertainty with Application to Data-Driven Problems","year":2010,"lang":"en","type":"article","venue":"Operations Research","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":1895,"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; Probabilistic logic; Ambiguity; Portfolio optimization; Stochastic programming; Portfolio; Robust optimization; Covariance; Moment (physics); Covariance matrix; Range (aeronautics); Probability distribution; Gaussian; Optimization problem; Mathematics; Algorithm; Finance; Artificial intelligence; Statistics","score_opus":0.19840338857256148,"score_gpt":0.45345395033015923,"score_spread":0.25505056175759777,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1968355947","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":[0.017332032,0.0000078372195,0.9696411,0.008673959,0.00009810441,0.0013008598,0.00025377172,0.000042419233,0.0026499059],"genre_scores_gemma":[0.8636986,0.00006555849,0.12797654,0.00013169087,0.00021895718,0.0005363431,0.00346794,0.000025183122,0.0038792023],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9955615,0.0002748469,0.00050883566,0.0008515954,0.0024238038,0.00037942134],"domain_scores_gemma":[0.99478203,0.00030283,0.000052513675,0.0016555395,0.0029391074,0.00026795585],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.004219231,0.000136899,0.00016117352,0.00047565554,0.0010111821,0.0010870805,0.0013710605,0.00010595374,0.0008217488],"category_scores_gemma":[0.00089719455,0.0000969395,0.000023329438,0.0027849763,0.00017740161,0.00088621,0.0003834665,0.0004139746,0.0005197996],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000023841045,0.00011117101,0.0005987646,8.935868e-7,0.000008678965,5.2451463e-7,0.00011942577,0.9730078,0.0007822291,0.013820614,0.006969972,0.004556082],"study_design_scores_gemma":[0.00022468457,0.00008614289,0.0014150076,0.0000053849776,0.0000049120267,0.000005353417,0.00021122825,0.96149015,0.00007377277,0.0004233588,0.035923664,0.00013632003],"about_ca_topic_score_codex":0.00046791192,"about_ca_topic_score_gemma":0.00544656,"teacher_disagreement_score":0.8463665,"about_ca_system_score_codex":0.00010782203,"about_ca_system_score_gemma":0.0005831501,"threshold_uncertainty_score":0.9999499},"labels":[],"label_agreement":null},{"id":"W1968723219","doi":"10.1016/j.jspi.2005.11.011","title":"Estimating conditional tail expectation with actuarial applications in view","year":2008,"lang":"en","type":"article","venue":"Journal of Statistical Planning and Inference","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":110,"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","funders":"Indiana University Bloomington","keywords":"Estimator; Mathematics; Parametric statistics; Conditional expectation; Econometrics; Construct (python library); Asymptotic analysis; Mathematical economics; Applied mathematics; Statistics; Computer science","score_opus":0.0892378011964783,"score_gpt":0.4007101723045575,"score_spread":0.31147237110807924,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1968723219","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":[0.19973235,0.00006066631,0.79963887,0.000058394846,0.000041140396,0.000046925215,0.000013629684,0.0000034067866,0.00040464112],"genre_scores_gemma":[0.89211506,0.00003713758,0.10770665,0.000029170602,0.000074292686,0.0000033377996,0.000014483693,0.0000028168224,0.000017054059],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9985858,0.00006686452,0.0005741254,0.0001224131,0.00055198616,0.00009881822],"domain_scores_gemma":[0.9972866,0.0018820801,0.00037804237,0.000063070285,0.00029838237,0.000091783295],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005090437,0.00007327901,0.00020979218,0.00018494875,0.0001401102,0.00007462577,0.00010702525,0.00003495093,0.00006610846],"category_scores_gemma":[0.0014761812,0.000047364847,0.000014378032,0.00026606343,0.00012887878,0.00037844843,0.000011860711,0.00016867241,0.0000048758293],"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":[0.00026853563,0.00015637661,0.7886259,0.000011187079,0.000023414139,0.00020815922,0.0049566356,0.137962,0.000060123948,0.025832381,0.0024662332,0.039429076],"study_design_scores_gemma":[0.0010047712,0.0004314474,0.85333556,0.00012086831,0.000019347584,0.00035665237,0.000988229,0.09017067,0.000020902895,0.052260965,0.001110339,0.00018027655],"about_ca_topic_score_codex":0.000008329406,"about_ca_topic_score_gemma":0.0000015338643,"teacher_disagreement_score":0.6923827,"about_ca_system_score_codex":0.000014665177,"about_ca_system_score_gemma":0.00014069561,"threshold_uncertainty_score":0.19314824},"labels":[],"label_agreement":null},{"id":"W1972646937","doi":"10.1080/10920277.2009.10597540","title":"An Option-Based Operational Risk Management Model for Pandemics","year":2009,"lang":"en","type":"article","venue":"North American Actuarial Journal","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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 Manitoba","funders":"","keywords":"Risk management; Pandemic; Risk analysis (engineering); Business; Computer science; Coronavirus disease 2019 (COVID-19); Finance; Medicine","score_opus":0.047244668263456094,"score_gpt":0.36741034729996547,"score_spread":0.3201656790365094,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1972646937","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":[0.2162772,0.000007765054,0.78242457,0.00042159692,0.00027672705,0.00019999269,0.000054321685,0.00002482502,0.0003130365],"genre_scores_gemma":[0.82519835,0.00028474454,0.17223543,0.0014248636,0.0006551045,0.000007459993,0.00005343974,0.000010658947,0.00012997376],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997331,0.00016231686,0.00073348504,0.00035511938,0.0011097745,0.0003082665],"domain_scores_gemma":[0.99786365,0.00021464247,0.00073808257,0.00034692924,0.00052752014,0.0003091892],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014251469,0.00017188708,0.00027559872,0.00033768572,0.000618161,0.0006358017,0.00060065754,0.000040543768,0.000037906455],"category_scores_gemma":[0.00033085517,0.00013030137,0.0001770898,0.0005790037,0.00008598396,0.000584545,0.000011418684,0.00023154641,0.000014190113],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021634874,0.000069098875,0.014289015,1.23039e-7,0.0000105383915,0.0000021598232,0.00009465178,0.61073154,0.0000038710646,0.0003527925,0.0016100848,0.37261975],"study_design_scores_gemma":[0.00092696876,0.00042022107,0.04531622,0.0000018560087,0.00004751805,0.000010100536,0.000101131365,0.941322,0.0000058739693,0.0074263783,0.0042478554,0.00017383568],"about_ca_topic_score_codex":0.000015799758,"about_ca_topic_score_gemma":0.00007384788,"teacher_disagreement_score":0.61018914,"about_ca_system_score_codex":0.000068784226,"about_ca_system_score_gemma":0.00027044,"threshold_uncertainty_score":0.613105},"labels":[],"label_agreement":null},{"id":"W1976713692","doi":"10.3138/infor.50.3.140","title":"Different Probability Distributions for Portfolio Selection in the Chance Constrained Compromise Programming Model","year":2012,"lang":"en","type":"article","venue":"INFOR Information Systems and Operational Research","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"Laurentian University","funders":"","keywords":"Compromise; Portfolio; Selection (genetic algorithm); Skew; Computer science; Mathematical optimization; Portfolio optimization; Econometrics; Mathematical economics; Economics; Mathematics; Finance; Artificial intelligence","score_opus":0.2169921108384988,"score_gpt":0.4396577849698724,"score_spread":0.22266567413137361,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1976713692","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":[0.48543492,0.00014198272,0.5029981,0.0011364933,0.00024212843,0.0056981654,0.000299616,0.000036950514,0.0040116357],"genre_scores_gemma":[0.9963545,0.000031848685,0.0017073483,0.000061447325,0.00011105272,0.0012604066,0.00025897345,0.0000032157864,0.00021120938],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99678177,0.00022620754,0.0009564537,0.00014247213,0.0014967393,0.00039637435],"domain_scores_gemma":[0.99732655,0.00080728385,0.00017839156,0.00017257803,0.0014014225,0.000113751885],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.010416113,0.00011287659,0.0001772582,0.000352155,0.00073153287,0.001123665,0.0002486524,0.000095695665,0.000013590492],"category_scores_gemma":[0.001861612,0.00006752053,0.000047631806,0.0007663312,0.00013532549,0.0029284786,0.000047609446,0.00020359122,0.00001868416],"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":[0.00020254285,0.00024467948,0.066029966,0.0000855478,0.000019063356,1.225185e-7,0.009017093,0.11781384,0.00005571879,0.75351995,0.007856896,0.045154598],"study_design_scores_gemma":[0.0006090246,0.00007955621,0.02459735,0.000019236046,0.000003215149,0.000018248887,0.0024392821,0.9064101,0.000034297664,0.0021186569,0.06354603,0.00012497636],"about_ca_topic_score_codex":0.0000821774,"about_ca_topic_score_gemma":0.000028956947,"teacher_disagreement_score":0.7885963,"about_ca_system_score_codex":0.000107329804,"about_ca_system_score_gemma":0.00019819294,"threshold_uncertainty_score":0.9999133},"labels":[],"label_agreement":null},{"id":"W1977073876","doi":"10.1093/jjfinec/nbs001","title":"Converting Tail-VaR to VaR: An Econometric Study","year":2012,"lang":"en","type":"article","venue":"Journal of Financial Econometrics","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":12,"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":"Value at risk; Econometrics; Vector autoregression; Economics; Currency; Risk management; Monetary economics; Finance","score_opus":0.13825375613268828,"score_gpt":0.373279548651269,"score_spread":0.23502579251858072,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1977073876","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":[0.98105145,0.00052941096,0.013495369,0.00018426658,0.0027893502,0.00023099089,0.000010894292,0.000009324664,0.0016989236],"genre_scores_gemma":[0.99442,0.0001482759,0.0034261001,0.00054387585,0.0012201334,0.0000030611156,9.736698e-7,0.000017413167,0.0002201983],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9962531,0.00018707642,0.00203325,0.00027565318,0.000788327,0.00046263458],"domain_scores_gemma":[0.9955227,0.0008785347,0.0016583613,0.0004371668,0.0008109734,0.00069230475],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.010450819,0.00018473764,0.0006612743,0.0066188416,0.00018961466,0.00031898226,0.0009195046,0.000111164896,0.00040562375],"category_scores_gemma":[0.012592548,0.00015245144,0.00020872802,0.008835132,0.000025053505,0.0025881326,0.00011670831,0.00026568995,0.00034721324],"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":[0.000057197973,0.0006888142,0.7694359,0.0000011494262,0.000016369393,0.000010880753,0.0018983738,0.0054472135,0.0000033584993,0.00033186743,0.0041933605,0.21791552],"study_design_scores_gemma":[0.0008520215,0.0015697107,0.8881918,0.000004636975,0.000041627147,0.00004689218,0.0016242673,0.0008242255,0.00006368114,0.00090848806,0.1055673,0.0003053357],"about_ca_topic_score_codex":0.000022806233,"about_ca_topic_score_gemma":0.000014784341,"teacher_disagreement_score":0.2176102,"about_ca_system_score_codex":0.0001844841,"about_ca_system_score_gemma":0.0001956136,"threshold_uncertainty_score":0.9957248},"labels":[],"label_agreement":null},{"id":"W1982561848","doi":"10.1007/s10479-013-1391-7","title":"Smoothing and parametric rules for stochastic mean-CVaR optimal execution strategy","year":2013,"lang":"en","type":"article","venue":"Annals of Operations Research","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"University of Waterloo","funders":"","keywords":"CVAR; Mathematical optimization; Computer science; Downside risk; Expected shortfall; Smoothing; Parametric statistics; Theory of computation; Portfolio; Discretization; Representation (politics); Algorithm; Mathematics","score_opus":0.4890830016918179,"score_gpt":0.5419786120347316,"score_spread":0.052895610342913735,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1982561848","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":[0.8493201,0.00055594527,0.14611486,0.0020239514,0.000051160376,0.00093030836,0.000040652383,0.000013760427,0.0009492269],"genre_scores_gemma":[0.9879525,0.00047963575,0.009708359,0.00004072269,0.00005958908,0.0001560927,0.00003004229,0.000010134005,0.0015629767],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99735004,0.0002900662,0.00055073225,0.00035226735,0.0011215399,0.00033537616],"domain_scores_gemma":[0.99440795,0.0015336969,0.000059888047,0.00035462232,0.0035020674,0.0001417919],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0046193902,0.00009389618,0.00020029896,0.0010371867,0.0005347556,0.0008143689,0.00034175825,0.000089061934,0.00026726574],"category_scores_gemma":[0.0049101813,0.00007216943,0.000059128066,0.0012826062,0.00021062684,0.0010378701,0.00008475811,0.00015647018,0.00012378077],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000048701466,0.00014666529,0.00030443686,0.000009127015,0.000028038889,7.05883e-7,0.0011454904,0.8342672,0.0011504533,0.029561529,0.019092053,0.11424562],"study_design_scores_gemma":[0.00028559848,0.00044213442,0.00587924,0.000017264148,0.0000051045995,0.000003833332,0.0021939552,0.97007656,0.0014950002,0.018361233,0.0011000097,0.0001400887],"about_ca_topic_score_codex":0.0006484455,"about_ca_topic_score_gemma":0.0000487982,"teacher_disagreement_score":0.13863231,"about_ca_system_score_codex":0.000009597955,"about_ca_system_score_gemma":0.0001669727,"threshold_uncertainty_score":0.7852978},"labels":[],"label_agreement":null},{"id":"W1982670170","doi":"10.1016/j.insmatheco.2014.09.009","title":"Multivariate reinsurance designs for minimizing an insurer’s capital requirement","year":2014,"lang":"en","type":"article","venue":"Insurance Mathematics and Economics","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":22,"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":"Zhejiang University; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China; University of Waterloo; Society of Actuaries","keywords":"Reinsurance; Multivariate statistics; Capital requirement; Multivariate analysis; Actuarial science; Capital (architecture); Econometrics; Capital adequacy ratio; Statistics; Mathematics; Economics; Microeconomics; Geography","score_opus":0.14203781841500418,"score_gpt":0.3529972611079621,"score_spread":0.2109594426929579,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1982670170","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":[0.80627316,0.000056466128,0.19165517,0.0001443801,0.00032385223,0.00032624134,0.00004929102,0.000029090257,0.0011423327],"genre_scores_gemma":[0.8659784,0.0002973743,0.13323613,0.00012663177,0.00012935126,0.000041381878,0.000009375942,0.000025965428,0.00015536764],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99813706,0.000050005896,0.0008836866,0.0004717473,0.00017681977,0.0002807112],"domain_scores_gemma":[0.9978704,0.0007314881,0.00050809025,0.0005592756,0.00018722848,0.00014354169],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025618745,0.00019719418,0.00042410448,0.00012743336,0.00025256418,0.00039089267,0.00036196472,0.000098546174,0.000013364169],"category_scores_gemma":[0.0008026859,0.00016639697,0.00008492235,0.00010764484,0.000074881056,0.0006071761,0.00004979913,0.0000645985,0.000026431984],"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":[0.000316459,0.00082226633,0.046027802,0.00016585735,0.00014137352,0.000002596106,0.023297137,0.101456456,0.0026144683,0.33513755,0.0010321577,0.4889859],"study_design_scores_gemma":[0.0013723491,0.00025154898,0.016577829,0.000038340597,0.00001696238,0.000012495514,0.00072450744,0.7276666,0.0007096331,0.24304071,0.009105196,0.00048382944],"about_ca_topic_score_codex":0.000019318037,"about_ca_topic_score_gemma":0.000047784153,"teacher_disagreement_score":0.62621015,"about_ca_system_score_codex":0.000027329774,"about_ca_system_score_gemma":0.000032417345,"threshold_uncertainty_score":0.6785472},"labels":[],"label_agreement":null},{"id":"W1983234616","doi":"10.1016/j.jspi.2012.01.008","title":"Large-sample confidence intervals for risk measures of location–scale families","year":2012,"lang":"en","type":"article","venue":"Journal of Statistical Planning and Inference","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Mathematics; Statistics; Confidence interval; Expected shortfall; Gumbel distribution; Estimator; Risk measure; Value at risk; Standard deviation; Sample (material); Sample size determination; Econometrics; Extreme value theory; Risk management","score_opus":0.140887317388506,"score_gpt":0.42290662576767035,"score_spread":0.28201930837916434,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1983234616","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":[0.08257696,0.00083337695,0.9159306,0.00003993319,0.00019824874,0.000052650816,0.00018942983,0.0000030629596,0.0001756788],"genre_scores_gemma":[0.9432433,0.0005174295,0.05609146,0.00003511376,0.00007826609,0.0000014540857,0.000004098297,0.0000036075173,0.000025273937],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99824685,0.00012946414,0.00080191786,0.00009921878,0.0005466287,0.00017593258],"domain_scores_gemma":[0.99131143,0.006664596,0.00073175185,0.00010740847,0.001036334,0.00014847056],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0032297508,0.00008158548,0.00030811547,0.00015638421,0.00010208339,0.000077494966,0.00018416399,0.00004940789,0.00004108337],"category_scores_gemma":[0.021212092,0.000054781824,0.000041103347,0.00017809214,0.00012217987,0.0004514765,0.000029346049,0.00013039913,0.0000028782529],"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":[0.000322878,0.00012206942,0.88234174,0.000022168791,0.000039331437,0.0000013140746,0.0069543724,0.005057937,0.0000927805,0.030210119,0.0060196808,0.0688156],"study_design_scores_gemma":[0.0010999044,0.0009367863,0.745397,0.00030438002,0.00014484937,0.000039088594,0.006874717,0.04187885,0.0008370385,0.18344301,0.018753523,0.00029086348],"about_ca_topic_score_codex":0.000027877802,"about_ca_topic_score_gemma":0.000004363146,"teacher_disagreement_score":0.86066633,"about_ca_system_score_codex":0.000007828581,"about_ca_system_score_gemma":0.00007299442,"threshold_uncertainty_score":0.98703265},"labels":[],"label_agreement":null},{"id":"W1983390870","doi":"10.1109/sc.companion.2012.162","title":"Data Challenges in High-Performance Risk Analytics","year":2012,"lang":"en","type":"article","venue":"","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Analytics; Pipeline (software); Risk management; Computer science; Data science; Risk analysis (engineering); Data analysis; Portfolio; Data mining; Business; Finance","score_opus":0.32898196371676053,"score_gpt":0.4032204226085975,"score_spread":0.07423845889183694,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1983390870","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":[0.93450963,0.0021743372,0.00670868,0.0007459186,0.0006017838,0.000100168705,0.00003927058,0.000039930655,0.055080295],"genre_scores_gemma":[0.9666963,0.02742703,0.00451872,0.000042629268,0.00012703665,0.0000012229063,0.000016597582,0.000004762808,0.0011656972],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9985244,0.000090672875,0.00036352518,0.0002517016,0.00054491765,0.00022474928],"domain_scores_gemma":[0.99829835,0.00029621008,0.0001304748,0.0011351522,0.000060866347,0.00007894961],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003687041,0.00007213726,0.00014082718,0.00021185691,0.00004931817,0.000051496867,0.0007679277,0.00005290231,0.00035215192],"category_scores_gemma":[0.0006898287,0.000047260757,0.0000126755,0.00053479575,0.000025520547,0.0012820633,0.00020725885,0.00008835271,0.00068666064],"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":[0.000005908092,0.000049517155,0.6953415,6.2212337e-7,0.000003423318,3.8506013e-7,0.0002012752,0.0043465504,6.8073103e-7,0.0019840638,0.0054582637,0.29260775],"study_design_scores_gemma":[0.0001734108,0.00001739521,0.7737666,0.000002821817,0.0000072482912,0.0000018101905,0.00037195493,0.12609012,0.000048394315,0.0011729774,0.098234914,0.000112375346],"about_ca_topic_score_codex":0.00006604453,"about_ca_topic_score_gemma":0.00021917938,"teacher_disagreement_score":0.29249537,"about_ca_system_score_codex":0.0000100888865,"about_ca_system_score_gemma":0.000018871197,"threshold_uncertainty_score":0.88258624},"labels":[],"label_agreement":null},{"id":"W1984167047","doi":"10.1016/j.disopt.2005.07.001","title":"Computing and minimizing the relative regret in combinatorial optimization with interval data","year":2005,"lang":"en","type":"article","venue":"Discrete Optimization","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"The Scarborough Hospital; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Regret; Mathematical optimization; Mathematics; Interval (graph theory); Minimax; Function (biology); Set (abstract data type); Minification; Computer science; Combinatorics; Statistics","score_opus":0.06296760241526711,"score_gpt":0.3605790427625292,"score_spread":0.2976114403472621,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1984167047","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":[0.00731531,0.00019532448,0.9851884,0.0025839005,0.00028488657,0.00047628258,0.000022875027,0.000051591538,0.003881425],"genre_scores_gemma":[0.68956554,0.00035771748,0.30900523,0.00019074438,0.00023661889,0.000007763082,0.00040351812,0.00003366317,0.00019922061],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9970641,0.00041954644,0.0007666958,0.00070330733,0.0007804183,0.00026590342],"domain_scores_gemma":[0.9976083,0.0006778223,0.00050454127,0.00085418933,0.0002759294,0.00007921239],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023222645,0.00021743015,0.0002922986,0.0002719841,0.00033102042,0.0005150644,0.0007436599,0.00011748491,0.00007247719],"category_scores_gemma":[0.0011163409,0.00013653006,0.00003134893,0.0012065062,0.00017780084,0.0022730904,0.00036040516,0.00023536496,0.00000771503],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008909644,0.00002392612,0.0052543897,0.0000012100321,0.00001169067,0.0000019751433,0.0015360706,0.9814086,8.5012175e-7,0.002118602,0.00034430064,0.009209283],"study_design_scores_gemma":[0.00101634,0.00006711279,0.0011288321,0.000047119225,0.000026121596,0.000011241989,0.0008185401,0.99498624,0.000010182077,0.00044145394,0.00125068,0.00019614998],"about_ca_topic_score_codex":0.0000480604,"about_ca_topic_score_gemma":0.00006939826,"teacher_disagreement_score":0.6822502,"about_ca_system_score_codex":0.000055610708,"about_ca_system_score_gemma":0.000072199975,"threshold_uncertainty_score":0.5567534},"labels":[],"label_agreement":null},{"id":"W1986284462","doi":"10.1016/j.mcm.2011.06.053","title":"The impact of distribution on value-at-risk measures","year":2011,"lang":"en","type":"article","venue":"Mathematical and Computer Modelling","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"National Natural Science Foundation of China","keywords":"Value at risk; Normality; Econometrics; Monte Carlo method; Risk management; Probabilistic logic; Logistic regression; Model risk; Risk measure; Financial risk; Computation; Measure (data warehouse); Logistic distribution; Computer science; Statistics; Mathematics; Actuarial science; Finance; Economics; Data mining; Portfolio","score_opus":0.1372947471908552,"score_gpt":0.3278487011146202,"score_spread":0.19055395392376503,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1986284462","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":[0.3245722,0.000044803473,0.6742612,0.000011291923,0.00002894981,0.00005565714,0.000006428555,0.00000911472,0.0010103411],"genre_scores_gemma":[0.99055547,0.00024304361,0.009053491,0.0000061698174,0.00003307154,0.0000023302987,0.0000018795175,0.000004426734,0.00010014591],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987587,0.00010468067,0.00039086712,0.00017392781,0.00043967715,0.00013217753],"domain_scores_gemma":[0.99877185,0.00061725755,0.00016403422,0.00026785416,0.000112125104,0.000066909284],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001200977,0.000091234426,0.0001707871,0.000030855274,0.00022157762,0.00007258176,0.00020246243,0.000040298168,0.000021349668],"category_scores_gemma":[0.00007821895,0.000041874224,0.00011365146,0.0001301434,0.00007828031,0.00006716613,0.000068913505,0.00006760387,0.000036590707],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015433683,0.00019124379,0.0027299707,0.0000036532304,0.000051453768,0.0000015591274,0.0023717554,0.65799177,0.000004122089,0.19838606,0.000945958,0.13716812],"study_design_scores_gemma":[0.0000617485,0.000088445806,0.00088022347,0.000008737353,0.000006624401,0.0000026205867,0.0000108665035,0.71500266,0.00006868469,0.28375158,0.000077869976,0.0000399515],"about_ca_topic_score_codex":0.000029082648,"about_ca_topic_score_gemma":4.760681e-7,"teacher_disagreement_score":0.66598326,"about_ca_system_score_codex":0.000013009987,"about_ca_system_score_gemma":0.000010270724,"threshold_uncertainty_score":0.17075814},"labels":[],"label_agreement":null},{"id":"W1987434446","doi":"10.1007/s10898-012-9969-1","title":"Portfolio selection under model uncertainty: a penalized moment-based optimization approach","year":2012,"lang":"en","type":"article","venue":"Journal of Global Optimization","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Ambiguity; Portfolio; Portfolio optimization; Moment (physics); Mathematical optimization; Flexibility (engineering); Realization (probability); Mathematics; Econometrics; Range (aeronautics); Robust optimization; Selection (genetic algorithm); Computer science; Project portfolio management; Downside risk; Artificial intelligence; Economics; Finance; Statistics; Project management","score_opus":0.06337797441665845,"score_gpt":0.35943060676135935,"score_spread":0.2960526323447009,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1987434446","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":[0.0040967152,0.00030996208,0.98740155,0.00043066707,0.0006354965,0.00030486615,0.00001717366,0.00004913905,0.006754445],"genre_scores_gemma":[0.47522154,0.00024633473,0.5234007,0.00044504294,0.00031093846,0.00000770612,0.00006034748,0.000026597361,0.0002807876],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99466866,0.0004134515,0.0017919193,0.00034365786,0.0022707665,0.00051151984],"domain_scores_gemma":[0.9946614,0.00013052765,0.0022589394,0.00033864978,0.0021905827,0.0004199271],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0034357372,0.0003232098,0.0005787962,0.0005237131,0.00026233788,0.00038117354,0.00051864446,0.00026672482,0.0005321922],"category_scores_gemma":[0.0006552889,0.00025378983,0.0003337131,0.0026611872,0.000074613745,0.0024039396,0.000050935698,0.00022700567,0.00001204164],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00032849194,0.00037752022,0.008056637,0.0000034122531,0.000046781737,9.933382e-7,0.00007101328,0.9834029,0.000009859445,0.0020606732,0.003878764,0.0017629364],"study_design_scores_gemma":[0.0015703161,0.0001282403,0.00061347726,0.000014463736,0.0001310342,0.00010264762,0.00019520373,0.9957291,0.000026854284,0.0007875334,0.00043776137,0.0002633659],"about_ca_topic_score_codex":0.000019638968,"about_ca_topic_score_gemma":0.0000023164505,"teacher_disagreement_score":0.47112483,"about_ca_system_score_codex":0.0006515683,"about_ca_system_score_gemma":0.0005797638,"threshold_uncertainty_score":0.9999914},"labels":[],"label_agreement":null},{"id":"W1987714019","doi":"10.1108/15265941011092068","title":"A simple parallel algorithm for large‐scale portfolio problems","year":2010,"lang":"en","type":"article","venue":"The Journal of Risk Finance","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"University of Manitoba; Ontario Tech University","funders":"","keywords":"Portfolio; Selection (genetic algorithm); Computer science; Mathematical optimization; Portfolio optimization; Variance (accounting); Scale (ratio); Quadratic programming; Quadratic equation; Function (biology); Modern portfolio theory; Process (computing); Simple (philosophy); Algorithm; Mathematics; Economics; Machine learning; Finance","score_opus":0.026035616477904144,"score_gpt":0.32328885607647595,"score_spread":0.2972532395985718,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1987714019","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":[0.2970444,0.0005564565,0.7004131,0.0003516051,0.000866911,0.00024804327,0.00008184887,0.000009386148,0.00042821374],"genre_scores_gemma":[0.80593896,0.012086159,0.17746049,0.00023025315,0.0010374375,0.000014106506,0.0000052631376,0.000034226134,0.0031931053],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9977815,0.000127831,0.000887668,0.00015644525,0.0007567594,0.00028979403],"domain_scores_gemma":[0.9961583,0.0009114985,0.0015935793,0.00048706343,0.0007722144,0.00007732244],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006838002,0.00012791484,0.00031512967,0.00014308425,0.00032772365,0.00010518695,0.0009293002,0.00008896353,0.00008856582],"category_scores_gemma":[0.0010488079,0.00006889209,0.0002043997,0.00049205107,0.00007006118,0.00040563318,0.000054225286,0.000435787,0.000036324564],"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":[0.00022516095,0.0002650514,0.02659251,0.0000021503095,0.00003798603,0.000009970976,0.0040304344,0.040071476,0.00021812013,0.001335314,0.15550077,0.77171105],"study_design_scores_gemma":[0.0012821613,0.0002369344,0.033802837,0.000011294703,0.000062919375,0.00017075737,0.00044938954,0.10888669,0.00026239193,0.11410192,0.7405544,0.00017828646],"about_ca_topic_score_codex":0.00002384947,"about_ca_topic_score_gemma":0.00013275146,"teacher_disagreement_score":0.7715328,"about_ca_system_score_codex":0.000008940803,"about_ca_system_score_gemma":0.00011957688,"threshold_uncertainty_score":0.2809338},"labels":[],"label_agreement":null},{"id":"W1990209519","doi":"10.1080/17442508.2010.523467","title":"Randomized stopping times and coherent multiperiod risk measures","year":2010,"lang":"en","type":"article","venue":"Stochastics","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"York University","funders":"","keywords":"Representation (politics); Randomized controlled trial; Stopping time; Feature (linguistics); Optimal stopping; Computer science; Mathematics; Statistics; Medicine; Philosophy; Linguistics","score_opus":0.043858785174434985,"score_gpt":0.3425239926315008,"score_spread":0.2986652074570658,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1990209519","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":[0.24194066,0.00036636446,0.7521841,0.0002056847,0.0013060442,0.0005498042,0.000028006174,0.000066137894,0.0033532032],"genre_scores_gemma":[0.9814999,0.0003773866,0.01690055,0.000050245,0.00016893636,0.00002003672,0.0000037788143,0.000012761472,0.0009663727],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99809873,0.00019231353,0.0004925006,0.00030994613,0.0007208479,0.00018564647],"domain_scores_gemma":[0.9972818,0.0016096462,0.00029575604,0.00035832523,0.00031884477,0.00013561509],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0029452431,0.0001414674,0.00040374548,0.00016105198,0.00025130762,0.00025590847,0.0002327646,0.00010043447,0.00017942228],"category_scores_gemma":[0.01019962,0.00009484464,0.00009324356,0.00021289266,0.000279898,0.00013342466,0.00007381044,0.00025695557,0.00008264941],"study_design_candidate":"design_other","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":[0.015600097,0.00017584859,0.016303742,0.0000055132955,0.00018798334,0.0000130603175,0.008767339,0.03295054,0.0021757034,0.033729017,0.008013053,0.8820781],"study_design_scores_gemma":[0.11365626,0.00010659992,0.0040397528,0.00002239222,0.00022712334,0.000035514044,0.0010262183,0.74468875,0.0004901757,0.0640351,0.070943825,0.0007282857],"about_ca_topic_score_codex":0.00005671984,"about_ca_topic_score_gemma":0.00008779686,"teacher_disagreement_score":0.8813498,"about_ca_system_score_codex":0.000005307093,"about_ca_system_score_gemma":0.0000500262,"threshold_uncertainty_score":0.9981379},"labels":[],"label_agreement":null},{"id":"W1991398958","doi":"10.3390/jrfm7030113","title":"Risk Measures and Portfolio Optimization","year":2014,"lang":"en","type":"article","venue":"Journal of risk and financial management","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":29,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Portfolio optimization; Portfolio; Spectral risk measure; Expected shortfall; Mathematical optimization; Value at risk; Geometric Brownian motion; Econometrics; Coherent risk measure; Risk measure; Optimization problem; Expected return; Computer science; Modern portfolio theory; Actuarial science; Mathematics; Risk management; Economics; Financial economics; Finance; Diffusion process","score_opus":0.017037771897253248,"score_gpt":0.27022514975174106,"score_spread":0.2531873778544878,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1991398958","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":[0.09206716,0.00082569936,0.90338624,0.00008457328,0.000438586,0.000097443866,0.0000047862886,0.000007335027,0.0030881574],"genre_scores_gemma":[0.9094454,0.06082233,0.029072186,0.00010625224,0.00028517283,0.000001413342,8.7593105e-7,0.0000088099,0.0002575581],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9980584,0.0002019372,0.0006857935,0.0001970112,0.000718321,0.00013854759],"domain_scores_gemma":[0.9983127,0.0001916403,0.00091407954,0.00017758418,0.00028487472,0.00011912288],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0038847572,0.00011598327,0.00027772572,0.0004438006,0.00022699453,0.00020345958,0.00018594797,0.000060873226,0.000022419315],"category_scores_gemma":[0.0014337837,0.00008158348,0.00007492824,0.00037771984,0.00005945224,0.00034331938,0.000076692726,0.00015032104,0.000004834419],"study_design_candidate":"design_other","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":[0.00007365972,0.000026566373,0.06743076,0.0000017833219,0.0000097795855,0.0000081611015,0.00023511997,0.050979603,4.5206295e-7,0.0026002408,0.0035766512,0.8750572],"study_design_scores_gemma":[0.0013661176,0.0003098285,0.41462043,0.000029255345,0.00021098346,0.000038956827,0.00032168237,0.02658051,0.0000108002705,0.045978803,0.51031154,0.00022110732],"about_ca_topic_score_codex":0.000022590762,"about_ca_topic_score_gemma":0.000013291945,"teacher_disagreement_score":0.87483615,"about_ca_system_score_codex":0.000010879688,"about_ca_system_score_gemma":0.000015645128,"threshold_uncertainty_score":0.3326878},"labels":[],"label_agreement":null},{"id":"W1991559757","doi":"10.2139/ssrn.1115729","title":"Comonotonic Measures of Multivariate Risks","year":2009,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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 British Columbia","funders":"","keywords":"Multivariate statistics; Multivariate analysis; Econometrics; Mathematics; Statistics; Computer science","score_opus":0.0985941659133074,"score_gpt":0.3885786455905135,"score_spread":0.28998447967720614,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1991559757","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":[0.62494224,0.0044950237,0.35946658,0.0016770021,0.00047040707,0.00023036041,0.00000340312,0.000042494445,0.00867246],"genre_scores_gemma":[0.98722583,0.010739531,0.0006602129,0.000073830095,0.00011394802,6.829441e-7,0.0000010101255,0.000007513208,0.0011774139],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9965036,0.00025282303,0.00073388586,0.00021520743,0.0011192954,0.0011752121],"domain_scores_gemma":[0.9984061,0.00015860346,0.0005795015,0.00031297182,0.00045092974,0.000091883405],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007385153,0.0001282155,0.00028455333,0.00029792052,0.00018165472,0.00010174675,0.00063380663,0.000083711304,0.000039160124],"category_scores_gemma":[0.0007799067,0.000089787805,0.0001823366,0.000572136,0.000043350512,0.00032635103,0.000019694107,0.00089455943,0.000048238868],"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":[0.00014807933,0.00013265046,0.007923179,2.2135038e-7,0.000064036416,0.0000024025667,0.00041309,0.010609712,0.0011758275,0.14881536,0.00032722802,0.8303882],"study_design_scores_gemma":[0.0009809587,0.00062600744,0.028065875,0.000009110235,0.000034448294,0.00015961583,0.0007697615,0.0044498765,0.0012798825,0.9536302,0.009802681,0.00019159495],"about_ca_topic_score_codex":0.000071708266,"about_ca_topic_score_gemma":0.00015547768,"teacher_disagreement_score":0.8301966,"about_ca_system_score_codex":0.00020522214,"about_ca_system_score_gemma":0.001323079,"threshold_uncertainty_score":0.3886465},"labels":[],"label_agreement":null},{"id":"W1991832688","doi":"10.1007/pl00011400","title":"A stochastic programming model using an endogenously determined worst case risk measure for dynamic asset allocation","year":2001,"lang":"en","type":"article","venue":"Mathematical Programming","topic":"Risk and Portfolio Optimization","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":"University of British Columbia","funders":"","keywords":"Stochastic game; Portfolio; Sharpe ratio; Dynamic programming; Risk measure; Stochastic programming; Measure (data warehouse); Portfolio optimization; Asset (computer security); Stochastic investment model; Mathematics; Mathematical optimization; Economics; Asset allocation; Expected utility hypothesis; Transaction cost; Risk aversion (psychology); Econometrics; Mathematical economics; Computer science; Microeconomics; Financial economics","score_opus":0.15378398146636194,"score_gpt":0.3958709482372792,"score_spread":0.24208696677091726,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1991832688","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":[0.31211218,0.000046953606,0.68602914,0.00003872207,0.000064671665,0.0015026864,0.00001209618,0.00015435356,0.000039234747],"genre_scores_gemma":[0.63363975,0.000003293209,0.3659176,0.0000138914165,0.000057767396,0.00021424287,0.000023310666,0.00004724611,0.000082891114],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9957721,0.00023360128,0.0012527113,0.0008133457,0.0011449662,0.00078322284],"domain_scores_gemma":[0.99658555,0.00093469885,0.00064415444,0.0008216936,0.00064499077,0.00036891008],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.004448161,0.00036876014,0.00055093743,0.00036377917,0.00081502524,0.00092300435,0.0005067365,0.00021447307,0.000027037173],"category_scores_gemma":[0.003982158,0.00029665165,0.00024514482,0.00095417135,0.00013208427,0.00079592795,0.00008338864,0.00023479512,0.00003010614],"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":[0.00009426593,0.0005857035,0.0002178804,0.00003113853,0.000036661047,0.00013903937,0.0025361136,0.23806779,0.0001854632,0.00057768484,0.00000798469,0.75752026],"study_design_scores_gemma":[0.0005516984,0.00017098148,0.0000069048206,0.000050320446,0.0001711749,0.001520186,0.0011446818,0.95860624,0.000016407967,0.037223056,0.00018551217,0.00035280545],"about_ca_topic_score_codex":0.000048045888,"about_ca_topic_score_gemma":0.00026451857,"teacher_disagreement_score":0.75716746,"about_ca_system_score_codex":0.00013633192,"about_ca_system_score_gemma":0.00013961799,"threshold_uncertainty_score":0.99994856},"labels":[],"label_agreement":null},{"id":"W1995695651","doi":"10.1007/s001860000073","title":"The efficient frontier for bounded assets","year":2000,"lang":"en","type":"article","venue":"Mathematical Methods of Operations Research","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"University of Waterloo","funders":"","keywords":"Efficient frontier; Portfolio; Bounded function; Constraint (computer-aided design); Upper and lower bounds; Selection (genetic algorithm); Variance (accounting); Project portfolio management; Budget constraint; Asset (computer security); Mathematical optimization; Universe; Mathematics; Econometrics; Economics; Mathematical economics; Computer science; Finance; Microeconomics","score_opus":0.4164572100295024,"score_gpt":0.6420881436023647,"score_spread":0.22563093357286224,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1995695651","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":[0.023543708,0.00015885684,0.9491084,0.0024851328,0.00008779323,0.00088924123,0.000012977552,0.000013098982,0.023700792],"genre_scores_gemma":[0.026451347,0.00008591392,0.94529176,0.000023540815,0.000059601338,0.0002646682,0.0000035654198,0.000014294637,0.027805295],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9950058,0.0018816482,0.00084973295,0.00030406658,0.0015958204,0.0003629262],"domain_scores_gemma":[0.98532546,0.012326574,0.000038181246,0.000831231,0.0013663456,0.00011223948],"candidate_categories":["metaresearch","insufficient_payload"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.032342598,0.00008800464,0.00025815665,0.00020606125,0.0010892453,0.00065723783,0.00081583887,0.000078774625,0.0016984111],"category_scores_gemma":[0.02086564,0.000046721478,0.00012913908,0.0010067441,0.0004272465,0.000119305994,0.00007458599,0.00018100273,0.0002748187],"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":[0.000089960784,0.0002925086,0.000020603238,0.000008463465,0.000026409674,4.487959e-7,0.00089462154,0.03278083,0.0007138749,0.31269068,0.012710251,0.63977134],"study_design_scores_gemma":[0.0001948527,0.00008781122,0.000118254204,0.000008709165,0.000006207357,0.0000024154908,0.00036280128,0.65661985,0.0026990476,0.27314577,0.06668888,0.000065418804],"about_ca_topic_score_codex":0.000011400812,"about_ca_topic_score_gemma":0.000008378534,"teacher_disagreement_score":0.63970596,"about_ca_system_score_codex":0.000027517692,"about_ca_system_score_gemma":0.00015274386,"threshold_uncertainty_score":0.9992142},"labels":[],"label_agreement":null},{"id":"W1995981016","doi":"10.1007/s11009-015-9444-9","title":"Vector-Valued Tail Value-at-Risk and Capital Allocation","year":2015,"lang":"en","type":"article","venue":"Methodology And Computing In Applied Probability","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Université du Québec à Trois-Rivières; Université du Québec; Concordia University; Université Laval; Actua","funders":"","keywords":"Orthant; Mathematics; Subadditivity; Bivariate analysis; Value at risk; Homogeneous; Capital allocation line; Econometrics; Invariant (physics); Risk model; Actuarial science; Risk management; Mathematical economics; Statistics; Combinatorics; Finance; Economics; Profit (economics); Microeconomics","score_opus":0.20406206819389552,"score_gpt":0.38860587873418645,"score_spread":0.18454381054029093,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1995981016","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":[0.8230619,0.00030521766,0.17450468,0.00015513103,0.00020813098,0.00032418125,0.0000019749345,0.000040105268,0.0013986948],"genre_scores_gemma":[0.7629631,0.00003315547,0.23682632,0.00007503244,0.000042518994,0.000009671401,0.000004494413,0.0000057451803,0.0000399618],"study_design_codex":"observational","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9954563,0.002490946,0.0007091595,0.0007324149,0.00035699134,0.00025423046],"domain_scores_gemma":[0.9959963,0.003017481,0.0003245203,0.00039097786,0.00013182808,0.00013886923],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.025652563,0.0001590132,0.00040378026,0.00014023039,0.00019706559,0.00006307456,0.00021119353,0.0001992674,0.00001210901],"category_scores_gemma":[0.005219983,0.00012972593,0.000030611023,0.00043498082,0.00032659108,0.000069161026,0.00029219314,0.0002400239,0.000014521244],"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":[0.00046058054,0.00010394111,0.71528226,0.000012501645,0.00001606579,0.0000021872431,0.009919199,0.03355212,0.00023894082,0.061521493,0.00019286752,0.17869785],"study_design_scores_gemma":[0.00091316894,0.000091236776,0.32651943,0.000004035072,0.000017993958,0.00001779474,0.0005838665,0.07799523,0.00032621052,0.59273756,0.0005871575,0.00020632862],"about_ca_topic_score_codex":0.0001789448,"about_ca_topic_score_gemma":0.000101132166,"teacher_disagreement_score":0.5312161,"about_ca_system_score_codex":0.00006384743,"about_ca_system_score_gemma":0.00007009575,"threshold_uncertainty_score":0.88907176},"labels":[],"label_agreement":null},{"id":"W1997198130","doi":"10.1080/10920277.2010.10597577","title":"Improving Skewness of Mean-Variance Portfolios","year":2010,"lang":"en","type":"article","venue":"North American Actuarial Journal","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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 New Brunswick","funders":"Natural Sciences and Engineering Research Council of Canada; Flinders University","keywords":"Skewness; Efficient frontier; Computer science; Portfolio; Variance (accounting); Context (archaeology); Asset allocation; Econometrics; Portfolio optimization; Asset (computer security); Product (mathematics); Actuarial science; Mathematical optimization; Economics; Mathematics; Financial economics","score_opus":0.02421548453836631,"score_gpt":0.322912169632663,"score_spread":0.2986966850942967,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1997198130","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":[0.89070183,0.000014988786,0.10406125,0.00022359731,0.0029880896,0.00011743847,0.000017074175,0.000024964109,0.0018507865],"genre_scores_gemma":[0.98379046,0.000109862354,0.014250733,0.00017315989,0.0014121858,0.000002274325,0.0000039227043,0.000020565023,0.00023682088],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99627525,0.00016434837,0.0012062765,0.00036170232,0.0015930735,0.0003993256],"domain_scores_gemma":[0.99546504,0.000411255,0.0022633357,0.00060612673,0.0008916286,0.00036260372],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019708867,0.00020140856,0.0005309528,0.0004533667,0.00027677967,0.00034229518,0.0010299376,0.0000673814,0.0004497133],"category_scores_gemma":[0.0025068566,0.00014675684,0.0002272473,0.0015451972,0.00039266417,0.00065738615,0.00009120862,0.0007333505,0.000059233855],"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":[0.0001804042,0.00007896574,0.1695423,9.81422e-7,0.000028930492,0.00003740056,0.00059129373,0.0013420206,0.0030680657,0.00036348167,0.0013240776,0.8234421],"study_design_scores_gemma":[0.002688787,0.00090549974,0.8916716,0.000018252955,0.0001592192,0.0012841389,0.0015958139,0.012818058,0.0037536744,0.007119614,0.07685517,0.0011301821],"about_ca_topic_score_codex":0.00047856141,"about_ca_topic_score_gemma":0.00088867237,"teacher_disagreement_score":0.8223119,"about_ca_system_score_codex":0.0000209477,"about_ca_system_score_gemma":0.00053798896,"threshold_uncertainty_score":0.59845704},"labels":[],"label_agreement":null},{"id":"W1999755164","doi":"10.1016/j.ejor.2009.01.061","title":"Tradeoff-based decomposition and decision-making in multiobjective programming","year":2009,"lang":"en","type":"article","venue":"European Journal of Operational Research","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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 Waterloo","funders":"","keywords":"Decomposition; Computer science; Mathematical optimization; Multiobjective programming; Multi-objective optimization; Operations research; Mathematics","score_opus":0.13138254451192238,"score_gpt":0.48951592855266274,"score_spread":0.35813338404074035,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1999755164","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":[0.8418684,0.0003441125,0.15207891,0.0017816428,0.000106484185,0.00021947894,0.0000022702393,0.0000055095256,0.0035931664],"genre_scores_gemma":[0.9261208,0.00006582259,0.0735113,0.0001268385,0.00014132881,7.151632e-7,0.0000015747603,0.0000072298776,0.00002436901],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9953501,0.0013613107,0.00082474627,0.00022337686,0.0020301358,0.00021029518],"domain_scores_gemma":[0.9962309,0.0019052444,0.00019041689,0.00013285535,0.0014231838,0.000117398544],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.016030842,0.00008285107,0.00016636653,0.0011350069,0.00026321632,0.0006205097,0.0003503897,0.00002413293,0.00006832849],"category_scores_gemma":[0.0042544776,0.00006090084,0.00005785606,0.000967201,0.000081602935,0.00065833365,0.000031376407,0.00041587328,0.000027255785],"study_design_candidate":"design_other","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":[0.0004082951,0.00015416213,0.0054317173,5.325589e-7,0.000004078426,0.00025221353,0.0007790482,0.057762753,0.0005844551,0.0010724697,0.000710654,0.93283963],"study_design_scores_gemma":[0.0025678226,0.0018077618,0.86178493,0.00035694966,0.0000057711404,0.00026827867,0.000936532,0.1079447,0.00032723148,0.014263009,0.009504919,0.00023209592],"about_ca_topic_score_codex":0.0000016417041,"about_ca_topic_score_gemma":0.0000055999726,"teacher_disagreement_score":0.93260753,"about_ca_system_score_codex":0.00007603426,"about_ca_system_score_gemma":0.00022601777,"threshold_uncertainty_score":0.5983589},"labels":[],"label_agreement":null},{"id":"W2005374667","doi":"10.1108/jrf-09-2014-0132","title":"Computing value-at-risk using genetic algorithm","year":2015,"lang":"en","type":"article","venue":"The Journal of Risk Finance","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"University of Manitoba","funders":"","keywords":"Value at risk; Computation; Volatility (finance); Selection (genetic algorithm); Computer science; Population; Monte Carlo method; Portfolio; Mathematical optimization; Genetic algorithm; Econometrics; Originality; Risk management; Set (abstract data type); Expected shortfall; Actuarial science; Algorithm; Finance; Economics; Mathematics; Statistics; Machine learning","score_opus":0.07691649418869266,"score_gpt":0.35202347813252555,"score_spread":0.2751069839438329,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2005374667","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":[0.5861306,0.0025388391,0.4105207,0.000054004027,0.00057642604,0.00003983457,0.000006511489,0.0000041063877,0.00012895466],"genre_scores_gemma":[0.7572553,0.008286593,0.23360722,0.000060703485,0.00052445324,1.07540814e-7,2.3554462e-7,0.00001452222,0.00025087703],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9968321,0.0007287623,0.00088517804,0.0001362304,0.0012038426,0.00021391775],"domain_scores_gemma":[0.99562806,0.0008678858,0.0023073358,0.00039348257,0.0007026151,0.00010059675],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0076082335,0.00011914662,0.00028738732,0.00014948408,0.0003526069,0.00009075058,0.0007737675,0.000054263346,0.000011387325],"category_scores_gemma":[0.0018949172,0.000067466746,0.00011593472,0.0006571114,0.00010351406,0.00024040978,0.0001348293,0.00030483337,0.00006728031],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000038345865,0.000013626118,0.023329206,1.4107414e-7,0.000007835734,0.000010498212,0.0015012696,0.72081155,0.000006246627,0.000012744918,0.0028401238,0.25142843],"study_design_scores_gemma":[0.00069465756,0.00014386952,0.08985018,0.000027752789,0.00009700301,0.0005696772,0.0005029738,0.87266743,0.00021849603,0.01567173,0.019409202,0.00014702826],"about_ca_topic_score_codex":0.00016560503,"about_ca_topic_score_gemma":0.0000059608765,"teacher_disagreement_score":0.25128138,"about_ca_system_score_codex":0.00007768727,"about_ca_system_score_gemma":0.00016942744,"threshold_uncertainty_score":0.2751214},"labels":[],"label_agreement":null},{"id":"W2006474481","doi":"10.1007/s11750-013-0276-x","title":"Comments on: Multicriteria decision systems for financial problems","year":2013,"lang":"en","type":"article","venue":"Top","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Laurentian University","funders":"","keywords":"Computer science; Mathematical economics; Management science; Economics","score_opus":0.08747867990037758,"score_gpt":0.37442683384914804,"score_spread":0.2869481539487705,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2006474481","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":[0.71090233,0.00030208373,0.27163,0.0014581092,0.0051933024,0.0030193415,0.00004938333,0.000097842196,0.007347588],"genre_scores_gemma":[0.985129,0.00008874698,0.007330671,0.0006516538,0.000297289,0.00027283956,0.000018771403,0.000015322372,0.0061957096],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9984905,0.00005359352,0.00046365772,0.00027814833,0.000518857,0.00019525764],"domain_scores_gemma":[0.99844563,0.00062869047,0.00014388195,0.0003803872,0.0003176429,0.00008376144],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00077547185,0.00009889442,0.00018526599,0.00012631525,0.00014670582,0.00036222665,0.00033263065,0.00007737762,0.00028344867],"category_scores_gemma":[0.0011268758,0.000067033325,0.00006254713,0.00022703319,0.000020650194,0.00026363524,0.000041247084,0.00004623948,0.0012851299],"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":[0.00006812455,0.00012302844,0.005146535,0.0000060709817,0.0000051194515,0.0000011985336,0.00049554225,0.010603196,0.0001530656,0.0025366384,0.6456157,0.33524576],"study_design_scores_gemma":[0.001282848,0.00030144613,0.014592786,0.00006107125,0.000007034565,0.0000037011657,0.00011522616,0.18389165,0.00020211264,0.021693474,0.777609,0.00023966107],"about_ca_topic_score_codex":0.000099347766,"about_ca_topic_score_gemma":0.000003795295,"teacher_disagreement_score":0.33500612,"about_ca_system_score_codex":0.000020283635,"about_ca_system_score_gemma":0.000025158011,"threshold_uncertainty_score":0.99949247},"labels":[],"label_agreement":null},{"id":"W2007214340","doi":"10.1016/j.jbankfin.2012.01.013","title":"Portfolio credit-risk optimization","year":2012,"lang":"en","type":"article","venue":"Journal of Banking & Finance","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"McMaster University","funders":"","keywords":"Expected shortfall; Portfolio optimization; Portfolio; Value at risk; Quantile; Credit risk; Econometrics; Conditional independence; Economics; Computer science; Actuarial science; Mathematics; Risk management; Finance; Statistics","score_opus":0.04316112291124744,"score_gpt":0.3339354217292109,"score_spread":0.29077429881796346,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2007214340","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":[0.36137828,0.003092621,0.6211522,0.00027929482,0.0038274862,0.00009283156,0.0000067829474,0.000018576295,0.01015189],"genre_scores_gemma":[0.9379047,0.005203204,0.054738898,0.000109785615,0.0012333167,9.915825e-7,0.0000014849461,0.00001464495,0.00079295214],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.99698615,0.00016434923,0.0011358063,0.0001516644,0.0012573444,0.00030470258],"domain_scores_gemma":[0.996169,0.0003382211,0.002321121,0.00032816356,0.0007387966,0.00010475419],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0046043023,0.00013182036,0.000331148,0.0004038328,0.00018345086,0.0001557425,0.0005076518,0.00009792005,0.00044911666],"category_scores_gemma":[0.0016562414,0.00009698456,0.00018597711,0.00091599737,0.000048157162,0.0017299204,0.000047976864,0.00027510725,0.0000620561],"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":[0.00006432641,0.000105400046,0.19950186,9.779092e-7,0.0000180111,0.000015341873,0.0006804707,0.64326686,0.000020244304,0.0012062095,0.039215475,0.115904845],"study_design_scores_gemma":[0.0012438057,0.00033203384,0.30043173,0.0001189273,0.0001248484,0.00060623756,0.0002610726,0.06553061,0.00075729546,0.011265529,0.6188115,0.00051642885],"about_ca_topic_score_codex":0.0000064599185,"about_ca_topic_score_gemma":6.9335215e-7,"teacher_disagreement_score":0.579596,"about_ca_system_score_codex":0.000047178935,"about_ca_system_score_gemma":0.00010592154,"threshold_uncertainty_score":0.49175125},"labels":[],"label_agreement":null},{"id":"W2010703796","doi":"10.1007/bf02887233","title":"A dynamic decision model for portfolio investment and assets management","year":2005,"lang":"en","type":"article","venue":"Journal of Zhejiang University Science","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Post-modern portfolio theory; Sharpe ratio; Portfolio; Economics; Investment strategy; Modern portfolio theory; Portfolio optimization; Selection (genetic algorithm); Investment (military); Investment performance; Replicating portfolio; Actuarial science; Application portfolio management; Decision rule; Efficient frontier; Microeconomics; Econometrics; Computer science; Return on investment; Project portfolio management; Financial economics; Artificial intelligence; Management; Project management","score_opus":0.03629538746537908,"score_gpt":0.33581612126509097,"score_spread":0.2995207337997119,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2010703796","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":[0.39810055,0.00009610781,0.59701216,0.0011948814,0.00017909994,0.00015757928,0.0000058600212,0.000006862731,0.003246915],"genre_scores_gemma":[0.72289795,0.0006496039,0.27215233,0.0002964552,0.000018533043,1.05436975e-7,3.30894e-7,0.00000348852,0.003981198],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99804914,0.000024058143,0.00034284187,0.0002441782,0.0011493405,0.00019044887],"domain_scores_gemma":[0.9984308,0.0001528299,0.0004384148,0.00021143758,0.0005473542,0.0002191897],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029167004,0.00008081262,0.00016221798,0.0009545994,0.00032554546,0.00016674022,0.0007481002,0.000032969463,0.000011922037],"category_scores_gemma":[0.00023837555,0.000063818115,0.00008046719,0.0009352882,0.00020568364,0.0016599485,0.00014985849,0.0000700173,0.0000065310273],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026999463,0.00015125393,0.0025178008,0.0000038171333,0.000026540758,0.00006911436,0.001138168,0.49050057,0.00049097964,0.017403185,0.016776165,0.4706524],"study_design_scores_gemma":[0.0007515954,0.00009651544,0.0093305325,0.000022606355,0.00003211698,0.00005151922,0.0006238823,0.94533926,0.00004157885,0.013194831,0.030410841,0.00010469879],"about_ca_topic_score_codex":0.0000016231871,"about_ca_topic_score_gemma":0.000008945537,"teacher_disagreement_score":0.4705477,"about_ca_system_score_codex":0.00018495896,"about_ca_system_score_gemma":0.00019927701,"threshold_uncertainty_score":0.26024273},"labels":[],"label_agreement":null},{"id":"W2014129541","doi":"10.1007/s10479-014-1761-9","title":"A new rank dependent utility approach to model risk averse preferences in portfolio optimization","year":2015,"lang":"en","type":"article","venue":"Annals of Operations Research","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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 Toronto","funders":"","keywords":"Portfolio; Portfolio optimization; Risk aversion (psychology); Computer science; Distortion (music); Theory of computation; Mathematical optimization; Rank (graph theory); Econometrics; Parametric statistics; Selection (genetic algorithm); Function (biology); Expected utility hypothesis; Mathematical economics; Economics; Mathematics; Artificial intelligence; Algorithm; Statistics; Financial economics","score_opus":0.5797632174622376,"score_gpt":0.5203476363812555,"score_spread":0.059415581080982105,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2014129541","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":[0.14475305,0.00015327823,0.80373126,0.0009199818,0.0000720495,0.00094081945,0.00005887708,0.000020279,0.04935038],"genre_scores_gemma":[0.9156183,0.0007563619,0.07873763,0.00006327885,0.000044620087,0.00006125387,0.00004044857,0.000011038554,0.004667094],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99427396,0.0009706881,0.0009204858,0.000606365,0.0028151304,0.0004133954],"domain_scores_gemma":[0.9950723,0.00024565516,0.00008985336,0.0008029335,0.0033061036,0.00048314274],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.012735449,0.00013736174,0.00030709777,0.0013935796,0.00021052305,0.0003790434,0.00084827025,0.00012936884,0.00021048586],"category_scores_gemma":[0.007028836,0.000109404915,0.00006882538,0.0030847825,0.00008954571,0.000960204,0.00023336301,0.00031035335,0.000117894124],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012020598,0.00019059649,0.0056888876,0.0000011465057,0.0000068363674,8.4313655e-7,0.0020608923,0.9522055,0.000005456474,0.0010454472,0.02342075,0.01525347],"study_design_scores_gemma":[0.00039569376,0.000110869245,0.0021665955,0.000007642756,0.000003010231,0.0000012869606,0.0014840652,0.9892449,0.00032555987,0.005501231,0.00064523355,0.0001139079],"about_ca_topic_score_codex":0.0037899858,"about_ca_topic_score_gemma":0.0009818436,"teacher_disagreement_score":0.7708652,"about_ca_system_score_codex":0.000034642035,"about_ca_system_score_gemma":0.0013846352,"threshold_uncertainty_score":0.84146804},"labels":[],"label_agreement":null},{"id":"W2016291901","doi":"10.1007/s00780-012-0200-5","title":"Bounds for the sum of dependent risks and worst Value-at-Risk with monotone marginal densities","year":2013,"lang":"en","type":"article","venue":"Finance and Stochastics","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":153,"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 Natural Science Foundation of China; Georgia Institute of Technology; National Science Foundation","keywords":"Monotone polygon; Mathematics; Upper and lower bounds; Mathematical finance; Marginal distribution; Value (mathematics); Distribution (mathematics); Applied mathematics; Combinatorics; Statistics; Random variable; Mathematical analysis; Economics","score_opus":0.052422142392076036,"score_gpt":0.3100022229815693,"score_spread":0.2575800805894933,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2016291901","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":[0.63361156,0.0009010975,0.3648358,0.00015046405,0.00007587173,0.00028366444,0.000046839978,0.0000043218797,0.000090369715],"genre_scores_gemma":[0.99129754,0.0018533116,0.0051883636,0.0000415433,0.000044589135,0.00004605989,0.000003043198,0.000007693437,0.0015178494],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99898326,0.000025218933,0.00026597705,0.00021474007,0.00034868406,0.00016212849],"domain_scores_gemma":[0.99840677,0.000806686,0.0002503454,0.00023956771,0.0002595436,0.000037107227],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047936718,0.00010671441,0.000196983,0.000060004382,0.0003417346,0.00012669098,0.00013659337,0.000051017,0.000012838885],"category_scores_gemma":[0.00022010187,0.000059526927,0.000026553542,0.00013907033,0.00024636093,0.00014780845,0.00006337226,0.00006958827,0.000005659538],"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":[0.0017138019,0.00017122237,0.4013835,0.00004800341,0.00017193619,0.000009503163,0.009667912,0.26016757,0.00014021514,0.044267066,0.017931322,0.26432794],"study_design_scores_gemma":[0.0013976682,0.0007505256,0.5431311,0.000046136003,0.00013407259,0.000041729356,0.0019141322,0.41692334,0.00026579364,0.026432533,0.008646179,0.00031675477],"about_ca_topic_score_codex":0.00028383936,"about_ca_topic_score_gemma":0.00013619552,"teacher_disagreement_score":0.35964742,"about_ca_system_score_codex":0.000008576078,"about_ca_system_score_gemma":0.000034552577,"threshold_uncertainty_score":0.26283807},"labels":[],"label_agreement":null},{"id":"W2026351326","doi":"10.1080/13504860600563143","title":"Liquidity Risk with Coherent Risk Measures","year":2006,"lang":"en","type":"article","venue":"Applied Mathematical Finance","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Market liquidity; Liquidity risk; Risk measure; Portfolio; Martingale (probability theory); Economics; Spectral risk measure; Actuarial science; Econometrics; Portfolio optimization; Financial economics; Business; Finance; Mathematics","score_opus":0.030650612770801923,"score_gpt":0.2857963265549404,"score_spread":0.2551457137841385,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2026351326","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":[0.41793424,0.00013256833,0.516494,0.00007718528,0.000052574982,0.00039835772,0.000026842545,0.00008496147,0.064799316],"genre_scores_gemma":[0.95909745,0.0005022211,0.039002582,0.00002756156,0.00009831671,0.00007752767,0.0000037965297,0.00001964664,0.0011709293],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9971274,0.00009156643,0.0006648326,0.00052070315,0.0012821999,0.0003132798],"domain_scores_gemma":[0.99763846,0.0008275028,0.0005151145,0.0007722448,0.00017927431,0.00006742558],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001765169,0.00021133645,0.00039690282,0.0000816694,0.0002493007,0.0001902815,0.00044843316,0.000103364306,0.00017348329],"category_scores_gemma":[0.00050374237,0.0001289369,0.00007961072,0.00058794173,0.00018052313,0.00012383999,0.0000626813,0.00022033982,0.0011671499],"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":[0.00031369933,0.0007080351,0.014359099,0.00001037013,0.000033697255,0.000014967997,0.00048311186,0.055294756,0.00012137212,0.70063376,0.034352258,0.19367488],"study_design_scores_gemma":[0.0005713806,0.000085464,0.0142478775,0.000019137997,0.000050792903,0.000007824443,0.000070862676,0.0074528605,0.0015560365,0.92646545,0.04914524,0.00032705648],"about_ca_topic_score_codex":0.000041224328,"about_ca_topic_score_gemma":0.000028457089,"teacher_disagreement_score":0.54116315,"about_ca_system_score_codex":0.000022863263,"about_ca_system_score_gemma":0.000045640278,"threshold_uncertainty_score":0.99961054},"labels":[],"label_agreement":null},{"id":"W2026737856","doi":"10.1016/j.na.2009.03.085","title":"Portfolio risk minimization and differential games","year":2009,"lang":"en","type":"article","venue":"Nonlinear Analysis","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"University of Calgary","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Hamilton–Jacobi–Bellman equation; Differential game; Risk measure; Dynamic risk measure; Markov chain; Time consistency; Mathematical optimization; Measure (data warehouse); Portfolio; Mathematical economics; Markov process; Stochastic differential equation; Computer science; Probability measure; Mathematics; Coherent risk measure; Portfolio optimization; Bellman equation; Applied mathematics; Economics; Finance","score_opus":0.026212882148966856,"score_gpt":0.3376331598429519,"score_spread":0.31142027769398506,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2026737856","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":[0.8262549,0.00019132468,0.16898409,0.00036636926,0.000092284194,0.00008522293,0.000053527237,0.000052994223,0.003919269],"genre_scores_gemma":[0.98492664,0.0011997997,0.009545683,0.00014590818,0.00017052793,0.0000012134025,0.00010347541,0.000006063508,0.0039006828],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99790674,0.0001469151,0.00056200725,0.00044984036,0.00076046924,0.00017402113],"domain_scores_gemma":[0.9986616,0.00016152469,0.00034123458,0.0004523144,0.00025402085,0.00012933071],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0005638954,0.00013865186,0.00037650033,0.00087106694,0.00016386382,0.00028401506,0.000221703,0.00009316618,0.0010364632],"category_scores_gemma":[0.0005644567,0.00010185431,0.00024526776,0.00268261,0.000044214725,0.00024031701,0.000030037963,0.00009308435,0.000052984127],"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":[0.00006697675,0.00018416844,0.53695273,6.706838e-7,0.0004199293,0.000008948292,0.00045387438,0.031077733,0.00007324425,0.00027338037,0.0040619564,0.42642638],"study_design_scores_gemma":[0.00027099258,0.00005539281,0.41160396,0.0000012152142,0.0008611005,0.0000019228082,0.00009555255,0.57359105,0.00013840898,0.0024174424,0.0107931495,0.0001698386],"about_ca_topic_score_codex":0.000049090107,"about_ca_topic_score_gemma":0.00006343459,"teacher_disagreement_score":0.5425133,"about_ca_system_score_codex":0.000009419599,"about_ca_system_score_gemma":0.000023027153,"threshold_uncertainty_score":0.99987674},"labels":[],"label_agreement":null},{"id":"W2027387834","doi":"10.1016/j.jbankfin.2009.11.013","title":"CAPM and APT-like models with risk measures","year":2009,"lang":"en","type":"article","venue":"Journal of Banking & Finance","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":30,"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":"Concordia University","keywords":"Capital asset pricing model; Bounded function; Portfolio; Spectral risk measure; Econometrics; Economics; Market risk; CVAR; Standard deviation; Market portfolio; Coherent risk measure; Risk measure; Dynamic risk measure; Risk–return spectrum; Actuarial science; Expected shortfall; Mathematics; Financial economics; Statistics","score_opus":0.04655913976880174,"score_gpt":0.3035687785908416,"score_spread":0.25700963882203987,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2027387834","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":[0.76131225,0.003017401,0.23186927,0.0005490163,0.00021321976,0.00005838384,0.0000024772753,0.0000080571135,0.002969922],"genre_scores_gemma":[0.9730626,0.0076990696,0.018670034,0.00017698019,0.00010311476,2.668386e-7,1.2580692e-7,0.0000052668693,0.0002825451],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9980976,0.000083414576,0.0005303571,0.00016808913,0.0009695453,0.00015098917],"domain_scores_gemma":[0.9981618,0.00014797317,0.0009379815,0.00020675597,0.00049323717,0.000052288986],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018808738,0.00010226955,0.0002704028,0.00019818792,0.00015269895,0.00017575845,0.00028355577,0.00004958627,0.000008236037],"category_scores_gemma":[0.00020181303,0.000062975836,0.00006152471,0.000400585,0.000047847534,0.0007659608,0.000013146706,0.00021025538,0.00000338107],"study_design_candidate":"design_other","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":[0.00022630647,0.000049115708,0.02002501,5.944129e-7,0.000015366193,0.00006949164,0.0013231081,0.33227623,0.000051807998,0.0026336042,0.004651316,0.6386781],"study_design_scores_gemma":[0.0023681866,0.0017102038,0.35902697,0.00029734534,0.00012251077,0.0011370992,0.0002804459,0.093981355,0.0004698738,0.42298722,0.1170433,0.00057546876],"about_ca_topic_score_codex":0.000011752796,"about_ca_topic_score_gemma":0.000011388858,"teacher_disagreement_score":0.6381026,"about_ca_system_score_codex":0.000016970282,"about_ca_system_score_gemma":0.00008580755,"threshold_uncertainty_score":0.25680798},"labels":[],"label_agreement":null},{"id":"W2027515466","doi":"10.1007/s11081-009-9095-1","title":"A two-stage stochastic mixed-integer programming approach to the index tracking problem","year":2009,"lang":"en","type":"article","venue":"Optimization and Engineering","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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 Toronto","funders":"","keywords":"Portfolio; Index (typography); Stochastic programming; Computer science; Mathematical optimization; Integer programming; Set (abstract data type); Integer (computer science); Tracking (education); Dynamic programming; Mathematics; Finance","score_opus":0.031243170206080174,"score_gpt":0.2926003849621429,"score_spread":0.2613572147560627,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2027515466","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":[0.0015694443,0.00007688527,0.99531883,0.00042118013,0.00013408382,0.00041089783,0.00000248209,0.000115842464,0.0019503261],"genre_scores_gemma":[0.85903585,0.000022704106,0.14009665,0.00015803768,0.00009613775,0.000030192508,0.000010758496,0.000017333341,0.0005323329],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984777,0.000035271918,0.00038723758,0.00034724784,0.00048658732,0.00026593293],"domain_scores_gemma":[0.9992456,0.000099145385,0.00009543084,0.00026764828,0.00015535497,0.0001368491],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009399498,0.0001647508,0.00017626035,0.00027585178,0.00018664016,0.00057949027,0.00025532226,0.000058431582,0.000016514483],"category_scores_gemma":[0.0003587804,0.000112664144,0.000046010697,0.0010454116,0.000014362036,0.0003595384,0.00003894156,0.00014134478,0.000007139624],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000061288265,0.000020665926,0.00010461603,0.000001822,0.000004040759,5.149588e-7,0.00067383295,0.96655303,0.000010245014,0.0016192987,0.0001346041,0.030871179],"study_design_scores_gemma":[0.00020016126,0.000028235287,0.00063104555,0.000015110402,0.0000071312675,0.000010216268,0.000336439,0.9936058,0.000008645417,0.00003900597,0.0049644387,0.00015376619],"about_ca_topic_score_codex":0.00000777923,"about_ca_topic_score_gemma":0.0000025426982,"teacher_disagreement_score":0.8574664,"about_ca_system_score_codex":0.000021755315,"about_ca_system_score_gemma":0.000019082032,"threshold_uncertainty_score":0.5588038},"labels":[],"label_agreement":null},{"id":"W2027778813","doi":"10.1016/j.compchemeng.2014.03.019","title":"Optimal scenario reduction framework based on distance of uncertainty distribution and output performance: I. Single reduction via mixed integer linear optimization","year":2014,"lang":"en","type":"article","venue":"Computers & Chemical Engineering","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":72,"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":"National Science Foundation","keywords":"Reduction (mathematics); Mathematical optimization; Range (aeronautics); Probabilistic logic; Integer (computer science); Computation; Measure (data warehouse); Linear programming; Computer science; Distribution (mathematics); Integer programming; Algorithm; Mathematics; Engineering; Data mining; Artificial intelligence","score_opus":0.01667038671667964,"score_gpt":0.24837768996661896,"score_spread":0.23170730324993932,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2027778813","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":[0.25758475,0.000016166816,0.74141645,0.00010368536,0.0006867421,0.00009992497,0.000004850719,0.00006778444,0.000019646981],"genre_scores_gemma":[0.8930775,0.000018146868,0.10647041,0.0000110941755,0.00025737542,0.000005972274,0.00013071734,0.000016874943,0.000011899039],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99819505,0.00004820789,0.0005543973,0.0004509318,0.00053031184,0.00022110103],"domain_scores_gemma":[0.9987997,0.00024517498,0.00023320557,0.00035082203,0.00025306086,0.00011802621],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005816019,0.00020406503,0.00029247892,0.00015910203,0.00007949589,0.00008087391,0.00020897837,0.00018163821,0.000006989652],"category_scores_gemma":[0.0006037881,0.00018570025,0.00007765146,0.0006140437,0.00009188816,0.0002724289,0.000052040992,0.00025116693,0.000003278798],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000082777646,0.000053246014,0.00006984847,0.000015271726,0.00000559143,1.7394973e-7,0.0000949058,0.9762278,0.0023164062,0.00011781464,0.00015602319,0.020860102],"study_design_scores_gemma":[0.00027295193,0.00011898547,0.00015212731,0.0001459458,0.000012850024,0.000008071969,0.000014866744,0.9826858,0.015759597,0.00005272976,0.0005881623,0.00018790169],"about_ca_topic_score_codex":0.0000042121646,"about_ca_topic_score_gemma":3.3710638e-8,"teacher_disagreement_score":0.63549274,"about_ca_system_score_codex":0.00012936471,"about_ca_system_score_gemma":0.000019668474,"threshold_uncertainty_score":0.7572636},"labels":[],"label_agreement":null},{"id":"W2028690092","doi":"10.1016/j.insmatheco.2013.09.017","title":"Complete mixability and asymptotic equivalence of worst-possible VaR and ES estimates","year":2013,"lang":"en","type":"article","venue":"Insurance Mathematics and Economics","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Equivalence (formal languages); Limit (mathematics); Mathematics; Applied mathematics; Value at risk; Value (mathematics); Expected shortfall; Mathematical analysis; Statistics; Discrete mathematics; Risk management; Economics","score_opus":0.0593064509207077,"score_gpt":0.29807919805214145,"score_spread":0.23877274713143376,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2028690092","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":[0.99580956,0.00041114236,0.0025817319,0.00014707091,0.000060403567,0.00019874967,0.000038145157,0.00001025162,0.0007429318],"genre_scores_gemma":[0.9625617,0.0020032367,0.035333045,0.00002790791,0.000011110821,0.000008009319,0.0000014673146,0.00000773179,0.00004576286],"study_design_codex":"observational","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99886554,0.000016882266,0.0005994608,0.0002697626,0.000103079925,0.00014524243],"domain_scores_gemma":[0.9983249,0.00086373463,0.0003131026,0.00029346708,0.00011455517,0.000090252004],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007197284,0.00012441743,0.00036724203,0.00008008711,0.00010084525,0.00026354534,0.00015502848,0.000052943982,0.00004789851],"category_scores_gemma":[0.00040314972,0.000096754295,0.000027406042,0.00009219568,0.00027826277,0.00040808026,0.000104980354,0.00004709142,0.000016622047],"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":[0.000015083887,0.00012326367,0.8938168,0.00022633505,0.000037490623,5.889009e-7,0.0031797916,0.0039160484,0.00039643003,0.023282299,0.00016606906,0.07483979],"study_design_scores_gemma":[0.00028889102,0.00006017574,0.29094896,0.00005126094,0.000011606246,0.00001977316,0.00042987792,0.33672917,0.00024137864,0.3708937,0.00013990482,0.00018531372],"about_ca_topic_score_codex":0.00005015131,"about_ca_topic_score_gemma":0.000014127367,"teacher_disagreement_score":0.60286784,"about_ca_system_score_codex":0.00000658023,"about_ca_system_score_gemma":0.000015302492,"threshold_uncertainty_score":0.39455256},"labels":[],"label_agreement":null},{"id":"W2030612922","doi":"10.1155/2010/976371","title":"Continuous Time Portfolio Selection under Conditional Capital at Risk","year":2010,"lang":"en","type":"article","venue":"Journal of Probability and Statistics","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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 Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Spectral risk measure; Quasiconvex function; Dynamic risk measure; Portfolio; Portfolio optimization; Measure (data warehouse); Risk measure; Selection (genetic algorithm); Econometrics; Coherent risk measure; Time consistency; Capital (architecture); Mathematics; Actuarial science; Expected shortfall; Economics; Computer science; Financial economics; Convex optimization; Regular polygon","score_opus":0.022435542528409316,"score_gpt":0.3075065108195431,"score_spread":0.2850709682911338,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2030612922","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":[0.74988467,0.000036224177,0.24879424,0.0001395175,0.00037069782,0.00009585941,0.00028215512,0.000006544423,0.000390081],"genre_scores_gemma":[0.8659246,0.0001512336,0.13219081,0.000059627444,0.00020960283,0.0000011362689,0.000022525603,0.000007492085,0.001432958],"study_design_codex":"observational","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9978463,0.00017791889,0.00085438107,0.00018508927,0.00079365796,0.00014266632],"domain_scores_gemma":[0.99666905,0.0009082691,0.00093473686,0.00013788286,0.0011820097,0.00016804859],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.002765304,0.000105429324,0.0002787489,0.00014087511,0.00023435772,0.00015006527,0.00014670308,0.00010607434,0.0016381505],"category_scores_gemma":[0.0029252928,0.00007668786,0.000068319736,0.00017727698,0.00022391875,0.00030299556,0.000036641097,0.0003614467,0.000055006596],"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":[0.000537723,0.00052114076,0.6830321,0.000011725347,0.00013332527,0.00004691254,0.0009441667,0.010796889,0.0020537958,0.17925599,0.07345393,0.04921228],"study_design_scores_gemma":[0.0005054612,0.00027324064,0.24319692,0.000003373505,0.00005351352,0.00041375728,0.000062904925,0.009104393,0.000107753825,0.7398447,0.0063163317,0.000117651594],"about_ca_topic_score_codex":0.000020669097,"about_ca_topic_score_gemma":0.00015888612,"teacher_disagreement_score":0.5605887,"about_ca_system_score_codex":0.00003979226,"about_ca_system_score_gemma":0.00016223393,"threshold_uncertainty_score":0.9992745},"labels":[],"label_agreement":null},{"id":"W2038051688","doi":"10.3138/infor.47.1.15","title":"Dotted Representations of Mean-Variance Efficient Frontiers and their Computation","year":2009,"lang":"en","type":"article","venue":"INFOR Information Systems and Operational Research","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"","keywords":"Efficient frontier; Variance (accounting); Portfolio; Computer science; Computation; Mathematical optimization; Variety (cybernetics); Selection (genetic algorithm); Frontier; Mathematics; Algorithm; Economics; Artificial intelligence","score_opus":0.08982958862662074,"score_gpt":0.41474373434920897,"score_spread":0.32491414572258825,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2038051688","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":[0.37854826,0.0007398731,0.5895685,0.0021395544,0.0005123012,0.0015980774,0.00016656499,0.000043725486,0.026683165],"genre_scores_gemma":[0.99744254,0.00017526875,0.0017668541,0.00009901736,0.00004419575,0.000020976582,0.000102053826,0.0000025235793,0.00034659205],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99702793,0.00020286477,0.001005641,0.00016686627,0.0014209236,0.0001757813],"domain_scores_gemma":[0.99647665,0.0005514958,0.00026132492,0.00020082411,0.0023977533,0.000111972324],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0042456766,0.00009792778,0.000208893,0.0007441683,0.00043377763,0.00082877075,0.00016896728,0.000079805286,0.000011935537],"category_scores_gemma":[0.0010988938,0.00006901979,0.00003112277,0.00093358604,0.00013876871,0.001699222,0.000047867496,0.00012882953,0.000024541483],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016889205,0.00005654097,0.0064453846,0.000029407034,0.00003362297,6.3401154e-7,0.018560154,0.65293366,0.00025764492,0.16553453,0.025848035,0.1301315],"study_design_scores_gemma":[0.0006128091,0.000121636076,0.03895542,0.000030715888,0.0000024345522,0.000016393826,0.009080975,0.926484,0.00015475617,0.0022752373,0.02214908,0.00011652247],"about_ca_topic_score_codex":0.00014457676,"about_ca_topic_score_gemma":0.0000042382335,"teacher_disagreement_score":0.6188943,"about_ca_system_score_codex":0.000031996813,"about_ca_system_score_gemma":0.00016524093,"threshold_uncertainty_score":0.7991856},"labels":[],"label_agreement":null},{"id":"W2039604625","doi":"10.1017/s0515036100014914","title":"Quantifying and Correcting the Bias in Estimated Risk Measures","year":2007,"lang":"en","type":"article","venue":"Astin Bulletin","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":21,"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":"Econometrics; Monte Carlo method; Statistics; Estimation; Computer science; Mathematics; Economics","score_opus":0.2776417928637508,"score_gpt":0.40950337000740095,"score_spread":0.13186157714365015,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2039604625","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":[0.9616549,0.00059034413,0.032493927,0.0009611381,0.00035671284,0.00015453703,0.000001927036,0.000044689783,0.0037418287],"genre_scores_gemma":[0.9946458,0.0002027152,0.0046075378,0.00012374201,0.000055751294,0.0000026584412,0.0000012184361,0.000009828164,0.00035077854],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9979725,0.0002976601,0.0005585584,0.00030889385,0.0005997243,0.00026270165],"domain_scores_gemma":[0.994837,0.004415664,0.0002861933,0.0002559745,0.00014391643,0.000061250685],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.012369226,0.00011069045,0.00016512227,0.00020965839,0.0002874209,0.00021617977,0.00025470555,0.00006363352,0.00015266114],"category_scores_gemma":[0.014806069,0.00006768858,0.000034098925,0.0007386516,0.000086531436,0.000050431725,0.00007525985,0.00023062548,0.0001914405],"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":[0.00002733914,0.000008272108,0.624548,3.4843572e-7,0.0000020739153,0.000007765675,0.0006251003,0.004758269,0.000019752011,0.00005051185,0.0028618895,0.36709064],"study_design_scores_gemma":[0.00040176776,0.000034278655,0.9140463,0.00004175946,0.00001114873,0.00006159213,0.0025382994,0.020398498,0.0004999785,0.0006167269,0.061167274,0.00018238129],"about_ca_topic_score_codex":0.00088392285,"about_ca_topic_score_gemma":0.0007705384,"teacher_disagreement_score":0.36690825,"about_ca_system_score_codex":0.000012855976,"about_ca_system_score_gemma":0.000019641318,"threshold_uncertainty_score":0.99349266},"labels":[],"label_agreement":null},{"id":"W2041781650","doi":"10.1007/s10589-007-9126-9","title":"Algorithm for cardinality-constrained quadratic optimization","year":2007,"lang":"en","type":"article","venue":"Computational Optimization and Applications","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":255,"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; Quadratic programming; Cardinality (data modeling); Mathematical optimization; Solver; Selection (genetic algorithm); Branch and bound; Quadratic equation; Optimization problem; Integer programming; Limit (mathematics); Integer (computer science); Computer science","score_opus":0.04823591415135936,"score_gpt":0.37538249784272015,"score_spread":0.3271465836913608,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2041781650","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":[0.00007344229,0.00011526363,0.9947697,0.00065808237,0.00012501822,0.0011777455,0.00010283315,0.000113657006,0.0028642765],"genre_scores_gemma":[0.03300549,0.00008711335,0.9644405,0.00047598736,0.0002678416,0.00022772062,0.00090036565,0.00002704609,0.00056788913],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99742985,0.0000734539,0.0009779282,0.000586645,0.00066607184,0.00026606707],"domain_scores_gemma":[0.99633706,0.0014023246,0.00042046912,0.00029861642,0.0013287691,0.0002127837],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001990587,0.00020129849,0.00027622946,0.0004335996,0.0006297922,0.00033608897,0.00026147626,0.00012625167,0.000120077384],"category_scores_gemma":[0.00031129824,0.0001901526,0.000112853406,0.0011926501,0.00016574319,0.00042331545,0.000045161396,0.00008535473,0.00002060676],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008707399,0.000050323484,0.00020157662,0.0000023982489,0.00001356594,2.585179e-7,0.000072603616,0.8306187,0.0000018023297,0.023357345,0.00033269852,0.14534001],"study_design_scores_gemma":[0.0006421173,0.000036351,0.00043639523,0.000004355396,0.000030420932,0.000015573365,0.0003195394,0.96333236,0.000013216322,0.024708195,0.010246634,0.00021487822],"about_ca_topic_score_codex":0.000005282716,"about_ca_topic_score_gemma":0.0000029318953,"teacher_disagreement_score":0.14512514,"about_ca_system_score_codex":0.00004758851,"about_ca_system_score_gemma":0.00012368102,"threshold_uncertainty_score":0.7754198},"labels":[],"label_agreement":null},{"id":"W2041879434","doi":"10.5267/j.ijiec.2013.03.006","title":"Project selection problem under uncertainty: An application of utility theory and chance constrained programming to a real case","year":2013,"lang":"en","type":"article","venue":"International Journal of Industrial Engineering Computations","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Selection (genetic algorithm); Mathematical optimization; Computer science; Operations research; Expected utility hypothesis; Management science; Engineering; Artificial intelligence; Economics; Mathematical economics; Mathematics","score_opus":0.06433651472861027,"score_gpt":0.3669836832637936,"score_spread":0.30264716853518336,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2041879434","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":[0.44449645,0.0000063167777,0.5545136,0.00034876983,0.00023291926,0.00033831902,0.000009106097,0.000016304968,0.00003825816],"genre_scores_gemma":[0.9711333,0.000008334204,0.028517751,0.000019168097,0.00026703847,0.000022494844,0.000008407287,0.000008339757,0.000015172159],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99815816,0.0001378212,0.0008270706,0.00017686967,0.00058665965,0.00011342282],"domain_scores_gemma":[0.99685377,0.0006044389,0.00052841543,0.00009209486,0.0018115485,0.000109713474],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017447026,0.0001049718,0.00019650467,0.00060969585,0.000058966027,0.00019030177,0.0002441695,0.0000807642,0.000015935984],"category_scores_gemma":[0.0008760741,0.00008869829,0.000055612352,0.0006078027,0.000043689175,0.0005409919,0.000034893666,0.00017738264,0.0000018679755],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007530012,0.0000700398,0.001421168,0.0000014262097,0.000056539186,0.0000072205808,0.0006739369,0.7419145,0.000501048,0.0062219505,0.000093858805,0.24896298],"study_design_scores_gemma":[0.0011771977,0.00040903772,0.0029737777,0.000071403185,0.00003549788,0.0012453025,0.0015846243,0.9811788,0.00023644044,0.009792698,0.0011021462,0.00019303964],"about_ca_topic_score_codex":0.0005081901,"about_ca_topic_score_gemma":0.000035452802,"teacher_disagreement_score":0.52663684,"about_ca_system_score_codex":0.00006661624,"about_ca_system_score_gemma":0.00019972467,"threshold_uncertainty_score":0.3617011},"labels":[],"label_agreement":null},{"id":"W2041963638","doi":"10.1016/j.insmatheco.2005.03.002","title":"Bounds on the value-at-risk for the sum of possibly dependent risks","year":2005,"lang":"en","type":"article","venue":"Insurance Mathematics and Economics","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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 Trois-Rivières","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mathematics; Upper and lower bounds; Value (mathematics); Order (exchange); Applied mathematics; Quality (philosophy); Value at risk; Matrix (chemical analysis); Econometrics; Mathematical optimization; Statistics; Risk management; Economics; Mathematical analysis; Physics; Finance","score_opus":0.09064205795996066,"score_gpt":0.33285267635714344,"score_spread":0.24221061839718278,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2041963638","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":[0.9863239,0.00029806214,0.0084575955,0.0013775624,0.0001832444,0.00034071459,0.00013734294,0.0000067141104,0.0028748426],"genre_scores_gemma":[0.9910054,0.0035220853,0.004518922,0.0002075875,0.000090921625,0.000027019809,0.0000013351628,0.000012107723,0.0006145723],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99885315,0.000028382583,0.0006001738,0.0001937369,0.00017585242,0.00014868869],"domain_scores_gemma":[0.9960727,0.002690183,0.00057472585,0.0005381549,0.000087292516,0.000036930665],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023571393,0.00011193745,0.0002211683,0.000053687385,0.00037706134,0.00019266241,0.00042530103,0.0000547321,0.00003670556],"category_scores_gemma":[0.00052784313,0.00005615459,0.000100977864,0.00008477376,0.000114666116,0.00011110526,0.00006432361,0.00007482416,0.00004135495],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020390442,0.00030096507,0.055732585,0.000025804557,0.00016987698,3.4834798e-7,0.007882339,0.42311382,0.000041174644,0.25458243,0.0061935694,0.25175315],"study_design_scores_gemma":[0.0007145157,0.000118926786,0.029985853,0.000021429845,0.000046611785,0.000010836976,0.0011050069,0.7411002,0.0012502294,0.1911215,0.034294516,0.00023040122],"about_ca_topic_score_codex":0.000026518925,"about_ca_topic_score_gemma":0.0001291905,"teacher_disagreement_score":0.31798634,"about_ca_system_score_codex":0.000026155618,"about_ca_system_score_gemma":0.000026688262,"threshold_uncertainty_score":0.2900089},"labels":[],"label_agreement":null},{"id":"W2042028954","doi":"10.1287/moor.1110.0513","title":"Penalized Sample Average Approximation Methods for Stochastic Mathematical Programs with Complementarity Constraints","year":2011,"lang":"en","type":"article","venue":"Mathematics of Operations Research","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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 Victoria","funders":"","keywords":"Mathematics; Complementarity (molecular biology); Estimator; Mathematical optimization; Rate of convergence; Constraint (computer-aided design); Applied mathematics; Convergence (economics); Sample size determination; Statistics; Computer science","score_opus":0.5520442039151218,"score_gpt":0.5557295844772944,"score_spread":0.003685380562172602,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2042028954","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":[0.015893355,0.000010674054,0.977766,0.00009821218,0.00002811794,0.0025007548,0.00006447871,0.000027846505,0.0036105944],"genre_scores_gemma":[0.2565844,0.0000040111295,0.74262834,0.0000061687283,0.000013907602,0.000402367,0.00006806905,0.000017474922,0.00027526295],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99614865,0.0005277072,0.0011163751,0.00037961768,0.0014405057,0.00038713266],"domain_scores_gemma":[0.9931082,0.0034157266,0.00016581353,0.00073710515,0.002431131,0.00014201159],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.010200545,0.00016209066,0.00045940845,0.0004717129,0.00046616216,0.0003110168,0.00065980444,0.00007931059,0.0018206708],"category_scores_gemma":[0.0062997737,0.00010926397,0.00009998214,0.0009459887,0.0006503132,0.0003599528,0.0001273069,0.00018485483,0.00004964127],"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":[0.00020626198,0.0026818553,0.00026800475,0.00020178521,0.00015133126,0.0000014880312,0.02747251,0.009348858,0.0010188462,0.87381005,0.0003692091,0.08446977],"study_design_scores_gemma":[0.00082734536,0.00041678053,0.000059097743,0.00006032385,0.000028017737,0.0000136587205,0.0037927292,0.6660129,0.0018576242,0.32660848,0.00016713241,0.00015592959],"about_ca_topic_score_codex":0.00008744399,"about_ca_topic_score_gemma":0.00009053037,"teacher_disagreement_score":0.656664,"about_ca_system_score_codex":0.00004418308,"about_ca_system_score_gemma":0.00023373755,"threshold_uncertainty_score":0.9990918},"labels":[],"label_agreement":null},{"id":"W2044323442","doi":"10.5555/2133036.2133162","title":"Risk-averse stochastic optimization: probabilistically-constrained models and algorithms for black-box distributions","year":2011,"lang":"en","type":"article","venue":"","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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; Computer science; Black box; Budget constraint; Approximation algorithm; Stochastic programming; Probability distribution; Set cover problem; Robust optimization; Vertex cover; Probabilistic logic; Constraint (computer-aided design); Set (abstract data type); Mathematics; Artificial intelligence","score_opus":0.1252051040714316,"score_gpt":0.3392558638077378,"score_spread":0.21405075973630622,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2044323442","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":[0.000827666,0.000015208697,0.9893073,0.0001435725,0.00014910121,0.00073731446,0.00034742636,0.00008790097,0.008384536],"genre_scores_gemma":[0.39703023,0.00011681881,0.60122603,0.00005690812,0.0000680437,0.000075715674,0.000082298975,0.00001636787,0.0013275943],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979854,0.00009350014,0.0006393422,0.0005415458,0.00045125379,0.00028892825],"domain_scores_gemma":[0.9975978,0.00078491477,0.00023994032,0.00040824624,0.00073060585,0.00023848072],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001212841,0.0001782035,0.00026831953,0.0001471663,0.00031450493,0.00016886678,0.00027210035,0.000121102945,0.0010692799],"category_scores_gemma":[0.0020278206,0.00013096226,0.00009914842,0.00047212257,0.00039683847,0.00052293105,0.00007230255,0.00008822341,0.00004852427],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000770364,0.00013559422,0.00020645536,0.0000028394095,0.000030708736,0.000002069227,0.0006906735,0.8459785,0.000001220353,0.13778198,0.0015457125,0.013547246],"study_design_scores_gemma":[0.0004917438,0.000091928305,0.00018293392,0.0000036546799,0.000058887585,0.0000060773264,0.00028481177,0.83317626,0.000011343194,0.16529046,0.00024339429,0.00015852011],"about_ca_topic_score_codex":0.000041263753,"about_ca_topic_score_gemma":0.000017890805,"teacher_disagreement_score":0.39620256,"about_ca_system_score_codex":0.000023074177,"about_ca_system_score_gemma":0.00010064093,"threshold_uncertainty_score":0.9998439},"labels":[],"label_agreement":null},{"id":"W2045969600","doi":"10.1016/j.spl.2014.12.016","title":"Subadditivity of Value-at-Risk for Bernoulli random variables","year":2014,"lang":"en","type":"article","venue":"Statistics & Probability Letters","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Subadditivity; Superadditivity; Mathematics; Bernoulli's principle; Random variable; Bernoulli process; Value (mathematics); Construct (python library); Applied mathematics; Statistical physics; Combinatorics; Statistics; Mathematical economics; Thermodynamics","score_opus":0.033217988820359444,"score_gpt":0.3109846246109201,"score_spread":0.27776663579056066,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2045969600","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":[0.12638135,0.000011111669,0.86930496,0.00059567974,0.0003500333,0.00068406237,0.0022193526,0.00002780063,0.0004256486],"genre_scores_gemma":[0.21662727,0.000041165666,0.78217494,0.00047581462,0.00013158195,0.00007701336,0.0002028956,0.000023529843,0.00024579227],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99636155,0.00078280526,0.0009896286,0.0006015942,0.000932123,0.0003322909],"domain_scores_gemma":[0.9883941,0.009434016,0.00071872317,0.0007971003,0.00053847977,0.000117583935],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0069739195,0.00019427124,0.000522577,0.00012270783,0.00025694675,0.000092524664,0.00044236716,0.000086568376,0.00020771683],"category_scores_gemma":[0.017168473,0.00015622764,0.00014754418,0.00033075438,0.00036662954,0.00016499916,0.00009381656,0.00011434778,0.000033677],"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":[0.0016321631,0.00046100857,0.22030446,0.00014454153,0.00013770367,0.0000024104693,0.0015880612,0.028441496,0.0023278268,0.40100735,0.20168684,0.14226612],"study_design_scores_gemma":[0.0017183638,0.00013884356,0.020508803,0.0000097450475,0.00008875767,0.0000015050617,0.0000056006606,0.03996179,0.00078939524,0.90723264,0.029300703,0.00024384014],"about_ca_topic_score_codex":0.0002098115,"about_ca_topic_score_gemma":0.00015871541,"teacher_disagreement_score":0.5062253,"about_ca_system_score_codex":0.0000712338,"about_ca_system_score_gemma":0.00006788795,"threshold_uncertainty_score":0.9911103},"labels":[],"label_agreement":null},{"id":"W2047206123","doi":"10.1016/j.cor.2013.11.010","title":"Using robust optimization for distribution and inventory planning for a large pulp producer","year":2013,"lang":"en","type":"article","venue":"Computers & Operations Research","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Université Laval","funders":"Linköpings Universitet","keywords":"Truck; Train; Operations research; Computer science; Robust optimization; Stock (firearms); Inventory management; Stock management; Safety stock; Plan (archaeology); Operations management; Business; Supply chain; Mathematical optimization; Economics; Marketing; Mathematics","score_opus":0.38007242243603634,"score_gpt":0.4906539403902115,"score_spread":0.11058151795417515,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2047206123","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":[0.06796938,0.00013833438,0.92863077,0.0009241585,0.00023813052,0.0019698055,0.00006348218,0.000025146872,0.000040771894],"genre_scores_gemma":[0.6066424,0.00006915647,0.39015165,0.000108657725,0.00047502064,0.0007255259,0.00083271286,0.000030812258,0.0009640527],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978499,0.000235112,0.0004256893,0.00049632887,0.00060022774,0.00039274397],"domain_scores_gemma":[0.996722,0.0005122456,0.000051397616,0.00031993483,0.0022652769,0.00012918016],"candidate_categories":["sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0032405695,0.00010626878,0.00016333883,0.00035835517,0.0013083089,0.0012797903,0.00028004797,0.00008607706,0.000055290864],"category_scores_gemma":[0.0017090641,0.0000897401,0.000050404433,0.00075453485,0.00007580075,0.0009759433,0.00013311261,0.00012029219,0.000011379483],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001637351,0.000044786088,0.0011114469,0.0000065676318,0.0000081067965,1.9403122e-7,0.0005137189,0.96884656,0.000101047764,0.0021266143,0.021381794,0.0058427667],"study_design_scores_gemma":[0.00046615538,0.000084199535,0.00047396563,0.000019766303,0.000005286857,0.0000029027062,0.0003873082,0.99096966,0.00010757133,0.00071180763,0.0066656466,0.00010575143],"about_ca_topic_score_codex":0.00005879033,"about_ca_topic_score_gemma":0.000014781204,"teacher_disagreement_score":0.53867304,"about_ca_system_score_codex":0.000089277026,"about_ca_system_score_gemma":0.00017305114,"threshold_uncertainty_score":0.99999183},"labels":[],"label_agreement":null},{"id":"W2048767445","doi":"10.1002/mde.1451","title":"Reducing estimation risk in optimal portfolio selection when short sales are allowed","year":2008,"lang":"en","type":"article","venue":"Managerial and Decision Economics","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":8,"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":"Concordia University; DePaul University; George Washington University","keywords":"Constraint (computer-aided design); Portfolio; Efficient frontier; Estimation; Economics; Econometrics; Selection (genetic algorithm); Investment (military); Sample (material); Portfolio optimization; Computer science; Financial economics; Mathematics","score_opus":0.04253525907269236,"score_gpt":0.2931060110947168,"score_spread":0.25057075202202445,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2048767445","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":[0.8961468,0.00008316579,0.101570934,0.00007249114,0.0006787089,0.00022482898,0.000013195476,0.00004263721,0.0011672619],"genre_scores_gemma":[0.93261033,0.0028504645,0.063773416,0.00006204045,0.00020294427,0.000012336913,0.000025144784,0.000020446681,0.0004428562],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99755377,0.00009696431,0.0010791736,0.0007008799,0.00030985373,0.00025937957],"domain_scores_gemma":[0.998498,0.00046893017,0.0004224563,0.00036783176,0.00010834724,0.00013441993],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017731367,0.00020799754,0.00043485645,0.0007021108,0.00034148048,0.0003192565,0.00028022696,0.00016426013,0.0001551194],"category_scores_gemma":[0.0008679404,0.00018603288,0.00009723951,0.0004203669,0.000084673964,0.00096166425,0.0001337042,0.000152254,0.00011226777],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003511834,0.000050273487,0.06771622,0.0000010514393,0.000011229108,0.00001910847,0.00037061612,0.5587321,0.00001267232,0.0003770405,0.005365605,0.3669929],"study_design_scores_gemma":[0.0007928265,0.00007561328,0.26226974,0.00001756165,0.00001640931,0.00007742523,0.00025462054,0.68485105,0.00007174136,0.043524686,0.0077248625,0.00032346518],"about_ca_topic_score_codex":0.00018781702,"about_ca_topic_score_gemma":0.0002617374,"teacher_disagreement_score":0.36666945,"about_ca_system_score_codex":0.00007657626,"about_ca_system_score_gemma":0.000056770394,"threshold_uncertainty_score":0.7586201},"labels":[],"label_agreement":null},{"id":"W2048883691","doi":"10.1002/nav.1","title":"Extending the MAD portfolio optimization model to incorporate downside risk aversion","year":2001,"lang":"en","type":"article","venue":"Naval Research Logistics (NRL)","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Downside risk; Portfolio optimization; Mathematical optimization; Stochastic dominance; Portfolio; Quadratic programming; Computer science; Variance (accounting); Modern portfolio theory; Mathematics; Econometrics; Economics","score_opus":0.43634205123823044,"score_gpt":0.5104956728919372,"score_spread":0.07415362165370676,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2048883691","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":[0.019882133,0.00014396294,0.9430053,0.0013743527,0.00039695905,0.00073022157,0.00006799343,0.00008120343,0.03431783],"genre_scores_gemma":[0.9359024,0.0062951604,0.04187441,0.00028308452,0.00034331332,0.000044465996,0.000052822747,0.000046262663,0.015158074],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9915973,0.0010701163,0.00093415304,0.0008898589,0.004577581,0.00093101105],"domain_scores_gemma":[0.9926202,0.002564581,0.00039861744,0.0013553947,0.0025627776,0.00049838296],"candidate_categories":["metaresearch","sts","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.016279137,0.00025904944,0.00033873366,0.0011099876,0.0014255977,0.00089597586,0.0015825817,0.00022604504,0.00051036564],"category_scores_gemma":[0.024602149,0.00016839054,0.00013398539,0.004124961,0.0003901043,0.00048367702,0.0007255279,0.0008601942,0.0011697089],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020689589,0.000055965116,0.0064797252,0.0000012541014,0.000009455988,0.0000649375,0.00021625399,0.9303446,0.00006352501,0.0048991116,0.032875206,0.024783079],"study_design_scores_gemma":[0.0002894661,0.00015369536,0.001599256,0.000015298654,0.000015800044,0.000017902305,0.0003574567,0.9124043,0.000090372676,0.06904472,0.01580205,0.00020968803],"about_ca_topic_score_codex":0.0005330469,"about_ca_topic_score_gemma":0.000118141455,"teacher_disagreement_score":0.9160203,"about_ca_system_score_codex":0.0002495529,"about_ca_system_score_gemma":0.00043870564,"threshold_uncertainty_score":0.9998744},"labels":[],"label_agreement":null},{"id":"W2049676934","doi":"10.1145/2535557.2535562","title":"Accounting for secondary uncertainty","year":2013,"lang":"en","type":"article","venue":"","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Dalhousie University","funders":"","keywords":"Computer science; Aggregate (composite); Event (particle physics); Portfolio; Computation; Key (lock); Core (optical fiber); Uncertainty analysis; Systematic risk; Data mining; Accounting; Econometrics; Algorithm; Simulation; Finance; Mathematics; Computer security; Economics","score_opus":0.061122283754894945,"score_gpt":0.3592230966170716,"score_spread":0.29810081286217666,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2049676934","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":[0.3231479,0.0000723943,0.31270218,0.0033131063,0.00090010033,0.00085359137,0.000012100241,0.00012715021,0.35887146],"genre_scores_gemma":[0.94454134,0.000017754577,0.020866731,0.0012481698,0.00015296998,0.00005334163,0.000009841549,0.000007497145,0.033102352],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9990436,0.0000188749,0.00029204204,0.00019579665,0.0003055159,0.00014417687],"domain_scores_gemma":[0.9987138,0.00053320895,0.000094068455,0.00022389732,0.0003883267,0.00004668498],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00082747475,0.000054803957,0.00009759591,0.00009087637,0.00011275757,0.00033602372,0.00024887495,0.000039144186,0.006470304],"category_scores_gemma":[0.0007268238,0.000034292014,0.00005974198,0.00024162116,0.000019481595,0.00056924485,0.000032191132,0.00003513126,0.0011244826],"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":[0.0000041454887,0.000009721044,0.007978725,7.203284e-7,0.000003657017,1.1378534e-7,0.00011774645,0.0030821392,0.000104945175,0.004686736,0.37849447,0.6055169],"study_design_scores_gemma":[0.00042088027,0.000037512447,0.021475252,0.0000020819873,0.000004148041,0.0000023503383,0.00096172374,0.20361367,0.00039271987,0.15289319,0.62001973,0.000176753],"about_ca_topic_score_codex":0.000145484,"about_ca_topic_score_gemma":0.000026852726,"teacher_disagreement_score":0.62139344,"about_ca_system_score_codex":0.000007394515,"about_ca_system_score_gemma":0.000041992742,"threshold_uncertainty_score":0.9996533},"labels":[],"label_agreement":null},{"id":"W2050485049","doi":"10.1016/j.insmatheco.2010.05.001","title":"Bias correction for estimated distortion risk measure using the bootstrap","year":2010,"lang":"en","type":"article","venue":"Insurance Mathematics and Economics","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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 Waterloo","funders":"","keywords":"Estimator; Mathematics; Measure (data warehouse); Distortion (music); Econometrics; Expected shortfall; Statistics; Portfolio; Computer science; Economics","score_opus":0.189054526075957,"score_gpt":0.3622035809816657,"score_spread":0.17314905490570873,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2050485049","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":[0.8503944,0.000036875877,0.14753345,0.000069616435,0.0013242044,0.00023755067,0.000041928568,0.000018386967,0.00034353847],"genre_scores_gemma":[0.9799081,0.00028930788,0.019511312,0.000023038694,0.00010678722,0.000013952978,0.000004514379,0.000013640782,0.0001293396],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990395,0.000020174552,0.00048811873,0.00020767689,0.00011567738,0.000128861],"domain_scores_gemma":[0.9983417,0.0005926262,0.00053367415,0.00032272292,0.00016183284,0.000047453133],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018313507,0.00010647391,0.00018668921,0.0000685342,0.00039027093,0.0003103968,0.00017390629,0.000085181804,0.000013315932],"category_scores_gemma":[0.001207597,0.00006992892,0.000069957605,0.00011995701,0.00008242223,0.0002369491,0.00001939327,0.00012499496,0.000008380556],"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":[0.000118092066,0.0002084054,0.24924763,0.000027711441,0.000082158374,7.1926814e-7,0.005707986,0.21596211,0.0009824272,0.011231362,0.0024853135,0.51394606],"study_design_scores_gemma":[0.00020241854,0.000018171733,0.011955025,0.000009077384,0.000020344263,0.000023112976,0.0002743027,0.94735867,0.00033229706,0.0371444,0.002546517,0.00011566776],"about_ca_topic_score_codex":0.000036537767,"about_ca_topic_score_gemma":0.00019430798,"teacher_disagreement_score":0.73139656,"about_ca_system_score_codex":0.000015252072,"about_ca_system_score_gemma":0.000033259694,"threshold_uncertainty_score":0.30016878},"labels":[],"label_agreement":null},{"id":"W2051744421","doi":"10.1016/j.mcm.2009.02.008","title":"Asset allocation using reliability method","year":2009,"lang":"en","type":"article","venue":"Mathematical and Computer Modelling","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Computer science; Reliability (semiconductor); Asset (computer security); Reliability engineering; Computer security; Engineering","score_opus":0.14549574562920067,"score_gpt":0.3967811583165963,"score_spread":0.2512854126873957,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2051744421","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":[0.09197323,0.000025555175,0.90682447,0.0003496425,0.000048573795,0.00008990051,6.6177563e-7,0.000033118657,0.000654862],"genre_scores_gemma":[0.3233487,0.000014739018,0.6763205,0.00016806995,0.00006713812,7.354865e-7,0.0000012544126,0.0000032073322,0.0000756899],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99847835,0.000119210796,0.00048039312,0.0003271482,0.00045226098,0.00014261472],"domain_scores_gemma":[0.99895597,0.00042116473,0.0000986765,0.00029011993,0.0001470021,0.00008709576],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018725592,0.00010060731,0.00022536509,0.000079797304,0.00011974084,0.00023252334,0.00016644428,0.00006214002,0.000027598679],"category_scores_gemma":[0.00006750389,0.00006974121,0.000054908236,0.00023357545,0.000022060964,0.00024103791,0.00004016716,0.000078704754,0.000024316456],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000040980085,0.00005234305,0.000060801016,0.0000029739072,0.0000021406702,8.7774055e-7,0.00034936744,0.8669162,0.000018004699,0.051408093,0.00007607676,0.08110903],"study_design_scores_gemma":[0.000046709116,0.000016336358,0.000047067624,0.000008245424,0.000005501703,0.00000785838,0.000010000068,0.6067772,0.000030255116,0.39281958,0.00017632107,0.000054941444],"about_ca_topic_score_codex":0.000003571912,"about_ca_topic_score_gemma":7.017567e-8,"teacher_disagreement_score":0.3414115,"about_ca_system_score_codex":0.000010599352,"about_ca_system_score_gemma":0.000013906069,"threshold_uncertainty_score":0.28439638},"labels":[],"label_agreement":null},{"id":"W2053801759","doi":"10.5539/ibr.v1n2p34","title":"A Mean- maximum Deviation Portfolio Optimization Model","year":2009,"lang":"en","type":"article","venue":"International Business Research","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Portfolio; Portfolio optimization; Mathematical optimization; Modern portfolio theory; Computer science; Linear programming; Absolute deviation; Line (geometry); Mathematics; Economics; Statistics; Financial economics","score_opus":0.17253146517661866,"score_gpt":0.45696025623398817,"score_spread":0.2844287910573695,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2053801759","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":[0.0135509465,0.00009200492,0.8858334,0.009455629,0.0005709712,0.00032195644,0.000021255995,0.00008483709,0.09006903],"genre_scores_gemma":[0.96258974,0.00082225335,0.024311988,0.00041143908,0.00041423767,0.000025970285,0.00017739978,0.00002040372,0.011226553],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9934258,0.00020682729,0.0007674155,0.0006174608,0.0045847194,0.00039779165],"domain_scores_gemma":[0.9913255,0.00035305426,0.00023003203,0.000546146,0.0074056606,0.00013962355],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0039966754,0.00016106229,0.00020744622,0.0016704964,0.00029364953,0.0008451194,0.0012556784,0.00014277818,0.0011544636],"category_scores_gemma":[0.0037866344,0.00013411879,0.000083173436,0.0031124684,0.00009238571,0.0014173595,0.00014043582,0.0002645547,0.00046242194],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009153165,0.00011582302,0.002223574,6.869728e-7,0.000008972774,0.000011186908,0.0001520203,0.9414534,0.00014267732,0.0063199154,0.012949609,0.03653061],"study_design_scores_gemma":[0.0003608521,0.000028414637,0.020798763,0.000014554853,0.0000033598233,0.000013977986,0.00006885849,0.87873846,0.000100407626,0.08920045,0.010517535,0.00015434042],"about_ca_topic_score_codex":0.000060211565,"about_ca_topic_score_gemma":0.000014771728,"teacher_disagreement_score":0.9490388,"about_ca_system_score_codex":0.00018122037,"about_ca_system_score_gemma":0.00031457187,"threshold_uncertainty_score":0.9997586},"labels":[],"label_agreement":null},{"id":"W2054467151","doi":"10.1142/s0219024911006802","title":"DYNAMIC PORTFOLIO SELECTION UNDER CAPITAL-AT-RISK WITH NO SHORT-SELLING CONSTRAINTS","year":2011,"lang":"en","type":"article","venue":"International Journal of Theoretical and Applied Finance","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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 Calgary","funders":"Mitacs","keywords":"Portfolio optimization; Portfolio; Downside risk; Constraint (computer-aided design); Mathematical optimization; Post-modern portfolio theory; Replicating portfolio; Computer science; Risk measure; Black–Litterman model; Optimization problem; Asset (computer security); Economics; Investment strategy; Econometrics; Microeconomics; Mathematics; Financial economics","score_opus":0.017752817381558882,"score_gpt":0.2814454631092467,"score_spread":0.2636926457276878,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2054467151","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":[0.7991732,0.000063329324,0.17650104,0.00009387176,0.00032696355,0.00006762131,0.000011849514,0.000008262873,0.023753893],"genre_scores_gemma":[0.9840788,0.0012134222,0.014204566,0.00010997863,0.000085871645,0.000002108303,0.0000020888483,0.000009887328,0.00029322464],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9981325,0.000041406154,0.00060850795,0.00022931509,0.00083509745,0.00015312908],"domain_scores_gemma":[0.99846935,0.00020282214,0.00046641444,0.00010186002,0.00067017303,0.000089398],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00089207693,0.00013104762,0.00022275388,0.00017960106,0.00009278698,0.00008930775,0.0003829195,0.00007754137,0.00082237035],"category_scores_gemma":[0.0001261871,0.000084321844,0.000070286806,0.00016780973,0.0006293488,0.00018471287,0.000055046876,0.0002276194,0.00004799447],"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":[0.0016700866,0.000170321,0.0148412855,0.0000011956589,0.00013473387,0.000055270073,0.00063243636,0.0075903037,0.00048621048,0.8898615,0.00035016594,0.084206484],"study_design_scores_gemma":[0.0020487122,0.0007122087,0.057306085,0.00010194119,0.00012590316,0.001751919,0.00071893283,0.027043276,0.005753426,0.8993993,0.004432443,0.00060587766],"about_ca_topic_score_codex":0.0000031131744,"about_ca_topic_score_gemma":0.0000044444873,"teacher_disagreement_score":0.18490568,"about_ca_system_score_codex":0.00004749142,"about_ca_system_score_gemma":0.000055881526,"threshold_uncertainty_score":0.9004379},"labels":[],"label_agreement":null},{"id":"W2054507424","doi":"10.4236/jmf.2013.33039","title":"Risk Measures and Nonlinear Expectations","year":2013,"lang":"en","type":"article","venue":"Journal of Mathematical Finance","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Western University","funders":"Korea Science and Engineering Foundation; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Coherent risk measure; Probability measure; Risk measure; Dynamic risk measure; Econometrics; Mathematics; Spectral risk measure; Mathematical economics; Converse; Measure (data warehouse); Economics; Choquet integral; Expected shortfall; Actuarial science; Computer science; Financial economics; Risk management; Statistics; Finance; Artificial intelligence","score_opus":0.048861636892746085,"score_gpt":0.33994916456104535,"score_spread":0.29108752766829926,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2054507424","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":[0.8002971,0.0006435673,0.19490734,0.0011600046,0.00011699483,0.0001009388,0.0000028877448,0.000005877879,0.002765288],"genre_scores_gemma":[0.8361916,0.0017275994,0.16104436,0.00004791911,0.00011395901,0.0000031576703,1.2469864e-7,0.000006555315,0.00086470763],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9983177,0.00008286921,0.0007207047,0.00009465754,0.00068431057,0.000099738754],"domain_scores_gemma":[0.99776125,0.00084175804,0.00057934807,0.00016471268,0.00057865586,0.00007426649],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010860855,0.00006798062,0.00023690386,0.00011955474,0.00008177489,0.00015306758,0.00021580624,0.000041038882,0.00015484788],"category_scores_gemma":[0.004610297,0.000040268773,0.00007564003,0.00024334717,0.000068477864,0.00045010902,0.000023767578,0.0001333906,0.0002972943],"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":[0.000059945225,0.0005495706,0.020800063,0.00001106505,0.00007141016,0.00003396728,0.007819015,0.004366184,0.00038908626,0.033756085,0.05940103,0.8727426],"study_design_scores_gemma":[0.0005903894,0.00021967407,0.036907125,0.00006538249,0.00004136702,0.00025032155,0.0015800039,0.06369962,0.0004493612,0.87319565,0.022811461,0.00018965932],"about_ca_topic_score_codex":0.000001996169,"about_ca_topic_score_gemma":7.19251e-7,"teacher_disagreement_score":0.87255293,"about_ca_system_score_codex":0.0000068929094,"about_ca_system_score_gemma":0.000032516433,"threshold_uncertainty_score":0.5519289},"labels":[],"label_agreement":null},{"id":"W2055400144","doi":"10.2139/ssrn.2335391","title":"Bounds on Capital Requirements for Bivariate Risk with Given Marginals and Partial Information on the Dependence","year":2013,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Bivariate analysis; Copula (linguistics); Mathematics; Majorization; Econometrics; Portfolio; Reinsurance; Capital requirement; Kullback–Leibler divergence; Economics; Statistics; Actuarial science; Discrete mathematics; Profit (economics)","score_opus":0.028215599256987196,"score_gpt":0.2999709534523807,"score_spread":0.2717553541953935,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2055400144","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":[0.8987328,0.00010292669,0.096359745,0.0030699468,0.00017711877,0.00053150445,0.000009347484,0.000012455654,0.0010041542],"genre_scores_gemma":[0.99715525,0.0017342406,0.00020638718,0.00023437073,0.00011351333,0.000033063712,0.000003701628,0.000007649688,0.0005118085],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9975685,0.00015780794,0.00040561336,0.00016752395,0.0008514318,0.00084916444],"domain_scores_gemma":[0.9985003,0.0003834769,0.00052358146,0.00021753891,0.00029813877,0.00007696117],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00421133,0.00013060024,0.00013432345,0.00016472417,0.00059313676,0.0007299589,0.0003269256,0.00005152796,0.000078552664],"category_scores_gemma":[0.000589373,0.000066253415,0.000052196367,0.0002141265,0.00005569482,0.0013106564,0.000022754266,0.00054871954,0.00015618764],"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":[0.0018873984,0.00017293904,0.04418941,0.0000040849573,0.00037203354,0.0000022002507,0.0035606418,0.014583659,0.000071370756,0.50076073,0.008015729,0.42637983],"study_design_scores_gemma":[0.0022922768,0.004299746,0.026959455,0.000045142475,0.00006763111,0.0001480502,0.0049454477,0.021410266,0.00028209455,0.92699236,0.012188577,0.00036897467],"about_ca_topic_score_codex":0.00011152716,"about_ca_topic_score_gemma":0.00011139286,"teacher_disagreement_score":0.42623162,"about_ca_system_score_codex":0.00014824908,"about_ca_system_score_gemma":0.00046717946,"threshold_uncertainty_score":0.703901},"labels":[],"label_agreement":null},{"id":"W2057974767","doi":"10.1145/1217856.1217860","title":"An approximation scheme for stochastic linear programming and its application to stochastic integer programs","year":2006,"lang":"en","type":"article","venue":"Journal of the ACM","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"University of Waterloo","funders":"","keywords":"Rounding; Vertex cover; Stochastic programming; Mathematical optimization; Mathematics; Linear programming; Generalization; Stochastic optimization; Integer (computer science); Integer programming; Approximation algorithm; Computer science","score_opus":0.054539277359258856,"score_gpt":0.3689163897992989,"score_spread":0.3143771124400401,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2057974767","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":[0.25882685,0.00007144934,0.73890394,0.001317996,0.00014764325,0.0007132705,0.0000017395406,0.000009542479,0.0000075662133],"genre_scores_gemma":[0.9134849,0.0000015326103,0.08594635,0.00005097684,0.00035672908,0.000041594627,0.0000034466582,0.000010901836,0.00010358786],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998414,0.00005649333,0.0006019808,0.00017212931,0.00060734694,0.0001480835],"domain_scores_gemma":[0.99775857,0.00020181817,0.0006536659,0.00050539157,0.00079066306,0.00008987386],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00178838,0.00009399635,0.00017460741,0.00017103975,0.00015223942,0.0001939151,0.0009258733,0.00005452027,0.0000023548598],"category_scores_gemma":[0.0024988025,0.000054766755,0.00008148338,0.00049377745,0.000025706113,0.00041209484,0.00010264849,0.00010614313,0.000006590511],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003649423,0.0004203784,0.00222155,0.000015051643,0.000028560535,6.400736e-7,0.001249176,0.5182161,0.0069373706,0.0029096238,0.0015344853,0.46610218],"study_design_scores_gemma":[0.0008356272,0.00065871177,0.0034711836,0.000077473014,0.000070918235,0.00008013507,0.00052007026,0.93401384,0.00079656387,0.054904412,0.0043536383,0.00021744407],"about_ca_topic_score_codex":0.000005147539,"about_ca_topic_score_gemma":0.000008154629,"teacher_disagreement_score":0.654658,"about_ca_system_score_codex":0.000029479568,"about_ca_system_score_gemma":0.0000476184,"threshold_uncertainty_score":0.29914805},"labels":[],"label_agreement":null},{"id":"W2058139875","doi":"10.1016/j.compchemeng.2015.01.003","title":"Optimal robust optimization approximation for chance constrained optimization problem","year":2015,"lang":"en","type":"article","venue":"Computers & Chemical Engineering","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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 Alberta","funders":"","keywords":"Mathematical optimization; Robust optimization; Optimization problem; Portfolio optimization; Constrained optimization; Random optimization; Set (abstract data type); Continuous optimization; Computer science; Constraint (computer-aided design); Mathematics; Multi-swarm optimization; Portfolio","score_opus":0.05689615215590723,"score_gpt":0.28365677500940667,"score_spread":0.22676062285349943,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2058139875","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":[0.002286369,0.00006333317,0.99564064,0.00029979402,0.0005587832,0.0005804119,0.000010893236,0.00025330664,0.0003065003],"genre_scores_gemma":[0.06390352,0.00001461487,0.93533814,0.00006110437,0.00029372735,0.00007587853,0.00021374188,0.000036362446,0.000062900195],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977549,0.000028886607,0.00069990347,0.00053336384,0.0006376586,0.00034534335],"domain_scores_gemma":[0.9982086,0.00031607953,0.00025278918,0.00032304323,0.00064565917,0.00025385822],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009214215,0.00024260937,0.000329652,0.0002509292,0.000068685964,0.0002862092,0.00043493533,0.00016550928,0.000016685492],"category_scores_gemma":[0.00091755076,0.00023094247,0.00010721646,0.000766841,0.00004676357,0.00063542183,0.000089618516,0.00012201985,0.00000890979],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000031756175,0.000030361301,0.0000146987695,0.000010019278,0.000011342132,8.055437e-7,0.00022880192,0.9934221,0.00035491816,0.0005842932,0.0015419303,0.0037689998],"study_design_scores_gemma":[0.00090030074,0.00004669954,0.0000021019812,0.000030397156,0.0000138539735,0.000012754234,0.000041416035,0.9953704,0.0022131817,0.0001378599,0.00096210523,0.00026896142],"about_ca_topic_score_codex":0.0000018718131,"about_ca_topic_score_gemma":5.5802992e-8,"teacher_disagreement_score":0.06161715,"about_ca_system_score_codex":0.000113674905,"about_ca_system_score_gemma":0.000081861595,"threshold_uncertainty_score":0.94175607},"labels":[],"label_agreement":null},{"id":"W2065163895","doi":"10.1016/s0166-218x(03)00462-1","title":"Interval data minmax regret network optimization problems","year":2003,"lang":"en","type":"article","venue":"Discrete Applied Mathematics","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":134,"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":"Mathematics; Regret; Interval (graph theory); Minimax; Logarithm; Shortest path problem; Combinatorics; Minimum spanning tree; Path (computing); Function (biology); Bounded function; Mathematical optimization; Enhanced Data Rates for GSM Evolution; Discrete mathematics; Graph; Statistics; Computer science","score_opus":0.12630091945201155,"score_gpt":0.3558418059577799,"score_spread":0.22954088650576837,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2065163895","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":[0.00081767363,0.00011217245,0.887346,0.000113372655,0.00028651409,0.00054531096,0.000034618824,0.00008817044,0.11065611],"genre_scores_gemma":[0.085826725,0.00033842688,0.91035765,0.000223993,0.00019843427,0.00005828382,0.00042986468,0.00007279578,0.0024938209],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9967274,0.00008404874,0.0010711943,0.0006543609,0.0010680988,0.00039486558],"domain_scores_gemma":[0.9964665,0.00045164142,0.0006020947,0.0021786292,0.00016309656,0.00013801559],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0031996402,0.00025339302,0.00043374256,0.00012540756,0.00020977436,0.0004121029,0.0013270363,0.00013967894,0.00059543556],"category_scores_gemma":[0.0009933676,0.00018697047,0.00007228618,0.00096323976,0.00009508234,0.00042094954,0.00029498004,0.00015623063,0.0002967624],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016113141,0.000114707356,0.00025381718,0.00002676931,0.00004454772,0.0000030195613,0.0016091901,0.7447596,0.000036449514,0.19120163,0.05598189,0.0059522446],"study_design_scores_gemma":[0.000544378,0.000039896797,0.000023552919,0.000052754076,0.00007677349,0.000020505116,0.0013815842,0.7346318,0.00015145609,0.19427247,0.06831574,0.0004891253],"about_ca_topic_score_codex":0.0000023554846,"about_ca_topic_score_gemma":0.00000804141,"teacher_disagreement_score":0.10816229,"about_ca_system_score_codex":0.00002269113,"about_ca_system_score_gemma":0.00006936775,"threshold_uncertainty_score":0.7624434},"labels":[],"label_agreement":null},{"id":"W2067009897","doi":"10.1016/j.insmatheco.2008.05.011","title":"Optimal reinsurance under VaR and CTE risk measures","year":2008,"lang":"en","type":"article","venue":"Insurance Mathematics and Economics","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":270,"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":"Reinsurance; Actuarial science; Risk measure; Econometrics; Measure (data warehouse); Economics; Financial economics; Computer science","score_opus":0.06983459065520538,"score_gpt":0.28707261572301845,"score_spread":0.2172380250678131,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2067009897","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":[0.9774364,0.000693394,0.018741757,0.00015346268,0.0001560836,0.00012893096,0.000047902042,0.00002610948,0.0026159622],"genre_scores_gemma":[0.9386037,0.032788914,0.028041685,0.00008638655,0.000059493163,0.0000068616782,0.0000016153141,0.000017521714,0.0003938365],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99855965,0.000036452748,0.00062587817,0.00037272464,0.00020021024,0.0002050819],"domain_scores_gemma":[0.9985567,0.00041027425,0.0003948454,0.00039945752,0.00011781746,0.00012095362],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010380164,0.00017099729,0.0003693033,0.0001132498,0.00035179086,0.00019057236,0.00021675316,0.00009059015,0.00001796711],"category_scores_gemma":[0.00033101047,0.00013842185,0.000056840112,0.00014352231,0.0002094065,0.00038188294,0.00006783735,0.000115491275,0.000051550218],"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":[0.00016763176,0.00028138873,0.48150918,0.000036013807,0.00016366238,0.00002328365,0.010978707,0.30713576,0.000077344965,0.032520358,0.0023937258,0.16471295],"study_design_scores_gemma":[0.0019365544,0.00015677372,0.43607214,0.00004961067,0.000042582687,0.00055884576,0.0012972918,0.33669156,0.0006028069,0.19606145,0.025472654,0.0010577269],"about_ca_topic_score_codex":0.000026058091,"about_ca_topic_score_gemma":0.000022111755,"teacher_disagreement_score":0.16365522,"about_ca_system_score_codex":0.00001429508,"about_ca_system_score_gemma":0.000035693138,"threshold_uncertainty_score":0.5644679},"labels":[],"label_agreement":null},{"id":"W2067516990","doi":"10.2143/ast.39.1.2038062","title":"Estimating the Variance of Bootstrapped Risk Measures","year":2009,"lang":"en","type":"article","venue":"Astin Bulletin","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Quantile; Estimator; Mathematics; Measure (data warehouse); Resampling; Variance (accounting); Statistics; Econometrics; Expected shortfall; Risk measure; Computer science; Risk management; Economics","score_opus":0.0497952230909039,"score_gpt":0.32997869572803795,"score_spread":0.28018347263713406,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2067516990","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":[0.15414982,0.0007623771,0.78584784,0.013891482,0.00067621906,0.00041533023,0.000021358752,0.000099509,0.04413604],"genre_scores_gemma":[0.95814455,0.000066445864,0.040330622,0.00024818335,0.00012721182,0.000002987233,0.0000012988227,0.0000054576903,0.0010732659],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9978695,0.00027701288,0.00057090254,0.0002586197,0.0008451616,0.00017881648],"domain_scores_gemma":[0.9978139,0.00090050086,0.0004966044,0.0004908459,0.00025003654,0.000048103924],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0032337164,0.00010417978,0.00018835648,0.0000716539,0.00020847471,0.00010060599,0.0005649778,0.00005016596,0.00044438933],"category_scores_gemma":[0.005226219,0.0000614781,0.000081694016,0.00045647437,0.000081965096,0.000046926434,0.000029910658,0.00014184674,0.00026156433],"study_design_candidate":"design_other","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":[0.00004847271,0.00005129285,0.015749121,7.664833e-7,0.000010677215,0.0000033803813,0.0006401771,0.18192564,0.00026902775,0.0013538491,0.072047874,0.72789973],"study_design_scores_gemma":[0.00087284035,0.00030409583,0.4444581,0.000064334454,0.00006475805,0.000027295595,0.0004047422,0.1321291,0.0013168177,0.051663727,0.3682747,0.00041950817],"about_ca_topic_score_codex":0.00006887805,"about_ca_topic_score_gemma":0.0000037687787,"teacher_disagreement_score":0.8039947,"about_ca_system_score_codex":0.000005503455,"about_ca_system_score_gemma":0.00003460863,"threshold_uncertainty_score":0.625665},"labels":[],"label_agreement":null},{"id":"W2067761883","doi":"10.1007/s10479-014-1584-8","title":"Optimal VaR-based risk management with reinsurance","year":2014,"lang":"en","type":"article","venue":"Annals of Operations Research","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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 Waterloo","funders":"","keywords":"Reinsurance; Monotonic function; Piecewise; Actuarial science; Imperfect; Mathematical optimization; Economics; Risk management; Computer science; Mathematical economics; Econometrics; Mathematics; Finance","score_opus":0.2638871786263751,"score_gpt":0.49962539047620635,"score_spread":0.23573821184983124,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2067761883","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":[0.6332206,0.00014270002,0.2942692,0.004213472,0.0000711507,0.0006337794,0.000039641025,0.000031303516,0.067378126],"genre_scores_gemma":[0.9571759,0.0006450205,0.037430655,0.000099064564,0.0000432452,0.000048003607,0.000013102035,0.000010349806,0.0045346743],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99601734,0.00079353916,0.00043669157,0.00037110376,0.00208208,0.00029923778],"domain_scores_gemma":[0.99608517,0.00059093436,0.00006502187,0.00082339183,0.0023235616,0.00011192831],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008234114,0.00008823133,0.00017363885,0.0006195578,0.00048662804,0.00029854014,0.00060347497,0.0000447379,0.00029460614],"category_scores_gemma":[0.0013046755,0.00005939115,0.000053707932,0.001755006,0.0002208139,0.00033534004,0.00007144665,0.00018594967,0.0002481411],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000916063,0.00010157632,0.0051475437,0.000003073142,0.000016487676,0.00000283571,0.00012539719,0.93840724,0.000027501002,0.00842286,0.007444868,0.040209003],"study_design_scores_gemma":[0.0007856066,0.00083721796,0.033880975,0.000043932225,0.00000877415,0.0000020755333,0.00040312676,0.85376364,0.006696246,0.0021694938,0.10119194,0.00021695471],"about_ca_topic_score_codex":0.00016702413,"about_ca_topic_score_gemma":0.000109077875,"teacher_disagreement_score":0.32395527,"about_ca_system_score_codex":0.00000687323,"about_ca_system_score_gemma":0.00009962752,"threshold_uncertainty_score":0.37427986},"labels":[],"label_agreement":null},{"id":"W2067796483","doi":"10.1137/130915315","title":"Distributionally Robust Stochastic Knapsack Problem","year":2014,"lang":"en","type":"article","venue":"SIAM Journal on Optimization","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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","funders":"","keywords":"Knapsack problem; Mathematical optimization; Continuous knapsack problem; Mathematics; Change-making problem; Relaxation (psychology); Stochastic programming; Robust optimization; Constraint (computer-aided design); Semidefinite programming; Independence (probability theory); Random variable; Upper and lower bounds","score_opus":0.04645706831941011,"score_gpt":0.31809869475742075,"score_spread":0.27164162643801065,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2067796483","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":[0.001231616,0.000039582552,0.9853478,0.0016941162,0.0006996354,0.00018249071,0.000013829904,0.000050931234,0.010740004],"genre_scores_gemma":[0.8299525,0.0003704112,0.16178526,0.0010225183,0.0015098517,0.00001849696,0.0002057627,0.00006732047,0.00506789],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9961897,0.000389691,0.00096836226,0.00038999013,0.0017314091,0.00033084198],"domain_scores_gemma":[0.99695617,0.00053483195,0.00076815276,0.0003750653,0.0010941175,0.00027168097],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0031291908,0.00021358131,0.00028703353,0.00043054088,0.0005480477,0.0007539784,0.00053392973,0.00013412503,0.0010666932],"category_scores_gemma":[0.0024923102,0.00015818165,0.000153856,0.0009412663,0.00006726675,0.00072238344,0.000045334287,0.00033512356,0.00038469044],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000061544306,0.00007119734,0.00025832895,7.6118533e-7,0.000010254324,0.0000022703366,0.000054694396,0.97058874,0.0000043939503,0.0074441163,0.009427862,0.012075857],"study_design_scores_gemma":[0.0007096659,0.0002795774,0.0007405311,0.000036331465,0.000019508534,0.00010725488,0.000046347224,0.97076255,0.000016382925,0.013974769,0.013071123,0.00023593879],"about_ca_topic_score_codex":0.0000015345804,"about_ca_topic_score_gemma":0.0000014349154,"teacher_disagreement_score":0.82872087,"about_ca_system_score_codex":0.00011213348,"about_ca_system_score_gemma":0.00013668218,"threshold_uncertainty_score":0.99984646},"labels":[],"label_agreement":null},{"id":"W2069326331","doi":"10.1007/s10479-006-0132-6","title":"Coherent multiperiod risk adjusted values and Bellman’s principle","year":2006,"lang":"en","type":"article","venue":"Annals of Operations Research","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":426,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Ambiguity; Theory of computation; Measure (data warehouse); Coherent risk measure; Risk measure; Mathematics; Expected shortfall; Stability (learning theory); Value (mathematics); Spectral risk measure; Dynamic risk measure; Mathematical economics; Risk management; Applied mathematics; Statistics; Computer science; Economics; Algorithm; Data mining; Financial economics","score_opus":0.3529172039971666,"score_gpt":0.5307843923733416,"score_spread":0.17786718837617504,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2069326331","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":[0.9907372,0.0010182359,0.0020048583,0.0011422933,0.000054209424,0.00041329765,0.000058746366,0.000016656377,0.004554508],"genre_scores_gemma":[0.98316616,0.0028123169,0.0025372957,0.00002521866,0.00008167382,0.00003604431,0.000027908762,0.000009312245,0.0113040805],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9965962,0.00067560293,0.0006032508,0.00035691363,0.0014831888,0.0002848557],"domain_scores_gemma":[0.9963684,0.0005835591,0.000068955575,0.0005089224,0.0023695237,0.000100656165],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0049736053,0.00009180224,0.00019659633,0.0005308685,0.00065263425,0.00034438624,0.00034927882,0.000075445845,0.00034297755],"category_scores_gemma":[0.0024229398,0.00006802062,0.000056120683,0.0010363524,0.00026698384,0.00040034237,0.00016180541,0.00019597044,0.000118354976],"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":[0.00017904656,0.000996571,0.29234183,0.000014921788,0.000069570415,0.000013246119,0.0040908335,0.416845,0.0043777856,0.021080839,0.12110585,0.13888451],"study_design_scores_gemma":[0.00074624276,0.00039430987,0.5495698,0.000026758671,0.0000103234825,0.000006334179,0.0016705933,0.35121807,0.01586317,0.011213009,0.069013245,0.00026816648],"about_ca_topic_score_codex":0.00214057,"about_ca_topic_score_gemma":0.0013057679,"teacher_disagreement_score":0.25722793,"about_ca_system_score_codex":0.000009043698,"about_ca_system_score_gemma":0.00012859206,"threshold_uncertainty_score":0.5019601},"labels":[],"label_agreement":null},{"id":"W2069942923","doi":"10.1007/s10589-012-9526-3","title":"Regularized robust optimization: the optimal portfolio execution case","year":2013,"lang":"en","type":"article","venue":"Computational Optimization and Applications","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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 Waterloo","funders":"","keywords":"Mathematical optimization; Hessian matrix; Robust optimization; Regularization (linguistics); Mathematics; Optimization problem; Convex optimization; Solution set; Computer science; Portfolio optimization; Stability (learning theory); Portfolio; Set (abstract data type); Regular polygon; Applied mathematics","score_opus":0.04433641405925529,"score_gpt":0.3134835759124052,"score_spread":0.2691471618531499,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2069942923","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":[0.0014819863,0.00016961414,0.98790914,0.0040101972,0.000115739815,0.0014606719,0.000029011782,0.00014654669,0.0046770903],"genre_scores_gemma":[0.2842819,0.00043184293,0.70821995,0.0014644783,0.00039206733,0.0012487781,0.0007180533,0.00005618403,0.0031867316],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9968785,0.00023211256,0.0010327625,0.0006904001,0.0008805771,0.0002856442],"domain_scores_gemma":[0.99625486,0.00077687396,0.0005488598,0.0006194719,0.0015637169,0.00023622977],"candidate_categories":["sts","scholarly_communication","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0008993314,0.00027205612,0.00027840486,0.00034983645,0.0013406689,0.0010619785,0.00044098706,0.00014955891,0.001949818],"category_scores_gemma":[0.00026346033,0.00019942185,0.0001136204,0.001808934,0.00030695487,0.0009921074,0.0001437777,0.00018369299,0.00020216237],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000006154913,0.00006108316,0.00025310117,0.0000015429075,0.000017865395,0.0000048956304,0.00011950526,0.9645469,0.000001859056,0.020306233,0.007062632,0.0076181805],"study_design_scores_gemma":[0.00048356276,0.000022960281,0.00057044165,0.0000042139022,0.00003491578,0.00066010025,0.0005924552,0.98409414,0.0000037608322,0.0070473137,0.006242903,0.00024321725],"about_ca_topic_score_codex":0.000047931873,"about_ca_topic_score_gemma":0.0000031136572,"teacher_disagreement_score":0.28279993,"about_ca_system_score_codex":0.000046810754,"about_ca_system_score_gemma":0.00013046483,"threshold_uncertainty_score":0.999975},"labels":[],"label_agreement":null},{"id":"W2071468246","doi":"10.1137/060650003","title":"Explicit Reformulations for Robust Optimization Problems with General Uncertainty Sets","year":2008,"lang":"en","type":"article","venue":"SIAM Journal on Optimization","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Robust optimization; Mathematics; Mathematical optimization; Optimization problem; Convex optimization; Class (philosophy); Set (abstract data type); Range (aeronautics); Regular polygon; Computer science","score_opus":0.11606457632831246,"score_gpt":0.34081999397819235,"score_spread":0.22475541764987989,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2071468246","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":[0.008534913,0.000059036476,0.98615235,0.0010565331,0.0004592144,0.0008346168,0.000032188414,0.00009073305,0.0027804065],"genre_scores_gemma":[0.13236713,0.0020545106,0.8592669,0.0006335674,0.0007167732,0.00012458963,0.0004688295,0.00011180405,0.004255928],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99600446,0.00021731427,0.0012074966,0.00057801785,0.0015299433,0.00046274424],"domain_scores_gemma":[0.9957398,0.0003277769,0.0010918239,0.00052151264,0.0020302138,0.00028885913],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0014644127,0.00033551647,0.00041672616,0.0007824421,0.0014466569,0.00044655957,0.00047732398,0.00019689281,0.0003165092],"category_scores_gemma":[0.0007059148,0.00023015079,0.00018850734,0.0014667739,0.000080101774,0.0014814379,0.000032867065,0.00028329066,0.000024942954],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025993798,0.00011598067,0.0012741886,0.0000024639571,0.000029003333,0.0000076254973,0.00039523916,0.9916198,0.0000051447864,0.0007015368,0.0032720983,0.0023169324],"study_design_scores_gemma":[0.001684082,0.000605323,0.0003322987,0.000040058127,0.00003152746,0.00031291833,0.00014459639,0.9923385,0.000031189928,0.0006478141,0.0035013095,0.00033037955],"about_ca_topic_score_codex":0.000010623821,"about_ca_topic_score_gemma":0.000009622572,"teacher_disagreement_score":0.12688549,"about_ca_system_score_codex":0.00021894631,"about_ca_system_score_gemma":0.00031139457,"threshold_uncertainty_score":0.9998533},"labels":[],"label_agreement":null},{"id":"W2072354128","doi":"10.1007/s10436-006-0068-5","title":"A PDE approach for risk measures for derivatives with regime switching","year":2007,"lang":"en","type":"article","venue":"Annals of Finance","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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 Calgary","funders":"","keywords":"Mathematical finance; Economics; Computer science; Mathematics; Econometrics; Mathematical economics; Financial economics","score_opus":0.1781555842386255,"score_gpt":0.41083402421100984,"score_spread":0.23267843997238435,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2072354128","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":[0.14134052,0.00054990244,0.85567075,0.0002792284,0.00003809822,0.0004020557,0.000026895694,0.000012165411,0.0016803471],"genre_scores_gemma":[0.8024556,0.0008612789,0.19572829,0.00013259186,0.00006425235,0.00003637811,0.0000050213407,0.000011287856,0.0007053063],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9985053,0.00003392769,0.0004412978,0.00032315677,0.00044153127,0.00025478555],"domain_scores_gemma":[0.997404,0.00083076087,0.00060592586,0.00032635374,0.00079454033,0.00003842355],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0037985886,0.00010892571,0.0002583493,0.00014458204,0.00017007884,0.000046278612,0.00031891305,0.000057218323,0.0000011192008],"category_scores_gemma":[0.0018251018,0.00007378566,0.00010657583,0.0004583388,0.000060677983,0.00030284913,0.000016083508,0.000053851927,0.0000012331595],"study_design_candidate":"design_other","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":[0.0019362564,0.00019929788,0.030139804,0.00001784812,0.00006107424,0.0000012313337,0.0029746885,0.038340278,0.00052980974,0.0139742885,0.022081349,0.88974404],"study_design_scores_gemma":[0.0028435993,0.0019518774,0.17389773,0.00013583885,0.00006106426,0.000010697625,0.0018437693,0.06420689,0.11453983,0.12813537,0.51143795,0.00093538646],"about_ca_topic_score_codex":0.000026218691,"about_ca_topic_score_gemma":0.00002259355,"teacher_disagreement_score":0.88880867,"about_ca_system_score_codex":0.0000031518161,"about_ca_system_score_gemma":0.00005173443,"threshold_uncertainty_score":0.3008892},"labels":[],"label_agreement":null},{"id":"W2078251720","doi":"10.1016/j.jmva.2014.12.001","title":"On aggregation sets and lower-convex sets","year":2014,"lang":"en","type":"article","venue":"Journal of Multivariate Analysis","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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 Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; University of Waterloo","keywords":"Mathematics; Combinatorics; Convex set; Set (abstract data type); Regular polygon; Subderivative; Scaling; Constraint (computer-aided design); Marginal distribution; Convex analysis; Connection (principal bundle); Discrete mathematics; Random variable; Convex optimization; Statistics; Geometry","score_opus":0.03515416755781014,"score_gpt":0.35870748545509135,"score_spread":0.3235533178972812,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2078251720","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":[0.8564985,0.00007224247,0.14088154,0.000819521,0.00030996595,0.00004710988,0.000004141833,0.000007560281,0.0013594461],"genre_scores_gemma":[0.9947251,0.00018818247,0.0044543073,0.00022115454,0.00008262398,4.2786934e-7,0.0000029641326,0.0000066796238,0.000318571],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9972473,0.00038584718,0.00090672594,0.00023067788,0.0010905046,0.00013896178],"domain_scores_gemma":[0.99654746,0.0010854821,0.0012433077,0.00030743514,0.0006563605,0.00015993175],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0037988867,0.00012630988,0.0005039802,0.0011641702,0.000120586694,0.00022761014,0.00027422683,0.00008931227,0.00021137952],"category_scores_gemma":[0.0028535074,0.0000819318,0.00031178395,0.0014545862,0.000036859707,0.00042943496,0.000031678086,0.00015809223,0.000043937274],"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":[0.00048280475,0.00030588507,0.095583536,0.000003272982,0.0016071697,0.00004678159,0.0017087559,0.33183777,0.00052990403,0.0027099429,0.0044507924,0.5607334],"study_design_scores_gemma":[0.0013765191,0.00041259872,0.24458009,0.00002789698,0.001150163,0.00002382463,0.00013732677,0.7185213,0.00035400287,0.025602505,0.00757098,0.0002428096],"about_ca_topic_score_codex":0.000043068423,"about_ca_topic_score_gemma":0.000023332756,"teacher_disagreement_score":0.5604906,"about_ca_system_score_codex":0.000024716588,"about_ca_system_score_gemma":0.0000308959,"threshold_uncertainty_score":0.3416121},"labels":[],"label_agreement":null},{"id":"W2078547130","doi":"10.1016/j.sepro.2011.11.045","title":"Coherent Distortion Risk Measures in Portfolio Selection","year":2012,"lang":"en","type":"article","venue":"Systems Engineering Procedia","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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 Waterloo","funders":"","keywords":"CVAR; Portfolio optimization; Risk measure; Portfolio; Expected shortfall; Mathematical optimization; Coherent risk measure; Spectral risk measure; Measure (data warehouse); Linear programming; Selection (genetic algorithm); Distortion (music); Dynamic risk measure; Computer science; Modern portfolio theory; Mathematics; Economics; Data mining; Artificial intelligence; Financial economics","score_opus":0.04425118424936799,"score_gpt":0.3046182600516697,"score_spread":0.26036707580230173,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2078547130","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":[0.7749483,0.0027914764,0.21485531,0.000013550945,0.0044651083,0.00065044477,0.0000099083,0.00027327443,0.00199262],"genre_scores_gemma":[0.9981247,0.0001944314,0.0004414763,0.0000030383787,0.0005093138,0.000084278,0.000007269072,0.00002180104,0.00061369274],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9978479,0.000078962024,0.0006568598,0.0002517734,0.0008108965,0.00035358567],"domain_scores_gemma":[0.99905014,0.0001152845,0.0002571514,0.00020716964,0.00022265344,0.00014760325],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002520039,0.00015727784,0.00024290083,0.00046319666,0.0000581803,0.000110391185,0.00016748995,0.00011628664,0.000020461088],"category_scores_gemma":[0.0014781107,0.00012948852,0.000049888044,0.0012474938,0.000008558914,0.0005599735,0.000019714613,0.00017313233,0.000109827706],"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":[0.000010251357,0.00004343751,0.6466513,0.000012790787,0.000010154819,7.6676946e-7,0.00075284916,0.3400247,0.00020640955,0.00051282096,0.0025947553,0.009179698],"study_design_scores_gemma":[0.0004896718,0.000058534708,0.6675499,0.0000800549,0.000029894149,0.000058862115,0.00038469175,0.20589003,0.0006376747,0.00009660785,0.12419288,0.00053118245],"about_ca_topic_score_codex":0.00018381492,"about_ca_topic_score_gemma":0.000033477078,"teacher_disagreement_score":0.2231764,"about_ca_system_score_codex":0.0001483574,"about_ca_system_score_gemma":0.000043304266,"threshold_uncertainty_score":0.52803886},"labels":[],"label_agreement":null},{"id":"W2079342235","doi":"10.4236/ti.2011.24024","title":"An Efficient and Concise Algorithm for Convex Quadratic Programming and Its Application to Markowitz’s Portfolio Selection Model","year":2011,"lang":"en","type":"article","venue":"Technology and Investment","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Selection (genetic algorithm); Portfolio; Mathematical optimization; Computer science; Quadratic programming; Regular polygon; Convex optimization; Quadratic model; Algorithm; Mathematics; Artificial intelligence; Economics; Machine learning; Finance","score_opus":0.04855942498656025,"score_gpt":0.3317873045300353,"score_spread":0.28322787954347506,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2079342235","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":[0.54904896,0.00033924,0.44902965,0.0001823866,0.000031852192,0.0010789909,0.000005826639,0.00009703746,0.00018604803],"genre_scores_gemma":[0.89178765,0.000083999206,0.107227,0.00034575874,0.000011250458,0.0003923387,0.0000064028595,0.000009381899,0.00013620805],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99881107,0.000026134612,0.000322199,0.00047847917,0.00016811388,0.00019399838],"domain_scores_gemma":[0.99931425,0.000028978646,0.00013878751,0.0001992628,0.00017968653,0.00013903395],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006674632,0.0001298675,0.00019560134,0.0004705107,0.00021456224,0.000049251565,0.00011900561,0.00016828462,0.0000060115267],"category_scores_gemma":[0.00007702476,0.00010579176,0.000014080128,0.0004856361,0.00012998693,0.0001487011,0.000054342254,0.00006887569,0.000003992584],"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":[0.00006456071,0.0001979077,0.0045220237,0.000009427881,0.000021255573,0.0000013003449,0.0016463638,0.001066859,0.0013661622,0.22963157,0.00025170998,0.7612209],"study_design_scores_gemma":[0.0003795624,0.0004960255,0.0011724476,0.000005806265,0.000026835185,0.000022558197,0.000452717,0.9404338,0.0030807909,0.052280646,0.0015011397,0.00014766562],"about_ca_topic_score_codex":0.000010264532,"about_ca_topic_score_gemma":0.000007883334,"teacher_disagreement_score":0.93936694,"about_ca_system_score_codex":0.000018057763,"about_ca_system_score_gemma":0.000033775606,"threshold_uncertainty_score":0.4314063},"labels":[],"label_agreement":null},{"id":"W2080125129","doi":"10.1145/2465848.2465853","title":"High performance risk aggregation","year":2013,"lang":"en","type":"article","venue":"","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Computer science; Aggregate (composite); Exploit; Distributed File System; Process (computing); Set (abstract data type); Scope (computer science); Monte Carlo method; Data set; Big data; Distributed computing; Data mining; Database; Parallel computing; Operating system","score_opus":0.032602267872433026,"score_gpt":0.2997624795502578,"score_spread":0.2671602116778248,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2080125129","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":[0.94348943,0.000016929796,0.02745484,0.0003198574,0.00027543143,0.00011682055,0.0000010419692,0.00004741731,0.028278215],"genre_scores_gemma":[0.9731382,0.0004486663,0.008660276,0.00013196781,0.00006755886,0.000010483146,0.0000032925318,0.000003862429,0.017535677],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99882954,0.000054060067,0.00029146686,0.00018205005,0.00053016574,0.00011268996],"domain_scores_gemma":[0.9990622,0.00015688947,0.00015357851,0.00030398427,0.00026914326,0.00005423206],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00063347584,0.00005795767,0.00008391851,0.00011576285,0.00012454056,0.00021148726,0.00021980845,0.000039294668,0.005599457],"category_scores_gemma":[0.0003954804,0.000035666744,0.000026983831,0.00043644934,0.000024599629,0.00083179324,0.000027136211,0.000051318402,0.00977679],"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":[0.000002348421,0.000009864584,0.17108369,1.856353e-7,0.000001999529,1.12462224e-7,0.00008285291,0.010681093,0.000019878433,0.0011492488,0.03512008,0.78184867],"study_design_scores_gemma":[0.00022217295,0.00005430111,0.7125517,0.0000026004427,0.0000046401497,0.0000023898156,0.00012673702,0.23562592,0.0027270315,0.027687414,0.020854121,0.00014098427],"about_ca_topic_score_codex":0.00042496945,"about_ca_topic_score_gemma":0.000010725993,"teacher_disagreement_score":0.78170764,"about_ca_system_score_codex":0.000008107126,"about_ca_system_score_gemma":0.000014724219,"threshold_uncertainty_score":0.99530953},"labels":[],"label_agreement":null},{"id":"W2080781888","doi":"10.2139/ssrn.1993084","title":"Optimal Risk Transfer with Multiple Reinsurers","year":2011,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"University of Calgary","funders":"","keywords":"Computer science","score_opus":0.03427966090304749,"score_gpt":0.277325780223567,"score_spread":0.24304611932051953,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2080781888","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":[0.5231803,0.0005061393,0.47211647,0.000093927745,0.00020045723,0.000103499195,0.0000038755284,0.000029038869,0.00376629],"genre_scores_gemma":[0.98755443,0.006158246,0.0031923067,0.000036563444,0.00012381356,0.0000034544114,0.0000015346208,0.000023217091,0.002906444],"study_design_codex":"observational","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9964481,0.00023553018,0.0004998101,0.0003201115,0.00093612476,0.001560303],"domain_scores_gemma":[0.9988845,0.0001435073,0.0001953766,0.00032877663,0.00029325119,0.00015461209],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004944088,0.00017247438,0.00022734981,0.000263586,0.000358327,0.00012446711,0.00058768183,0.00008775505,0.00028388243],"category_scores_gemma":[0.00030493492,0.00010668257,0.00014341787,0.0005820136,0.00008790791,0.0005431921,0.000015969063,0.0013493394,0.00014398414],"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":[0.0029962384,0.00040946342,0.58930266,0.0000010829596,0.0005663611,0.00006542143,0.0070587294,0.030324439,0.00012602,0.04904803,0.0015831073,0.31851846],"study_design_scores_gemma":[0.017094256,0.009262421,0.15710472,0.000067440444,0.00073890906,0.006819015,0.037942972,0.033561826,0.0052409624,0.6601047,0.06901444,0.0030483834],"about_ca_topic_score_codex":0.0001442563,"about_ca_topic_score_gemma":0.0016372545,"teacher_disagreement_score":0.6110566,"about_ca_system_score_codex":0.00015312202,"about_ca_system_score_gemma":0.0011492324,"threshold_uncertainty_score":0.5862283},"labels":[],"label_agreement":null},{"id":"W2081081544","doi":"10.1287/mnsc.2014.2059","title":"Decision Making Under Uncertainty When Preference Information Is Incomplete","year":2015,"lang":"en","type":"article","venue":"Management Science","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Stochastic dominance; Expected utility hypothesis; Mathematical optimization; Decision maker; Preference; Certainty; Utility maximization problem; Optimal decision; Decision problem; Maximization; Portfolio; Stochastic programming; Computer science; Stochastic optimization; Optimization problem; Mathematical economics; Portfolio optimization; Robust optimization; Complete information; Utility maximization; Economics; Mathematics; Operations research; Microeconomics; Decision tree; Artificial intelligence","score_opus":0.179464039186951,"score_gpt":0.38354594880588844,"score_spread":0.20408190961893743,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2081081544","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":[0.05822234,0.000022676217,0.8041207,0.0011445128,0.0010370174,0.0004323627,0.0000069887624,0.00008987416,0.1349235],"genre_scores_gemma":[0.95522135,0.000042930318,0.04149568,0.0020017638,0.00002736564,0.000012901212,0.000003784555,0.0000041893154,0.0011900134],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9948619,0.00005836112,0.00063471944,0.00049344625,0.003580075,0.00037149544],"domain_scores_gemma":[0.9978721,0.00015652362,0.00032584628,0.0008590019,0.000600705,0.00018581498],"candidate_categories":["scholarly_communication","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.005488187,0.00014498264,0.00015680799,0.0008793237,0.00039481433,0.001335616,0.0017899981,0.000038632556,0.00024513164],"category_scores_gemma":[0.0007406215,0.00010711573,0.000043876706,0.002504634,0.0002705507,0.0039588804,0.00078085926,0.0000796252,0.0014751954],"study_design_candidate":"design_other","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":[0.00005103696,0.000025163956,0.006009077,0.000002827138,0.0000053417907,0.0000023649168,0.002462723,0.19613795,0.0000044350636,0.029950783,0.052340463,0.7130078],"study_design_scores_gemma":[0.00052251795,0.00006589351,0.03454786,0.000045140107,0.000011672323,0.000003777879,0.0027614767,0.3118631,0.000027593762,0.44901145,0.20084499,0.0002945229],"about_ca_topic_score_codex":0.000039637434,"about_ca_topic_score_gemma":0.000016392696,"teacher_disagreement_score":0.89699906,"about_ca_system_score_codex":0.00017206295,"about_ca_system_score_gemma":0.00010741453,"threshold_uncertainty_score":0.9997011},"labels":[],"label_agreement":null},{"id":"W2081991282","doi":"10.1016/s0165-1889(03)00056-3","title":"Capital growth with security","year":2003,"lang":"en","type":"article","venue":"Journal of Economic Dynamics and Control","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":71,"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","keywords":"Economics; Probabilistic logic; Investment (military); Capital allocation line; Set (abstract data type); Capital (architecture); Mathematical economics; Cash flow; Sequence (biology); Microeconomics; Expected utility hypothesis; Mathematical optimization; Econometrics; Computer science; Mathematics; Finance; Statistics","score_opus":0.008909324021779665,"score_gpt":0.25655875854000776,"score_spread":0.2476494345182281,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2081991282","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":[0.95462877,0.00023640651,0.036327567,0.0004909886,0.00048639008,0.000061792656,0.000013919323,0.000002308527,0.007751856],"genre_scores_gemma":[0.99875385,0.0003249146,0.0003996686,0.00009099688,0.00008151722,4.7918326e-7,3.792479e-7,0.000005164218,0.00034306126],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99904543,0.00005736785,0.00050122105,0.000117949574,0.00017231601,0.00010569534],"domain_scores_gemma":[0.9987809,0.00025639572,0.00054786087,0.00010871615,0.00019842625,0.00010770027],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014392078,0.0000767771,0.00026061138,0.00013216544,0.000056471774,0.00018049803,0.00014874568,0.000043270913,0.00005645433],"category_scores_gemma":[0.00017485154,0.00004932089,0.0000677103,0.000052589116,0.000044599157,0.00034579023,0.000006534107,0.000109186,0.00000896201],"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":[0.0003273612,0.00008112436,0.40023717,0.0000024403585,0.00016871702,0.00005751319,0.0005662444,0.014392823,0.00001376697,0.5767509,0.0009160457,0.0064859055],"study_design_scores_gemma":[0.0075051454,0.0009843358,0.02373874,0.000019228804,0.00012617603,0.0016646689,0.0024760775,0.40321776,0.000024571336,0.5557245,0.004111033,0.00040777028],"about_ca_topic_score_codex":0.000011810831,"about_ca_topic_score_gemma":0.00013754974,"teacher_disagreement_score":0.38882494,"about_ca_system_score_codex":0.000045393874,"about_ca_system_score_gemma":0.00013599131,"threshold_uncertainty_score":0.20112476},"labels":[],"label_agreement":null},{"id":"W2085469254","doi":"10.1017/asb.2013.19","title":"VAR-BASED OPTIMAL PARTIAL HEDGING","year":2013,"lang":"en","type":"article","venue":"Astin Bulletin","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"CVAR; Expected shortfall; Constraint (computer-aided design); Budget constraint; Economics; Market neutral; Mathematical optimization; Risk management; Econometrics; Actuarial science; Computer science; Mathematical economics; Microeconomics; Mathematics; Financial economics; Finance","score_opus":0.04688793760607535,"score_gpt":0.31886923888867136,"score_spread":0.271981301282596,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2085469254","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":[0.51858693,0.00014175262,0.41294372,0.020661512,0.001211756,0.0006137678,0.000010143807,0.0002676517,0.04556279],"genre_scores_gemma":[0.95801693,0.000008769967,0.033885904,0.000855993,0.00026141928,0.00004321693,0.000009935952,0.00001783075,0.0068999897],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9978263,0.00015737931,0.00050939026,0.00040528065,0.0007746752,0.0003270197],"domain_scores_gemma":[0.9983103,0.00064446486,0.00018376185,0.00043762516,0.000268864,0.00015496465],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0010948414,0.00013949753,0.0001933055,0.00015117809,0.00017741905,0.0003792306,0.0004360257,0.0000745884,0.023580013],"category_scores_gemma":[0.0013466639,0.00010728892,0.00009364594,0.00038860267,0.000075501666,0.000107065745,0.00007110053,0.00011861161,0.017703151],"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":[0.00004427862,0.00010049151,0.035804085,0.0000020386503,0.000010287042,0.000017395989,0.00020352412,0.17185506,0.00047618788,0.00068866974,0.6235795,0.16721846],"study_design_scores_gemma":[0.0005334174,0.00007737624,0.014080842,0.000012172608,0.000008879862,0.000006375994,0.00013807585,0.13265564,0.0017324885,0.0005696031,0.84991944,0.00026571535],"about_ca_topic_score_codex":0.00011874079,"about_ca_topic_score_gemma":0.0000014268121,"teacher_disagreement_score":0.43943003,"about_ca_system_score_codex":0.000011576987,"about_ca_system_score_gemma":0.000052939384,"threshold_uncertainty_score":0.9830617},"labels":[],"label_agreement":null},{"id":"W2086041263","doi":"10.3905/jod.2005.580517","title":"Life after VaR","year":2005,"lang":"en","type":"article","venue":"The Journal of Derivatives","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":12,"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":"Value at risk; Tail risk; Extreme value theory; Quantile; Econometrics; Expected shortfall; Economics; Time horizon; Measure (data warehouse); Coherent risk measure; Horizon; Vector autoregression; Cutoff; Actuarial science; Mathematics; Computer science; Statistics; Risk management; Finance","score_opus":0.058662480802129956,"score_gpt":0.3579669452314382,"score_spread":0.29930446442930825,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2086041263","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":[0.9358918,0.0014258502,0.04526863,0.0129742455,0.00021457642,0.000048939513,0.0000013512359,0.0000052558694,0.004169368],"genre_scores_gemma":[0.9928861,0.00070132554,0.0039947596,0.0011342898,0.00039326664,3.9448076e-7,6.2758076e-8,0.000004650972,0.0008851334],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9983608,0.00025754212,0.0005395467,0.00005507883,0.0006888061,0.0000982215],"domain_scores_gemma":[0.9981919,0.00066512503,0.0005069325,0.00019333846,0.00033659767,0.000106087406],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023025114,0.00006353903,0.00015031503,0.00013088562,0.000076386954,0.000076014614,0.00046323633,0.00002425347,0.00063781027],"category_scores_gemma":[0.002326131,0.00002904738,0.00007550507,0.00034392157,0.00008311625,0.000548584,0.00003875333,0.00011836539,0.00012444226],"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":[0.0021381876,0.00042959288,0.10012652,0.000003213046,0.00030485407,0.00003798182,0.069891825,0.09946349,0.0037145903,0.003065713,0.2799678,0.44085625],"study_design_scores_gemma":[0.0010908096,0.00039216297,0.36040297,0.000040847393,0.00007489755,0.00022558415,0.0055614584,0.004168868,0.006483966,0.029341606,0.59192675,0.0002900872],"about_ca_topic_score_codex":9.751664e-7,"about_ca_topic_score_gemma":0.0000019189786,"teacher_disagreement_score":0.44056615,"about_ca_system_score_codex":0.000009276931,"about_ca_system_score_gemma":0.00008395661,"threshold_uncertainty_score":0.6983575},"labels":[],"label_agreement":null},{"id":"W2086311721","doi":"10.3905/jwm.2003.442378","title":"Risk-Adjusted Performance of Funds of Hedge Funds Using a Modified Sharpe Ratio","year":2003,"lang":"en","type":"article","venue":"˜The œjournal of wealth management","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":206,"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":"Sharpe ratio; Hedge fund; Institutional investor; Asset (computer security); Econometrics; Fund of funds; Economics; Actuarial science; Business; Financial economics; Monetary economics; Finance; Computer science; Portfolio","score_opus":0.10182606183260808,"score_gpt":0.352316883440537,"score_spread":0.25049082160792896,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2086311721","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":[0.84990674,0.00068550574,0.1388979,0.000093645904,0.00060178293,0.0003243592,0.000007151948,0.0000055807054,0.009477347],"genre_scores_gemma":[0.99070555,0.0032136512,0.0048287604,0.00008158554,0.0000514641,0.0000014620349,8.8130577e-7,0.0000113651995,0.0011052553],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99598795,0.00046976077,0.0017126978,0.00016628446,0.0014159067,0.00024737255],"domain_scores_gemma":[0.99549526,0.00025321846,0.0029742804,0.0005289505,0.0006430141,0.0001052809],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0075679156,0.00014578442,0.0004232343,0.0005498071,0.00018917475,0.000038619874,0.0006855367,0.000053497708,0.00015165853],"category_scores_gemma":[0.0001378711,0.00009263162,0.00014671737,0.0011591577,0.00009072558,0.0003429002,0.000080244645,0.00018369262,0.00000867752],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00061766553,0.00028586347,0.048451215,0.00011351749,0.00020893899,0.00000974314,0.0019224283,0.8880221,0.00016866313,0.003193435,0.005264877,0.051741518],"study_design_scores_gemma":[0.005111176,0.0017958184,0.14408527,0.00033248283,0.0007636546,0.000113457434,0.0077810003,0.80145,0.0016024552,0.0052744127,0.031148275,0.00054195203],"about_ca_topic_score_codex":0.000045962144,"about_ca_topic_score_gemma":0.000018490866,"teacher_disagreement_score":0.14079884,"about_ca_system_score_codex":0.000052782565,"about_ca_system_score_gemma":0.00012798756,"threshold_uncertainty_score":0.37774077},"labels":[],"label_agreement":null},{"id":"W2086713197","doi":"10.3390/risks2040411","title":"Measuring Risk When Expected Losses Are Unbounded","year":2014,"lang":"en","type":"article","venue":"Risks","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Expected value; Expected shortfall; Coherent risk measure; Conditional probability; Value (mathematics); Value at risk; Pareto principle; Conditional expectation; Actuarial science; Mathematics; Econometrics; Mathematical economics; Computer science; Risk management; Statistics; Economics; Finance","score_opus":0.24106361904638038,"score_gpt":0.3731557394998607,"score_spread":0.13209212045348032,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2086713197","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":[0.83786833,0.000359745,0.13391183,0.00028824215,0.00070102845,0.00017312041,0.000015546124,0.00018893529,0.02649323],"genre_scores_gemma":[0.9935503,0.00026504864,0.0039391965,0.00009155859,0.00025426986,0.000008616544,0.0000047733024,0.000016848024,0.001869407],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99733317,0.00040345517,0.0004945652,0.00044687415,0.0010596504,0.000262291],"domain_scores_gemma":[0.99760914,0.0006530494,0.00048733916,0.0007406915,0.00037196878,0.00013780929],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00190018,0.00014930632,0.00027352394,0.00024021916,0.00032304245,0.00039506238,0.0005354651,0.000105003666,0.0004831796],"category_scores_gemma":[0.004887575,0.000111136316,0.000107526546,0.000517243,0.00006931139,0.00032884325,0.00007944752,0.00016287217,0.0010061397],"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":[0.000045661025,0.00006445456,0.81768775,0.0000020357488,0.000026447018,0.000005738316,0.001403561,0.01085695,0.00006194736,0.0009797221,0.026350377,0.14251535],"study_design_scores_gemma":[0.0008629878,0.0000754079,0.6685822,0.000029410508,0.000046712106,0.000009265872,0.00088870554,0.029017726,0.0018218586,0.11204804,0.18615042,0.0004672435],"about_ca_topic_score_codex":0.00044833057,"about_ca_topic_score_gemma":0.0002699847,"teacher_disagreement_score":0.15980004,"about_ca_system_score_codex":0.000028089193,"about_ca_system_score_gemma":0.000035369474,"threshold_uncertainty_score":0.9997717},"labels":[],"label_agreement":null},{"id":"W2087295105","doi":"10.1134/s1064562407030052","title":"On a property of multidimensional normal distributions and its application to the computation of options","year":2007,"lang":"en","type":"article","venue":"Doklady Mathematics","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Bank of Canada; University of Alberta","funders":"","keywords":"Mathematics; Property (philosophy); Computation; Applied mathematics; Calculus (dental); Algorithm; Epistemology; Medicine","score_opus":0.05619199598957699,"score_gpt":0.36529460474965736,"score_spread":0.3091026087600804,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2087295105","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":[0.36389792,0.000023372872,0.6346608,0.0003375037,0.000040490515,0.00039409826,0.00004715627,0.000008500346,0.0005901479],"genre_scores_gemma":[0.9766772,0.000015011326,0.02309248,0.000027250408,0.000015833393,0.000011417785,0.000017857736,0.0000045718784,0.00013836853],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985855,0.00003577809,0.00057434017,0.00013721477,0.0005730664,0.00009408283],"domain_scores_gemma":[0.9982609,0.0006823894,0.00030417653,0.00022056962,0.00047829072,0.00005365845],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016291237,0.00007015658,0.00014633186,0.00012994559,0.00011669773,0.000015286567,0.00014277955,0.000039467897,0.000011618449],"category_scores_gemma":[0.00084946904,0.00003602744,0.000037101112,0.00056360837,0.000044469227,0.00007854171,0.000049707094,0.000049516668,0.000038035065],"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":[0.00014304579,0.0013304601,0.0013937132,0.00006273707,0.000045126322,0.0000011018599,0.007887019,0.17937338,0.016022967,0.73087114,0.0034740758,0.059395242],"study_design_scores_gemma":[0.00065502065,0.00034450923,0.029464807,0.000100235804,0.000061126906,0.000021069483,0.001479732,0.8942659,0.0204641,0.049454544,0.003433864,0.0002551087],"about_ca_topic_score_codex":0.000007955355,"about_ca_topic_score_gemma":0.000014243872,"teacher_disagreement_score":0.7148925,"about_ca_system_score_codex":0.000011708006,"about_ca_system_score_gemma":0.000023671564,"threshold_uncertainty_score":0.14691563},"labels":[],"label_agreement":null},{"id":"W2087610528","doi":"10.1145/2695664.2695899","title":"Dynamic optimization of multi-layered reinsurance treaties","year":2015,"lang":"en","type":"article","venue":"","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Dalhousie University; Canadian Bioethics Society","funders":"Conselho Nacional de Desenvolvimento Científico e Tecnológico; Dalhousie University","keywords":"Reinsurance; Computer science; Pareto principle; Hedge; Mathematical optimization; Optimization problem; Risk management; Multi-objective optimization; Finance; Economics; Algorithm; Mathematics","score_opus":0.14269140322002297,"score_gpt":0.39076896811284784,"score_spread":0.24807756489282487,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2087610528","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":[0.045399047,0.00022300915,0.9360095,0.0002569308,0.00027105893,0.00014627645,0.000010072622,0.000052615793,0.017631477],"genre_scores_gemma":[0.83483064,0.0001808902,0.15360767,0.000030318737,0.000008311626,0.0000037318925,0.0000111449135,0.000007177927,0.011320135],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983541,0.00010016017,0.0005248491,0.0002239536,0.00068620953,0.0001107364],"domain_scores_gemma":[0.99844205,0.00014283211,0.00026162795,0.00038322038,0.00068332785,0.00008693793],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008644908,0.00008799244,0.0002043072,0.00020049581,0.000038834507,0.00005998035,0.00027472252,0.000059967024,0.0001259429],"category_scores_gemma":[0.0011183275,0.000060568593,0.000052688345,0.0006654951,0.00006150153,0.00040211898,0.000033547323,0.00003234727,0.00007568817],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000029477833,0.000038707105,0.009982426,5.370094e-7,0.000004089009,6.460548e-7,0.00035809382,0.9759442,0.000032059055,0.00035024274,0.0012881691,0.011971313],"study_design_scores_gemma":[0.000569932,0.000054080272,0.0022390005,0.0000035457497,0.0000040731347,0.00000216698,0.0006800955,0.9941004,0.00035037208,0.001152358,0.0007610904,0.00008290584],"about_ca_topic_score_codex":0.00008270147,"about_ca_topic_score_gemma":0.00005315142,"teacher_disagreement_score":0.7894316,"about_ca_system_score_codex":0.000021150829,"about_ca_system_score_gemma":0.00007320718,"threshold_uncertainty_score":0.24699154},"labels":[],"label_agreement":null},{"id":"W2090235487","doi":"10.1142/s0219024913500416","title":"ALLOCATING SYSTEMIC RISK IN A REGULATORY PERSPECTIVE","year":2013,"lang":"en","type":"article","venue":"International Journal of Theoretical and Applied Finance","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"University of Toronto","funders":"","keywords":"Axiom; Perspective (graphical); Systemic risk; Axiomatic system; Mathematical economics; Aggregate (composite); Economics; Computer science; Risk analysis (engineering); Microeconomics; Mathematical optimization; Mathematics; Business; Artificial intelligence; Financial crisis","score_opus":0.010865694857771965,"score_gpt":0.29713429320677903,"score_spread":0.2862685983490071,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2090235487","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":[0.9774111,0.00031453735,0.012198375,0.0013481495,0.00025655728,0.00008835747,0.0000028122563,0.0000035494525,0.008376548],"genre_scores_gemma":[0.9962773,0.0007876088,0.0025954442,0.00011133856,0.00014096357,0.0000045612724,2.2174466e-7,0.000004460651,0.000078105295],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99838823,0.000068190755,0.0006106271,0.00015901322,0.00067313906,0.00010078274],"domain_scores_gemma":[0.9983005,0.00043925617,0.00048711593,0.00010187363,0.0006242758,0.00004701532],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012903408,0.000075552874,0.00018973571,0.00018990072,0.00003271385,0.00013347178,0.00042986515,0.000051399074,0.00009862214],"category_scores_gemma":[0.000833466,0.000050659026,0.000053361546,0.00016281019,0.00022303812,0.00020053404,0.000063084895,0.00020114706,0.000036400823],"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":[0.00007206185,0.000033399992,0.0059430865,4.2546625e-7,0.000013199631,0.000007272974,0.0006261062,0.003935098,0.00023541185,0.935459,0.00029485824,0.053380072],"study_design_scores_gemma":[0.0005919443,0.000040549257,0.040871277,0.000059761624,0.0000060147836,0.00014002055,0.001495767,0.010610041,0.00035922456,0.9452865,0.00044238922,0.00009648525],"about_ca_topic_score_codex":0.000016515032,"about_ca_topic_score_gemma":0.0000011693049,"teacher_disagreement_score":0.053283587,"about_ca_system_score_codex":0.00005792449,"about_ca_system_score_gemma":0.000039680108,"threshold_uncertainty_score":0.2065815},"labels":[],"label_agreement":null},{"id":"W2091867238","doi":"10.1016/j.dam.2005.04.015","title":"Complexity of minimizing the total flow time with interval data and minmax regret criterion","year":2006,"lang":"en","type":"article","venue":"Discrete Applied Mathematics","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":71,"is_retracted":false,"has_abstract":false,"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":"Natural Sciences and Engineering Research Council of Canada","keywords":"Regret; Mathematics; Minimax; Interval (graph theory); Mathematical optimization; Scheduling (production processes); Time complexity; Combinatorics; Interval data; Statistics","score_opus":0.08831245293251806,"score_gpt":0.32945453873333586,"score_spread":0.2411420858008178,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2091867238","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":[0.5870184,0.00014970433,0.36209694,0.00084428815,0.0000892106,0.00082483125,0.0004362645,0.00006399224,0.048476387],"genre_scores_gemma":[0.756875,0.000018195504,0.24212825,0.000035375586,0.00006929825,0.000009027904,0.00018439285,0.000021219379,0.0006592878],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982574,0.000032453612,0.00059031363,0.0003122663,0.00065185496,0.00015572747],"domain_scores_gemma":[0.9980468,0.00040063838,0.00037911625,0.0010461095,0.00008868505,0.000038612267],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010719087,0.00014806195,0.00032605368,0.00006895716,0.0001220934,0.00017702616,0.0006201256,0.000048225815,0.00009899274],"category_scores_gemma":[0.00010933247,0.000078518235,0.00003239473,0.00027278642,0.00039587618,0.00018719408,0.00033865706,0.00007949083,0.000021327582],"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":[0.0018885549,0.0018171169,0.0037659733,0.00068833696,0.00058303203,0.00005515949,0.05599845,0.027809918,0.029994804,0.49092647,0.20828253,0.17818965],"study_design_scores_gemma":[0.00064883655,0.000085000946,0.0015937425,0.00007819474,0.00010116181,0.000058451875,0.0029330843,0.88219845,0.0011909388,0.109128885,0.0016768643,0.00030637122],"about_ca_topic_score_codex":0.000014979311,"about_ca_topic_score_gemma":0.000013801443,"teacher_disagreement_score":0.85438854,"about_ca_system_score_codex":0.0000062975673,"about_ca_system_score_gemma":0.000021424608,"threshold_uncertainty_score":0.32018808},"labels":[],"label_agreement":null},{"id":"W2092181454","doi":"10.1007/s10479-006-0136-2","title":"Credit risk optimization using factor models","year":2006,"lang":"en","type":"article","venue":"Annals of Operations Research","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"University of Waterloo","funders":"","keywords":"Portfolio optimization; Portfolio; Computer science; Mathematical optimization; Theory of computation; Credit risk; Modern portfolio theory; Curse of dimensionality; Optimization problem; Mathematics; Economics; Actuarial science; Machine learning; Algorithm; Finance","score_opus":0.6231119004332903,"score_gpt":0.5580227427264584,"score_spread":0.06508915770683199,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2092181454","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":[0.34338316,0.0002782936,0.6468681,0.00066728913,0.0001023363,0.00033161495,0.0001390108,0.000021317745,0.008208832],"genre_scores_gemma":[0.9647579,0.0010037757,0.031481057,0.000023407209,0.00019117538,0.000012452367,0.000046046527,0.000015056555,0.0024690968],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99576676,0.00068444054,0.00075299386,0.00036511038,0.002098317,0.00033238364],"domain_scores_gemma":[0.9943275,0.00048930326,0.000103950486,0.0005868778,0.0044006873,0.0000916714],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003932675,0.00010147837,0.00020303625,0.000935746,0.0006656357,0.00048071487,0.0004946247,0.0001048915,0.0006491089],"category_scores_gemma":[0.0018625164,0.000081125814,0.00009220046,0.0020753234,0.00017140748,0.0013214002,0.000106194246,0.00020812683,0.0000635437],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013514146,0.00007547537,0.0018793383,7.493713e-7,0.000005891994,9.160877e-7,0.0001516179,0.9857884,0.00019507576,0.0029377646,0.0067771967,0.0021740643],"study_design_scores_gemma":[0.00011521639,0.000051940304,0.0012671482,0.000006197686,0.00000261096,0.0000013861533,0.0001444121,0.98660177,0.0021011408,0.0084406845,0.0011821875,0.00008528976],"about_ca_topic_score_codex":0.0025389346,"about_ca_topic_score_gemma":0.00019544979,"teacher_disagreement_score":0.6213748,"about_ca_system_score_codex":0.000018964196,"about_ca_system_score_gemma":0.00026138063,"threshold_uncertainty_score":0.71072876},"labels":[],"label_agreement":null},{"id":"W2094531960","doi":"10.5539/ijef.v6n9p151","title":"Alternative Measures for Modeling Risk and Expected Utility Theory (Risk Adjustment, Measurement and Attitude)","year":2014,"lang":"en","type":"article","venue":"International Journal of Economics and Finance","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Expected utility hypothesis; Modern portfolio theory; Prospect theory; Capital asset pricing model; Expected return; Economics; Econometrics; Risk-seeking; Subjective expected utility; Portfolio; Actuarial science; Microeconomics; Mathematical economics; Finance","score_opus":0.08750778059144146,"score_gpt":0.3254586337799066,"score_spread":0.23795085318846512,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2094531960","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":[0.67261475,0.0019627765,0.32458004,0.00013791629,0.00045595568,0.00007056629,0.000038617625,0.0000014135699,0.00013796757],"genre_scores_gemma":[0.93666524,0.05872742,0.004302367,0.000054820383,0.00022009197,0.0000030645422,8.5639834e-7,0.0000053026197,0.000020835143],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99876845,0.00010830452,0.0005782144,0.00021985809,0.00023858085,0.00008659285],"domain_scores_gemma":[0.99780494,0.00036409966,0.000813236,0.00009708633,0.00087112765,0.000049535836],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003998197,0.00009680397,0.00021680084,0.00014268993,0.00009972347,0.00016614761,0.00022053733,0.000038573195,0.0000040478762],"category_scores_gemma":[0.0013458933,0.0000755352,0.00006025629,0.000027563248,0.00006328911,0.0003483826,0.000053675296,0.00009372245,5.119802e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005426496,0.000049275717,0.021007184,0.0000011756025,0.00016274586,7.4926726e-7,0.0007735064,0.1531417,0.0000075409785,0.035848834,0.00012762808,0.788337],"study_design_scores_gemma":[0.00091137335,0.00009237472,0.013765076,0.000016230379,0.000024510406,0.000015491478,0.000101155136,0.69559175,0.000048375354,0.28084806,0.008502188,0.00008339286],"about_ca_topic_score_codex":0.000039222145,"about_ca_topic_score_gemma":0.00006137348,"teacher_disagreement_score":0.7882536,"about_ca_system_score_codex":0.000033982567,"about_ca_system_score_gemma":0.00003827661,"threshold_uncertainty_score":0.3080236},"labels":[],"label_agreement":null},{"id":"W2097512955","doi":"10.71781/15327","title":"On some aspects of coherent risk measures and their applications","year":2011,"lang":"en","type":"dissertation","venue":"Library and Archives Canada (Government of Canada)","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Property (philosophy); Lebesgue integration; Class (philosophy); Mathematics; Dual (grammatical number); Pure mathematics; Regular polygon; Topological space; Space (punctuation); Bounded function; Coherent risk measure; Topology (electrical circuits); Discrete mathematics; Risk management; Computer science; Combinatorics; Mathematical analysis; Epistemology; Artificial intelligence; Value at risk","score_opus":0.011655424881826552,"score_gpt":0.20166045656660397,"score_spread":0.19000503168477742,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2097512955","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":[0.18936016,0.0026448015,0.00044878884,0.00029837008,0.00046840715,0.0007399848,0.001284385,0.000012385973,0.8047427],"genre_scores_gemma":[0.98352295,0.0034140912,0.00019695767,0.00013742407,0.000036421694,0.000024846639,0.000057011257,0.00002121872,0.012589077],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9969479,0.00010807061,0.0005408175,0.0003651536,0.0018724386,0.00016562092],"domain_scores_gemma":[0.99798554,0.00067965407,0.00078465464,0.0003536055,0.0000037387551,0.0001928327],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000043746775,0.00022978574,0.0004041232,0.00008128821,0.0001706675,0.000033068303,0.00034451106,0.000059629725,0.000043726763],"category_scores_gemma":[0.000022983306,0.0001712618,0.000044816272,0.0001285092,0.00006876062,0.00023779726,0.000046946385,0.00016240108,7.1644672e-9],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009218268,0.00008849254,0.009564791,0.00009544518,0.00018142258,0.000007477899,0.0006690339,0.0002623996,0.0015833743,0.71002483,0.002172646,0.27442828],"study_design_scores_gemma":[0.0007096306,0.00032696084,0.1565502,0.00028746962,0.00012520162,0.0000037050227,0.010385356,0.0013862456,0.1443495,0.6265433,0.058523186,0.00080929557],"about_ca_topic_score_codex":0.0030247595,"about_ca_topic_score_gemma":0.060920272,"teacher_disagreement_score":0.7941628,"about_ca_system_score_codex":0.0000033421481,"about_ca_system_score_gemma":0.0012568694,"threshold_uncertainty_score":0.9562155},"labels":[],"label_agreement":null},{"id":"W2100110221","doi":"10.7551/mitpress/7503.003.0197","title":"The Robustness-Performance Tradeoff in Markov Decision Processes","year":2007,"lang":"en","type":"book-chapter","venue":"The MIT Press eBooks","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"McGill University","funders":"","keywords":"Robustness (evolution); Computer science; Markov decision process; Markov chain; Markov model; Markov process; Mathematics; Machine learning; Statistics; Chemistry","score_opus":0.11643579191583656,"score_gpt":0.33792463920825955,"score_spread":0.221488847292423,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2100110221","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.001211405,0.004455532,0.0037611632,0.000110900415,0.00072091015,0.00087585475,0.00001727434,0.000056682744,0.9887903],"genre_scores_gemma":[0.013538302,0.007373654,0.0005692758,0.00015608795,0.00036142874,0.00005001735,0.000007780646,0.00007817988,0.9778653],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9954759,0.00009321212,0.0012925515,0.00064378476,0.0020223663,0.00047220953],"domain_scores_gemma":[0.99379796,0.0034280159,0.00072907563,0.001499572,0.00045019793,0.0000951977],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0043415176,0.00043664232,0.00047740788,0.000286329,0.00055274356,0.000551331,0.0024572846,0.0004038129,0.000077199365],"category_scores_gemma":[0.0005827713,0.00021017858,0.00015575592,0.000092301845,0.00042468795,0.00019087784,0.0002900107,0.0006937854,0.0000748448],"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":[0.00038562738,0.000012607381,0.000058425805,0.000017727252,0.000031179734,0.000019848474,0.00063785183,0.009386467,0.0000010004766,0.012802709,0.010142477,0.9665041],"study_design_scores_gemma":[0.00031188023,0.00004909988,0.00021035755,0.00023872784,0.00004435926,0.000025794898,0.000034672456,0.005384182,0.00014509124,0.018191194,0.97499704,0.00036758734],"about_ca_topic_score_codex":0.00003402869,"about_ca_topic_score_gemma":0.00045012805,"teacher_disagreement_score":0.9661365,"about_ca_system_score_codex":0.000049926748,"about_ca_system_score_gemma":0.0002064271,"threshold_uncertainty_score":0.8570834},"labels":[],"label_agreement":null},{"id":"W2100282405","doi":"10.1016/j.jmva.2015.01.006","title":"Extreme negative dependence and risk aggregation","year":2015,"lang":"en","type":"article","venue":"Journal of Multivariate Analysis","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":25,"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":"Mathematics; Independent and identically distributed random variables; Sequence (biology); Random variable; Infinity; Marginal distribution; Distribution (mathematics); Probabilistic logic; Combinatorics; Statistics; Applied mathematics; Mathematical analysis","score_opus":0.1119160543952564,"score_gpt":0.37091951970598874,"score_spread":0.2590034653107324,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2100282405","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":[0.64433527,0.00037904465,0.35419983,0.00021895998,0.0001650429,0.00004702909,0.0000057783986,0.0000056613108,0.00064337754],"genre_scores_gemma":[0.972742,0.0010924205,0.02564959,0.000025201569,0.000105701445,5.0027114e-7,0.0000011826006,0.000004799205,0.00037860073],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9968674,0.0005398386,0.00092578627,0.00020873398,0.0013341993,0.00012404592],"domain_scores_gemma":[0.99502563,0.00081019395,0.0018983099,0.00023989269,0.0017782059,0.00024777107],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0053865337,0.00010947538,0.00043476681,0.0009920012,0.00010043666,0.00023062126,0.0003013662,0.000071095405,0.00007463161],"category_scores_gemma":[0.007954952,0.000070845635,0.00023118591,0.0021839845,0.000051568135,0.0009170188,0.00005061158,0.00017651408,0.0000201443],"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":[0.0002760641,0.000075468386,0.6782386,4.7287156e-7,0.0011584987,0.000037527745,0.004206834,0.1975419,0.000082620965,0.00019610536,0.00072350784,0.11746243],"study_design_scores_gemma":[0.0018241919,0.0002793777,0.42195806,0.000014878271,0.0027535437,0.00004759557,0.002806323,0.53401166,0.00047672432,0.033107214,0.0024730796,0.0002473533],"about_ca_topic_score_codex":0.0005101857,"about_ca_topic_score_gemma":0.00020700142,"teacher_disagreement_score":0.33646974,"about_ca_system_score_codex":0.000042740376,"about_ca_system_score_gemma":0.00010424455,"threshold_uncertainty_score":0.95233953},"labels":[],"label_agreement":null},{"id":"W2101898852","doi":"10.2139/ssrn.671981","title":"Portfolio Selection with Parameter and Model Uncertainty: A Multi-Prior Approach","year":2004,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":276,"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 Hospital; University of British Columbia","funders":"","keywords":"Selection (genetic algorithm); Portfolio; Black–Litterman model; Computer science; Model selection; Portfolio optimization; Econometrics; Artificial intelligence; Economics; Replicating portfolio; Financial economics","score_opus":0.0365434685570825,"score_gpt":0.312952607780441,"score_spread":0.27640913922335847,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2101898852","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":[0.30352983,0.00047651737,0.69500124,0.00021951254,0.00003716735,0.00014167863,0.0000011528435,0.000026669744,0.000566231],"genre_scores_gemma":[0.96136415,0.0031025824,0.033047598,0.00011685226,0.00009462735,0.0000093077915,0.000003268945,0.000023822182,0.0022377963],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99682057,0.00008863656,0.00047916317,0.00043213536,0.00082437025,0.0013551377],"domain_scores_gemma":[0.9989213,0.000069310256,0.0003378682,0.00021257278,0.0003000261,0.00015893408],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029294004,0.00020364916,0.00026866447,0.00035880643,0.00039958116,0.00032273517,0.00029493688,0.000109850174,0.000011665298],"category_scores_gemma":[0.00022991798,0.00013113362,0.00008579084,0.0007261878,0.00008597993,0.00058997824,0.000031062023,0.0010616274,0.000013065259],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021033162,0.00014793266,0.00578011,0.0000011139442,0.00008216065,0.0000035971425,0.0004780012,0.9071028,0.000059427384,0.026996352,0.00006720352,0.059070975],"study_design_scores_gemma":[0.0027995445,0.0005565776,0.0011414151,0.000012013949,0.00006928415,0.0025172075,0.0018273933,0.5678356,0.000053230862,0.42250034,0.00034113746,0.00034621236],"about_ca_topic_score_codex":0.00010603106,"about_ca_topic_score_gemma":0.00059043424,"teacher_disagreement_score":0.6619536,"about_ca_system_score_codex":0.00044333367,"about_ca_system_score_gemma":0.002263816,"threshold_uncertainty_score":0.5347474},"labels":[],"label_agreement":null},{"id":"W2104195238","doi":"10.1016/j.procs.2013.05.403","title":"A MapReduce Framework for Analysing Portfolios of Catastrophic Risk with Secondary Uncertainty","year":2013,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Dalhousie University","funders":"","keywords":"Computer science; Uncertainty quantification; Data science; Risk analysis (engineering); Machine learning","score_opus":0.028165541505720874,"score_gpt":0.3194657560178787,"score_spread":0.2913002145121578,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2104195238","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":[0.32445997,0.000047236947,0.67449075,0.00018193769,0.00029278413,0.00037769304,0.000016526183,0.000027161173,0.00010591073],"genre_scores_gemma":[0.6303573,0.000023682034,0.36932656,0.00009063283,0.00013416345,0.000032570297,0.0000034854172,0.0000068488025,0.000024718212],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99675846,0.000048150403,0.00062659435,0.0007972894,0.0013375652,0.0004319386],"domain_scores_gemma":[0.9960155,0.0005957771,0.000717305,0.00071058294,0.0017373341,0.00022351764],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020356132,0.00017730669,0.00033781404,0.0005796788,0.0003549437,0.00051069946,0.0013511562,0.000060952145,0.00007004262],"category_scores_gemma":[0.0009412579,0.00011724831,0.00008999725,0.0033085893,0.0005985412,0.0012610052,0.00018486664,0.0001771446,0.000028015267],"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":[0.000073726194,0.00013840687,0.08612986,0.000023377363,0.000037984697,0.0000028761774,0.0027195222,0.19302054,0.00035392767,0.0043555307,0.0041302,0.70901406],"study_design_scores_gemma":[0.00038757667,0.00037845963,0.04528076,0.000057626286,0.000035739115,0.00003116506,0.00021488804,0.86370116,0.0012608417,0.08770602,0.00063179136,0.0003139511],"about_ca_topic_score_codex":0.00011948821,"about_ca_topic_score_gemma":0.00000881844,"teacher_disagreement_score":0.7087001,"about_ca_system_score_codex":0.000032355143,"about_ca_system_score_gemma":0.00069264445,"threshold_uncertainty_score":0.49246866},"labels":[],"label_agreement":null},{"id":"W2104402102","doi":"","title":"Tractable Objectives for Robust Policy Optimization","year":2012,"lang":"en","type":"article","venue":"","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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 Alberta","funders":"","keywords":"Mathematical optimization; Regret; Computer science; Robust optimization; Context (archaeology); Optimization problem; Mathematics; Machine learning","score_opus":0.12430614919059262,"score_gpt":0.40539702499330765,"score_spread":0.281090875802715,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2104402102","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":[0.0012898482,0.000089530426,0.91379714,0.00061425386,0.00031345815,0.00024314199,0.000008218883,0.00005304506,0.083591394],"genre_scores_gemma":[0.5876483,0.00018296299,0.37832633,0.00043592125,0.00090367615,0.000046830366,0.000026695006,0.000020906606,0.03240842],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987158,0.000055890956,0.0003336689,0.00019593108,0.00039911375,0.00029958136],"domain_scores_gemma":[0.9987336,0.0004590491,0.0001396647,0.00025521818,0.00028463642,0.000127829],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013467425,0.00009048767,0.00014196274,0.0003074755,0.0001722128,0.0001648611,0.000203507,0.00007057737,0.00069177797],"category_scores_gemma":[0.0018163365,0.00006334073,0.00007899486,0.0007894853,0.000026314052,0.0012895998,0.000025086974,0.000034797744,0.000106349755],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002269062,0.00008455697,0.0084043,6.991062e-7,0.0000058462883,4.0218474e-8,0.00047769403,0.9342748,0.00002030431,0.011710895,0.01683848,0.02815973],"study_design_scores_gemma":[0.000621932,0.00008337011,0.009851204,0.0000029350215,0.000017309649,0.000007943963,0.0012258554,0.86840415,0.0011692822,0.00502116,0.113300435,0.00029442413],"about_ca_topic_score_codex":0.000056729223,"about_ca_topic_score_gemma":0.000006647996,"teacher_disagreement_score":0.5863584,"about_ca_system_score_codex":0.000030730884,"about_ca_system_score_gemma":0.000074640615,"threshold_uncertainty_score":0.7574484},"labels":[],"label_agreement":null},{"id":"W2105055176","doi":"10.1142/s0217595913400022","title":"A SMOOTHING PENALIZED SAMPLE AVERAGE APPROXIMATION METHOD FOR STOCHASTIC PROGRAMS WITH SECOND-ORDER STOCHASTIC DOMINANCE CONSTRAINTS","year":2013,"lang":"en","type":"article","venue":"Asia Pacific Journal of Operational Research","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":7,"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":"McMaster University","keywords":"Mathematics; Smoothing; Mathematical optimization; Stochastic dominance; Sample size determination; Applied mathematics; Rate of convergence; Bellman equation; Function (biology); Discretization; Minification; Penalty method; Exponential function; Sample (material); Convergence (economics); Computer science; Statistics; Mathematical analysis","score_opus":0.11957889688163752,"score_gpt":0.4312643474946223,"score_spread":0.31168545061298475,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2105055176","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":[0.015982524,0.00008869202,0.97897625,0.0019642406,0.000150763,0.0015636962,0.000029654237,0.000008536847,0.0012356623],"genre_scores_gemma":[0.7000574,0.000009042473,0.29828426,0.00002910898,0.00015943892,0.00017546493,0.00003373134,0.000020384014,0.0012311523],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99395925,0.0007393856,0.0011796107,0.0004198852,0.003202793,0.00049909565],"domain_scores_gemma":[0.9845381,0.0055209505,0.00058301794,0.00036000618,0.008735706,0.00026222432],"candidate_categories":["metaresearch","scholarly_communication","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.012110071,0.00019522195,0.00045199052,0.0007182045,0.0005881428,0.001337619,0.00070546666,0.000111023204,0.0017542585],"category_scores_gemma":[0.011330595,0.00012806372,0.00012742993,0.0012042555,0.00033721927,0.0014836137,0.00006991095,0.00052853575,0.00007142544],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0025191465,0.00091734057,0.0013768704,0.000054786844,0.0003707259,0.00003140948,0.009417902,0.47462523,0.0046001053,0.042069737,0.02149824,0.4425185],"study_design_scores_gemma":[0.006026294,0.0020605046,0.0022280065,0.0002653629,0.000043774253,0.0008028119,0.011598454,0.855289,0.00040847465,0.11519428,0.0055239517,0.0005590868],"about_ca_topic_score_codex":0.00003768493,"about_ca_topic_score_gemma":0.000020423342,"teacher_disagreement_score":0.6840749,"about_ca_system_score_codex":0.00011516187,"about_ca_system_score_gemma":0.0011870101,"threshold_uncertainty_score":0.9996991},"labels":[],"label_agreement":null},{"id":"W2105819246","doi":"10.1287/opre.2014.1318","title":"The Value of Stochastic Modeling in Two-Stage Stochastic Programs with Cost Uncertainty","year":2014,"lang":"en","type":"article","venue":"Operations Research","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Stochastic programming; Mathematical optimization; Computer science; Stochastic modelling; Heuristic; Stochastic optimization; Expected value; Value (mathematics); Linear programming; Bellman equation; Function (biology); Stochastic investment model; Random variable; Stochastic process; Mathematics; Economics","score_opus":0.21360839487146607,"score_gpt":0.4764161017725425,"score_spread":0.26280770690107647,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2105819246","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":[0.23889332,0.000040342897,0.7581904,0.0005300684,0.00004810397,0.0008937898,0.0000057759053,0.000010338353,0.0013878754],"genre_scores_gemma":[0.9960566,0.000015620251,0.0018527778,0.0000101635715,0.000041681888,0.00019857637,0.000014860209,0.000011926989,0.0017977544],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99604803,0.0007968249,0.0005358785,0.000349317,0.0018767011,0.0003932444],"domain_scores_gemma":[0.9963188,0.0014326271,0.000044274817,0.00067179155,0.0014322741,0.00010019079],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009086373,0.00009697081,0.00017738443,0.0003847833,0.0007141931,0.0005616899,0.0006529761,0.000038042956,0.000037161877],"category_scores_gemma":[0.003540891,0.000053367283,0.000032370306,0.0019704655,0.00030754635,0.0002566883,0.00010693854,0.00032323351,0.00005283766],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000053214277,0.00004910803,0.0001943407,8.3249967e-7,0.0000035539513,4.640157e-7,0.00051897694,0.9517191,0.00002838583,0.022097358,0.000023477527,0.025311187],"study_design_scores_gemma":[0.00041527773,0.00013352717,0.00007141003,0.000025216179,0.000002218582,0.0000015534399,0.0014981782,0.9957636,0.000009079008,0.0017737008,0.00023762308,0.000068594505],"about_ca_topic_score_codex":0.0018936022,"about_ca_topic_score_gemma":0.010387779,"teacher_disagreement_score":0.75716335,"about_ca_system_score_codex":0.00006098806,"about_ca_system_score_gemma":0.00037233287,"threshold_uncertainty_score":0.57966244},"labels":[],"label_agreement":null},{"id":"W2105979176","doi":"10.1002/mcda.1475","title":"A New VNS Metaheuristic Using MADS as a Local Optimizer","year":2012,"lang":"en","type":"article","venue":"Journal of Multi-Criteria Decision Analysis","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Royal Military College of Canada; Hydro-Québec","funders":"","keywords":"Metaheuristic; Mathematical optimization; Variable neighborhood search; Local optimum; Computer science; Local search (optimization); Variable (mathematics); Portfolio; Neighbourhood (mathematics); Mathematics; Economics; Finance","score_opus":0.1453728942564561,"score_gpt":0.46303869649328305,"score_spread":0.31766580223682694,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2105979176","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":[0.13790344,0.0014494733,0.85901195,0.00016506884,0.0011815438,0.00008273322,0.000008552722,0.0000119510405,0.00018527763],"genre_scores_gemma":[0.6181991,0.00035329786,0.3797883,0.00019345261,0.0003784922,5.613081e-7,0.000003006586,0.000021168484,0.0010626139],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9926393,0.0006297549,0.0028056107,0.0004057383,0.0029998783,0.0005197109],"domain_scores_gemma":[0.9932831,0.0014637221,0.0019123477,0.00082998216,0.0014997342,0.0010111256],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0072176033,0.00033529266,0.0013702846,0.0033333553,0.00021291117,0.00065836764,0.001065642,0.00020390784,0.006783187],"category_scores_gemma":[0.005285089,0.00022504067,0.001491331,0.0049973456,0.000083636965,0.0016019401,0.00019268508,0.00032489686,0.00036887464],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012883293,0.00087657175,0.038948514,0.000002826606,0.0026430823,0.00022630653,0.00222487,0.51989216,0.0017204523,0.0001272112,0.02869727,0.4033524],"study_design_scores_gemma":[0.0030570917,0.00017640459,0.022016257,0.000052297706,0.005342427,0.0006134947,0.0013619731,0.92942715,0.00049318705,0.002137293,0.034739684,0.00058276905],"about_ca_topic_score_codex":0.00015473351,"about_ca_topic_score_gemma":0.000024671768,"teacher_disagreement_score":0.48029566,"about_ca_system_score_codex":0.00014381204,"about_ca_system_score_gemma":0.0002696994,"threshold_uncertainty_score":0.99412477},"labels":[],"label_agreement":null},{"id":"W2108381265","doi":"","title":"Portfolio Risk Management with CVaR-Like Constraints","year":2009,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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 New Brunswick; University of Manitoba","funders":"","keywords":"CVAR; Downside risk; Portfolio; Portfolio optimization; Asset allocation; Skewness; Asset (computer security); Expected shortfall; Variance (accounting); Econometrics; Computer science; Post-modern portfolio theory; Actuarial science; Economics; Mathematical optimization; Mathematics; Replicating portfolio; Financial economics","score_opus":0.014333521063152726,"score_gpt":0.2941881979589179,"score_spread":0.27985467689576515,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2108381265","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":[0.19472973,0.0025988142,0.6128953,0.0015117062,0.0006627368,0.0005261641,0.000009091071,0.00012402101,0.18694241],"genre_scores_gemma":[0.966926,0.020151252,0.002439921,0.00031795254,0.00016202868,0.000002012798,0.0000032903301,0.000013590484,0.009983978],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99570197,0.00016858305,0.00061519863,0.00037970397,0.0013537185,0.0017808253],"domain_scores_gemma":[0.9985256,0.00007426368,0.0005707244,0.00041047484,0.0002468102,0.00017211279],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005201815,0.00020110123,0.00025660105,0.00041093543,0.00038366797,0.00030987995,0.0006380176,0.000069990405,0.00032439778],"category_scores_gemma":[0.00009086301,0.00013128165,0.00012903316,0.00083393895,0.000101389385,0.00043965297,0.000025386309,0.0011188156,0.00020880884],"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":[0.00012924329,0.000087638495,0.011097845,2.6968672e-7,0.000117613614,0.000060072805,0.00009873844,0.0027630108,0.0000032661799,0.1404895,0.0029083788,0.84224445],"study_design_scores_gemma":[0.0016572293,0.0009968082,0.019736454,0.000016825188,0.00011598936,0.0019368696,0.0029031981,0.0006992107,0.00001711082,0.93306345,0.038485095,0.00037177547],"about_ca_topic_score_codex":0.000011375779,"about_ca_topic_score_gemma":0.000091083086,"teacher_disagreement_score":0.84187263,"about_ca_system_score_codex":0.00026864058,"about_ca_system_score_gemma":0.00082551106,"threshold_uncertainty_score":0.53535104},"labels":[],"label_agreement":null},{"id":"W2108591779","doi":"10.3138/infor.47.1.23","title":"Multi-Attribute Portfolio Selection with Genetic Optimization Algorithms","year":2009,"lang":"en","type":"article","venue":"INFOR Information Systems and Operational Research","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Chinese Academy of Sciences; National Natural Science Foundation of China","keywords":"Selection (genetic algorithm); Computer science; Portfolio; Quality control and genetic algorithms; Portfolio optimization; Genetic algorithm; Algorithm; Mathematical optimization; Machine learning; Artificial intelligence; Meta-optimization; Mathematics; Economics; Financial economics","score_opus":0.11207607931676752,"score_gpt":0.4081918378083836,"score_spread":0.2961157584916161,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2108591779","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":[0.025630694,0.00018410491,0.96563756,0.00074640673,0.00023352583,0.0013487533,0.000059891223,0.000082821876,0.006076257],"genre_scores_gemma":[0.94808376,0.0004677587,0.047284547,0.00037420148,0.0002510561,0.00011122258,0.00032808576,0.00001103447,0.0030883201],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9952026,0.00020450566,0.0011166258,0.00026837847,0.0028390265,0.00036883872],"domain_scores_gemma":[0.99470574,0.00024968185,0.00027951124,0.00026915708,0.0042810156,0.00021486952],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.003973002,0.00017288048,0.00023779985,0.0011101066,0.0008283845,0.0024240727,0.00028088051,0.0001552397,0.000119722805],"category_scores_gemma":[0.0008036968,0.000121165125,0.00003752026,0.0018345595,0.00009218122,0.0045132725,0.00004308473,0.00025085453,0.00019407729],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005578577,0.000029861036,0.010282933,0.000005091141,0.000012399578,0.0000016023437,0.000591208,0.95702046,0.0000147886785,0.003864416,0.005765151,0.022356305],"study_design_scores_gemma":[0.0008280013,0.000328933,0.05088304,0.000022426322,0.0000044585713,0.00011128591,0.00091378536,0.88975495,0.000039152357,0.00005535418,0.0568674,0.00019119983],"about_ca_topic_score_codex":0.00016752908,"about_ca_topic_score_gemma":0.000011989115,"teacher_disagreement_score":0.9224531,"about_ca_system_score_codex":0.00010023681,"about_ca_system_score_gemma":0.00039857675,"threshold_uncertainty_score":0.9986115},"labels":[],"label_agreement":null},{"id":"W2109521279","doi":"10.2143/ast.38.2.2033355","title":"Economic Capital Allocations for Non-negative Portfolios of Dependent Risks","year":2008,"lang":"en","type":"article","venue":"Astin Bulletin","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"York University","funders":"","keywords":"Portfolio; Context (archaeology); Multivariate statistics; Capital (architecture); Economics; Economic capital; Decomposition; Econometrics; Poisson distribution; Risk-adjusted return on capital; Actuarial science; Microeconomics; Financial economics; Financial capital; Mathematics; Capital formation; Statistics; Geography","score_opus":0.08867106082031231,"score_gpt":0.3560634217238997,"score_spread":0.2673923609035874,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2109521279","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":[0.91809183,0.00006596245,0.0672889,0.0012840321,0.0005142602,0.0006799081,0.00016258263,0.00003188338,0.011880659],"genre_scores_gemma":[0.98025656,0.0001614079,0.01410687,0.00007513554,0.00012396097,0.00006521132,0.000022568433,0.000015690128,0.0051725837],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.9981964,0.00006006893,0.00074929546,0.00037940632,0.00040030334,0.00021450344],"domain_scores_gemma":[0.99767095,0.0010223688,0.00047038306,0.00039596102,0.00034206884,0.00009824919],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0008562595,0.0001363491,0.00028358685,0.0002119317,0.0002023803,0.000034343262,0.00040377886,0.00008042289,0.0012732475],"category_scores_gemma":[0.001142283,0.00011763699,0.00014102957,0.00016657959,0.00013778337,0.00008588258,0.000062405554,0.00007461158,0.0007645373],"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":[0.00032196008,0.00037745823,0.18569584,0.000009952504,0.00013936059,0.000022786864,0.006143603,0.14378096,0.00045261436,0.0044564507,0.627132,0.031467024],"study_design_scores_gemma":[0.0072573572,0.0012669232,0.5751427,0.0000771604,0.0001815144,0.0003258363,0.0062398845,0.04744876,0.029852198,0.025668517,0.304742,0.0017971492],"about_ca_topic_score_codex":0.0004906295,"about_ca_topic_score_gemma":0.000082897066,"teacher_disagreement_score":0.38944685,"about_ca_system_score_codex":0.000035577006,"about_ca_system_score_gemma":0.00017012491,"threshold_uncertainty_score":0.99963975},"labels":[],"label_agreement":null},{"id":"W2115300132","doi":"10.2139/ssrn.1855623","title":"Coupled Risk Measures and Their Empirical Estimation When Losses Follow Heavy-Tailed Distributions","year":2011,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Western University","funders":"","keywords":"Econometrics; Estimation; Statistics; Heavy-tailed distribution; Environmental science; Economics; Mathematics; Probability distribution","score_opus":0.0701234112896679,"score_gpt":0.32796508210446884,"score_spread":0.2578416708148009,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2115300132","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":[0.3923747,0.0017556546,0.60470384,0.00044134475,0.00015805627,0.00012045063,0.000015105767,0.000033518354,0.00039729805],"genre_scores_gemma":[0.9888917,0.009108728,0.0014150154,0.000033821932,0.00010892301,0.0000067588553,0.00001184612,0.000014396016,0.00040878222],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9968364,0.00036533969,0.00064832816,0.00032904057,0.0006748255,0.0011460126],"domain_scores_gemma":[0.99826854,0.00033019538,0.00047908464,0.00030835718,0.00043255094,0.00018124128],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0056397994,0.00019928736,0.00029847442,0.00022826945,0.0007062617,0.0002761106,0.0004069602,0.00011685756,0.00012427851],"category_scores_gemma":[0.0019858768,0.0001279219,0.00015661419,0.00044396616,0.00012221813,0.0006119695,0.00004920105,0.00093275757,0.00005919337],"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":[0.0007600986,0.00041369916,0.40698975,0.0000018043768,0.00042312196,0.000009041821,0.007188663,0.0034529464,0.000087264096,0.024169542,0.0030676331,0.55343646],"study_design_scores_gemma":[0.00084425777,0.00037104826,0.02296892,0.0000089070445,0.00007342136,0.0003241092,0.0016985433,0.03944026,0.00012532448,0.9307904,0.0031146123,0.00024017633],"about_ca_topic_score_codex":0.00014519897,"about_ca_topic_score_gemma":0.0011312002,"teacher_disagreement_score":0.90662086,"about_ca_system_score_codex":0.00024432215,"about_ca_system_score_gemma":0.0011531083,"threshold_uncertainty_score":0.5432066},"labels":[],"label_agreement":null},{"id":"W2117390786","doi":"10.1108/eb043499","title":"Dimension Reduction in the Computation of Value‐at‐Risk","year":2002,"lang":"en","type":"article","venue":"The Journal of Risk Finance","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":12,"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":"Value at risk; Portfolio; Dimensionality reduction; Computation; Dimension (graph theory); Econometrics; Order (exchange); Economics; Reduction (mathematics); Curse of dimensionality; Value (mathematics); Financial economics; Risk management; Computer science; Mathematics; Finance; Statistics; Algorithm","score_opus":0.04901928401463922,"score_gpt":0.32076507495587575,"score_spread":0.2717457909412365,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2117390786","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":[0.9850629,0.0021238634,0.011408962,0.0006176113,0.00036440918,0.000089602196,0.0000037996062,0.000002173518,0.00032667888],"genre_scores_gemma":[0.9742679,0.0241115,0.0013836179,0.00002368616,0.00008442497,4.1146717e-7,2.7786314e-7,0.000004170857,0.0001239846],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9971178,0.00097213325,0.0008077027,0.000091710426,0.00090717955,0.00010348108],"domain_scores_gemma":[0.9965324,0.00087805785,0.002001399,0.00026716237,0.0003050259,0.00001599259],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0062949928,0.00007469736,0.00019303859,0.00017026388,0.00018926765,0.000027177672,0.00044342986,0.000042446805,0.000020102383],"category_scores_gemma":[0.0010227041,0.00003521351,0.00008764166,0.00083737524,0.00009543737,0.0002733443,0.000029452438,0.0002568752,0.000028972812],"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":[0.00015637382,0.00009001294,0.01633932,7.219231e-7,0.000006251414,0.0000033778836,0.010450871,0.7987426,0.00016463705,0.00019799428,0.012641352,0.16120648],"study_design_scores_gemma":[0.001446369,0.00055065745,0.64872086,0.00010712157,0.00012686104,0.0005053181,0.0027568263,0.28165373,0.0018246828,0.052570254,0.00954695,0.00019035501],"about_ca_topic_score_codex":0.00007495451,"about_ca_topic_score_gemma":0.000014063838,"teacher_disagreement_score":0.63238156,"about_ca_system_score_codex":0.000029353188,"about_ca_system_score_gemma":0.000015172996,"threshold_uncertainty_score":0.21817313},"labels":[],"label_agreement":null},{"id":"W2120195113","doi":"10.5539/mas.v2n5p122","title":"Adaptive Multicut Aggregation Method for Solving Two-stage Stochastic Convex Programming with Recourse","year":2008,"lang":"en","type":"article","venue":"Modern Applied Science","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Mathematical optimization; Computer science; Convergence (economics); Regular polygon; Simple (philosophy); Computation; Stochastic programming; Rate of convergence; Convex optimization; Stage (stratigraphy); Mathematics; Algorithm; Key (lock); Biology","score_opus":0.10409604148366133,"score_gpt":0.38296725164287937,"score_spread":0.278871210159218,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2120195113","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":[0.021828681,0.000026291857,0.97537255,0.000088885354,0.00013901482,0.001164793,0.000008957205,0.0000910696,0.0012797313],"genre_scores_gemma":[0.6161489,0.0000041089343,0.38305223,0.00006244584,0.000040127517,0.00013284388,0.000002836311,0.000013570314,0.0005429442],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99612564,0.00004041446,0.0004896616,0.0010123763,0.001762064,0.0005698344],"domain_scores_gemma":[0.9972399,0.0007889217,0.00046367676,0.0005536898,0.0007350256,0.00021880025],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0038361815,0.00021468847,0.0003134634,0.000408616,0.001213844,0.000312827,0.00077866664,0.000059017057,0.000019192003],"category_scores_gemma":[0.00067861215,0.00015602181,0.00005894897,0.0015563188,0.0007255029,0.0007429151,0.00009481336,0.00013925273,0.000033461012],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026210377,0.000078896526,0.00026352933,0.0000024971732,0.000008950353,0.0000058697783,0.0067762155,0.50128305,0.011655423,0.00352314,0.000072649,0.4760677],"study_design_scores_gemma":[0.00078007666,0.000104624036,0.00020139263,0.0000114482145,0.000013284581,0.000019531826,0.00094302295,0.989235,0.0031518221,0.0050056432,0.00028911652,0.00024506604],"about_ca_topic_score_codex":0.000045241988,"about_ca_topic_score_gemma":0.000053570267,"teacher_disagreement_score":0.5943202,"about_ca_system_score_codex":0.00009361963,"about_ca_system_score_gemma":0.0004788138,"threshold_uncertainty_score":0.933603},"labels":[],"label_agreement":null},{"id":"W2120518702","doi":"10.1109/cdc.2009.5400796","title":"Parametric regret in uncertain Markov decision processes","year":2009,"lang":"en","type":"article","venue":"","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"McGill University","funders":"","keywords":"Regret; Markov decision process; Parametric statistics; Computer science; Markov process; Markov chain; Decision theory; Mathematical optimization; Artificial intelligence; Machine learning; Mathematics; Statistics","score_opus":0.07366231025112745,"score_gpt":0.3976961143989476,"score_spread":0.3240338041478201,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2120518702","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":[0.6844789,0.0011526925,0.13744071,0.0031107648,0.00041268682,0.00056345435,0.000004925974,0.00015033124,0.17268553],"genre_scores_gemma":[0.9623886,0.00088727643,0.031628765,0.00057147257,0.00003397612,0.0000037956452,0.0000038648204,0.0000046134724,0.004477668],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9975725,0.000082907616,0.00062652834,0.00042229777,0.0010644045,0.00023140958],"domain_scores_gemma":[0.9976296,0.0013942178,0.00014443632,0.00042757875,0.00031463246,0.00008953935],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0018859442,0.000116590025,0.00021997062,0.0009703445,0.0000627645,0.00023224513,0.00049642747,0.000092090246,0.00057630154],"category_scores_gemma":[0.0089698285,0.00007444927,0.00004413964,0.006674742,0.000023895967,0.00047807087,0.000028832144,0.00009131908,0.00027362254],"study_design_candidate":"design_other","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":[0.0000741042,0.00008875105,0.05296556,7.7670165e-7,9.178402e-7,0.000018147955,0.000118034506,0.011082217,0.000005035666,0.0007897568,0.028584247,0.9062725],"study_design_scores_gemma":[0.001478413,0.00045538574,0.42161974,0.000065621614,0.000008814687,0.000034672594,0.0007475661,0.06909423,0.0006711205,0.4055341,0.09964336,0.0006469726],"about_ca_topic_score_codex":0.00004579297,"about_ca_topic_score_gemma":0.00009983318,"teacher_disagreement_score":0.90562546,"about_ca_system_score_codex":0.0000307836,"about_ca_system_score_gemma":0.00012093464,"threshold_uncertainty_score":0.999378},"labels":[],"label_agreement":null},{"id":"W2120552230","doi":"10.2139/ssrn.2373149","title":"How Superadditive Can a Risk Measure Be?","year":2013,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Superadditivity; Measure (data warehouse); Risk measure; Business; Computer science; Mathematics; Economics; Financial economics; Mathematical economics; Data mining","score_opus":0.026585782066678013,"score_gpt":0.28127407608376476,"score_spread":0.25468829401708676,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2120552230","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":[0.820616,0.004098939,0.12673718,0.038643412,0.0010669263,0.0006495525,0.00005913897,0.00009345614,0.008035367],"genre_scores_gemma":[0.9772882,0.008161469,0.00031503965,0.00020965548,0.00040982672,0.00001129971,0.0000054175603,0.000019408377,0.013579701],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9956777,0.00041129166,0.00040862776,0.00032335453,0.0013710435,0.0018079507],"domain_scores_gemma":[0.99796325,0.00027374938,0.00039071604,0.00033667585,0.00083423674,0.00020134616],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004291527,0.00018024837,0.00025099466,0.00029111572,0.0004852095,0.0009895896,0.000648179,0.000105971056,0.00042848347],"category_scores_gemma":[0.0019304138,0.000121273726,0.00018838169,0.00064511836,0.00007013114,0.0009141055,0.000038468603,0.0016441358,0.00019016935],"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":[0.000047773872,0.00009533379,0.044332255,4.6901053e-7,0.00024767863,0.000008739268,0.0016537305,0.0007179319,0.00020350213,0.046118356,0.036086183,0.87048805],"study_design_scores_gemma":[0.0009458563,0.00044094527,0.010600215,0.000008353289,0.00005598777,0.00062363496,0.014351733,0.0020216072,0.00018552295,0.92211777,0.04825241,0.00039595508],"about_ca_topic_score_codex":0.0004269138,"about_ca_topic_score_gemma":0.0026827804,"teacher_disagreement_score":0.87599945,"about_ca_system_score_codex":0.00039530694,"about_ca_system_score_gemma":0.0017964852,"threshold_uncertainty_score":0.9542635},"labels":[],"label_agreement":null},{"id":"W2124851762","doi":"10.1109/acc.2008.4586625","title":"A Markovian regime-switching stochastic differential game for portfolio risk minimization","year":2008,"lang":"en","type":"article","venue":"","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"University of Calgary","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Differential game; Hamilton–Jacobi–Bellman equation; Markov process; Mathematical optimization; Markov chain; Portfolio; Computer science; Mathematical economics; Measure (data warehouse); Stochastic differential equation; Mathematics; Applied mathematics; Bellman equation; Economics; Finance","score_opus":0.06834476709409196,"score_gpt":0.334114307358692,"score_spread":0.2657695402646001,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2124851762","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":[0.15578273,0.000037322392,0.83852845,0.00015512822,0.0007194089,0.00044721918,0.000025174742,0.000095848285,0.0042086937],"genre_scores_gemma":[0.97080064,0.00024220033,0.010679622,0.00012613142,0.00041307547,0.000042395604,0.00005350778,0.000029892046,0.017612554],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9971008,0.00013347079,0.00084744184,0.0006013893,0.00096566486,0.00035128635],"domain_scores_gemma":[0.9975267,0.0007526096,0.000558379,0.00057497196,0.00040378651,0.0001835516],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0007447009,0.00021197318,0.00034187097,0.00044076075,0.00043811495,0.0001799666,0.00043046483,0.00013261834,0.0012512825],"category_scores_gemma":[0.0022070417,0.00015682654,0.00021197174,0.00067608524,0.00006006679,0.0004678305,0.00006607192,0.00012056686,0.00011713783],"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":[0.0013624197,0.000649572,0.05274645,0.000011138329,0.0001819864,0.00003955675,0.0051472145,0.21653841,0.00029788646,0.009669783,0.35191745,0.36143816],"study_design_scores_gemma":[0.0024867167,0.00026236,0.03281189,0.000020318463,0.000108209366,0.00009173018,0.00042676934,0.9050893,0.00018705722,0.01943176,0.038381297,0.0007025799],"about_ca_topic_score_codex":0.00006481417,"about_ca_topic_score_gemma":0.00003465861,"teacher_disagreement_score":0.82784885,"about_ca_system_score_codex":0.000028846756,"about_ca_system_score_gemma":0.00010920261,"threshold_uncertainty_score":0.9996617},"labels":[],"label_agreement":null},{"id":"W2125280627","doi":"10.5267/j.msl.2014.4.011","title":"Asset management using an extended Markowitz theorem","year":2014,"lang":"en","type":"article","venue":"Management Science Letters","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Asset (computer security); Computer science; Asset management; Mathematical economics; Business; Mathematics; Economics; Econometrics; Finance; Computer security","score_opus":0.05170423005707668,"score_gpt":0.3514242381365369,"score_spread":0.2997200080794602,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2125280627","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":[0.35260868,0.00000510129,0.5572011,0.0023273951,0.0010126024,0.00049719185,0.0000018679959,0.00012194487,0.086224146],"genre_scores_gemma":[0.9342572,0.000028228797,0.05815852,0.006032943,0.000104821636,0.000015699658,0.000005024505,0.000017526243,0.0013799965],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99455076,0.00024011747,0.0005490648,0.0011309996,0.0028729455,0.00065608026],"domain_scores_gemma":[0.99789274,0.00008480462,0.00026743443,0.0014758481,0.00006445587,0.00021469922],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.008480598,0.00022283476,0.00021502715,0.0011756192,0.000706538,0.0012677752,0.002331062,0.000031365405,0.00020402313],"category_scores_gemma":[0.00010540502,0.00017348278,0.00008312333,0.0028090863,0.0005516488,0.0017213986,0.000526984,0.000091830276,0.0002585291],"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":[0.000052566673,0.0002890106,0.02199221,0.00001788931,0.00005052948,0.00010595528,0.00054089166,0.13288333,0.0033643055,0.21393736,0.019810973,0.606955],"study_design_scores_gemma":[0.0012066559,0.00013324499,0.2563831,0.00004626652,0.00011431711,0.000018485684,0.002178416,0.57541084,0.0006302893,0.053035706,0.10975462,0.0010880437],"about_ca_topic_score_codex":0.000014046746,"about_ca_topic_score_gemma":0.0000031707707,"teacher_disagreement_score":0.6058669,"about_ca_system_score_codex":0.00009989672,"about_ca_system_score_gemma":0.0000067560054,"threshold_uncertainty_score":0.99976903},"labels":[],"label_agreement":null},{"id":"W2126905415","doi":"10.1287/moor.2014.0704","title":"A Mixed Value and Policy Iteration Method for Stochastic Control with Universally Measurable Policies","year":2015,"lang":"en","type":"article","venue":"Mathematics of Operations Research","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Mathematics; Bounded function; Convergence (economics); Mathematical optimization; Bellman equation; Power iteration; Function (biology); Markov decision process; Optimal control; Average cost; Context (archaeology); Value (mathematics); Applied mathematics; Iterative method; Markov process","score_opus":0.33334413430034004,"score_gpt":0.500800905692265,"score_spread":0.16745677139192494,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2126905415","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":[0.044111878,0.000047429075,0.95112437,0.0022564372,0.000021945434,0.00087360374,0.00004480828,0.00001285483,0.001506689],"genre_scores_gemma":[0.69616205,0.000010244858,0.30150148,0.000022836595,0.00004844979,0.000072263276,0.000009126293,0.000014158948,0.0021594302],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9973286,0.00038220818,0.00045710264,0.00022971032,0.0013683324,0.00023407835],"domain_scores_gemma":[0.99396825,0.001817665,0.000080287406,0.00038864755,0.003591456,0.00015371999],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0074982718,0.00009925648,0.0002736037,0.0008280907,0.00031052897,0.00045193022,0.00029457032,0.000059606286,0.000009951143],"category_scores_gemma":[0.00874678,0.000067676985,0.00003356341,0.0011045387,0.00016399761,0.00043726742,0.00005125966,0.00009543439,0.000010420141],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009262503,0.00011670346,0.00007589139,0.000014125343,0.0000315377,4.7298295e-7,0.0071766586,0.67925733,0.0009335354,0.3089528,0.0012855887,0.0020627151],"study_design_scores_gemma":[0.0011226104,0.00035250554,0.000088487985,0.000024734949,0.00001655513,0.000011841687,0.004298586,0.95444727,0.00058331806,0.038642753,0.00032780846,0.00008354407],"about_ca_topic_score_codex":0.00042179183,"about_ca_topic_score_gemma":0.00041464614,"teacher_disagreement_score":0.65205014,"about_ca_system_score_codex":0.00006026918,"about_ca_system_score_gemma":0.00075037876,"threshold_uncertainty_score":0.999603},"labels":[],"label_agreement":null},{"id":"W2129591712","doi":"10.1111/j.1467-9965.2010.00443.x","title":"INDIFFERENCE PRICE WITH GENERAL SEMIMARTINGALES","year":2010,"lang":"en","type":"article","venue":"Mathematical Finance","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":43,"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":"Duality (order theory); Mathematical economics; Economics; Probability measure; Mathematics; Measure (data warehouse); Space (punctuation); Regular polygon; Function (biology); Econometrics; Computer science; Pure mathematics; Mathematical analysis","score_opus":0.03709765329046002,"score_gpt":0.3328434513271378,"score_spread":0.2957457980366778,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2129591712","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":[0.8729143,0.0000111496565,0.108077906,0.00036652607,0.000094925526,0.00011449463,0.000003190428,0.00004037223,0.01837715],"genre_scores_gemma":[0.88152456,0.0000190875,0.11158895,0.000110392706,0.00007828835,0.000017679244,0.0000013385537,0.000010301501,0.0066493903],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99826247,0.00003530585,0.00040763261,0.00032269096,0.00073125365,0.000240644],"domain_scores_gemma":[0.9985385,0.0004830813,0.00019640512,0.0005447325,0.00016457887,0.00007268754],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000864875,0.00012362962,0.00022758057,0.00007078363,0.00012323998,0.00017185492,0.00046722253,0.00007676488,0.00042663686],"category_scores_gemma":[0.001434182,0.00007238893,0.000041001444,0.00050749764,0.00014590847,0.00021503052,0.00006204193,0.00022419968,0.0007071637],"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":[0.000080177015,0.00043437904,0.07894604,0.000020343121,0.000015410178,0.000054074768,0.0034736178,0.0006928287,0.006365181,0.79247844,0.010508352,0.10693115],"study_design_scores_gemma":[0.0004419234,0.00013137325,0.092462525,0.000045061824,0.000013254048,0.00011432143,0.000064166336,0.06574814,0.005577964,0.7960648,0.038868085,0.00046841972],"about_ca_topic_score_codex":0.0000021746114,"about_ca_topic_score_gemma":0.0000071615736,"teacher_disagreement_score":0.10646273,"about_ca_system_score_codex":0.000005150926,"about_ca_system_score_gemma":0.000047396858,"threshold_uncertainty_score":0.9089395},"labels":[],"label_agreement":null},{"id":"W2133183405","doi":"10.1111/j.1539-6975.2012.01473.x","title":"Designing a Countercyclical Insurance Program for Systemic Risk","year":2012,"lang":"en","type":"article","venue":"Journal of Risk & Insurance","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Social Sciences and Humanities Research Council; Wilfrid Laurier University; University of Waterloo","funders":"","keywords":"Solvency; Systemic risk; Construct (python library); Actuarial science; Capital requirement; Economics; Capital (architecture); Value at risk; Business; Financial crisis; Monetary economics; Risk management; Finance; Macroeconomics; Computer science; Microeconomics","score_opus":0.05028341999256518,"score_gpt":0.36882285206382437,"score_spread":0.3185394320712592,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2133183405","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":[0.8288869,0.0048493617,0.16435584,0.00006666738,0.0011986262,0.00041542374,0.000070012386,0.00003092847,0.00012621052],"genre_scores_gemma":[0.9521224,0.004984399,0.04183227,0.000056201392,0.00088834646,0.000031023792,0.0000013813412,0.000025381805,0.00005858552],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9956481,0.0005535202,0.0016907185,0.0002492998,0.0013656695,0.0004927186],"domain_scores_gemma":[0.99268115,0.00214602,0.0031477087,0.0004016643,0.001332119,0.00029135228],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008309012,0.00021946519,0.000630135,0.00030835278,0.00030546077,0.00025817918,0.0007294572,0.0001537942,0.000016728887],"category_scores_gemma":[0.005500329,0.0001493229,0.00039944597,0.0007744173,0.00009852196,0.0014104381,0.000028695255,0.00046723138,0.00005528961],"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":[0.0003198802,0.00017510304,0.8173931,0.0000071269196,0.000038596005,0.000005283652,0.0010378886,0.0047336966,0.00017446512,0.000066910725,0.0029401663,0.17310774],"study_design_scores_gemma":[0.002012141,0.00046051203,0.9221949,0.00021042662,0.00008325915,0.0008370704,0.0006212369,0.0030780137,0.00061733,0.0021847817,0.06735403,0.00034631044],"about_ca_topic_score_codex":0.000022673132,"about_ca_topic_score_gemma":0.000005330605,"teacher_disagreement_score":0.17276143,"about_ca_system_score_codex":0.00009526235,"about_ca_system_score_gemma":0.00010437493,"threshold_uncertainty_score":0.6584805},"labels":[],"label_agreement":null},{"id":"W2133626546","doi":"10.1007/s11009-008-9089-z","title":"Properties of Distortion Risk Measures","year":2008,"lang":"en","type":"article","venue":"Methodology And Computing In Applied Probability","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":116,"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":"Mathematics; Distortion (music); Coherent risk measure; CVAR; Expected shortfall; Risk measure; Spectral risk measure; Dynamic risk measure; Value at risk; Measure (data warehouse); Portfolio; Risk analysis (engineering); Completeness (order theory); Actuarial science; Econometrics; Risk management; Computer science; Data mining; Economics; Finance; Business","score_opus":0.3930101946453399,"score_gpt":0.3764521564179393,"score_spread":0.01655803822740065,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2133626546","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":[0.7475001,0.00022600827,0.25107986,0.000030325033,0.00008977751,0.00018970827,8.140648e-7,0.000018940698,0.0008644663],"genre_scores_gemma":[0.8917149,0.00013468934,0.10808512,0.00001806627,0.00002017524,0.0000065856743,6.277929e-7,0.0000033406961,0.000016505375],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99663025,0.0017602533,0.0007238488,0.00041837085,0.00031229795,0.00015497745],"domain_scores_gemma":[0.9978034,0.0013707258,0.0003548732,0.00031524347,0.000119890916,0.000035859615],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.012824359,0.00009964008,0.0003822615,0.0001300788,0.00017605555,0.000009467357,0.00019385236,0.00011577271,0.000006704155],"category_scores_gemma":[0.0036725565,0.00007119222,0.000043130407,0.00040800186,0.00047098153,0.00004290448,0.00009462224,0.00017935842,0.0000030935787],"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":[0.00023097979,0.00008629388,0.72748744,0.000009041865,0.0000074943778,6.9928694e-7,0.003798877,0.02054537,0.00093360466,0.0024956614,0.000024822777,0.24437973],"study_design_scores_gemma":[0.0004413742,0.00005885171,0.803153,0.000009391569,0.000011646145,0.0000157472,0.00022948737,0.01020323,0.008512189,0.17677292,0.00043251365,0.00015968576],"about_ca_topic_score_codex":0.00011611813,"about_ca_topic_score_gemma":0.00004231929,"teacher_disagreement_score":0.24422003,"about_ca_system_score_codex":0.000017463646,"about_ca_system_score_gemma":0.00004628182,"threshold_uncertainty_score":0.44446927},"labels":[],"label_agreement":null},{"id":"W2134335798","doi":"10.1080/10920277.2011.10597635","title":"Capital Allocation Using the Bootstrap","year":2011,"lang":"en","type":"article","venue":"North American Actuarial Journal","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Capital allocation line; Distortion (music); Econometrics; Variance (accounting); Bootstrapping (finance); Resampling; Capital (architecture); Monotone polygon; Selection bias; Mathematics; Statistics; Economics; Computer science; Microeconomics","score_opus":0.17635680960020172,"score_gpt":0.37069947253724667,"score_spread":0.19434266293704494,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2134335798","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":[0.90620124,0.000025494646,0.090244286,0.0002119274,0.0010834913,0.00009493073,0.0000037942389,0.000016252094,0.0021185786],"genre_scores_gemma":[0.9928224,0.00014724594,0.0057197427,0.00030564528,0.0008857015,0.0000013010667,0.000002103387,0.0000120324685,0.00010383321],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99760216,0.00026474643,0.00061827974,0.00021462771,0.0010193186,0.00028088942],"domain_scores_gemma":[0.998049,0.0001738058,0.0008564492,0.00035250664,0.00038033788,0.00018788932],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001189144,0.0001337531,0.00020849565,0.00020065434,0.0004993836,0.00035583318,0.00070823176,0.000028886829,0.00048631156],"category_scores_gemma":[0.00052215974,0.0000759116,0.00014191309,0.00088365364,0.00030905206,0.000525863,0.00004485807,0.00030382717,0.00008635276],"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":[0.0003208284,0.00012340082,0.39733887,3.2793233e-7,0.000084692525,0.000047925703,0.012375152,0.008954884,0.00021691536,0.00040339716,0.0046928027,0.5754408],"study_design_scores_gemma":[0.0006787183,0.00045470448,0.9596852,0.0000055059336,0.00010245746,0.0008094375,0.006702022,0.010867284,0.00023698094,0.004119053,0.015905516,0.000433123],"about_ca_topic_score_codex":0.00061538233,"about_ca_topic_score_gemma":0.00034306743,"teacher_disagreement_score":0.5750077,"about_ca_system_score_codex":0.00004363435,"about_ca_system_score_gemma":0.0002645609,"threshold_uncertainty_score":0.5324771},"labels":[],"label_agreement":null},{"id":"W2136037928","doi":"10.2143/ast.42.2.2182809","title":"Average Value-at-Risk Minimizing Reinsurance under Wang's Premium Principle with Constraints","year":2012,"lang":"en","type":"article","venue":"Astin Bulletin","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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 Waterloo","funders":"","keywords":"Reinsurance; Constraint (computer-aided design); Budget constraint; Probabilistic logic; Actuarial science; Value (mathematics); Economics; Mathematical economics; Mathematical optimization; Mathematics; Microeconomics; Statistics","score_opus":0.03788769855233587,"score_gpt":0.3110000056682177,"score_spread":0.27311230711588186,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2136037928","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":[0.7882978,0.00051808223,0.12319736,0.0018423061,0.0008242041,0.00053239707,0.000052517564,0.00016032252,0.08457503],"genre_scores_gemma":[0.9636751,0.00012263532,0.023223229,0.00036463965,0.00022951116,0.000018797102,0.000012828051,0.000031078973,0.012322213],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.99674004,0.00035761902,0.0006164434,0.0005194821,0.0011672021,0.00059920654],"domain_scores_gemma":[0.99728376,0.0010455207,0.00050935656,0.0006586276,0.00022821114,0.00027451766],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0026530272,0.00024520746,0.00031559198,0.00013964184,0.00037764417,0.00014366335,0.0004391565,0.000121034995,0.0044880044],"category_scores_gemma":[0.0013283804,0.0001755137,0.00008936994,0.0004576014,0.00026583916,0.00018692682,0.00016336584,0.00022927788,0.002686639],"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":[0.00049323286,0.00022154785,0.78684616,0.000009387967,0.00007282423,0.000028996448,0.0020887516,0.1002676,0.00026211128,0.004063895,0.06641769,0.039227795],"study_design_scores_gemma":[0.0015354151,0.0001335693,0.28791186,0.00007353398,0.000045286637,0.00013210035,0.0004991215,0.0026982194,0.002323055,0.0005630186,0.7034281,0.0006566937],"about_ca_topic_score_codex":0.00004510486,"about_ca_topic_score_gemma":0.000008695294,"teacher_disagreement_score":0.6370104,"about_ca_system_score_codex":0.00006575029,"about_ca_system_score_gemma":0.00006739332,"threshold_uncertainty_score":0.9980899},"labels":[],"label_agreement":null},{"id":"W2138340154","doi":"10.1287/opre.1070.0433","title":"Optimal Dynamic Trading Strategies with Risk Limits","year":2007,"lang":"en","type":"article","venue":"Operations Research","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":27,"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":"Limit (mathematics); Portfolio; Risk measure; Value at risk; Econometrics; Coherent risk measure; Spectral risk measure; Economics; Actuarial science; Control (management); Measure (data warehouse); Expected shortfall; Risk management; Computer science; Mathematics; Financial economics; Finance","score_opus":0.16309333884309335,"score_gpt":0.4942684075279156,"score_spread":0.33117506868482227,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2138340154","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":[0.6990068,0.00009167147,0.26735666,0.00033487132,0.00006410369,0.0002602606,0.0000107489,0.00003059885,0.032844305],"genre_scores_gemma":[0.96403927,0.00029216876,0.02855705,0.000011545058,0.00006852613,0.000017692379,0.000015897054,0.00001444418,0.0069834306],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9963179,0.0003674367,0.00042820282,0.00042899078,0.0019807566,0.00047666312],"domain_scores_gemma":[0.9971632,0.0009333253,0.00003759157,0.00052764104,0.0011693429,0.00016890278],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00935646,0.00010592399,0.00014318858,0.00081070967,0.0011902833,0.0015502373,0.0005235187,0.000080682206,0.000481063],"category_scores_gemma":[0.0011233367,0.00007079919,0.00003934889,0.0024608981,0.0002296858,0.0010482521,0.00004509464,0.00043792895,0.00036354252],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014133436,0.00013147478,0.0078653395,0.0000014125214,0.000024364987,0.00004734679,0.0031130542,0.89349824,0.0013978542,0.012626247,0.0025010395,0.07865228],"study_design_scores_gemma":[0.00057289307,0.0005226155,0.037774097,0.000016311984,0.000009942982,0.00003890069,0.018760648,0.92708683,0.0013889958,0.0017435947,0.011804391,0.00028077414],"about_ca_topic_score_codex":0.00027162844,"about_ca_topic_score_gemma":0.0038646404,"teacher_disagreement_score":0.26503247,"about_ca_system_score_codex":0.00006903643,"about_ca_system_score_gemma":0.00040796754,"threshold_uncertainty_score":0.99948627},"labels":[],"label_agreement":null},{"id":"W2143299583","doi":"10.1080/14697680701213868","title":"Portfolio optimization under the Value-at-Risk constraint","year":2007,"lang":"en","type":"article","venue":"Quantitative Finance","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Portfolio optimization; Portfolio; Constraint (computer-aided design); Value at risk; Economics; Value (mathematics); Expected shortfall; Mathematical optimization; Financial economics; Econometrics; Mathematical economics; Actuarial science; Risk management; Mathematics; Finance; Statistics","score_opus":0.0721367049900727,"score_gpt":0.379992870718648,"score_spread":0.3078561657285753,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2143299583","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":[0.15651067,0.0005931564,0.8166094,0.0006019901,0.0005148747,0.00027457942,0.000037311172,0.000043003918,0.024815025],"genre_scores_gemma":[0.9213831,0.002131997,0.06996818,0.0005938512,0.000077384764,0.000011482148,0.000020675507,0.000022498645,0.005790836],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9970374,0.00028106396,0.00080082944,0.0005107839,0.0009953487,0.00037453906],"domain_scores_gemma":[0.9958112,0.0021983557,0.00077700673,0.000599778,0.0005441613,0.000069496265],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0051712897,0.00019255542,0.00025262023,0.00020916793,0.0006153303,0.00014265327,0.00054110633,0.00010150947,0.00038259116],"category_scores_gemma":[0.0016496491,0.000121490644,0.00012898611,0.0014315735,0.00045893656,0.00037963668,0.00009490039,0.00019805282,0.0004935199],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000076858196,0.000034659904,0.0075798705,4.2000434e-7,0.0000133872145,0.000009207496,0.00066879473,0.732458,0.00002127675,0.24087529,0.0066218902,0.01164033],"study_design_scores_gemma":[0.0011635948,0.00039834436,0.26722765,0.000034337572,0.00006332088,0.00005389176,0.005468935,0.49941355,0.0012431655,0.08073888,0.1434439,0.0007504364],"about_ca_topic_score_codex":0.00007672963,"about_ca_topic_score_gemma":0.000083238105,"teacher_disagreement_score":0.76487243,"about_ca_system_score_codex":0.00007506616,"about_ca_system_score_gemma":0.0001002491,"threshold_uncertainty_score":0.6343364},"labels":[],"label_agreement":null},{"id":"W2147060207","doi":"10.1239/jap/1409932674","title":"Asymptotic Bounds for the Distribution of the Sum of Dependent Random Variables","year":2014,"lang":"en","type":"article","venue":"Journal of Applied Probability","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; University of Cambridge","keywords":"Mathematics; Random variable; Limit (mathematics); Combinatorics; Distribution (mathematics); Mathematical proof; Marginal distribution; Value (mathematics); Expected value; Discrete mathematics; Applied mathematics; Statistics; Mathematical analysis","score_opus":0.03345529852883366,"score_gpt":0.29882868617425523,"score_spread":0.26537338764542157,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2147060207","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":[0.5378249,0.00008154757,0.45915177,0.0006045206,0.0005281145,0.00070589833,0.000032185126,0.0000030913354,0.001067963],"genre_scores_gemma":[0.99798805,0.00003677833,0.001823427,0.00002313928,0.00008608539,0.000008593051,0.0000016210033,0.0000037418788,0.000028566166],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9973641,0.00019396296,0.0012114639,0.00013832982,0.00096990465,0.00012220773],"domain_scores_gemma":[0.99405205,0.0029075933,0.0016070071,0.0005100563,0.0008815577,0.00004174517],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.012510247,0.00008637209,0.0003605069,0.000037056532,0.0001499542,0.000055164048,0.0007097434,0.000068722526,0.00002700868],"category_scores_gemma":[0.0027078232,0.000037303438,0.0002543981,0.00034638194,0.00021068576,0.000103943465,0.00006470558,0.00012724179,8.070449e-7],"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":[0.006678011,0.0009944049,0.0895911,0.00011721205,0.0002757262,1.7018775e-7,0.0017581016,0.5822325,0.002756118,0.178184,0.006935826,0.13047685],"study_design_scores_gemma":[0.003559478,0.0002601696,0.076039225,0.000035563487,0.00023972895,0.000009711887,0.00030571097,0.027066441,0.012622844,0.8677561,0.011977353,0.00012767124],"about_ca_topic_score_codex":0.000009133949,"about_ca_topic_score_gemma":0.0000123450955,"teacher_disagreement_score":0.6895721,"about_ca_system_score_codex":0.000041593685,"about_ca_system_score_gemma":0.00016401375,"threshold_uncertainty_score":0.43358266},"labels":[],"label_agreement":null},{"id":"W2151534421","doi":"10.5267/j.dsl.2012.12.001","title":"A fuzzy compromise programming approach for the Black-Litterman portfolio selection model","year":2012,"lang":"en","type":"article","venue":"Decision Science Letters","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Black–Litterman model; Portfolio; Fuzzy logic; Mathematical optimization; Computer science; Portfolio optimization; Econometrics; Economics; Mathematics; Replicating portfolio; Artificial intelligence; Financial economics","score_opus":0.09478110072037044,"score_gpt":0.3782452984271142,"score_spread":0.28346419770674375,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2151534421","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":[0.2437563,0.00004526413,0.7527306,0.0010597972,0.00048253482,0.00083412696,0.0000046905516,0.000060076447,0.0010266007],"genre_scores_gemma":[0.81328285,0.000021042753,0.18367866,0.0022946172,0.00028472324,0.00011330931,0.000005478918,0.00001866009,0.000300685],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9941934,0.00008557009,0.0008524117,0.00083708175,0.003082339,0.00094920484],"domain_scores_gemma":[0.99673295,0.00110825,0.0004453715,0.0009293046,0.00044715792,0.00033697716],"candidate_categories":["sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.011446063,0.00025017367,0.00029404098,0.00071610365,0.0013600263,0.0011753185,0.001910915,0.0000853652,0.00002344099],"category_scores_gemma":[0.0016575067,0.0001467946,0.00021932353,0.0034885923,0.0008455487,0.0023051822,0.00022129859,0.00020895345,0.00009289545],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014363581,0.00019006287,0.02375149,0.0000027775927,0.000012402471,7.535829e-7,0.0021937839,0.52492416,0.0064182393,0.0021231903,0.052795835,0.3874437],"study_design_scores_gemma":[0.00043841402,0.000032038188,0.010282174,0.0000057336824,0.000024814268,0.000026127109,0.0004113618,0.9662103,0.0005183821,0.0022603157,0.019517856,0.0002725051],"about_ca_topic_score_codex":0.000012423945,"about_ca_topic_score_gemma":0.0000015354846,"teacher_disagreement_score":0.56952655,"about_ca_system_score_codex":0.00010746695,"about_ca_system_score_gemma":0.00010926498,"threshold_uncertainty_score":0.99994004},"labels":[],"label_agreement":null},{"id":"W2152196758","doi":"","title":"A General Projection Property for Distribution Families","year":2009,"lang":"en","type":"article","venue":"","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":31,"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":"Unimodality; Mathematics; Property (philosophy); Univariate; Covariance; Mathematical optimization; Dimension (graph theory); Projection (relational algebra); Distribution (mathematics); Projection pursuit; Selection (genetic algorithm); Applied mathematics; Portfolio; Combinatorics; Statistics; Computer science; Algorithm; Artificial intelligence; Economics; Mathematical analysis; Multivariate statistics","score_opus":0.08278549029093941,"score_gpt":0.3775448693693817,"score_spread":0.2947593790784423,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2152196758","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":[0.093352295,0.000022101032,0.8820028,0.0021386764,0.00032552882,0.00066186587,0.000027521688,0.00011152226,0.021357717],"genre_scores_gemma":[0.93687546,0.00011879972,0.012961586,0.00040938362,0.0002547647,0.00003884387,0.00013397427,0.0000044965072,0.049202677],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99896616,0.00003424022,0.00027840814,0.00023367036,0.00035457482,0.00013295695],"domain_scores_gemma":[0.9993505,0.000051607007,0.00007864513,0.00017733184,0.0003052451,0.000036706868],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006598226,0.00006743958,0.0000997836,0.00007469913,0.00015019244,0.0001556233,0.00012933747,0.000048583963,0.000047649566],"category_scores_gemma":[0.00049891096,0.00003291568,0.00006557934,0.00041666423,0.000016729662,0.0003284303,0.000008078515,0.000027350487,0.00004205871],"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":[0.00007880894,0.00006429325,0.002477953,5.3651416e-7,0.000002628753,2.8291865e-7,0.00013284756,0.0016077722,0.000517059,0.008960967,0.15152913,0.83462775],"study_design_scores_gemma":[0.000506394,0.00042148924,0.0342335,0.000002856051,0.000009641445,0.000007210956,0.0002472129,0.24163066,0.0034842216,0.031552665,0.6877124,0.00019176638],"about_ca_topic_score_codex":0.000051332994,"about_ca_topic_score_gemma":0.000023545166,"teacher_disagreement_score":0.8690412,"about_ca_system_score_codex":0.000022572314,"about_ca_system_score_gemma":0.000039429357,"threshold_uncertainty_score":0.15006791},"labels":[],"label_agreement":null},{"id":"W2155813287","doi":"","title":"How much international exposure is advantageous in a domestic portfolio from a Canadian perspective","year":2006,"lang":"en","type":"dissertation","venue":"Summit (Simon Fraser University)","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Portfolio; Asset allocation; Financial economics; Bond; Asset (computer security); Economics; Cash; Perspective (graphical); Globalization; Investment (military); Business; Finance; Political science; Market economy","score_opus":0.014913606436695538,"score_gpt":0.27530627228988885,"score_spread":0.2603926658531933,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2155813287","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":[0.7840038,0.00069093425,0.00035346136,0.0013871442,0.0027813439,0.00063627143,0.0016568305,0.00008282201,0.20840742],"genre_scores_gemma":[0.84271854,0.00056535925,0.00026313498,0.00012684344,0.00018549906,0.0000024094916,0.0027913852,0.000039564908,0.15330726],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.9964793,0.00016077209,0.00046774736,0.0011573682,0.0012595926,0.00047522833],"domain_scores_gemma":[0.99721855,0.00025429926,0.0005349933,0.0006345787,0.0010166314,0.00034091834],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00026439677,0.00044112754,0.0005527347,0.0042134607,0.00021349607,0.00049880595,0.0014328978,0.0006124177,0.0012526453],"category_scores_gemma":[0.00043234098,0.00047431194,0.0002726547,0.0023494624,0.00006594469,0.00090374233,0.000058589838,0.0005588171,0.00015343336],"study_design_candidate":"observational","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":[0.00040231502,0.00016404016,0.84519386,0.0000048067573,0.00016812645,0.005802309,0.0009941792,0.001019252,0.0000029188861,0.0024395965,0.1350999,0.0087086605],"study_design_scores_gemma":[0.0019594356,0.00009307756,0.04614228,0.00011871858,0.00016830974,1.1569448e-7,0.3208609,0.0010858782,0.000115666924,0.015813172,0.6125457,0.0010967522],"about_ca_topic_score_codex":0.31502452,"about_ca_topic_score_gemma":0.98537856,"teacher_disagreement_score":0.7990516,"about_ca_system_score_codex":0.001148696,"about_ca_system_score_gemma":0.0011341702,"threshold_uncertainty_score":0.9997709},"labels":[],"label_agreement":null},{"id":"W2155945500","doi":"10.1080/10556780903334682","title":"Robust portfolio selection based on a joint ellipsoidal uncertainty set","year":2009,"lang":"en","type":"article","venue":"Optimization methods & software","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":55,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Portfolio; Robust optimization; Selection (genetic algorithm); Ellipsoid; Mathematics; Portfolio optimization; Mathematical optimization; Modern portfolio theory; Set (abstract data type); Project portfolio management; Computer science; Artificial intelligence; Economics; Financial economics","score_opus":0.13396194156147348,"score_gpt":0.40509937454343903,"score_spread":0.2711374329819656,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2155945500","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":[0.00048124758,0.00005412141,0.9932135,0.000879578,0.0005629333,0.00049571716,0.000029892193,0.00045245307,0.0038306103],"genre_scores_gemma":[0.011171122,0.00013092726,0.9835339,0.002294883,0.00022578568,0.000028552688,0.00022917712,0.000044758945,0.002340874],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9939399,0.0015685799,0.00127151,0.0010901857,0.0016026674,0.00052715215],"domain_scores_gemma":[0.9957004,0.001134661,0.0007768781,0.0008892422,0.0011879853,0.00031081465],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0057773483,0.00043244482,0.0006002192,0.0009938475,0.0005500369,0.0005183629,0.0005299623,0.00033454003,0.0025883554],"category_scores_gemma":[0.007013357,0.00035927564,0.00030491283,0.0033119677,0.00007387428,0.0006159252,0.000039329534,0.0003663658,0.0001193171],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010102922,0.00010032174,0.0011210358,0.0000015961084,0.0000070398687,0.0000039249,0.000092614995,0.8478164,0.000022430902,0.0001676868,0.009294136,0.14127176],"study_design_scores_gemma":[0.0006592015,0.00034724636,0.0018242883,0.000024382785,0.000037055517,0.000012598179,0.000078783036,0.98205316,0.0003917558,0.0031785953,0.010968456,0.00042448696],"about_ca_topic_score_codex":0.000025025243,"about_ca_topic_score_gemma":0.0000051747093,"teacher_disagreement_score":0.14084727,"about_ca_system_score_codex":0.00018373388,"about_ca_system_score_gemma":0.0003084163,"threshold_uncertainty_score":0.9998859},"labels":[],"label_agreement":null},{"id":"W2163689154","doi":"10.1287/inte.33.2.12.14465","title":"Applying Operations Research Techniques to Financial Markets","year":2003,"lang":"en","type":"article","venue":"INFORMS Journal on Applied Analytics","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"University of British Columbia","funders":"University of Nottingham; London School of Economics and Political Science","keywords":"Financial market; Equity (law); Debt; Finance; Business; Financial modeling; Market data; Financial engineering; Economics","score_opus":0.13052742231364783,"score_gpt":0.42996205898317885,"score_spread":0.299434636669531,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2163689154","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":[0.039749987,0.00003124759,0.37397078,0.0013524868,0.0007705894,0.0015486602,0.000013788279,0.00009578395,0.58246666],"genre_scores_gemma":[0.9406807,0.0005100705,0.048872266,0.0032294,0.0005661817,0.00013678352,0.0000047626354,0.00003004401,0.005969809],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9958791,0.000098638724,0.0009934658,0.0002580825,0.0023111955,0.00045948627],"domain_scores_gemma":[0.9976558,0.00037820838,0.00013976515,0.00046874408,0.00098588,0.00037159614],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.009318704,0.00016121937,0.00026889282,0.0014578891,0.0008836121,0.001159137,0.000633738,0.00014128086,0.00034391656],"category_scores_gemma":[0.0039564143,0.00010939024,0.00010830495,0.0024772221,0.00006715057,0.0003638247,0.000068332556,0.0007320645,0.00073215424],"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":[0.00017746007,0.00014306282,0.00079852226,0.0000021680191,0.000024716843,0.00003603579,0.0006829465,0.12590192,0.00030083518,0.28655174,0.12328118,0.4620994],"study_design_scores_gemma":[0.00024317145,0.00014121873,0.0004853263,0.000018592627,0.00000725559,0.000053420506,0.0005367234,0.003449237,0.0025565424,0.032218844,0.960068,0.00022169262],"about_ca_topic_score_codex":0.0000031720092,"about_ca_topic_score_gemma":0.000015421208,"teacher_disagreement_score":0.9009307,"about_ca_system_score_codex":0.00014045446,"about_ca_system_score_gemma":0.0004415373,"threshold_uncertainty_score":0.99987775},"labels":[],"label_agreement":null},{"id":"W2164843905","doi":"10.1109/cso.2011.188","title":"Optimal Portfolios with Power and Log Utilities","year":2011,"lang":"en","type":"article","venue":"","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Western University; Wilfrid Laurier University","funders":"","keywords":"Portfolio; Portfolio optimization; Gaussian; Capital (architecture); Computer science; Power (physics); Mathematical optimization; Modern portfolio theory; Risk model; Risk-adjusted return on capital; Econometrics; Actuarial science; Mathematics; Economics; Finance; Microeconomics; Financial capital","score_opus":0.08030035123566305,"score_gpt":0.31246252048171286,"score_spread":0.23216216924604982,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2164843905","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":[0.57251954,0.000088356355,0.016201485,0.00007533576,0.00008527485,0.0000900882,0.0000028742406,0.00004716028,0.4108899],"genre_scores_gemma":[0.9619577,0.00008211877,0.019671699,0.00017074995,0.000012883336,0.0000033645545,0.0000010586443,0.0000067728156,0.018093646],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9988278,0.000031806856,0.00025452758,0.0002737049,0.00045067404,0.00016147159],"domain_scores_gemma":[0.9992622,0.00008266159,0.00008419999,0.00030134185,0.00016804753,0.00010154643],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0004524575,0.00009593599,0.00014159176,0.00015151557,0.00007819327,0.000099957884,0.00017959242,0.000046541703,0.0050119674],"category_scores_gemma":[0.000097388416,0.000053521657,0.000026107688,0.0002726185,0.00014284393,0.00040197492,0.000050715473,0.000047917398,0.0001280945],"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":[0.0005533295,0.00020785189,0.7810359,0.0000024186065,0.00006369888,0.00011858173,0.014058071,0.00057986425,0.000022591998,0.093356594,0.061492633,0.04850849],"study_design_scores_gemma":[0.0015843944,0.0014164742,0.7779936,0.000017593442,0.00005056985,0.00035187262,0.021646416,0.007007371,0.0024178124,0.03145705,0.15508945,0.00096735905],"about_ca_topic_score_codex":0.000080737904,"about_ca_topic_score_gemma":0.00002349516,"teacher_disagreement_score":0.39279625,"about_ca_system_score_codex":0.000003233033,"about_ca_system_score_gemma":0.000034826695,"threshold_uncertainty_score":0.9958976},"labels":[],"label_agreement":null},{"id":"W2165923723","doi":"10.1017/s0515036100014756","title":"Optimal Retention for a Stop-loss Reinsurance Under the VaR and CTE Risk Measures","year":2007,"lang":"en","type":"article","venue":"Astin Bulletin","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":213,"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":"Changjiang Scholar Program of Chinese Ministry of Education; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Reinsurance; Mathematics; Multivariate statistics; Poisson distribution; Distribution (mathematics); Binomial distribution; Binomial (polynomial); Multivariate normal distribution; Mathematical optimization; Applied mathematics; Statistics; Economics; Actuarial science","score_opus":0.056782277804696524,"score_gpt":0.3278571570165889,"score_spread":0.2710748792118924,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2165923723","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":[0.46202648,0.0006868978,0.5312,0.0036328465,0.00032098388,0.00037098714,0.000021962736,0.0000400067,0.0016998637],"genre_scores_gemma":[0.97941077,0.00054559845,0.01593558,0.00027157716,0.00015682967,0.000018235902,0.000005629513,0.000015558977,0.0036402438],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99797285,0.00018555007,0.0004832266,0.00039930848,0.00068123784,0.0002778454],"domain_scores_gemma":[0.9972163,0.0016836104,0.0003161183,0.00036511826,0.00035273403,0.00006613441],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0072222594,0.00013032703,0.00016835344,0.00009744967,0.00045215423,0.00019340661,0.00030212995,0.000086985725,0.00011948392],"category_scores_gemma":[0.0020108027,0.00008121469,0.00008808678,0.00031185328,0.00015013303,0.00007363931,0.000054534245,0.00013970211,0.00010072356],"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":[0.0016453151,0.000106030595,0.0542378,0.000008039596,0.00007974861,0.000016109248,0.0017122752,0.07641302,0.00044186818,0.007432536,0.092965305,0.76494193],"study_design_scores_gemma":[0.0009484937,0.00013843583,0.21789116,0.000019309302,0.000045125616,0.0000314676,0.000914831,0.0055249343,0.0007744487,0.006098203,0.76736385,0.00024977044],"about_ca_topic_score_codex":0.00006697594,"about_ca_topic_score_gemma":0.000023025734,"teacher_disagreement_score":0.7646922,"about_ca_system_score_codex":0.0000141473065,"about_ca_system_score_gemma":0.000022963071,"threshold_uncertainty_score":0.34776506},"labels":[],"label_agreement":null},{"id":"W2169070995","doi":"","title":"Multivariate Tweedie Distributions and Some Related Capital-at-Risk Analysis","year":2010,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"York University","funders":"","keywords":"Multivariate statistics; Univariate; Poisson distribution; Inverse Gaussian distribution; Exponential family; Econometrics; Normal-inverse Gaussian distribution; Mathematics; Multivariate analysis; Gaussian; Statistics; Applied mathematics; Distribution (mathematics); Mathematical analysis; Gaussian process; Physics; Gaussian random field","score_opus":0.011894822904460022,"score_gpt":0.3027868461355225,"score_spread":0.29089202323106245,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2169070995","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":[0.97131205,0.0012201912,0.025724016,0.0007737236,0.0004076251,0.00008391011,0.00004420058,0.000031186704,0.00040311227],"genre_scores_gemma":[0.98876274,0.007116819,0.00034263195,0.000021172309,0.00015495425,0.0000025089196,0.000029330049,0.00001161465,0.0035582085],"study_design_codex":"observational","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9966917,0.00020722196,0.00070977485,0.00038065645,0.00069728476,0.0013133474],"domain_scores_gemma":[0.99815995,0.00033535267,0.0006073063,0.00038313365,0.00029599806,0.00021825553],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006007438,0.00016812024,0.00031646926,0.00055561424,0.000873559,0.0002710891,0.00040140952,0.00016543192,0.00028796087],"category_scores_gemma":[0.0014860355,0.00012150487,0.00025005688,0.0014603899,0.00012963783,0.0005872667,0.00009431916,0.0020476968,0.0001402025],"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":[0.000115466544,0.0001829425,0.5414372,6.007335e-7,0.0022005134,0.000015653291,0.0013082179,0.0040077935,0.0021404277,0.3627073,0.0008631537,0.08502072],"study_design_scores_gemma":[0.0007907671,0.0001154681,0.17367989,0.000001433094,0.0006387799,0.00026729755,0.0006101533,0.011881485,0.00008166925,0.80714136,0.004538834,0.00025282998],"about_ca_topic_score_codex":0.0002699868,"about_ca_topic_score_gemma":0.0036056489,"teacher_disagreement_score":0.4444341,"about_ca_system_score_codex":0.00021526635,"about_ca_system_score_gemma":0.00056662725,"threshold_uncertainty_score":0.8896338},"labels":[],"label_agreement":null},{"id":"W2170996874","doi":"10.3905/jwm.2006.644221","title":"Optimal Portfolio Allocation Using Funds of Hedge Funds","year":2006,"lang":"en","type":"article","venue":"˜The œjournal of wealth management","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Université du Québec à Montréal","funders":"","keywords":"Hedge fund; Alternative beta; Portfolio; Global assets under management; Business; Fund of funds; Economics; Institutional investor; Finance; Corporate governance; Market liquidity","score_opus":0.05890982937587599,"score_gpt":0.3555782478227789,"score_spread":0.29666841844690295,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2170996874","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":[0.4614696,0.001266003,0.5109602,0.0008030443,0.0011421243,0.0003950221,0.0000053677268,0.000014798435,0.023943838],"genre_scores_gemma":[0.9837141,0.0008608778,0.011865074,0.00016167836,0.00027529168,0.0000013766573,0.0000033079982,0.000013787386,0.003104507],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.9961195,0.00021865917,0.0016347824,0.00017609759,0.0015865582,0.00026437416],"domain_scores_gemma":[0.9966765,0.0001666205,0.002022195,0.00046630393,0.00057745184,0.00009093347],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005652467,0.00014301733,0.00032346547,0.00061516423,0.00018071769,0.000075712785,0.0007437531,0.000053317613,0.00015071558],"category_scores_gemma":[0.000032285247,0.0000929369,0.00015308528,0.0010184356,0.00008287152,0.00036045696,0.00011739936,0.0001357847,0.000019434834],"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":[0.0002418424,0.00026192458,0.013570676,0.000028699895,0.00009254324,0.00003369033,0.00037847002,0.8855915,0.00016256189,0.0047800383,0.044155847,0.050702214],"study_design_scores_gemma":[0.0044542616,0.0013112471,0.2749421,0.0002623277,0.0008579769,0.0004226608,0.0074129174,0.26758844,0.00069351064,0.025978155,0.41521344,0.00086296874],"about_ca_topic_score_codex":0.00016219642,"about_ca_topic_score_gemma":0.000034649827,"teacher_disagreement_score":0.6180031,"about_ca_system_score_codex":0.00009037116,"about_ca_system_score_gemma":0.0001037233,"threshold_uncertainty_score":0.37898567},"labels":[],"label_agreement":null},{"id":"W2171793383","doi":"10.1007/s11579-010-0023-0","title":"Risk measures on the space of infinite sequences","year":2010,"lang":"en","type":"article","venue":"Mathematics and Financial Economics","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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 de Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Fonds Québécois de la Recherche sur la Nature et les Technologies","keywords":"Risk measure; Coherent risk measure; Dynamic risk measure; Mathematics; Mathematical finance; Spectral risk measure; Measure (data warehouse); Generalization; Statistic; Risk management; Space (punctuation); Dimension (graph theory); Extension (predicate logic); Actuarial science; Statistics; Value at risk; Computer science; Combinatorics; Expected shortfall; Data mining; Economics; Finance; Mathematical analysis","score_opus":0.04752174764149962,"score_gpt":0.28233150111893324,"score_spread":0.2348097534774336,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2171793383","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":[0.9831268,0.000018449758,0.0036521247,0.00043429714,0.00032594736,0.00010655654,0.000030477966,0.0000056838567,0.01229965],"genre_scores_gemma":[0.99127865,0.0010820248,0.0072879433,0.00008291269,0.00008278498,0.000004410694,6.0699574e-7,0.000006135216,0.00017450615],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99918705,0.00002066714,0.0004132222,0.00014548094,0.00014106547,0.00009249161],"domain_scores_gemma":[0.99823713,0.00086842466,0.00042978913,0.00033910453,0.00008926917,0.000036295172],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019131877,0.00008314749,0.00019564971,0.00007734361,0.00014082424,0.00011223621,0.00026213724,0.00007196871,0.000042471456],"category_scores_gemma":[0.0025139046,0.000048627076,0.000054550732,0.00012076414,0.00014937407,0.00008181602,0.000040907704,0.00013960457,0.000033120035],"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":[0.000011493299,0.000043522632,0.0052561727,0.000002430214,0.000006161528,3.2809623e-7,0.0020377594,0.0016413091,0.00009682624,0.9292508,0.0010030636,0.060650162],"study_design_scores_gemma":[0.00017406805,0.00008784669,0.007800776,0.000009893613,0.000016575843,0.0000048284483,0.00046343252,0.037354816,0.001621162,0.85896516,0.09332786,0.00017361082],"about_ca_topic_score_codex":0.000028759987,"about_ca_topic_score_gemma":0.0001783463,"teacher_disagreement_score":0.09232479,"about_ca_system_score_codex":0.0000032349715,"about_ca_system_score_gemma":0.000055610104,"threshold_uncertainty_score":0.30095604},"labels":[],"label_agreement":null},{"id":"W2172061102","doi":"10.1016/j.insmatheco.2014.10.004","title":"Extreme value analysis of the Haezendonck–Goovaerts risk measure with a general Young function","year":2014,"lang":"en","type":"article","venue":"Insurance Mathematics and Economics","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Measure (data warehouse); Value (mathematics); Function (biology); Mathematics; Extreme value theory; Statistics; Econometrics; Computer science; Data mining; Biology","score_opus":0.03751673121706231,"score_gpt":0.2490333513720493,"score_spread":0.21151662015498698,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2172061102","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":[0.91675115,0.00005906276,0.07974496,0.000055178618,0.00013195511,0.00010876449,0.00002973895,0.000008604191,0.0031106009],"genre_scores_gemma":[0.9922381,0.00031602828,0.0070866067,0.00004258587,0.000040749826,0.0000056056674,0.000002535663,0.000010158683,0.00025764582],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99874043,0.000069176436,0.0005579857,0.000253245,0.0002532322,0.00012591715],"domain_scores_gemma":[0.9982525,0.00024667714,0.0007235806,0.0005740419,0.00015536413,0.000047818823],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015270831,0.00012346929,0.0003953521,0.00017019281,0.00016125676,0.00013219597,0.0002494655,0.000057948335,0.000020007563],"category_scores_gemma":[0.00027896967,0.000070752656,0.00013648719,0.0005650885,0.00008938892,0.00017313495,0.00004231953,0.00007150904,0.0000050347103],"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":[0.000030083442,0.00004835986,0.6600544,0.0000060118496,0.00029802733,1.14996894e-7,0.0010191953,0.30047122,0.000026173531,0.01595838,0.0001128527,0.021975184],"study_design_scores_gemma":[0.0002511249,0.000033509892,0.33333576,0.000010186968,0.0002861287,0.0000041941885,0.00012011155,0.6441823,0.000062243285,0.020870142,0.0007341909,0.00011006291],"about_ca_topic_score_codex":0.00008755641,"about_ca_topic_score_gemma":0.00043559753,"teacher_disagreement_score":0.34371114,"about_ca_system_score_codex":0.000014623878,"about_ca_system_score_gemma":0.00002436036,"threshold_uncertainty_score":0.28852096},"labels":[],"label_agreement":null},{"id":"W2174240154","doi":"10.1016/j.jmva.2017.02.005","title":"Domains of weak continuity of statistical functionals with a view toward robust statistics","year":2017,"lang":"en","type":"preprint","venue":"Journal of Multivariate Analysis","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Actua; University of Waterloo","funders":"Bundesministerium für Bildung und Forschung; Deutsche Forschungsgemeinschaft","keywords":"Estimator; Robustness (evolution); Mathematics; M-estimator; Parametric statistics; Robust statistics; Applied mathematics; Homogeneity (statistics); Mathematical optimization; Econometrics; Statistics","score_opus":0.09504663097290719,"score_gpt":0.39567791907594096,"score_spread":0.30063128810303374,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2174240154","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":[0.016708203,0.00035294783,0.9799461,0.00016152041,0.00034098,0.00016666148,0.0015714723,0.0000038952544,0.0007482593],"genre_scores_gemma":[0.7651244,0.0016829219,0.23247437,0.000012969564,0.00013120081,0.0000024933752,0.00008616571,0.0000171424,0.00046836963],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99229026,0.0007095038,0.0032501388,0.00045286113,0.0030690124,0.00022824465],"domain_scores_gemma":[0.9792788,0.0021587156,0.011382839,0.0010117888,0.005940354,0.00022748802],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0061286683,0.00032699778,0.002629427,0.0014263979,0.00011084434,0.00024181968,0.0011947046,0.00027224675,0.0007505909],"category_scores_gemma":[0.005359777,0.00020713564,0.00077368657,0.00085081934,0.0003303046,0.00026199422,0.00031808126,0.000605268,0.0000055883675],"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":[0.0012042618,0.00060112576,0.08576141,0.000102390455,0.010274162,0.00009128878,0.00109782,0.86688834,0.000074752774,0.005215362,0.0032356598,0.025453394],"study_design_scores_gemma":[0.0034424944,0.0008501936,0.7259543,0.0006083512,0.023138404,0.000048049245,0.00090034853,0.19500926,0.00018705244,0.0436128,0.0054369955,0.0008117475],"about_ca_topic_score_codex":0.0007015785,"about_ca_topic_score_gemma":0.0002923018,"teacher_disagreement_score":0.7484162,"about_ca_system_score_codex":0.000071028764,"about_ca_system_score_gemma":0.00083437347,"threshold_uncertainty_score":0.84467465},"labels":[],"label_agreement":null},{"id":"W2182891966","doi":"10.70930/tac/9xlt05n6","title":"Toward Categorical Risk Measure Theory","year":2014,"lang":"en","type":"article","venue":"Theory and applications of categories","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Axiom; Ambiguity; Mathematics; Categorical variable; Measure (data warehouse); Mathematical economics; Value (mathematics); Axiomatic system; Computer science; Statistics; Data mining","score_opus":0.03037482880003825,"score_gpt":0.3094262105761289,"score_spread":0.2790513817760907,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2182891966","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":[0.017782811,0.00075116573,0.95259726,0.00017931087,0.00006537178,0.00023531682,0.00001670072,0.00005371851,0.028318362],"genre_scores_gemma":[0.9969974,0.000505965,0.00074986345,0.00005895672,0.00013124337,0.00007418212,0.000010847808,0.000010609637,0.0014609266],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9977964,0.0007776233,0.00048488536,0.00033012623,0.00045735913,0.00015359602],"domain_scores_gemma":[0.9960238,0.002558567,0.00033145523,0.00063451316,0.0003511119,0.000100572004],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0071946713,0.00013390675,0.00027056946,0.00016404639,0.00027764816,0.00008936338,0.00045981767,0.00009386894,0.00014195882],"category_scores_gemma":[0.0016203354,0.000094361814,0.000072671835,0.0006019633,0.0004799803,0.0002106474,0.000076504366,0.00012535194,0.00008313484],"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":[0.000069673304,0.000025747035,0.0012018367,0.0000024479175,0.000010024218,6.75468e-8,0.0006280902,0.00016128687,0.000035598474,0.78497124,0.00024594503,0.21264806],"study_design_scores_gemma":[0.0001489284,0.00003652048,0.0018088528,0.0000013271346,0.000040031842,0.000005289755,0.0012562656,0.00018993414,0.0010758333,0.94998246,0.045342438,0.00011212933],"about_ca_topic_score_codex":0.000014589954,"about_ca_topic_score_gemma":0.0000026678279,"teacher_disagreement_score":0.9792146,"about_ca_system_score_codex":0.0000063885777,"about_ca_system_score_gemma":0.000040359664,"threshold_uncertainty_score":0.38479632},"labels":[],"label_agreement":null},{"id":"W2184515612","doi":"","title":"Fuzzy Liability Driven Pension Fund Management","year":2014,"lang":"en","type":"article","venue":"","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Pension; Pension fund; Fuzzy logic; Actuarial science; Valuation (finance); Pension plan; Business; Target date fund; Finance; Liability; Economics; Computer science; Institutional investor; Artificial intelligence; Open-end fund","score_opus":0.09013101779956224,"score_gpt":0.36903529886414965,"score_spread":0.2789042810645874,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2184515612","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":[0.12040947,0.000007651312,0.08058704,0.00080528896,0.00040942078,0.0001753122,7.9536744e-7,0.00006709105,0.7975379],"genre_scores_gemma":[0.9550855,0.000060585833,0.0141099775,0.00035437391,0.00005946379,0.0000041832964,0.0000035911203,0.0000048967913,0.030317388],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.998215,0.00014737321,0.0003563577,0.00036989903,0.0007597824,0.00015157793],"domain_scores_gemma":[0.9987312,0.00026307828,0.00009287924,0.0006723777,0.0001519871,0.000088482084],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0019417561,0.00008068353,0.00013632384,0.00012771432,0.00011386156,0.00014741307,0.00026113968,0.000043100637,0.00088144833],"category_scores_gemma":[0.00028640876,0.000052329247,0.00006487621,0.0004006655,0.00003981336,0.00021111575,0.00013087253,0.000044012817,0.0018815504],"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":[0.00003867753,0.00013676568,0.10349479,0.000004366992,0.000013582095,0.000005931649,0.00025067525,0.018132925,0.0000646673,0.18401709,0.13345526,0.5603853],"study_design_scores_gemma":[0.00035354652,0.00006931402,0.1689953,0.000004310977,0.000012370458,0.0000026984296,0.00022255964,0.04212863,0.00013925116,0.14838812,0.63949716,0.0001867577],"about_ca_topic_score_codex":0.000020885016,"about_ca_topic_score_gemma":0.000030410045,"teacher_disagreement_score":0.8346761,"about_ca_system_score_codex":0.00001355589,"about_ca_system_score_gemma":0.0000067039073,"threshold_uncertainty_score":0.9988956},"labels":[],"label_agreement":null},{"id":"W2186630404","doi":"10.82308/2492","title":"Robust network design","year":2010,"lang":"en","type":"article","venue":"eScholarship@McGill (McGill)","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"McGill University","funders":"University of Pennsylvania","keywords":"Counterexample; Network planning and design; Mathematical optimization; Conjecture; Set (abstract data type); Computer science; Tree (set theory); Generality; Robustness (evolution); Routing (electronic design automation); Mathematics; Theoretical computer science; Discrete mathematics; Combinatorics","score_opus":0.08989797324482983,"score_gpt":0.29824595550532107,"score_spread":0.20834798226049123,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2186630404","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":[0.7663781,0.00016798747,0.0022152239,0.00025650449,0.007815836,0.0012281159,0.00026211448,0.00073496986,0.22094113],"genre_scores_gemma":[0.92042375,0.00017592304,0.07215651,0.000738923,0.00025941047,0.00004527313,0.000019799845,0.00008747828,0.0060929144],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9940557,0.00074104744,0.0012217305,0.0011823241,0.0018829432,0.00091625383],"domain_scores_gemma":[0.99490327,0.0016232335,0.0005686877,0.001595036,0.00074253976,0.000567214],"candidate_categories":["metaepi_narrow","sts","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0078807045,0.00045062182,0.0005492586,0.00037445428,0.0016899476,0.00043263656,0.0016402846,0.00048531225,0.0019308599],"category_scores_gemma":[0.005192404,0.00037666748,0.00028980232,0.0021110007,0.00016111461,0.001631034,0.00032587064,0.0012389526,0.0031369398],"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":[0.00015413579,0.00018660355,0.0016004214,0.0000035344522,0.00005779205,0.000083572195,0.000006357374,0.1693856,0.010079604,0.22779252,0.0013267251,0.58932316],"study_design_scores_gemma":[0.00085368723,0.00015721776,0.003260711,0.000022503396,0.000056151337,0.0000992223,0.000047894948,0.008423424,0.010567805,0.29728636,0.67830807,0.00091696257],"about_ca_topic_score_codex":0.000074689975,"about_ca_topic_score_gemma":0.0002952341,"teacher_disagreement_score":0.67698133,"about_ca_system_score_codex":0.000088112414,"about_ca_system_score_gemma":0.00005933098,"threshold_uncertainty_score":0.9998685},"labels":[],"label_agreement":null},{"id":"W2188123775","doi":"","title":"Optimal scenario set partitioning for multistage stochastic programming with the progressive hedging algorithm","year":2013,"lang":"en","type":"article","venue":"PolyPublie (École Polytechnique de Montréal)","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Mathematical optimization; Partition (number theory); Branching (polymer chemistry); Set (abstract data type); Tree (set theory); Algorithm; Mathematics; Computer science; Lagrangian relaxation; Combinatorics","score_opus":0.02513170987983816,"score_gpt":0.2976098122025453,"score_spread":0.27247810232270714,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2188123775","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":[0.04628253,0.00052272604,0.94558495,0.003849025,0.000096384436,0.0032135793,0.000047424055,0.00035229637,0.00005109917],"genre_scores_gemma":[0.4721355,0.000015853417,0.52168334,0.0005821461,0.00018688166,0.0041811187,0.000055885128,0.00006165864,0.001097616],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99654067,0.00018273604,0.000686457,0.00069010997,0.0009834421,0.0009165998],"domain_scores_gemma":[0.99665755,0.0007508297,0.0006338249,0.0008754323,0.00078240124,0.00029999058],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0018258569,0.00036370035,0.00039404142,0.00036781366,0.0010483675,0.001760612,0.00083138695,0.00018018045,0.00008191398],"category_scores_gemma":[0.00074019347,0.00022811866,0.00017410725,0.0010136162,0.00025707646,0.0009909982,0.00016653197,0.00035333345,0.000043481694],"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":[0.000118522454,0.00017832313,0.008635376,0.000010638125,0.00007934913,0.000034355173,0.0022796153,0.35918438,0.0001734911,0.0021077069,0.007815336,0.6193829],"study_design_scores_gemma":[0.0006542969,0.000286019,0.0053807716,0.000056396984,0.00004556933,0.00011237035,0.0018626093,0.98301494,0.0005048931,0.0010859984,0.006593197,0.00040296422],"about_ca_topic_score_codex":0.0016846276,"about_ca_topic_score_gemma":0.0005737542,"teacher_disagreement_score":0.62383056,"about_ca_system_score_codex":0.00017367692,"about_ca_system_score_gemma":0.00024405442,"threshold_uncertainty_score":0.9992757},"labels":[],"label_agreement":null},{"id":"W2195005603","doi":"10.2139/ssrn.2699474","title":"Regress Under Stress: A Simple Least-Squares Method for Integrating Economic Scenarios with Risk Simulations","year":2015,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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 Waterloo; Fields Institute for Research in Mathematical Sciences","funders":"","keywords":"Simple (philosophy); Stress (linguistics); Econometrics; Least-squares function approximation; Computer science; Mathematics; Statistics","score_opus":0.05641635733717868,"score_gpt":0.38690865952206155,"score_spread":0.33049230218488285,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2195005603","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":[0.08065112,0.0006889687,0.9172037,0.0005807923,0.00017631784,0.00026715553,0.000057204295,0.000028596027,0.0003461432],"genre_scores_gemma":[0.9768307,0.00044049285,0.021169005,0.00005685752,0.00038203096,0.000011308117,0.000022571738,0.00003332677,0.001053712],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99671674,0.00039522364,0.00067520037,0.00038535506,0.00057737523,0.001250124],"domain_scores_gemma":[0.9970803,0.0010498823,0.0007946059,0.0003506447,0.0005283388,0.00019621775],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0061233966,0.0002028701,0.00031719916,0.00032617088,0.0005464036,0.00048022493,0.00052117935,0.00009373278,0.00004690639],"category_scores_gemma":[0.0012913821,0.00013235146,0.00016795516,0.00033790377,0.00005339763,0.00069928635,0.00003851635,0.0008797947,0.00002613566],"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":[0.00018886897,0.000031187486,0.017328123,5.720759e-7,0.000107827946,6.481708e-7,0.0005082576,0.8757388,0.00000459208,0.029212069,0.0007223663,0.07615667],"study_design_scores_gemma":[0.0012341036,0.00044996056,0.000238547,0.000012411959,0.00007430043,0.00009993783,0.014232607,0.44813505,0.000034791537,0.5311794,0.004110677,0.00019819704],"about_ca_topic_score_codex":0.0004527945,"about_ca_topic_score_gemma":0.018001543,"teacher_disagreement_score":0.89617956,"about_ca_system_score_codex":0.00070602266,"about_ca_system_score_gemma":0.0031531414,"threshold_uncertainty_score":0.9999174},"labels":[],"label_agreement":null},{"id":"W2202143919","doi":"10.2139/ssrn.2618345","title":"Empirical Performance of Backtesting Methods for Expected Shortfall","year":2015,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Dairy Farmers of Ontario","funders":"","keywords":"Expected shortfall; Econometrics; Computer science; Economics; Risk management; Finance","score_opus":0.19049718778677296,"score_gpt":0.4854976059325435,"score_spread":0.2950004181457705,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2202143919","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":[0.421896,0.0012026982,0.5749616,0.0002531254,0.0002744777,0.00011578087,9.315572e-7,0.000016162938,0.0012792493],"genre_scores_gemma":[0.89243484,0.0011070946,0.104423665,0.000051342613,0.00027531743,0.0000068757577,0.0000030626177,0.000018175173,0.0016796166],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9968963,0.00031348356,0.0007844626,0.00022920233,0.0006484447,0.0011281096],"domain_scores_gemma":[0.9973573,0.0007391895,0.00048519846,0.00024272998,0.001022953,0.00015263732],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.015723644,0.000119988326,0.0002944724,0.00025477223,0.00014026504,0.00008046984,0.0005248955,0.000080263366,0.00001993797],"category_scores_gemma":[0.004320738,0.000085232154,0.00013699225,0.00068924675,0.000049816652,0.00037942108,0.000042664957,0.00056473626,0.000012498193],"study_design_candidate":"design_other","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":[0.0004037866,0.00010392938,0.1520014,0.0000025759714,0.0000797892,8.240878e-7,0.0012274818,0.010213262,0.0004099911,0.006585424,0.0026938433,0.8262777],"study_design_scores_gemma":[0.0028258828,0.0033081751,0.0075365375,0.000032748107,0.000101229605,0.0009165881,0.0076718656,0.25854763,0.0023700332,0.676974,0.039143905,0.0005713804],"about_ca_topic_score_codex":0.0000073672973,"about_ca_topic_score_gemma":0.00002721736,"teacher_disagreement_score":0.8257063,"about_ca_system_score_codex":0.0002393507,"about_ca_system_score_gemma":0.002469727,"threshold_uncertainty_score":0.5449532},"labels":[],"label_agreement":null},{"id":"W2204235168","doi":"10.1016/j.insmatheco.2015.12.003","title":"Marginal Indemnification Function formulation for optimal reinsurance","year":2015,"lang":"en","type":"article","venue":"Insurance Mathematics and Economics","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":95,"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; University of Waterloo","keywords":"Reinsurance; Function (biology); Mathematics; Mathematical optimization; Actuarial science; Business; Biology","score_opus":0.13134742619503445,"score_gpt":0.3339147880857693,"score_spread":0.20256736189073485,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2204235168","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":[0.6777655,0.00008402184,0.31974083,0.00020665473,0.00030005057,0.00027147544,0.0000289479,0.000021686443,0.0015808363],"genre_scores_gemma":[0.9478366,0.00022101388,0.051303376,0.00006291547,0.00012434262,0.000041362735,0.000024016,0.000014639705,0.00037173755],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99874884,0.000015345368,0.00062122685,0.00028486387,0.00017314973,0.00015655173],"domain_scores_gemma":[0.99860084,0.00024877797,0.00042752599,0.00029889622,0.00032148973,0.000102463055],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016897396,0.00011987093,0.00022867607,0.00012709475,0.00012961535,0.00025529036,0.00016037881,0.00008970687,0.0000074312907],"category_scores_gemma":[0.00047697767,0.000104023646,0.00005362635,0.00014690513,0.00002737041,0.0006430754,0.000024514966,0.000050573715,0.00004183357],"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":[0.0006162058,0.00022528834,0.06815513,0.00005447088,0.000056517685,6.0133164e-7,0.0040764264,0.2514622,0.00012736271,0.393592,0.004424245,0.27720958],"study_design_scores_gemma":[0.0009731587,0.00013091078,0.023585953,0.000010976831,0.00001323663,0.000012009013,0.0005418804,0.713631,0.00011602671,0.23433316,0.0264312,0.00022049311],"about_ca_topic_score_codex":0.000004699042,"about_ca_topic_score_gemma":0.000008791995,"teacher_disagreement_score":0.4621688,"about_ca_system_score_codex":0.000038068178,"about_ca_system_score_gemma":0.00005083067,"threshold_uncertainty_score":0.42419612},"labels":[],"label_agreement":null},{"id":"W2207788829","doi":"","title":"OPTIMAL INVESTMENT FOR INSTITUTIONAL INVESTORS UNDER VALUE-AT-RISK CONSTRAINTS IN CHINESE STOCK MARKETS","year":2011,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Portfolio optimization; Constraint (computer-aided design); Value at risk; Tracking error; Econometrics; Portfolio; Economics; Stock market; Monte Carlo method; Vector autoregression; Financial economics; Actuarial science; Risk management; Mathematics; Finance; Statistics","score_opus":0.0492486708686548,"score_gpt":0.32263103793557996,"score_spread":0.27338236706692515,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2207788829","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":[0.8959852,0.0007947712,0.09562522,0.00020608804,0.0005241426,0.0003795173,0.000019874586,0.00001967309,0.0064455494],"genre_scores_gemma":[0.99055135,0.0020341242,0.0053350083,0.00031730896,0.00015525092,0.000026273781,0.000011303438,0.000020429601,0.0015489424],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9958953,0.00034081927,0.00089023565,0.00042863376,0.0008913032,0.0015536565],"domain_scores_gemma":[0.99842495,0.00029591948,0.00054748054,0.00028517863,0.00022662012,0.00021983136],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0076670456,0.00024438964,0.00031501998,0.00056853116,0.00046699584,0.000094452815,0.00059567235,0.000143962,0.00030286788],"category_scores_gemma":[0.0011396821,0.00017856872,0.00020410396,0.0006282243,0.00030953027,0.000560272,0.000068609625,0.0009394138,0.000060701706],"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":[0.0011920157,0.00047392203,0.36349037,0.0000029738537,0.00025461477,0.0000197773,0.002121555,0.0540464,0.00007058536,0.53447974,0.0014934713,0.042354587],"study_design_scores_gemma":[0.0024852525,0.00040486566,0.15014043,0.0000141474375,0.00003450496,0.0005534505,0.00101394,0.009472177,0.000045883036,0.83317703,0.0023218258,0.00033646738],"about_ca_topic_score_codex":0.00008984241,"about_ca_topic_score_gemma":0.00096657575,"teacher_disagreement_score":0.29869732,"about_ca_system_score_codex":0.0012427096,"about_ca_system_score_gemma":0.0033849154,"threshold_uncertainty_score":0.72818214},"labels":[],"label_agreement":null},{"id":"W2214593275","doi":"10.2139/ssrn.2572508","title":"Risk Bounds for Factor Models","year":2015,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"University of Waterloo","funders":"","keywords":"Factor (programming language); Computer science; Mathematics; Programming language","score_opus":0.10719103087446484,"score_gpt":0.3716141173239719,"score_spread":0.26442308644950707,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2214593275","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":[0.1163075,0.0018457677,0.8778703,0.00047635302,0.0006065146,0.00015427812,0.000018048797,0.000025983643,0.0026952291],"genre_scores_gemma":[0.9843924,0.0048570097,0.0019963384,0.000067033216,0.00045810474,0.0000063553075,0.0000037644986,0.000019203208,0.008199803],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9966962,0.00015085445,0.00051141944,0.00024675168,0.0009450625,0.0014497611],"domain_scores_gemma":[0.9981844,0.0002343124,0.0004224171,0.00026939923,0.00067721464,0.00021230326],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007236587,0.00012675079,0.00020792196,0.00022704543,0.0002906756,0.00035589485,0.0005626591,0.00008606115,0.000026148024],"category_scores_gemma":[0.0013196812,0.00008770261,0.00017256585,0.00036072356,0.000034280216,0.00077763933,0.000030229514,0.00072124496,0.000080850434],"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":[0.00045653744,0.00012506894,0.015584141,6.863692e-7,0.00017410869,0.0000023810858,0.0019479404,0.1052898,0.000023930577,0.3126899,0.021406496,0.54229903],"study_design_scores_gemma":[0.0007105152,0.0002949234,0.00012977712,0.0000012696913,0.00001645475,0.00008173537,0.0012422099,0.051666535,0.000017042752,0.9155184,0.030205447,0.00011566266],"about_ca_topic_score_codex":0.000042598076,"about_ca_topic_score_gemma":0.0003105303,"teacher_disagreement_score":0.875874,"about_ca_system_score_codex":0.00042016956,"about_ca_system_score_gemma":0.0029187296,"threshold_uncertainty_score":0.5177701},"labels":[],"label_agreement":null},{"id":"W2216812937","doi":"10.1016/j.asoc.2015.11.005","title":"Evolutionary multi-objective optimization algorithms for fuzzy portfolio selection","year":2015,"lang":"en","type":"article","venue":"Applied Soft Computing","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":129,"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":"Portfolio; Computer science; Mathematical optimization; Crossover; Portfolio optimization; Cardinality (data modeling); Selection (genetic algorithm); Evolutionary algorithm; Genetic algorithm; Optimization problem; Skewness; Modern portfolio theory; Fuzzy logic; Algorithm; Mathematics; Econometrics; Data mining; Economics; Machine learning; Artificial intelligence; Finance","score_opus":0.10323691650600501,"score_gpt":0.3649082193145247,"score_spread":0.2616713028085197,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2216812937","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":[0.0040143463,0.00008960681,0.98723173,0.00007253727,0.0007163415,0.00073612673,0.000009619012,0.0002442138,0.0068855016],"genre_scores_gemma":[0.4780825,0.000009208079,0.5207044,0.00015096423,0.0003898953,0.00003641816,0.00008203032,0.000030623803,0.0005139558],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99714005,0.000092871225,0.0007727844,0.0007072639,0.0008885649,0.00039847344],"domain_scores_gemma":[0.9971729,0.00063070003,0.000526151,0.0002874216,0.0011800856,0.00020273785],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023230363,0.00022669838,0.00032488484,0.00039266451,0.00048332897,0.00021295843,0.00035158434,0.0001718425,0.000022257003],"category_scores_gemma":[0.0010389119,0.00020958809,0.000111377165,0.0013341686,0.00006324472,0.0003333658,0.00011656458,0.00015490118,0.000080356825],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000591731,0.00007083754,0.0023888375,0.0000014243083,0.000017062997,6.478755e-7,0.000690858,0.9282689,0.000025066496,0.0015169039,0.007314499,0.059645753],"study_design_scores_gemma":[0.0011575696,0.0000698534,0.0016167964,0.000005606468,0.000020210377,0.000014797447,0.00091193995,0.98322797,0.00011432405,0.008305872,0.004293429,0.00026164667],"about_ca_topic_score_codex":0.00003671761,"about_ca_topic_score_gemma":0.0000050179806,"teacher_disagreement_score":0.47406816,"about_ca_system_score_codex":0.0001726906,"about_ca_system_score_gemma":0.00025751244,"threshold_uncertainty_score":0.8546754},"labels":[],"label_agreement":null},{"id":"W2220181446","doi":"10.1007/978-2-8178-0112-4_5","title":"Méthodes de simulation stochastique","year":2013,"lang":"fr","type":"book-chapter","venue":"","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Humanities; Mathematics; Physics; Philosophy","score_opus":0.17162214061662923,"score_gpt":0.4143171044692111,"score_spread":0.24269496385258188,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2220181446","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00022648503,0.00023130176,0.5675824,0.0004765422,0.0004917498,0.00034796464,0.000015303607,0.000051981093,0.43057624],"genre_scores_gemma":[0.1058395,0.0015865283,0.11149018,0.00052556786,0.00047599946,0.000013611457,0.000038346712,0.00005882245,0.7799714],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.996541,0.00020608546,0.0010772193,0.000725612,0.0010293402,0.00042070012],"domain_scores_gemma":[0.9950438,0.002476333,0.0005982777,0.00070409523,0.00086148595,0.0003160146],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0019344488,0.00046053974,0.0005313418,0.00044046136,0.00027418855,0.0004833954,0.0005485546,0.00084961584,0.0540312],"category_scores_gemma":[0.00096819527,0.0003756678,0.00027406708,0.00014780049,0.0002749485,0.00069769006,0.00012962183,0.0003680784,0.013645859],"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":[0.000008873811,0.0000105590625,0.00018172897,0.0000024521796,0.000013870237,0.0000040878062,0.00009305775,0.39692488,0.0000068533805,0.42938173,0.0009842629,0.17238767],"study_design_scores_gemma":[0.00012461343,0.00006222684,0.00028226024,0.00004994604,0.000035302583,0.000009924557,0.000034142384,0.57019037,0.000021544043,0.19195719,0.23690617,0.00032632163],"about_ca_topic_score_codex":0.00037617152,"about_ca_topic_score_gemma":0.00007655417,"teacher_disagreement_score":0.45609227,"about_ca_system_score_codex":0.00012852979,"about_ca_system_score_gemma":0.0002956834,"threshold_uncertainty_score":0.9998695},"labels":[],"label_agreement":null},{"id":"W2255521334","doi":"10.1016/j.compchemeng.2016.02.005","title":"Linear programming-based scenario reduction using transportation distance","year":2016,"lang":"en","type":"article","venue":"Computers & Chemical Engineering","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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 Alberta","funders":"","keywords":"Reduction (mathematics); Mathematical optimization; Linear programming; Set (abstract data type); Stochastic programming; Computer science; Minification; Dynamic programming; Algorithm; Mathematics","score_opus":0.03656416774600321,"score_gpt":0.2955963544794652,"score_spread":0.25903218673346196,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2255521334","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":[0.23648378,0.000029147712,0.762715,0.00014953094,0.0004138146,0.000085921616,0.0000033182948,0.00011341362,0.0000061074415],"genre_scores_gemma":[0.85396355,0.0000047596377,0.14576903,0.0000137871475,0.00019317039,0.000004843036,0.000011721702,0.000016155127,0.000022960345],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985401,0.000013007674,0.00041367492,0.00035426745,0.00045494077,0.00022399165],"domain_scores_gemma":[0.9992388,0.00014662783,0.000113439055,0.00025289124,0.00013421239,0.00011406193],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027122756,0.0001380091,0.000172547,0.00012887814,0.000044007185,0.00006688579,0.00023644112,0.00007950083,0.000013375681],"category_scores_gemma":[0.00014042149,0.00009904673,0.00009231437,0.00050115905,0.000040288596,0.00027508958,0.000009812065,0.000077593395,0.000012488258],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000033997498,0.00004056897,0.00089299027,0.000008906287,0.000009792836,0.0000061084916,0.00014502525,0.7579539,0.10810852,0.0002419795,0.0002403913,0.13231781],"study_design_scores_gemma":[0.0004154323,0.00001695832,0.00030370927,0.00009536712,0.000010815883,0.0000050590634,0.0000064463425,0.93795437,0.05076877,0.00010049221,0.010105525,0.00021708487],"about_ca_topic_score_codex":0.0000056147833,"about_ca_topic_score_gemma":3.4302872e-7,"teacher_disagreement_score":0.6174798,"about_ca_system_score_codex":0.0000816947,"about_ca_system_score_gemma":0.00003472953,"threshold_uncertainty_score":0.40390086},"labels":[],"label_agreement":null},{"id":"W2260727221","doi":"","title":"Capital Planning under Uncertainty at Fletcher Challenge Canada","year":2002,"lang":"en","type":"article","venue":"","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":17,"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":"Newsprint; Time horizon; Pace; Operations research; Strategic planning; Production (economics); Computer science; Capital (architecture); Engineering; Economics; Business; Marketing; Finance; Microeconomics","score_opus":0.1273547988853066,"score_gpt":0.3308484087249456,"score_spread":0.203493609839639,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2260727221","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":[0.71462125,0.0009094278,0.0025386354,0.0057191276,0.0007342777,0.0001233057,0.000010138876,0.000058586353,0.27528524],"genre_scores_gemma":[0.9291347,0.000111806046,0.00021606438,0.00076200423,0.00008026363,0.0000028449424,0.0000044973285,0.000007690256,0.06968012],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9981655,0.000053610813,0.00031502364,0.00030452045,0.0009217841,0.00023956726],"domain_scores_gemma":[0.99905974,0.0002689158,0.000092317554,0.00033064437,0.00011483442,0.00013354247],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003276219,0.000107844164,0.00014620685,0.00007205733,0.0001696184,0.00007679627,0.00027873443,0.000059977443,0.012078206],"category_scores_gemma":[0.00013664449,0.00007410473,0.000042104697,0.00026836642,0.000030342733,0.00015721214,0.000067802524,0.00007266507,0.00047197888],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009122671,0.000030510397,0.010813882,6.151641e-7,0.000015152155,0.00004101891,0.0018239644,0.38784352,0.000013487959,0.001846977,0.58449155,0.013070206],"study_design_scores_gemma":[0.00066100026,0.00006688849,0.01424813,0.000007729779,0.000011850934,0.000026687834,0.0057060383,0.29693407,0.00006562891,0.0057972996,0.6759292,0.00054549315],"about_ca_topic_score_codex":0.1362283,"about_ca_topic_score_gemma":0.51655734,"teacher_disagreement_score":0.380329,"about_ca_system_score_codex":0.00014100347,"about_ca_system_score_gemma":0.00006619283,"threshold_uncertainty_score":0.9888249},"labels":[],"label_agreement":null},{"id":"W2265464138","doi":"10.1111/j.1467-9965.2009.00378.x","title":"MAXIMIZING THE GROWTH RATE UNDER RISK CONSTRAINTS","year":2009,"lang":"en","type":"article","venue":"Mathematical Finance","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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 British Columbia","funders":"","keywords":"Economics; Portfolio; Risk aversion (psychology); Econometrics; Ergodic theory; Context (archaeology); Incomplete markets; Isoelastic utility; Maximization; Constant (computer programming); Financial market; Mathematics; Value at risk; Expected utility hypothesis; Mathematical economics; Microeconomics; Financial economics; Risk management; Computer science","score_opus":0.05859409147533349,"score_gpt":0.33854722224530104,"score_spread":0.27995313076996753,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2265464138","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":[0.19587244,0.00012404313,0.74110854,0.0063019893,0.00015839115,0.00027234896,0.000009876394,0.000064953005,0.056087438],"genre_scores_gemma":[0.98643476,0.00024201609,0.010901577,0.0006596956,0.00004995104,0.0000054535612,7.8859006e-7,0.0000063154907,0.0016994734],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9982035,0.0001866026,0.0005185172,0.00028741857,0.00055553095,0.00024840713],"domain_scores_gemma":[0.9977621,0.0012878542,0.00023755134,0.000511034,0.00015128509,0.00005016735],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0024882206,0.00012606056,0.00022907586,0.000055347366,0.00025930017,0.00022273012,0.0005378075,0.00006302045,0.0003200998],"category_scores_gemma":[0.0028614104,0.00006700429,0.000096853444,0.0005498179,0.0002255555,0.00018970964,0.00003803782,0.00017227135,0.0011914185],"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":[0.000014957661,0.000080539125,0.00060248206,0.0000014052081,0.000005965213,0.000011182517,0.0004790778,0.0024558813,0.000037127404,0.89616746,0.006746658,0.093397245],"study_design_scores_gemma":[0.00015394491,0.000030139096,0.020188956,0.000014979187,0.000009717458,0.00001940128,0.00010223948,0.017682029,0.00021524818,0.95907354,0.002400967,0.000108860484],"about_ca_topic_score_codex":0.0000018527866,"about_ca_topic_score_gemma":6.5615984e-7,"teacher_disagreement_score":0.7905623,"about_ca_system_score_codex":0.000012358845,"about_ca_system_score_gemma":0.000032153486,"threshold_uncertainty_score":0.9995863},"labels":[],"label_agreement":null},{"id":"W2270667121","doi":"","title":"Optimal payoffs under state-dependent constraints","year":2013,"lang":"en","type":"preprint","venue":"RePEc: Research Papers in Economics","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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 Waterloo","funders":"","keywords":"Portfolio; Cumulative prospect theory; Mathematical economics; Expected utility hypothesis; State (computer science); Portfolio optimization; Economics; Computer science; Financial economics","score_opus":0.0924752405090118,"score_gpt":0.3942373062730656,"score_spread":0.3017620657640538,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2270667121","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":[0.82114863,0.00014830235,0.00075491576,0.0007791965,0.0014812609,0.0014831588,0.00019979688,0.00007257528,0.17393218],"genre_scores_gemma":[0.9560862,0.019182518,0.0037473373,0.00015346278,0.00023993358,0.00023329657,0.00008649044,0.00008444237,0.020186283],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9929109,0.00087815383,0.0017616658,0.0017729511,0.0015566435,0.0011196859],"domain_scores_gemma":[0.9942795,0.0019579215,0.00057806954,0.0020561668,0.0006719478,0.00045641902],"candidate_categories":["metaepi_narrow","scholarly_communication","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.008287817,0.00043698546,0.00083603035,0.0013023989,0.00022997304,0.0013186828,0.0021209244,0.0006274452,0.0025464832],"category_scores_gemma":[0.0018374715,0.00038846242,0.00026942993,0.00033701828,0.0009158123,0.00034718547,0.0021244679,0.0020976504,0.0006936492],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000052006995,0.00010669184,0.007452313,0.000008818384,0.00005390925,0.000035247867,0.00040607702,0.54927695,0.000020644819,0.0004959359,0.00084256404,0.44124883],"study_design_scores_gemma":[0.0046680844,0.0005464809,0.07518898,0.00045004077,0.00004498894,0.0001395756,0.013920694,0.55742663,0.001038611,0.21189576,0.1304915,0.0041886754],"about_ca_topic_score_codex":0.00021077301,"about_ca_topic_score_gemma":0.00025557348,"teacher_disagreement_score":0.43706015,"about_ca_system_score_codex":0.0007435373,"about_ca_system_score_gemma":0.0014393757,"threshold_uncertainty_score":0.9998567},"labels":[],"label_agreement":null},{"id":"W2271306490","doi":"10.2139/ssrn.2273124","title":"Optimal Bounds on the Value-at-Risk Given Moment Information","year":2013,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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 New Brunswick; University of Fredericton; University of Manitoba","funders":"","keywords":"Moment (physics); Value (mathematics); Mathematics; Mathematical optimization; Applied mathematics; Statistics; Combinatorics; Econometrics; Physics","score_opus":0.01669002739424807,"score_gpt":0.2801155891124607,"score_spread":0.26342556171821263,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2271306490","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":[0.9021796,0.00036203794,0.07105577,0.0118505685,0.0005180086,0.00041246027,0.0000057751977,0.000033231358,0.013582526],"genre_scores_gemma":[0.9881658,0.0048270463,0.00024754604,0.0004276201,0.00019492614,0.000019441344,0.0000056184363,0.000010082152,0.006101951],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9962824,0.00029674114,0.000647867,0.00016077537,0.0013639645,0.0012482635],"domain_scores_gemma":[0.99825287,0.0003229396,0.0006205648,0.00037308363,0.0003255707,0.0001049816],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0052414844,0.00015061653,0.0001554269,0.0002464883,0.0008398607,0.0007376195,0.00069137715,0.000074028234,0.0007640964],"category_scores_gemma":[0.00054688647,0.00008124917,0.00014632824,0.0004524305,0.00005952975,0.0012523351,0.00007687619,0.0010807015,0.0031107715],"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":[0.0001900141,0.000114200644,0.0105433995,8.5338513e-7,0.00019854063,0.0000016235613,0.002894419,0.1906561,0.00004891546,0.3020797,0.07796673,0.41530553],"study_design_scores_gemma":[0.0010847534,0.0011037887,0.0162075,0.0000122314195,0.00004629559,0.0003626634,0.006668485,0.06516535,0.00016199928,0.65964454,0.24914606,0.00039631603],"about_ca_topic_score_codex":0.00012883607,"about_ca_topic_score_gemma":0.00005232549,"teacher_disagreement_score":0.41490918,"about_ca_system_score_codex":0.000721012,"about_ca_system_score_gemma":0.0006876177,"threshold_uncertainty_score":0.9976654},"labels":[],"label_agreement":null},{"id":"W2273410570","doi":"10.1007/s10479-015-1822-8","title":"Robust scenario-based value-at-risk optimization","year":2015,"lang":"en","type":"article","venue":"Annals of Operations Research","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"TD Bank Group","funders":"","keywords":"Theory of computation; Heuristic; Value at risk; Computer science; Mathematical optimization; Computational complexity theory; Expected shortfall; Value (mathematics); Robust optimization; Optimization algorithm; Algorithm; Risk management; Mathematics; Machine learning; Finance; Economics","score_opus":0.6708934013309007,"score_gpt":0.5373646680948094,"score_spread":0.13352873323609127,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2273410570","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":[0.24131861,0.0010218173,0.68972105,0.017588094,0.00048013148,0.0013402584,0.00018444347,0.00009408048,0.048251513],"genre_scores_gemma":[0.9296517,0.00096116296,0.058985583,0.00027040427,0.00014767726,0.00005620127,0.00014466896,0.000028432234,0.009754197],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99387306,0.0013944432,0.0007655155,0.00045668322,0.0031207132,0.00038956848],"domain_scores_gemma":[0.9908585,0.0007183878,0.00011352408,0.0008831799,0.007105369,0.00032105364],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.013484234,0.00012085887,0.00024035099,0.0010297318,0.00065681443,0.00041781386,0.00072410644,0.000124996,0.0005711604],"category_scores_gemma":[0.012479272,0.00009477915,0.00009804352,0.0027437664,0.00026980508,0.0006661924,0.00017982387,0.00024883664,0.0004938746],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006741108,0.0001124215,0.0046377946,9.991237e-7,0.000007938762,0.0000020075913,0.00037219876,0.93145263,0.00002208157,0.0012684696,0.05885494,0.003201104],"study_design_scores_gemma":[0.00038862342,0.00022016947,0.0010070354,0.000009598818,0.000004093999,0.0000016809274,0.00036952895,0.9810271,0.0024273742,0.00085484417,0.013579392,0.000110503155],"about_ca_topic_score_codex":0.00076109584,"about_ca_topic_score_gemma":0.0002758416,"teacher_disagreement_score":0.68833303,"about_ca_system_score_codex":0.000051505227,"about_ca_system_score_gemma":0.00081136805,"threshold_uncertainty_score":0.99583906},"labels":[],"label_agreement":null},{"id":"W2276376831","doi":"10.1515/demo-2015-0009","title":"Seven Proofs for the Subadditivity ofExpected Shortfall","year":2015,"lang":"en","type":"article","venue":"Dependence Modeling","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":49,"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; Eidgenössische Technische Hochschule Zürich; Swiss Finance Institute","keywords":"Subadditivity; Mathematical proof; Estimator; Mathematics; Consistency (knowledge bases); Mathematical economics; Calculus (dental); Discrete mathematics; Statistics; Medicine","score_opus":0.32735448186219834,"score_gpt":0.4163499826407276,"score_spread":0.08899550077852925,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2276376831","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":[0.08638508,0.0002778024,0.91010034,0.0007510037,0.0004399205,0.00047174084,0.000022073922,0.00006413417,0.0014879118],"genre_scores_gemma":[0.99169815,0.000049276816,0.0065838275,0.0001425154,0.00021068094,0.000085876185,0.000013067591,0.000014169685,0.0012024686],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99750423,0.000111116795,0.00045830797,0.00042825236,0.0012168313,0.00028123244],"domain_scores_gemma":[0.99743885,0.00076004775,0.00014754705,0.00057681656,0.0009416889,0.00013506712],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003767939,0.00012944455,0.00018820957,0.0001072166,0.00026269414,0.0002888535,0.0007327417,0.000087254855,0.000036522626],"category_scores_gemma":[0.0031604634,0.00007797932,0.0001021993,0.0004604425,0.000037962054,0.00054287235,0.0000973103,0.00013198445,0.0000730242],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006476625,0.00002474182,0.0017546744,7.102293e-7,0.000009926337,0.0000028028458,0.0013150686,0.94690174,0.000030085039,0.00081362907,0.0053126547,0.043769177],"study_design_scores_gemma":[0.00021040185,0.000028553628,0.00011595507,0.0000045035154,0.000013597726,0.000008263216,0.0011055052,0.9706433,0.00012487514,0.023875,0.0037509715,0.00011911581],"about_ca_topic_score_codex":0.00013920295,"about_ca_topic_score_gemma":0.00025607538,"teacher_disagreement_score":0.905313,"about_ca_system_score_codex":0.000039122,"about_ca_system_score_gemma":0.00021731872,"threshold_uncertainty_score":0.37835982},"labels":[],"label_agreement":null},{"id":"W2291375884","doi":"10.2139/ssrn.2597286","title":"Diversification Limit of Quantiles Under Dependence Uncertainty","year":2015,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Quantile; Diversification (marketing strategy); Econometrics; Limit (mathematics); Economics; Business; Mathematics","score_opus":0.10326143402481794,"score_gpt":0.3579872705481102,"score_spread":0.2547258365232923,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2291375884","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":[0.66788113,0.0021369257,0.32486093,0.0013825336,0.00048532162,0.00011461378,0.0000039459783,0.000022075339,0.0031125064],"genre_scores_gemma":[0.9939616,0.004038475,0.00034671908,0.000038434577,0.00008963895,8.4015056e-7,0.0000029988964,0.000006721427,0.0015146266],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99709195,0.00019649151,0.0005181418,0.00020835298,0.0012169648,0.00076812267],"domain_scores_gemma":[0.9981128,0.00019859071,0.00051653606,0.00028013607,0.0007700572,0.00012190725],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0063154446,0.000096038246,0.00018032515,0.00024125635,0.00012766564,0.00009292934,0.0006202785,0.00007162619,0.000040514104],"category_scores_gemma":[0.0009167749,0.000069916794,0.00009771595,0.00056914653,0.000070497794,0.00047318486,0.000046175257,0.0005014085,0.000100157755],"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":[0.00032625912,0.00016621879,0.06416242,0.0000011969414,0.00011103025,0.0000034072652,0.001276572,0.18770781,0.0003701237,0.5939261,0.003051106,0.14889775],"study_design_scores_gemma":[0.0006270317,0.00030713214,0.0050458647,0.000007577501,0.000029914556,0.00014860954,0.014544546,0.009163356,0.00023610737,0.966476,0.0032633743,0.00015045589],"about_ca_topic_score_codex":0.00014493713,"about_ca_topic_score_gemma":0.0005643245,"teacher_disagreement_score":0.37254995,"about_ca_system_score_codex":0.0003216983,"about_ca_system_score_gemma":0.0021839961,"threshold_uncertainty_score":0.38743153},"labels":[],"label_agreement":null},{"id":"W2295380485","doi":"10.1515/apjri-2015-0009","title":"On the Use of Long-Term Risk Measures as an Approach to Communicating Risks","year":2015,"lang":"en","type":"article","venue":"Asia-Pacific Journal of Risk and Insurance","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Western University","funders":"","keywords":"Black swan theory; Actuarial science; Irrational number; Term (time); Time consistency; Risk measure; Volatility (finance); Extreme value theory; Risk analysis (engineering); Economics; Business; Econometrics; Financial economics; Mathematics; Statistics","score_opus":0.2472356810878239,"score_gpt":0.37475660308698117,"score_spread":0.12752092199915727,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2295380485","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":[0.9789047,0.0010835158,0.015681157,0.00017134429,0.00018684365,0.00017098905,0.000041180505,0.000008013743,0.0037522034],"genre_scores_gemma":[0.9858311,0.0074767456,0.006478841,0.00005175134,0.00007581911,0.0000025422196,0.0000014601836,0.000014399036,0.00006733387],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99574846,0.0014009594,0.0010234652,0.00024786207,0.0013737766,0.00020545245],"domain_scores_gemma":[0.9943917,0.0016510586,0.0017120205,0.00081277307,0.0011223028,0.000310148],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0069205,0.00018014392,0.00044099914,0.00028288865,0.00030664675,0.00032371836,0.0009557323,0.00009002583,0.0000066205175],"category_scores_gemma":[0.0072361785,0.00010030892,0.00012682556,0.00069184066,0.00020709111,0.0007955024,0.00008172396,0.00053754426,0.00001619596],"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":[0.00040353695,0.00019162551,0.7126853,0.0000011398442,0.000034650337,0.0000046872888,0.006065235,0.02032223,0.000024659708,0.0005725661,0.0019195266,0.2577749],"study_design_scores_gemma":[0.0009856794,0.0008173412,0.97198325,0.00009633175,0.00005942682,0.00023620657,0.006726069,0.003429697,0.00040698153,0.008978018,0.00601119,0.00026980438],"about_ca_topic_score_codex":0.00022333699,"about_ca_topic_score_gemma":0.000022062957,"teacher_disagreement_score":0.259298,"about_ca_system_score_codex":0.000021100524,"about_ca_system_score_gemma":0.00009969022,"threshold_uncertainty_score":0.86629045},"labels":[],"label_agreement":null},{"id":"W2298735872","doi":"10.2139/ssrn.2744142","title":"Quantile-Based Risk Sharing","year":2016,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"University of Waterloo","funders":"","keywords":"Quantile; Econometrics; Computer science; Economics","score_opus":0.03736075992914437,"score_gpt":0.3347476517696116,"score_spread":0.2973868918404672,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2298735872","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":[0.40232128,0.0007388374,0.59312874,0.0014577986,0.00039568925,0.000068000605,0.0000041835865,0.000037877646,0.0018475918],"genre_scores_gemma":[0.98706216,0.0062416666,0.00039237982,0.000069863265,0.0002605738,0.0000019479683,5.6226753e-7,0.00001592425,0.0059549334],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9964496,0.00017040966,0.0005575027,0.0003245377,0.00092950015,0.0015684565],"domain_scores_gemma":[0.99832714,0.0004114433,0.00048517232,0.00040193272,0.0002537546,0.00012054089],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008532049,0.00012812101,0.00018386889,0.0003197372,0.0003285643,0.00021836344,0.0007842929,0.000071449846,0.00032656328],"category_scores_gemma":[0.0014326643,0.000067682886,0.00017202228,0.00047238445,0.000048443493,0.00046427883,0.000041478885,0.00066314224,0.00059040997],"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":[0.000117505726,0.000047491456,0.2678657,2.5696252e-7,0.000045935612,0.0000051062993,0.000059027694,0.0021088757,0.00035196776,0.088016294,0.0011160687,0.64026576],"study_design_scores_gemma":[0.0012499037,0.00025057688,0.00793473,0.000018562925,0.000025986497,0.0001289707,0.00029381752,0.0049306466,0.00041034544,0.96435803,0.020170474,0.00022797761],"about_ca_topic_score_codex":0.000036639605,"about_ca_topic_score_gemma":0.00026676542,"teacher_disagreement_score":0.8763417,"about_ca_system_score_codex":0.00032149977,"about_ca_system_score_gemma":0.001293141,"threshold_uncertainty_score":0.7588723},"labels":[],"label_agreement":null},{"id":"W2299216252","doi":"","title":"A Fast and Accurate Algorithm for Stochastic Integer Programming, Applied to Stochastic Shift Scheduling","year":2012,"lang":"en","type":"article","venue":"Les Cahiers du GERAD","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Mathematical optimization; Stochastic programming; Computer science; Scheduling (production processes); Heuristic; Integer programming; Dynamic programming; Column generation; Algorithm; Mathematics","score_opus":0.03724072600934392,"score_gpt":0.3240968000928531,"score_spread":0.28685607408350916,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2299216252","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":[0.05559293,0.00021278688,0.9421377,0.0003490683,0.00050906726,0.0009561956,0.000023481085,0.00007740051,0.00014134435],"genre_scores_gemma":[0.750664,0.0000071272093,0.2480202,0.00027679754,0.00041366194,0.00023128602,0.000017538096,0.00003100134,0.00033839318],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99776006,0.000053523057,0.0005376814,0.00049355486,0.00056222925,0.0005929438],"domain_scores_gemma":[0.99812543,0.0007228166,0.0002342548,0.00032878888,0.0001740389,0.00041465572],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015064244,0.00025299907,0.0003677015,0.0003564812,0.00043124345,0.0004062717,0.00032768835,0.00017118189,0.000031577634],"category_scores_gemma":[0.0009768124,0.00020008857,0.00009052997,0.000607136,0.00017010407,0.000380727,0.00008463499,0.00020983114,0.00008590926],"study_design_candidate":"design_other","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":[0.00007822635,0.00007267191,0.00026573124,0.0000052831842,0.0000311068,0.0000011185591,0.010161452,0.028955858,0.00003645496,0.015076947,0.00045448221,0.9448607],"study_design_scores_gemma":[0.0048693977,0.00079841376,0.00648888,0.0001165411,0.0003517243,0.00011341233,0.01835057,0.86664814,0.00029185825,0.054121125,0.045041054,0.0028088633],"about_ca_topic_score_codex":0.000014648831,"about_ca_topic_score_gemma":0.000008781456,"teacher_disagreement_score":0.9420518,"about_ca_system_score_codex":0.000056455454,"about_ca_system_score_gemma":0.000046170422,"threshold_uncertainty_score":0.8159375},"labels":[],"label_agreement":null},{"id":"W2302257968","doi":"10.1016/j.ifacol.2015.09.124","title":"Chance Constrained Planning and Scheduling under Uncertainty using Robust Optimization Approximation","year":2015,"lang":"en","type":"article","venue":"IFAC-PapersOnLine","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"University of Alberta","funders":"","keywords":"Mathematical optimization; Robust optimization; A priori and a posteriori; Computer science; Scheduling (production processes); Optimization problem; Approximation algorithm; Job shop scheduling; Mathematics","score_opus":0.2350612720353123,"score_gpt":0.3900678926469056,"score_spread":0.15500662061159332,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2302257968","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":[0.23918849,0.00041946972,0.75843126,0.0006565867,0.00027276497,0.00020595298,0.000015389958,0.00007090545,0.0007391889],"genre_scores_gemma":[0.27830502,0.000056579414,0.72091824,0.00023787594,0.00019974391,0.0000041751046,0.00008064029,0.00001783154,0.00017988116],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976974,0.00014157906,0.0005700237,0.00050562376,0.00080374733,0.00028162223],"domain_scores_gemma":[0.99838793,0.00023520779,0.00037794383,0.0002790033,0.00050366606,0.00021623932],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001597415,0.00020711662,0.0002999583,0.00026543866,0.00024001376,0.00030420368,0.00020623076,0.00014861206,0.000057452955],"category_scores_gemma":[0.0011218073,0.00017191672,0.000048562924,0.00075567974,0.00013870768,0.00073075434,0.00006650026,0.00014869317,0.0000120673185],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000043008247,0.000023112263,0.0018805256,0.000002600988,0.000009893238,0.0000035972887,0.0009158953,0.99175644,0.00026274638,0.00032133106,0.000004851029,0.0047760075],"study_design_scores_gemma":[0.00077840383,0.000042246025,0.00020215876,0.000039091276,0.000020792297,0.000033028613,0.004150535,0.99354357,0.00006638342,0.00083478366,0.000076041215,0.00021295523],"about_ca_topic_score_codex":0.000052450738,"about_ca_topic_score_gemma":0.00001183297,"teacher_disagreement_score":0.03911653,"about_ca_system_score_codex":0.00007972081,"about_ca_system_score_gemma":0.00017590924,"threshold_uncertainty_score":0.701056},"labels":[],"label_agreement":null},{"id":"W2309428600","doi":"10.1017/asb.2015.12","title":"DISAPPOINTMENT AVERSION PREMIUM PRINCIPLE","year":2015,"lang":"en","type":"article","venue":"Astin Bulletin","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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 Calgary","funders":"","keywords":"Disappointment; Monotonic function; Mathematical economics; Economics; Arrow; Expected utility hypothesis; Convexity; Ambiguity aversion; Econometrics; Mathematics; Financial economics; Computer science; Ambiguity; Mathematical analysis; Psychology","score_opus":0.1421523116232034,"score_gpt":0.3890612957241557,"score_spread":0.2469089841009523,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2309428600","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":[0.51467,0.0003796477,0.08884421,0.02029519,0.003169275,0.00092812587,0.000027534332,0.0003339086,0.37135208],"genre_scores_gemma":[0.93418723,0.000045328256,0.014970247,0.00046107545,0.00026434704,0.000016958404,0.000022934502,0.000018490693,0.05001337],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9977318,0.0001508695,0.00041443406,0.00036685137,0.0011153934,0.00022063032],"domain_scores_gemma":[0.9985907,0.00025145474,0.00017589447,0.00046871346,0.00027911202,0.00023412108],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0020411802,0.00011579948,0.00016126299,0.000108947446,0.00010552143,0.0001320327,0.00039945316,0.00006243142,0.002105588],"category_scores_gemma":[0.002074364,0.00008451336,0.00006323327,0.0003227478,0.000054776316,0.00007585281,0.0001887194,0.00008491472,0.009146612],"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":[0.000084934145,0.00008220657,0.033459216,9.627149e-7,0.000006068988,0.00001319933,0.0006325938,0.008528887,0.00003520617,0.0014458207,0.9237263,0.031984605],"study_design_scores_gemma":[0.00041833366,0.00008722022,0.0048604542,0.000006327245,0.000005360178,0.000007924661,0.00033003368,0.0022681565,0.00032704652,0.0018379487,0.9897331,0.0001181203],"about_ca_topic_score_codex":0.00003302595,"about_ca_topic_score_gemma":0.0000032338257,"teacher_disagreement_score":0.41951722,"about_ca_system_score_codex":0.000048824164,"about_ca_system_score_gemma":0.00006884824,"threshold_uncertainty_score":0.9988066},"labels":[],"label_agreement":null},{"id":"W2311001255","doi":"10.1287/opre.2016.1483","title":"Robust Optimization of Sums of Piecewise Linear Functions with Application to Inventory Problems","year":2016,"lang":"en","type":"article","venue":"Operations Research","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Mathematical optimization; Robust optimization; Newsvendor model; Linear programming; Piecewise linear function; Mathematics; Computer science; Semidefinite programming; Optimization problem; Column generation; Set (abstract data type); Norm (philosophy)","score_opus":0.19865479882426987,"score_gpt":0.4180923603225724,"score_spread":0.21943756149830254,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2311001255","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":[0.05787945,0.00003317922,0.9367047,0.0014682555,0.000046372697,0.0008794746,0.000043936187,0.000017754981,0.002926876],"genre_scores_gemma":[0.94961095,0.00019319318,0.0391025,0.000016229063,0.00006884945,0.00027799167,0.000042277316,0.000020059299,0.010667945],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99687046,0.0003105112,0.0006482939,0.0003952692,0.001555093,0.0002203538],"domain_scores_gemma":[0.9949067,0.00032751652,0.00009449829,0.0007588731,0.0037707377,0.00014162321],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002940876,0.00009189702,0.00019101861,0.0009151853,0.00026620962,0.00007103087,0.0004282488,0.00007537991,0.00041119583],"category_scores_gemma":[0.001353449,0.00005402842,0.000041175204,0.0029968987,0.00022488633,0.00051798834,0.00009277921,0.00009481349,0.00017983426],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000047996724,0.00009509067,0.0052681207,0.0000035842859,0.000009131181,1.4312737e-7,0.00032305124,0.9733946,0.0056618922,0.0015360122,0.0017684791,0.011891948],"study_design_scores_gemma":[0.0010601637,0.00089800666,0.002991813,0.0001209435,0.000018946295,0.0000044194176,0.00092513993,0.9627519,0.012301927,0.00041124158,0.018258564,0.00025691855],"about_ca_topic_score_codex":0.00026728897,"about_ca_topic_score_gemma":0.00063624297,"teacher_disagreement_score":0.8976022,"about_ca_system_score_codex":0.00005763188,"about_ca_system_score_gemma":0.00033721916,"threshold_uncertainty_score":0.4502306},"labels":[],"label_agreement":null},{"id":"W2315126152","doi":"10.1007/s11081-016-9314-5","title":"Factor-based robust index tracking","year":2016,"lang":"en","type":"article","venue":"Optimization and Engineering","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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 Toronto","funders":"","keywords":"Tracking error; Benchmark (surveying); Computer science; Conic section; Tracking (education); Portfolio; Efficient frontier; Portfolio optimization; Mathematical optimization; Robust optimization; Sharpe ratio; Mathematics; Artificial intelligence; Finance; Control (management)","score_opus":0.0582568812469636,"score_gpt":0.28799536107110624,"score_spread":0.22973847982414264,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2315126152","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":[0.014156065,0.000039284427,0.9845805,0.00034306603,0.0002033962,0.00006208051,0.00000507602,0.0000930827,0.0005174303],"genre_scores_gemma":[0.9665068,0.00016032816,0.032748677,0.00005486322,0.00005503506,0.000004316034,0.0000032889625,0.000016596583,0.0004500922],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989675,0.000020753492,0.00027935245,0.00023618678,0.0003415008,0.00015465273],"domain_scores_gemma":[0.9992701,0.00023787147,0.00007706074,0.00018800609,0.00012915749,0.000097833974],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029371353,0.00010724694,0.00012515817,0.00025708284,0.00007134664,0.00016587492,0.00012300575,0.00007028146,0.0003814457],"category_scores_gemma":[0.00053804787,0.000069095746,0.000034379565,0.00038521664,0.000018020468,0.0004354064,0.000019816087,0.00004090795,0.000016788317],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000036273093,0.0000048781803,0.0121951355,9.85632e-7,0.0000023218192,8.376752e-7,0.000033409673,0.9651198,0.00017737075,0.00013615341,0.00009838142,0.02222713],"study_design_scores_gemma":[0.00033640864,0.000011078216,0.013493526,0.000018282964,0.0000025294878,0.0000021292,0.00001330921,0.98150355,0.00029224664,0.000023710203,0.0041748015,0.00012841998],"about_ca_topic_score_codex":0.0000025633376,"about_ca_topic_score_gemma":0.0000011163123,"teacher_disagreement_score":0.95235074,"about_ca_system_score_codex":0.000019036223,"about_ca_system_score_gemma":0.000019044248,"threshold_uncertainty_score":0.41765627},"labels":[],"label_agreement":null},{"id":"W2319014419","doi":"10.5849/forsci.12-090","title":"A Robust Model for Protecting Road-Building and Harvest-Scheduling Decisions from Timber Estimate Errors","year":2014,"lang":"en","type":"article","venue":"Forest Science","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Pacific Insight Electronics (Canada); Western Forest Products; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Robustness (evolution); Mathematical optimization; Scheduling (production processes); Computer science; Budget constraint; Constraint (computer-aided design); Operations research; Mathematics; Economics","score_opus":0.13463662354985573,"score_gpt":0.3824254790778019,"score_spread":0.24778885552794616,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2319014419","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":[0.457084,0.00001614069,0.54213744,0.00014650292,0.00015527151,0.0002365224,0.000008857628,0.000034903645,0.00018035091],"genre_scores_gemma":[0.58986646,0.0000069636226,0.40981686,0.000051566723,0.000050770457,0.000030121735,0.0000020002362,0.000010197435,0.00016509167],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9968179,0.00006263308,0.0005622193,0.00092672306,0.001140275,0.0004902435],"domain_scores_gemma":[0.9969821,0.0013825403,0.00032513833,0.00058359286,0.00044910854,0.00027753232],"candidate_categories":["metaresearch","sts"],"consensus_categories":[],"category_scores_codex":[0.006204969,0.00017966727,0.00025643423,0.00038124423,0.0014747399,0.0010007918,0.00091001164,0.00007862538,0.000013513286],"category_scores_gemma":[0.019003028,0.00013519493,0.00007085359,0.0011675225,0.0004045957,0.0015322063,0.0002793052,0.00015694548,0.000028494183],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012709112,0.000010201715,0.0044157663,6.626352e-7,0.0000012989155,4.1549308e-7,0.00043255542,0.9681703,0.00056206953,0.0054035904,0.00005296268,0.02093751],"study_design_scores_gemma":[0.00021599005,0.0000322063,0.0060462556,0.000038209837,0.000008239045,0.0000050067033,0.00007734464,0.8539212,0.00015921211,0.13918678,0.0001472903,0.00016226835],"about_ca_topic_score_codex":0.000146394,"about_ca_topic_score_gemma":0.00022793883,"teacher_disagreement_score":0.1337832,"about_ca_system_score_codex":0.00003334973,"about_ca_system_score_gemma":0.0001769664,"threshold_uncertainty_score":0.9998252},"labels":[],"label_agreement":null},{"id":"W2339592748","doi":"10.1007/s42488-019-00007-w","title":"Multiportfolio optimization with CVaR risk measure","year":2019,"lang":"en","type":"article","venue":"Journal of Data Information and Management","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":16,"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 Windsor","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"CVAR; Risk measure; Expected shortfall; Coherent risk measure; Measure (data warehouse); Sensitivity (control systems); Convexity; Mathematical optimization; Computer science; Econometrics; Risk analysis (engineering); Economics; Risk management; Mathematics; Data mining; Business; Financial economics; Engineering; Finance","score_opus":0.04343219132625476,"score_gpt":0.31298020191493037,"score_spread":0.2695480105886756,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2339592748","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":[0.010131503,0.00009049681,0.9723918,0.0003624756,0.00031871107,0.00041631956,0.00004492412,0.0000128083175,0.016230976],"genre_scores_gemma":[0.5270388,0.024705483,0.4447623,0.0012996551,0.00016376252,0.0000060331413,0.00040105937,0.000020418738,0.0016024854],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99784553,0.000054362034,0.0008124813,0.000107302825,0.0010859964,0.000094331066],"domain_scores_gemma":[0.9975472,0.000054596887,0.0013429915,0.00051705533,0.00045942923,0.00007873679],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028075052,0.00008711776,0.0001687756,0.0004722156,0.00007421238,0.0003745462,0.0005231368,0.00003315974,0.00016781817],"category_scores_gemma":[0.00014319716,0.000053302305,0.000025411093,0.00040855346,0.000018555555,0.0071035847,0.00015984826,0.00009443051,0.000084158615],"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":[0.00021396797,0.00003538782,0.019693645,0.000016699687,0.00010279965,0.000004426204,0.00046081722,0.46640572,3.9117512e-7,0.0010779183,0.027498385,0.48448983],"study_design_scores_gemma":[0.0022290421,0.00020865184,0.018184584,0.000052533178,0.00010325558,0.00005540116,0.0021689294,0.35593772,0.0000048400816,0.00018804717,0.6207059,0.00016107605],"about_ca_topic_score_codex":0.0000069442817,"about_ca_topic_score_gemma":0.0000034217721,"teacher_disagreement_score":0.59320754,"about_ca_system_score_codex":0.0000152545435,"about_ca_system_score_gemma":0.000032152555,"threshold_uncertainty_score":0.5149928},"labels":[],"label_agreement":null},{"id":"W2350106457","doi":"","title":"The Bounds of the Value-at-Risk for Functions of Dependent Risks","year":2004,"lang":"en","type":"article","venue":"Systems Engineering","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":2,"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":"Copula (linguistics); Value at risk; Liberian dollar; Econometrics; Economics; Expected shortfall; Risk measure; Coherent risk measure; Actuarial science; Value (mathematics); Mathematics; Mathematical economics; Risk management; Financial economics; Statistics; Finance; Portfolio","score_opus":0.04730084453919938,"score_gpt":0.3099096801667443,"score_spread":0.26260883562754495,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2350106457","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":[0.43251598,0.0014438495,0.56100285,0.00007786497,0.003451504,0.00059638364,0.00007151741,0.00002915004,0.00081090425],"genre_scores_gemma":[0.9983231,0.00008362384,0.00034676274,0.000001437298,0.00007732336,0.00003556186,9.964823e-7,0.000009871834,0.0011213339],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99860454,0.000042957217,0.0005374811,0.00013211979,0.0005472576,0.00013562464],"domain_scores_gemma":[0.9984294,0.00052827835,0.00033181184,0.00048664436,0.0001932264,0.00003063217],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016533715,0.00007652569,0.0001571042,0.00007378989,0.00023278096,0.00005544153,0.00035979823,0.00004913499,0.0000022126476],"category_scores_gemma":[0.00088503043,0.000039370694,0.0001316103,0.00036859236,0.000031922886,0.00007027119,0.0000540694,0.00006209652,0.000005608744],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000005974075,0.0000057306606,0.0034417403,0.000007069133,0.000018737795,5.778387e-8,0.00018758664,0.99075186,0.00044130636,0.004373514,0.00018521793,0.0005812207],"study_design_scores_gemma":[0.0025892763,0.00027704446,0.086922936,0.00029596267,0.00023281379,0.00005415791,0.00325971,0.7319348,0.01755768,0.005537369,0.15077624,0.0005620465],"about_ca_topic_score_codex":0.00034288448,"about_ca_topic_score_gemma":0.000049694354,"teacher_disagreement_score":0.5658071,"about_ca_system_score_codex":0.00005456,"about_ca_system_score_gemma":0.000048540573,"threshold_uncertainty_score":0.17903864},"labels":[],"label_agreement":null},{"id":"W2395276158","doi":"10.2139/ssrn.2779334","title":"Some Mathematical Aspects of Price Optimisation","year":2016,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Actua","funders":"","keywords":"Mathematical economics; Computer science; Econometrics; Economics; Management science","score_opus":0.029729879083745952,"score_gpt":0.32147759996699604,"score_spread":0.2917477208832501,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2395276158","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":[0.2657957,0.0016850636,0.71473473,0.003742428,0.00036417847,0.00018171572,0.0000031544478,0.000033811368,0.013459203],"genre_scores_gemma":[0.9873176,0.007680261,0.0010934575,0.000031354954,0.00026772995,0.0000016212408,3.7772276e-7,0.000011088563,0.0035964746],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9970666,0.00013720542,0.00066797074,0.00018812237,0.0010066996,0.0009333792],"domain_scores_gemma":[0.99838436,0.00045161048,0.00050469936,0.0002719179,0.00030063634,0.00008675162],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0053339535,0.000099735546,0.00021526228,0.00024701824,0.00009781885,0.000061061815,0.0004441916,0.00006878774,0.00027288307],"category_scores_gemma":[0.0014658509,0.00005297898,0.000121128425,0.00034719432,0.0000640593,0.00064976,0.000032822147,0.0003123259,0.00025294683],"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":[0.000030958716,0.000049142556,0.0006240012,6.764103e-7,0.000026477865,0.0000011624116,0.000102044236,0.0002245588,0.0011598415,0.90130633,0.00021155154,0.09626323],"study_design_scores_gemma":[0.0004045476,0.00016045161,0.0005390504,0.000014478669,0.000011308568,0.0001791391,0.00026956305,0.0003980378,0.0012065173,0.9955996,0.0011362957,0.000081011916],"about_ca_topic_score_codex":0.000002393223,"about_ca_topic_score_gemma":0.000009369391,"teacher_disagreement_score":0.7215219,"about_ca_system_score_codex":0.00025291028,"about_ca_system_score_gemma":0.0011742866,"threshold_uncertainty_score":0.32512045},"labels":[],"label_agreement":null},{"id":"W2396268331","doi":"10.1007/s10898-016-0438-0","title":"Robust optimization approximation for joint chance constrained optimization problem","year":2016,"lang":"en","type":"article","venue":"Journal of Global Optimization","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"University of Alberta","funders":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Mathematical optimization; Bounding overwatch; Robust optimization; Optimization problem; Mathematics; Convexity; Constrained optimization; Probabilistic logic; Constraint (computer-aided design); Continuous optimization; Computer science; Multi-swarm optimization; Artificial intelligence","score_opus":0.07599725735112603,"score_gpt":0.32296229761482476,"score_spread":0.24696504026369873,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2396268331","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":[0.0003972564,0.00015144696,0.99179596,0.0036452576,0.0010082246,0.00096420717,0.00009760429,0.00006787724,0.00187215],"genre_scores_gemma":[0.060121436,0.0010178108,0.93768203,0.00019963969,0.00048343948,0.000034718716,0.00006540439,0.0000413327,0.00035416664],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.994365,0.00032005028,0.0026572605,0.00055916404,0.0016502416,0.00044828423],"domain_scores_gemma":[0.99024546,0.00041135526,0.0037481794,0.00044752212,0.0048754825,0.0002719755],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0035889214,0.00036440088,0.0006974011,0.0004708377,0.00029849846,0.00044032175,0.00060972007,0.00030246988,0.00037031886],"category_scores_gemma":[0.003819137,0.00024200334,0.00035984884,0.0017028955,0.00015349852,0.0028893445,0.000065300876,0.00011261878,0.0000132994155],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000310085,0.00012801767,0.00068983214,0.000009113653,0.00004367458,0.000002571006,0.00006628374,0.972838,0.000069852365,0.0022878523,0.0030867972,0.020467918],"study_design_scores_gemma":[0.0028579652,0.0004317289,0.00012469565,0.00014226399,0.00009061236,0.00011139413,0.00012682838,0.99160075,0.00021667386,0.0031116798,0.0008577072,0.0003277238],"about_ca_topic_score_codex":0.000003997789,"about_ca_topic_score_gemma":0.0000035368664,"teacher_disagreement_score":0.05972418,"about_ca_system_score_codex":0.00047774738,"about_ca_system_score_gemma":0.0004421776,"threshold_uncertainty_score":0.986861},"labels":[],"label_agreement":null},{"id":"W2398968425","doi":"","title":"Portfolio Selection Subject to Vague Experts' Judgments","year":2006,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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 Manitoba; Ontario Tech University","funders":"","keywords":"Portfolio; Indeterminacy (philosophy); Fuzzy logic; Modern portfolio theory; Variance (accounting); Selection (genetic algorithm); Econometrics; Proxy (statistics); Computer science; Mathematical economics; Mathematics; Economics; Artificial intelligence; Machine learning; Financial economics; Epistemology","score_opus":0.023258232125925753,"score_gpt":0.32205042187165694,"score_spread":0.2987921897457312,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2398968425","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":[0.7276655,0.0010849333,0.2513293,0.0010824385,0.0009911042,0.00030338636,0.0000029394364,0.00009119477,0.017449226],"genre_scores_gemma":[0.9729753,0.0011721742,0.0006343578,0.00023770922,0.00084802933,0.000010064807,0.000006281092,0.000027987073,0.024088083],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99506027,0.00019547736,0.0008347905,0.00043843285,0.0014036348,0.0020673943],"domain_scores_gemma":[0.9986663,0.00010464619,0.000368948,0.00028506058,0.00040661244,0.00016844175],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0045846873,0.00021281134,0.00027184497,0.0007107193,0.00045529826,0.00038972686,0.00057340495,0.0001114798,0.00031014692],"category_scores_gemma":[0.00030884807,0.00016599115,0.0001802252,0.0015437745,0.00002456029,0.00057750056,0.00004655876,0.0007002308,0.00036067163],"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":[0.0005243242,0.0005908187,0.27253658,0.0000014992926,0.00019303561,0.000037135072,0.00061886146,0.0580466,0.005274176,0.14221165,0.16985995,0.35010538],"study_design_scores_gemma":[0.0015993629,0.0010795702,0.026636152,0.000018294428,0.000058524016,0.0019185301,0.0023129922,0.0027052192,0.0030206975,0.7880777,0.17173102,0.0008419337],"about_ca_topic_score_codex":0.0004315868,"about_ca_topic_score_gemma":0.0019539343,"teacher_disagreement_score":0.64586604,"about_ca_system_score_codex":0.00074947043,"about_ca_system_score_gemma":0.0013253653,"threshold_uncertainty_score":0.6768923},"labels":[],"label_agreement":null},{"id":"W2403086122","doi":"10.1007/s10203-016-0175-3","title":"Capital allocation to alternatives with a multivariate ladder gamma return distribution","year":2016,"lang":"en","type":"article","venue":"Decisions in Economics and Finance","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"York University","funders":"","keywords":"Capital allocation line; Risk aversion (psychology); Capital (architecture); Economics; Econometrics; Investment (military); Actuarial science; Multivariate statistics; Expected return; Rate of return; Cash; Financial economics; Microeconomics; Statistics; Finance; Mathematics; Expected utility hypothesis","score_opus":0.03282061407611075,"score_gpt":0.3074346465206579,"score_spread":0.27461403244454713,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2403086122","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":[0.8373901,0.000043605505,0.16029014,0.0016301194,0.00013516872,0.00014987352,0.00009624063,0.0000059927547,0.00025872196],"genre_scores_gemma":[0.9802554,0.012589865,0.0063266465,0.00007353455,0.000034793906,0.000024439962,0.000009700832,0.000007658567,0.00067790627],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99876946,0.000036850975,0.00043345097,0.0004541684,0.00013557683,0.00017048],"domain_scores_gemma":[0.99876314,0.00052361697,0.00017564162,0.00035106408,0.00013012497,0.00005642749],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006668567,0.00011035511,0.00018395633,0.00016020746,0.00008925634,0.000118394775,0.00023655775,0.000055593933,0.0000073807673],"category_scores_gemma":[0.00070429937,0.00006646584,0.000025487574,0.0002680788,0.00006183435,0.00046033016,0.00007505336,0.00004565105,0.00006479797],"study_design_candidate":"design_other","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":[0.00014672858,0.000042209253,0.022026516,6.894833e-8,0.0000051695993,0.000004214024,0.0006008862,0.009081147,0.00008027363,0.024829129,0.0005103159,0.9426733],"study_design_scores_gemma":[0.0015543968,0.00016603932,0.6301158,0.00006383821,0.000004718143,0.000017394792,0.00024166038,0.03799301,0.00045702563,0.051789284,0.27722213,0.00037468047],"about_ca_topic_score_codex":0.00003898529,"about_ca_topic_score_gemma":0.00032572585,"teacher_disagreement_score":0.94229865,"about_ca_system_score_codex":0.00005387286,"about_ca_system_score_gemma":0.00004294137,"threshold_uncertainty_score":0.2710398},"labels":[],"label_agreement":null},{"id":"W2411298013","doi":"","title":"A Gradual Nonconvexification Method or Minimizing Value-at-Risk","year":2014,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Value (mathematics); Value at risk; Mathematics; Econometrics; Economics; Actuarial science; Statistics; Risk management; Finance","score_opus":0.04591467456758235,"score_gpt":0.3750131024960113,"score_spread":0.3290984279284289,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2411298013","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":[0.07159926,0.0004808802,0.92342377,0.0010045358,0.00047778126,0.00012696428,0.000002432706,0.000041097614,0.0028432868],"genre_scores_gemma":[0.933505,0.010157968,0.041694224,0.0003098976,0.00094261905,0.000010675618,0.000008687273,0.000046283916,0.013324667],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99474114,0.0010190635,0.0009061367,0.0004610785,0.0012715794,0.0016009946],"domain_scores_gemma":[0.99679846,0.001238084,0.0009489427,0.00049266545,0.00034476715,0.00017710982],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.021206131,0.00019877878,0.00033227468,0.0003943016,0.00069575716,0.00028362704,0.00074933015,0.00012816807,0.0001742682],"category_scores_gemma":[0.0033983595,0.00013047973,0.00020181898,0.0008382014,0.000051563024,0.00046734142,0.00007237407,0.0011544751,0.00037896898],"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":[0.0003573831,0.00007237421,0.009487061,0.0000010664238,0.00009286043,0.0000026078353,0.00083430496,0.00796778,0.00029426737,0.13654982,0.0023769778,0.84196347],"study_design_scores_gemma":[0.0014027182,0.000639909,0.0035302686,0.000010679511,0.00010379533,0.0010111043,0.0018628751,0.07814508,0.00054920174,0.83880806,0.07355629,0.00038000225],"about_ca_topic_score_codex":0.00007250134,"about_ca_topic_score_gemma":0.0005091847,"teacher_disagreement_score":0.88172954,"about_ca_system_score_codex":0.00050987647,"about_ca_system_score_gemma":0.0012183889,"threshold_uncertainty_score":0.7349664},"labels":[],"label_agreement":null},{"id":"W2436733851","doi":"10.1016/j.apm.2016.10.015","title":"Assessing contaminated land cleanup costs and strategies","year":2016,"lang":"en","type":"article","venue":"Applied Mathematical Modelling","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"","keywords":"Contaminated land; USable; Context (archaeology); Environmental remediation; Provisioning; Risk analysis (engineering); Computer science; Order (exchange); Key (lock); Operations research; Environmental economics; Engineering; Economics; Contamination; Business; Finance; Computer security","score_opus":0.10302126162808643,"score_gpt":0.36444346754123463,"score_spread":0.2614222059131482,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2436733851","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":[0.23053494,0.00002501354,0.73214126,0.00012626048,0.000025242141,0.0001243787,0.0000014141975,0.000047521724,0.036973994],"genre_scores_gemma":[0.9679942,0.00010109078,0.031499553,0.00004124243,0.000033921417,0.000011257804,0.0000010874307,0.000014016313,0.0003036256],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99835175,0.000041139432,0.0005024099,0.00034838077,0.0005291136,0.00022721048],"domain_scores_gemma":[0.9978562,0.0015006829,0.0001462491,0.00027661442,0.00010614732,0.00011408892],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011708584,0.00013700733,0.00027877645,0.00010416369,0.00013113488,0.00063435896,0.00018355458,0.00009283303,0.00012119741],"category_scores_gemma":[0.00015041551,0.00007465102,0.000032755925,0.0001930946,0.000115381576,0.00057576736,0.000059238104,0.00007018213,0.00019713504],"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":[0.000032110536,0.000058734167,0.0003147964,0.000011877715,0.000015306037,0.000006079756,0.0005008825,0.012367908,0.0016762194,0.7832745,0.00014875196,0.20159286],"study_design_scores_gemma":[0.00039228649,0.00001304958,0.00008687921,0.000051941206,0.000014165478,0.000007075417,0.00079816027,0.2819281,0.0006000212,0.7156363,0.0003131549,0.00015887577],"about_ca_topic_score_codex":0.0000037498255,"about_ca_topic_score_gemma":0.0000015150526,"teacher_disagreement_score":0.73745924,"about_ca_system_score_codex":0.000019300885,"about_ca_system_score_gemma":0.000029579383,"threshold_uncertainty_score":0.6117138},"labels":[],"label_agreement":null},{"id":"W2469383975","doi":"10.58079/oums","title":"Bounding sums of random variables, part 1","year":2012,"lang":"en","type":"article","venue":"OpenEdition (OpenEdition)","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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 National de la Recherche Scientifique; AXA Research Fund","keywords":"Bounding overwatch; Mathematics; Quantile; Random variable; Marginal distribution; Econometrics; Statistics; Computer science; Artificial intelligence","score_opus":0.06283477635835082,"score_gpt":0.3273761431268472,"score_spread":0.2645413667684964,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2469383975","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":[0.12867711,0.0030665104,0.34419855,0.021398574,0.031232657,0.0035172107,0.0013577656,0.00047073234,0.46608087],"genre_scores_gemma":[0.98347163,0.0005848206,0.0045215595,0.0036246057,0.0014445957,0.00013790242,0.0006709026,0.00003277732,0.0055112005],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9952804,0.00046829286,0.001380575,0.00049198046,0.001820321,0.0005584651],"domain_scores_gemma":[0.9961995,0.0010819439,0.00086727424,0.00077448366,0.0007209718,0.00035583612],"candidate_categories":["scholarly_communication","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0055382997,0.0002724157,0.0005793548,0.00044046892,0.0004774911,0.00038335877,0.0006913401,0.00019627003,0.01127737],"category_scores_gemma":[0.0018320916,0.00022702743,0.00021445488,0.001247118,0.00018370834,0.018286284,0.00015485704,0.00018434665,0.002002834],"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":[0.0006666773,0.00076158956,0.02914599,0.000030516421,0.00014234959,0.000013588401,0.00073690386,0.004378468,0.0015357148,0.5713165,0.3619439,0.029327793],"study_design_scores_gemma":[0.0031187444,0.00014038716,0.03131448,0.00010619929,0.00010858591,0.00005573441,0.0005587141,0.0011109359,0.009270203,0.017112352,0.93650126,0.0006023881],"about_ca_topic_score_codex":0.0000473991,"about_ca_topic_score_gemma":0.00007497734,"teacher_disagreement_score":0.8547945,"about_ca_system_score_codex":0.00007426888,"about_ca_system_score_gemma":0.00014005549,"threshold_uncertainty_score":0.99877423},"labels":[],"label_agreement":null},{"id":"W2485530614","doi":"10.1137/1.9781611973082.126","title":"Risk-Averse Stochastic Optimization: Probabilistically-Constrained Models and Algorithms for Black-Box Distributions: (Extended Abstract)","year":2011,"lang":"en","type":"article","venue":"","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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 Waterloo","funders":"","keywords":"Black box; Computer science; Mathematical optimization; Budget constraint; Probabilistic logic; Probability distribution; Cover (algebra); Stochastic optimization; Set cover problem; Constraint (computer-aided design); Robust optimization; Stochastic programming; Set (abstract data type); Mathematics; Artificial intelligence","score_opus":0.11515895155728707,"score_gpt":0.3409449435604,"score_spread":0.22578599200311295,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2485530614","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":[0.001313937,0.000027674358,0.98840463,0.0001305403,0.00017361093,0.0009368142,0.0003658419,0.00011104489,0.008535911],"genre_scores_gemma":[0.5429604,0.00018285948,0.4555733,0.000056928566,0.00007472055,0.00008998326,0.00010240084,0.000020016063,0.00093940523],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99742,0.00007733084,0.00087900716,0.0006775001,0.00058781303,0.0003583376],"domain_scores_gemma":[0.9969963,0.0009738241,0.00037022005,0.00049236236,0.00088646053,0.00028082545],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0016069544,0.00023503858,0.00033890762,0.00018108582,0.0003308408,0.0002267809,0.00033539935,0.00016546712,0.0015022393],"category_scores_gemma":[0.0025417623,0.00017521196,0.00012487671,0.00046499673,0.00045036434,0.0007298872,0.000074131865,0.00012693123,0.00006518355],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020300072,0.00032201744,0.00013957395,0.00000766763,0.00006205483,0.0000062548606,0.00092918426,0.83658266,0.0000035334044,0.12132672,0.0018480191,0.038569335],"study_design_scores_gemma":[0.000663084,0.00010247495,0.0007233123,0.0000065143763,0.00006497714,0.000008633566,0.00036702445,0.7956477,0.000019947089,0.20199674,0.00019160146,0.00020803224],"about_ca_topic_score_codex":0.000053149768,"about_ca_topic_score_gemma":0.000019802355,"teacher_disagreement_score":0.5416465,"about_ca_system_score_codex":0.000032587872,"about_ca_system_score_gemma":0.00013454392,"threshold_uncertainty_score":0.9994105},"labels":[],"label_agreement":null},{"id":"W2489923096","doi":"10.1016/b978-1-78548-008-9.50005-8","title":"Robust Portfolio Allocation with Systematic Risk Contribution Restrictions11The authors gratefully acknowledge financial support of the chair QuantValley/Risk Foundation “Quantitative Management Initiative”. The second author gratefully acknowledges financial support of NSERC Canada.","year":2015,"lang":"en","type":"book-chapter","venue":"Elsevier eBooks","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"University of Toronto","funders":"","keywords":"Portfolio; Portfolio optimization; Modern portfolio theory; Actuarial science; Systematic risk; Risk management; Economics; Application portfolio management; Value at risk; Project portfolio management; Expected shortfall; Financial risk; Financial economics; Finance; Management; Project management","score_opus":0.06352008616646501,"score_gpt":0.29639340720625457,"score_spread":0.23287332103978955,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2489923096","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0116329845,0.0018617862,0.014413049,0.00078629993,0.00855728,0.01565789,0.0043340246,0.00015528382,0.9426014],"genre_scores_gemma":[0.1129961,0.00089582155,0.0008741552,0.00015512516,0.00037266128,0.0005129451,0.0005308246,0.00014937499,0.883513],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9903314,0.0013474614,0.0034970476,0.0010956466,0.0031428242,0.0005856316],"domain_scores_gemma":[0.9819599,0.0010050066,0.007787549,0.0017791095,0.0072318832,0.00023658815],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.008645676,0.00089909736,0.0017635403,0.0007555919,0.0009767096,0.00019975733,0.0015808245,0.0005925177,0.00060502905],"category_scores_gemma":[0.004125717,0.0005275494,0.00048376832,0.0007550951,0.0007467325,0.00039558197,0.00031146652,0.0009927062,0.000108527354],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0031549511,0.0006150563,0.0047673187,0.0050806687,0.0033133158,0.00016054818,0.014886864,0.012676401,0.000022476697,0.38343465,0.33900955,0.2328782],"study_design_scores_gemma":[0.0025604877,0.0015628462,0.011202325,0.0029385919,0.0035507397,0.000061075305,0.0016912168,0.0017007318,0.00030359876,0.065036364,0.9077071,0.0016849466],"about_ca_topic_score_codex":0.00051130826,"about_ca_topic_score_gemma":0.1521852,"teacher_disagreement_score":0.5686975,"about_ca_system_score_codex":0.0006353616,"about_ca_system_score_gemma":0.0070152082,"threshold_uncertainty_score":0.9997176},"labels":[],"label_agreement":null},{"id":"W249136499","doi":"10.4000/economiepublique.461","title":"L’évaluation des risques dans les projets publics","year":2003,"lang":"fr","type":"article","venue":"Économie publique/Public economics","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Ministère des Transports","funders":"","keywords":"Publics; Valuation (finance); Political science; Economic rent; Humanities; Economics; Philosophy; Finance; Microeconomics","score_opus":0.1355044333844986,"score_gpt":0.3248757799899201,"score_spread":0.18937134660542151,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W249136499","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":[0.52751476,0.0040158085,0.03407061,0.026455447,0.0052829906,0.0012522925,0.00022518524,0.00027410945,0.4009088],"genre_scores_gemma":[0.6757712,0.05151323,0.09920136,0.0032685657,0.0016518747,0.00046472796,0.0005541826,0.00037104462,0.16720381],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99131405,0.0015931301,0.0029847673,0.0019239123,0.00045588904,0.001728229],"domain_scores_gemma":[0.992216,0.0014772044,0.0015552785,0.0018919661,0.0018114959,0.0010480669],"candidate_categories":["metaresearch","metaepi_narrow","scholarly_communication","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.011366709,0.0008988414,0.0011058104,0.0016287077,0.0011830361,0.0067179254,0.0017945938,0.0011417753,0.0032830723],"category_scores_gemma":[0.008909677,0.0009679082,0.0005873194,0.0016465922,0.0012755981,0.010375459,0.00031289246,0.0007220937,0.0009534706],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000018860253,0.00030161318,0.057819013,0.000018634912,0.0001238402,0.0000031322563,0.001653343,0.009840144,0.000003852735,0.44577962,0.011707352,0.4727306],"study_design_scores_gemma":[0.0009588447,0.00017143105,0.0056544216,0.00002618682,0.000073181676,0.00009251345,0.0022264975,0.07547382,0.00037183132,0.14752136,0.76647323,0.0009566448],"about_ca_topic_score_codex":0.0018318221,"about_ca_topic_score_gemma":0.0066776644,"teacher_disagreement_score":0.7547659,"about_ca_system_score_codex":0.0021957033,"about_ca_system_score_gemma":0.0068805083,"threshold_uncertainty_score":0.9998244},"labels":[],"label_agreement":null},{"id":"W2493908268","doi":"10.1287/mnsc.2020.3851","title":"Inverse Optimization of Convex Risk Functions","year":2020,"lang":"en","type":"preprint","venue":"Management Science","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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 Ottawa","funders":"","keywords":"Mathematical optimization; Portfolio optimization; Optimization problem; Convexity; Mathematics; Convex analysis; Convex optimization; Computer science; Portfolio; Regular polygon; Finance; Economics","score_opus":0.0870370668642074,"score_gpt":0.35083644777548145,"score_spread":0.2637993809112741,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2493908268","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":[0.005674274,0.000040125033,0.91144407,0.0009446252,0.0020818575,0.0006934211,0.00006929608,0.00009216707,0.07896014],"genre_scores_gemma":[0.83996373,0.003186913,0.14837958,0.0004509726,0.00016056742,0.00007023879,0.000079717436,0.000029923887,0.0076783746],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9947201,0.00015321377,0.00091319514,0.0012024979,0.0027320122,0.00027899037],"domain_scores_gemma":[0.99655694,0.0001348893,0.0012072736,0.0013454205,0.00056934176,0.00018612358],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0038040918,0.00022269816,0.00038379888,0.00109613,0.00035951455,0.0004946981,0.0022123468,0.00010211295,0.00053853524],"category_scores_gemma":[0.0013237827,0.00018914296,0.00015597057,0.0036357127,0.0006970163,0.00051967247,0.0023903418,0.00028211702,0.00032105733],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011989254,0.00003361871,0.005716046,0.000013456512,0.000018013148,0.000003389689,0.0002781081,0.9654576,0.00000764578,0.0019164128,0.012031407,0.0145122865],"study_design_scores_gemma":[0.00022011263,0.000040248444,0.008404264,0.00003082137,0.00010054927,5.0156706e-7,0.0008006182,0.9593162,0.00007965263,0.018851811,0.011900013,0.00025521126],"about_ca_topic_score_codex":0.000074649586,"about_ca_topic_score_gemma":0.000008996698,"teacher_disagreement_score":0.83428943,"about_ca_system_score_codex":0.0000738855,"about_ca_system_score_gemma":0.0001716784,"threshold_uncertainty_score":0.7713026},"labels":[],"label_agreement":null},{"id":"W2495331872","doi":"10.1017/ccol0521825512.007","title":"Uncertainty and Incommensurabilities","year":2005,"lang":"en","type":"book-chapter","venue":"Cambridge University Press eBooks","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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; Université de Montréal","funders":"","keywords":"Computer science","score_opus":0.05727552535292435,"score_gpt":0.2677526684932406,"score_spread":0.21047714314031624,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2495331872","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0005791678,0.00023523746,0.00034660852,0.00009010372,0.00019199887,0.00024514043,0.0002672364,0.000073550356,0.99797094],"genre_scores_gemma":[0.0034886359,0.0013214943,0.00021248081,0.00010867377,0.00014860075,3.0892616e-7,0.000030008368,0.000023393266,0.9946664],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99803966,0.000081155915,0.00036155,0.0006303256,0.00067264534,0.00021463072],"domain_scores_gemma":[0.9978806,0.00049314735,0.00035106431,0.0007084312,0.0003753183,0.00019145134],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005099361,0.00030729797,0.0004588806,0.0003375265,0.00025043974,0.00016378565,0.00058345136,0.00033739934,0.00002349575],"category_scores_gemma":[0.000091780945,0.00029183476,0.00016153607,0.000015468791,0.00041801238,0.00018197513,0.00042477582,0.00031040836,0.000030390198],"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":[0.0000613555,0.0000043492523,0.000041017553,0.0000055547357,0.00003585504,0.000040932366,0.00009655048,0.00019228003,0.0000011045781,0.8896395,0.08557052,0.024310976],"study_design_scores_gemma":[0.00030448375,0.00003330682,0.000076956,0.00002853637,0.00006006675,0.000013353592,0.00011100812,0.0006440151,0.000009674888,0.00036849527,0.9980332,0.00031688923],"about_ca_topic_score_codex":0.00014040155,"about_ca_topic_score_gemma":0.000014515226,"teacher_disagreement_score":0.9124627,"about_ca_system_score_codex":0.000114935836,"about_ca_system_score_gemma":0.00009649657,"threshold_uncertainty_score":0.9999534},"labels":[],"label_agreement":null},{"id":"W2501681817","doi":"10.3390/risks6010023","title":"Desirable Portfolios in Fixed Income Markets: Application to Credit Risk Premiums","year":2018,"lang":"en","type":"article","venue":"Risks","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Fixed income; Portfolio; Cash flow; Replicating portfolio; Arbitrage; Bond; Portfolio optimization; Economics; Financial economics; Econometrics; Business; Actuarial science; Finance","score_opus":0.07044548236380661,"score_gpt":0.3917648262224021,"score_spread":0.3213193438585955,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2501681817","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":[0.76819706,0.0000909987,0.19754419,0.00031208253,0.0006308422,0.0009045702,0.00004039029,0.00011386164,0.03216601],"genre_scores_gemma":[0.98807275,0.00024074368,0.008557001,0.00021371183,0.00038847502,0.00008396071,0.000011655266,0.000020714411,0.0024109776],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99703604,0.00025792434,0.0007961179,0.00064468815,0.0008971478,0.00036810213],"domain_scores_gemma":[0.997681,0.00040451981,0.00036987642,0.0009591756,0.00037812602,0.00020726136],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0036365227,0.00017266409,0.00028513419,0.00059514685,0.00019510898,0.00017995173,0.00071364414,0.00015907416,0.00064572075],"category_scores_gemma":[0.002011511,0.00012682578,0.00006952826,0.0021313652,0.00007707949,0.00040627175,0.00014373858,0.0001730172,0.0022726615],"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":[0.00019228536,0.0000808715,0.7080584,0.0000016471361,0.000008372586,0.000004342541,0.0005699125,0.0045693903,0.00016845291,0.0002473675,0.031247098,0.25485188],"study_design_scores_gemma":[0.0004502039,0.0001204401,0.82260466,0.000017152113,0.000012879985,0.0000045745633,0.00014331756,0.03566266,0.0011739641,0.011727475,0.12782408,0.00025861876],"about_ca_topic_score_codex":0.0009613963,"about_ca_topic_score_gemma":0.00042419866,"teacher_disagreement_score":0.25459325,"about_ca_system_score_codex":0.000079421974,"about_ca_system_score_gemma":0.00008173438,"threshold_uncertainty_score":0.99850416},"labels":[],"label_agreement":null},{"id":"W2514227992","doi":"10.21314/jor.2009.191","title":"Min-Max robust and CVaR robust mean-variance portfolios","year":2009,"lang":"en","type":"article","venue":"The Journal of Risk","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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 Waterloo","funders":"","keywords":"CVAR; Variance (accounting); Econometrics; Statistics; Mathematics; Computer science; Economics; Portfolio; Expected shortfall; Financial economics","score_opus":0.04861479097870843,"score_gpt":0.31094742568092104,"score_spread":0.26233263470221263,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2514227992","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":[0.77781993,0.005797257,0.20168196,0.004768379,0.0009871095,0.000210278,0.000018363216,0.000024454275,0.008692267],"genre_scores_gemma":[0.9769969,0.012818707,0.0084507,0.0003269431,0.00039349846,2.0147063e-7,4.7207695e-7,0.000008995178,0.0010035732],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9973538,0.00036057865,0.00086032995,0.00015388294,0.0010616959,0.00020973454],"domain_scores_gemma":[0.9969186,0.0007603534,0.0012625724,0.0004150004,0.00048594526,0.00015753797],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005524187,0.0001401791,0.00032568516,0.00023482076,0.00025721436,0.00018979293,0.00067216466,0.00007415337,0.00016953217],"category_scores_gemma":[0.0012859633,0.00007431079,0.00011321842,0.0006221029,0.00009211189,0.0005399778,0.000039215698,0.00033029917,0.000041631738],"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":[0.00053942826,0.00017631652,0.012906476,0.0000013025002,0.00006713737,0.000103321465,0.006009762,0.5586717,0.0001727833,0.0014020918,0.08821087,0.33173877],"study_design_scores_gemma":[0.005338293,0.0024699722,0.44545552,0.00022950677,0.0010716204,0.004159666,0.008379433,0.10552433,0.0012326593,0.27250797,0.15231368,0.0013173567],"about_ca_topic_score_codex":0.000028893404,"about_ca_topic_score_gemma":0.000020204108,"teacher_disagreement_score":0.4531474,"about_ca_system_score_codex":0.000018532088,"about_ca_system_score_gemma":0.000077929246,"threshold_uncertainty_score":0.3030306},"labels":[],"label_agreement":null},{"id":"W2516949334","doi":"10.2139/ssrn.2658669","title":"Risk Aversion in Risk Measures and Risk Sharing","year":2015,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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 Waterloo","funders":"","keywords":"Risk aversion (psychology); Actuarial science; Business; Economics; Financial economics; Expected utility hypothesis","score_opus":0.04537679487256991,"score_gpt":0.3200889990445574,"score_spread":0.2747122041719875,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2516949334","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":[0.9584942,0.0050475583,0.034656532,0.000106418,0.00026511247,0.00009495092,0.0000054639363,0.000018588626,0.0013111801],"genre_scores_gemma":[0.8679099,0.1308028,0.00036140243,0.000011614352,0.00014929936,0.0000012638316,9.880579e-7,0.000012356147,0.0007503724],"study_design_codex":"observational","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99615467,0.0005689297,0.0005848951,0.00038076026,0.0011039082,0.0012068343],"domain_scores_gemma":[0.9983042,0.00023789697,0.00067576184,0.00029234213,0.00027217582,0.0002176513],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.021292876,0.00015448206,0.00024796478,0.00048640065,0.00034566261,0.00029466403,0.0004838213,0.00010802694,0.000018976163],"category_scores_gemma":[0.0037176241,0.00011304879,0.0000853121,0.0006459656,0.000052204716,0.00062773057,0.00010420742,0.0019809138,0.000087711975],"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":[0.0000939464,0.000020658501,0.79380804,1.216882e-7,0.00002619822,0.0000033197173,0.0006279615,0.010203948,0.0000029474318,0.0011280738,0.00032881537,0.19375595],"study_design_scores_gemma":[0.0018938403,0.00032450794,0.08845734,0.000011773771,0.000071656905,0.00021119637,0.00540575,0.026287138,0.000028558597,0.8691623,0.007891585,0.00025435633],"about_ca_topic_score_codex":0.0014090798,"about_ca_topic_score_gemma":0.0048277117,"teacher_disagreement_score":0.86803424,"about_ca_system_score_codex":0.0004323652,"about_ca_system_score_gemma":0.00083897315,"threshold_uncertainty_score":0.8606195},"labels":[],"label_agreement":null},{"id":"W2517804492","doi":"10.3390/jrfm10010005","title":"Accurate Evaluation of Expected Shortfall for Linear Portfolios with Elliptically Distributed Risk Factors","year":2017,"lang":"en","type":"article","venue":"Journal of risk and financial management","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Expected shortfall; Quantile; Multivariate statistics; Econometrics; Statistics; Computer science; Risk management; Mathematics; Economics; Finance","score_opus":0.06482728450339825,"score_gpt":0.35670570109988425,"score_spread":0.291878416596486,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2517804492","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":[0.60786223,0.00009469124,0.39114562,0.000029491579,0.00023498302,0.00031648262,0.00010680754,0.0000033106128,0.00020639073],"genre_scores_gemma":[0.9892006,0.0030658196,0.007505255,0.0000061930896,0.00014117343,0.0000068145505,0.0000120957775,0.000009004756,0.00005302241],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9971657,0.00013170949,0.00093926187,0.00022834101,0.0013575281,0.00017749787],"domain_scores_gemma":[0.99487066,0.00023737155,0.0026924189,0.00042092474,0.0016685894,0.000110016124],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004183067,0.00015171849,0.00041046878,0.00028117574,0.00040800145,0.00019534482,0.00045731955,0.00007465534,0.000024257672],"category_scores_gemma":[0.003560318,0.00009448151,0.00014687442,0.00021804983,0.000105336745,0.0004821177,0.00007602525,0.00013806553,0.0000011264294],"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":[0.0013988585,0.00026254146,0.50183743,0.0000137244315,0.0001293776,0.000021282032,0.0008692788,0.051072937,0.000006669772,0.0017338557,0.002114846,0.4405392],"study_design_scores_gemma":[0.0022449803,0.00053654413,0.9514081,0.0000473333,0.000692007,0.000004305257,0.0005514281,0.02144087,0.00008614421,0.010258182,0.012565121,0.00016497778],"about_ca_topic_score_codex":0.00004035389,"about_ca_topic_score_gemma":0.000051976665,"teacher_disagreement_score":0.4495707,"about_ca_system_score_codex":0.000028792441,"about_ca_system_score_gemma":0.00009526994,"threshold_uncertainty_score":0.42622903},"labels":[],"label_agreement":null},{"id":"W2522507952","doi":"10.2139/ssrn.3073013","title":"Robust Empirical Optimization is Almost the Same as Mean-Variance Optimization","year":2017,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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 British Columbia","funders":"","keywords":"Robust optimization; Variance (accounting); Mathematics; Stochastic optimization; Econometrics; Mathematical optimization; Statistics; Economics","score_opus":0.08086324046274919,"score_gpt":0.37195754375052426,"score_spread":0.2910943032877751,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2522507952","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":[0.0043442757,0.00086583424,0.9641083,0.019660328,0.00082801736,0.00026340707,0.0000058700275,0.000038984475,0.009885002],"genre_scores_gemma":[0.8774965,0.04642484,0.032179542,0.003696035,0.0021010952,0.000025134892,0.000032061376,0.00011573493,0.037929084],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99492455,0.00037470853,0.00087523425,0.00055017794,0.0016440116,0.001631297],"domain_scores_gemma":[0.9960821,0.00029664332,0.0013481664,0.0012770421,0.0008266667,0.00016937204],"candidate_categories":["sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0074039856,0.00026981806,0.00032806644,0.0002122206,0.002989001,0.002232953,0.0022144807,0.0001986206,0.0006892529],"category_scores_gemma":[0.00261466,0.00017118882,0.00022427346,0.00050920236,0.00021463337,0.0016598399,0.00017104311,0.0013857022,0.0002482459],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000070476344,0.0000430628,0.0035045678,3.7050498e-7,0.000052139465,0.000004015272,0.00055452564,0.96424794,0.0000026215673,0.0115704695,0.0036265152,0.016323276],"study_design_scores_gemma":[0.0010068329,0.000261235,0.0012737091,0.000018826833,0.00008009187,0.00070193806,0.0017693685,0.83972716,0.000047284284,0.14035028,0.01436617,0.0003971201],"about_ca_topic_score_codex":0.00013032334,"about_ca_topic_score_gemma":0.0002753647,"teacher_disagreement_score":0.93192875,"about_ca_system_score_codex":0.00040783072,"about_ca_system_score_gemma":0.0021459316,"threshold_uncertainty_score":0.99880284},"labels":[],"label_agreement":null},{"id":"W2523743667","doi":"10.3390/risks4040033","title":"Multivariate TVaR-Based Risk Decomposition for Vector-Valued Portfolios","year":2016,"lang":"en","type":"article","venue":"Risks","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Université du Québec à Trois-Rivières; Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Bivariate analysis; Capital allocation line; Multivariate statistics; Value at risk; Portfolio; Risk management; Actuarial science; Orthant; Business; Enterprise risk management; Economic capital; Project portfolio management; Financial risk; Econometrics; Economics; Finance; Mathematics; Statistics; Human capital; Microeconomics; Incentive","score_opus":0.1351923790012696,"score_gpt":0.4422744245479462,"score_spread":0.3070820455466766,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2523743667","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":[0.18016043,0.00006634715,0.81626594,0.0006574923,0.000724774,0.0005540764,0.00023865986,0.00012003648,0.0012122225],"genre_scores_gemma":[0.9750982,0.00009787232,0.023359438,0.00014125215,0.00023146237,0.00007409739,0.000025796351,0.00002728537,0.0009445997],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99739164,0.00027274038,0.00067895174,0.00056853297,0.0007385815,0.00034953046],"domain_scores_gemma":[0.9967504,0.0014020657,0.0005671511,0.0006538019,0.0004589256,0.0001676209],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021871447,0.0001928279,0.0002928449,0.00030837118,0.00029963147,0.00014798937,0.0004212427,0.00015058235,0.00046000822],"category_scores_gemma":[0.00209118,0.00011431192,0.00023634522,0.00045624332,0.00006593994,0.00036185558,0.000036337216,0.00007670337,0.0005094192],"study_design_candidate":"design_other","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":[0.0013312394,0.0004412897,0.13215035,0.0000068262993,0.000107050946,0.000015833655,0.0003874451,0.018398656,0.011296372,0.009439095,0.04281669,0.78360915],"study_design_scores_gemma":[0.008463112,0.00063018454,0.5834622,0.00009094467,0.000193459,0.000009196469,0.000084193794,0.21704675,0.031443384,0.060515787,0.09705192,0.0010089063],"about_ca_topic_score_codex":0.00021898882,"about_ca_topic_score_gemma":0.000032148957,"teacher_disagreement_score":0.7949378,"about_ca_system_score_codex":0.000058999864,"about_ca_system_score_gemma":0.00010454495,"threshold_uncertainty_score":0.6547723},"labels":[],"label_agreement":null},{"id":"W2524120659","doi":"10.2139/ssrn.2841909","title":"A Theory for Measures of Tail Risk","year":2016,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Tail risk; Actuarial science; Econometrics; Business; Economics","score_opus":0.04527616310383574,"score_gpt":0.33033288182629994,"score_spread":0.2850567187224642,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2524120659","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":[0.08986506,0.001955666,0.9062267,0.0005273119,0.00021923997,0.00011525267,0.0000126735595,0.000010281278,0.0010678358],"genre_scores_gemma":[0.9789796,0.013501321,0.000588275,0.000021919763,0.00017725468,0.000004377682,3.1101283e-7,0.000011055045,0.0067158816],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99741405,0.00030366532,0.0005165954,0.00017510545,0.0006692778,0.0009213091],"domain_scores_gemma":[0.9975803,0.0010298097,0.0005774312,0.00024352148,0.00050724414,0.00006173082],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.015698623,0.00008801715,0.00019174094,0.00020813952,0.00016283918,0.000044799435,0.00046945075,0.000058175152,0.00008204053],"category_scores_gemma":[0.00401169,0.000043521883,0.00019441183,0.00024754336,0.00006899522,0.00026244787,0.000019480362,0.00027343698,0.000033814107],"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":[0.000223437,0.000022940321,0.008803125,2.573655e-7,0.00005758242,1.8689187e-7,0.00010313695,0.00012708761,0.00045961342,0.22758792,0.0008225483,0.7617922],"study_design_scores_gemma":[0.0006781955,0.00026295878,0.000840928,0.000007051631,0.00002688207,0.000041759817,0.0005411532,0.000079613244,0.00072535075,0.980176,0.016547207,0.00007293318],"about_ca_topic_score_codex":0.000008277942,"about_ca_topic_score_gemma":0.00013824426,"teacher_disagreement_score":0.9056384,"about_ca_system_score_codex":0.00012769664,"about_ca_system_score_gemma":0.0011801248,"threshold_uncertainty_score":0.54408604},"labels":[],"label_agreement":null},{"id":"W2527784883","doi":"10.3390/jrfm9040011","title":"Portfolios Dominating Indices: Optimization with Second-Order Stochastic Dominance Constraints vs. Minimum and Mean Variance Portfolios","year":2016,"lang":"en","type":"article","venue":"Journal of risk and financial management","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"","keywords":"Stochastic dominance; Variance (accounting); Portfolio optimization; Portfolio; Sample mean and sample covariance; Mathematics; Rate of return on a portfolio; Modern portfolio theory; Econometrics; Sample (material); Statistics; Mathematical optimization; Economics; Finance","score_opus":0.011882762439686204,"score_gpt":0.2565888413457037,"score_spread":0.2447060789060175,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2527784883","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":[0.17605433,0.0003892004,0.82166046,0.00018657537,0.0003308369,0.00029319545,0.000024593484,0.000009812047,0.001050986],"genre_scores_gemma":[0.9550268,0.00409823,0.039948806,0.0001349408,0.00017637078,0.000007912913,0.0000011971175,0.00001814665,0.000587619],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9971576,0.00012532083,0.0011019254,0.00042483796,0.0008828186,0.00030753925],"domain_scores_gemma":[0.99670535,0.00041919516,0.0018388173,0.00029359368,0.0005561066,0.000186934],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023492922,0.00024928906,0.0005210593,0.00061231176,0.00026804858,0.00020651882,0.0003582763,0.00010215197,0.00016561852],"category_scores_gemma":[0.0010247102,0.00014790952,0.00006868523,0.0007214269,0.00030549648,0.0008540872,0.00014519176,0.00016997328,0.00000514493],"study_design_candidate":"design_other","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":[0.0009569361,0.00012629425,0.020189121,0.000023314491,0.0000647698,0.00026822355,0.0015995116,0.014523421,0.000018216713,0.0034410928,0.0015540307,0.95723504],"study_design_scores_gemma":[0.048659515,0.0058191847,0.72985333,0.0041118893,0.0020411937,0.0030330976,0.01076552,0.04172924,0.00032039508,0.07488391,0.0746853,0.004097426],"about_ca_topic_score_codex":0.000007719934,"about_ca_topic_score_gemma":0.000030350186,"teacher_disagreement_score":0.95313764,"about_ca_system_score_codex":0.000035284058,"about_ca_system_score_gemma":0.00010428129,"threshold_uncertainty_score":0.6031575},"labels":[],"label_agreement":null},{"id":"W2529309826","doi":"10.1287/mnsc.2018.3108","title":"“Dice”-sion–Making Under Uncertainty: When Can a Random Decision Reduce Risk?","year":2019,"lang":"en","type":"article","venue":"Management Science","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Group for Research in Decision Analysis; HEC Montréal","funders":"Engineering and Physical Sciences Research Council","keywords":"Quasiconvex function; Ambiguity; Risk measure; Measure (data warehouse); Mathematical optimization; Coherent risk measure; Mathematics; Stochastic programming; Optimal decision; Computer science; Econometrics; Regular polygon; Convex optimization; Decision tree; Economics; Convex combination; Artificial intelligence; Data mining","score_opus":0.04337342957922691,"score_gpt":0.3605695141329323,"score_spread":0.3171960845537054,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2529309826","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":[0.6477269,0.00012460416,0.23954955,0.0019559267,0.0027372001,0.0012960288,0.000009730092,0.00014515249,0.106454924],"genre_scores_gemma":[0.9746319,0.00030871516,0.013992103,0.0007405656,0.000055683566,0.000017314218,0.000002641959,0.000013419321,0.010237668],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9935457,0.00017178338,0.00071633543,0.0012460138,0.003723911,0.00059627375],"domain_scores_gemma":[0.9969329,0.00064775086,0.00047561585,0.0014550047,0.00030637017,0.00018236236],"candidate_categories":["scholarly_communication","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.008885548,0.00021983037,0.0003099578,0.0010475258,0.0007521306,0.0010738974,0.0022731589,0.000057407688,0.0009581306],"category_scores_gemma":[0.000975066,0.00015606721,0.00011991277,0.0032877324,0.00033062522,0.0009646399,0.00076642126,0.00016194428,0.0013457515],"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":[0.00020028558,0.000068749774,0.024700563,0.000004271822,0.000017858174,0.000013774304,0.0011072843,0.44076627,0.00017161139,0.013656684,0.011384155,0.50790846],"study_design_scores_gemma":[0.0042283973,0.00017533696,0.09444038,0.00019790327,0.000084976586,0.000012564062,0.0054922556,0.40920374,0.00023164488,0.3228371,0.16215792,0.00093775505],"about_ca_topic_score_codex":0.00014190067,"about_ca_topic_score_gemma":0.00007621916,"teacher_disagreement_score":0.5069707,"about_ca_system_score_codex":0.00016319391,"about_ca_system_score_gemma":0.00010998349,"threshold_uncertainty_score":0.9999631},"labels":[],"label_agreement":null},{"id":"W2529415110","doi":"10.1287/moor.2016.0796","title":"Proper Efficiency and Tradeoffs in Multiple Criteria and Stochastic Optimization","year":2016,"lang":"en","type":"article","venue":"Mathematics of Operations Research","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Dalhousie University","funders":"","keywords":"Mathematics; Equivalence (formal languages); Mathematical optimization; Stochastic programming; Stochastic optimization; Generalization; Bounded function; Expected value; Linear programming; Optimization problem; Mathematical economics; Discrete mathematics; Statistics","score_opus":0.25606256179635734,"score_gpt":0.4637190489282441,"score_spread":0.20765648713188678,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2529415110","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":[0.36846495,0.0001559372,0.6284941,0.0014188851,0.000030023932,0.00057235744,0.000014827192,0.000009104991,0.0008398192],"genre_scores_gemma":[0.93802,0.00023256648,0.061020296,0.0000068126087,0.000012730082,0.00003504819,0.000001687863,0.000008870871,0.0006620015],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997927,0.00023115128,0.00055598374,0.00026560796,0.00083389116,0.00018633738],"domain_scores_gemma":[0.9973618,0.0015204396,0.000039653667,0.0003033698,0.0007037345,0.000071020884],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0039544883,0.000077414385,0.00018340765,0.0006022503,0.00018047796,0.00020933452,0.00021078443,0.0000573915,0.000121733625],"category_scores_gemma":[0.009638925,0.000044300083,0.000015086407,0.00072307995,0.0003049993,0.00046102257,0.00011188353,0.000073468946,0.000015164045],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010471271,0.0012018366,0.0033124269,0.00007037544,0.000020102041,0.000007717695,0.023642339,0.8655515,0.017997885,0.036812708,0.00092419365,0.050354242],"study_design_scores_gemma":[0.000512281,0.00009565491,0.00051306887,0.00006886975,0.0000025855793,0.000009277525,0.0011787938,0.994375,0.0003915651,0.0027130113,0.00006369982,0.00007618428],"about_ca_topic_score_codex":0.00003464528,"about_ca_topic_score_gemma":0.0000503864,"teacher_disagreement_score":0.56955504,"about_ca_system_score_codex":0.00001926612,"about_ca_system_score_gemma":0.00007585115,"threshold_uncertainty_score":0.9987033},"labels":[],"label_agreement":null},{"id":"W2530901793","doi":"10.3390/jrfm10020011","title":"The Solvency II Standard Formula, Linear Geometry, and Diversification","year":2017,"lang":"en","type":"article","venue":"Journal of risk and financial management","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Subadditivity; Mathematics; Convexity; Monotone polygon; Risk measure; Diversification (marketing strategy); Quantile; Solvency; Coherent risk measure; Econometrics; Mathematical economics; Pure mathematics; Mathematical analysis; Economics; Finance; Portfolio; Market liquidity; Geometry","score_opus":0.030498996327194886,"score_gpt":0.31547825999456713,"score_spread":0.28497926366737225,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2530901793","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":[0.7444303,0.0020329782,0.24869704,0.001309574,0.0011203914,0.00020230353,0.000018222463,0.000004832955,0.0021843414],"genre_scores_gemma":[0.9542442,0.041833002,0.0030387943,0.0000254018,0.00018400389,8.7011745e-7,3.309568e-7,0.0000031950105,0.00067022914],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9987366,0.000036905643,0.00041443156,0.0001231285,0.00057403033,0.00011486204],"domain_scores_gemma":[0.9983824,0.00012250425,0.0008966068,0.00029132492,0.0002427721,0.000064376094],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0027617898,0.00007052025,0.00015387149,0.00015480735,0.0020990998,0.00041017996,0.00039576236,0.000035336892,0.000006502417],"category_scores_gemma":[0.0010561729,0.0000403016,0.00006105355,0.000105695326,0.00010716292,0.00043529813,0.00024661305,0.00010677696,0.0000031592397],"study_design_candidate":"design_other","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":[0.00013404102,0.000013325382,0.04025374,0.0000015195935,0.00000796937,0.000008181739,0.0003934794,0.00008588156,6.796717e-7,0.0054418226,0.0033538318,0.9503055],"study_design_scores_gemma":[0.0005916389,0.0002035022,0.4322621,0.0000137201605,0.000048761598,0.000007127669,0.00040186066,0.0006609573,0.0000107667,0.035209376,0.5305231,0.00006708126],"about_ca_topic_score_codex":0.000015556328,"about_ca_topic_score_gemma":0.000028712057,"teacher_disagreement_score":0.95023847,"about_ca_system_score_codex":0.000013272161,"about_ca_system_score_gemma":0.000018706285,"threshold_uncertainty_score":0.99920005},"labels":[],"label_agreement":null},{"id":"W2549877593","doi":"10.1287/mnsc.2016.2593","title":"Minimizing Risk Exposure When the Choice of a Risk Measure Is Ambiguous","year":2017,"lang":"en","type":"article","venue":"Management Science","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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 Ottawa; HEC Montréal","funders":"","keywords":"Dynamic risk measure; Coherent risk measure; Decision maker; Time consistency; Risk measure; Commit; Variance (accounting); Actuarial science; Spectral risk measure; Measure (data warehouse); Econometrics; Economics; Computer science; Expected shortfall; Risk management; Finance","score_opus":0.08624745809410747,"score_gpt":0.3626847094171182,"score_spread":0.2764372513230108,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2549877593","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":[0.6814583,0.0004338825,0.060269725,0.004419493,0.0021159933,0.0010828745,0.000062568324,0.000082608385,0.25007457],"genre_scores_gemma":[0.98776096,0.0007490503,0.005398174,0.00019236124,0.000061011815,0.000011780401,4.424022e-7,0.000007350557,0.005818855],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9954653,0.00016008345,0.0005255752,0.000675504,0.0028224757,0.00035111269],"domain_scores_gemma":[0.99534506,0.00025672058,0.0012991297,0.0026464816,0.00035378968,0.0000988032],"candidate_categories":["sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.009062504,0.00014774772,0.0002100323,0.00030337207,0.0024907328,0.0013297336,0.0046241567,0.0000391795,0.0001773674],"category_scores_gemma":[0.004380669,0.00008767213,0.00011311276,0.00077307934,0.0010154074,0.001083176,0.0008112393,0.0001474359,0.00024623718],"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":[0.000021623995,0.000054284254,0.34217632,0.0000038134683,0.000026889076,0.0000050672375,0.0033522195,0.002694075,0.000041106836,0.0012501542,0.025231289,0.6251432],"study_design_scores_gemma":[0.00040908295,0.000044737302,0.91272205,0.00002532135,0.000073609626,9.869335e-7,0.0012089448,0.010573733,0.0003766988,0.015275654,0.059106294,0.00018291632],"about_ca_topic_score_codex":0.00060447055,"about_ca_topic_score_gemma":0.00013165177,"teacher_disagreement_score":0.62496024,"about_ca_system_score_codex":0.00002503361,"about_ca_system_score_gemma":0.00003977113,"threshold_uncertainty_score":0.999707},"labels":[],"label_agreement":null},{"id":"W2564296586","doi":"10.1287/ijoc.2016.0717","title":"Strengthened Benders Cuts for Stochastic Integer Programs with Continuous Recourse","year":2016,"lang":"en","type":"article","venue":"INFORMS journal on computing","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"University of Toronto","funders":"Office of Naval Research; National Science Foundation","keywords":"Benders' decomposition; Mathematical optimization; Integer programming; Mathematics; Integer (computer science); Stochastic programming; Branch and cut; Linear programming relaxation; Relaxation (psychology); Variable (mathematics); Space (punctuation); Computer science","score_opus":0.06866419041123342,"score_gpt":0.35013955302237865,"score_spread":0.28147536261114525,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2564296586","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":[0.20790212,0.000015379179,0.7891076,0.00069044455,0.00068538735,0.00029616308,0.0000035159364,0.000046691162,0.0012526625],"genre_scores_gemma":[0.9864484,0.000013786137,0.011997844,0.00021786286,0.00036394346,0.0000043393834,0.0000023797536,0.000020237048,0.0009311784],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99748594,0.000041040323,0.0008658684,0.00024448347,0.00093203934,0.0004306113],"domain_scores_gemma":[0.9966232,0.001166412,0.000931439,0.0002575463,0.00080289925,0.00021845946],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002219285,0.00019317362,0.00031191032,0.00034269772,0.00035576307,0.00055656774,0.00046936361,0.00007626394,0.000044839544],"category_scores_gemma":[0.0011676324,0.000085241554,0.00014496225,0.0003570323,0.00008133838,0.0006293288,0.00004108002,0.00019981807,0.000058003916],"study_design_candidate":"design_other","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":[0.00026271096,0.000039173417,0.0027016916,8.534573e-7,0.00003289424,0.000007350828,0.000505271,0.029990725,0.0000054920574,0.00049207394,0.0013070159,0.96465474],"study_design_scores_gemma":[0.028824355,0.018227102,0.0090235295,0.004089216,0.00025734043,0.0034437736,0.015583166,0.67065376,0.0007470935,0.07322449,0.17237829,0.003547909],"about_ca_topic_score_codex":0.0000024669532,"about_ca_topic_score_gemma":0.000008106676,"teacher_disagreement_score":0.96110684,"about_ca_system_score_codex":0.00006894516,"about_ca_system_score_gemma":0.0001571095,"threshold_uncertainty_score":0.53669953},"labels":[],"label_agreement":null},{"id":"W2569664619","doi":"10.1137/15m100866x","title":"Lower Bounds on the Probability of a Finite Union of Events","year":2016,"lang":"en","type":"article","venue":"SIAM Journal on Discrete Mathematics","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":8,"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":"Mathematics; Combinatorics; Upper and lower bounds; Event (particle physics); Discrete mathematics; Pairwise comparison; Finite set; Class (philosophy); Statistics; Mathematical analysis","score_opus":0.07988994126648957,"score_gpt":0.34764671378984713,"score_spread":0.26775677252335756,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2569664619","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":[0.9189737,0.000026429638,0.065320075,0.0033726548,0.00042810934,0.00032120134,0.000037747854,0.000009715045,0.011510401],"genre_scores_gemma":[0.9958283,0.00015894836,0.002543058,0.000050802966,0.000043540243,0.0000025472564,4.031092e-7,0.000011126319,0.0013613127],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99677485,0.00033367448,0.0010772408,0.00015462039,0.0014978497,0.00016175516],"domain_scores_gemma":[0.9950798,0.0025354014,0.0012283815,0.0006598331,0.00041671938,0.00007986704],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0051051755,0.00013240283,0.00031607162,0.00016729796,0.0001091617,0.000051112762,0.0006002738,0.00006370448,0.00040824345],"category_scores_gemma":[0.0047520883,0.00005056275,0.00022355071,0.00039967516,0.00014730402,0.00017253193,0.000054865723,0.00013961286,0.00006561067],"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":[0.002037087,0.0061418344,0.03776876,0.00022785271,0.000610591,0.000041829524,0.019113066,0.026187409,0.0058423793,0.71783483,0.027754508,0.15643983],"study_design_scores_gemma":[0.0006864398,0.0010097582,0.0040201857,0.0007196613,0.000033242846,0.00002124009,0.0005165722,0.0030116555,0.0033492395,0.9830708,0.0033870363,0.00017416065],"about_ca_topic_score_codex":8.1068623e-7,"about_ca_topic_score_gemma":0.000001909221,"teacher_disagreement_score":0.26523596,"about_ca_system_score_codex":0.00003277687,"about_ca_system_score_gemma":0.000076657925,"threshold_uncertainty_score":0.5689037},"labels":[],"label_agreement":null},{"id":"W2578607773","doi":"10.1007/s10107-018-1339-4","title":"Two-stage linear decision rules for multi-stage stochastic programming","year":2018,"lang":"en","type":"article","venue":"Mathematical Programming","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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 Toronto","funders":"U.S. Department of Energy; National Science Foundation","keywords":"Mathematics; Upper and lower bounds; Linear programming; Mathematical optimization; Stochastic programming; Value (mathematics); Function (biology); Dual (grammatical number); Affine transformation; Bellman equation; Statistics; Pure mathematics","score_opus":0.1949070732070776,"score_gpt":0.4531267254404512,"score_spread":0.2582196522333736,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2578607773","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":[0.0330415,0.00006924662,0.96381533,0.00009121723,0.00034084186,0.0017847825,0.00002403171,0.00026411342,0.0005689372],"genre_scores_gemma":[0.13390361,0.0000033299648,0.86165625,0.00006444234,0.00039942545,0.00025193917,0.000022511538,0.00006297665,0.0036355043],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9953516,0.00011163148,0.0014088482,0.0008693312,0.0014171435,0.0008414487],"domain_scores_gemma":[0.9952929,0.0021918635,0.00048606985,0.0008790137,0.0008034214,0.00034671868],"candidate_categories":["metaresearch","metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0043068863,0.00036313382,0.0006021295,0.00035741218,0.0006329505,0.0008160684,0.0008457448,0.00018161231,0.00042705017],"category_scores_gemma":[0.009804679,0.0002608237,0.00031140837,0.00083524804,0.00040047383,0.00047264915,0.0002434617,0.00020516463,0.0011709561],"study_design_candidate":"design_other","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":[0.00014028717,0.00049796695,0.00025218652,0.000047582274,0.0000299422,0.000009126706,0.0014114146,0.0013462855,0.000060156035,0.030782273,0.00023099616,0.9651918],"study_design_scores_gemma":[0.0017070626,0.00046580363,0.000051024173,0.00014134517,0.00005635298,0.000014747784,0.0015783836,0.8654474,0.0003164706,0.03133289,0.09835755,0.0005309512],"about_ca_topic_score_codex":0.000012235451,"about_ca_topic_score_gemma":0.000033833414,"teacher_disagreement_score":0.9646608,"about_ca_system_score_codex":0.000051718453,"about_ca_system_score_gemma":0.00007709503,"threshold_uncertainty_score":0.9999844},"labels":[],"label_agreement":null},{"id":"W2582478035","doi":"10.5267/j.dsl.2017.1.001","title":"A multi period portfolio selection using chance constrained programming","year":2017,"lang":"en","type":"article","venue":"Decision Science Letters","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Selection (genetic algorithm); Portfolio; Mathematical optimization; Computer science; Operations research; Economics; Mathematics; Artificial intelligence; Financial economics","score_opus":0.11965466829998851,"score_gpt":0.4223155566344343,"score_spread":0.3026608883344458,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2582478035","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":[0.62101793,0.000015513702,0.376318,0.0010092517,0.001014306,0.00026272878,0.0000029913435,0.00005175832,0.00030750403],"genre_scores_gemma":[0.8042202,0.000015470983,0.19482727,0.0006186681,0.00013731254,0.000009814309,8.536831e-7,0.0000133722115,0.00015703101],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9939134,0.000076211036,0.0008542609,0.0012103423,0.003228563,0.0007171985],"domain_scores_gemma":[0.9962687,0.00023593423,0.0010193003,0.0014517102,0.0007093649,0.00031500522],"candidate_categories":["sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.006570356,0.0002481291,0.0003544392,0.0009893792,0.003751759,0.0045659933,0.002647068,0.00009322942,0.00016511176],"category_scores_gemma":[0.007528718,0.00019240726,0.00015652554,0.0019124952,0.0017006437,0.0034004839,0.00036392978,0.00021182897,0.00016567187],"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":[0.00006688301,0.00008021715,0.107106276,0.0000011645326,0.0000072919033,0.00007074538,0.00083629234,0.010071591,0.14587241,0.0002493507,0.0012962149,0.73434156],"study_design_scores_gemma":[0.0017047644,0.00010669231,0.23232101,0.00009630769,0.000026417281,0.00041759352,0.00083382067,0.7281039,0.00724242,0.001122963,0.027108071,0.00091607124],"about_ca_topic_score_codex":0.00008991481,"about_ca_topic_score_gemma":0.000029775847,"teacher_disagreement_score":0.7334255,"about_ca_system_score_codex":0.00013152522,"about_ca_system_score_gemma":0.00029598305,"threshold_uncertainty_score":0.99754524},"labels":[],"label_agreement":null},{"id":"W2585021503","doi":"10.1515/strm-2015-0028","title":"Improved algorithms for computing worst Value-at-Risk","year":2017,"lang":"en","type":"article","venue":"Statistics & Risk Modeling","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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 Waterloo","funders":"","keywords":"Mathematics; Homogeneous; Context (archaeology); Algorithm; Matrix (chemical analysis); Value (mathematics); Mathematical optimization; Combinatorics; Statistics","score_opus":0.10459415956340136,"score_gpt":0.4068966317897994,"score_spread":0.30230247222639806,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2585021503","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":[0.041369725,0.000114434224,0.95463514,0.00005936763,0.0009964659,0.00041525514,0.0017068703,0.000057110126,0.0006456078],"genre_scores_gemma":[0.45242086,0.0006307032,0.54591274,0.000028681647,0.0002534678,0.000009608441,0.000060679573,0.000033346645,0.0006498931],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9967532,0.00015076212,0.001010155,0.0007479728,0.0008225326,0.0005153622],"domain_scores_gemma":[0.9944766,0.0013263064,0.0019293667,0.0012288506,0.0008419113,0.0001969719],"candidate_categories":["metaresearch","sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.003811166,0.00025388665,0.00042151328,0.00016873126,0.0033383926,0.001055096,0.0010040283,0.00013057802,0.00003497223],"category_scores_gemma":[0.010226359,0.00021512203,0.0001413435,0.00012528675,0.00011222308,0.00033002315,0.00033319392,0.00025762623,0.000065722474],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000059638078,0.000023266386,0.008147122,0.0000034041161,0.000028220096,0.0000027873155,0.00060421234,0.6708252,0.00001651821,0.0031738072,0.0016824087,0.31543344],"study_design_scores_gemma":[0.0006423091,0.000057423647,0.0008979581,0.000011442593,0.000074253396,0.0000019396577,0.00011049094,0.87286615,0.000031181127,0.12388497,0.0011721395,0.00024975502],"about_ca_topic_score_codex":0.0012127706,"about_ca_topic_score_gemma":0.00021036601,"teacher_disagreement_score":0.41105112,"about_ca_system_score_codex":0.00007191961,"about_ca_system_score_gemma":0.00010544912,"threshold_uncertainty_score":0.9999819},"labels":[],"label_agreement":null},{"id":"W2588272059","doi":"","title":"FUZZY PORTFOLIO OPTIMIZATION MODEL WITH ESTIMATION OF RESULTS","year":2016,"lang":"en","type":"article","venue":"The Journal of Internet Banking and Commerce","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Computer science; Fuzzy logic; Variance (accounting); Portfolio; Reliability (semiconductor); Portfolio optimization; Mathematical optimization; Fuzzy number; Modern portfolio theory; Econometrics; Fuzzy set; Artificial intelligence; Mathematics; Economics; Finance","score_opus":0.043401485942087585,"score_gpt":0.31513458732564936,"score_spread":0.27173310138356177,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2588272059","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":[0.21819595,0.00009488464,0.77613074,0.0013420706,0.00006198187,0.00004383229,0.0000037607151,0.0000045438996,0.0041222535],"genre_scores_gemma":[0.98620486,0.0006168397,0.011724794,0.000089898735,0.000028745077,3.085599e-7,7.8693563e-7,0.0000067354454,0.001326999],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99832684,0.00015771034,0.0007550132,0.00009039295,0.0005807005,0.000089341134],"domain_scores_gemma":[0.9975265,0.0005909995,0.0011342136,0.00022868093,0.00047702758,0.00004253621],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026434863,0.00008523638,0.00020079203,0.00018340658,0.000048623522,0.00005345414,0.00036217168,0.00003745796,0.0000246485],"category_scores_gemma":[0.0004456215,0.00003437844,0.00004165724,0.00022034021,0.00009805115,0.0003874899,0.00004605315,0.00008476641,0.0000020343825],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00062813226,0.000020184305,0.0008018503,0.0000011147829,0.000019243482,0.0000011920747,0.0010836854,0.92776644,0.00004714904,0.00081615447,0.0032956053,0.065519266],"study_design_scores_gemma":[0.0012849206,0.00045869895,0.0018116032,0.00032056903,0.00006519397,0.00022990667,0.0003080454,0.982042,0.000675558,0.012375135,0.00031834276,0.00011002183],"about_ca_topic_score_codex":0.000022550083,"about_ca_topic_score_gemma":0.0000051958277,"teacher_disagreement_score":0.76800895,"about_ca_system_score_codex":0.000014040245,"about_ca_system_score_gemma":0.000042836033,"threshold_uncertainty_score":0.14019121},"labels":[],"label_agreement":null},{"id":"W2589132521","doi":"","title":"Generalized Marginal Risk","year":2009,"lang":"en","type":"preprint","venue":"Munich Personal RePEc Archive (Ludwig Maximilian University of Munich)","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Portfolio; Position (finance); Modern portfolio theory; Portfolio optimization; Economics; Application portfolio management; Replicating portfolio; Metric (unit); Post-modern portfolio theory; Cash flow; Actuarial science; Project portfolio management; Financial economics; Finance; Operations management","score_opus":0.05150623655717985,"score_gpt":0.2958669660252884,"score_spread":0.24436072946810855,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2589132521","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":[0.78442013,0.0030374506,0.038529977,0.0030333714,0.0012917469,0.0018137506,0.0041930866,0.00038583297,0.16329467],"genre_scores_gemma":[0.65383035,0.064623095,0.22075959,0.0005221963,0.00065573776,0.000004219721,0.0019423863,0.00018587698,0.057476565],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9913527,0.0021616204,0.0011425103,0.0018217689,0.0027329482,0.0007884621],"domain_scores_gemma":[0.9923798,0.0010091267,0.0020903097,0.0030199185,0.00092901953,0.00057182246],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0031329794,0.0007845475,0.0015278315,0.0013169071,0.0009094845,0.00019465084,0.004881745,0.0006313608,0.001786285],"category_scores_gemma":[0.0011310002,0.00082704966,0.0013764233,0.0008083514,0.001020011,0.00037460504,0.003171962,0.001967144,0.00022901592],"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":[0.006238689,0.0020697669,0.05403762,0.00020553732,0.0017713734,0.0014116312,0.063097306,0.09420558,0.00047696367,0.012568891,0.18581259,0.5781041],"study_design_scores_gemma":[0.0045614513,0.0006782999,0.12700595,0.0005267305,0.00086312124,0.0000948311,0.011069328,0.18709634,0.00006558195,0.1381713,0.52738243,0.0024846378],"about_ca_topic_score_codex":0.0056180065,"about_ca_topic_score_gemma":0.0026720387,"teacher_disagreement_score":0.5756194,"about_ca_system_score_codex":0.00023564731,"about_ca_system_score_gemma":0.0008850765,"threshold_uncertainty_score":0.999418},"labels":[],"label_agreement":null},{"id":"W2592047957","doi":"10.2139/ssrn.2907499","title":"A Model-Free Continuum of Degrees of Risk Aversion","year":2017,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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 Waterloo","funders":"","keywords":"Econometrics; Economics","score_opus":0.04643339628610982,"score_gpt":0.33843541964418755,"score_spread":0.29200202335807773,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2592047957","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":[0.8536248,0.0009433616,0.14088526,0.00020726623,0.00023051977,0.00007175734,0.000017967133,0.000006015184,0.0040130387],"genre_scores_gemma":[0.9856881,0.0101485355,0.0022378042,0.000004011606,0.00007430044,4.2322196e-7,7.093179e-7,0.0000080609825,0.0018380348],"study_design_codex":"observational","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9974449,0.00011603639,0.0006356662,0.00017479467,0.00091104204,0.00071754557],"domain_scores_gemma":[0.9964792,0.00017757215,0.0018707082,0.0008776978,0.0005302542,0.00006453521],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0058324104,0.00009981542,0.00029708282,0.00021797496,0.0003767629,0.00010145484,0.0016097492,0.00007893151,0.000041324638],"category_scores_gemma":[0.0031356756,0.00007061192,0.0001974378,0.0001241767,0.00013759319,0.0005123333,0.00015065898,0.00059441174,0.0000120345685],"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":[0.0008499718,0.00026999315,0.38313416,0.000004614164,0.00027041233,0.000005386927,0.0017385856,0.12529713,0.0017026978,0.15104038,0.007926711,0.32775995],"study_design_scores_gemma":[0.0013357872,0.0002449731,0.021808475,0.00002096143,0.000058040918,0.000050384253,0.00089177745,0.07953005,0.00072101667,0.89476883,0.00044083077,0.00012888887],"about_ca_topic_score_codex":0.00018026937,"about_ca_topic_score_gemma":0.0021927739,"teacher_disagreement_score":0.74372846,"about_ca_system_score_codex":0.000076563585,"about_ca_system_score_gemma":0.0009530188,"threshold_uncertainty_score":0.37539232},"labels":[],"label_agreement":null},{"id":"W2597535316","doi":"","title":"Robust optimization of sums of piecewise linear functions with application to inventory problems","year":2015,"lang":"en","type":"article","venue":"Les Cahiers du GERAD","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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","funders":"","keywords":"Mathematical optimization; Newsvendor model; Linear programming; Robust optimization; Mathematics; Piecewise linear function; Computation; Column generation; Set (abstract data type); Optimization problem; Computer science; Algorithm","score_opus":0.06477491058583826,"score_gpt":0.28609883806500125,"score_spread":0.221323927479163,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2597535316","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":[0.12792651,0.000075469114,0.86881876,0.00033666057,0.00012669335,0.00041457484,0.00001506514,0.000032972865,0.0022532742],"genre_scores_gemma":[0.86859673,0.000092253096,0.1286773,0.000108327244,0.000117616066,0.00007283067,0.000090500784,0.000027334527,0.0022171193],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998053,0.000099964855,0.0005986034,0.00030925032,0.0007995801,0.00013957555],"domain_scores_gemma":[0.9977855,0.000092955954,0.00046211015,0.00046970224,0.0010154641,0.000174275],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001026051,0.00012073627,0.00025940326,0.00037812334,0.00008949607,0.000032160773,0.0002776934,0.000112869464,0.00003227277],"category_scores_gemma":[0.00043053093,0.00009151493,0.000054746593,0.0013967138,0.00017054626,0.0002861455,0.000030648618,0.000093303584,0.000028549373],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000080182486,0.000059031026,0.017624307,0.000005106177,0.0000124751605,3.5037192e-7,0.0018478624,0.970185,0.00010144235,0.0015972647,0.0029707698,0.005516234],"study_design_scores_gemma":[0.0020775795,0.0009832454,0.0040440313,0.000057891546,0.000114279756,0.000017022494,0.0042206123,0.9338268,0.0021008844,0.004343866,0.047670696,0.00054309046],"about_ca_topic_score_codex":0.00015234693,"about_ca_topic_score_gemma":0.000068826295,"teacher_disagreement_score":0.7406702,"about_ca_system_score_codex":0.000058928825,"about_ca_system_score_gemma":0.00012926661,"threshold_uncertainty_score":0.37318704},"labels":[],"label_agreement":null},{"id":"W2600745543","doi":"10.1007/s10107-017-1135-6","title":"Binary decision rules for multistage adaptive mixed-integer optimization","year":2017,"lang":"en","type":"article","venue":"Mathematical Programming","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":91,"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":"Scalability; Mathematical optimization; Binary number; Decision rule; Binary decision diagram; Computer science; Decision problem; Optimal decision; Optimization problem; Toolbox; Decision tree; Mathematics; Data mining; Algorithm; Artificial intelligence","score_opus":0.11749159385617258,"score_gpt":0.40547075357691104,"score_spread":0.28797915972073845,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2600745543","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":[0.007132225,0.000054068525,0.98778164,0.0002697679,0.00035254817,0.0009060056,0.000018586643,0.00009708144,0.0033880468],"genre_scores_gemma":[0.28895068,0.000018585453,0.7094857,0.000024423516,0.00011054559,0.00012654356,0.000017556857,0.000030231908,0.0012357546],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9971881,0.00007347385,0.0008416358,0.0005632694,0.00093783025,0.00039567627],"domain_scores_gemma":[0.9958724,0.0017600453,0.00060927647,0.0010777973,0.00050279556,0.00017771967],"candidate_categories":["metaresearch","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.002541068,0.00022371147,0.0004162289,0.00019321324,0.0010573467,0.0014523938,0.00096290663,0.00016187075,0.00021515021],"category_scores_gemma":[0.010077399,0.0001582382,0.0002255322,0.0001522926,0.00021764038,0.00080840226,0.0002386863,0.00012281096,0.00030309186],"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":[0.00019768687,0.00030561865,0.00053498877,0.00001985788,0.000027026526,0.000015013665,0.00048435552,0.040418,0.000014693406,0.049735,0.002607793,0.90563995],"study_design_scores_gemma":[0.00056361826,0.00015045199,0.00029091845,0.000081782455,0.000028833183,0.0000073219935,0.00042463746,0.87611336,0.00008279176,0.10971922,0.012303934,0.00023311401],"about_ca_topic_score_codex":0.000007650336,"about_ca_topic_score_gemma":0.000006095699,"teacher_disagreement_score":0.90540683,"about_ca_system_score_codex":0.000037913585,"about_ca_system_score_gemma":0.00003668138,"threshold_uncertainty_score":0.9995842},"labels":[],"label_agreement":null},{"id":"W2601927946","doi":"10.5539/ijsp.v6n3p1","title":"Estimation of Multivariate Smooth Functions via Convex Programs","year":2017,"lang":"en","type":"article","venue":"International Journal of Statistics and Probability","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Estimator; Mathematics; Smoothness; Mathematical optimization; Multivariate statistics; Function (biology); Applied mathematics; Upper and lower bounds; Convex function; Regular polygon; Statistics","score_opus":0.06888472118884144,"score_gpt":0.38872709268769373,"score_spread":0.3198423714988523,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2601927946","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":[0.16892426,0.00002819598,0.82875824,0.0005546676,0.0009799916,0.00011171112,0.000111514266,0.0000029546602,0.0005284646],"genre_scores_gemma":[0.9168918,0.00006631182,0.08282803,0.000012691732,0.000070478614,0.0000015251214,0.000011441443,0.000003468984,0.00011425137],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99798536,0.00007838247,0.0008474052,0.00013720067,0.000878348,0.00007331588],"domain_scores_gemma":[0.99550474,0.00037333166,0.0016411386,0.00024012162,0.0021649173,0.00007577647],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019266651,0.000075233496,0.00020847558,0.000112898495,0.00014112148,0.0003160775,0.00047370567,0.00004329562,0.00007716197],"category_scores_gemma":[0.0035954618,0.000054187487,0.00005767798,0.000048609178,0.00023825343,0.00045459022,0.000075635806,0.00010139324,0.000004122682],"study_design_candidate":"design_other","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":[0.00015172163,0.00016333956,0.07055868,0.0000051766183,0.000057235873,0.000007143924,0.00027597032,0.0047057164,0.000056596396,0.013329603,0.00036146093,0.9103274],"study_design_scores_gemma":[0.00080711086,0.00024643543,0.3496734,0.000036514186,0.00003618662,0.000039689618,0.00004430745,0.16722776,0.00011453656,0.4772395,0.0044374694,0.00009709564],"about_ca_topic_score_codex":0.00011229512,"about_ca_topic_score_gemma":0.000035145687,"teacher_disagreement_score":0.9102303,"about_ca_system_score_codex":0.000025627887,"about_ca_system_score_gemma":0.0000939045,"threshold_uncertainty_score":0.43043634},"labels":[],"label_agreement":null},{"id":"W2606820485","doi":"10.1007/s10898-017-0581-2","title":"A Utility Theory Based Interactive Approach to Robustness in Linear Optimization","year":2017,"lang":"en","type":"article","venue":"Journal of Global Optimization","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Robust optimization; Robustness (evolution); Linear programming; Probabilistic logic; Decision maker; Flexibility (engineering); Convex optimization; Optimization problem","score_opus":0.06205060786525529,"score_gpt":0.38402882627284857,"score_spread":0.3219782184075933,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2606820485","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":[0.010374179,0.000029008226,0.9758708,0.0005531998,0.00086928904,0.0002907971,0.000014827226,0.000012864499,0.0119850235],"genre_scores_gemma":[0.60945606,0.000069455404,0.39004633,0.00016358918,0.0001342735,0.0000037369487,0.000009859048,0.000011821289,0.00010483624],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99635935,0.0005412711,0.0012987419,0.0003750179,0.0011771541,0.00024847148],"domain_scores_gemma":[0.9954025,0.00033554714,0.001942991,0.00070597156,0.0013964276,0.00021657416],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0043980437,0.00020613139,0.00047591983,0.0003760725,0.00028260617,0.00062084006,0.0011531875,0.00016441807,0.00015657724],"category_scores_gemma":[0.009664451,0.00016129199,0.00017678703,0.0009030703,0.00009071408,0.0020083303,0.00012007517,0.00021749403,0.000008353893],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007406966,0.00025250978,0.018279554,0.0000020669993,0.0000127266485,0.000008222201,0.00011369669,0.97031915,7.639742e-7,0.00037456278,0.0004199814,0.0094760675],"study_design_scores_gemma":[0.001048094,0.00010368774,0.015251761,0.00004150837,0.000024122293,0.00002332329,0.00023921637,0.98208165,0.000021190344,0.0006305717,0.00037621154,0.00015866596],"about_ca_topic_score_codex":0.000025181744,"about_ca_topic_score_gemma":0.00001686087,"teacher_disagreement_score":0.59908193,"about_ca_system_score_codex":0.00020303058,"about_ca_system_score_gemma":0.00031927737,"threshold_uncertainty_score":0.99867755},"labels":[],"label_agreement":null},{"id":"W2609002022","doi":"10.1080/10920277.2017.1283236","title":"The Optimal Write-Down Coefficients in a Percentage for a Catastrophe Bond","year":2017,"lang":"en","type":"article","venue":"North American Actuarial Journal","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Bond; Statistics; Mathematics; Econometrics; Actuarial science; Psychology; Economics; Finance","score_opus":0.045251101070788176,"score_gpt":0.37469867864348405,"score_spread":0.3294475775726959,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2609002022","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":[0.9534447,0.000032383712,0.04283304,0.0012872105,0.0014610925,0.00033990448,0.000074055504,0.000009662268,0.00051794417],"genre_scores_gemma":[0.99361837,0.000267298,0.00488319,0.00015722758,0.0006679629,0.000013468208,0.000010023892,0.00001713601,0.00036535284],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99710566,0.00014348257,0.00078735285,0.0003435154,0.0010714745,0.00054853124],"domain_scores_gemma":[0.99678916,0.0006355857,0.0012453217,0.0007600688,0.00031094268,0.00025894662],"candidate_categories":["sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0021863752,0.00017971326,0.00034560196,0.00019740255,0.0022629038,0.0025406787,0.0017820996,0.00003639226,0.000055559107],"category_scores_gemma":[0.0026291946,0.000107055675,0.00019866778,0.00029014514,0.0005563955,0.0005064712,0.00014594817,0.00036514943,0.000052545638],"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":[0.0008400397,0.00010929112,0.2934796,4.4796755e-7,0.000025209862,0.00004780816,0.00089997373,0.009785631,0.000026467009,0.00006071536,0.01718422,0.6775406],"study_design_scores_gemma":[0.0028511118,0.0006640644,0.7771618,0.000011991525,0.000035447705,0.00014290828,0.0023457792,0.018809361,0.000026577121,0.0002927575,0.19730662,0.00035161656],"about_ca_topic_score_codex":0.0002559286,"about_ca_topic_score_gemma":0.0013579521,"teacher_disagreement_score":0.677189,"about_ca_system_score_codex":0.00008038299,"about_ca_system_score_gemma":0.0002951863,"threshold_uncertainty_score":0.999036},"labels":[],"label_agreement":null},{"id":"W2609429053","doi":"10.2139/ssrn.2956962","title":"Characterization, Robustness and Aggregation of Signed Choquet Integrals","year":2018,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"University of Waterloo","funders":"","keywords":"Robustness (evolution); Mathematics; Choquet integral; Characterization (materials science); Econometrics; Mathematical economics; Computer science; Artificial intelligence; Fuzzy logic; Chemistry; Materials science","score_opus":0.025303723060104883,"score_gpt":0.31625604989208034,"score_spread":0.29095232683197547,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2609429053","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":[0.67911637,0.00020601723,0.3197307,0.00036543654,0.00016935643,0.00007116513,0.0000024124708,0.0000090714275,0.00032947326],"genre_scores_gemma":[0.994123,0.003399141,0.00041685675,0.000043856362,0.0002958473,0.0000015472928,0.000008775401,0.00000989275,0.0017011226],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9978859,0.00016939653,0.0005838764,0.00019686304,0.00054135313,0.0006226519],"domain_scores_gemma":[0.99824363,0.00010024984,0.00058488786,0.00018574652,0.0008211297,0.000064345826],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0039220857,0.00010186817,0.00020064783,0.00029597082,0.00019464127,0.00013688832,0.0002837968,0.00007337812,0.0001112564],"category_scores_gemma":[0.000683075,0.00007315576,0.00005189193,0.0006097403,0.00012867418,0.00060325227,0.000029144983,0.00031784285,0.000016068787],"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":[0.00025460744,0.00009693891,0.030229993,0.0000028636055,0.0000960357,0.000001458622,0.0014221441,0.00056538096,0.021042265,0.12969217,0.0003691294,0.816227],"study_design_scores_gemma":[0.0024279351,0.002024423,0.046181753,0.00009842162,0.00009872637,0.00083736464,0.004202542,0.036420528,0.02508077,0.8723331,0.009611664,0.0006827467],"about_ca_topic_score_codex":0.000015188766,"about_ca_topic_score_gemma":0.00015921002,"teacher_disagreement_score":0.81554425,"about_ca_system_score_codex":0.00008589566,"about_ca_system_score_gemma":0.00063610985,"threshold_uncertainty_score":0.29832053},"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":"W2660893828","doi":"10.1007/s00521-017-3014-8","title":"A chance-constrained portfolio selection model with random-rough variables","year":2017,"lang":"en","type":"article","venue":"Neural Computing and Applications","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"York University","funders":"Iran National Science Foundation","keywords":"Portfolio; Mathematical optimization; Stochastic programming; Computer science; Quadratic programming; Random variable; Interval (graph theory); Flexibility (engineering); Portfolio optimization; Selection (genetic algorithm); Expected return; Linear programming; Mathematics; Finance; Statistics; Artificial intelligence; Economics","score_opus":0.055363661300707266,"score_gpt":0.3561586430444284,"score_spread":0.30079498174372116,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2660893828","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":[0.17634682,0.00004534379,0.80581295,0.00062427175,0.000069118694,0.00043921443,0.000009147287,0.0001071315,0.016546004],"genre_scores_gemma":[0.98723704,0.00005562496,0.0113648595,0.00011287737,0.00022981645,0.000037794725,0.0000075414177,0.000009756457,0.00094470073],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987142,0.0000365508,0.00031154646,0.00042379953,0.00032437834,0.00018951853],"domain_scores_gemma":[0.9984953,0.00019106406,0.00040015872,0.00046311578,0.00036007445,0.00009028762],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0005643306,0.0001264158,0.00020171933,0.00009188961,0.0015786854,0.00059521943,0.0003446794,0.000054755295,0.000011894658],"category_scores_gemma":[0.00021157863,0.00009097024,0.00003809255,0.00024378348,0.00014642008,0.0002876167,0.0000708803,0.00011857711,0.000009274727],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013680373,0.00009774609,0.027108323,0.000006497316,0.000029309906,0.0000018802714,0.0003162923,0.5603962,0.0005561944,0.030165756,0.0028906965,0.37829432],"study_design_scores_gemma":[0.0007614768,0.000031986103,0.005385082,0.000008499256,0.000019017,0.000027217106,0.00004637249,0.9795258,0.00012570033,0.0099867685,0.003942013,0.0001400717],"about_ca_topic_score_codex":0.00004738754,"about_ca_topic_score_gemma":0.000015188308,"teacher_disagreement_score":0.8108902,"about_ca_system_score_codex":0.0000079253105,"about_ca_system_score_gemma":0.000051468574,"threshold_uncertainty_score":0.9997211},"labels":[],"label_agreement":null},{"id":"W2738692001","doi":"10.1016/j.ijpe.2017.07.018","title":"A constrained cluster-based approach for tracking the S&P 500 index","year":2017,"lang":"en","type":"article","venue":"International Journal of Production Economics","topic":"Risk and Portfolio Optimization","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":"University of Toronto","funders":"","keywords":"Benchmark (surveying); Portfolio; Computer science; Tracking (education); Index (typography); Portfolio optimization; Mathematical optimization; Cluster analysis; Linear programming; Project portfolio management; Finance; Mathematics; Artificial intelligence; Algorithm; Economics","score_opus":0.15579693434693032,"score_gpt":0.4015623934948394,"score_spread":0.2457654591479091,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2738692001","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":[0.23841362,0.000034054643,0.73134327,0.021431718,0.00676892,0.00027729638,0.000024387704,0.00000769196,0.001699044],"genre_scores_gemma":[0.9805437,0.00008567549,0.016371455,0.00026351208,0.002006448,0.0000065888908,0.000007629463,0.000010105386,0.00070491404],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984007,0.00005943763,0.00083945505,0.00021676371,0.00037679417,0.0001068779],"domain_scores_gemma":[0.9955164,0.00026160086,0.002198195,0.00042758565,0.0015451704,0.000051011833],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0036130573,0.00009435142,0.00018404434,0.00025473387,0.00034011263,0.0009826004,0.0013756242,0.00005545478,0.000038890666],"category_scores_gemma":[0.0040677222,0.00006349173,0.0002036606,0.00004160339,0.00017500292,0.0009981601,0.00004493289,0.00014152826,0.000007355768],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00056173996,0.00012788431,0.017682247,0.0000017146685,0.00013983143,8.999373e-7,0.0003823663,0.8030452,0.00009475308,0.0023820577,0.0073282397,0.16825305],"study_design_scores_gemma":[0.0048232423,0.00020064412,0.030301211,0.00004249053,0.00008973257,0.00075522816,0.0012328175,0.46198505,0.0028073955,0.05722748,0.44005188,0.00048281436],"about_ca_topic_score_codex":0.00000729399,"about_ca_topic_score_gemma":0.000018320108,"teacher_disagreement_score":0.74213004,"about_ca_system_score_codex":0.00008086932,"about_ca_system_score_gemma":0.0002198065,"threshold_uncertainty_score":0.9475238},"labels":[],"label_agreement":null},{"id":"W2760746524","doi":"10.1016/j.cie.2017.09.021","title":"A conditional value-at-risk based methodology to intermediate-term planning of crude oil tanker fleet","year":2017,"lang":"en","type":"article","venue":"Computers & Industrial Engineering","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"McMaster University","funders":"","keywords":"Charter; Hedge; Crude oil; Business; Risk management; Finance; Operations research; Environmental economics; Economics; Engineering","score_opus":0.21314515051157487,"score_gpt":0.39059266602695425,"score_spread":0.17744751551537938,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2760746524","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":[0.5023224,0.00002178095,0.4941384,0.00016006113,0.0029770746,0.0000907746,0.000055986733,0.000044436925,0.0001891245],"genre_scores_gemma":[0.95997506,0.00000793632,0.038835082,0.00010593353,0.0008481452,0.000014263642,0.000039088045,0.000024140421,0.00015036402],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99771595,0.00019367112,0.00072318193,0.0004300887,0.00062773016,0.0003093534],"domain_scores_gemma":[0.9964536,0.0018985824,0.0005702377,0.0007043753,0.00015934426,0.00021388309],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021446445,0.00020760077,0.0004897004,0.0004832147,0.00024275055,0.00020778371,0.0010427176,0.00022263841,0.000095286494],"category_scores_gemma":[0.0047745127,0.0001896046,0.00015258529,0.00023439675,0.00008268586,0.00026474663,0.0003556541,0.00028857336,0.000034866785],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012171686,0.000017412363,0.018829145,0.0000029716268,0.000041703155,0.000022923781,0.00030976327,0.9377883,0.0011608966,0.0002680259,0.0045461683,0.03689099],"study_design_scores_gemma":[0.0039303377,0.000310743,0.12280783,0.00026928246,0.000080467216,0.000023103717,0.000055667504,0.83415586,0.010636696,0.00045741815,0.02659211,0.00068047806],"about_ca_topic_score_codex":0.000035526526,"about_ca_topic_score_gemma":0.0000019160948,"teacher_disagreement_score":0.45765266,"about_ca_system_score_codex":0.00007407435,"about_ca_system_score_gemma":0.00009372716,"threshold_uncertainty_score":0.77318513},"labels":[],"label_agreement":null},{"id":"W2768613765","doi":"10.1007/s10100-017-0508-5","title":"Stability advances in robust portfolio optimization under parallelepiped uncertainty","year":2017,"lang":"en","type":"article","venue":"Central European Journal of Operations Research","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":116,"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":"Portfolio optimization; Parallelepiped; Portfolio; Robust optimization; Computer science; Mathematical optimization; Expected return; Modern portfolio theory; Econometrics; Linear programming; Stock (firearms); Risk aversion (psychology); Rate of return on a portfolio; Economics; Mathematics; Expected utility hypothesis; Financial economics","score_opus":0.27702698869082065,"score_gpt":0.4599977237827984,"score_spread":0.18297073509197775,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2768613765","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":[0.5477338,0.0013294192,0.40528634,0.004415453,0.0010260828,0.00066824106,0.000021823209,0.000017277222,0.03950161],"genre_scores_gemma":[0.9848952,0.005836671,0.008322683,0.00003398938,0.00024636046,0.0000022226468,0.000009713141,0.000018363235,0.0006347418],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99307257,0.0026948373,0.0014078014,0.00036066966,0.0019429526,0.000521157],"domain_scores_gemma":[0.9956662,0.00033182325,0.00039035483,0.00088142924,0.0024232338,0.00030697032],"candidate_categories":["scholarly_communication","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.015827496,0.00014100558,0.00031168066,0.00061229436,0.0010821141,0.0016933692,0.0016655925,0.00004430941,0.0009446221],"category_scores_gemma":[0.007214798,0.00010258759,0.00012415153,0.000608702,0.00040446664,0.0029007045,0.00019526952,0.000617263,0.000057947153],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001654154,0.00012169302,0.0126454085,0.0000014366065,0.000006299088,0.00007279264,0.0002956391,0.9715488,0.000044391145,0.00083830813,0.0007617631,0.013498052],"study_design_scores_gemma":[0.00339895,0.0004470626,0.21033219,0.00009062466,0.000013752679,0.00007578279,0.0026174549,0.7646065,0.00019731034,0.0012530163,0.016619092,0.00034831706],"about_ca_topic_score_codex":0.00006612773,"about_ca_topic_score_gemma":0.0003810643,"teacher_disagreement_score":0.4371615,"about_ca_system_score_codex":0.00016677,"about_ca_system_score_gemma":0.00049529615,"threshold_uncertainty_score":0.99996865},"labels":[],"label_agreement":null},{"id":"W2768676031","doi":"10.1287/opre.2020.2041","title":"Calibration of Distributionally Robust Empirical Optimization Models","year":2017,"lang":"en","type":"preprint","venue":"Operations Research","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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 British Columbia","funders":"Japan Society for the Promotion of Science; Natural Sciences and Engineering Research Council of Canada; Ministry of Education, India","keywords":"Robustness (evolution); Resampling; Sample size determination; Mathematics; Statistics; Econometrics; Mathematical optimization; Computer science","score_opus":0.5642433531899352,"score_gpt":0.5464717258657695,"score_spread":0.017771627324165706,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2768676031","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":[0.006570741,0.00023817031,0.9751542,0.0036865762,0.00042859645,0.0009240248,0.00071831234,0.00003438987,0.012244959],"genre_scores_gemma":[0.9165817,0.0018346164,0.06805159,0.0000328005,0.00042307304,0.0002519163,0.0035601638,0.000041410254,0.00922271],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99191916,0.001253473,0.0013322049,0.00096424186,0.0041386844,0.00039224344],"domain_scores_gemma":[0.9901489,0.00065245887,0.00030126644,0.0021793875,0.0065192785,0.00019868732],"candidate_categories":["sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00761357,0.00023996127,0.0005021476,0.00097615836,0.00132226,0.0022132103,0.0019605006,0.0005703196,0.00062117766],"category_scores_gemma":[0.0065524103,0.00019963644,0.00020375641,0.0008560604,0.00048147302,0.0012192777,0.0013320816,0.0009633475,0.000051067116],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000029012099,0.00012341475,0.00059113133,0.0000071246013,0.000019311832,0.000002330775,0.00030525282,0.97551733,0.000017497088,0.009220817,0.012556006,0.0016107549],"study_design_scores_gemma":[0.00017810927,0.000044813863,0.00037130667,0.000045514902,0.000011998998,0.0000023911184,0.000108898086,0.97952,0.00018020243,0.018317716,0.0010366976,0.00018235017],"about_ca_topic_score_codex":0.0005097643,"about_ca_topic_score_gemma":0.0002670987,"teacher_disagreement_score":0.910011,"about_ca_system_score_codex":0.00017583829,"about_ca_system_score_gemma":0.0023231197,"threshold_uncertainty_score":0.9999779},"labels":[],"label_agreement":null},{"id":"W2768937018","doi":"10.1287/mnsc.2017.2849","title":"Discrete Nonlinear Optimization by State-Space Decompositions","year":2017,"lang":"en","type":"article","venue":"Management Science","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"University of Toronto","funders":"","keywords":"Mathematical optimization; Linear programming; Computer science; Submodular set function; Nonlinear system; Optimization problem; Integer programming; Relaxation (psychology); Shortest path problem; Mathematics; Graph; Theoretical computer science","score_opus":0.035263087107945824,"score_gpt":0.38009095762583067,"score_spread":0.3448278705178848,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2768937018","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":[0.014056133,0.0000243621,0.8707286,0.0037779994,0.00060433114,0.00033569126,0.00002989227,0.00006337195,0.11037963],"genre_scores_gemma":[0.76947725,0.00080112513,0.19745994,0.00034964448,0.00005902693,0.00002758696,0.000027753747,0.000017142025,0.031780563],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99696076,0.00004180152,0.00035683406,0.00066030846,0.0016315173,0.00034879072],"domain_scores_gemma":[0.99773973,0.00006003225,0.00039019826,0.0014267951,0.00021898105,0.00016428978],"candidate_categories":["sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0023162568,0.00012738856,0.00013538152,0.00033595247,0.0024421823,0.0028965198,0.002360324,0.000023231913,0.00019060791],"category_scores_gemma":[0.00040465783,0.00010073282,0.000049460654,0.0007877395,0.00063140574,0.00227132,0.0005924302,0.00006246359,0.00029079957],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000023868975,0.00011238543,0.0154617885,0.000003278647,0.000013945987,0.000016655993,0.00031853298,0.86848634,0.00032960754,0.00806407,0.039685022,0.067484476],"study_design_scores_gemma":[0.0003976921,0.000041846033,0.018651886,0.0000144498235,0.000016966858,0.0000019654726,0.00023304422,0.91100377,0.0009896576,0.00588752,0.06245678,0.00030445316],"about_ca_topic_score_codex":0.00004291776,"about_ca_topic_score_gemma":0.000011107486,"teacher_disagreement_score":0.7554211,"about_ca_system_score_codex":0.000047465204,"about_ca_system_score_gemma":0.000031107764,"threshold_uncertainty_score":0.9988565},"labels":[],"label_agreement":null},{"id":"W2769807024","doi":"10.2139/ssrn.3062767","title":"Optimal Reinsurance in a Market of Multiple Reinsurers Under Law-Invariant Convex Risk Measures","year":2017,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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 Waterloo","funders":"","keywords":"Reinsurance; Invariant (physics); Econometrics; Regular polygon; Mathematics; Mathematical economics; Actuarial science; Economics; Law and economics; Financial economics","score_opus":0.03798002439639975,"score_gpt":0.3198357934488526,"score_spread":0.28185576905245285,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2769807024","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":[0.8731171,0.0030775315,0.105932355,0.0015491646,0.0009428506,0.00034447564,0.000027875752,0.00002540293,0.01498328],"genre_scores_gemma":[0.9810365,0.01572798,0.000991949,0.000045170404,0.000119145705,0.000003825767,0.0000010617819,0.000019810692,0.0020545856],"study_design_codex":"observational","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99539137,0.0005683509,0.0009715247,0.0003924455,0.0012654938,0.001410816],"domain_scores_gemma":[0.99661773,0.00046262966,0.001502126,0.00086433825,0.00043210288,0.00012109047],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.015079302,0.00019760481,0.0004675352,0.00028057394,0.00064666,0.00034310823,0.0013351819,0.000148392,0.000075495045],"category_scores_gemma":[0.0035963538,0.000150428,0.00020075263,0.00026833225,0.00026351205,0.0007803795,0.000088135275,0.0014151195,0.00002290501],"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":[0.002131152,0.00032469258,0.64862204,0.000004559144,0.00035675737,0.00005330354,0.00094206637,0.12587465,0.0007822298,0.1404552,0.0022578004,0.07819555],"study_design_scores_gemma":[0.0064883707,0.00054374343,0.33031183,0.00009585829,0.00008080443,0.00044708187,0.00339696,0.05156972,0.0010381374,0.5955597,0.009749525,0.000718277],"about_ca_topic_score_codex":0.001221582,"about_ca_topic_score_gemma":0.011727263,"teacher_disagreement_score":0.4551045,"about_ca_system_score_codex":0.00026737244,"about_ca_system_score_gemma":0.0013882065,"threshold_uncertainty_score":0.6544088},"labels":[],"label_agreement":null},{"id":"W2774431399","doi":"10.2139/ssrn.3079998","title":"Quantile-Based Risk Sharing with Heterogeneous Beliefs","year":2017,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Quantile; Econometrics; Computer science; Economics","score_opus":0.03910327262449213,"score_gpt":0.338486942034175,"score_spread":0.29938366940968286,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2774431399","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":[0.8236363,0.00091729814,0.17213416,0.0006596297,0.00031743234,0.00012657438,0.0000044932976,0.000034223987,0.0021698726],"genre_scores_gemma":[0.9938799,0.0030896682,0.0006556887,0.000058419828,0.00026484125,0.000003063202,0.000001491015,0.000024452322,0.002022435],"study_design_codex":"observational","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99633896,0.00010924662,0.00047938988,0.00040697306,0.0010671039,0.001598333],"domain_scores_gemma":[0.99736166,0.00012243041,0.0010777601,0.0009933606,0.00029770142,0.0001471066],"candidate_categories":["sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0053272857,0.00018025227,0.00025357187,0.00022399075,0.0017615884,0.0012865063,0.0016015845,0.0000795464,0.000087689885],"category_scores_gemma":[0.0006745662,0.00011695308,0.00015089719,0.00016541638,0.000105307074,0.00056854513,0.000077991936,0.0011584711,0.00014346051],"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":[0.00038495963,0.00010738039,0.6866585,0.0000010742704,0.00016420006,0.000057212867,0.00018976857,0.0891089,0.0000518663,0.017402094,0.00022715234,0.20564689],"study_design_scores_gemma":[0.0055386694,0.0025531184,0.07965543,0.00008834842,0.00020225433,0.0024171274,0.00089118176,0.10450563,0.0012424021,0.78511864,0.016588276,0.0011989444],"about_ca_topic_score_codex":0.00024313443,"about_ca_topic_score_gemma":0.0024724237,"teacher_disagreement_score":0.7677165,"about_ca_system_score_codex":0.00021215218,"about_ca_system_score_gemma":0.0013189417,"threshold_uncertainty_score":0.99975026},"labels":[],"label_agreement":null},{"id":"W2776446206","doi":"10.5539/ass.v14n1p40","title":"Review on Reformulation of the Mean-Variance Model with Real-life Trading Restrictions","year":2017,"lang":"en","type":"article","venue":"Asian Social Science","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Cardinality (data modeling); Mathematical optimization; Solver; Context (archaeology); Perspective (graphical); Quadratic programming; Branch and bound; Computer science; Relaxation (psychology); Portfolio; Integer (computer science); Selection (genetic algorithm); Lift (data mining); Mathematics; Economics; Artificial intelligence; Finance","score_opus":0.1201505689291434,"score_gpt":0.4057354930678625,"score_spread":0.2855849241387191,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2776446206","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":[0.019239752,0.00010826087,0.019980894,0.013356065,0.00037800602,0.00049152237,0.000012593917,0.000028709776,0.9464042],"genre_scores_gemma":[0.9972628,0.0009863526,0.00079895766,0.00028967924,0.00006273002,0.000005722303,3.4828622e-7,0.0000041171816,0.0005893199],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.9977415,0.00006576829,0.00033682273,0.00031229143,0.0013651007,0.00017851563],"domain_scores_gemma":[0.9980168,0.000053579268,0.0007443071,0.000780731,0.00032848524,0.000076116296],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0022330282,0.00007809453,0.00016672458,0.000079349455,0.0025329408,0.00022211629,0.001372672,0.000038701706,0.000011436527],"category_scores_gemma":[0.0016056325,0.000042407446,0.000064719316,0.0011675707,0.00065013865,0.00090507715,0.00007518091,0.0000966397,0.00000788623],"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":[0.00005898015,0.00010300463,0.012097205,0.000024970028,0.000011194406,0.00000137969,0.0056248927,0.004688183,0.00061036856,0.5063349,0.011219619,0.45922527],"study_design_scores_gemma":[0.00041171417,0.00010045095,0.86985743,0.0006212939,0.000048361508,0.0000037421926,0.0006168848,0.07167768,0.00035768587,0.052251063,0.003733718,0.00031996364],"about_ca_topic_score_codex":0.00011108945,"about_ca_topic_score_gemma":0.000061658335,"teacher_disagreement_score":0.978023,"about_ca_system_score_codex":0.00006398454,"about_ca_system_score_gemma":0.00042739525,"threshold_uncertainty_score":0.99876565},"labels":[],"label_agreement":null},{"id":"W2786931536","doi":"10.1007/978-0-387-74759-0_466","title":"Operations Research and Financial Markets","year":2008,"lang":"en","type":"book-chapter","venue":"Encyclopedia of Optimization","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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 British Columbia","funders":"","keywords":"Business; Finance; Financial market; Financial system","score_opus":0.09175422378166155,"score_gpt":0.3638844289880762,"score_spread":0.2721302052064147,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2786931536","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00025742917,0.001274194,0.03372451,0.0002277704,0.0005230182,0.0005831495,0.00007021187,0.000032636955,0.9633071],"genre_scores_gemma":[0.0012190238,0.13755612,0.032080155,0.000041425355,0.00043878437,0.00002048717,0.00023536505,0.0000610474,0.8283476],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.9959591,0.00013093062,0.0011114161,0.00068325194,0.0018658855,0.00024942358],"domain_scores_gemma":[0.9963718,0.00061704783,0.00027767528,0.00066244544,0.0019254744,0.0001455338],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0017658657,0.000271893,0.0005001026,0.001111792,0.00040024798,0.000121443125,0.00045161764,0.0005066583,0.0013858118],"category_scores_gemma":[0.0022747682,0.00024273983,0.0000972423,0.000394307,0.0004233443,0.00045338497,0.0001946598,0.00041263402,0.0000994908],"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":[0.00014098917,0.00010084608,0.00038486643,0.000016712143,0.000029335772,0.000035268087,0.0015902891,0.7015521,0.0000016590166,0.035278294,0.19307199,0.06779764],"study_design_scores_gemma":[0.00047662883,0.00022653783,0.00073238503,0.000092872084,0.00003203364,0.00003290822,0.000037556143,0.07029586,0.000008470084,0.011607449,0.91596514,0.00049215276],"about_ca_topic_score_codex":0.000021661397,"about_ca_topic_score_gemma":0.000034567678,"teacher_disagreement_score":0.7228932,"about_ca_system_score_codex":0.000044796758,"about_ca_system_score_gemma":0.00045750086,"threshold_uncertainty_score":0.99952704},"labels":[],"label_agreement":null},{"id":"W2787207162","doi":"10.1007/s10479-018-2792-4","title":"Computation of the corrected Cornish–Fisher expansion using the response surface methodology: application to VaR and CVaR","year":2018,"lang":"en","type":"article","venue":"Annals of Operations Research","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Université Laval","funders":"Fonds de Recherche du Québec-Société et Culture; Université Laval","keywords":"Quantile; Kurtosis; CVAR; Skewness; Econometrics; Value at risk; Mathematics; Computation; Moment (physics); Taylor series; Portfolio; Risk management; Expected shortfall; Statistics; Economics; Algorithm; Mathematical analysis","score_opus":0.6406208474316435,"score_gpt":0.6007372589748402,"score_spread":0.03988358845680329,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2787207162","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":[0.84556067,0.00006407412,0.14845191,0.005073824,0.00009733098,0.00057749584,0.000012781973,0.0000059276617,0.000155976],"genre_scores_gemma":[0.98416644,0.00007096586,0.0150607,0.00019931415,0.00003963035,0.000011549146,0.0000032134153,0.000008076697,0.00044012954],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9937884,0.0041252854,0.0005267812,0.00029383233,0.0010821152,0.00018353897],"domain_scores_gemma":[0.9920239,0.0026288629,0.00011304306,0.00060263084,0.004569188,0.000062393345],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.016106231,0.000074826996,0.00017302208,0.00024708678,0.000740696,0.00013178779,0.00048594235,0.00007483342,0.000041153213],"category_scores_gemma":[0.008057289,0.00004222606,0.00004174636,0.0024413299,0.00055406353,0.00022508169,0.00022797241,0.000145265,0.000020089892],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013019767,0.00015342842,0.010323409,0.0000059544655,0.000034600787,4.2801008e-7,0.012450064,0.5052353,0.4040847,0.0015871511,0.021270005,0.04355296],"study_design_scores_gemma":[0.00026453126,0.00046437437,0.10996108,0.00004282953,0.000011724489,0.000009352144,0.0034904347,0.7303131,0.14761053,0.0030455375,0.0046541113,0.00013239149],"about_ca_topic_score_codex":0.0005889013,"about_ca_topic_score_gemma":0.00018273591,"teacher_disagreement_score":0.2564742,"about_ca_system_score_codex":0.000012344772,"about_ca_system_score_gemma":0.00024048526,"threshold_uncertainty_score":0.96459097},"labels":[],"label_agreement":null},{"id":"W2788490140","doi":"10.1287/opre.2017.1679","title":"A Practicable Robust Counterpart Formulation for Decomposable Functions: A Network Congestion Case Study","year":2018,"lang":"en","type":"article","venue":"Operations Research","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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; HEC Montréal","funders":"","keywords":"Robust optimization; Mathematical optimization; Bounding overwatch; Computer science; Optimization problem; Queue; Convex optimization; Function (biology); Nonlinear system; Mathematics; Regular polygon","score_opus":0.4597327066755495,"score_gpt":0.5606816333251312,"score_spread":0.10094892664958172,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2788490140","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":[0.5229822,0.00006744616,0.46407986,0.0012773213,0.0008060426,0.003854379,0.00003529269,0.000073224255,0.0068242685],"genre_scores_gemma":[0.9650631,0.000027036347,0.017764784,0.00005309912,0.00082086097,0.0007338046,0.000061369225,0.000020312107,0.015455664],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9962202,0.0007494157,0.00067893247,0.0005840336,0.0012810283,0.00048640583],"domain_scores_gemma":[0.99169165,0.0018397655,0.00007490384,0.0007322055,0.0055155205,0.00014596544],"candidate_categories":["sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.011339294,0.0001255643,0.0001995383,0.00047954294,0.0033864847,0.0016024921,0.00029058784,0.00009928987,0.00077277346],"category_scores_gemma":[0.004714413,0.00010182937,0.00005683115,0.0022646983,0.00010782333,0.0015887571,0.000104669976,0.00021671702,0.0007479344],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00032488105,0.00074926636,0.016292362,0.0000025998213,0.00005136506,0.00005431298,0.0022672084,0.80509067,0.000063337204,0.00494619,0.1579719,0.012185909],"study_design_scores_gemma":[0.0008014841,0.0011783297,0.00077955204,0.000008856402,0.00002203968,0.0002456836,0.007590658,0.90488946,0.000028302646,0.0011213985,0.08319268,0.0001415436],"about_ca_topic_score_codex":0.0010522173,"about_ca_topic_score_gemma":0.012373577,"teacher_disagreement_score":0.44631508,"about_ca_system_score_codex":0.00012165455,"about_ca_system_score_gemma":0.00039568148,"threshold_uncertainty_score":0.99943393},"labels":[],"label_agreement":null},{"id":"W2791378733","doi":"10.1016/j.crma.2018.03.010","title":"Expected Shortfall and optimal hedging payoff","year":2018,"lang":"en","type":"article","venue":"Comptes Rendus Mathématique","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Université de Rennes 1; Université du Québec à Montréal; HSBC Bank USA","keywords":"Stochastic game; Mathematics; Mathematical economics; Minimisation (clinical trials); Random variable; Statistics","score_opus":0.06769237690367078,"score_gpt":0.36819495605711794,"score_spread":0.30050257915344714,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2791378733","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":[0.90168077,0.0006287094,0.08033122,0.0004894314,0.00045096767,0.00021867876,0.000009998164,0.00029108804,0.01589913],"genre_scores_gemma":[0.9718507,0.00033265346,0.026121968,0.00020582406,0.00026982508,0.000014750123,0.000010128722,0.000028662265,0.0011654734],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978811,0.00016532368,0.00060064154,0.00048640638,0.0005747996,0.00029172515],"domain_scores_gemma":[0.998247,0.00042717616,0.00023306134,0.00056885916,0.00035532514,0.0001685377],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0009300947,0.00019165508,0.00031974295,0.0002680564,0.0002665728,0.00040185612,0.00044258425,0.00012267433,0.0010088389],"category_scores_gemma":[0.0006248842,0.00014616486,0.0000607333,0.0005654397,0.00022085616,0.0003741529,0.0001966192,0.00014991868,0.0006934807],"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":[0.00020416285,0.000335388,0.15264526,0.000024276216,0.0001289753,0.00010610079,0.018288432,0.0028657285,0.0045892363,0.032130957,0.46444085,0.32424065],"study_design_scores_gemma":[0.0016562799,0.0007361375,0.18205869,0.00017481529,0.00006796552,0.00040641232,0.003741589,0.466858,0.011098037,0.03738419,0.29408258,0.0017352841],"about_ca_topic_score_codex":0.000021664137,"about_ca_topic_score_gemma":0.000017418744,"teacher_disagreement_score":0.46399227,"about_ca_system_score_codex":0.00002071726,"about_ca_system_score_gemma":0.00004920058,"threshold_uncertainty_score":0.9999044},"labels":[],"label_agreement":null},{"id":"W2794154484","doi":"10.1007/s11228-018-0476-5","title":"Computation of the Epsilon-Subdifferential of Convex Piecewise Linear-Quadratic Functions in Optimal Worst-Case Time","year":2018,"lang":"en","type":"article","venue":"Set-Valued and Variational Analysis","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Okanagan University College; University of British Columbia, Okanagan Campus","funders":"British Columbia Knowledge Development Fund; Natural Sciences and Engineering Research Council of Canada; Canada Foundation for Innovation","keywords":"Proper convex function; Convex analysis; Convex combination; Piecewise linear function; Regular polygon; Logarithmically convex function; Convex function; Convex optimization; Subderivative","score_opus":0.043630747610200964,"score_gpt":0.344245910834639,"score_spread":0.30061516322443804,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2794154484","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":[0.7759997,0.000032881144,0.22314283,0.00022198296,0.00012344054,0.000120966724,0.000092304945,0.000006366352,0.00025947127],"genre_scores_gemma":[0.9941654,0.000008184026,0.0052448628,0.000033749642,0.000067472836,0.0000040270875,0.000079615354,0.0000047800713,0.00039186142],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976948,0.00032731882,0.00083988067,0.00029214687,0.0007280077,0.00011786691],"domain_scores_gemma":[0.9980737,0.00040933522,0.0005460565,0.00025188734,0.00066478556,0.000054247048],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001162245,0.00011467197,0.00037752299,0.0005969274,0.00018297049,0.000049148708,0.00017034497,0.000079391495,0.00060452556],"category_scores_gemma":[0.00047373437,0.00007658062,0.00019004024,0.003023329,0.00019177934,0.00019074572,0.00006790965,0.00007822739,0.000026409638],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00029774898,0.00043746622,0.2720245,0.00001137738,0.0009888365,0.0000073861174,0.004002972,0.70715004,0.0006549712,0.007366475,0.00075002614,0.006308158],"study_design_scores_gemma":[0.00036490377,0.000049276365,0.15369265,0.0000068587456,0.00038867505,0.000005400924,0.0001908617,0.84217906,0.00007566496,0.0029505442,0.000023593158,0.000072501476],"about_ca_topic_score_codex":0.0002335161,"about_ca_topic_score_gemma":0.00016388888,"teacher_disagreement_score":0.2181657,"about_ca_system_score_codex":0.000015828904,"about_ca_system_score_gemma":0.000089620095,"threshold_uncertainty_score":0.6619131},"labels":[],"label_agreement":null},{"id":"W2795388653","doi":"10.1287/opre.2021.2212","title":"Shortfall Risk Models When Information on Loss Function Is Incomplete","year":2022,"lang":"en","type":"article","venue":"Operations Research","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Group for Research in Decision Analysis; HEC Montréal","funders":"","keywords":"Function (biology); Complete information; Computer science; Risk analysis (engineering); Medicine; Economics; Mathematical economics","score_opus":0.27562440018042544,"score_gpt":0.44732016697909677,"score_spread":0.17169576679867132,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2795388653","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":[0.50247556,0.00013229848,0.33616924,0.01021752,0.0014268782,0.002237758,0.0007884073,0.00016586711,0.14638644],"genre_scores_gemma":[0.98610723,0.00022719608,0.0015808912,0.0006681297,0.000098352524,0.00029801353,0.00024220753,0.000012373343,0.010765606],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99383706,0.0011391698,0.00063119084,0.00036650742,0.0036946805,0.00033138512],"domain_scores_gemma":[0.9973239,0.0004302408,0.00007105441,0.0008039184,0.0012470898,0.00012384611],"candidate_categories":["sts","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0069833566,0.000110999106,0.00014642441,0.0012348868,0.0030815923,0.0009803415,0.0007029673,0.00005755437,0.0047201016],"category_scores_gemma":[0.0008143596,0.000093485265,0.00007612407,0.0018980425,0.00008666441,0.0021659953,0.00037322272,0.00066699815,0.0017511655],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009604409,0.00005772145,0.0007934774,5.067725e-7,0.000008794503,0.0000010798984,0.0025361483,0.83505857,0.000009531681,0.016900487,0.10746829,0.037069317],"study_design_scores_gemma":[0.0002447742,0.00026642627,0.000955653,0.0000014790457,0.0000036782774,0.0000033730785,0.0013077741,0.69938064,0.00002883299,0.03087864,0.2668297,0.000099054334],"about_ca_topic_score_codex":0.00089974405,"about_ca_topic_score_gemma":0.00015396629,"teacher_disagreement_score":0.48363164,"about_ca_system_score_codex":0.00018785412,"about_ca_system_score_gemma":0.0003282902,"threshold_uncertainty_score":0.99902606},"labels":[],"label_agreement":null},{"id":"W2800267686","doi":"10.32920/24088725.v1","title":"The strong Fatou property of risk measures","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Natural Sciences and Engineering Research Council of Canada","keywords":"Quasiconvex function; Converse; Mathematics; Invariant (physics); Pure mathematics; Property (philosophy); Discrete mathematics; Philosophy; Epistemology; Geometry","score_opus":0.23728120045416237,"score_gpt":0.3996413957655625,"score_spread":0.16236019531140014,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2800267686","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":[0.06772153,0.0029757598,0.65793216,0.007175129,0.013554495,0.003393817,0.00060251856,0.000825332,0.24581927],"genre_scores_gemma":[0.9063002,0.015160564,0.0035731415,0.000022993945,0.00023708389,0.000051328814,0.000020844172,0.00003722008,0.07459666],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9961919,0.00040181374,0.0008928285,0.00048081478,0.0018410375,0.00019156335],"domain_scores_gemma":[0.9957002,0.0014322656,0.00075000135,0.0012997753,0.000757244,0.000060517155],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0056024645,0.00016261384,0.0003213749,0.0001700682,0.00023179628,0.0003741737,0.0013953137,0.00017658016,0.00009695096],"category_scores_gemma":[0.0056773243,0.0000590732,0.0002197282,0.00042528793,0.00013867886,0.0000755992,0.00079960545,0.00035036795,0.0002764737],"study_design_candidate":"design_other","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":[0.00005025676,0.0000319425,0.058545258,0.0000040224813,0.00009034383,0.0000020518664,0.00084861077,0.18440661,0.0000061846235,0.0033451302,0.14133705,0.61133254],"study_design_scores_gemma":[0.000421076,0.000074022064,0.108100586,0.00009012449,0.00013912641,0.0000021894357,0.004284728,0.15924971,0.0011974537,0.3117534,0.4139629,0.000724691],"about_ca_topic_score_codex":0.0014222251,"about_ca_topic_score_gemma":0.0011471539,"teacher_disagreement_score":0.83857864,"about_ca_system_score_codex":0.000015967547,"about_ca_system_score_gemma":0.00021811611,"threshold_uncertainty_score":0.6796698},"labels":[],"label_agreement":null},{"id":"W2806795684","doi":"10.29252/iors.8.1.78","title":"Event-driven and Attribute-driven Robustness","year":2017,"lang":"en","type":"article","venue":"Iranian Journal of Operations Research","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":2,"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 Tabriz; University of Toronto; Carnegie Mellon University","keywords":"Robustness (evolution); Robust optimization; Polytope; Computer science; Mathematical optimization; Conservatism; Event (particle physics); Regular polygon; Convex optimization; Artificial intelligence; Mathematics","score_opus":0.3481642263916119,"score_gpt":0.5292341026410323,"score_spread":0.18106987624942045,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2806795684","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":[0.94032156,0.0002535061,0.048993364,0.00845139,0.00039091124,0.00021986797,0.00001935472,0.000004758777,0.0013452641],"genre_scores_gemma":[0.99043185,0.0007702481,0.0058887084,0.000018147182,0.00030870474,0.0000032185085,0.000001762698,0.000010280841,0.0025671008],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9963642,0.0005468809,0.00073473115,0.00022638138,0.001851822,0.0002760211],"domain_scores_gemma":[0.99521303,0.00034896896,0.00022054843,0.00067986577,0.003267581,0.00027002094],"candidate_categories":["sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.005917624,0.00009360477,0.00026708588,0.00062629435,0.0019965626,0.0024587254,0.0013139998,0.00008274015,0.0003173137],"category_scores_gemma":[0.0051647555,0.00006572589,0.00008978359,0.00036976387,0.00034803426,0.0016087872,0.00020801765,0.0004571516,0.000075312215],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012352849,0.00017865116,0.16379324,0.0000047381213,0.00006809974,0.00025595922,0.0020863607,0.7561237,0.0019031496,0.0024973,0.01866356,0.054301705],"study_design_scores_gemma":[0.003168793,0.000964093,0.3471974,0.00016693897,0.000039744788,0.0007787315,0.0031151902,0.6009273,0.00092605647,0.00177552,0.040513758,0.0004264719],"about_ca_topic_score_codex":0.000036462996,"about_ca_topic_score_gemma":0.00021622666,"teacher_disagreement_score":0.18340416,"about_ca_system_score_codex":0.000041753632,"about_ca_system_score_gemma":0.0003750306,"threshold_uncertainty_score":0.9993027},"labels":[],"label_agreement":null},{"id":"W2809417058","doi":"10.1007/s10479-018-2942-8","title":"Reduced cost-based variable fixing in two-stage stochastic programming","year":2018,"lang":"en","type":"article","venue":"Annals of Operations Research","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"École de Technologie Supérieure; Transport Canada","funders":"","keywords":"Mathematical optimization; Stochastic programming; Theory of computation; Variable (mathematics); Heuristic; Set (abstract data type); Random variable; Range (aeronautics); Integer programming; Computer science; Reduction (mathematics); Measure (data warehouse); Mathematics; Linear programming; Algorithm; Statistics","score_opus":0.4932199997891655,"score_gpt":0.5907253176075067,"score_spread":0.09750531781834121,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2809417058","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":[0.7556957,0.00014762924,0.22348635,0.004352487,0.00025022423,0.0018686678,0.00004842911,0.000042590786,0.014107914],"genre_scores_gemma":[0.98335403,0.00001780344,0.013043066,0.00009335712,0.00010116376,0.00010970652,0.000021215154,0.000011675816,0.00324796],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99636674,0.00059725175,0.0006638594,0.0003922771,0.0015126606,0.00046723022],"domain_scores_gemma":[0.99495137,0.0008744307,0.000059062633,0.0005930291,0.0033983495,0.00012378691],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010127509,0.00009094182,0.00020233633,0.0011449666,0.00045153694,0.00044772672,0.00056253787,0.000055588607,0.0007745823],"category_scores_gemma":[0.0075937603,0.000075458796,0.000044200206,0.0036338773,0.00030665094,0.000506915,0.00010077439,0.00023763074,0.0001796413],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012159502,0.00029562958,0.0019689451,0.0000050292188,0.000009979002,0.000005616002,0.0012364339,0.9363845,0.005015999,0.009115976,0.004287682,0.04155263],"study_design_scores_gemma":[0.0006511521,0.00033981373,0.0014847236,0.000073075396,0.0000021537664,0.0000014401908,0.0010044118,0.9636806,0.015166704,0.0015139154,0.015910808,0.00017116261],"about_ca_topic_score_codex":0.0011801097,"about_ca_topic_score_gemma":0.0010303559,"teacher_disagreement_score":0.22765835,"about_ca_system_score_codex":0.000024113286,"about_ca_system_score_gemma":0.00065829506,"threshold_uncertainty_score":0.90909886},"labels":[],"label_agreement":null},{"id":"W2884230765","doi":"10.1080/1350486x.2018.1492347","title":"Optimal Expected-Shortfall Portfolio Selection with Copula-Induced Dependence","year":2018,"lang":"en","type":"article","venue":"Applied Mathematical Finance","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Concordia University","funders":"","keywords":"Copula (linguistics); Expected shortfall; Portfolio; Selection (genetic algorithm); Econometrics; Economics; Computer science; Financial economics; Artificial intelligence","score_opus":0.048219501297631306,"score_gpt":0.33134095545198894,"score_spread":0.28312145415435763,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2884230765","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":[0.5816692,0.000018032919,0.3436825,0.00006285672,0.00009310394,0.00040402915,0.000002487625,0.00012927713,0.073938526],"genre_scores_gemma":[0.9091823,0.000038983137,0.088799946,0.00010236379,0.00020096233,0.00008975581,0.0000033966753,0.000030329973,0.0015519259],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9965303,0.00004479837,0.0007651937,0.00079177856,0.001391779,0.00047617577],"domain_scores_gemma":[0.9980146,0.00031518732,0.00036038065,0.00071779714,0.00045530664,0.00013667325],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00097118295,0.00027941464,0.0004527229,0.00018925054,0.00029345852,0.00025524898,0.00060982787,0.00017191206,0.001019853],"category_scores_gemma":[0.0003660723,0.00019559021,0.000068785565,0.0014843103,0.00021507898,0.00032107762,0.00009518126,0.0002132075,0.0017971363],"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":[0.0007819152,0.00075579307,0.0048071304,0.000019896193,0.00007384402,0.00006086205,0.002233327,0.0047136536,0.0061574965,0.8446864,0.020591836,0.115117855],"study_design_scores_gemma":[0.004295914,0.0030384876,0.02798304,0.0003142846,0.00025477647,0.0009871688,0.002278234,0.12973887,0.121680625,0.65217316,0.053313263,0.003942164],"about_ca_topic_score_codex":0.0000068152567,"about_ca_topic_score_gemma":0.000011425917,"teacher_disagreement_score":0.32751313,"about_ca_system_score_codex":0.000042905627,"about_ca_system_score_gemma":0.00011944882,"threshold_uncertainty_score":0.99989337},"labels":[],"label_agreement":null},{"id":"W2885501161","doi":"10.1051/ps/2018015","title":"A consistent estimator to the orthant-based tail value-at-risk","year":2018,"lang":"en","type":"article","venue":"ESAIM Probability and Statistics","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Concordia University; McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Orthant; Mathematics; Bivariate analysis; Estimator; Consistency (knowledge bases); Statistics; Dimension (graph theory); Applied mathematics; Combinatorics; Discrete mathematics","score_opus":0.06705301575537304,"score_gpt":0.34645768058892873,"score_spread":0.2794046648335557,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2885501161","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":[0.16073088,0.00006828765,0.8339866,0.0021196615,0.0004613686,0.0006510338,0.0008136411,0.000043094624,0.0011254741],"genre_scores_gemma":[0.61616194,0.00004388266,0.38134986,0.0010141014,0.00011963262,0.0000415387,0.000026446136,0.000014246459,0.0012283403],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9973728,0.00041045062,0.00060452893,0.0005200356,0.00082236936,0.00026977996],"domain_scores_gemma":[0.9963814,0.0017765595,0.00023636062,0.00074751844,0.0006390864,0.00021905662],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0032168606,0.00015907922,0.0002426573,0.000075488526,0.00078769505,0.0002486122,0.00033699116,0.000069830065,0.00035881755],"category_scores_gemma":[0.007976317,0.000095318166,0.000047937712,0.00045642987,0.00053301203,0.00007644321,0.00014428682,0.000114780836,0.00041998678],"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":[0.00068374263,0.00035402962,0.31649777,0.00003170331,0.000054788037,0.000018427305,0.003953004,0.024507938,0.000025144427,0.1447166,0.15337034,0.3557865],"study_design_scores_gemma":[0.0007343411,0.000782083,0.10981377,0.000019181,0.0000899569,0.000014299855,0.00024268783,0.4225845,0.00021660134,0.21469966,0.25039142,0.0004114858],"about_ca_topic_score_codex":0.00014997655,"about_ca_topic_score_gemma":0.0012825975,"teacher_disagreement_score":0.45543107,"about_ca_system_score_codex":0.000048995284,"about_ca_system_score_gemma":0.00017654413,"threshold_uncertainty_score":0.9548972},"labels":[],"label_agreement":null},{"id":"W2887644249","doi":"10.1287/ijoc.2020.1042","title":"The Value of Randomized Solutions in Mixed-Integer Distributionally Robust Optimization Problems","year":2021,"lang":"en","type":"article","venue":"INFORMS journal on computing","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":12,"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; Group for Research in Decision Analysis; HEC Montréal","funders":"","keywords":"Dice; Integer (computer science); Randomized response; Randomization; Outcome (game theory); Mathematical optimization; Integer programming; Value (mathematics); Process (computing); Randomized controlled trial; Computer science; Mathematics; Mathematical economics; Statistics; Medicine","score_opus":0.051763821103933606,"score_gpt":0.3192376499714898,"score_spread":0.26747382886755616,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2887644249","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":[0.017364053,0.00034549233,0.9756995,0.001497833,0.00093644235,0.00020097406,0.000005827354,0.000013068122,0.0039368533],"genre_scores_gemma":[0.98429984,0.0009714343,0.014034019,0.00014832547,0.00017824746,0.000003935919,0.000025061556,0.000010009677,0.00032913304],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.995884,0.00044344176,0.001975451,0.00016978027,0.0012228034,0.00030449848],"domain_scores_gemma":[0.9937569,0.0032933992,0.0011771648,0.00025086128,0.0014346765,0.000086982545],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.01100114,0.0001290886,0.00044879623,0.00025433837,0.00063714525,0.0005469491,0.0004222618,0.00007770119,0.00004912494],"category_scores_gemma":[0.0088215,0.00007143986,0.000295506,0.001151414,0.00013138099,0.00043696558,0.000121411846,0.00038520005,0.000014405893],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004467857,0.000038251103,0.00082777464,0.0000010514545,0.000022909197,0.00000540272,0.0001976595,0.96377784,0.0000025669378,0.01978158,0.0005048738,0.014393309],"study_design_scores_gemma":[0.008837906,0.000026211292,0.001252246,0.00010447443,0.000011307596,0.000110411755,0.00033790324,0.97822094,0.0000375835,0.009122648,0.0018435937,0.00009479886],"about_ca_topic_score_codex":0.000009049724,"about_ca_topic_score_gemma":0.000012689968,"teacher_disagreement_score":0.96693575,"about_ca_system_score_codex":0.000094069386,"about_ca_system_score_gemma":0.0003854561,"threshold_uncertainty_score":0.99952763},"labels":[],"label_agreement":null},{"id":"W2888369039","doi":"10.48550/arxiv.1808.07339","title":"Scenario-based Risk Evaluation","year":2018,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Risk analysis (engineering); Business","score_opus":0.26504338815592554,"score_gpt":0.29310400605878445,"score_spread":0.02806061790285891,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2888369039","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":[0.6446422,0.0000572261,0.34072152,0.00010823385,0.0014357348,0.000570635,0.00007678521,0.00011547167,0.012272199],"genre_scores_gemma":[0.9957243,0.0002583749,0.0010119027,0.000080086684,0.00020788018,0.0000015100218,0.000099574936,0.000021822105,0.002594554],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99604696,0.0009915422,0.00048237748,0.0014359334,0.0007387496,0.00030445238],"domain_scores_gemma":[0.9946909,0.00047805437,0.0010350842,0.0018199469,0.0017795041,0.00019649799],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0048060566,0.0003089921,0.0003884259,0.0007333928,0.00033756005,0.0002844347,0.0014072323,0.0004583964,0.0016904706],"category_scores_gemma":[0.0018088574,0.0002948952,0.00032545757,0.001279235,0.00022691458,0.00030547948,0.00053160684,0.00047802567,0.0012308044],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007844529,0.00005724352,0.060189817,0.0000028898403,0.00003048656,0.000017106328,0.00008970384,0.92647743,0.000001363589,0.0013965219,0.006724954,0.0049340515],"study_design_scores_gemma":[0.0005699029,0.000049235034,0.0116454195,0.0000217624,0.00021301385,5.250349e-7,0.00007656482,0.87592804,0.0000462288,0.1052472,0.0058870628,0.00031505976],"about_ca_topic_score_codex":0.00026160112,"about_ca_topic_score_gemma":0.00018554993,"teacher_disagreement_score":0.35108212,"about_ca_system_score_codex":0.00025663487,"about_ca_system_score_gemma":0.000755044,"threshold_uncertainty_score":0.9999503},"labels":[],"label_agreement":null},{"id":"W2888836939","doi":"10.1080/00207543.2018.1508901","title":"Optimising data-driven network under limited resource: a partial diversification approach","year":2018,"lang":"en","type":"article","venue":"International Journal of Production Research","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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 Toronto","funders":"Fundamental Research Funds for the Central Universities; Marcus och Amalia Wallenbergs minnesfond; National Natural Science Foundation of China","keywords":"Diversification (marketing strategy); Mathematical optimization; Cardinality (data modeling); Computer science; Linear programming; Integer programming; Dual (grammatical number); Database transaction; Operations research; Data mining; Engineering; Mathematics; Business; Database","score_opus":0.4759175268010841,"score_gpt":0.5129229023720254,"score_spread":0.0370053755709413,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2888836939","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":[0.34638813,0.0005521802,0.57278496,0.04778309,0.012652686,0.0008179881,0.000045410645,0.000079930236,0.018895615],"genre_scores_gemma":[0.960306,0.00034062486,0.025505405,0.0001139872,0.011519273,0.0000028025124,0.00007077218,0.000016848182,0.0021242723],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99204177,0.00090390106,0.0009553256,0.0005433795,0.0052446034,0.00031103176],"domain_scores_gemma":[0.9887257,0.00044128802,0.0007140629,0.0008184994,0.009144466,0.00015595736],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01578752,0.000105760846,0.00019237107,0.0011414081,0.0004246127,0.0007178529,0.0026872926,0.00009518102,0.0002909051],"category_scores_gemma":[0.0063378797,0.000082226565,0.000081479455,0.0017136803,0.00040340945,0.0014247595,0.000561925,0.00049667293,0.00015407609],"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":[0.001276123,0.00037707353,0.00908806,0.0000019543158,0.00024621768,0.000018370922,0.0014092537,0.40557757,0.0013520211,0.0025536476,0.48965418,0.08844555],"study_design_scores_gemma":[0.0012810093,0.00049258827,0.017052393,0.00009444128,0.00005126295,0.00056618254,0.004209689,0.24606101,0.0026684194,0.013632924,0.7135491,0.00034099765],"about_ca_topic_score_codex":0.000021350608,"about_ca_topic_score_gemma":0.0000045885886,"teacher_disagreement_score":0.6139179,"about_ca_system_score_codex":0.00016279225,"about_ca_system_score_gemma":0.00028974228,"threshold_uncertainty_score":0.7587491},"labels":[],"label_agreement":null},{"id":"W2893895554","doi":"10.1287/ijoc.2017.0795","title":"Risk Averse Shortest Paths: A Computational Study","year":2018,"lang":"en","type":"article","venue":"INFORMS journal on computing","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Shortest path problem; Measure (data warehouse); Mathematical optimization; Risk measure; Variance (accounting); Mathematics; Path (computing); Regular polygon; Implementation; Arc length; Computer science; Arc (geometry); Discrete mathematics; Data mining","score_opus":0.062020384140820846,"score_gpt":0.3816068146451262,"score_spread":0.31958643050430535,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2893895554","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":[0.8335867,0.000007752144,0.15694864,0.00007100275,0.0010726671,0.00016258925,0.0000042165516,0.00004215604,0.008104298],"genre_scores_gemma":[0.9916256,0.000012787957,0.0067544375,0.00041470432,0.0010052499,5.299928e-7,0.0000022222616,0.000011247747,0.00017321113],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99622357,0.00016418644,0.0012046294,0.0002611737,0.0018367907,0.00030966193],"domain_scores_gemma":[0.9964309,0.00092627417,0.001022829,0.00029024135,0.001114991,0.00021477258],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0050499886,0.00017448326,0.0002643659,0.0005262489,0.0010904439,0.00092512596,0.0005940347,0.000058059963,0.00027569625],"category_scores_gemma":[0.0017527763,0.00011354597,0.00013542209,0.00085046544,0.00009037745,0.00064502825,0.00012644342,0.00044823973,0.0011316943],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000060062823,0.00015468328,0.25434434,1.7835713e-7,0.00003346779,0.000041307463,0.0038370397,0.40436798,4.266183e-7,0.00021250303,0.004391072,0.33255693],"study_design_scores_gemma":[0.0012482194,0.0015450355,0.27014598,0.000028384178,0.000020273985,0.00026417495,0.0034943246,0.6970125,0.0000061602614,0.009608698,0.016360907,0.0002653499],"about_ca_topic_score_codex":0.000014637347,"about_ca_topic_score_gemma":0.000010896799,"teacher_disagreement_score":0.33229157,"about_ca_system_score_codex":0.000062083156,"about_ca_system_score_gemma":0.00014445247,"threshold_uncertainty_score":0.99964607},"labels":[],"label_agreement":null},{"id":"W2901514607","doi":"10.1287/opre.2018.1736","title":"Technical Note—Closed-Form Solutions for Worst-Case Law Invariant Risk Measures with Application to Robust Portfolio Optimization","year":2018,"lang":"en","type":"article","venue":"Operations Research","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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 Ottawa","funders":"","keywords":"CVAR; Spectral risk measure; Portfolio; Value at risk; Expected shortfall; Invariant (physics); Mathematics; Risk measure; Portfolio optimization; Dynamic risk measure; Stochastic dominance; Coherent risk measure; Class (philosophy); Mathematical optimization; Risk management; Computer science; Econometrics; Economics","score_opus":0.17700525950768378,"score_gpt":0.43664414526065204,"score_spread":0.25963888575296823,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2901514607","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":[0.0019116868,0.000028615787,0.9825305,0.0020874054,0.000102823986,0.0026658175,0.00015130617,0.00009711692,0.010424764],"genre_scores_gemma":[0.6756152,0.000116823976,0.32066527,0.00015059202,0.00041805106,0.0012784592,0.00013663255,0.000040486433,0.0015785061],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9950567,0.00047914995,0.00082867587,0.00092361006,0.0019551085,0.00075677637],"domain_scores_gemma":[0.9916604,0.00073333614,0.00011023739,0.0013024773,0.0058028586,0.00039070132],"candidate_categories":["sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.009720248,0.00021291751,0.00027577,0.0009690037,0.0043383483,0.0011725571,0.000731909,0.0002207106,0.00026287773],"category_scores_gemma":[0.0037659886,0.0001588192,0.000085606094,0.004006883,0.0005199913,0.00093990745,0.00023870605,0.00037253456,0.0004025499],"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":[0.00016866293,0.00017157747,0.000115476345,0.0000013183669,0.0000102368895,0.000009333,0.00039042076,0.91305727,0.00038370615,0.04519045,0.010235841,0.030265722],"study_design_scores_gemma":[0.00059950975,0.00080423674,0.00019134306,0.0000152437015,0.00003086933,0.00016833095,0.00049168454,0.95146906,0.000627646,0.0020545954,0.043258335,0.00028911344],"about_ca_topic_score_codex":0.0019746984,"about_ca_topic_score_gemma":0.027347472,"teacher_disagreement_score":0.6737035,"about_ca_system_score_codex":0.00021747101,"about_ca_system_score_gemma":0.00057312055,"threshold_uncertainty_score":0.99986434},"labels":[],"label_agreement":null},{"id":"W2902988089","doi":"10.1016/j.insmatheco.2020.06.007","title":"Comparative risk aversion in RDEU with applications to optimal underwriting of securities issuance","year":2020,"lang":"en","type":"article","venue":"Insurance Mathematics and Economics","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Research Grants Council, University Grants Committee","keywords":"Underwriting; Risk aversion (psychology); Business; Actuarial science; Investment banking; Equity (law); Finance; Economics; Financial economics; Expected utility hypothesis","score_opus":0.08030331701863773,"score_gpt":0.32464923649030153,"score_spread":0.24434591947166379,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2902988089","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":[0.9218388,0.00009382901,0.0755149,0.0002540166,0.000010772466,0.00024368639,0.000061405626,0.0000075259554,0.0019750441],"genre_scores_gemma":[0.9356615,0.000631541,0.06357024,0.00007591061,0.000019748099,0.000017191505,0.0000019669683,0.0000057733687,0.000016157872],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990991,0.000016350414,0.00046574578,0.00021495776,0.000104383274,0.000099490186],"domain_scores_gemma":[0.9990924,0.00029372337,0.0003045042,0.00015420055,0.00008372212,0.00007144082],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033441544,0.00008713275,0.00030915657,0.00007903021,0.00006723748,0.00007363836,0.00016924484,0.00003041173,0.000011390305],"category_scores_gemma":[0.00006990473,0.000072619354,0.000022512097,0.00026357823,0.000062666644,0.00017838374,0.00004733493,0.00006545167,0.00001915032],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012380785,0.00012908205,0.07428908,0.00007416848,0.000028777662,0.0000014933808,0.0493868,0.83251894,0.00006454516,0.030888233,0.00026480615,0.012230293],"study_design_scores_gemma":[0.0011563251,0.00029453856,0.021563124,0.0001366754,0.00001738796,0.000008351301,0.041103702,0.8966282,0.0016242996,0.030828753,0.0061660158,0.00047262028],"about_ca_topic_score_codex":0.000014007386,"about_ca_topic_score_gemma":0.000048340786,"teacher_disagreement_score":0.06410929,"about_ca_system_score_codex":0.000011056047,"about_ca_system_score_gemma":0.0000231694,"threshold_uncertainty_score":0.29613313},"labels":[],"label_agreement":null},{"id":"W2905414953","doi":"10.1016/j.ejor.2019.09.019","title":"Weak Comonotonicity","year":2018,"lang":"en","type":"preprint","venue":"European Journal of Operational Research","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Western University; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Diversification (marketing strategy); Value at risk; Portfolio; Maximization; Value (mathematics); Economics; Expected shortfall; Econometrics; Actuarial science; Microeconomics; Mathematics; Financial economics; Risk management; Finance; Business; Statistics","score_opus":0.34232166411899717,"score_gpt":0.501639199687339,"score_spread":0.15931753556834188,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2905414953","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":[0.3859078,0.0015735065,0.07311012,0.013629269,0.0046446635,0.00072704005,0.00010809893,0.000025394358,0.5202741],"genre_scores_gemma":[0.9622833,0.0017745752,0.01478742,0.00017572664,0.0039074966,0.0000026176708,0.000028644985,0.00005094465,0.016989276],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9841085,0.006749585,0.0016547802,0.0004796904,0.006655914,0.00035153455],"domain_scores_gemma":[0.984849,0.0013921653,0.0008311532,0.00076076004,0.011908638,0.00025832717],"candidate_categories":["metaresearch","scholarly_communication","insufficient_payload"],"consensus_categories":["metaresearch","insufficient_payload"],"category_scores_codex":[0.05867273,0.00019346381,0.0004332248,0.0012292225,0.00046809527,0.0017213706,0.002775423,0.000094236355,0.0017542047],"category_scores_gemma":[0.011729927,0.00013490068,0.0002801405,0.00070333976,0.00041512636,0.00041649753,0.0014493007,0.00202104,0.0017806866],"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":[0.0002850741,0.0001801173,0.0036935995,0.0000058901796,0.00009067678,0.00028828852,0.00077994703,0.04754472,0.00020195507,0.0040612775,0.90744305,0.035425417],"study_design_scores_gemma":[0.0007696106,0.0008168529,0.037923668,0.00019761038,0.000017703298,0.00019997443,0.00028060487,0.016626105,0.0004550062,0.020287963,0.9220839,0.0003409625],"about_ca_topic_score_codex":0.0000067362103,"about_ca_topic_score_gemma":0.0000040636673,"teacher_disagreement_score":0.5763755,"about_ca_system_score_codex":0.00013408049,"about_ca_system_score_gemma":0.0016339882,"threshold_uncertainty_score":0.99931496},"labels":[],"label_agreement":null},{"id":"W2910908669","doi":"10.1111/mafi.12211","title":"An efficient approach to quantile capital allocation and sensitivity analysis","year":2019,"lang":"en","type":"article","venue":"Mathematical Finance","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Simons Foundation","keywords":"Quantile; Estimator; Nonparametric statistics; Value at risk; Econometrics; Capital allocation line; Expected shortfall; Random variable; Mathematics; Quantile function; Statistics; Economics; Risk management; Finance","score_opus":0.03259980768883214,"score_gpt":0.3292719535883797,"score_spread":0.29667214589954755,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2910908669","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":[0.5905118,0.000008849925,0.40666288,0.000074762705,0.000023553626,0.00015290259,0.0000044093904,0.000014528854,0.0025462864],"genre_scores_gemma":[0.9645604,0.0000074933596,0.03467958,0.00005996657,0.000014095666,0.000008328327,0.000006989666,0.0000057684947,0.00065738463],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983219,0.00011117824,0.00034387453,0.00046201787,0.0005951675,0.00016584819],"domain_scores_gemma":[0.9987766,0.0003118321,0.00009856385,0.00058456353,0.0001375768,0.000090853144],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017388939,0.00010169053,0.00030991243,0.00020024176,0.00006657422,0.00016020704,0.0001267933,0.00005454905,0.00004819766],"category_scores_gemma":[0.0003967491,0.00007428689,0.00006530273,0.001164825,0.000038376966,0.00014813202,0.000048482387,0.00005424472,0.00058484665],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026012845,0.000493515,0.011942182,0.000010523973,0.000030206784,0.0000029343148,0.0034414134,0.78157586,0.00050463283,0.19236486,0.00020130604,0.009406566],"study_design_scores_gemma":[0.00007343121,0.000040266197,0.05054746,0.000003992884,0.000029669538,0.0000054392312,0.00023818604,0.94377434,0.00014687688,0.0048159496,0.00021020415,0.00011418561],"about_ca_topic_score_codex":0.000012729654,"about_ca_topic_score_gemma":0.0000041294106,"teacher_disagreement_score":0.37404856,"about_ca_system_score_codex":0.000012280466,"about_ca_system_score_gemma":0.000015008917,"threshold_uncertainty_score":0.7517216},"labels":[],"label_agreement":null},{"id":"W2911474331","doi":"10.1109/cdc.2018.8619684","title":"Robustness to Incorrect System Models in Stochastic Control and Application to Data-Driven Learning","year":2018,"lang":"en","type":"article","venue":"","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Queen's University","funders":"","keywords":"Robustness (evolution); Mathematical optimization; Convergence (economics); Robust control; Computer science; Control theory (sociology); Mathematics; Optimal control; Control system; Control (management); Engineering","score_opus":0.05750930063175896,"score_gpt":0.3500750968973967,"score_spread":0.2925657962656377,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2911474331","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":[0.050347477,0.000007377763,0.9465363,0.00033124568,0.00011820808,0.00053226034,0.0000077906125,0.000051339957,0.002068],"genre_scores_gemma":[0.99008155,0.0000019547774,0.009223699,0.00016264766,0.00008931818,0.0000429448,0.000009425907,0.000008433468,0.00038002554],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998331,0.0001264891,0.00040038064,0.0005348828,0.00044039395,0.00016685715],"domain_scores_gemma":[0.99862564,0.0003177836,0.00009603783,0.0005662535,0.0002516546,0.000142647],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016423531,0.0000924612,0.00021452429,0.00033413572,0.00011496808,0.00017111024,0.0004956447,0.000049826926,0.00001755514],"category_scores_gemma":[0.0005240864,0.000070175854,0.000010581181,0.0008337702,0.000025149779,0.00042425178,0.00019614008,0.000062050094,0.00015392031],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000040385083,0.000006605472,0.0010570667,7.4399316e-7,0.0000017160394,3.8550002e-7,0.00032687353,0.9648618,0.000029717943,0.0005907943,0.0010085639,0.032075312],"study_design_scores_gemma":[0.00024131127,0.00006385157,0.0010883851,0.000014373375,0.000004833153,0.000003781601,0.00072894315,0.99707717,0.0000050771378,0.00011743652,0.0005597046,0.000095114236],"about_ca_topic_score_codex":0.00026962438,"about_ca_topic_score_gemma":0.00064462726,"teacher_disagreement_score":0.9397341,"about_ca_system_score_codex":0.000035135443,"about_ca_system_score_gemma":0.0000340326,"threshold_uncertainty_score":0.28616884},"labels":[],"label_agreement":null},{"id":"W2911799554","doi":"10.1080/01605682.2018.1507424","title":"A robust optimization approach to model supply and demand uncertainties in inventory systems","year":2019,"lang":"en","type":"article","venue":"Journal of the Operational Research Society","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"McMaster University","funders":"","keywords":"Mathematical optimization; Robust optimization; Computer science; Operations research; Tree (set theory); Robustness (evolution); Scheduling (production processes); Mathematics","score_opus":0.1968097072148363,"score_gpt":0.39835964848768435,"score_spread":0.20154994127284806,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2911799554","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":[0.6693227,0.0009888755,0.3184037,0.005402572,0.00044311024,0.0010847837,0.000013714748,0.000004561955,0.0043359473],"genre_scores_gemma":[0.94906324,0.00077911484,0.04226471,0.00023457722,0.00013560602,0.000013583424,0.000003009558,0.00001044803,0.0074957088],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99600744,0.00053070893,0.00059434754,0.00019763916,0.0024732875,0.00019658609],"domain_scores_gemma":[0.9975486,0.00039828743,0.0001732808,0.00021939514,0.0015428155,0.00011761203],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.011302592,0.0000786552,0.00020146427,0.00021987267,0.0002374305,0.0005451718,0.0005494603,0.000080865786,0.000024824772],"category_scores_gemma":[0.0010442677,0.00004511959,0.00010883755,0.00090006203,0.0000938938,0.0006566101,0.0001752545,0.0003673294,0.000005623589],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003355177,0.000039974784,0.016971536,0.000004825802,0.000010657476,1.9080018e-7,0.0018921166,0.96359134,0.000093399125,0.0020017107,0.015263941,0.00009675487],"study_design_scores_gemma":[0.00033657617,0.000044888955,0.00195678,0.000035165598,0.0000025108543,0.00001281222,0.0026725982,0.9933883,0.0000124580265,0.0007268415,0.0007577427,0.000053340547],"about_ca_topic_score_codex":0.000039181097,"about_ca_topic_score_gemma":0.0000054252673,"teacher_disagreement_score":0.27974048,"about_ca_system_score_codex":0.00017158691,"about_ca_system_score_gemma":0.00056709285,"threshold_uncertainty_score":0.5257104},"labels":[],"label_agreement":null},{"id":"W2913120741","doi":"10.1109/cdc.2018.8618733","title":"Robustness to Incorrect Priors in Infinite Horizon Stochastic Control","year":2018,"lang":"en","type":"article","venue":"","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Queen's University","funders":"","keywords":"Robustness (evolution); Prior probability; Mathematical optimization; Convergence (economics); Optimal control; Computer science; Weak convergence; Mathematics; Artificial intelligence; Bayesian probability; Economics","score_opus":0.04341690640399378,"score_gpt":0.35583541509118016,"score_spread":0.3124185086871864,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2913120741","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":[0.26541427,0.0000062383656,0.7212351,0.0005098371,0.000913412,0.00027610478,0.000002882134,0.000037488386,0.011604645],"genre_scores_gemma":[0.99292696,0.0000037273896,0.0035259684,0.0004888026,0.0001754327,0.000016410298,0.0000013363422,0.000009288709,0.0028521072],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99803805,0.00012604406,0.0005353145,0.00037597038,0.00066661206,0.00025800342],"domain_scores_gemma":[0.9983744,0.0006009142,0.00010956104,0.00040718255,0.00036251743,0.0001453951],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0016512866,0.00011884457,0.00024222044,0.0005994787,0.0000817743,0.00016527515,0.0004209331,0.00007561653,0.0006803391],"category_scores_gemma":[0.002282495,0.00008401653,0.000041956988,0.0017251988,0.000061505976,0.00027907323,0.00006548516,0.00008060467,0.0008206253],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020612948,0.00005491712,0.016045876,4.4965083e-7,0.0000051068128,0.000006645093,0.0008319258,0.819472,0.000055018845,0.0008826913,0.012733922,0.14970532],"study_design_scores_gemma":[0.0016100648,0.00095336034,0.024440905,0.000025331865,0.000010866573,0.000013287057,0.00089930056,0.95722175,0.00018574443,0.0018706908,0.012337348,0.00043134385],"about_ca_topic_score_codex":0.00013930928,"about_ca_topic_score_gemma":0.00088374154,"teacher_disagreement_score":0.72751266,"about_ca_system_score_codex":0.0000313447,"about_ca_system_score_gemma":0.00008787419,"threshold_uncertainty_score":0.9999573},"labels":[],"label_agreement":null},{"id":"W2913171886","doi":"10.1016/j.ejor.2019.01.043","title":"Almost Robust Discrete Optimization","year":2019,"lang":"en","type":"article","venue":"European Journal of Operational Research","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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 Toronto","funders":"","keywords":"Mathematical optimization; Robust optimization; Robustness (evolution); Computer science; Binary number; Linear programming; Optimization problem; Dependency (UML); Discrete optimization; Decision maker; Mathematics","score_opus":0.21660367295538616,"score_gpt":0.44467976105589285,"score_spread":0.2280760881005067,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2913171886","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":[0.20392689,0.00049747544,0.44707438,0.008316201,0.0015311888,0.00052660366,0.000021184698,0.000017907969,0.33808815],"genre_scores_gemma":[0.9540833,0.00039050094,0.028398052,0.00015612398,0.00059542025,7.1975455e-7,0.000016035901,0.000027787924,0.016332017],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99238837,0.0023413044,0.0009948725,0.00025238807,0.0037735412,0.00024954462],"domain_scores_gemma":[0.99430746,0.0008466179,0.0003114911,0.0003445707,0.0039957967,0.00019408987],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.021379866,0.00009757763,0.00019874642,0.0006959502,0.00026395303,0.0007864794,0.0009263101,0.000026438092,0.00397152],"category_scores_gemma":[0.0039022064,0.00006593292,0.00010899988,0.0010820464,0.000095644806,0.0011113732,0.00014794993,0.00045027473,0.0020031692],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009471769,0.00003325622,0.005206335,8.245473e-7,0.00001178854,0.000047485537,0.00023821801,0.96450543,0.0002720314,0.001689117,0.02299297,0.0049078334],"study_design_scores_gemma":[0.0027250508,0.0017515132,0.053968284,0.000118438875,0.000014406156,0.00044230264,0.001895357,0.63125694,0.000533245,0.0014861715,0.30535913,0.00044915196],"about_ca_topic_score_codex":0.0000024564047,"about_ca_topic_score_gemma":7.419282e-7,"teacher_disagreement_score":0.75015646,"about_ca_system_score_codex":0.000054542405,"about_ca_system_score_gemma":0.00033330725,"threshold_uncertainty_score":0.9987739},"labels":[],"label_agreement":null},{"id":"W2914448910","doi":"10.1142/s0219024920500454","title":"BEHAVIORAL PORTFOLIO CHOICE UNDER HYPERBOLIC ABSOLUTE RISK AVERSION","year":2020,"lang":"en","type":"article","venue":"International Journal of Theoretical and Applied Finance","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Western University","funders":"","keywords":"Isoelastic utility; Cumulative prospect theory; Expected utility hypothesis; Von Neumann–Morgenstern utility theorem; Prospect theory; Portfolio; Distortion function; Ambiguity aversion; Risk aversion (psychology); Subjective expected utility; Distortion (music); Spectral risk measure; Mathematical economics; Piecewise; Mathematics; Function (biology); Economics; Mathematical optimization; Modern portfolio theory; Computer science; Microeconomics; Finance; Statistics","score_opus":0.0351557163145786,"score_gpt":0.3341226036691542,"score_spread":0.2989668873545756,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2914448910","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":[0.9540732,0.0001674507,0.034282077,0.005488064,0.00050571765,0.00006575614,0.0000242381,0.000010538687,0.005382947],"genre_scores_gemma":[0.9942882,0.0017198361,0.0023531767,0.0010690702,0.0004652589,9.792202e-7,0.0000021885232,0.0000083323475,0.00009292341],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9979185,0.00004753832,0.0006368104,0.00022266488,0.0010381346,0.00013639459],"domain_scores_gemma":[0.9985108,0.00030438363,0.0005583117,0.000109166285,0.00036633282,0.0001510136],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00061659655,0.00011655471,0.00024338056,0.000111158624,0.000071789706,0.00015776753,0.00063974626,0.000076664255,0.0003288802],"category_scores_gemma":[0.00036504678,0.000081717684,0.000115323994,0.00021257804,0.000272106,0.00021444028,0.00013298517,0.00027563848,0.000065676584],"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":[0.0007622211,0.00017452499,0.0066631455,0.0000010553417,0.00005734668,0.000056767127,0.00049134716,0.011077183,0.0007109991,0.7600125,0.0076956507,0.21229723],"study_design_scores_gemma":[0.003658215,0.0005994147,0.07722503,0.000050310846,0.00014211074,0.0002579549,0.00054291124,0.02330824,0.0031523777,0.6494112,0.24106292,0.000589299],"about_ca_topic_score_codex":0.0000045245765,"about_ca_topic_score_gemma":4.998392e-7,"teacher_disagreement_score":0.23336728,"about_ca_system_score_codex":0.000019937826,"about_ca_system_score_gemma":0.00004430833,"threshold_uncertainty_score":0.36010078},"labels":[],"label_agreement":null},{"id":"W2920569591","doi":"10.5430/ijfr.v10n2p36","title":"An Extended Stochastic Goal Mixed Integer Programming for Optimal Portfolio Selection in the Amman Stock Exchange","year":2019,"lang":"en","type":"article","venue":"International Journal of Financial Research","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Portfolio; Portfolio optimization; Computer science; Mathematical optimization; Stochastic programming; Stock exchange; Post-modern portfolio theory; Modern portfolio theory; Stock (firearms); Replicating portfolio; Integer programming; Dynamic programming; Geometric Brownian motion; Econometrics; Operations research; Economics; Finance; Mathematics; Engineering","score_opus":0.11762679302112135,"score_gpt":0.47804230411607385,"score_spread":0.3604155110949525,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2920569591","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":[0.8573923,0.00009005353,0.13953312,0.00076982204,0.0011929092,0.0006959188,0.0000114050445,0.0000063858706,0.00030806096],"genre_scores_gemma":[0.9953255,0.000042325515,0.0029012207,0.0000660776,0.00097339484,0.00004452135,0.000008535646,0.000013688068,0.00062473794],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.994373,0.0004951112,0.000902217,0.00029694173,0.0035403834,0.0003923155],"domain_scores_gemma":[0.9940992,0.0012235992,0.0004590024,0.00019022818,0.0039232187,0.00010473546],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.013155635,0.00012226935,0.0002467287,0.0014570847,0.00013712618,0.00054463575,0.0016974893,0.000115360795,0.00018392803],"category_scores_gemma":[0.0046928674,0.00008131322,0.00016748576,0.0010195565,0.000087036045,0.0008934101,0.00007635734,0.0006223698,0.000036339366],"study_design_candidate":"design_other","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":[0.0013254464,0.00033649505,0.0057911123,0.0000026041191,0.000017070037,0.000039161307,0.0015066323,0.018553993,0.00044876608,0.0026872181,0.0042228955,0.9650686],"study_design_scores_gemma":[0.0044254814,0.005199483,0.7584521,0.00018210826,0.000021602644,0.00047347846,0.0011836848,0.13408418,0.0005150407,0.009883084,0.0851405,0.00043926455],"about_ca_topic_score_codex":0.000073399446,"about_ca_topic_score_gemma":0.00019394243,"teacher_disagreement_score":0.96462935,"about_ca_system_score_codex":0.00019371451,"about_ca_system_score_gemma":0.00061115786,"threshold_uncertainty_score":0.56181395},"labels":[],"label_agreement":null},{"id":"W2923640833","doi":"10.1016/j.insmatheco.2019.04.005","title":"Optimal insurance under rank-dependent expected utility","year":2019,"lang":"en","type":"article","venue":"Insurance Mathematics and Economics","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Indemnity; Schedule; Rank (graph theory); Distortion (music); Expected utility hypothesis; Actuarial science; Mathematical economics; Function (biology); Reinsurance; Moral hazard; Computer science; Economics; Incentive; Mathematics; Microeconomics; Combinatorics","score_opus":0.04933321618184101,"score_gpt":0.2991809422276545,"score_spread":0.2498477260458135,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2923640833","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":[0.9803016,0.00019558996,0.009333539,0.0001669212,0.00047669729,0.00029101982,0.00007773056,0.000038799386,0.009118097],"genre_scores_gemma":[0.9889263,0.0009981272,0.008913171,0.00012268327,0.000046583813,0.000010708801,0.0000052262617,0.000019924546,0.00095726707],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.998096,0.000036642676,0.000842811,0.00049178995,0.00027040744,0.0002623685],"domain_scores_gemma":[0.99820864,0.0004154745,0.00040717333,0.0007105675,0.00014634605,0.00011180353],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011312389,0.00019979582,0.00046834137,0.00012635149,0.00011792264,0.00033503107,0.0003980345,0.00011710395,0.00031819806],"category_scores_gemma":[0.00016643033,0.00016510741,0.00009275582,0.00019110592,0.00008182578,0.00046376165,0.00010422049,0.00012655735,0.00049135723],"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":[0.00020086768,0.00048334166,0.7244926,0.00007064123,0.00011944951,0.0000046780597,0.0047198506,0.1750855,0.00026042006,0.039253563,0.00085408747,0.054455005],"study_design_scores_gemma":[0.0027919977,0.00013092876,0.4505527,0.000059634087,0.000018638802,0.00007687709,0.0039821067,0.41474426,0.000917185,0.119154304,0.0065601403,0.0010112182],"about_ca_topic_score_codex":0.000016162812,"about_ca_topic_score_gemma":0.00001876749,"teacher_disagreement_score":0.27393988,"about_ca_system_score_codex":0.000027995044,"about_ca_system_score_gemma":0.000047268244,"threshold_uncertainty_score":0.67328846},"labels":[],"label_agreement":null},{"id":"W2935647296","doi":"10.1287/opre.2018.1835","title":"Robust Dual Dynamic Programming","year":2019,"lang":"en","type":"article","venue":"Operations Research","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"McGill University","funders":"Engineering and Physical Sciences Research Council; Imperial College London","keywords":"Mathematical optimization; Bounding overwatch; Dynamic programming; Computer science; Scheme (mathematics); Dual (grammatical number); Stylized fact; Upper and lower bounds; Robust optimization; Embedding; Algorithm; Mathematics","score_opus":0.2675650150968807,"score_gpt":0.49470294421885547,"score_spread":0.22713792912197478,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2935647296","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":[0.92422587,0.00015305664,0.023107644,0.0029440895,0.0004433815,0.0011284176,0.000012340601,0.000061797495,0.047923386],"genre_scores_gemma":[0.8991879,0.0001013352,0.013131846,0.000026652328,0.000055113716,0.00005099919,0.000031894473,0.000011518331,0.08740274],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9966334,0.0003910888,0.00034708338,0.0004067871,0.0018752762,0.00034633974],"domain_scores_gemma":[0.9976973,0.00038564004,0.00001821649,0.0006457676,0.0011484311,0.00010465326],"candidate_categories":["scholarly_communication","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0049426993,0.000070459195,0.00011980449,0.0005462215,0.0005017834,0.0011242669,0.00042847884,0.000069569964,0.002399222],"category_scores_gemma":[0.0013534529,0.00005201154,0.0000457008,0.0019926943,0.00009032916,0.0005687737,0.0001446921,0.00027150562,0.008027744],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000024651345,0.00015994527,0.017709892,0.0000026295704,0.000012871406,0.000011847226,0.0012303534,0.7045076,0.0014115921,0.013991323,0.010506132,0.2504312],"study_design_scores_gemma":[0.00030512936,0.00014815082,0.007208119,0.000008572064,0.0000019388763,0.00001269811,0.001854439,0.82019246,0.00016353141,0.00075321825,0.16920193,0.0001498294],"about_ca_topic_score_codex":0.00013449656,"about_ca_topic_score_gemma":0.0004891644,"teacher_disagreement_score":0.25028136,"about_ca_system_score_codex":0.000054975342,"about_ca_system_score_gemma":0.00024217804,"threshold_uncertainty_score":0.9999127},"labels":[],"label_agreement":null},{"id":"W2937729660","doi":"10.1016/j.insmatheco.2020.01.004","title":"Is the inf-convolution of law-invariant preferences law-invariant?","year":2020,"lang":"en","type":"article","venue":"Insurance Mathematics and Economics","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"University of Waterloo; Actua","funders":"Natural Sciences and Engineering Research Council of Canada; China Scholarship Council; Society of Actuaries","keywords":"Invariant (physics); Convexity; Mathematics; Monotonic function; Mathematical economics; Pure mathematics; Law; Economics; Mathematical analysis; Political science","score_opus":0.10870476048232611,"score_gpt":0.301059560518384,"score_spread":0.1923548000360579,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2937729660","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":[0.9496116,0.00028747105,0.010524203,0.004211761,0.00021857531,0.00031625683,0.00015152316,0.000025622547,0.034652997],"genre_scores_gemma":[0.9920304,0.0011898949,0.005234391,0.0013946327,0.00007558479,0.000007744458,0.0000030993021,0.000010093356,0.00005415281],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9985323,0.000039554157,0.0008018406,0.00027768925,0.00019609783,0.00015254742],"domain_scores_gemma":[0.99839395,0.00047907507,0.0005338679,0.0003825046,0.00012544385,0.00008517109],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00091963075,0.00013624069,0.00035124566,0.00003520358,0.00017511235,0.0002361899,0.00043675772,0.0000813451,0.000075947275],"category_scores_gemma":[0.00023326853,0.00008784935,0.00008030856,0.00015687002,0.00022029395,0.00034565167,0.000098153265,0.00009356739,0.00006262221],"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":[0.000029596991,0.000055954642,0.0054619983,0.00003825086,0.000045971778,7.712224e-7,0.010975465,0.0043113157,0.00008007307,0.97293305,0.00094413094,0.005123396],"study_design_scores_gemma":[0.0006317534,0.00013738559,0.005428798,0.00004613906,0.000036156373,0.000013293156,0.0017076802,0.32585174,0.00171434,0.6413385,0.022754278,0.00033991894],"about_ca_topic_score_codex":0.00011825951,"about_ca_topic_score_gemma":0.00008718628,"teacher_disagreement_score":0.33159456,"about_ca_system_score_codex":0.0000087219505,"about_ca_system_score_gemma":0.000040857853,"threshold_uncertainty_score":0.35823926},"labels":[],"label_agreement":null},{"id":"W294061239","doi":"10.2139/ssrn.2530287","title":"Is 1/n Really Better than Optimal Mean-Variance Portfolio?","year":2014,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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 British Columbia","funders":"","keywords":"Portfolio; Variance (accounting); Modern portfolio theory; Mathematics; Econometrics; Statistics; Economics; Actuarial science; Financial economics; Accounting","score_opus":0.023018791532577892,"score_gpt":0.31263754380799075,"score_spread":0.28961875227541284,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W294061239","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":[0.33787358,0.0009873804,0.6237532,0.006606024,0.00094618433,0.00017702507,0.0000060515395,0.00007459825,0.02957597],"genre_scores_gemma":[0.97350436,0.0044680084,0.0029872241,0.0013978499,0.0010379445,0.0000038487697,0.000004760801,0.00003568773,0.016560294],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9944948,0.00031515944,0.00088849344,0.00051500584,0.0015700597,0.0022164746],"domain_scores_gemma":[0.9978482,0.00022308773,0.0006316173,0.0006393697,0.00044314543,0.00021454509],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009555718,0.0002451958,0.00036400792,0.00036597977,0.0004078352,0.00046450848,0.0010934371,0.00015254757,0.00052814174],"category_scores_gemma":[0.00052241614,0.00018172759,0.00026482844,0.0007290992,0.00007596374,0.0007370358,0.0000786941,0.0013982102,0.00058312964],"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":[0.00028297785,0.00018145888,0.064313926,0.0000020977873,0.0002819987,0.00002562962,0.0014996816,0.0075357365,0.00036343696,0.24590638,0.044906262,0.6347004],"study_design_scores_gemma":[0.0011452659,0.0005834844,0.010864492,0.000017330132,0.000060500854,0.001046898,0.0008586264,0.012532207,0.0002807879,0.8152744,0.15680565,0.00053031754],"about_ca_topic_score_codex":0.000066224675,"about_ca_topic_score_gemma":0.00022368641,"teacher_disagreement_score":0.6356308,"about_ca_system_score_codex":0.00024295328,"about_ca_system_score_gemma":0.0012327933,"threshold_uncertainty_score":0.7495147},"labels":[],"label_agreement":null},{"id":"W2941381621","doi":"10.1145/3313276.3316391","title":"Approximation algorithms for distributionally-robust stochastic optimization with black-box distributions","year":2019,"lang":"en","type":"article","venue":"","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Probability distribution; Mathematical optimization; Stochastic optimization; Computer science; Robust optimization; Stochastic programming; Black box; Optimization problem; Algorithm; Mathematics; Artificial intelligence; Statistics","score_opus":0.05390955991847635,"score_gpt":0.3232666681542887,"score_spread":0.2693571082358124,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2941381621","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":[0.004551439,0.000013075413,0.990285,0.0008162304,0.0002449018,0.0011894208,0.00043674547,0.00010342433,0.0023597653],"genre_scores_gemma":[0.67204463,0.000022207947,0.3159729,0.00010775354,0.00017798919,0.00017299071,0.0042257244,0.000033853383,0.0072419774],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974093,0.00006494749,0.0006163121,0.00058192696,0.0010100252,0.00031749607],"domain_scores_gemma":[0.9971763,0.0006077877,0.0003401003,0.0005152896,0.0012295713,0.00013096046],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010076617,0.0002014854,0.0002738745,0.00019572697,0.0002603437,0.00034670878,0.00035861525,0.00011671054,0.00089124776],"category_scores_gemma":[0.00066713826,0.00013919952,0.00010877076,0.0010735146,0.00010713767,0.000773478,0.00004656821,0.000088557725,0.0002955932],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000069783404,0.00007154351,0.0007292048,0.000002802689,0.000014952637,2.964826e-7,0.000046595167,0.96897584,0.00000920901,0.024682764,0.0031390938,0.002257909],"study_design_scores_gemma":[0.00091074244,0.00015837674,0.0013158285,0.000011439104,0.000030199286,0.000007253449,0.00025540218,0.9902096,0.00009127036,0.004741391,0.002028294,0.00024020043],"about_ca_topic_score_codex":0.000010276678,"about_ca_topic_score_gemma":0.0000074934837,"teacher_disagreement_score":0.6743121,"about_ca_system_score_codex":0.000094992305,"about_ca_system_score_gemma":0.00015304991,"threshold_uncertainty_score":0.9758538},"labels":[],"label_agreement":null},{"id":"W2943278754","doi":"","title":"\"Dice\"-sion making under uncertainty: When can a random decision reduce risk?","year":2016,"lang":"en","type":"article","venue":"Infoscience (Ecole Polytechnique Fédérale de Lausanne)","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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","funders":"","keywords":"Dice; Computer science; Risk analysis (engineering); Statistics; Mathematics; Business","score_opus":0.043364469050784865,"score_gpt":0.3512088051251451,"score_spread":0.30784433607436024,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2943278754","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":[0.23855267,0.00016370809,0.75061405,0.0034207962,0.0008093223,0.00091719406,0.0000969255,0.00043103483,0.0049943007],"genre_scores_gemma":[0.94810766,0.0009676858,0.04497424,0.0014836988,0.00022285493,0.0001607661,0.0000050465883,0.00004498599,0.0040330333],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9930922,0.0006148404,0.0014685881,0.001324083,0.0023302513,0.0011700391],"domain_scores_gemma":[0.99328166,0.0028405504,0.0010749198,0.0017023607,0.0006238593,0.00047662837],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.007904278,0.00046052167,0.0006268969,0.0010934615,0.0008607476,0.0006665867,0.0023914792,0.0004207804,0.0007812397],"category_scores_gemma":[0.007070363,0.00030273653,0.00030261613,0.0021994908,0.00061708264,0.0014601795,0.0005036365,0.00039261303,0.00030287262],"study_design_candidate":"design_other","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":[0.00074775703,0.00017890573,0.04636099,0.000005025953,0.000020532443,0.000050830262,0.0014572516,0.022224842,0.018815894,0.005729386,0.03779866,0.86660993],"study_design_scores_gemma":[0.0069286013,0.0010184296,0.04261527,0.0015013253,0.00012376711,0.00031913116,0.0014680212,0.07423763,0.05775907,0.6191552,0.19200906,0.002864475],"about_ca_topic_score_codex":0.00071987975,"about_ca_topic_score_gemma":0.0009579659,"teacher_disagreement_score":0.86374545,"about_ca_system_score_codex":0.00042198,"about_ca_system_score_gemma":0.00070872094,"threshold_uncertainty_score":0.9999425},"labels":[],"label_agreement":null},{"id":"W2949564271","doi":"10.2139/ssrn.2836281","title":"Gini-Type Measures of Risk and Variability: Gini Shortfall, Capital Allocations, and Heavy-Tailed Risks","year":2016,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Western University; University of Waterloo; York University","funders":"","keywords":"Economics; Capital (architecture); Expected shortfall; Actuarial science; Inequality; Econometrics; Risk management; Finance; Mathematics; Geography","score_opus":0.050227611361306004,"score_gpt":0.3282954384922362,"score_spread":0.2780678271309302,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2949564271","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":[0.90782565,0.0041278508,0.08646898,0.0009188786,0.00022035111,0.00014389164,0.000014431364,0.00001781908,0.00026213727],"genre_scores_gemma":[0.94163173,0.057312,0.0005499491,0.000020065649,0.00011792528,0.0000023678165,0.0000014080834,0.000013385623,0.0003511462],"study_design_codex":"observational","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99669886,0.0005163875,0.0007307798,0.00037833472,0.0008056138,0.000870004],"domain_scores_gemma":[0.99746615,0.0009182129,0.00048691925,0.0003633411,0.0006027581,0.0001626261],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010506122,0.00016846131,0.00033030886,0.00026899576,0.00026733425,0.00013009665,0.0003259712,0.0001188951,0.000066238485],"category_scores_gemma":[0.0039298525,0.00010051843,0.00007590861,0.00039932804,0.00025896798,0.0005130416,0.00006571114,0.0005607664,0.000014804002],"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":[0.00027990164,0.00007086363,0.5077659,0.0000013815737,0.000110471,0.0000010903766,0.0005846744,0.00013471436,0.00028789087,0.039514903,0.0002307859,0.45101747],"study_design_scores_gemma":[0.0010476187,0.00044707398,0.09898575,0.000020573965,0.0000983147,0.00022521506,0.0014102319,0.00038233132,0.0002625971,0.8939037,0.002974356,0.0002422538],"about_ca_topic_score_codex":0.00015690723,"about_ca_topic_score_gemma":0.0008974176,"teacher_disagreement_score":0.8543888,"about_ca_system_score_codex":0.00014273073,"about_ca_system_score_gemma":0.0012414678,"threshold_uncertainty_score":0.47046846},"labels":[],"label_agreement":null},{"id":"W2949773839","doi":"10.48550/arxiv.1311.5686","title":"High Performance Risk Aggregation: Addressing the Data Processing Challenge the Hadoop MapReduce Way","year":2013,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Computer science; Aggregate (composite); Exploit; Distributed File System; Big data; Process (computing); Set (abstract data type); Data set; Scope (computer science); Distributed computing; Database; Data mining; Parallel computing; Operating system","score_opus":0.3497576000742727,"score_gpt":0.2873531752415266,"score_spread":0.06240442483274611,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2949773839","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":[0.91911685,0.001415464,0.06898174,0.0017219648,0.0015591831,0.0008970237,0.00013015006,0.00015532954,0.0060223],"genre_scores_gemma":[0.98562473,0.008604334,0.0005747099,0.000091544505,0.00044530578,0.0000033327738,0.00009840894,0.00002904893,0.0045286124],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9963331,0.0006384626,0.00055963313,0.0014877232,0.0005795809,0.00040146118],"domain_scores_gemma":[0.9930064,0.00065722765,0.0014402043,0.0041080224,0.0006680653,0.00012006418],"candidate_categories":["sts","scholarly_communication","open_science"],"consensus_categories":[],"category_scores_codex":[0.002873453,0.00038221964,0.00038382152,0.00022742644,0.0015606863,0.0010849952,0.006081264,0.0003137384,0.0002809837],"category_scores_gemma":[0.0005903505,0.00022491252,0.00012763827,0.0011311854,0.00046960398,0.0015601993,0.003210023,0.0010955335,0.000402789],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000047954305,0.00006133605,0.006936885,0.000026511314,0.000065618246,0.000018169572,0.0013683212,0.80216014,0.0000015294044,0.002872861,0.0070679616,0.17937268],"study_design_scores_gemma":[0.00025903783,0.000023502987,0.009683902,0.00016482214,0.00016840699,0.000005416114,0.0011728618,0.9569346,0.00003070343,0.02305184,0.0081260605,0.00037888598],"about_ca_topic_score_codex":0.00043616016,"about_ca_topic_score_gemma":0.00008088772,"teacher_disagreement_score":0.1789938,"about_ca_system_score_codex":0.000086462096,"about_ca_system_score_gemma":0.00032246517,"threshold_uncertainty_score":0.99995196},"labels":[],"label_agreement":null},{"id":"W2949873667","doi":"10.48550/arxiv.1308.3814","title":"A Mixed Value and Policy Iteration Method for Stochastic Control with Universally Measurable Policies","year":2013,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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 Alberta","funders":"","keywords":"Mathematics; Bounded function; Convergence (economics); Bellman equation; Function (biology); Mathematical optimization; Power iteration; Average cost; Value (mathematics); Context (archaeology); Optimal control; Markov decision process; Applied mathematics; Iterative method; Economics; Markov process","score_opus":0.13654237918992942,"score_gpt":0.27273179711356405,"score_spread":0.13618941792363462,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2949873667","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":[0.12354852,0.000042076957,0.8736938,0.00046054524,0.00016191309,0.0009771775,0.00010091271,0.0000660967,0.00094890944],"genre_scores_gemma":[0.9843482,0.000093034556,0.0103693735,0.0001516446,0.00012441762,0.000005902195,0.00003289115,0.000028685869,0.0048458623],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99757856,0.00041357212,0.00035328665,0.0010080136,0.00028651062,0.00036004567],"domain_scores_gemma":[0.99632144,0.0010976078,0.00058742124,0.00072106504,0.0010420055,0.00023044816],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0011566204,0.0003470722,0.00056101155,0.00092173094,0.0003129397,0.00046569866,0.00063796283,0.00029298195,0.00002945667],"category_scores_gemma":[0.00067343167,0.00029530353,0.00015758118,0.00086482376,0.00015955455,0.00058915204,0.00023039946,0.00024871505,0.000024476056],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024125895,0.000027564813,0.0009600981,0.000010345958,0.000090695205,0.000006176752,0.0003892134,0.87121177,0.000026819771,0.12520169,0.00051113835,0.0013232097],"study_design_scores_gemma":[0.0016253926,0.00015017555,0.0020266061,0.000044329474,0.00020694161,0.0000072169096,0.0004954348,0.8900462,0.000024464101,0.10432063,0.00069518137,0.0003574677],"about_ca_topic_score_codex":0.0018606049,"about_ca_topic_score_gemma":0.0003460799,"teacher_disagreement_score":0.86332446,"about_ca_system_score_codex":0.00016788348,"about_ca_system_score_gemma":0.000534869,"threshold_uncertainty_score":0.99994993},"labels":[],"label_agreement":null},{"id":"W2950492904","doi":"10.48550/arxiv.1701.04102","title":"Two-stage Linear Decision Rules for Multi-stage Stochastic Programming","year":2017,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Division of Civil, Mechanical and Manufacturing Innovation; Advanced Scientific Computing Research; U.S. Department of Energy; Office of Science; National Science Foundation","keywords":"Mathematics; Linear programming; Upper and lower bounds; Mathematical optimization; Value (mathematics); Dual (grammatical number); Stochastic programming; Function (biology); Affine transformation; Statistics; Pure mathematics","score_opus":0.35129853536203565,"score_gpt":0.35610831844400703,"score_spread":0.004809783081971386,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2950492904","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":[0.16927178,0.000089735484,0.8273107,0.000023037983,0.0011282279,0.0011379413,0.00032370543,0.00012932876,0.0005855535],"genre_scores_gemma":[0.88799375,0.00027660927,0.07871858,0.000029350462,0.00025497694,0.000008614855,0.00017232756,0.000060750895,0.03248501],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99631757,0.00016150376,0.00067401334,0.0018302873,0.00045171156,0.0005649045],"domain_scores_gemma":[0.9939785,0.0010098937,0.001376652,0.0023724185,0.00094108435,0.0003214515],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00207811,0.00049428974,0.0007156085,0.0007303447,0.0008375662,0.0007107351,0.0026613094,0.00047743833,0.00013612969],"category_scores_gemma":[0.002796881,0.00046515424,0.0005789561,0.00038473218,0.00026554547,0.00060137105,0.0014312683,0.0005331241,0.00030376326],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025750595,0.00012865537,0.0027499967,0.00002075041,0.00005194458,0.00009291764,0.00018492305,0.9638483,0.000004778482,0.009966726,0.00026520257,0.022428317],"study_design_scores_gemma":[0.0016482639,0.00007081756,0.0006509875,0.00011038244,0.00011027897,0.000001564399,0.00039672432,0.96371907,0.000021731674,0.015535952,0.017162353,0.00057186146],"about_ca_topic_score_codex":0.0002672706,"about_ca_topic_score_gemma":0.0003461242,"teacher_disagreement_score":0.7485921,"about_ca_system_score_codex":0.00014717306,"about_ca_system_score_gemma":0.0003467429,"threshold_uncertainty_score":0.99978},"labels":[],"label_agreement":null},{"id":"W2951405180","doi":"10.48550/arxiv.1310.2274","title":"Accounting for Secondary Uncertainty: Efficient Computation of Portfolio Risk Measures on Multi and Many Core Architectures","year":2013,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Computer science; Aggregate (composite); Portfolio; Computation; Event (particle physics); Fraction (chemistry); Systematic risk; Multi-core processor; Core (optical fiber); Uncertainty analysis; Data mining; Econometrics; Parallel computing; Algorithm; Finance; Simulation; Mathematics","score_opus":0.14658932572757521,"score_gpt":0.2756830555994775,"score_spread":0.12909372987190226,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2951405180","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":[0.72196794,0.00010073181,0.27625093,0.00001450155,0.00031333734,0.0006495006,0.00014572444,0.000039038816,0.00051831914],"genre_scores_gemma":[0.99710155,0.00031012922,0.0020162244,0.00004232935,0.00006214713,0.0000028940326,0.000050631956,0.000023462937,0.00039060766],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975556,0.00019061235,0.0005765106,0.001048343,0.0003541327,0.00027476784],"domain_scores_gemma":[0.99624646,0.00090672984,0.0013315192,0.0005829019,0.00080046966,0.0001319341],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0013816966,0.0003257313,0.00054509635,0.0007979498,0.0002596816,0.00015170805,0.0005958737,0.00029160027,0.000039927636],"category_scores_gemma":[0.0007630691,0.00028787157,0.0002561099,0.0004762077,0.00021730538,0.00008349207,0.0003984271,0.00044931663,0.000015117966],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016030966,0.000068895955,0.016544301,0.000025608191,0.000060862065,0.0000066119433,0.00036532714,0.9533502,0.000013598813,0.0020500445,0.00040312568,0.0269511],"study_design_scores_gemma":[0.00088042585,0.0000891096,0.032830093,0.00007927612,0.000109442575,0.0000018560804,0.00041492196,0.928659,0.00006290943,0.036236826,0.00034218113,0.00029393908],"about_ca_topic_score_codex":0.00052736397,"about_ca_topic_score_gemma":0.0000792874,"teacher_disagreement_score":0.27513364,"about_ca_system_score_codex":0.00006934367,"about_ca_system_score_gemma":0.00017548964,"threshold_uncertainty_score":0.9999573},"labels":[],"label_agreement":null},{"id":"W2951526564","doi":"10.2139/ssrn.3038297","title":"Weighted Risk Capital Allocations in the Presence of Systematic Risk","year":2017,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Western University; York University","funders":"","keywords":"Systematic risk; Business; Actuarial science; Economics; Finance","score_opus":0.032626703261959746,"score_gpt":0.33622239917771596,"score_spread":0.3035956959157562,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2951526564","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":[0.9548626,0.0029938447,0.03893949,0.0007795236,0.0002856285,0.000429885,0.0000098760165,0.0000070211545,0.0016921113],"genre_scores_gemma":[0.9832291,0.015823025,0.00015971328,0.000008471303,0.000081440616,0.000011187374,8.185609e-7,0.0000066601833,0.0006795756],"study_design_codex":"observational","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99609226,0.0009799829,0.00086416554,0.00019441821,0.0010829906,0.0007861652],"domain_scores_gemma":[0.99569863,0.00093892077,0.0019793597,0.0010170997,0.00032070934,0.00004526952],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.016887061,0.00010950915,0.00028488753,0.00022603458,0.0008308022,0.0004185595,0.0020995173,0.00006203612,0.000021653466],"category_scores_gemma":[0.0063725975,0.000059189075,0.000136842,0.00031195153,0.00012439808,0.0005800029,0.000054281674,0.0010946861,0.000052124247],"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":[0.00011521725,0.00043080983,0.6858051,0.00009006062,0.00031717645,0.00001448678,0.017281678,0.010869361,0.00004455235,0.2528226,0.0011170896,0.031091861],"study_design_scores_gemma":[0.0005949548,0.00018513111,0.08163851,0.00019641014,0.00011462964,0.00020643957,0.01326307,0.015355458,0.000027939233,0.88814604,0.00011984325,0.00015155216],"about_ca_topic_score_codex":0.00081834575,"about_ca_topic_score_gemma":0.0052519045,"teacher_disagreement_score":0.63532346,"about_ca_system_score_codex":0.000110534485,"about_ca_system_score_gemma":0.0008687572,"threshold_uncertainty_score":0.7629055},"labels":[],"label_agreement":null},{"id":"W2952077562","doi":"10.48550/arxiv.1311.5685","title":"Data Challenges in High-Performance Risk Analytics","year":2013,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Analytics; Pipeline (software); Risk management; Risk analysis (engineering); Data analysis; Portfolio; Data science; Computer science; Business; Finance; Data mining","score_opus":0.3791707720152268,"score_gpt":0.2781383826847389,"score_spread":0.10103238933048792,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2952077562","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":[0.9785902,0.00046264954,0.013361214,0.00014842975,0.0006781174,0.00028565436,0.0002192787,0.00006315377,0.0061912974],"genre_scores_gemma":[0.91494155,0.081984825,0.0005590988,0.000018388624,0.00009598467,5.275343e-7,0.00012195817,0.000016969425,0.0022606917],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9968581,0.0003277336,0.0005292212,0.0016184415,0.00033072426,0.0003357564],"domain_scores_gemma":[0.99469763,0.00042382022,0.00071948423,0.0037052312,0.00030615443,0.00014765454],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001993098,0.00029450786,0.0005042235,0.00080197566,0.0001167345,0.00017350132,0.003670654,0.000389851,0.00024600097],"category_scores_gemma":[0.0005395308,0.00027581226,0.00008263721,0.0010593137,0.00012721121,0.0009044611,0.0028104293,0.0007079429,0.00079616724],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002444538,0.000060147388,0.10832641,0.000009780401,0.000032324442,0.00004326706,0.00010863114,0.8697368,2.0771104e-7,0.004878591,0.0016200034,0.015159395],"study_design_scores_gemma":[0.00032178708,0.000025756264,0.089706756,0.000039334584,0.00006237654,9.736463e-7,0.00025878838,0.8642707,0.0000041530784,0.039888583,0.005090921,0.00032985493],"about_ca_topic_score_codex":0.0006363007,"about_ca_topic_score_gemma":0.00070559257,"teacher_disagreement_score":0.081522174,"about_ca_system_score_codex":0.0001000276,"about_ca_system_score_gemma":0.00017433615,"threshold_uncertainty_score":0.9999818},"labels":[],"label_agreement":null},{"id":"W2952780795","doi":"10.48550/arxiv.1207.1077","title":"On the mixing set with a knapsack constraint","year":2012,"lang":"en","type":"preprint","venue":"London School of Economics and Political Science Research Online (London School of Economics and Political Science)","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Knapsack problem; Cardinality (data modeling); Mathematics; Continuous knapsack problem; Constraint (computer-aided design); Combinatorics; Set (abstract data type); Mixing (physics); Polytope; Class (philosophy); Discrete mathematics; Mathematical optimization; Computer science","score_opus":0.1307119423795123,"score_gpt":0.4135485694157115,"score_spread":0.28283662703619916,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2952780795","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":[0.9647437,0.00028662704,0.000102758255,0.016452268,0.0004568353,0.0010276176,0.0010056214,0.000015009173,0.015909564],"genre_scores_gemma":[0.9927509,0.003307805,0.0021933212,0.00091738626,0.000503181,0.00003226702,0.000022353262,0.000038155267,0.00023467539],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9884023,0.0005389059,0.0026116548,0.0023384409,0.0016732671,0.0044354266],"domain_scores_gemma":[0.9817312,0.0063940845,0.0009655332,0.0021069497,0.0021191025,0.006683153],"candidate_categories":["metaresearch","metaepi_narrow","sts","scholarly_communication"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.031379923,0.00070289493,0.0015694171,0.0020780093,0.0011435571,0.0024001447,0.0039268997,0.0004804024,0.00056598283],"category_scores_gemma":[0.015711365,0.0004582364,0.0002983885,0.0016123465,0.021783285,0.0014851053,0.0032604176,0.0022016524,0.0001050056],"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":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015581124,0.00018823221,0.010736029,0.000028280132,0.00003225092,0.0000026440898,0.000084369516,0.0015612236,0.00007699058,0.98520684,0.00019509331,0.0017322582],"study_design_scores_gemma":[0.0013949532,0.0011017444,0.04677259,0.0002977025,0.00007643231,0.000095821,0.0027417613,0.06608459,0.0022568814,0.873244,0.004790634,0.001142906],"about_ca_topic_score_codex":0.0027982346,"about_ca_topic_score_gemma":0.00037082666,"teacher_disagreement_score":0.11196284,"about_ca_system_score_codex":0.00095850247,"about_ca_system_score_gemma":0.009451684,"threshold_uncertainty_score":0.9997869},"labels":[],"label_agreement":null},{"id":"W2955582860","doi":"10.3390/jrfm12030107","title":"CVaR Regression Based on the Relation between CVaR and Mixed-Quantile Quadrangles","year":2019,"lang":"en","type":"article","venue":"Journal of risk and financial management","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":18,"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":"CVAR; Mathematics; Quadrangle; Quantile; Linear regression; Quantile regression; Regression analysis; Mathematical optimization; Expected shortfall; Applied mathematics; Statistics; Risk management; Economics","score_opus":0.02641005917818693,"score_gpt":0.289801360357669,"score_spread":0.26339130117948206,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2955582860","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":[0.9512342,0.00044347896,0.044794913,0.00066623,0.00048222535,0.00021740768,0.000008287964,0.0000057162824,0.0021475367],"genre_scores_gemma":[0.9949158,0.0030908268,0.001466617,0.000110393456,0.00014365718,0.00000115739,0.0000014459886,0.000006169195,0.00026397107],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99822795,0.00020198715,0.0005265048,0.00018166301,0.00074468297,0.00011718019],"domain_scores_gemma":[0.9982615,0.00068098574,0.0006498586,0.00022721617,0.00012086883,0.00005957839],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002768141,0.00010891122,0.00023568865,0.00030434984,0.00021031812,0.0001076644,0.00020046666,0.00006599621,0.000045042718],"category_scores_gemma":[0.00044323617,0.00005701948,0.000080009886,0.00033461492,0.000041947398,0.00023235613,0.000055197776,0.00018400946,0.000029745135],"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":[0.0002266455,0.0000544947,0.37461147,0.0000074632644,0.000010314683,0.000015378222,0.00038533437,0.003079726,0.000005787747,0.006952517,0.006617378,0.6080335],"study_design_scores_gemma":[0.00062140636,0.00028001177,0.88261336,0.00009638764,0.000051652205,0.000002719757,0.00028749072,0.0039195283,0.000030970914,0.013709591,0.09829602,0.000090859394],"about_ca_topic_score_codex":0.000007748033,"about_ca_topic_score_gemma":0.0000027987253,"teacher_disagreement_score":0.60794264,"about_ca_system_score_codex":0.000013382444,"about_ca_system_score_gemma":0.000018400553,"threshold_uncertainty_score":0.23251869},"labels":[],"label_agreement":null},{"id":"W2963210780","doi":"10.3390/jrfm12020083","title":"Multi-Period Investment Strategies under Cumulative Prospect Theory","year":2019,"lang":"en","type":"article","venue":"Journal of risk and financial management","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Cumulative prospect theory; Portfolio; Novelty; Asset (computer security); Investment strategy; Modern portfolio theory; Investment (military); Selection (genetic algorithm); Prospect theory; Benchmark (surveying); Econometrics; Economics; Sensitivity (control systems); Portfolio optimization; Computer science; Actuarial science; Financial economics; Microeconomics; Engineering; Artificial intelligence; Psychology; Political science","score_opus":0.02971044772960261,"score_gpt":0.3157693199976229,"score_spread":0.2860588722680203,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2963210780","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":[0.6410232,0.0011044462,0.34729722,0.00012283187,0.00093591184,0.00041607703,0.0000074632476,0.000010612689,0.00908224],"genre_scores_gemma":[0.9844722,0.0024699436,0.010914191,0.00019021271,0.00010002816,0.0000025591428,6.717027e-7,0.000007721909,0.0018424635],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.9981714,0.0001621352,0.0006336805,0.00020651161,0.00066987635,0.00015642315],"domain_scores_gemma":[0.99872905,0.00013988365,0.0006450636,0.0002057296,0.00020230334,0.00007795246],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022212148,0.00013002637,0.00028946187,0.00031260625,0.00012030648,0.00025873762,0.00024547806,0.000051150546,0.00009689478],"category_scores_gemma":[0.0001682573,0.00008404962,0.00010950028,0.00034919707,0.00006496267,0.0005388954,0.00008975838,0.00015895728,0.000051626026],"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":[0.0006302112,0.00034854395,0.07720016,0.00001754143,0.00009663442,0.000100940255,0.0075986944,0.05998348,0.00003535906,0.52430224,0.0022351893,0.32745102],"study_design_scores_gemma":[0.0018397674,0.0003880909,0.55890995,0.000044827168,0.000080243815,0.000017977118,0.009469216,0.0020703052,0.000022111193,0.36799306,0.058953177,0.00021130948],"about_ca_topic_score_codex":0.0000079095835,"about_ca_topic_score_gemma":0.000010863272,"teacher_disagreement_score":0.48170978,"about_ca_system_score_codex":0.00003350923,"about_ca_system_score_gemma":0.00006131026,"threshold_uncertainty_score":0.3427444},"labels":[],"label_agreement":null},{"id":"W2964262527","doi":"10.1007/s00780-019-00393-4","title":"Distributional compatibility for change of measures","year":2019,"lang":"en","type":"article","venue":"Finance and Stochastics","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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 Waterloo","funders":"","keywords":"Mathematics; Uncountable set; Random variable; Combinatorics; Probability measure; Probability distribution; Regular polygon; Discrete mathematics; Statistics; Geometry","score_opus":0.14518957572363195,"score_gpt":0.38097271504876573,"score_spread":0.23578313932513378,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2964262527","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":[0.5564589,0.00021301309,0.4421843,0.000106575826,0.00020897403,0.00028192034,0.0003519517,0.0000043818527,0.00018997198],"genre_scores_gemma":[0.99741954,0.0000683861,0.0022157226,0.000027364918,0.000044299115,0.000012267504,0.000027109925,0.0000022025674,0.00018311411],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99919623,0.000014027638,0.00024193508,0.00016360776,0.00029715284,0.00008704488],"domain_scores_gemma":[0.9990172,0.00032691978,0.00013748505,0.00017464315,0.00032428672,0.000019506673],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005243012,0.00005167489,0.00016127228,0.000035415233,0.000046265548,0.000017086624,0.00010377575,0.000036937352,0.00001625627],"category_scores_gemma":[0.00038336418,0.000039067836,0.00003645162,0.0001395197,0.000051562176,0.000096064025,0.000022805698,0.000027238539,0.0000098754435],"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":[0.00043660944,0.00020192767,0.55110765,0.000038983337,0.00001782596,5.241657e-7,0.0014752651,0.011448779,0.00019785091,0.22353218,0.0073975143,0.20414491],"study_design_scores_gemma":[0.00085111405,0.00038606257,0.75380427,0.000030268628,0.000013815044,0.0000016513527,0.00013391388,0.13096456,0.00028129568,0.054817926,0.058527105,0.00018802698],"about_ca_topic_score_codex":0.000008458051,"about_ca_topic_score_gemma":0.0000058361097,"teacher_disagreement_score":0.44096062,"about_ca_system_score_codex":0.000005398414,"about_ca_system_score_gemma":0.000025306848,"threshold_uncertainty_score":0.159314},"labels":[],"label_agreement":null},{"id":"W2965384330","doi":"10.1111/mafi.12270","title":"Risk functionals with convex level sets","year":2020,"lang":"en","type":"article","venue":"Mathematical Finance","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":26,"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; Society of Actuaries","keywords":"Mathematics; Identifiability; Quantile; Dimension (graph theory); Coherent risk measure; Choquet integral; Property (philosophy); Monotone polygon; Measure (data warehouse); Characterization (materials science); Class (philosophy); Risk measure; Regular polygon; Mathematical economics; Applied mathematics; Econometrics; Pure mathematics; Expected shortfall; Risk management; Computer science; Statistics; Economics; Artificial intelligence; Finance","score_opus":0.1720744904209427,"score_gpt":0.3513906394661631,"score_spread":0.17931614904522042,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2965384330","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":[0.1232437,0.000079442136,0.8526162,0.004575234,0.00007425024,0.0002134821,0.000060991362,0.000066136476,0.019070558],"genre_scores_gemma":[0.9413428,0.00013401659,0.05393542,0.0011073591,0.000079490936,0.000018845672,0.000004644377,0.000015254876,0.0033621762],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9981353,0.00006669774,0.00042214507,0.00034634996,0.00085816364,0.00017133317],"domain_scores_gemma":[0.998533,0.0006579795,0.00021045507,0.00030881912,0.00018590916,0.0001038226],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00061779184,0.00011798304,0.00027573033,0.00003671329,0.00011344519,0.0001087562,0.00029738917,0.0000544712,0.0009860015],"category_scores_gemma":[0.0020782459,0.00007185171,0.00005880419,0.00058995024,0.00009249631,0.0002018086,0.000047893827,0.00012418612,0.0033115975],"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":[0.000545216,0.00045605228,0.050890703,0.00004581231,0.000090884605,0.000102087804,0.0052436315,0.038444396,0.000089459594,0.41002196,0.24560274,0.24846706],"study_design_scores_gemma":[0.0011033007,0.00041310486,0.050957903,0.000051368086,0.00004787826,0.00004061352,0.00030968714,0.26772746,0.00062349933,0.46542174,0.21275431,0.0005491278],"about_ca_topic_score_codex":0.0000019782503,"about_ca_topic_score_gemma":9.564503e-7,"teacher_disagreement_score":0.8180991,"about_ca_system_score_codex":0.0000070743945,"about_ca_system_score_gemma":0.00004587048,"threshold_uncertainty_score":0.9999272},"labels":[],"label_agreement":null},{"id":"W2966766085","doi":"10.2139/ssrn.3419388","title":"Distributional Transforms, Probability Distortions, and Their Applications","year":2019,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"University of Waterloo","funders":"","keywords":"Econometrics; Mathematics; Computer science; Statistics; Economics","score_opus":0.018309944476577987,"score_gpt":0.2918052920172554,"score_spread":0.27349534754067745,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2966766085","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":[0.32143798,0.0019518314,0.6725802,0.0016630549,0.0001264896,0.00043651316,0.00004511537,0.000026516223,0.0017323061],"genre_scores_gemma":[0.9953182,0.0027828154,0.00016461516,0.000030997828,0.00009658579,0.000019702577,0.000027705235,0.0000065432478,0.001552868],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9979636,0.000089458445,0.0004557628,0.00027302888,0.0004394087,0.00077874353],"domain_scores_gemma":[0.99908465,0.00015962392,0.00017990383,0.00025734762,0.0002224403,0.000096027834],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003511019,0.00011149308,0.00016456847,0.00009827059,0.0003112564,0.00014303967,0.00030028645,0.000061397004,0.00012164627],"category_scores_gemma":[0.0001084948,0.00007078857,0.000102534876,0.0003876818,0.00007259115,0.00039822154,0.000021161635,0.0006225637,0.000100766556],"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":[0.00004563042,0.000109537774,0.083995275,0.000001868225,0.00004202746,1.3006948e-7,0.00017026193,0.00039834942,0.000113796785,0.6778111,0.00024781027,0.2370642],"study_design_scores_gemma":[0.0002729471,0.00008160255,0.010444351,0.000002127923,0.000007401751,0.0001443568,0.0006615231,0.00059957005,0.000025931251,0.9307145,0.056939192,0.000106505664],"about_ca_topic_score_codex":0.000010287649,"about_ca_topic_score_gemma":0.0001274104,"teacher_disagreement_score":0.67388016,"about_ca_system_score_codex":0.0003265799,"about_ca_system_score_gemma":0.0010036129,"threshold_uncertainty_score":0.2886674},"labels":[],"label_agreement":null},{"id":"W2968840856","doi":"10.48550/arxiv.1908.04040","title":"Near-optimal robust bilevel optimization","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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","funders":"","keywords":"Bilevel optimization; Computer science; Robust optimization; Mathematical optimization; Business; Optimization problem; Mathematics","score_opus":0.20517920011379034,"score_gpt":0.25187211438425144,"score_spread":0.0466929142704611,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2968840856","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":[0.14015724,0.00005613563,0.848263,0.00008408449,0.0013755583,0.00046313574,0.00007391043,0.00014196911,0.009384936],"genre_scores_gemma":[0.9586504,0.00086405175,0.019221004,0.00010436176,0.00014904262,0.0000010668278,0.0001615084,0.00004565034,0.0208029],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99648577,0.00032281238,0.00060960674,0.0016764546,0.00047749392,0.00042785308],"domain_scores_gemma":[0.9958828,0.0003914567,0.00082731905,0.0018475297,0.00081923866,0.00023163595],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.001180563,0.0004266235,0.0005923943,0.0005861468,0.00029029066,0.00063469453,0.0017682754,0.0006346489,0.0012914097],"category_scores_gemma":[0.000473765,0.00041883017,0.0003709063,0.0014710143,0.00019557355,0.000656378,0.001234575,0.0006107504,0.0013773064],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006855558,0.000057876594,0.012761623,0.00000690584,0.000039011444,0.000048348636,0.0001360632,0.97989357,0.0000010910846,0.002030054,0.004243871,0.0007130616],"study_design_scores_gemma":[0.00045782104,0.000043498556,0.0016174616,0.00003079774,0.00008174444,0.0000037449972,0.00015741361,0.99003226,0.000011602367,0.0034413831,0.003637135,0.00048515506],"about_ca_topic_score_codex":0.00013751014,"about_ca_topic_score_gemma":0.00001648527,"teacher_disagreement_score":0.829042,"about_ca_system_score_codex":0.00017943366,"about_ca_system_score_gemma":0.0005361801,"threshold_uncertainty_score":0.9998264},"labels":[],"label_agreement":null},{"id":"W2969212598","doi":"10.1287/opre.2021.2159","title":"Adjustable Robust Optimization Reformulations of Two-Stage Worst-Case Regret Minimization Problems","year":2021,"lang":"en","type":"article","venue":"Operations Research","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":31,"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":"Regret; Robust optimization; Mathematical optimization; Computer science; Minification; Optimization problem; Stochastic optimization; Set (abstract data type); Exploit; Computation; Robustness (evolution); Affine transformation; Stochastic programming; Mathematics; Algorithm","score_opus":0.3457333605305196,"score_gpt":0.4837777289242717,"score_spread":0.13804436839375206,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2969212598","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":[0.06807714,0.0005411277,0.8923307,0.0020159604,0.00032771216,0.0012184861,0.00020971651,0.000077824,0.035201307],"genre_scores_gemma":[0.7402784,0.0010104802,0.1905276,0.000049571634,0.00015773466,0.00014269815,0.00079471344,0.000046914225,0.06699185],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9944093,0.0011635468,0.0012106479,0.0007015733,0.0020688316,0.00044609146],"domain_scores_gemma":[0.9905906,0.00065647165,0.00014881381,0.0012393886,0.0071725147,0.00019222927],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0044988296,0.00016811611,0.00031402978,0.0010259426,0.0011863532,0.00079567597,0.00043901074,0.0001584924,0.003187633],"category_scores_gemma":[0.005184039,0.00014332896,0.00012470884,0.006411343,0.00019309994,0.001505669,0.00023329805,0.00033535878,0.00012486758],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013419684,0.00015968879,0.0016196498,0.0000074950312,0.000016073353,0.00006055727,0.00078770344,0.98227215,0.00032719484,0.010362916,0.0018083361,0.0025647923],"study_design_scores_gemma":[0.000609218,0.000060455972,0.00012739492,0.000029473731,0.0000138883,0.00011838227,0.0026285734,0.98883873,0.0016628527,0.0005452409,0.0052030995,0.0001626705],"about_ca_topic_score_codex":0.00058203057,"about_ca_topic_score_gemma":0.0023725613,"teacher_disagreement_score":0.7018031,"about_ca_system_score_codex":0.00013022704,"about_ca_system_score_gemma":0.00089793693,"threshold_uncertainty_score":0.9977236},"labels":[],"label_agreement":null},{"id":"W2971343933","doi":"10.48550/arxiv.1904.07381","title":"Approximation Algorithms for Distributionally Robust Stochastic Optimization with Black-Box Distributions","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Vertex cover; Mathematical optimization; Rounding; Approximation algorithm; Robust optimization; Probability distribution; Computer science; Optimization problem; Set cover problem; Stochastic optimization; Mathematics; Algorithm; Set (abstract data type)","score_opus":0.13873070320307423,"score_gpt":0.25493576761384573,"score_spread":0.1162050644107715,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2971343933","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":[0.0103918305,0.00002312273,0.9845125,0.00020117298,0.00051942555,0.0015592696,0.0020599633,0.00013011276,0.0006025666],"genre_scores_gemma":[0.9636947,0.00011365854,0.026574064,0.000025876416,0.00016760723,0.000014407796,0.0063799787,0.00003944364,0.00299029],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9968587,0.00017509535,0.00059945235,0.0014534588,0.0004892764,0.00042400998],"domain_scores_gemma":[0.9951278,0.00064538134,0.00097641,0.0011528353,0.0018952595,0.00020232942],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0009701051,0.00042970545,0.0005412797,0.00043557913,0.00039671318,0.00039349202,0.0010183096,0.00044378734,0.0001438722],"category_scores_gemma":[0.00065834535,0.00039293905,0.000295328,0.0013256788,0.0002595956,0.000638129,0.00039332733,0.00038336654,0.00011279845],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017719777,0.00010966434,0.0006921284,0.000017272056,0.0000689493,0.0000074080435,0.000055764776,0.9686415,7.569495e-7,0.028597943,0.0013254517,0.00030596377],"study_design_scores_gemma":[0.0009310319,0.0001043069,0.00070317823,0.000060817103,0.00021619994,0.0000038116157,0.00019672904,0.97397083,0.000010239426,0.022931678,0.0003863173,0.0004848838],"about_ca_topic_score_codex":0.000033071603,"about_ca_topic_score_gemma":0.000019647552,"teacher_disagreement_score":0.9579385,"about_ca_system_score_codex":0.00038879167,"about_ca_system_score_gemma":0.0005622808,"threshold_uncertainty_score":0.99985224},"labels":[],"label_agreement":null},{"id":"W2971439546","doi":"10.1057/s41260-019-00132-6","title":"Sensitivity of optimal portfolio problems to time-varying parameters: simulation analysis","year":2019,"lang":"en","type":"article","venue":"Journal of Asset Management","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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 Alberta","funders":"","keywords":"Expected shortfall; Portfolio optimization; Portfolio; Sensitivity (control systems); Diversification (marketing strategy); Mathematical optimization; Asset allocation; Computer science; Econometrics; Economics; Mathematics; Financial economics; Engineering; Business","score_opus":0.041550197748854274,"score_gpt":0.3471638455101629,"score_spread":0.3056136477613086,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2971439546","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":[0.68713546,0.000015614667,0.3086323,0.00016821006,0.00017151643,0.0002663676,0.0000048902766,0.000006238058,0.00359935],"genre_scores_gemma":[0.96683884,0.00004034297,0.031392764,0.000079811056,0.000030017285,9.1815366e-7,0.0000049803675,0.000007757053,0.001604585],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99655014,0.00025055188,0.001194357,0.00024489348,0.0015826246,0.00017744659],"domain_scores_gemma":[0.9969698,0.0004958915,0.0013726376,0.00042011924,0.0006260869,0.00011546872],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0056614936,0.00012628283,0.000535343,0.0016223387,0.000038587506,0.00015578564,0.000291098,0.0000470875,0.00033068238],"category_scores_gemma":[0.00026608518,0.00009586746,0.00034088147,0.0022545522,0.000015039195,0.00057489955,0.00012048476,0.00009442757,0.00014616674],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007142588,0.000071342285,0.03153995,0.000005453248,0.00043413258,0.000023576435,0.00015895037,0.9526598,0.00019259809,0.000038074602,0.0013024091,0.013502284],"study_design_scores_gemma":[0.0005091246,0.00029480233,0.07636057,0.000046706697,0.0006374776,0.0000092589335,0.00026472658,0.91574347,0.00025782606,0.00044722037,0.0052460046,0.00018281065],"about_ca_topic_score_codex":0.000016967246,"about_ca_topic_score_gemma":0.0000027751726,"teacher_disagreement_score":0.27970335,"about_ca_system_score_codex":0.00005118937,"about_ca_system_score_gemma":0.00002437652,"threshold_uncertainty_score":0.39093614},"labels":[],"label_agreement":null},{"id":"W2974223901","doi":"10.1016/j.ejor.2019.08.040","title":"Pareto solutions in multicriteria optimization under uncertainty","year":2019,"lang":"en","type":"article","venue":"European Journal of Operational Research","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Dalhousie University","funders":"University of Colorado Denver; Lancaster University","keywords":"Mathematical optimization; Uncountable set; Pareto principle; Mathematics; Multi-objective optimization; Leverage (statistics); Robustness (evolution); Computer science; Countable set; Discrete mathematics","score_opus":0.2795321122757877,"score_gpt":0.4655253884037594,"score_spread":0.18599327612797167,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2974223901","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":[0.79246324,0.000399225,0.1535383,0.0069031897,0.0010777692,0.0005286072,0.000020567639,0.000012125269,0.045056973],"genre_scores_gemma":[0.98900205,0.00020930536,0.0080114035,0.00013843593,0.0002029419,0.0000012192722,0.000012975123,0.00001544022,0.0024062067],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9932107,0.002844983,0.0010357599,0.00024208757,0.0023666052,0.00029987592],"domain_scores_gemma":[0.9956513,0.0009595881,0.00020274971,0.00027969002,0.002764081,0.00014260034],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.020481346,0.000090495276,0.00019365104,0.00094479526,0.00020398683,0.00049869163,0.000651749,0.000030647974,0.0022201915],"category_scores_gemma":[0.0031766475,0.00006664195,0.000078658675,0.001189324,0.00009458565,0.0008275047,0.00013232828,0.00043208324,0.000736082],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000084536645,0.00006624841,0.0053954916,7.5386015e-7,0.0000063773396,0.000028032591,0.00030875203,0.98311776,0.0004949435,0.0016455341,0.0059063826,0.0029451875],"study_design_scores_gemma":[0.0015714593,0.00046525663,0.080591165,0.00007791122,0.0000043561427,0.00009054745,0.0019572682,0.8866434,0.000072719624,0.0016508173,0.02666848,0.00020663267],"about_ca_topic_score_codex":0.000012856605,"about_ca_topic_score_gemma":0.000015944242,"teacher_disagreement_score":0.19653884,"about_ca_system_score_codex":0.0001257582,"about_ca_system_score_gemma":0.00041030505,"threshold_uncertainty_score":0.9986919},"labels":[],"label_agreement":null},{"id":"W2974378149","doi":"10.1287/mnsc.2020.3617","title":"An Axiomatic Foundation for the Expected Shortfall","year":2020,"lang":"en","type":"article","venue":"Management Science","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":110,"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; University of Waterloo","funders":"","keywords":"Expected shortfall; Coherent risk measure; Diversification (marketing strategy); Axiom; Portfolio; Dynamic risk measure; Spectral risk measure; Actuarial science; Value at risk; Modern portfolio theory; Risk measure; Basel II; Risk management; Basel III; Economics; Financial economics; Business; Capital requirement; Profit (economics); Microeconomics; Mathematics; Finance","score_opus":0.14094196720961763,"score_gpt":0.4108089590384625,"score_spread":0.2698669918288449,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2974378149","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":[0.026163723,0.000016945702,0.95673084,0.0059827613,0.00035805907,0.0007685427,0.00000143785,0.00007642795,0.009901242],"genre_scores_gemma":[0.98580354,0.000041721523,0.012251925,0.0012564217,0.00007539967,0.000056863875,0.0000041307076,0.000005065836,0.0005049255],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99768674,0.000038990238,0.00031258338,0.0004736201,0.0012779105,0.00021015834],"domain_scores_gemma":[0.9988516,0.00019672733,0.00013393497,0.00053391675,0.00017468474,0.00010912837],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023775685,0.00007777235,0.000086954715,0.00015547614,0.0006250503,0.0009240738,0.0015716506,0.00001289804,0.00012843429],"category_scores_gemma":[0.00056912977,0.000046180725,0.000039308117,0.002285067,0.0002076947,0.0009770733,0.00011955236,0.000027938135,0.00017620777],"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":[0.000046853118,0.00006993698,0.0045305085,0.000007801441,0.000015086623,0.0000031513434,0.0037136117,0.060460456,0.00080461777,0.10599234,0.01020449,0.81415117],"study_design_scores_gemma":[0.00018861167,0.00009176385,0.025351003,0.0000023071775,0.000017987559,4.4689517e-7,0.0022364592,0.91781765,0.00018421093,0.009627263,0.044370484,0.000111792295],"about_ca_topic_score_codex":0.000005499663,"about_ca_topic_score_gemma":0.0000054587736,"teacher_disagreement_score":0.95963985,"about_ca_system_score_codex":0.000020267873,"about_ca_system_score_gemma":0.000025310037,"threshold_uncertainty_score":0.89108646},"labels":[],"label_agreement":null},{"id":"W2982150252","doi":"10.1017/asb.2019.31","title":"MULTIVARIATE GEOMETRIC TAIL- AND RANGE-VALUE-AT-RISK","year":2019,"lang":"en","type":"article","venue":"Astin Bulletin","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Concordia University; University of Waterloo","funders":"","keywords":"Multivariate statistics; Univariate; Generalization; Range (aeronautics); Multivariate analysis; Value at risk; Value (mathematics); Econometrics; Mathematics; Statistics; Economics; Risk management; Engineering; Mathematical analysis","score_opus":0.026136785090656246,"score_gpt":0.30050577169800347,"score_spread":0.2743689866073472,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2982150252","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":[0.9674127,0.0008560473,0.007866062,0.0014830603,0.000629431,0.00037426112,0.00003057892,0.000069773334,0.021278102],"genre_scores_gemma":[0.9523672,0.0004869121,0.0086142775,0.00025899167,0.00010080506,0.000008961212,0.000008124111,0.000021391585,0.03813336],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.9975146,0.0002739869,0.00049708824,0.0005765434,0.0008415997,0.0002961633],"domain_scores_gemma":[0.99726176,0.0015722483,0.00033633856,0.00051135954,0.00017457391,0.00014369683],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0025223133,0.00016646822,0.0002866824,0.00045505498,0.00018469087,0.00017594254,0.00034726755,0.000108709864,0.006803221],"category_scores_gemma":[0.0033534744,0.0001249417,0.00007515191,0.0010438946,0.000060911232,0.00008141577,0.00023943282,0.00015381376,0.012202271],"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":[0.00012834101,0.000045124118,0.78727365,0.0000040147324,0.000016858761,0.0000093582985,0.00031952682,0.0071089943,0.00014025002,0.0004073133,0.07321354,0.13133307],"study_design_scores_gemma":[0.0010744196,0.00008821259,0.38126868,0.000008132493,0.000018100109,0.000013675549,0.00009375524,0.0073579964,0.00012521377,0.0006285786,0.60910016,0.00022306468],"about_ca_topic_score_codex":0.00030273932,"about_ca_topic_score_gemma":0.0000066825796,"teacher_disagreement_score":0.53588665,"about_ca_system_score_codex":0.000024158004,"about_ca_system_score_gemma":0.00002138893,"threshold_uncertainty_score":0.9941047},"labels":[],"label_agreement":null},{"id":"W2984785540","doi":"10.1007/s10107-019-01447-3","title":"Nonlinear chance-constrained problems with applications to hydro scheduling","year":2019,"lang":"en","type":"article","venue":"Mathematical Programming","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Polytechnique Montréal","funders":"Air Force Office of Scientific Research; Ministero dell’Istruzione, dell’Università e della Ricerca","keywords":"Mathematical optimization; Solver; Mathematics; Nonlinear programming; Nonlinear system; Integer programming; Scheduling (production processes)","score_opus":0.051191507727888726,"score_gpt":0.3425473852442398,"score_spread":0.29135587751635106,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2984785540","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":[0.08797263,0.00002101192,0.89842016,0.0006925909,0.000043077238,0.0020847926,0.0000030024826,0.00015319185,0.01060952],"genre_scores_gemma":[0.4167415,0.000003032838,0.5812908,0.00011217215,0.00007611978,0.00032533414,0.000007027375,0.000023701761,0.0014203711],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99754685,0.00003906001,0.00060532964,0.00051092147,0.0009182129,0.0003796276],"domain_scores_gemma":[0.99834865,0.00037473752,0.00018396607,0.0006253026,0.00024505812,0.00022227189],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0013097302,0.00017258414,0.0003292655,0.00019307437,0.00013599322,0.00036735178,0.00044912478,0.000074887714,0.00028730545],"category_scores_gemma":[0.0003528038,0.00011345148,0.000073228606,0.0012309349,0.00007376728,0.00022781605,0.00008343454,0.00014323439,0.0029525654],"study_design_candidate":"design_other","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":[0.00005984812,0.00072784774,0.005012835,0.0001342406,0.00005913882,0.0000072271614,0.0028873445,0.03750047,0.0008771708,0.08797477,0.0000739575,0.8646851],"study_design_scores_gemma":[0.0018250946,0.0010673325,0.00015918197,0.0005065783,0.000089778216,0.00011960609,0.0029226374,0.6215347,0.0019374434,0.16716677,0.20121963,0.00145125],"about_ca_topic_score_codex":0.0000024178632,"about_ca_topic_score_gemma":0.0000036049116,"teacher_disagreement_score":0.86323386,"about_ca_system_score_codex":0.00001940111,"about_ca_system_score_gemma":0.00005393884,"threshold_uncertainty_score":0.9978238},"labels":[],"label_agreement":null},{"id":"W2987526535","doi":"10.1016/j.insmatheco.2019.10.007","title":"Convex risk functionals: Representation and applications","year":2019,"lang":"en","type":"article","venue":"Insurance Mathematics and Economics","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"University of Waterloo; Actua","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China; National Outstanding Youth Science Fund Project of National Natural Science Foundation of China; Higher Education Discipline Innovation Project; Society of Actuaries","keywords":"Mathematics; Invariant (physics); Representation (politics); Regular polygon; Reinsurance; Representation theorem; Convex analysis; Convex optimization; Variance (accounting); Applied mathematics; Pure mathematics; Mathematical optimization; Actuarial science; Economics; Law","score_opus":0.044439351526659066,"score_gpt":0.3116396053015381,"score_spread":0.26720025377487905,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2987526535","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":[0.96567225,0.00020135283,0.025094839,0.000118757904,0.00014082581,0.0002832662,0.000041878553,0.00001533329,0.008431514],"genre_scores_gemma":[0.98622614,0.0048770057,0.0076705907,0.00007736978,0.00006381888,0.00003168264,0.000008026101,0.000009654734,0.0010357244],"study_design_codex":"observational","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9991293,0.000016799648,0.0004007255,0.00026659208,0.00010266355,0.00008389459],"domain_scores_gemma":[0.9987421,0.0004944343,0.00031532583,0.00031227243,0.00008320232,0.000052647134],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006624804,0.00007702343,0.0001893698,0.000083968334,0.00010606855,0.0002015303,0.00009988581,0.000047577334,0.00008650402],"category_scores_gemma":[0.00012877231,0.00006468427,0.000029845207,0.00011721842,0.00004897507,0.00027030034,0.000039564686,0.0000522465,0.00024293811],"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":[0.00001800534,0.00006591342,0.6996781,0.000019924393,0.000035203877,2.3643751e-7,0.0010794564,0.013547561,0.000033663826,0.09493855,0.000595775,0.18998756],"study_design_scores_gemma":[0.0006701616,0.00004051417,0.18139924,0.000011278315,0.000018660123,0.00003131357,0.0015209276,0.234841,0.00008191356,0.52622455,0.05488524,0.00027520026],"about_ca_topic_score_codex":0.000011658814,"about_ca_topic_score_gemma":0.00000770759,"teacher_disagreement_score":0.5182789,"about_ca_system_score_codex":0.000008011066,"about_ca_system_score_gemma":0.000014056037,"threshold_uncertainty_score":0.31225592},"labels":[],"label_agreement":null},{"id":"W2996984992","doi":"10.2139/ssrn.3508860","title":"Portfolio Optimization under Correlation Constraint","year":2019,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"McMaster University","funders":"","keywords":"Constraint (computer-aided design); Correlation; Portfolio optimization; Portfolio; Computer science; Econometrics; Mathematical optimization; Mathematics; Economics; Financial economics","score_opus":0.021377340056577297,"score_gpt":0.3057633723025269,"score_spread":0.2843860322459496,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2996984992","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":[0.07990876,0.0005022657,0.8958556,0.00066336297,0.000993799,0.00021280276,0.0000019469283,0.000040311337,0.021821167],"genre_scores_gemma":[0.9803402,0.0033796825,0.0014476477,0.00019388678,0.00018470216,0.0000018823071,0.000016332639,0.00002083895,0.014414823],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.996345,0.00019008123,0.00074848987,0.00033325035,0.0011112669,0.0012719245],"domain_scores_gemma":[0.99830896,0.00020037642,0.0005887768,0.00033623137,0.00045324236,0.00011240481],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.005216207,0.00016130361,0.00023814505,0.00041467423,0.00020863162,0.00029420116,0.00041579542,0.00013582085,0.0016788512],"category_scores_gemma":[0.0003076378,0.00012513819,0.00015051734,0.0007497827,0.000050600054,0.00078103057,0.000034862645,0.0009900396,0.0008026018],"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":[0.000033068198,0.000026375701,0.016024904,2.4545201e-7,0.000030545576,0.0000011433726,0.00005905376,0.78979445,0.000021191625,0.16708326,0.00044074486,0.026484987],"study_design_scores_gemma":[0.001387948,0.00035577107,0.0040105605,0.000013446481,0.000040553863,0.00097227766,0.00464335,0.32974526,0.0000256609,0.6525115,0.005914206,0.0003795011],"about_ca_topic_score_codex":0.000016598242,"about_ca_topic_score_gemma":0.00004118378,"teacher_disagreement_score":0.90043145,"about_ca_system_score_codex":0.000453837,"about_ca_system_score_gemma":0.0018905725,"threshold_uncertainty_score":0.9999754},"labels":[],"label_agreement":null},{"id":"W2997362232","doi":"10.1287/opre.2022.2366","title":"Lagrangian Dual Decision Rules for Multistage Stochastic Mixed-Integer Programming","year":2022,"lang":"en","type":"article","venue":"Operations Research","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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; HEC Montréal","funders":"","keywords":"Dual polyhedron; Bounding overwatch; Lagrangian relaxation; Mathematical optimization; Dual (grammatical number); Integer programming; Lagrangian; Augmented Lagrangian method; Multiplier (economics); Mathematics; Integer (computer science); Lagrange multiplier; Computer science; Linear programming; Stochastic programming; Decision problem; Satisfiability; Applied mathematics; Algorithm; Artificial intelligence; Combinatorics","score_opus":0.1816251271320773,"score_gpt":0.4820940679830823,"score_spread":0.300468940851005,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2997362232","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":[0.121842444,0.00020120862,0.87295574,0.0012700118,0.0005872867,0.0017977527,0.0003067765,0.0000564981,0.0009822858],"genre_scores_gemma":[0.9332061,0.000020770127,0.04888978,0.000042292257,0.00016767828,0.0013095033,0.00024838475,0.000029455001,0.016086062],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9951073,0.00060956203,0.0005624956,0.00057746546,0.0026511366,0.000492055],"domain_scores_gemma":[0.99560493,0.0022689295,0.000041503215,0.00060577807,0.0013212392,0.00015764573],"candidate_categories":["sts","scholarly_communication","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.008502083,0.000116368705,0.00018507845,0.0009173005,0.0032474075,0.001044034,0.0006776841,0.000054621032,0.0012434286],"category_scores_gemma":[0.005555854,0.00009435802,0.00011490586,0.0017280054,0.00013046709,0.00039116477,0.00038536682,0.00037710182,0.00031582976],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001359558,0.00022394487,0.0001811785,0.0000017994632,0.000011868258,0.000010506944,0.0012915769,0.6576086,0.0002608262,0.013486879,0.027764073,0.29902276],"study_design_scores_gemma":[0.0006766951,0.00032777956,0.0004454109,0.000006963913,0.0000061776263,0.00001848286,0.005253936,0.7102786,0.00007866825,0.003062138,0.27966225,0.0001829425],"about_ca_topic_score_codex":0.00021544739,"about_ca_topic_score_gemma":0.00071126036,"teacher_disagreement_score":0.824066,"about_ca_system_score_codex":0.00012852787,"about_ca_system_score_gemma":0.00031225054,"threshold_uncertainty_score":0.99999297},"labels":[],"label_agreement":null},{"id":"W2999941322","doi":"10.2139/ssrn.967234","title":"Improving Performance By Constraining Portfolio Norms: A Generalized Approach to Portfolio Optimization","year":2007,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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 British Columbia","funders":"","keywords":"Portfolio; Portfolio optimization; Black–Litterman model; Post-modern portfolio theory; Economics; Computer science; Actuarial science; Econometrics; Mathematical optimization; Business; Mathematical economics; Financial economics; Replicating portfolio; Mathematics","score_opus":0.023570985744108652,"score_gpt":0.29861066510615675,"score_spread":0.2750396793620481,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2999941322","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":[0.17949672,0.00072734914,0.8000647,0.00010145882,0.00034539946,0.00028134845,0.000004695449,0.000063977495,0.018914344],"genre_scores_gemma":[0.9551068,0.003522069,0.03423887,0.00044884736,0.00048713028,0.000010545488,0.000042581214,0.000056729605,0.006086409],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9929936,0.00013163361,0.0015078717,0.00065111247,0.0016616259,0.003054135],"domain_scores_gemma":[0.9974487,0.00013685471,0.0009222319,0.00045897436,0.0006017173,0.00043156065],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.015444294,0.0003433321,0.00046231726,0.0009084506,0.000638673,0.0004917724,0.0008910134,0.00019737982,0.00015135664],"category_scores_gemma":[0.0006467519,0.00027693206,0.00019434656,0.0018528905,0.000085269065,0.0010828179,0.000092415205,0.0012976647,0.00005528314],"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":[0.00042087393,0.0002464989,0.018641308,0.000004396159,0.00013904444,0.000012164026,0.0008438433,0.41297153,0.00092364114,0.017338905,0.00589511,0.54256266],"study_design_scores_gemma":[0.008570954,0.0024532685,0.0034951938,0.000081932114,0.0002920816,0.008146916,0.022923218,0.8765752,0.002703816,0.029571014,0.041642938,0.0035434738],"about_ca_topic_score_codex":0.00008066428,"about_ca_topic_score_gemma":0.000040819566,"teacher_disagreement_score":0.7756101,"about_ca_system_score_codex":0.00065149664,"about_ca_system_score_gemma":0.0018861982,"threshold_uncertainty_score":0.9999683},"labels":[],"label_agreement":null},{"id":"W2999984739","doi":"10.1017/asb.2019.39","title":"BILATERAL RISK SHARING WITH HETEROGENEOUS BELIEFS AND EXPOSURE CONSTRAINTS","year":2020,"lang":"en","type":"article","venue":"Astin Bulletin","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Constraint (computer-aided design); Monotone polygon; Rank (graph theory); Expected utility hypothesis; Distortion (music); Monotonic function; Function (biology); Mathematical optimization; Mathematical economics; Econometrics; Computer science; Economics; Mathematics; Combinatorics","score_opus":0.04390057193014945,"score_gpt":0.2777916528633823,"score_spread":0.23389108093323285,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2999984739","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":[0.9830201,0.00019950149,0.009882427,0.0037023844,0.00007790525,0.00017243631,0.000021986903,0.00007201285,0.0028512068],"genre_scores_gemma":[0.9911492,0.00011221879,0.007445868,0.00067340396,0.00011704886,0.0000051780094,0.0000045005645,0.000014422336,0.00047818082],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9983725,0.00008424406,0.00035303892,0.0005173364,0.00046889007,0.00020400266],"domain_scores_gemma":[0.9990615,0.00021942561,0.00018578219,0.00022709981,0.000108322194,0.00019785017],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0005019498,0.00014098825,0.00020231074,0.00005848483,0.00015043943,0.00025020784,0.0002638256,0.000057879817,0.0012648273],"category_scores_gemma":[0.0005359519,0.00009728553,0.00003585113,0.00023916067,0.00015069841,0.000054139375,0.00011975911,0.00013842985,0.00039536098],"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":[0.00022293642,0.000025235804,0.7885234,0.000004557358,0.00003395455,0.00014495317,0.0020115143,0.02088965,0.00017209709,0.00013708681,0.008165077,0.17966956],"study_design_scores_gemma":[0.007600503,0.0049257237,0.47250694,0.00016983788,0.00017323566,0.0010521074,0.0016533855,0.054028194,0.0028982481,0.0032723402,0.44947228,0.0022472034],"about_ca_topic_score_codex":0.000036120546,"about_ca_topic_score_gemma":0.0000074106483,"teacher_disagreement_score":0.44130722,"about_ca_system_score_codex":0.000004598802,"about_ca_system_score_gemma":0.000019389923,"threshold_uncertainty_score":0.99964815},"labels":[],"label_agreement":null},{"id":"W3003409770","doi":"10.2139/ssrn.3454958","title":"Bridging the Gap between Strategic Allocation and Investment Risk","year":2019,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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; University of Toronto","funders":"","keywords":"Bridging (networking); Business; Investment (military); Risk management; Industrial organization; Finance; Computer science; Computer security; Political science; Politics","score_opus":0.048707692401279,"score_gpt":0.3214800739031334,"score_spread":0.2727723815018544,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3003409770","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":[0.9748692,0.0016076105,0.018405864,0.0014460171,0.00015971568,0.00016199927,0.0000020570037,0.0000119081105,0.0033356599],"genre_scores_gemma":[0.9905333,0.007175798,0.00008080118,0.0001298001,0.00025769166,0.0000016691016,0.0000029138785,0.000008679937,0.0018093562],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9976147,0.00029567393,0.00039754435,0.00021864448,0.00066822453,0.0008052486],"domain_scores_gemma":[0.9989082,0.0002324175,0.00039829465,0.00026049267,0.00012863317,0.00007193091],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0073257745,0.00010388949,0.00014701855,0.00013084609,0.0003205607,0.00035696756,0.000355579,0.000049038197,0.00004195642],"category_scores_gemma":[0.00014589995,0.000059445276,0.000059429643,0.00033993658,0.00004356079,0.00032647006,0.00003746175,0.0008700757,0.00014285286],"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":[0.00003231131,0.000026016547,0.3587871,0.0000011408214,0.0001404266,9.789943e-7,0.0007703314,0.0077116564,0.00013056703,0.39785993,0.00051405054,0.23402548],"study_design_scores_gemma":[0.0003753379,0.00018212893,0.02631437,0.000005576167,0.00003790714,0.000090788184,0.0024888162,0.009380493,0.00003772703,0.9588423,0.002128204,0.00011635952],"about_ca_topic_score_codex":0.00007861101,"about_ca_topic_score_gemma":0.00013758105,"teacher_disagreement_score":0.56098235,"about_ca_system_score_codex":0.00016134942,"about_ca_system_score_gemma":0.00076940516,"threshold_uncertainty_score":0.37800944},"labels":[],"label_agreement":null},{"id":"W3008447057","doi":"10.1515/strm-2020-0006","title":"On the extension property of dilatation monotone risk measures","year":2020,"lang":"en","type":"preprint","venue":"Statistics & Risk Modeling","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Monotone polygon; Mathematics; Monotonic function; Convexity; Combinatorics; Extension (predicate logic); Invariant (physics); Norm (philosophy); Quasiconvex function; Pure mathematics; Regular polygon; Mathematical analysis; Mathematical physics; Convex set","score_opus":0.19137976055555467,"score_gpt":0.3709122768625082,"score_spread":0.17953251630695355,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3008447057","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":[0.04726739,0.00029164992,0.9478047,0.0003284628,0.00052983256,0.0007862263,0.0023638709,0.000051669776,0.0005762197],"genre_scores_gemma":[0.905891,0.00989909,0.08358904,0.00008497827,0.00011334661,0.00003905124,0.00019891889,0.000048828042,0.0001357394],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9936878,0.0010085299,0.0015644635,0.0009977048,0.002477246,0.00026425542],"domain_scores_gemma":[0.9921843,0.0021241368,0.0025039266,0.0013385365,0.0017192453,0.00012989495],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.004079274,0.00038089763,0.0006526101,0.0002584853,0.0004142454,0.00030389414,0.00090034615,0.00024956776,0.000054974975],"category_scores_gemma":[0.020732813,0.00019813764,0.00018470154,0.00041939016,0.00011313734,0.00010963472,0.00046763328,0.0011243373,0.0000953146],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014790885,0.000037648177,0.0004976033,0.000008338814,0.00004220548,0.0000032635212,0.0012244956,0.91372377,0.000035056244,0.006595374,0.0038642033,0.07382014],"study_design_scores_gemma":[0.00010534643,0.000050128558,0.00024717086,0.00005046833,0.00010402054,3.4239108e-7,0.00013935176,0.5992907,0.00006969496,0.39961123,0.00017389169,0.00015762345],"about_ca_topic_score_codex":0.001592589,"about_ca_topic_score_gemma":0.00010350147,"teacher_disagreement_score":0.8642156,"about_ca_system_score_codex":0.00005139349,"about_ca_system_score_gemma":0.00028660803,"threshold_uncertainty_score":0.987516},"labels":[],"label_agreement":null},{"id":"W3008948251","doi":"10.2139/ssrn.3127285","title":"Risk Measures Derived From a Regulatorrs Perspective on the Regulatory Capital Requirements for Insurers","year":2017,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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 Waterloo","funders":"","keywords":"Capital requirement; Business; Perspective (graphical); Actuarial science; Capital adequacy ratio; Risk analysis (engineering); Economics; Computer science; Incentive","score_opus":0.058541058909318176,"score_gpt":0.35054667417080765,"score_spread":0.2920056152614895,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3008948251","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":[0.9761318,0.0012916767,0.016437754,0.0031746828,0.0007712536,0.0003919466,0.000032944463,0.000021444579,0.001746519],"genre_scores_gemma":[0.9946091,0.0032295296,0.00020003313,0.00011578053,0.0005324376,0.000016408207,0.000002538989,0.000027192702,0.0012669464],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9958907,0.0003714645,0.0005221065,0.00047835836,0.0014649621,0.0012724291],"domain_scores_gemma":[0.99617445,0.00054435007,0.0012785437,0.0011285511,0.00075288187,0.000121191304],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.008388517,0.00022033813,0.00027977486,0.0001652277,0.002703609,0.000808176,0.0015701117,0.00011965999,0.000042589134],"category_scores_gemma":[0.0055552213,0.00013196893,0.00030932046,0.00011943556,0.00023302554,0.00061437883,0.00007403101,0.0010126425,0.000051830568],"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":[0.0021128333,0.00027913155,0.099923715,7.1933334e-7,0.0016207295,0.000008526353,0.008652262,0.00359363,0.0017063776,0.6620035,0.009176131,0.21092242],"study_design_scores_gemma":[0.0010561771,0.00031851398,0.08239126,0.000012742406,0.00007076366,0.000016263506,0.011657101,0.000658255,0.000592936,0.9010184,0.0020037354,0.00020387513],"about_ca_topic_score_codex":0.0005085803,"about_ca_topic_score_gemma":0.0019016652,"teacher_disagreement_score":0.23901485,"about_ca_system_score_codex":0.0009938017,"about_ca_system_score_gemma":0.0014597147,"threshold_uncertainty_score":0.99859476},"labels":[],"label_agreement":null},{"id":"W3009441102","doi":"10.1016/j.insmatheco.2020.02.010","title":"On sums of two counter-monotonic risks","year":2020,"lang":"en","type":"article","venue":"Insurance Mathematics and Economics","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Actua; Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada; Fondation de l’Université Laval","keywords":"Monotonic function; Subadditivity; Mathematics; Expected shortfall; Independent and identically distributed random variables; Value at risk; Bivariate analysis; Diversification (marketing strategy); Value (mathematics); Econometrics; Random variable; Statistics; Risk management; Combinatorics; Economics; Business","score_opus":0.14341728612584595,"score_gpt":0.3626358689502403,"score_spread":0.21921858282439435,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3009441102","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":[0.9841943,0.00006755284,0.0069981287,0.00033397888,0.00010493945,0.000112645655,0.000049631322,0.0000113973,0.008127474],"genre_scores_gemma":[0.9942536,0.0009630966,0.0043920237,0.0002971073,0.000033534783,0.0000033836902,0.0000015003237,0.000009909918,0.00004584442],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989616,0.000013523476,0.00057320466,0.00021470193,0.00013159963,0.0001053689],"domain_scores_gemma":[0.9988399,0.0003860627,0.00036953785,0.00025860616,0.00006725454,0.0000786199],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000491318,0.00009803577,0.0003022843,0.000055528584,0.00004737462,0.000098604454,0.00023170831,0.00003930921,0.00005330147],"category_scores_gemma":[0.00027748506,0.00007802674,0.000057666777,0.00011493952,0.00005928224,0.00013586249,0.000042216816,0.000062226456,0.0000957237],"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":[0.00023839503,0.00032005788,0.100313574,0.00011602734,0.00012446569,0.0000072692465,0.013900879,0.25486293,0.00037276297,0.48884553,0.0047349003,0.13616319],"study_design_scores_gemma":[0.0011089537,0.00022114434,0.005778726,0.000034881232,0.000013630634,0.0000066185644,0.00050613366,0.7271059,0.0014385857,0.25787368,0.0056201178,0.00029160315],"about_ca_topic_score_codex":0.000011020726,"about_ca_topic_score_gemma":0.000009135499,"teacher_disagreement_score":0.47224298,"about_ca_system_score_codex":0.000008738342,"about_ca_system_score_gemma":0.00002298694,"threshold_uncertainty_score":0.3181838},"labels":[],"label_agreement":null},{"id":"W3010762676","doi":"10.48550/arxiv.2003.07915","title":"The value of randomized strategies in distributionally robust risk averse network interdiction games","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"CVAR; Mathematical optimization; Interdiction; Computer science; Relaxation (psychology); Convex optimization; Mathematics; Expected shortfall; Regular polygon; Risk management","score_opus":0.08790647729152554,"score_gpt":0.2450310449307071,"score_spread":0.15712456763918156,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3010762676","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":[0.23898675,0.00023254441,0.7551632,0.00024391359,0.0010127173,0.0006681777,0.00011418095,0.000073516996,0.0035049964],"genre_scores_gemma":[0.9925795,0.0064393235,0.00037676463,0.000015604315,0.0001217374,0.000002362296,0.000061410276,0.000013234633,0.00039005477],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9961954,0.0015772243,0.0008753213,0.0007403043,0.00035664992,0.00025510215],"domain_scores_gemma":[0.99485683,0.0025330088,0.0013827805,0.0006904991,0.00044014136,0.00009674875],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003952617,0.00025200882,0.00079578353,0.00020938164,0.00023223912,0.00024656966,0.0011041649,0.00025194528,0.000050215785],"category_scores_gemma":[0.0017973682,0.0001870939,0.00054306147,0.0012081655,0.00046632736,0.0003263052,0.00070437597,0.000614127,0.00004038849],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0050005163,0.000022744156,0.007927961,0.0000036907743,0.00009003221,0.000017981443,0.00018139383,0.86691594,3.7467092e-7,0.11820613,0.0012385247,0.00039468493],"study_design_scores_gemma":[0.0062839966,0.000019027006,0.004945711,0.000045042136,0.00012311742,6.316821e-7,0.0009889038,0.616007,0.0000034857667,0.37075263,0.0006787608,0.00015171479],"about_ca_topic_score_codex":0.00046357306,"about_ca_topic_score_gemma":0.0002979918,"teacher_disagreement_score":0.75478643,"about_ca_system_score_codex":0.00011881007,"about_ca_system_score_gemma":0.0003716381,"threshold_uncertainty_score":0.7629468},"labels":[],"label_agreement":null},{"id":"W3011455298","doi":"10.1080/03155986.2020.1730676","title":"A survey of nonlinear robust optimization","year":2020,"lang":"en","type":"article","venue":"INFOR Information Systems and Operational Research","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":46,"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":"Robust optimization; Mathematical optimization; Optimization problem; Ellipsoid; Computer science; Set (abstract data type); Polyhedron; Nonlinear system; Nonlinear programming; Mathematics","score_opus":0.29757167266280166,"score_gpt":0.43267051838708126,"score_spread":0.1350988457242796,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3011455298","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":[0.07330871,0.000539485,0.87371486,0.0034744164,0.0005885413,0.002391445,0.0010737256,0.000077804274,0.044831034],"genre_scores_gemma":[0.9934644,0.00026536008,0.0049543018,0.00029140007,0.00010689724,0.000031283507,0.0005646877,0.000006632659,0.00031502527],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9956024,0.0003530492,0.0012664604,0.00015795429,0.0024389743,0.00018110663],"domain_scores_gemma":[0.9929233,0.0009019481,0.00027626875,0.00020026127,0.0055135186,0.00018470621],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0073378496,0.00009346286,0.00023103828,0.00044271018,0.00026880018,0.00087898236,0.00031216344,0.00010187371,0.00014208158],"category_scores_gemma":[0.007840823,0.00007021977,0.00003087093,0.0016650801,0.00010928038,0.0028841542,0.00011671817,0.00016134398,0.00018825872],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007304341,0.000008256841,0.013695389,0.000015775622,0.000008208868,2.0001114e-7,0.0013033481,0.9675025,0.0000044609164,0.0047901478,0.0076280963,0.0049706222],"study_design_scores_gemma":[0.00032409816,0.00009593137,0.01284453,0.000009981625,0.0000011739834,0.0000026863806,0.000897078,0.9422368,0.00002414252,0.0000117372265,0.043475565,0.00007629913],"about_ca_topic_score_codex":0.0006755245,"about_ca_topic_score_gemma":0.00001863921,"teacher_disagreement_score":0.9201557,"about_ca_system_score_codex":0.000021954345,"about_ca_system_score_gemma":0.0003725357,"threshold_uncertainty_score":0.9386764},"labels":[],"label_agreement":null},{"id":"W3011682496","doi":"10.1109/cdc40024.2019.9029801","title":"Robustness to Incorrect Models in Average-Cost Optimal Stochastic Control","year":2019,"lang":"en","type":"preprint","venue":"","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Queen's University","funders":"","keywords":"Robustness (evolution); Mathematics; Mathematical optimization; Optimal control; Convergence (economics); Ergodicity; State variable; Control variable; Applied mathematics; Statistics; Economics","score_opus":0.07458419289337358,"score_gpt":0.35313995979519003,"score_spread":0.27855576690181644,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3011682496","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":[0.023035612,0.000046307327,0.9608522,0.00059103424,0.0031118763,0.0021638384,0.00009113597,0.00007025025,0.010037736],"genre_scores_gemma":[0.97493315,0.00003546735,0.013427931,0.0005399764,0.00014783164,0.00020464668,0.000043653643,0.000038515926,0.010628805],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9950606,0.0003063506,0.001279525,0.0013146708,0.0015494687,0.00048935635],"domain_scores_gemma":[0.99624,0.001089865,0.00040959046,0.0014268408,0.00057983847,0.0002538327],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0028940442,0.00042706216,0.0009800523,0.0011885855,0.00006541833,0.0006232406,0.0015479587,0.0004644591,0.00072687684],"category_scores_gemma":[0.0012047652,0.0003293991,0.00021948677,0.0008336591,0.0000359281,0.00039505528,0.0008458724,0.0006494381,0.00068561663],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015691831,0.000051098923,0.00045908595,0.000003217591,0.000011497804,0.000010502716,0.00033051913,0.98288655,0.0000011712385,0.00023774199,0.005579947,0.010271727],"study_design_scores_gemma":[0.00075468095,0.000040595827,0.0003900571,0.000058482572,0.0000140518705,0.0000065583467,0.00013108914,0.9955058,0.000005488293,0.0023628122,0.00030617043,0.00042420256],"about_ca_topic_score_codex":0.00041606763,"about_ca_topic_score_gemma":0.00021308572,"teacher_disagreement_score":0.95189756,"about_ca_system_score_codex":0.00015248674,"about_ca_system_score_gemma":0.0005027089,"threshold_uncertainty_score":0.9999158},"labels":[],"label_agreement":null},{"id":"W3012489855","doi":"10.1109/cdc40024.2019.9029532","title":"Convex Symmetric Stochastic Dynamic Teams and Their Mean-Field Limit","year":2019,"lang":"en","type":"article","venue":"","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Queen's University","funders":"","keywords":"Linear-quadratic-Gaussian control; Mathematics; Mathematical optimization; Concentration of measure; Optimal control; Field (mathematics); Regular polygon; Applied mathematics; Computer science; Discrete mathematics; Pure mathematics","score_opus":0.023442877196394953,"score_gpt":0.30725170619734116,"score_spread":0.28380882900094623,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3012489855","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":[0.706313,0.00047484232,0.20075157,0.0010144006,0.00069154013,0.0003673961,0.0000051212487,0.00007870309,0.09030341],"genre_scores_gemma":[0.98583263,0.00015810305,0.0006167791,0.00050197693,0.000020347601,0.0000031709455,0.000002646828,0.000008652545,0.012855674],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99859035,0.000052804877,0.00034358236,0.00036872106,0.00045472596,0.00018982556],"domain_scores_gemma":[0.99754184,0.0015779753,0.00012061048,0.0005147747,0.00014364772,0.00010115416],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0008421914,0.00012517643,0.00023674167,0.0004044284,0.00006841138,0.00019564855,0.00038519327,0.00009003696,0.00092690415],"category_scores_gemma":[0.0010096139,0.000073433104,0.000056409954,0.000938556,0.000030693165,0.00028702206,0.00013678207,0.00010131791,0.0010001464],"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":[0.00010696776,0.000120989054,0.044205144,0.0000070453475,0.000056072186,0.0000039076476,0.0019123233,0.009081869,0.0006295769,0.024538292,0.014439291,0.9048985],"study_design_scores_gemma":[0.0009881124,0.0005061957,0.016750552,0.000017093067,0.000018269266,0.000034801422,0.0042807036,0.91507447,0.0005566084,0.05388655,0.0073995492,0.0004871248],"about_ca_topic_score_codex":0.000056921624,"about_ca_topic_score_gemma":0.00004255174,"teacher_disagreement_score":0.90599257,"about_ca_system_score_codex":0.000014438338,"about_ca_system_score_gemma":0.000037590857,"threshold_uncertainty_score":0.9999864},"labels":[],"label_agreement":null},{"id":"W3020943580","doi":"10.1080/24754269.2020.1758500","title":"Optimal reinsurance designs based on risk measures: a review","year":2020,"lang":"en","type":"review","venue":"Statistical Theory and Related Fields","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":58,"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; National Natural Science Foundation of China","keywords":"Reinsurance; Actuarial science; Risk management; Computer science; Risk analysis (engineering); Key (lock); Management science; Business; Economics; Finance","score_opus":0.10055824149736668,"score_gpt":0.4055722149285359,"score_spread":0.3050139734311692,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3020943580","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[4.0440586e-8,0.6911211,0.3003997,0.00012836324,0.0002960947,0.00051391363,0.00029750945,0.000041692783,0.007201595],"genre_scores_gemma":[0.000034782664,0.99525815,0.0030778165,0.00051254575,0.00006358888,0.0000432158,0.000097610995,0.000036133515,0.00087618013],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9917188,0.0047535324,0.0014401776,0.00088874897,0.00090411503,0.00029457375],"domain_scores_gemma":[0.9847329,0.013497401,0.0006720523,0.0006469604,0.00014033279,0.00031031205],"candidate_categories":["metaresearch","metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.006287992,0.00045690915,0.0019029072,0.00017445236,0.00025713767,0.0001345334,0.0005125068,0.00076914893,0.0012323554],"category_scores_gemma":[0.018154131,0.0002737644,0.00038151085,0.0009285255,0.00024311508,0.00007540772,0.0000615424,0.0015591712,0.00056547753],"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":[0.00007356864,0.000024032877,6.3748394e-7,0.0008489263,0.000067975365,0.00012437157,0.000030263911,0.0002988674,2.1763753e-9,0.04135661,0.0076121725,0.94956255],"study_design_scores_gemma":[0.00017726334,0.00026310404,0.0000014884598,0.0097815925,0.0008526655,0.000033582688,0.0000073648193,0.0023004478,5.9655825e-8,0.03875043,0.94747686,0.00035511443],"about_ca_topic_score_codex":0.0000020433347,"about_ca_topic_score_gemma":3.0062373e-7,"teacher_disagreement_score":0.9492075,"about_ca_system_score_codex":0.000021648553,"about_ca_system_score_gemma":0.0002556773,"threshold_uncertainty_score":0.99997145},"labels":[],"label_agreement":null},{"id":"W3021970279","doi":"10.3905/jpm.2007.684751","title":"Robust Portfolio Optimization","year":2007,"lang":"en","type":"article","venue":"The Journal of Portfolio Management","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":167,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Intertek (Canada)","funders":"","keywords":"Portfolio optimization; Portfolio; Robust optimization; Black–Litterman model; Computer science; Project portfolio management; Application portfolio management; Post-modern portfolio theory; Asset allocation; Modern portfolio theory; Estimation; Asset (computer security); Mathematical optimization; Economics; Replicating portfolio; Financial economics; Project management; Mathematics","score_opus":0.07816600764453831,"score_gpt":0.33968253625926537,"score_spread":0.2615165286147271,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3021970279","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":[0.01679054,0.0004577019,0.85860497,0.0006280453,0.0012882297,0.00028612092,0.000001554583,0.000025222444,0.12191761],"genre_scores_gemma":[0.9165739,0.007456096,0.05287538,0.0011398643,0.00090272643,0.0000023280452,0.000006674577,0.000052824664,0.02099024],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.99465966,0.00019581527,0.0020206955,0.00021910899,0.0024939107,0.00041082807],"domain_scores_gemma":[0.99583113,0.0003703995,0.0020374306,0.0007376989,0.00081474136,0.00020862294],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.018187597,0.00021268829,0.00036668777,0.0011035104,0.00024808882,0.00019875128,0.0013576573,0.000078332145,0.0012932232],"category_scores_gemma":[0.00032278616,0.0001234596,0.00023258149,0.0018271449,0.00010362508,0.0006664022,0.00018779613,0.00024809732,0.000094930096],"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":[0.0002184325,0.00010810768,0.002902007,0.0000026062144,0.00010977087,0.00021035914,0.00025998155,0.8421768,0.000009785646,0.0036241063,0.088474475,0.061903577],"study_design_scores_gemma":[0.005025895,0.0011042386,0.13100524,0.00017613254,0.0015150163,0.0022140467,0.016468728,0.07350627,0.0010109261,0.03505212,0.7314693,0.0014521342],"about_ca_topic_score_codex":0.000013060391,"about_ca_topic_score_gemma":0.000007375521,"teacher_disagreement_score":0.8997833,"about_ca_system_score_codex":0.00008237365,"about_ca_system_score_gemma":0.0000547547,"threshold_uncertainty_score":0.9996197},"labels":[],"label_agreement":null},{"id":"W3022460099","doi":"10.48550/arxiv.2005.02950","title":"Modality for Scenario Analysis and Maximum Likelihood Allocation","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Econometrics; Conditional probability distribution; Capital allocation line; Distribution (mathematics); Conditional expectation; Set (abstract data type); Mathematics; Statistics; Computer science; Economics; Microeconomics","score_opus":0.16601147592633841,"score_gpt":0.27168570028158956,"score_spread":0.10567422435525115,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3022460099","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":[0.2282766,0.00004334308,0.7694346,0.0007896679,0.00019310195,0.00038724625,0.00009744012,0.000060234084,0.00071782],"genre_scores_gemma":[0.995377,0.00059221877,0.002870103,0.00012348604,0.000079277685,0.0000019137915,0.00017307098,0.000013501095,0.00076939823],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975418,0.00018658086,0.00042449808,0.0013671486,0.0002389787,0.00024102192],"domain_scores_gemma":[0.997427,0.0003386812,0.0005301137,0.0008853091,0.0005719935,0.00024691876],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0010629791,0.0002496751,0.00054326083,0.000631468,0.00020156703,0.00027863745,0.00076444057,0.00030674707,0.00006860571],"category_scores_gemma":[0.00046017487,0.00024999407,0.0004007016,0.0019832691,0.00009971848,0.00028385114,0.0006057189,0.00025185585,0.000040383144],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00027518818,0.00010395729,0.17253126,0.00003394473,0.0008078566,0.000028526258,0.0005986635,0.79192513,0.000023665301,0.019179896,0.0013776508,0.013114232],"study_design_scores_gemma":[0.00029402968,0.000029425335,0.019149011,0.0000058138644,0.0007623145,3.2812773e-7,0.00016819542,0.63136,0.00003126177,0.34700876,0.00095539744,0.00023547142],"about_ca_topic_score_codex":0.00038202392,"about_ca_topic_score_gemma":0.00038452056,"teacher_disagreement_score":0.76710045,"about_ca_system_score_codex":0.000085450934,"about_ca_system_score_gemma":0.00019222804,"threshold_uncertainty_score":0.99999523},"labels":[],"label_agreement":null},{"id":"W3023307415","doi":"","title":"Quantile-Based Risk Sharing with Heterogeneous Beliefs","year":2017,"lang":"en","type":"article","venue":"RePEc: Research Papers in Economics","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Quantile; Pareto principle; Competitive equilibrium; Economics; Pareto optimal; Shapley value; Mathematical economics; Value at risk; Econometrics; Microeconomics; Mathematical optimization; Game theory; Mathematics; Multi-objective optimization; Risk management","score_opus":0.09178761781332014,"score_gpt":0.39542167890817526,"score_spread":0.30363406109485513,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3023307415","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":[0.9362744,0.000033423235,0.00017296041,0.0002568824,0.00015997978,0.00028622954,0.000018349194,0.000023329054,0.06277441],"genre_scores_gemma":[0.99401045,0.0021627126,0.0014445997,0.000041950596,0.00009500267,0.000032976022,0.0000047383205,0.000028220655,0.0021793467],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99725527,0.00017935391,0.0005439892,0.0008044113,0.00065074436,0.00056625553],"domain_scores_gemma":[0.9962784,0.00075599266,0.00040313427,0.0021503223,0.00021476505,0.00019739488],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.004643047,0.00015617296,0.0003022935,0.00045744886,0.000942225,0.0010975434,0.0016890633,0.000119178134,0.00016386379],"category_scores_gemma":[0.0022850377,0.00012227066,0.000091993315,0.00015686551,0.0003748624,0.0004000677,0.00030172107,0.0004079534,0.00008448829],"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":[0.0001398213,0.000051774092,0.5434763,0.0000018418682,0.0000135649525,0.000042789787,0.00011202965,0.15165704,0.000015880183,0.00015640922,0.000036278554,0.30429628],"study_design_scores_gemma":[0.0025076708,0.0005161462,0.33062074,0.00008727776,0.000010024381,0.000031019226,0.0004204905,0.6053918,0.001281236,0.004921256,0.053555623,0.0006567189],"about_ca_topic_score_codex":0.00028816788,"about_ca_topic_score_gemma":0.0014576629,"teacher_disagreement_score":0.45373476,"about_ca_system_score_codex":0.00013906471,"about_ca_system_score_gemma":0.00022774351,"threshold_uncertainty_score":0.99993944},"labels":[],"label_agreement":null},{"id":"W3023398061","doi":"10.3982/te2937","title":"The no‐upward‐crossing condition, comparative statics, and the moral‐hazard problem","year":2020,"lang":"en","type":"article","venue":"Theoretical Economics","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Kellogg's (Canada)","funders":"","keywords":"Comparative statics; Parameterized complexity; Monotone polygon; Smoothness; Moral hazard; Mathematical economics; Order (exchange); Exponential function; Computer science; Derivative (finance); Mathematics; Mathematical optimization; Applied mathematics; Economics; Mathematical analysis; Microeconomics; Algorithm","score_opus":0.06420774488540855,"score_gpt":0.33964929685339634,"score_spread":0.2754415519679878,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3023398061","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":[0.6632267,0.00061554916,0.058719058,0.071769536,0.00072524056,0.0014752523,0.00017627444,0.00014120019,0.20315115],"genre_scores_gemma":[0.99491876,0.00074819126,0.002404655,0.0016229156,0.00011880884,0.000011501025,0.0000077549175,0.000009946731,0.00015748634],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9985979,0.00025014958,0.0005579361,0.00024342803,0.0001597071,0.00019086828],"domain_scores_gemma":[0.99680436,0.0024584602,0.00017195602,0.00026074427,0.00016851893,0.00013593803],"candidate_categories":["sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0016908366,0.00012090394,0.00027605568,0.000018112278,0.0006099835,0.0012405665,0.00039664932,0.00004572799,0.00015295556],"category_scores_gemma":[0.0006422619,0.000059572827,0.000060887978,0.00012135861,0.0042994926,0.0001860937,0.00010100388,0.0001511968,0.00030137144],"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":[0.00015932201,0.000004173367,0.00018169895,8.8001116e-7,0.000015059321,3.044859e-7,0.0026267574,0.004850671,0.0000013405906,0.9835469,0.0052774916,0.0033353656],"study_design_scores_gemma":[0.00047719706,0.00003400292,0.00016207338,0.0000017250288,0.000011048799,0.0000020790437,0.0007735708,0.27790272,0.000043790547,0.6999747,0.020539902,0.00007720151],"about_ca_topic_score_codex":0.0000019066421,"about_ca_topic_score_gemma":0.0000043868786,"teacher_disagreement_score":0.331692,"about_ca_system_score_codex":0.000015831298,"about_ca_system_score_gemma":0.00007147361,"threshold_uncertainty_score":0.9997962},"labels":[],"label_agreement":null},{"id":"W3024141413","doi":"10.1017/asb.2020.12","title":"OPTIMAL INSURANCE CONTRACTS UNDER DISTORTION RISK MEASURES WITH AMBIGUITY AVERSION","year":2020,"lang":"en","type":"article","venue":"Astin Bulletin","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Western University; University of Calgary","funders":"","keywords":"Ambiguity; Ambiguity aversion; Indemnity; Distortion (music); Actuarial science; Econometrics; Economics; Risk aversion (psychology); Measure (data warehouse); Risk measure; Spectral risk measure; Expected utility hypothesis; Mathematical economics; Probability distribution; Mathematics; Risk management; Statistics; Computer science; Financial economics; Expected shortfall; Finance","score_opus":0.057247742643353805,"score_gpt":0.2883095073649033,"score_spread":0.2310617647215495,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3024141413","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":[0.75125057,0.00016149718,0.23693511,0.00688432,0.00019782584,0.00023319329,0.00003683973,0.00010793021,0.004192723],"genre_scores_gemma":[0.99375343,0.00022945282,0.0045337942,0.00077023415,0.00014399775,0.000006764779,0.000013585629,0.000016537671,0.0005322244],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99725175,0.00028654287,0.00045386163,0.0005292647,0.0012329257,0.00024563787],"domain_scores_gemma":[0.99830556,0.00038456306,0.00041984778,0.00030571426,0.00035816536,0.00022616793],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0010697738,0.00017504062,0.00026205537,0.000067763634,0.00026665855,0.00017570255,0.00035102406,0.00008790983,0.0006530323],"category_scores_gemma":[0.002023035,0.00012462525,0.00007550411,0.00048556275,0.00011001206,0.00014875097,0.000061135084,0.0002313909,0.0011868505],"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":[0.00142232,0.00012913512,0.34298772,0.000003604076,0.000042815132,0.000043888424,0.0012983378,0.40999496,0.00036904577,0.00020394337,0.12305463,0.12044961],"study_design_scores_gemma":[0.0016269735,0.00046883,0.6360404,0.00002624641,0.000041768682,0.0000124877015,0.00055658695,0.007921035,0.0013048357,0.00027402941,0.35129696,0.00042986101],"about_ca_topic_score_codex":0.00014295489,"about_ca_topic_score_gemma":0.000023648865,"teacher_disagreement_score":0.40207392,"about_ca_system_score_codex":0.000030259927,"about_ca_system_score_gemma":0.00005660508,"threshold_uncertainty_score":0.9995908},"labels":[],"label_agreement":null},{"id":"W3025788976","doi":"10.1080/03155986.2020.1730675","title":"Generalization bounds for regularized portfolio selection with market side information","year":2020,"lang":"en","type":"article","venue":"INFOR Information Systems and Operational Research","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":17,"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":"","keywords":"Volatility (finance); Portfolio optimization; Exploit; Portfolio; Investment strategy; Econometrics; Computer science; Financial market; Mathematical optimization; Mathematics; Economics; Finance","score_opus":0.10679910511170018,"score_gpt":0.3865618332264635,"score_spread":0.2797627281147633,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3025788976","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":[0.026793284,0.000079499114,0.9147205,0.004453936,0.00037961573,0.00392117,0.0002848816,0.00014630679,0.04922083],"genre_scores_gemma":[0.982492,0.00023084978,0.009136188,0.0021221235,0.00057146227,0.00074333826,0.0017196774,0.00002151015,0.0029628247],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9949875,0.0002183716,0.0014567369,0.00023075996,0.0027477155,0.0003589481],"domain_scores_gemma":[0.9929867,0.00050042797,0.0004534734,0.00021936517,0.005562597,0.00027742964],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0052167843,0.00018995245,0.00029050582,0.0008256834,0.0009427105,0.003686064,0.0002947105,0.00017922603,0.00014492926],"category_scores_gemma":[0.0028399746,0.00013957545,0.00005804829,0.0017529817,0.000106942505,0.01132306,0.0000739148,0.00020317693,0.00015079476],"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":[0.0025898302,0.000036803613,0.012748168,0.00021002843,0.00011616981,0.0000010082741,0.007918329,0.26261932,0.0001988626,0.25580466,0.39881226,0.058944564],"study_design_scores_gemma":[0.0009618272,0.0002373372,0.002226367,0.000014366304,0.0000053852714,0.000018857838,0.0012188106,0.48758024,0.000083938474,0.0002720776,0.5072364,0.00014440587],"about_ca_topic_score_codex":0.00015431084,"about_ca_topic_score_gemma":0.000015968697,"teacher_disagreement_score":0.9556987,"about_ca_system_score_codex":0.000097568205,"about_ca_system_score_gemma":0.0006128662,"threshold_uncertainty_score":0.9973482},"labels":[],"label_agreement":null},{"id":"W3034518788","doi":"10.1287/ijoc.2019.0928","title":"Exploiting the Structure of Two-Stage Robust Optimization Models with Exponential Scenarios","year":2020,"lang":"en","type":"article","venue":"INFORMS journal on computing","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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; Université de Montréal; Concordia University","funders":"","keywords":"Mathematical optimization; Benders' decomposition; Robust optimization; Linear programming; Heuristic; Integer programming; Mathematics; Relaxation (psychology); Facility location problem; Computer science; Convergence (economics); Algorithm","score_opus":0.09638433598586557,"score_gpt":0.3186392904963311,"score_spread":0.22225495451046554,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3034518788","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":[0.30609936,0.000014984377,0.6923727,0.00039966253,0.00017126215,0.000108286134,0.00000413945,0.00001908094,0.0008105402],"genre_scores_gemma":[0.9501916,0.000023801376,0.04865378,0.00069899845,0.00038940832,1.652977e-7,0.0000042339016,0.000014775903,0.000023208497],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99705464,0.00009090036,0.0010026097,0.000198871,0.0014098403,0.00024315863],"domain_scores_gemma":[0.9973942,0.000328065,0.0013415762,0.00022232711,0.0005630788,0.0001507722],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00097626826,0.00016681378,0.00027166854,0.00017410467,0.0005104707,0.00057434896,0.00063112244,0.00005445139,0.000120090815],"category_scores_gemma":[0.00041571015,0.00008373549,0.00010294997,0.00078821485,0.00006162784,0.0010819512,0.0001078669,0.00045584358,0.0000053285817],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000094783354,0.000007185432,0.00075483206,0.00000200035,0.000016472144,0.000010396905,0.00314192,0.97951275,0.000058079153,0.0008025064,0.00011402617,0.015485069],"study_design_scores_gemma":[0.0006949394,0.00016260144,0.00007699741,0.000050153958,0.000010741745,0.000064853986,0.0019561865,0.99574727,0.00044031464,0.0005051135,0.00017114118,0.00011970983],"about_ca_topic_score_codex":0.000008862361,"about_ca_topic_score_gemma":0.0000026054752,"teacher_disagreement_score":0.64409226,"about_ca_system_score_codex":0.000025486679,"about_ca_system_score_gemma":0.0001195916,"threshold_uncertainty_score":0.553846},"labels":[],"label_agreement":null},{"id":"W3034608894","doi":"10.1016/j.insmatheco.2020.06.008","title":"Optimal reinsurance with multiple reinsurers: Distortion risk measures, distortion premium principles, and heterogeneous beliefs","year":2020,"lang":"en","type":"article","venue":"Insurance Mathematics and Economics","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":25,"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":"Reinsurance; Distortion (music); Constraint (computer-aided design); Actuarial science; Econometrics; Upper and lower bounds; Ceteris paribus; Economics; Computer science; Mathematics; Microeconomics; Telecommunications","score_opus":0.055499916482817435,"score_gpt":0.2532351163027808,"score_spread":0.19773519981996335,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3034608894","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":[0.9262682,0.00074606726,0.071665265,0.00029210479,0.00013839002,0.00036414134,0.00014197739,0.000056173612,0.00032767773],"genre_scores_gemma":[0.9746015,0.0059612785,0.019067997,0.00008354392,0.00010962753,0.000024786972,0.00001662814,0.000034661967,0.000099978286],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978332,0.000059127007,0.00087376346,0.000649191,0.00032752554,0.0002572303],"domain_scores_gemma":[0.99807656,0.00025699634,0.0008160624,0.00043996717,0.00015997386,0.0002504424],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00085814105,0.00026866538,0.00047841456,0.000078183446,0.00030320502,0.0003281465,0.00025066888,0.000118688025,0.000007934223],"category_scores_gemma":[0.00070102926,0.00021308773,0.00006740155,0.00018979893,0.00016386966,0.0004824181,0.00008483128,0.00017322575,0.000017807954],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00048407004,0.00017067614,0.45724475,0.0000871627,0.00011490588,0.00001472801,0.007584057,0.46316633,0.00010873684,0.0009895616,0.00027576913,0.06975927],"study_design_scores_gemma":[0.0026824973,0.0005656434,0.130991,0.00011775042,0.000093318326,0.00015842766,0.0006567903,0.8135785,0.0012681988,0.0036002006,0.045105733,0.0011819644],"about_ca_topic_score_codex":0.00003258165,"about_ca_topic_score_gemma":0.00017106606,"teacher_disagreement_score":0.35041216,"about_ca_system_score_codex":0.000049753333,"about_ca_system_score_gemma":0.00004349006,"threshold_uncertainty_score":0.86894655},"labels":[],"label_agreement":null},{"id":"W3035455029","doi":"10.1017/asb.2020.14","title":"DISTORTION RISKMETRICS ON GENERAL SPACES","year":2020,"lang":"en","type":"article","venue":"Astin Bulletin","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":53,"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":"Distortion (music); Convexity; Class (philosophy); Choquet integral; Mathematical economics; Mathematics; Computer science; Econometrics; Economics; Artificial intelligence; Financial economics; Telecommunications","score_opus":0.11791846516686341,"score_gpt":0.3464442253482551,"score_spread":0.22852576018139167,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3035455029","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":[0.7343881,0.00037132812,0.1419422,0.05843294,0.0013093672,0.00036319756,0.000038776932,0.0002499534,0.062904134],"genre_scores_gemma":[0.98426104,0.00015222066,0.0058404217,0.0026213313,0.0005822608,0.0000065797035,0.000016622625,0.000016333752,0.006503219],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9979211,0.00014070542,0.00039360972,0.0004080946,0.0009561142,0.00018034049],"domain_scores_gemma":[0.9987955,0.00042329356,0.00021595394,0.00025202066,0.00014261495,0.000170594],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00070385926,0.000119267555,0.000175392,0.00015532896,0.00012333193,0.00020064968,0.0003385347,0.00006471955,0.0021093308],"category_scores_gemma":[0.0042056683,0.00009087685,0.000080775215,0.0009823429,0.000038950173,0.00005149071,0.00006023784,0.00012944028,0.005616351],"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":[0.00008694174,0.00004003091,0.029595619,0.0000012152611,0.000005898793,0.000014423689,0.0004017851,0.025811255,0.00006414619,0.0014872518,0.80979097,0.13270047],"study_design_scores_gemma":[0.00023710234,0.00017016886,0.018725801,0.0000033600836,0.000007058858,0.0000016183317,0.00008818251,0.007857498,0.00042235843,0.000601493,0.97173667,0.00014867609],"about_ca_topic_score_codex":0.00003097197,"about_ca_topic_score_gemma":0.0000016919022,"teacher_disagreement_score":0.24987292,"about_ca_system_score_codex":0.000018106819,"about_ca_system_score_gemma":0.000021192465,"threshold_uncertainty_score":0.9988029},"labels":[],"label_agreement":null},{"id":"W3036119246","doi":"10.1007/s10898-020-00915-x","title":"Generalized risk parity portfolio optimization: an ADMM approach","year":2020,"lang":"en","type":"article","venue":"Journal of Global Optimization","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Portfolio optimization; Mathematics; Mathematical optimization; Portfolio; Optimization problem; Coherent risk measure; Convex optimization; Risk measure; Regular polygon; Economics; Finance","score_opus":0.06904678787804504,"score_gpt":0.3482783259159813,"score_spread":0.27923153803793627,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3036119246","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":[0.013566902,0.0003786451,0.97830385,0.0010437947,0.000577945,0.00024839552,0.000058609094,0.000059833797,0.0057620006],"genre_scores_gemma":[0.28496972,0.0019486899,0.7109031,0.0010543766,0.0009321125,0.000003248306,0.00008517035,0.000029877121,0.00007373943],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99419445,0.00079546025,0.0020698088,0.0005247842,0.0020894278,0.00032606022],"domain_scores_gemma":[0.99366254,0.00010463294,0.0028652865,0.00047070463,0.0021823482,0.0007144625],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024519574,0.00030547119,0.0007022616,0.00018789027,0.00028149335,0.0006269694,0.0010104364,0.00024595385,0.0007918344],"category_scores_gemma":[0.0027223933,0.00023918087,0.00032616768,0.0026205417,0.00008957788,0.0023963347,0.00010013771,0.0003097692,0.000023292516],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00032744644,0.00016855399,0.02484667,0.0000021101894,0.000045316327,0.000016179309,0.00017528232,0.9598369,0.000002417893,0.0008416669,0.00976618,0.0039713057],"study_design_scores_gemma":[0.0014364125,0.00039740177,0.0018946955,0.000006458023,0.00012662067,0.00011694467,0.00025930488,0.99092686,0.000015769645,0.0008594614,0.00369396,0.00026608628],"about_ca_topic_score_codex":0.000023376937,"about_ca_topic_score_gemma":0.0000026884063,"teacher_disagreement_score":0.2714028,"about_ca_system_score_codex":0.00013706414,"about_ca_system_score_gemma":0.0003711388,"threshold_uncertainty_score":0.9753513},"labels":[],"label_agreement":null},{"id":"W3036346356","doi":"10.1287/moor.2019.1043","title":"On the Scenario-Tree Optimal-Value Error for Stochastic Programming Problems","year":2020,"lang":"en","type":"article","venue":"Mathematics of Operations Research","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Polytechnique Montréal","funders":"","keywords":"Mathematical optimization; Tree (set theory); Mathematics; Stochastic programming; Discretization; Node (physics); Bellman equation; Dynamic programming; Computer science; Expected value","score_opus":0.4065420398833334,"score_gpt":0.48614134322049746,"score_spread":0.07959930333716408,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3036346356","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":[0.055255532,0.000049214053,0.92293084,0.016714087,0.000052488223,0.0030652734,0.000033374938,0.000031683394,0.0018675042],"genre_scores_gemma":[0.8357803,0.000014775623,0.16150445,0.00012320293,0.00008930398,0.00060587673,0.000023025814,0.00003238338,0.0018266318],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9965228,0.00022581048,0.000718427,0.00033159248,0.0018846289,0.00031676586],"domain_scores_gemma":[0.9943374,0.0033362359,0.0000933782,0.00059758907,0.0015115937,0.00012380273],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.005612512,0.00011840204,0.00024591407,0.00024231903,0.00066015276,0.00055423,0.0009415136,0.00006459483,0.00016617341],"category_scores_gemma":[0.018053435,0.00006824746,0.000104025225,0.0013238328,0.00022281003,0.0001936115,0.00015097963,0.0002479472,0.00017649219],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000021946018,0.0001899412,0.000006914418,0.000026393092,0.000017205455,4.4539394e-7,0.007041778,0.8103902,0.00052169623,0.17328267,0.004734079,0.0037667376],"study_design_scores_gemma":[0.00020720328,0.00038044812,0.000007166255,0.000042238065,0.000008018637,0.000001394692,0.0027907356,0.97979397,0.0004477209,0.014186149,0.0020531642,0.00008181975],"about_ca_topic_score_codex":0.0000164369,"about_ca_topic_score_gemma":0.000029531533,"teacher_disagreement_score":0.7805248,"about_ca_system_score_codex":0.00002490771,"about_ca_system_score_gemma":0.0002175506,"threshold_uncertainty_score":0.9902179},"labels":[],"label_agreement":null},{"id":"W3040430149","doi":"10.1287/moor.2019.1035","title":"Surplus-Invariant Risk Measures","year":2020,"lang":"en","type":"article","venue":"Mathematics of Operations Research","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Toronto Metropolitan University","funders":"","keywords":"Mathematics; Invariant (physics); Lattice (music); Dual (grammatical number); Pure mathematics; Mathematical economics; Discrete mathematics","score_opus":0.4733203417074584,"score_gpt":0.49657254792778727,"score_spread":0.023252206220328864,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3040430149","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":[0.29647478,0.0003375085,0.64956826,0.00960679,0.0001482747,0.0011509605,0.00013593455,0.00007458276,0.0425029],"genre_scores_gemma":[0.9414058,0.0006132387,0.05632199,0.000044352306,0.000098021694,0.000024262463,0.0000071736526,0.00001553308,0.0014696503],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9953877,0.0005693861,0.00080493116,0.00029800448,0.0027064339,0.00023359408],"domain_scores_gemma":[0.99586546,0.0012211233,0.000088815046,0.0006188299,0.0020255859,0.00018017097],"candidate_categories":["metaresearch","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.007854681,0.00009133604,0.00026899384,0.00033231656,0.00039943669,0.000380756,0.00081703987,0.00006860261,0.00058193004],"category_scores_gemma":[0.021923639,0.000064659886,0.00008391293,0.001814501,0.00017222615,0.00029462343,0.00018255622,0.0002747263,0.0007859584],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000085819134,0.0008978894,0.0072832266,0.0000532678,0.00013597228,0.000021619648,0.04814309,0.5706295,0.010795839,0.21185683,0.09576334,0.054333616],"study_design_scores_gemma":[0.00035034385,0.00019115068,0.00069629936,0.000016890664,0.000014203511,0.0000040415075,0.0039782883,0.95004874,0.0065453644,0.024093973,0.013903115,0.000157614],"about_ca_topic_score_codex":0.00011391556,"about_ca_topic_score_gemma":0.00009503375,"teacher_disagreement_score":0.64493096,"about_ca_system_score_codex":0.000017066513,"about_ca_system_score_gemma":0.00024042149,"threshold_uncertainty_score":0.9999921},"labels":[],"label_agreement":null},{"id":"W3042850524","doi":"10.1287/opre.2021.0765","title":"Convolution Bounds on Quantile Aggregation","year":2024,"lang":"en","type":"article","venue":"Operations Research","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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 Waterloo","funders":"","keywords":"Quantile; Convolution (computer science); Mathematics; Computer science; Econometrics; Artificial intelligence","score_opus":0.34920467722749954,"score_gpt":0.5549997028613539,"score_spread":0.20579502563385438,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3042850524","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":[0.6543293,0.0021456818,0.1179251,0.01695962,0.0029410997,0.0013832567,0.00009229192,0.00038345996,0.20384018],"genre_scores_gemma":[0.9524107,0.0004755966,0.0007812393,0.000051897026,0.0002460294,0.00006201006,0.000058362908,0.000012849556,0.045901336],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9963578,0.00050883496,0.0003804517,0.0004630429,0.0020230997,0.00026676085],"domain_scores_gemma":[0.99767315,0.0008136304,0.000011727157,0.0004979102,0.0009093967,0.00009416665],"candidate_categories":["scholarly_communication","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0054185363,0.00008144554,0.00010067686,0.0010636128,0.00079400983,0.0023937242,0.00033515794,0.00008779612,0.0014260166],"category_scores_gemma":[0.0022276642,0.00005990252,0.000059166836,0.0027549532,0.0001392087,0.0007412091,0.0000559826,0.00031865458,0.008619649],"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":[0.000037115966,0.00011349196,0.0006679819,0.000004779308,0.000016484131,0.00002217074,0.001527489,0.11802517,0.0018764917,0.46160877,0.19054203,0.22555803],"study_design_scores_gemma":[0.000089386696,0.00013853714,0.001097287,0.0000304401,0.0000023565515,0.0000060512407,0.0003637777,0.6891996,0.0012860702,0.007625503,0.30007154,0.00008942014],"about_ca_topic_score_codex":0.00018716126,"about_ca_topic_score_gemma":0.00024336613,"teacher_disagreement_score":0.57117444,"about_ca_system_score_codex":0.000113589034,"about_ca_system_score_gemma":0.00036184487,"threshold_uncertainty_score":0.9994868},"labels":[],"label_agreement":null},{"id":"W3044015367","doi":"10.3390/e22080805","title":"Option Portfolio Selection with Generalized Entropic Portfolio Optimization","year":2020,"lang":"en","type":"article","venue":"Entropy","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Royal Bank of Canada; York University","funders":"Natural Sciences and Engineering Research Council of Canada; Oracle","keywords":"Portfolio; Portfolio optimization; Mathematical optimization; Selection (genetic algorithm); Computer science; Post-modern portfolio theory; Entropy (arrow of time); Modern portfolio theory; Optimization problem; Replicating portfolio; Econometrics; Economics; Mathematics; Financial economics; Artificial intelligence","score_opus":0.05251601547229127,"score_gpt":0.3106451233655948,"score_spread":0.2581291078933035,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3044015367","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":[0.10491963,0.0001292212,0.8883622,0.0020169741,0.00034625802,0.000473032,0.0000097126,0.00026466383,0.0034782859],"genre_scores_gemma":[0.93187356,0.0006600102,0.06265968,0.0014521922,0.0009264057,0.0000381497,0.00014067921,0.000055020308,0.0021942875],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9964098,0.00022842575,0.0007885616,0.00074786454,0.0014342703,0.00039111343],"domain_scores_gemma":[0.99816644,0.00008010902,0.0005538485,0.00035731084,0.0005062697,0.00033600314],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00045211162,0.00027746728,0.00039231763,0.00029947073,0.00024940257,0.00039671815,0.0003914466,0.00013444343,0.0028742452],"category_scores_gemma":[0.00041856943,0.00020689965,0.00013073623,0.001968374,0.000062463434,0.0008115672,0.00005490294,0.0001862434,0.00038385484],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00037250842,0.000067093,0.03285416,0.0000024693115,0.00004135305,0.000022612852,0.0002923288,0.9291428,0.001000229,0.012517324,0.0182818,0.005405355],"study_design_scores_gemma":[0.0024035135,0.0005651531,0.0065332176,0.000010570166,0.00009088299,0.000049647213,0.00019482085,0.91031235,0.0028360935,0.002313176,0.07417102,0.00051957584],"about_ca_topic_score_codex":0.000038048864,"about_ca_topic_score_gemma":0.000005224867,"teacher_disagreement_score":0.82695395,"about_ca_system_score_codex":0.00007163119,"about_ca_system_score_gemma":0.00014098984,"threshold_uncertainty_score":0.9980373},"labels":[],"label_agreement":null},{"id":"W3047044748","doi":"10.1057/s41260-020-00179-w","title":"A robust framework for risk parity portfolios","year":2020,"lang":"en","type":"article","venue":"Journal of Asset Management","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"University of Toronto","funders":"Mitacs","keywords":"Portfolio optimization; Portfolio; Robustness (evolution); Asset allocation; Econometrics; Computer science; Mathematical optimization; Covariance matrix; Coherent risk measure; Risk measure; Spectral risk measure; Expected shortfall; Parity (physics); Mathematics; Economics; Algorithm; Financial economics","score_opus":0.15768955882621294,"score_gpt":0.37838229817138197,"score_spread":0.22069273934516903,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3047044748","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":[0.011999977,0.0001449218,0.97782755,0.0057754847,0.0005991103,0.0002830475,0.000017420683,0.0000134949,0.0033389865],"genre_scores_gemma":[0.5879814,0.0024765523,0.40634862,0.0018182934,0.00081003614,0.000008128298,0.0000038243356,0.000020327241,0.0005328035],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9972865,0.00011500583,0.0010107463,0.00022851505,0.0011671666,0.00019209308],"domain_scores_gemma":[0.9972339,0.0004309989,0.0014262869,0.00026967094,0.00042335055,0.00021578466],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027723578,0.000119339915,0.00033341846,0.0002287049,0.000118956115,0.000277209,0.00065973884,0.00006836415,0.0002330705],"category_scores_gemma":[0.0019204887,0.000084792715,0.00027933405,0.0006788264,0.000022050337,0.00036689802,0.00010398299,0.00023072565,0.000056982914],"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":[0.0004616351,0.00019551386,0.04927853,0.000021537468,0.0003347133,0.0001478187,0.00061164,0.16419224,0.0000031763839,0.027215498,0.590835,0.16670272],"study_design_scores_gemma":[0.0013722073,0.00066298415,0.046975724,0.000048384278,0.0003233389,0.000021193771,0.0015768252,0.039512433,0.00004864344,0.18319304,0.72598135,0.00028388127],"about_ca_topic_score_codex":0.0000022278136,"about_ca_topic_score_gemma":0.000001266625,"teacher_disagreement_score":0.57598144,"about_ca_system_score_codex":0.000026536327,"about_ca_system_score_gemma":0.000034292076,"threshold_uncertainty_score":0.34577465},"labels":[],"label_agreement":null},{"id":"W3088437139","doi":"10.1109/compsac48688.2020.00-75","title":"Portfolio Optimization Using a Novel Data-Driven EWMA Covariance Model with Big Data","year":2020,"lang":"en","type":"article","venue":"","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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 Manitoba","funders":"","keywords":"Portfolio optimization; Covariance matrix; Sharpe ratio; Portfolio; Econometrics; Covariance; Stochastic volatility; Volatility (finance); Lasso (programming language); Computer science; Mathematics; Statistics; Economics; Finance","score_opus":0.583205013751827,"score_gpt":0.4101024790724303,"score_spread":0.17310253467939668,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3088437139","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":[0.00080750446,0.000034968707,0.99185103,0.0014419707,0.0001541311,0.00026329415,0.0005452151,0.000102102305,0.0047998056],"genre_scores_gemma":[0.088739835,0.00015870007,0.9071558,0.0017215601,0.00028548797,0.0000022995214,0.0011676385,0.00003454938,0.00073413167],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9963966,0.000055294266,0.00069752755,0.0012762916,0.001298233,0.0002760722],"domain_scores_gemma":[0.9959608,0.00012948786,0.00042019898,0.0028507856,0.0003945618,0.0002441902],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009302975,0.00021282247,0.00033621865,0.00015382396,0.0001945942,0.00050231,0.0029190513,0.00009266868,0.00034405943],"category_scores_gemma":[0.000845594,0.00014912197,0.000025815505,0.0017256323,0.00008207177,0.0024239966,0.001186853,0.00013050193,0.00005716414],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006476045,0.00003939211,0.00087433454,0.0000014237964,0.000017659731,0.000005336108,0.000112916925,0.98327196,0.000063379484,0.00054999907,0.010972701,0.004026133],"study_design_scores_gemma":[0.0005532027,0.000033034987,0.00004377397,0.000008258946,0.000045045937,0.000017247026,0.00012592845,0.99009603,0.000015014994,0.00012295155,0.008706425,0.00023308376],"about_ca_topic_score_codex":0.00012008802,"about_ca_topic_score_gemma":0.00005162794,"teacher_disagreement_score":0.087932326,"about_ca_system_score_codex":0.00001820397,"about_ca_system_score_gemma":0.0004796371,"threshold_uncertainty_score":0.6081017},"labels":[],"label_agreement":null},{"id":"W3089375171","doi":"10.3390/jrfm13100237","title":"Risk Return Trade-Off in Relaxed Risk Parity Portfolio Optimization","year":2020,"lang":"en","type":"article","venue":"Journal of risk and financial management","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Toronto; University of New Brunswick","funders":"Mitacs","keywords":"Parity (physics); Portfolio; Interest rate parity; Portfolio optimization; Economics; Rate of return on a portfolio; Econometrics; Financial economics; Monetary economics; Physics; Interest rate; Particle physics","score_opus":0.022529808937978443,"score_gpt":0.2749612427385968,"score_spread":0.25243143380061833,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3089375171","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":[0.39500856,0.0023535923,0.59824204,0.0009600445,0.0008124374,0.00041916757,0.000058171845,0.000024754616,0.002121231],"genre_scores_gemma":[0.8712951,0.109445535,0.018592218,0.0002415057,0.00033803948,0.0000021004257,0.0000037722573,0.00001509215,0.00006661445],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9964014,0.00045738075,0.0014933661,0.0003790533,0.0010120566,0.0002567214],"domain_scores_gemma":[0.99717313,0.00026280832,0.0019236815,0.00024967495,0.00015753723,0.00023317063],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0037487673,0.00020289792,0.00051952974,0.00050646544,0.00023201502,0.00020054838,0.00041596856,0.00014011328,0.00008187213],"category_scores_gemma":[0.0027976783,0.00015730817,0.00019097015,0.0013783771,0.00006465945,0.00059190375,0.000120184566,0.00061434665,0.000010802764],"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":[0.0003944756,0.000092227696,0.32574847,0.0000047776684,0.000017946559,0.00012875046,0.001566017,0.17924207,5.117338e-7,0.00042685503,0.0081297085,0.4842482],"study_design_scores_gemma":[0.002660531,0.0004553624,0.74429554,0.000044663444,0.00025384926,0.000018794088,0.0010323634,0.09360217,0.000012162936,0.015029318,0.14225797,0.00033725792],"about_ca_topic_score_codex":0.000061574516,"about_ca_topic_score_gemma":0.00004513044,"teacher_disagreement_score":0.5796498,"about_ca_system_score_codex":0.0000428432,"about_ca_system_score_gemma":0.000054169675,"threshold_uncertainty_score":0.6414841},"labels":[],"label_agreement":null},{"id":"W3091479065","doi":"10.1007/978-3-030-58942-4_7","title":"A Learning-Based Algorithm to Quickly Compute Good Primal Solutions for Stochastic Integer Programs","year":2020,"lang":"en","type":"book-chapter","venue":"Archivio istituzionale della ricerca (Alma Mater Studiorum Università di Bologna)","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Université de Montréal; Polytechnique Montréal; Mila - Quebec Artificial Intelligence Institute","funders":"","keywords":"Integer programming; Computer science; Integer (computer science); Mathematical optimization; Realization (probability); Stochastic programming; Linear programming; Algorithm; Mathematics","score_opus":0.0468196479111407,"score_gpt":0.2772422406303271,"score_spread":0.23042259271918641,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3091479065","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00007794005,0.00022671213,0.8258826,0.0039281896,0.0009907713,0.0022732175,0.0009495613,0.00031373822,0.16535725],"genre_scores_gemma":[0.019587703,0.00024479852,0.05798485,0.0015191111,0.00094469753,0.0001553521,0.0025866095,0.0002707003,0.9167062],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.993989,0.000193299,0.0010616406,0.0019248105,0.0017255129,0.001105721],"domain_scores_gemma":[0.99544716,0.0010464644,0.0007953354,0.0007924009,0.0012824971,0.0006361326],"candidate_categories":["metaepi_narrow","sts","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00092829927,0.0009236859,0.0012923025,0.001116764,0.0013193946,0.00032968842,0.0016401839,0.0003992704,0.00023487913],"category_scores_gemma":[0.00023867979,0.0008713136,0.0007442456,0.0004938197,0.00051735155,0.00027601956,0.001329756,0.00079919735,0.0011813869],"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":[0.001285979,0.0005802801,0.0003350637,0.000102713035,0.0011478061,0.00047682942,0.0019882373,0.23540753,0.000021019265,0.53021485,0.05797048,0.17046924],"study_design_scores_gemma":[0.0017159671,0.0018474596,0.00033786517,0.0002565114,0.000544788,0.000049001905,0.00035445113,0.1294652,0.0000049869436,0.008621076,0.8553835,0.0014191649],"about_ca_topic_score_codex":0.000016778864,"about_ca_topic_score_gemma":0.00007601308,"teacher_disagreement_score":0.79741305,"about_ca_system_score_codex":0.0003403156,"about_ca_system_score_gemma":0.0007145301,"threshold_uncertainty_score":0.99998075},"labels":[],"label_agreement":null},{"id":"W3093001417","doi":"10.1007/s00780-023-00497-y","title":"Optimal insurance under maxmin expected utility","year":2023,"lang":"en","type":"article","venue":"Finance and Stochastics","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Indemnity; Expected utility hypothesis; Ambiguity; Mathematical economics; Unobservable; Mathematical finance; Ex-ante; Actuarial science; Prior probability; Knightian uncertainty; Econometrics; Ambiguity aversion; Economics; Mathematics; Computer science; Bayesian probability; Financial economics; Statistics","score_opus":0.09140263138684938,"score_gpt":0.35850681054068706,"score_spread":0.26710417915383766,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3093001417","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":[0.7371059,0.00017953252,0.26091534,0.00025455703,0.00032200958,0.000100777936,0.000055549797,0.00007189827,0.000994434],"genre_scores_gemma":[0.9937441,0.0005264312,0.0025107395,0.00007850834,0.0000669765,0.000011017679,0.000015106889,0.000008171488,0.0030389442],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99855554,0.00003760532,0.00033821256,0.00035294768,0.00048041195,0.00023530354],"domain_scores_gemma":[0.9990038,0.00031927784,0.00011518607,0.0003347986,0.00017316353,0.000053745625],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00053493,0.000111083544,0.00019426494,0.00014323564,0.00018637272,0.00010196798,0.00021178722,0.00007921584,0.00004762666],"category_scores_gemma":[0.0005525726,0.000089934794,0.000039178583,0.0011125183,0.00010762224,0.00017896561,0.00008189416,0.00009646243,0.00026267316],"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":[0.00031044267,0.00015156834,0.0891309,0.000011360189,0.000027402813,0.00008527488,0.004330845,0.46990088,0.00015633019,0.030294226,0.09062454,0.31497622],"study_design_scores_gemma":[0.0004165414,0.000081183185,0.73295206,0.000014201499,0.0000053909325,0.000008604537,0.0005828338,0.22471942,0.0000658258,0.014183967,0.026740279,0.00022968694],"about_ca_topic_score_codex":0.000009105598,"about_ca_topic_score_gemma":0.0000077162085,"teacher_disagreement_score":0.6438212,"about_ca_system_score_codex":0.00000684745,"about_ca_system_score_gemma":0.000044332828,"threshold_uncertainty_score":0.36674345},"labels":[],"label_agreement":null},{"id":"W3093094698","doi":"10.1016/j.ejor.2023.02.014","title":"Portfolio selection: A target-distribution approach","year":2023,"lang":"en","type":"article","venue":"European Journal of Operational Research","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"HEC Montréal","funders":"Belgian Federal Science Policy Office; Fonds De La Recherche Scientifique - FNRS","keywords":"Efficient frontier; Portfolio; Portfolio optimization; Variance (accounting); Selection (genetic algorithm); Econometrics; Computer science; Modern portfolio theory; Asset (computer security); Gaussian; Rate of return on a portfolio; Post-modern portfolio theory; Mathematical optimization; Investment strategy; Mathematics; Economics; Replicating portfolio; Finance; Market liquidity; Artificial intelligence","score_opus":0.2537355990549406,"score_gpt":0.4552823099094369,"score_spread":0.20154671085449632,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3093094698","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":[0.29721174,0.0005468516,0.3971454,0.008287747,0.0015345899,0.0006390996,0.000096648495,0.00012037428,0.29441756],"genre_scores_gemma":[0.9770197,0.00034423292,0.0062121176,0.00006732229,0.001181596,0.0000028591817,0.00010700067,0.000022644563,0.015042523],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99174696,0.002439238,0.0010256137,0.00027464685,0.0041744607,0.00033906655],"domain_scores_gemma":[0.9941558,0.0005802707,0.0002664996,0.00021509809,0.0045533283,0.00022895238],"candidate_categories":["metaresearch","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.03481383,0.00010009669,0.00019437748,0.00094884925,0.0005800039,0.0006541599,0.00074034993,0.000032968826,0.00094340387],"category_scores_gemma":[0.0059729395,0.000072155206,0.00013195841,0.0041148732,0.00012165454,0.00072173035,0.0001309419,0.00054853305,0.0017134472],"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":[0.00014569839,0.0001224138,0.006952591,0.0000020771545,0.000033708686,0.00019772236,0.00038639578,0.123917036,0.0007036735,0.009781532,0.83994585,0.017811324],"study_design_scores_gemma":[0.00088694546,0.0005673655,0.11254235,0.000019958312,0.000007114236,0.0005560781,0.0009375247,0.083457634,0.0005269241,0.004657279,0.7956371,0.00020373976],"about_ca_topic_score_codex":0.0000027377362,"about_ca_topic_score_gemma":3.563433e-7,"teacher_disagreement_score":0.67980796,"about_ca_system_score_codex":0.00008353492,"about_ca_system_score_gemma":0.00052191835,"threshold_uncertainty_score":0.99996984},"labels":[],"label_agreement":null},{"id":"W3093810628","doi":"10.1007/s10732-020-09460-y","title":"A reduced cost-based restriction and refinement matheuristic for stochastic network design problem","year":2020,"lang":"en","type":"article","venue":"Journal of Heuristics","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Université du Québec à Montréal; Université de Montréal; Transport Canada","funders":"","keywords":"Mathematical optimization; Computer science; Quality (philosophy); Variable (mathematics); Network planning and design; Computational complexity theory; Space (punctuation); Algorithm; Mathematics","score_opus":0.17383762443154727,"score_gpt":0.3683970957695745,"score_spread":0.19455947133802726,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3093810628","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":[0.00271399,0.00028854219,0.99360466,0.002477069,0.00037695694,0.00045910056,0.0000150251135,0.0000123724285,0.000052277534],"genre_scores_gemma":[0.60746205,0.00034636853,0.3904556,0.0006289608,0.000965465,0.000016997676,0.0000059754248,0.00002861131,0.00008996418],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99761635,0.00017031742,0.0010753436,0.00020347246,0.0007300682,0.0002044602],"domain_scores_gemma":[0.996009,0.0016723844,0.0010958214,0.0001467092,0.000838292,0.00023777394],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020491057,0.00013250136,0.00034177123,0.0001428187,0.00016074463,0.0001796972,0.00024280348,0.00006681653,0.000019050814],"category_scores_gemma":[0.0052162516,0.00009769775,0.000090206246,0.0004912537,0.00004183254,0.000109330045,0.000025751375,0.00016552975,0.0000060594084],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007047896,0.000048723126,0.00019291164,0.000010932519,0.000019557168,0.000017164379,0.00016647954,0.88883954,0.00010316798,0.00055599597,0.0985103,0.010830451],"study_design_scores_gemma":[0.0020860399,0.0022353877,0.0008189,0.00008782283,0.00022293703,0.000054839995,0.00011715795,0.9224207,0.000089917616,0.032782488,0.03884152,0.00024228942],"about_ca_topic_score_codex":0.0000010763669,"about_ca_topic_score_gemma":3.4103326e-7,"teacher_disagreement_score":0.6047481,"about_ca_system_score_codex":0.00003597982,"about_ca_system_score_gemma":0.00023651491,"threshold_uncertainty_score":0.6244717},"labels":[],"label_agreement":null},{"id":"W3094839603","doi":"","title":"Data-driven distributionally robust capacitated facility location problem","year":2020,"lang":"en","type":"article","venue":"RePEc: Research Papers in Economics","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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; Dalhousie University","funders":"","keywords":"Facility location problem; Mathematical optimization; Lagrangian relaxation; Column generation; Robust optimization; Benchmark (surveying); Relaxation (psychology); Mathematics; Ambiguity; Decision maker; Set (abstract data type); Linear programming; Optimization problem; Computer science; Operations research","score_opus":0.19766362154521924,"score_gpt":0.38416657457238684,"score_spread":0.1865029530271676,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3094839603","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":[0.78992915,0.00021453745,0.027791228,0.023940273,0.0006121899,0.003455556,0.008081723,0.00030275978,0.14567256],"genre_scores_gemma":[0.9929802,0.001290107,0.003298663,0.00015016136,0.000099023964,0.00003374811,0.0015120194,0.000012093391,0.00062401075],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99667645,0.00041615567,0.00082465855,0.00092658936,0.0007328087,0.00042335343],"domain_scores_gemma":[0.99721414,0.00078506314,0.00017964131,0.001006901,0.0005515872,0.00026264926],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0039156065,0.00013325438,0.00026431982,0.0001917136,0.00021157466,0.00028354846,0.0013503658,0.00012100522,0.00027023524],"category_scores_gemma":[0.005157749,0.00012062389,0.00004666619,0.00093628885,0.0002540152,0.0006963384,0.00042888,0.00040396466,0.00021960156],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014328538,0.000105100706,0.04361369,0.000010789567,0.000024064935,0.000010588662,0.00052277546,0.68302685,0.00008417224,0.00085343403,0.0044025285,0.26720273],"study_design_scores_gemma":[0.0005555399,0.00009670735,0.02462746,0.000011993115,0.0000039084266,0.0000044710096,0.0010177447,0.7989279,0.00004984484,0.0018795846,0.17257336,0.0002515028],"about_ca_topic_score_codex":0.000040693452,"about_ca_topic_score_gemma":0.0002184636,"teacher_disagreement_score":0.26695123,"about_ca_system_score_codex":0.00022977544,"about_ca_system_score_gemma":0.0005108593,"threshold_uncertainty_score":0.617468},"labels":[],"label_agreement":null},{"id":"W3094948588","doi":"10.1287/moor.2022.1299","title":"Maximum Spectral Measures of Risk with Given Risk Factor Marginal Distributions","year":2022,"lang":"en","type":"article","venue":"Mathematics of Operations Research","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":10,"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 Waterloo","funders":"","keywords":"Mathematics; Risk measure; Measure (data warehouse); Upper and lower bounds; Duality (order theory); Mathematical optimization; Applied mathematics; Expected shortfall; Metric (unit); Bellman equation; Function (biology); Wasserstein metric; Constraint (computer-aided design); Combinatorics; Mathematical analysis; Risk management; Computer science; Finance","score_opus":0.19479692925859743,"score_gpt":0.4235596820432506,"score_spread":0.2287627527846532,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3094948588","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":[0.8230623,0.00010184363,0.17001441,0.0003399242,0.000054605902,0.0005576255,0.0031514233,0.00001633073,0.0027015072],"genre_scores_gemma":[0.9641448,0.00019376322,0.03474286,0.0000012353671,0.00002337896,0.0000646925,0.000040151248,0.00001339231,0.0007756965],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9945286,0.00084776815,0.0007535053,0.00028413418,0.0033049516,0.00028103692],"domain_scores_gemma":[0.9960555,0.0012787642,0.00022657397,0.0007778426,0.001565251,0.00009609371],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0053196275,0.00011143024,0.00030874697,0.0006210653,0.0010946381,0.00015779669,0.00081055646,0.000037351405,0.0022059272],"category_scores_gemma":[0.003941087,0.000079755526,0.000099808756,0.0020106386,0.00032284166,0.00022538957,0.00024983246,0.00048046806,0.000037267822],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00030672114,0.0026608906,0.070623614,0.000041157644,0.00024905292,0.000016028758,0.020711118,0.801421,0.002192802,0.07068738,0.0087983515,0.022291875],"study_design_scores_gemma":[0.0031222075,0.0030768309,0.12043107,0.00008816866,0.00020693844,0.00010014585,0.04475681,0.535891,0.020396924,0.25525972,0.015693251,0.000976933],"about_ca_topic_score_codex":0.00036301365,"about_ca_topic_score_gemma":0.00032600513,"teacher_disagreement_score":0.26553,"about_ca_system_score_codex":0.0000770813,"about_ca_system_score_gemma":0.00035320548,"threshold_uncertainty_score":0.99870616},"labels":[],"label_agreement":null},{"id":"W3095328311","doi":"10.1287/opre.2020.1990","title":"The Discrete Moment Problem with Nonconvex Shape Constraints","year":2020,"lang":"en","type":"article","venue":"Operations Research","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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 British Columbia","funders":"","keywords":"Moment (physics); Mathematical optimization; Mathematics; Piecewise; Newsvendor model; Second moment of area; Distribution (mathematics); Computation; Applied mathematics; Algorithm; Mathematical analysis; Geometry","score_opus":0.2198915502003134,"score_gpt":0.4631863808632026,"score_spread":0.24329483066288918,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3095328311","genre_codex":"commentary","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":[0.14878881,0.0004942671,0.2295925,0.33219895,0.0002472046,0.0046168286,0.00012281023,0.00014912794,0.2837895],"genre_scores_gemma":[0.984594,0.0003410201,0.0060788677,0.0002693201,0.000118927645,0.0001362077,0.0000147017445,0.000012010663,0.008434942],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99651,0.00044903028,0.0003579297,0.00036886972,0.001993577,0.0003205717],"domain_scores_gemma":[0.99771833,0.00056397094,0.000027039958,0.00040656817,0.0010695151,0.00021459495],"candidate_categories":["sts","scholarly_communication","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0031060653,0.00008263097,0.00011152715,0.00010192582,0.0014296583,0.0014826682,0.0006747472,0.000037924197,0.0011645787],"category_scores_gemma":[0.0008088472,0.000040220733,0.00003186622,0.0015250903,0.00057769835,0.00033997133,0.00014334288,0.00028359145,0.0010003335],"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":[0.00030296197,0.0001247146,0.010231454,0.000005754274,0.00008476453,0.000048297978,0.010914549,0.18905717,0.0013527854,0.107403725,0.11916394,0.5613099],"study_design_scores_gemma":[0.0004571195,0.0004979575,0.0013094584,0.000011768147,0.0000049890396,0.0000098382725,0.005312121,0.63804966,0.0006792117,0.0012037829,0.3522833,0.00018083188],"about_ca_topic_score_codex":0.00003941701,"about_ca_topic_score_gemma":0.00019281165,"teacher_disagreement_score":0.8358052,"about_ca_system_score_codex":0.00002843488,"about_ca_system_score_gemma":0.00041613524,"threshold_uncertainty_score":0.99987036},"labels":[],"label_agreement":null},{"id":"W3096847493","doi":"10.1007/s11590-021-01754-9","title":"Complexity of near-optimal robust versions of multilevel optimization problems","year":2021,"lang":"en","type":"article","venue":"Optimization Letters","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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","funders":"Région Hauts-de-France","keywords":"Robustness (evolution); Computational intelligence; Robust optimization; Computational complexity theory; Optimization problem; Adversarial system","score_opus":0.13047109769552634,"score_gpt":0.3247271779096579,"score_spread":0.19425608021413154,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3096847493","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":[0.01927915,0.00006226371,0.97648287,0.0020378954,0.00036937042,0.00028642965,0.00009395942,0.00004481608,0.0013432518],"genre_scores_gemma":[0.18218802,0.00024775643,0.8162504,0.0004244767,0.000047026067,0.000011030072,0.0003863698,0.000033685024,0.00041125214],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9962645,0.0003301735,0.0013254372,0.00056667643,0.0012341486,0.0002790302],"domain_scores_gemma":[0.9962114,0.00037468836,0.0009897588,0.0007467265,0.0015481217,0.00012931132],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00085763197,0.0002240613,0.0005090913,0.00032548094,0.00023858239,0.00015071855,0.00047150967,0.00014305697,0.0019173075],"category_scores_gemma":[0.001231202,0.00021170585,0.00021372824,0.0017625821,0.00047916258,0.0006953422,0.00015825545,0.00013902801,0.000019687903],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000034523666,0.00015107902,0.0025729977,0.000012158892,0.00002993841,0.0000035474263,0.0005624138,0.9912583,0.00085759914,0.00037909718,0.0032304823,0.0009078197],"study_design_scores_gemma":[0.0007854441,0.000038643648,0.0010685929,0.000038329963,0.000039840637,0.000008613058,0.00023969947,0.99461854,0.0021210075,0.000063645464,0.00077199674,0.00020566255],"about_ca_topic_score_codex":0.000051311843,"about_ca_topic_score_gemma":0.0000070937217,"teacher_disagreement_score":0.16290887,"about_ca_system_score_codex":0.000046335965,"about_ca_system_score_gemma":0.00021074379,"threshold_uncertainty_score":0.99899507},"labels":[],"label_agreement":null},{"id":"W3104737672","doi":"10.3390/jrfm13110285","title":"Neural Network Predictive Modeling on Dynamic Portfolio Management—A Simulation-Based Portfolio Optimization Approach","year":2020,"lang":"en","type":"article","venue":"Journal of risk and financial management","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"","keywords":"CVAR; Portfolio; Portfolio optimization; Computer science; Sharpe ratio; Rate of return on a portfolio; Econometrics; Project portfolio management; Volatility (finance); Expected shortfall; Asset allocation; Investment strategy; Benchmark (surveying); Post-modern portfolio theory; Black–Litterman model; Artificial neural network; Replicating portfolio; Economics; Machine learning; Finance; Profit (economics); Microeconomics","score_opus":0.029563718504815373,"score_gpt":0.28578997198667344,"score_spread":0.25622625348185807,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3104737672","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":[0.008459807,0.00030836664,0.9868126,0.0001528992,0.00041436913,0.00041640847,0.000010761289,0.0000326376,0.003392167],"genre_scores_gemma":[0.90240717,0.0025963862,0.0939248,0.0006504981,0.0003036524,0.0000070751626,0.000015658825,0.000021211741,0.0000735774],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99656266,0.0001719705,0.0012136801,0.00047241364,0.0012762222,0.0003030593],"domain_scores_gemma":[0.9978893,0.0002125505,0.0010506228,0.00029755247,0.0003216413,0.00022833415],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014029768,0.00025868384,0.00045126813,0.00055240165,0.00030153024,0.00025891108,0.00050813286,0.00010195627,0.000028933655],"category_scores_gemma":[0.00031399925,0.00020896802,0.00020715658,0.0013396577,0.00003776302,0.00052890705,0.0001301273,0.00029445192,0.0000044772805],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004340227,0.000077944766,0.0020355342,0.000010065268,0.000030632797,0.000058233018,0.00014585062,0.9103501,4.4139647e-8,0.0010033087,0.00069976907,0.08515454],"study_design_scores_gemma":[0.001184627,0.00030476227,0.0033348103,0.000031288833,0.00015497176,0.0000025381114,0.00024554742,0.9896678,2.5044565e-7,0.0024018073,0.0024734666,0.00019818064],"about_ca_topic_score_codex":0.0000027229166,"about_ca_topic_score_gemma":5.2703246e-7,"teacher_disagreement_score":0.89394736,"about_ca_system_score_codex":0.000052025,"about_ca_system_score_gemma":0.00003716381,"threshold_uncertainty_score":0.85214686},"labels":[],"label_agreement":null},{"id":"W3105408634","doi":"10.1007/s10107-024-02128-6","title":"Optimizing distortion riskmetrics with distributional uncertainty","year":2024,"lang":"en","type":"article","venue":"Mathematical Programming","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":"University of Waterloo; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mathematics; Distortion (music); Mathematical optimization; Applied mathematics; Econometrics; Computer science","score_opus":0.06577574395816546,"score_gpt":0.364925118855046,"score_spread":0.29914937489688054,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3105408634","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":[0.020576779,0.00038677023,0.97486824,0.00038302742,0.00018094383,0.00026385937,0.000013674909,0.00025222625,0.0030744567],"genre_scores_gemma":[0.91387403,0.000021420103,0.08491702,0.000018741155,0.00012045186,0.000047185607,0.00004740076,0.000019813779,0.00093392923],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9971715,0.00007351085,0.00059323834,0.0004492265,0.0013958556,0.00031663827],"domain_scores_gemma":[0.9981698,0.0010385055,0.00011116723,0.00031646204,0.00021219013,0.00015188073],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019774707,0.00016273822,0.00023882155,0.00027172,0.00019926437,0.0010264587,0.00027287952,0.000081309794,0.00030269212],"category_scores_gemma":[0.0014869503,0.00009816579,0.000120299504,0.0020296222,0.00013603223,0.00040348846,0.00006260521,0.0001997285,0.0004164701],"study_design_candidate":"design_other","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":[0.00003884056,0.00018368443,0.0010483129,0.000056388424,0.000049387058,0.00007492019,0.0010803556,0.013350321,0.000013496962,0.15146159,0.0018293767,0.83081335],"study_design_scores_gemma":[0.00034075236,0.0002491206,0.00042247353,0.00024712563,0.00011959662,0.0001340419,0.000885658,0.5561844,0.00012945427,0.2641488,0.17657885,0.0005596881],"about_ca_topic_score_codex":0.0000054095235,"about_ca_topic_score_gemma":0.000003276027,"teacher_disagreement_score":0.89329726,"about_ca_system_score_codex":0.00010615222,"about_ca_system_score_gemma":0.000074280346,"threshold_uncertainty_score":0.9898164},"labels":[],"label_agreement":null},{"id":"W3106280978","doi":"10.2139/ssrn.3476392","title":"Central-planned Portfolio Selection, Pareto Frontier, and Pareto Improvement","year":2019,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Pareto principle; Frontier; Portfolio; Selection (genetic algorithm); Efficient frontier; Economics; Pareto analysis; Computer science; Financial economics; Operations management; Geography","score_opus":0.012400757365731822,"score_gpt":0.2802209001666112,"score_spread":0.2678201428008794,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3106280978","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":[0.943295,0.0032254902,0.046643183,0.0009104598,0.0013477702,0.00043360572,0.000007525582,0.000057107394,0.0040798546],"genre_scores_gemma":[0.9717775,0.012718534,0.00023076903,0.00020069232,0.00029432887,0.000005299703,0.000008753138,0.000023850303,0.014740298],"study_design_codex":"observational","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.995355,0.00013851619,0.0007774606,0.0004908304,0.0010643182,0.002173891],"domain_scores_gemma":[0.9985262,0.00009270628,0.000499619,0.00030281104,0.0003421927,0.0002364555],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0035960032,0.00022857962,0.00034641387,0.00033215046,0.00030699294,0.00039418845,0.00043076434,0.00012445152,0.00040084976],"category_scores_gemma":[0.00019787153,0.000172646,0.00012672585,0.00059262145,0.000038922193,0.0006416119,0.00005733189,0.0009557546,0.00013060981],"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":[0.0003272166,0.00010869522,0.7357615,0.0000028537943,0.00020819796,0.0000063704406,0.0003348202,0.0025135914,0.00054651056,0.026135772,0.016302904,0.21775156],"study_design_scores_gemma":[0.004051436,0.0036118398,0.08227925,0.000035708432,0.00013895184,0.0015083668,0.008878667,0.014090227,0.0009148744,0.7032821,0.18006574,0.0011428063],"about_ca_topic_score_codex":0.000088287685,"about_ca_topic_score_gemma":0.000655824,"teacher_disagreement_score":0.6771464,"about_ca_system_score_codex":0.00045354792,"about_ca_system_score_gemma":0.0013865241,"threshold_uncertainty_score":0.70403},"labels":[],"label_agreement":null},{"id":"W3106631555","doi":"10.1287/ijoc.2020.0959","title":"Linearized Robust Counterparts of Two-Stage Robust Optimization Problems with Applications in Operations Management","year":2020,"lang":"en","type":"article","venue":"INFORMS journal on computing","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Group for Research in Decision Analysis; HEC Montréal; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"","keywords":"Robust optimization; Linearization; Mathematical optimization; Bilinear interpolation; Newsvendor model; Scheme (mathematics); Conic section; Optimization problem; Computer science; Linear programming; Mathematics; Nonlinear system","score_opus":0.07741789821834659,"score_gpt":0.3308340246991044,"score_spread":0.2534161264807578,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3106631555","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":[0.023154266,0.000018951954,0.9708586,0.00045961232,0.0000764075,0.0005144049,0.0000057182633,0.000026802361,0.004885241],"genre_scores_gemma":[0.77351606,0.00012704458,0.22546743,0.00049031613,0.0001370608,0.000010979898,0.000028389908,0.000018193688,0.00020451557],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997361,0.00005785622,0.0012253933,0.00023914085,0.0009050174,0.00021160931],"domain_scores_gemma":[0.99838865,0.0001580271,0.00060280255,0.00022663808,0.0004797268,0.00014415725],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001223958,0.00015571891,0.00028999016,0.0003993804,0.00027832718,0.00047403594,0.00043640798,0.000045396217,0.00008170918],"category_scores_gemma":[0.00012441624,0.000103266575,0.0000657258,0.0014535579,0.000045048317,0.00067175133,0.00008214714,0.00027864124,0.000017778106],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000041316704,0.00005110561,0.0051041646,0.0000069099992,0.000016219214,0.000005835529,0.00095014164,0.98667336,0.0000034783998,0.0013967236,0.000085049185,0.0056656827],"study_design_scores_gemma":[0.0012138465,0.00014671883,0.000550322,0.000079398545,0.000010651441,0.000013029342,0.00075265375,0.9946061,0.000027686505,0.00006177088,0.0024089387,0.0001289021],"about_ca_topic_score_codex":0.000012906277,"about_ca_topic_score_gemma":0.00002025704,"teacher_disagreement_score":0.7503618,"about_ca_system_score_codex":0.000051571034,"about_ca_system_score_gemma":0.000090736736,"threshold_uncertainty_score":0.45711395},"labels":[],"label_agreement":null},{"id":"W3107553371","doi":"10.2139/ssrn.3709680","title":"Data-Driven Distributionally Robust Risk Parity Portfolio Optimization","year":2020,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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 Toronto","funders":"","keywords":"Portfolio optimization; Robust optimization; Portfolio; Parity (physics); Econometrics; Economics; Mathematical optimization; Computer science; Financial economics; Mathematics","score_opus":0.07986261174280108,"score_gpt":0.33008210261274795,"score_spread":0.2502194908699469,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3107553371","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":[0.009209007,0.0008643622,0.98418075,0.0036709607,0.00027006675,0.00015474633,0.00032797517,0.00006329867,0.0012588182],"genre_scores_gemma":[0.94987047,0.03255285,0.014003231,0.0005057369,0.0012958688,0.000004045029,0.0009493425,0.00003992438,0.0007785419],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99495184,0.0004058613,0.000979535,0.0006467647,0.0015401191,0.0014758476],"domain_scores_gemma":[0.9972837,0.00022623548,0.00092897995,0.00066212786,0.0005776589,0.00032130475],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0056976713,0.00021625787,0.00033127543,0.00014992594,0.0005852399,0.0004837962,0.0018170284,0.00013043877,0.0005612356],"category_scores_gemma":[0.003475463,0.00017111593,0.000147744,0.0011435978,0.00007575216,0.0013521096,0.00027346192,0.0017081138,0.00020333538],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008954408,0.000053357056,0.02577149,5.166787e-7,0.000082728315,0.0000069995854,0.000053340736,0.9261432,0.0000029633309,0.016607054,0.01179433,0.019394452],"study_design_scores_gemma":[0.00079724443,0.00023256536,0.0031900299,0.0000047357357,0.00010360516,0.0002065096,0.0006215464,0.88433284,0.0000058813,0.0790528,0.031132573,0.00031967077],"about_ca_topic_score_codex":0.0000388282,"about_ca_topic_score_gemma":0.00016165942,"teacher_disagreement_score":0.97017753,"about_ca_system_score_codex":0.00030693752,"about_ca_system_score_gemma":0.0026285476,"threshold_uncertainty_score":0.74209994},"labels":[],"label_agreement":null},{"id":"W3108819839","doi":"","title":"Optimal reinsurance and investment problem under fractional power utility function","year":2020,"lang":"en","type":"article","venue":"International Conference on Industrial Engineering and Operations Management","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Reinsurance; Investment (military); Function (biology); Mathematical optimization; Computer science; Mathematics; Economics; Finance","score_opus":0.1293216208293079,"score_gpt":0.31930201811949266,"score_spread":0.18998039729018476,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3108819839","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":[0.2033665,0.000102998354,0.66085285,0.049649727,0.00298309,0.0013597804,0.000121066776,0.000248955,0.081315055],"genre_scores_gemma":[0.9953002,0.00014452594,0.0026933567,0.0007912955,0.00016310028,0.000040562907,0.000031707757,0.000007511978,0.00082774524],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99847925,0.00003913841,0.00037241753,0.00040067974,0.00059627794,0.00011222626],"domain_scores_gemma":[0.99943066,0.00006928669,0.00006257588,0.0001275922,0.00018452361,0.00012535446],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037338262,0.00013825545,0.0001284969,0.00016376143,0.00013195809,0.00048201732,0.00016449536,0.00007210166,0.0004117177],"category_scores_gemma":[0.00017577068,0.000120364486,0.00002862974,0.00021924124,0.000035362988,0.00037895792,0.00008697817,0.00018694378,0.000033626075],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010012772,0.000036565547,0.0003227786,0.0000016760134,0.00006557031,0.000002596264,0.0001342763,0.60203934,0.000062083716,0.38512158,0.0026836556,0.009429757],"study_design_scores_gemma":[0.0010278012,0.00020532057,0.0073886486,0.00003267521,0.000020824229,0.0000035842163,0.00049817236,0.90168095,0.000039213603,0.0018508631,0.087028205,0.00022377018],"about_ca_topic_score_codex":0.00001531807,"about_ca_topic_score_gemma":0.0000025092545,"teacher_disagreement_score":0.7919337,"about_ca_system_score_codex":0.000030999792,"about_ca_system_score_gemma":0.000032574895,"threshold_uncertainty_score":0.49083212},"labels":[],"label_agreement":null},{"id":"W3110436430","doi":"10.1007/s10479-020-03886-0","title":"Stochastic efficiency and inefficiency in portfolio optimization with incomplete information: a set-valued probability approach","year":2020,"lang":"en","type":"article","venue":"Annals of Operations Research","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Acadia University","funders":"","keywords":"Inefficiency; Complete information; Theory of computation; Set (abstract data type); Mathematical optimization; Portfolio; Portfolio optimization; Probability measure; Computer science; Metric (unit); Mathematics; Mathematical economics; Algorithm; Economics; Statistics","score_opus":0.371803939000855,"score_gpt":0.45643776027668737,"score_spread":0.08463382127583235,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3110436430","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":[0.27872503,0.00010309307,0.7124015,0.0032261084,0.000018448784,0.0012884845,0.000042539104,0.000024297933,0.004170496],"genre_scores_gemma":[0.98071426,0.00008277518,0.01879698,0.00018480935,0.00002443731,0.00007490104,0.00007342652,0.000007766499,0.00004065603],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9958831,0.0005082729,0.0009640141,0.00042789624,0.0018753062,0.00034139468],"domain_scores_gemma":[0.9964521,0.00029551072,0.0001136005,0.00042036106,0.0025162934,0.0002021486],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005013256,0.00013870896,0.00030274366,0.0008405013,0.00032484252,0.0004055116,0.00049211294,0.00008050983,0.000122012614],"category_scores_gemma":[0.004738226,0.00010107925,0.00003517681,0.004921002,0.00038069513,0.0015539208,0.00017999299,0.00028165738,0.000021255673],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000096563286,0.000106876185,0.0017550293,0.000012283701,0.0000041340836,8.243488e-7,0.0033466583,0.98897016,0.000009658388,0.0035565093,0.00038111937,0.0017602069],"study_design_scores_gemma":[0.00041449175,0.00037592623,0.0032510299,0.000014005957,0.0000023310947,0.000005283527,0.0013561879,0.99387324,0.00004518278,0.00034553022,0.00019951205,0.00011727828],"about_ca_topic_score_codex":0.00019773046,"about_ca_topic_score_gemma":0.000043482443,"teacher_disagreement_score":0.70198923,"about_ca_system_score_codex":0.00001759165,"about_ca_system_score_gemma":0.0004615157,"threshold_uncertainty_score":0.5672441},"labels":[],"label_agreement":null},{"id":"W3112192998","doi":"10.2139/ssrn.3708379","title":"Bayes Risk, Elicitability, and the Expected Shortfall","year":2020,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"University of Waterloo","funders":"","keywords":"Expected shortfall; Bayes' theorem; Econometrics; Actuarial science; Economics; Bayesian probability; Risk management; Statistics; Mathematics; Finance","score_opus":0.0315365879904773,"score_gpt":0.3078857027878049,"score_spread":0.2763491147973276,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3112192998","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":[0.8467939,0.0086201755,0.119962536,0.020665722,0.00022545218,0.00030110337,0.0000051249544,0.00005571648,0.0033702794],"genre_scores_gemma":[0.9774384,0.021283144,0.00018056516,0.00042532518,0.00031949492,0.0000033603633,9.62579e-7,0.000010819542,0.00033792842],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99668187,0.0006576813,0.00056612026,0.0003071935,0.0008215063,0.0009656265],"domain_scores_gemma":[0.9982745,0.0007657789,0.00033468613,0.00024776242,0.00022848662,0.00014875972],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0075591295,0.0001318263,0.00025698505,0.00007910357,0.00045547597,0.00035218647,0.0005936157,0.00005895633,0.000114746814],"category_scores_gemma":[0.0036799782,0.00006909605,0.00013712957,0.0005899855,0.00017450206,0.0003013042,0.00009404912,0.0013537122,0.00005592249],"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":[0.0013406741,0.00007829121,0.22390491,0.0000017474226,0.00029225234,0.000007623042,0.009557676,0.0036078934,0.00007626497,0.26310074,0.0052550947,0.49277684],"study_design_scores_gemma":[0.0019668485,0.00027213403,0.010611602,0.0000027613826,0.000062547435,0.0002042637,0.0075863115,0.018360097,0.000028924967,0.9494184,0.0112900585,0.00019604535],"about_ca_topic_score_codex":0.00008019747,"about_ca_topic_score_gemma":0.0004575679,"teacher_disagreement_score":0.6863177,"about_ca_system_score_codex":0.00007725573,"about_ca_system_score_gemma":0.000599617,"threshold_uncertainty_score":0.5881281},"labels":[],"label_agreement":null},{"id":"W3120078791","doi":"","title":"Dynamic mean semi-variance portfolio selection","year":2003,"lang":"en","type":"article","venue":"Lecture notes in computer science","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Efficient frontier; Portfolio; Portfolio optimization; Downside risk; Variance (accounting); Selection (genetic algorithm); Mathematical optimization; Modern portfolio theory; Optimization problem; Mathematics; Computer science; Econometrics; Economics; Financial economics; Artificial intelligence","score_opus":0.02247822449264877,"score_gpt":0.31778966941669995,"score_spread":0.2953114449240512,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3120078791","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":[0.072781935,0.00010617446,0.9246916,0.00028342986,0.0013506035,0.00016060389,0.000001049348,0.000060364968,0.0005642191],"genre_scores_gemma":[0.8172208,0.000046031688,0.18211582,0.0005178467,0.00005364116,0.0000043050254,0.0000010190005,0.000008266038,0.000032279837],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99633116,0.00019482066,0.000555205,0.0009812664,0.0014244348,0.00051311916],"domain_scores_gemma":[0.9980826,0.00051704684,0.00022870823,0.00063643366,0.00039267793,0.00014256148],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003662117,0.00019581484,0.0002508579,0.00075387687,0.0003443451,0.00057217706,0.0009933381,0.000107419575,0.00013622994],"category_scores_gemma":[0.0015320565,0.00015419011,0.00006565825,0.006606555,0.0002909971,0.00081416784,0.00010181971,0.00025611927,0.00008460139],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000048070256,0.00003326921,0.0164706,7.675623e-7,0.000001535204,0.000007979737,0.00046033048,0.6177204,0.0006411505,0.00059281004,0.000057391153,0.3640089],"study_design_scores_gemma":[0.00019944801,0.00006276815,0.008089316,0.000012684687,0.0000025814168,0.00008376385,0.0000023938526,0.907827,0.004377597,0.0776668,0.0014469873,0.00022866472],"about_ca_topic_score_codex":0.000037393325,"about_ca_topic_score_gemma":0.00021595208,"teacher_disagreement_score":0.7444388,"about_ca_system_score_codex":0.00014484725,"about_ca_system_score_gemma":0.00037998505,"threshold_uncertainty_score":0.62876904},"labels":[],"label_agreement":null},{"id":"W3120430762","doi":"10.2139/ssrn.3707709","title":"Dependence and Risk Attitudes: Neutrality and Aversion","year":2020,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Neutrality; Risk aversion (psychology); Psychology; Economics; Econometrics; Social psychology; Actuarial science; Political science; Financial economics; Law; Expected utility hypothesis","score_opus":0.03804108793550343,"score_gpt":0.32579442644289075,"score_spread":0.2877533385073873,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3120430762","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":[0.95482206,0.0030748746,0.03995485,0.0016765551,0.000075209246,0.00006752845,0.0000028635104,0.00001598052,0.00031008886],"genre_scores_gemma":[0.9298483,0.0693851,0.00028928844,0.00014944332,0.00010511608,4.0033285e-7,5.248708e-7,0.0000053779977,0.00021641096],"study_design_codex":"observational","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9980119,0.00019517571,0.00030596222,0.00028674016,0.000521204,0.00067901803],"domain_scores_gemma":[0.9991886,0.00016939102,0.00023027878,0.00011565991,0.00010316424,0.00019288191],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0031844391,0.000097643635,0.00015676823,0.00006902549,0.0003161848,0.00023635267,0.0002392592,0.00005746951,0.000039726823],"category_scores_gemma":[0.0005914885,0.00007076201,0.000045023593,0.0002691171,0.00006241182,0.0004698868,0.00007128316,0.0008710461,0.000031487267],"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":[0.00012758422,0.000018002895,0.79185563,0.0000018922289,0.000051483898,0.000007650254,0.00094670116,0.000848464,0.000117576456,0.015832396,0.00053030386,0.1896623],"study_design_scores_gemma":[0.002299398,0.0012852922,0.33536607,0.000020131003,0.00014515729,0.0011490883,0.0065060216,0.020562876,0.00024688026,0.6147139,0.01710044,0.0006047379],"about_ca_topic_score_codex":0.00007030657,"about_ca_topic_score_gemma":0.00028720533,"teacher_disagreement_score":0.5988815,"about_ca_system_score_codex":0.00005828146,"about_ca_system_score_gemma":0.00037283785,"threshold_uncertainty_score":0.37843102},"labels":[],"label_agreement":null},{"id":"W3120902719","doi":"10.2139/ssrn.3664590","title":"Linking Risk Management Under Expected Shortfall to Loss-Averse Behavior","year":2020,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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 Laval","funders":"","keywords":"Expected shortfall; Risk management; Economics; Actuarial science; Business; Econometrics; Risk analysis (engineering); Finance","score_opus":0.042926095575010564,"score_gpt":0.33364591822340056,"score_spread":0.29071982264839,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3120902719","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":[0.5031914,0.0006125391,0.489032,0.002390262,0.00046369666,0.00041426448,0.0000064230585,0.00008956743,0.003799815],"genre_scores_gemma":[0.9874889,0.006918034,0.0024462878,0.0008127355,0.00040424947,0.000011846929,0.0000053675444,0.000028773271,0.0018837965],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99577457,0.0002006968,0.00069098803,0.00047399124,0.0013186247,0.0015411442],"domain_scores_gemma":[0.99868536,0.00008454402,0.00033393645,0.00032374248,0.00023463744,0.00033780094],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025511438,0.00019644186,0.00025274593,0.0003147199,0.00036464765,0.00035599407,0.00081946235,0.00008323125,0.00028597415],"category_scores_gemma":[0.00019892675,0.00015677222,0.00018598879,0.0011694245,0.00003036381,0.00034787916,0.00014421939,0.0011878518,0.0006455423],"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":[0.00037343282,0.00022689563,0.17957957,0.0000023695557,0.00031347654,0.00019532524,0.0027928068,0.060657367,0.000117513,0.087785214,0.004544346,0.6634117],"study_design_scores_gemma":[0.005116457,0.0025866097,0.08754914,0.00006684683,0.0009294904,0.0009267698,0.043424636,0.011463369,0.00030576077,0.6672571,0.1781989,0.0021749255],"about_ca_topic_score_codex":0.0000217693,"about_ca_topic_score_gemma":0.00022347172,"teacher_disagreement_score":0.66123676,"about_ca_system_score_codex":0.00038545323,"about_ca_system_score_gemma":0.0004637758,"threshold_uncertainty_score":0.8297355},"labels":[],"label_agreement":null},{"id":"W3121232053","doi":"10.1080/14697688.2018.1484151","title":"Optimal mean-reversion strategy in the presence of bid-ask spread and delays in capital allocations","year":2018,"lang":"en","type":"article","venue":"Quantitative Finance","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Sharpe ratio; Transaction cost; Mean reversion; Economics; Portfolio; Bid price; Bid–ask spread; Expected return; Stock (firearms); Ask price; Trading strategy; Econometrics; Financial economics; Portfolio optimization; Microeconomics; Stock exchange; Finance","score_opus":0.08991190916917638,"score_gpt":0.3871454622858689,"score_spread":0.29723355311669253,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3121232053","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":[0.98649395,0.00047891235,0.010460615,0.0002927548,0.000066710745,0.00020717044,0.000020124744,0.0000036781491,0.0019760886],"genre_scores_gemma":[0.9919958,0.0007825966,0.006916068,0.000029319026,0.000012551619,0.000010984207,0.000003530471,0.0000037578266,0.00024538336],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.99850714,0.00023671966,0.00040985402,0.0002910596,0.00040378174,0.00015147068],"domain_scores_gemma":[0.9983028,0.0009352337,0.00020533851,0.0002722755,0.00026667558,0.000017647655],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014033545,0.000089058085,0.00016488691,0.000210398,0.00007827032,0.00004903801,0.0003770698,0.00004933434,0.000023072611],"category_scores_gemma":[0.00084623136,0.000060731738,0.000024761313,0.0011091199,0.0003614689,0.000489813,0.000047112608,0.00009526849,0.00003941834],"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":[0.00048581112,0.0004718194,0.20731395,0.000014639005,0.000017275202,0.0000418172,0.12602782,0.18750972,0.0022219012,0.43170255,0.008646138,0.035546556],"study_design_scores_gemma":[0.00078922015,0.0009845466,0.74018955,0.000101641876,0.0000082529405,0.000008231287,0.018421866,0.21144816,0.0014625649,0.023470558,0.0028513223,0.00026405606],"about_ca_topic_score_codex":0.00059691153,"about_ca_topic_score_gemma":0.001685074,"teacher_disagreement_score":0.5328756,"about_ca_system_score_codex":0.000011416961,"about_ca_system_score_gemma":0.00007392087,"threshold_uncertainty_score":0.24765684},"labels":[],"label_agreement":null},{"id":"W3121277148","doi":"10.1007/s00780-017-0328-4","title":"Risk bounds for factor models","year":2017,"lang":"en","type":"article","venue":"Finance and Stochastics","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":51,"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; Upper and lower bounds; Independence (probability theory); Regular polygon; Conditional independence; Joint probability distribution; Value at risk; Mathematical finance; Marginal distribution; Expected shortfall; Risk measure; Combinatorics; Range (aeronautics); Measure (data warehouse); Statistics; Portfolio; Random variable; Mathematical analysis; Economics; Risk management; Computer science; Geometry","score_opus":0.14544552929197296,"score_gpt":0.39566672867371583,"score_spread":0.25022119938174286,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3121277148","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":[0.108574696,0.00016507658,0.8888643,0.00013621748,0.00038575218,0.00014729827,0.00022276599,0.000009765954,0.0014941574],"genre_scores_gemma":[0.9841548,0.00088960544,0.011717702,0.000037106336,0.00012514093,0.000014345487,0.0000037297368,0.00000711155,0.003050408],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99910194,0.000010302867,0.00022502104,0.00024716178,0.00025889743,0.00015666113],"domain_scores_gemma":[0.99866956,0.00024361265,0.00032313738,0.0005176047,0.00020363991,0.00004245083],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036671016,0.000084699015,0.00016352395,0.000048464957,0.0008600272,0.00045780337,0.00034244056,0.00006585477,0.0000074950726],"category_scores_gemma":[0.0012968995,0.00006363336,0.000047933132,0.00004239181,0.00009807958,0.00041250495,0.000060634706,0.000057888337,0.000014825591],"study_design_candidate":"design_other","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":[0.00017847132,0.000058758174,0.018603586,0.0000052513456,0.000019259352,0.0000037908533,0.0018772105,0.08462362,0.000016697928,0.11479609,0.020190498,0.75962675],"study_design_scores_gemma":[0.00041511538,0.00010570787,0.030443931,0.000007278975,0.00001119608,0.0000015477223,0.000039620398,0.65992457,0.000022005106,0.2604127,0.04847521,0.00014114223],"about_ca_topic_score_codex":0.000020755535,"about_ca_topic_score_gemma":0.000021119018,"teacher_disagreement_score":0.87714654,"about_ca_system_score_codex":0.000005143263,"about_ca_system_score_gemma":0.000035579244,"threshold_uncertainty_score":0.6614721},"labels":[],"label_agreement":null},{"id":"W3121304191","doi":"10.1016/j.jbankfin.2021.106297","title":"Adjusted Expected Shortfall","year":2020,"lang":"en","type":"preprint","venue":"Journal of Banking & Finance","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Actua; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Expected shortfall; Stochastic dominance; Econometrics; Value at risk; Benchmark (surveying); Tail risk; Economics; Actuarial science; Statistics; Mathematics; Risk management; Finance","score_opus":0.1358453735710938,"score_gpt":0.37081082546223093,"score_spread":0.23496545189113713,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3121304191","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":[0.6476851,0.009513358,0.3106382,0.0066318447,0.011066919,0.00052065006,0.000056158096,0.00009659665,0.013791207],"genre_scores_gemma":[0.97163534,0.0054213433,0.02085886,0.00029530228,0.0011006901,0.0000032673768,0.000007836124,0.000030458552,0.0006468869],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99476856,0.00024598057,0.002122569,0.000502146,0.002097402,0.00026332398],"domain_scores_gemma":[0.99345154,0.00040569733,0.0038680434,0.00067160884,0.00148719,0.00011591525],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019734006,0.00029252406,0.0009581769,0.000508369,0.000107140615,0.00042108848,0.0017598064,0.00032583534,0.00022394031],"category_scores_gemma":[0.0020942655,0.00022738904,0.00050918077,0.0009003742,0.00006445087,0.00034691853,0.00047850335,0.0012775395,0.00006508822],"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":[0.0006125234,0.00024487328,0.03228004,0.00003796968,0.00023686777,0.0014155391,0.006156685,0.34722403,0.0004332205,0.0029349485,0.20927262,0.39915067],"study_design_scores_gemma":[0.0017119155,0.00061739073,0.2119672,0.00154776,0.00030400444,0.0005119189,0.00046413008,0.04982756,0.0013646444,0.19235283,0.5378945,0.001436166],"about_ca_topic_score_codex":0.000013917026,"about_ca_topic_score_gemma":0.000007920023,"teacher_disagreement_score":0.3977145,"about_ca_system_score_codex":0.00009025088,"about_ca_system_score_gemma":0.0005907384,"threshold_uncertainty_score":0.9272656},"labels":[],"label_agreement":null},{"id":"W3121344495","doi":"","title":"Aggregating risks with partial dependence information","year":2016,"lang":"en","type":"preprint","venue":"RePEc: Research Papers in Economics","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Upper and lower bounds; Regular polygon; Mathematics; Order (exchange); Distribution (mathematics); Combinatorics; Applied mathematics; Mathematical optimization; Mathematical analysis; Economics","score_opus":0.10955405125313292,"score_gpt":0.40693252861069346,"score_spread":0.2973784773575605,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3121344495","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":[0.6387106,0.000097981145,0.005258062,0.0011853944,0.0011418517,0.0016896779,0.00015831336,0.00010353259,0.3516546],"genre_scores_gemma":[0.988879,0.006382579,0.0029639273,0.00008009291,0.00025347053,0.00016908387,0.000044407374,0.000028170229,0.0011992268],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99549204,0.00047930086,0.0012747988,0.00078524905,0.001290429,0.00067820156],"domain_scores_gemma":[0.99549615,0.0015527793,0.000827146,0.0013611831,0.0005755153,0.00018724795],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0071947896,0.00026561134,0.00046565628,0.00095247757,0.00025158908,0.00087193464,0.0013081076,0.00041285952,0.0001848812],"category_scores_gemma":[0.0040161437,0.00018717103,0.0001139881,0.000340962,0.00029944663,0.0010840968,0.0009913233,0.0010920105,0.00012581811],"study_design_candidate":"design_other","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":[0.00013473106,0.0000210295,0.047447328,0.000009360844,0.000019171017,0.000010643991,0.0005055377,0.087209776,0.0000037832863,0.00036638422,0.0001432287,0.864129],"study_design_scores_gemma":[0.003847635,0.0005843338,0.060466796,0.001494565,0.000033484575,0.00009210607,0.004468341,0.4623515,0.0014428953,0.06601614,0.39668795,0.0025142652],"about_ca_topic_score_codex":0.000107570544,"about_ca_topic_score_gemma":0.00025991138,"teacher_disagreement_score":0.86161476,"about_ca_system_score_codex":0.0004025163,"about_ca_system_score_gemma":0.0009748815,"threshold_uncertainty_score":0.8408086},"labels":[],"label_agreement":null},{"id":"W3121441896","doi":"10.1137/140981046","title":"How Superadditive Can a Risk Measure Be?","year":2015,"lang":"en","type":"article","venue":"SIAM Journal on Financial Mathematics","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"University of Waterloo","funders":"","keywords":"Coherent risk measure; Risk measure; Superadditivity; Spectral risk measure; Dynamic risk measure; Measure (data warehouse); Expected shortfall; Tail risk; Diversification (marketing strategy); Econometrics; Mathematics; Portfolio; Computer science; Economics; Mathematical economics; Data mining; Financial economics; Business","score_opus":0.12908514052895428,"score_gpt":0.33464464290750656,"score_spread":0.20555950237855228,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3121441896","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":[0.61719453,0.0006634678,0.31659964,0.021238437,0.0060827257,0.0009794933,0.00050271326,0.00015823591,0.03658076],"genre_scores_gemma":[0.95945233,0.00056958623,0.03098197,0.00076722866,0.0016597724,0.000011524746,0.0000076018173,0.000052198255,0.0064977757],"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99607337,0.00026405504,0.0006864698,0.0002537764,0.0023899819,0.00033234493],"domain_scores_gemma":[0.9965282,0.00055384944,0.0008020361,0.0004241059,0.0012743734,0.00041741828],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.003921491,0.00022303386,0.00044339953,0.00033495115,0.00034186625,0.0008015627,0.0005891396,0.00015212942,0.0000712905],"category_scores_gemma":[0.021589026,0.00014863955,0.00020566757,0.0007257647,0.0000797582,0.00041248248,0.00004907825,0.00054857973,0.00013713892],"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":[0.00032149453,0.0010732675,0.006018851,0.000011477584,0.000098530545,0.00053140084,0.03335038,0.007468316,0.000051338357,0.08072485,0.6584011,0.21194904],"study_design_scores_gemma":[0.002190746,0.0011983779,0.0025633646,0.00016022095,0.000101210055,0.00067252055,0.0068466575,0.0069446857,0.0005718108,0.65833473,0.31969693,0.0007187624],"about_ca_topic_score_codex":0.000006334535,"about_ca_topic_score_gemma":0.000039211034,"teacher_disagreement_score":0.57760984,"about_ca_system_score_codex":0.00011342269,"about_ca_system_score_gemma":0.0005582822,"threshold_uncertainty_score":0.98665255},"labels":[],"label_agreement":null},{"id":"W3121563380","doi":"10.1017/asb.2020.22","title":"RISK MEASURES DERIVED FROM A REGULATOR’S PERSPECTIVE ON THE REGULATORY CAPITAL REQUIREMENTS FOR INSURERS","year":2020,"lang":"en","type":"article","venue":"Astin Bulletin","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Subadditivity; Regulator; Capital requirement; Monotonic function; Robustness (evolution); Mathematics; Econometrics; Quantile; Risk measure; Actuarial science; Risk analysis (engineering); Computer science; Economics; Business; Microeconomics; Finance; Discrete mathematics; Incentive","score_opus":0.09292295234196796,"score_gpt":0.32407492066936067,"score_spread":0.2311519683273927,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3121563380","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":[0.93565124,0.00038430214,0.014926524,0.042601477,0.00048722376,0.00096968375,0.00020930298,0.00010978333,0.0046604318],"genre_scores_gemma":[0.99429834,0.00006748218,0.0026180064,0.0020829109,0.00037758835,0.00005523852,0.0000135598975,0.000028923612,0.00045795477],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99680054,0.00043312146,0.00051302335,0.0007141861,0.0012670124,0.00027209768],"domain_scores_gemma":[0.9965662,0.0017181138,0.0004336767,0.00057727174,0.0005355484,0.00016918947],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0015836984,0.00021651789,0.00027825317,0.00007587255,0.0004196416,0.0002022052,0.00067171577,0.00010276806,0.0008539048],"category_scores_gemma":[0.010064478,0.00014001898,0.00019225293,0.00032991142,0.00015887062,0.00007319032,0.0000934236,0.00019445599,0.00065628946],"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":[0.0018702623,0.00016500289,0.03646014,0.0000024869037,0.000321294,0.0000128790025,0.024377054,0.012892268,0.0027234303,0.015590921,0.86753696,0.038047284],"study_design_scores_gemma":[0.0036513119,0.0010030665,0.27846348,0.000061136714,0.00019680541,0.000002680969,0.03023948,0.008679385,0.0104828365,0.053260077,0.61289036,0.0010693567],"about_ca_topic_score_codex":0.00026858304,"about_ca_topic_score_gemma":0.000026303594,"teacher_disagreement_score":0.2546466,"about_ca_system_score_codex":0.00007649849,"about_ca_system_score_gemma":0.00008124615,"threshold_uncertainty_score":0.99827415},"labels":[],"label_agreement":null},{"id":"W3121682060","doi":"10.2139/ssrn.3720332","title":"Maximum Spectral Measures of Risk with Given Risk Factor Marginal Distributions","year":2020,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Mathematics; Measure (data warehouse); Wasserstein metric; Risk measure; Upper and lower bounds; Mathematical optimization; Applied mathematics; Duality (order theory); Expected shortfall; Constraint (computer-aided design); Metric (unit); Function (biology); Mathematical analysis; Combinatorics; Risk management; Computer science","score_opus":0.04752477288594668,"score_gpt":0.3116074714124667,"score_spread":0.26408269852652,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3121682060","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":[0.24155235,0.0030457424,0.7506412,0.0011887571,0.0005726461,0.00036562403,0.0018264661,0.000053255197,0.00075394526],"genre_scores_gemma":[0.9604196,0.036785316,0.0018739149,0.000009253788,0.0005622036,0.0000068298737,0.00006134393,0.00003991135,0.00024162521],"study_design_codex":"observational","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99282897,0.0008398264,0.0012780469,0.0007703299,0.0023990136,0.0018838334],"domain_scores_gemma":[0.9948124,0.00040296032,0.0028344602,0.00073966663,0.0009101662,0.00030031096],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0044142124,0.00047010885,0.000862713,0.0004293873,0.00046186423,0.000389709,0.0015622413,0.00030656025,0.00025571077],"category_scores_gemma":[0.001951983,0.0003245507,0.00056711765,0.00079885457,0.00020825963,0.00027529802,0.00027983714,0.0068649864,0.00006543912],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0025618267,0.0004974087,0.48096094,0.000021409227,0.002815782,0.0000710548,0.0023286394,0.12657386,0.00009510818,0.033671513,0.0033851743,0.3470173],"study_design_scores_gemma":[0.00086732616,0.0006010225,0.07452419,0.000049146776,0.00039453336,0.00023776048,0.0010814912,0.0016594307,0.00016991976,0.91603774,0.0038853567,0.00049207185],"about_ca_topic_score_codex":0.00042254687,"about_ca_topic_score_gemma":0.001865332,"teacher_disagreement_score":0.88236624,"about_ca_system_score_codex":0.00075552263,"about_ca_system_score_gemma":0.0061225123,"threshold_uncertainty_score":0.99992067},"labels":[],"label_agreement":null},{"id":"W3121752429","doi":"","title":"Deterring Repeat Offenders with Escalating Penalty Schedules: A Bayesian Approach","year":2015,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Thompson Rivers University","funders":"","keywords":"Bayesian probability; Deterrence (psychology); Economics; Computer science; Econometrics; Computer security; Artificial intelligence; Law and economics","score_opus":0.0722583838536898,"score_gpt":0.3245440088518188,"score_spread":0.252285624998129,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3121752429","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":[0.20019516,0.00063843234,0.7884579,0.00025563984,0.00013290708,0.0001287278,5.285426e-7,0.000042469514,0.010148261],"genre_scores_gemma":[0.9804928,0.00036756028,0.017607195,0.00006248233,0.00025505977,0.0000043491195,0.0000029994203,0.000028271708,0.0011793086],"study_design_codex":"observational","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9953257,0.0002864027,0.0006429044,0.00043091728,0.0015635695,0.0017505098],"domain_scores_gemma":[0.9982616,0.00011709288,0.0005063144,0.0004057815,0.00039788347,0.000311384],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008901613,0.00020574265,0.00029465422,0.00034120216,0.00034395748,0.00044782957,0.0006227579,0.00009015448,0.000018650382],"category_scores_gemma":[0.00076411024,0.0001335354,0.00011756871,0.000787602,0.0000746261,0.00079712906,0.000059366597,0.0013427978,0.00003435755],"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":[0.0007880036,0.0002776138,0.3669098,0.000006248746,0.00037903385,0.00005180679,0.0053131334,0.18302254,0.00006749942,0.09265035,0.0007581748,0.34977582],"study_design_scores_gemma":[0.0048996583,0.0017700683,0.0032665653,0.00008202797,0.00014346103,0.006731852,0.09018649,0.30145058,0.00016288899,0.58708453,0.0030513315,0.0011705224],"about_ca_topic_score_codex":0.000056286015,"about_ca_topic_score_gemma":0.00033870048,"teacher_disagreement_score":0.7802976,"about_ca_system_score_codex":0.0005593187,"about_ca_system_score_gemma":0.0025910835,"threshold_uncertainty_score":0.58338624},"labels":[],"label_agreement":null},{"id":"W3121975445","doi":"10.1017/s0515036100015300","title":"Economic Capital Allocations for Non-negative Portfolios of Dependent Risks","year":2008,"lang":"en","type":"article","venue":"Astin Bulletin","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"York University","funders":"","keywords":"Context (archaeology); Portfolio; Capital (architecture); Economic capital; Economics; Multivariate statistics; Decomposition; Poisson distribution; Risk-adjusted return on capital; Econometrics; Financial economics; Actuarial science; Microeconomics; Financial capital; Mathematics; Capital formation; Geography; Statistics","score_opus":0.08867106082031231,"score_gpt":0.3560634217238997,"score_spread":0.2673923609035874,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3121975445","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":[0.91809183,0.00006596245,0.0672889,0.0012840321,0.0005142602,0.0006799081,0.00016258263,0.00003188338,0.011880659],"genre_scores_gemma":[0.98025656,0.0001614079,0.01410687,0.00007513554,0.00012396097,0.00006521132,0.000022568433,0.000015690128,0.0051725837],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.9981964,0.00006006893,0.00074929546,0.00037940632,0.00040030334,0.00021450344],"domain_scores_gemma":[0.99767095,0.0010223688,0.00047038306,0.00039596102,0.00034206884,0.00009824919],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0008562595,0.0001363491,0.00028358685,0.0002119317,0.0002023803,0.000034343262,0.00040377886,0.00008042289,0.0012732475],"category_scores_gemma":[0.001142283,0.00011763699,0.00014102957,0.00016657959,0.00013778337,0.00008588258,0.000062405554,0.00007461158,0.0007645373],"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":[0.00032196008,0.00037745823,0.18569584,0.000009952504,0.00013936059,0.000022786864,0.006143603,0.14378096,0.00045261436,0.0044564507,0.627132,0.031467024],"study_design_scores_gemma":[0.0072573572,0.0012669232,0.5751427,0.0000771604,0.0001815144,0.0003258363,0.0062398845,0.04744876,0.029852198,0.025668517,0.304742,0.0017971492],"about_ca_topic_score_codex":0.0004906295,"about_ca_topic_score_gemma":0.000082897066,"teacher_disagreement_score":0.38944685,"about_ca_system_score_codex":0.000035577006,"about_ca_system_score_gemma":0.00017012491,"threshold_uncertainty_score":0.99963975},"labels":[],"label_agreement":null},{"id":"W3122103973","doi":"10.3390/risks10110205","title":"Coherent Diversification Measures in Portfolio Theory: An Axiomatic Foundation","year":2022,"lang":"en","type":"article","venue":"Risks","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"Social Sciences and Humanities Research Council of Canada; Fonds de Recherche du Québec-Société et Culture","keywords":"Diversification (marketing strategy); Axiom; Portfolio; Modern portfolio theory; Mathematical economics; Mathematics; Rank correlation; Econometrics; Economics; Financial economics; Statistics; Business","score_opus":0.24207877671444444,"score_gpt":0.4203837325813149,"score_spread":0.17830495586687045,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3122103973","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":[0.95686924,0.00012444597,0.032816604,0.00020305053,0.00053677324,0.00041156088,0.000013834576,0.00006355113,0.0089609455],"genre_scores_gemma":[0.9984439,0.00007208398,0.00035082732,0.00008881221,0.000037733807,0.00006029299,0.00008472485,0.000009618116,0.00085201283],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9968567,0.0008655767,0.0004937353,0.0003587711,0.0012587047,0.0001664932],"domain_scores_gemma":[0.998762,0.00022554737,0.000313947,0.00050156924,0.00012744502,0.00006946026],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0047135046,0.00009710152,0.00015963044,0.00046301703,0.00035770563,0.00015753595,0.00049181597,0.000035989608,0.0022630729],"category_scores_gemma":[0.0004908257,0.000084565734,0.000052444593,0.001069168,0.000035192636,0.00048830494,0.00009705201,0.00013063932,0.00017959824],"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":[0.0001810982,0.0003624724,0.19955587,0.0000016486941,0.000014680654,0.000012335112,0.004927007,0.09271112,0.00021818752,0.030910136,0.0034610336,0.6676444],"study_design_scores_gemma":[0.0009989322,0.00026052605,0.6445467,0.0000059651575,0.000033704935,0.00001563181,0.0104768295,0.091661625,0.00018789938,0.20554711,0.045877136,0.00038794463],"about_ca_topic_score_codex":0.00029059965,"about_ca_topic_score_gemma":0.0001308892,"teacher_disagreement_score":0.6672565,"about_ca_system_score_codex":0.00012350154,"about_ca_system_score_gemma":0.00007700341,"threshold_uncertainty_score":0.998649},"labels":[],"label_agreement":null},{"id":"W3122152566","doi":"","title":"What about Underevaluating Operational Value at Risk in the Banking Sector","year":2007,"lang":"en","type":"preprint","venue":"RePEc: Research Papers in Economics","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Operational risk; Poisson distribution; Econometrics; Generalized Pareto distribution; Negative binomial distribution; Log-normal distribution; Value at risk; Extreme value theory; Variance (accounting); Relevance (law); Reliability (semiconductor); Distribution (mathematics); Economics; Statistics; Computer science; Mathematics; Risk management; Finance; Accounting; Physics","score_opus":0.14717054065129426,"score_gpt":0.43631367256355114,"score_spread":0.2891431319122569,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3122152566","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":[0.91465807,0.0008697858,0.00022051483,0.000828508,0.0015093306,0.0013223272,0.000045312383,0.000019699542,0.080526434],"genre_scores_gemma":[0.9250609,0.068255246,0.0025492983,0.00043233635,0.00064522505,0.00017889752,0.0001216892,0.00005457482,0.0027018248],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9914201,0.0026529087,0.0017177141,0.001352183,0.0020435387,0.00081355067],"domain_scores_gemma":[0.98968965,0.007668451,0.00058994273,0.0015487385,0.0003769475,0.00012626336],"candidate_categories":["metaresearch","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.044445667,0.0003136213,0.0005341387,0.0012333149,0.0005690592,0.002116898,0.0020086323,0.0005063871,0.00082423363],"category_scores_gemma":[0.005895585,0.0002395249,0.00021777066,0.00066517235,0.0002832336,0.00059327926,0.0014001018,0.0022074226,0.00007663112],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000059652706,0.000055680386,0.06861343,0.0000071388217,0.000019477731,0.000017957918,0.003160072,0.70512694,0.000005812636,0.00093917706,0.00017584916,0.22181877],"study_design_scores_gemma":[0.0010317131,0.000081968436,0.14980622,0.00038461608,0.000014163367,0.000027884826,0.008990226,0.7431581,0.00005914609,0.06718309,0.028562581,0.0007003129],"about_ca_topic_score_codex":0.00027979963,"about_ca_topic_score_gemma":0.0031503139,"teacher_disagreement_score":0.22111847,"about_ca_system_score_codex":0.001366751,"about_ca_system_score_gemma":0.00094678457,"threshold_uncertainty_score":0.998919},"labels":[],"label_agreement":null},{"id":"W3122308759","doi":"","title":"Robust Portfolio Allocation with Systematic Risk Contribution Restrictions","year":2012,"lang":"en","type":"preprint","venue":"RePEc: Research Papers in Economics","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Core Research for Evolutional Science and Technology; Natural Sciences and Engineering Research Council of Canada; Fondation du Risque","keywords":"Portfolio; Portfolio optimization; Systematic risk; Expected shortfall; Modern portfolio theory; Replicating portfolio; Robustness (evolution); Application portfolio management; Economics; Actuarial science; Spectral risk measure; Risk management; Post-modern portfolio theory; Econometrics; Project portfolio management; Computer science; Financial economics; Finance","score_opus":0.08412836372845652,"score_gpt":0.35781529480957175,"score_spread":0.27368693108111525,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3122308759","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":[0.7903109,0.0022844272,0.01951529,0.0010008307,0.0038436376,0.011585467,0.0005153481,0.00034910368,0.170595],"genre_scores_gemma":[0.9698789,0.024361905,0.0016924285,0.000021125346,0.00033590873,0.0006393745,0.00021824347,0.00005690179,0.002795183],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99277675,0.0017441644,0.0019529059,0.0012073506,0.0014306054,0.00088821887],"domain_scores_gemma":[0.992221,0.002416907,0.0015231932,0.0022074373,0.0012773507,0.0003541393],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.014641016,0.00038776326,0.0009857322,0.0017340988,0.00047226652,0.00070666656,0.0011557005,0.0006399359,0.00014432218],"category_scores_gemma":[0.0066611255,0.000309394,0.00021196109,0.0009897529,0.00027271936,0.0005111915,0.00057744153,0.0017207563,0.00012013193],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018177682,0.00029850556,0.10908119,0.0003718682,0.00017833228,0.000018020051,0.00052211824,0.855758,0.0000065217573,0.0021844504,0.0005343343,0.03086485],"study_design_scores_gemma":[0.0038799334,0.0005752777,0.25030574,0.0059647546,0.00054265535,0.00020608735,0.006881754,0.68260103,0.00017592616,0.024682125,0.021091215,0.0030935223],"about_ca_topic_score_codex":0.00035305443,"about_ca_topic_score_gemma":0.00068822416,"teacher_disagreement_score":0.17956804,"about_ca_system_score_codex":0.0011255266,"about_ca_system_score_gemma":0.0009280657,"threshold_uncertainty_score":0.9999358},"labels":[],"label_agreement":null},{"id":"W3122454094","doi":"10.48550/arxiv.1701.05967","title":"Fatou Property, representations, and extensions of law-invariant risk\\n measures on general Orlicz spaces","year":2017,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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; University of Lethbridge","funders":"","keywords":"Mathematics; Invariant (physics); Bounded function; Norm (philosophy); Pure mathematics; Regular polygon; Property (philosophy); Space (punctuation); Mathematical analysis; Law; Geometry","score_opus":0.262866638630152,"score_gpt":0.2969339494895916,"score_spread":0.03406731085943959,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3122454094","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":[0.92582774,0.00026769695,0.029926382,0.0004911375,0.00078971026,0.0007472902,0.00021688099,0.00008411812,0.04164907],"genre_scores_gemma":[0.9802203,0.005959648,0.0010038618,0.00005019281,0.00009580465,0.0000014070677,0.000022454655,0.000019094581,0.012627246],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9971063,0.00052940776,0.00046376247,0.00121449,0.00044655675,0.00023944513],"domain_scores_gemma":[0.9954604,0.00040104162,0.001190464,0.002011558,0.0007388254,0.00019774416],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013450067,0.00029431275,0.00054584374,0.00044939306,0.0006196337,0.00035356867,0.0010971294,0.00029182018,0.00005827395],"category_scores_gemma":[0.0014034932,0.00021112263,0.00021458116,0.00028772917,0.0004631684,0.00036626676,0.0009226363,0.00042729257,0.000050141854],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00030730225,0.00021521724,0.092936,0.000016176584,0.00019238114,0.00020915747,0.00085883576,0.73438036,0.00010875858,0.15542045,0.008275275,0.007080092],"study_design_scores_gemma":[0.0023324203,0.00034504276,0.103829056,0.00035120585,0.0007439936,0.000024922272,0.0011333422,0.47354847,0.0012736653,0.37377536,0.041110847,0.0015316688],"about_ca_topic_score_codex":0.004595401,"about_ca_topic_score_gemma":0.0007679764,"teacher_disagreement_score":0.2608319,"about_ca_system_score_codex":0.000040497398,"about_ca_system_score_gemma":0.00019926441,"threshold_uncertainty_score":0.8609331},"labels":[],"label_agreement":null},{"id":"W3122474220","doi":"10.48550/arxiv.1403.5841","title":"Measuring the lack of monotonicity in functions","year":2014,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Western University","funders":"Direktorat Jenderal Pendidikan Tinggi; Natural Sciences and Engineering Research Council of Canada","keywords":"Monotonic function; Parametric statistics; Reliability (semiconductor); Computer science; Econometrics; Parametric model; Nonparametric statistics; Mathematical optimization; Mathematical economics; Applied mathematics; Mathematics; Statistics","score_opus":0.3156409792644662,"score_gpt":0.27508436827251387,"score_spread":0.04055661099195235,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3122474220","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":[0.8630014,0.00003927357,0.12320286,0.0001114933,0.000432449,0.00023633656,0.000014096178,0.000024909767,0.012937199],"genre_scores_gemma":[0.99691147,0.00031100033,0.00008763903,0.000034216566,0.000041159998,6.7599575e-7,0.000005506735,0.000008377357,0.0025999844],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981768,0.00037708494,0.00041717652,0.00060543645,0.00024418725,0.00017928415],"domain_scores_gemma":[0.9974394,0.0005325784,0.00049962854,0.0011446005,0.0003214303,0.0000624039],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019139276,0.00015911214,0.0003330972,0.0004142899,0.00011376919,0.00006386726,0.0011493652,0.00020107836,0.0001097019],"category_scores_gemma":[0.0005209794,0.0001224646,0.00020507246,0.0010130699,0.00015021117,0.00014960297,0.00061851164,0.00041935907,0.00012389077],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000030297464,0.000031525033,0.07456106,0.000003293127,0.0000139251115,0.0000055836163,0.0001491424,0.9180321,0.0000047406793,0.005794776,0.00043596813,0.0009376084],"study_design_scores_gemma":[0.0005021383,0.00003260504,0.07558145,0.00006744151,0.00007613027,0.0000012986499,0.0006682167,0.8295915,0.00015419982,0.088228494,0.004774823,0.00032169724],"about_ca_topic_score_codex":0.00052761135,"about_ca_topic_score_gemma":0.0005388727,"teacher_disagreement_score":0.13391006,"about_ca_system_score_codex":0.00007781014,"about_ca_system_score_gemma":0.00014336595,"threshold_uncertainty_score":0.49939612},"labels":[{"model":"gpt","categories":[],"domain":null,"study_design":"theoretical_or_conceptual","genre":"other","about_ca_system":false,"about_ca_topic":false,"confidence":"high"},{"model":"grok","categories":[],"domain":null,"study_design":"theoretical_or_conceptual","genre":"methods","about_ca_system":false,"about_ca_topic":false,"confidence":"low"},{"model":"opus","categories":[],"domain":null,"study_design":"theoretical_or_conceptual","genre":"methods","about_ca_system":false,"about_ca_topic":false,"confidence":"medium"}],"label_agreement":"agree"},{"id":"W3122512969","doi":"","title":"Vigilant Measures of Risk and the Demand for Contingent Claims","year":2012,"lang":"en","type":"preprint","venue":"Econstor (Econstor)","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Natural Sciences and Engineering Research Council of Canada; Northwestern University","keywords":"Risk measure; Mathematics; Probability measure; Random variable; Mathematical economics; Measure (data warehouse); Constraint (computer-aided design); Invariant (physics); Maximization; Distortion (music); Econometrics; Economics; Mathematical optimization; Discrete mathematics; Computer science; Statistics; Finance","score_opus":0.06314867143154107,"score_gpt":0.3180720470847552,"score_spread":0.25492337565321416,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3122512969","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":[0.93592894,0.01556394,0.034248643,0.0003420015,0.004933908,0.0017966697,0.0007659248,0.00005633711,0.0063636415],"genre_scores_gemma":[0.99017817,0.0051262537,0.0031298876,0.00010334642,0.00059749116,0.00016615336,0.000031878444,0.00003748822,0.00062936026],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9953423,0.0007008404,0.0017736843,0.00085539505,0.000860659,0.0004670953],"domain_scores_gemma":[0.9904467,0.004920249,0.0024635291,0.001248443,0.00068557204,0.00023551432],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.010236141,0.00045525795,0.0013118059,0.00038085997,0.0003987732,0.00029861627,0.0009524846,0.00047110041,0.0002619774],"category_scores_gemma":[0.003485266,0.00028686895,0.00058309734,0.00018459345,0.0011526026,0.00022094442,0.0005202875,0.0005346713,0.000049986003],"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":[0.00032376102,0.00006549437,0.9565448,0.000029067447,0.00021459389,0.0000010675421,0.0023668373,0.0013180722,0.0000099113795,0.0052151703,0.012419346,0.021491844],"study_design_scores_gemma":[0.011285107,0.00019113063,0.54283303,0.00045378463,0.0020284918,0.00015184826,0.002624591,0.019055583,0.0013957134,0.15435669,0.26315284,0.0024711816],"about_ca_topic_score_codex":0.00017076766,"about_ca_topic_score_gemma":0.0002722789,"teacher_disagreement_score":0.4137118,"about_ca_system_score_codex":0.000063010964,"about_ca_system_score_gemma":0.00036267683,"threshold_uncertainty_score":0.99995834},"labels":[],"label_agreement":null},{"id":"W3122562706","doi":"10.1142/s2010139211000109","title":"The Uncertainty Premium in an Ambiguous Economy","year":2011,"lang":"en","type":"article","venue":"Quarterly Journal of Finance","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":39,"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":"Lunds Universitet; York University","keywords":"Equity premium puzzle; Ambiguity aversion; Ambiguity; Risk premium; Economics; Liquidity premium; Odds; Risk aversion (psychology); Volatility risk premium; Econometrics; Financial economics; Actuarial science; Expected utility hypothesis; Monetary economics; Mathematics; Logistic regression; Computer science; Statistics","score_opus":0.07378510645709488,"score_gpt":0.3325122230943171,"score_spread":0.25872711663722225,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3122562706","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":[0.9892236,0.00042371306,0.0039526764,0.00030170212,0.0005274171,0.00007318864,0.0000012824082,0.0000037755156,0.00549262],"genre_scores_gemma":[0.9979923,0.0002565915,0.0011734418,0.00006507833,0.00009347946,0.0000020515486,1.8757683e-7,0.000004925618,0.00041194324],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9983117,0.0001741306,0.00087201875,0.00014408375,0.00031247237,0.00018562407],"domain_scores_gemma":[0.99833345,0.00011654173,0.00076310436,0.00035344556,0.00037359388,0.00005986401],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024529293,0.00008377639,0.00019733649,0.00014524916,0.00010751964,0.00015685172,0.0007804737,0.00004756579,0.000033810873],"category_scores_gemma":[0.0001102633,0.000048561284,0.00008156022,0.00031639735,0.00007788657,0.0009005638,0.0000073763604,0.00017511436,0.000034237986],"study_design_candidate":"design_other","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":[0.00020600916,0.00009569271,0.021758161,4.5180855e-7,0.0000051563447,0.000052263324,0.0067440183,0.0026300577,0.000014659644,0.0026056438,0.0018026648,0.9640852],"study_design_scores_gemma":[0.0013185999,0.003670739,0.6216382,0.000046941117,0.000012850575,0.00021451575,0.0043144086,0.013029754,0.00019128912,0.26417243,0.09106357,0.00032672714],"about_ca_topic_score_codex":0.0000869306,"about_ca_topic_score_gemma":0.00022199296,"teacher_disagreement_score":0.96375847,"about_ca_system_score_codex":0.000027865324,"about_ca_system_score_gemma":0.00011509694,"threshold_uncertainty_score":0.19802718},"labels":[],"label_agreement":null},{"id":"W3122632845","doi":"10.1111/mafi.12140","title":"ASYMPTOTIC EQUIVALENCE OF RISK MEASURES UNDER DEPENDENCE UNCERTAINTY","year":2016,"lang":"en","type":"article","venue":"Mathematical Finance","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":29,"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; China Scholarship Council; University of Waterloo","keywords":"Coherent risk measure; Risk measure; Equivalence (formal languages); Dynamic risk measure; Mathematics; Measure (data warehouse); Spectral risk measure; Time consistency; Econometrics; Expected shortfall; Risk management; Statistics; Applied mathematics; Actuarial science; Computer science; Economics; Discrete mathematics; Financial economics","score_opus":0.07907319673504086,"score_gpt":0.34815530239935044,"score_spread":0.26908210566430957,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3122632845","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":[0.26533625,0.0001568674,0.7296943,0.00039112289,0.00011809298,0.00012837973,0.000026533591,0.00003064408,0.0041178237],"genre_scores_gemma":[0.9868362,0.0011621973,0.010265324,0.00003694452,0.00002992989,0.000009667472,3.0440714e-7,0.000010325556,0.0016491052],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99690914,0.00018442005,0.00081036147,0.0004173138,0.0013929075,0.00028587016],"domain_scores_gemma":[0.99507564,0.003231222,0.00044825076,0.0007975276,0.00037053716,0.00007683086],"candidate_categories":["metaresearch","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0022908896,0.00015202793,0.00037477235,0.000107869084,0.00009040403,0.000047408295,0.0007515604,0.000092691495,0.00039241544],"category_scores_gemma":[0.0085604815,0.000079638274,0.00012743377,0.00055063126,0.00029194492,0.00029607373,0.00011079685,0.000084527725,0.00083897426],"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":[0.000087334745,0.00023807003,0.01741411,0.000014096422,0.000022129621,0.000008419827,0.00061072956,0.016043346,0.0013191495,0.5113102,0.0017892207,0.4511432],"study_design_scores_gemma":[0.00031638044,0.000057551228,0.019224571,0.00014589919,0.000018483777,0.0000103641605,0.00007395991,0.007293534,0.0026816584,0.9683293,0.001657804,0.000190498],"about_ca_topic_score_codex":0.000011256143,"about_ca_topic_score_gemma":0.000009021755,"teacher_disagreement_score":0.7215,"about_ca_system_score_codex":0.000029204442,"about_ca_system_score_gemma":0.00007324433,"threshold_uncertainty_score":0.99993896},"labels":[],"label_agreement":null},{"id":"W3122931019","doi":"10.2139/ssrn.3423042","title":"An Axiomatic Foundation for the Expected Shortfall","year":2019,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Western University; University of Waterloo","funders":"","keywords":"Foundation (evidence); Axiom; Expected shortfall; Mathematical economics; Economics; Actuarial science; Financial economics; Mathematics; Political science; Law","score_opus":0.03426613644600812,"score_gpt":0.3610152652298627,"score_spread":0.3267491287838546,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3122931019","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":[0.44182602,0.00055262726,0.55468017,0.0009046079,0.00067481585,0.00043736293,0.0000011107634,0.000027421114,0.00089588086],"genre_scores_gemma":[0.9941711,0.0012993368,0.0005253225,0.00010010694,0.0002936596,0.000013234741,0.000008903371,0.000015697293,0.0035726307],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9973841,0.00015320323,0.0004940017,0.00022926967,0.00074278377,0.000996646],"domain_scores_gemma":[0.99826586,0.000543384,0.00032999762,0.00043746427,0.00036122685,0.000062089035],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0061212787,0.00010891306,0.00015882018,0.00017074135,0.0003624049,0.00045520772,0.00072879164,0.000057039382,0.00021829605],"category_scores_gemma":[0.00041255628,0.000061417275,0.0001268244,0.00044223695,0.000026634443,0.0006906319,0.000015692596,0.00042871851,0.00024437552],"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":[0.00022048535,0.000115966155,0.019775959,0.0000016146333,0.0001436159,6.146742e-7,0.00121608,0.018396001,0.00085558667,0.31718177,0.0009410747,0.64115125],"study_design_scores_gemma":[0.0009579208,0.0007763691,0.0072245803,0.0000062436447,0.000051506562,0.00019622289,0.007513164,0.15047231,0.0000819605,0.8142124,0.018280463,0.00022690072],"about_ca_topic_score_codex":0.000019380626,"about_ca_topic_score_gemma":0.00034574623,"teacher_disagreement_score":0.64092433,"about_ca_system_score_codex":0.00021272381,"about_ca_system_score_gemma":0.00096098916,"threshold_uncertainty_score":0.43895784},"labels":[],"label_agreement":null},{"id":"W3123051355","doi":"","title":"NON-ARBITRAGE UNDER ADDITIONAL INFORMATION FOR THIN SEMIMARTINGALE MODELS","year":2016,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Martingale (probability theory); Semimartingale; Jump; Mathematical economics; Mathematics; Local martingale; Bounded function; Arbitrage; Profit (economics); Econometrics; Economics; Applied mathematics; Financial economics; Microeconomics; Mathematical analysis","score_opus":0.035771300176198104,"score_gpt":0.2840875837339458,"score_spread":0.2483162835577477,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3123051355","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":[0.0032017636,0.00010064168,0.8708131,0.008272382,0.00041219158,0.00076691725,0.0025646247,0.00015507423,0.11371334],"genre_scores_gemma":[0.7522877,0.00073486014,0.19013129,0.0011746981,0.00021504314,0.00087319635,0.013105673,0.000105373525,0.0413722],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9936939,0.0023117778,0.0013860831,0.00079206296,0.0013910248,0.00042516884],"domain_scores_gemma":[0.9830375,0.006676628,0.0015497655,0.0021444552,0.006364943,0.0002267592],"candidate_categories":["metaepi_narrow","scholarly_communication","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.015235385,0.0003731567,0.00046722888,0.00050641125,0.00056830543,0.0012177367,0.0017713571,0.00045456926,0.0013714794],"category_scores_gemma":[0.00521402,0.0003123913,0.000396995,0.0004886178,0.00020426293,0.0013840739,0.0009434272,0.0005024882,0.00025315242],"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":[0.00013435152,0.0005457076,0.0005989539,0.00011192801,0.00016639734,0.0000018278606,0.01352394,0.052758988,0.0003262702,0.37985843,0.42749342,0.12447978],"study_design_scores_gemma":[0.0006816026,5.7497607e-7,0.000998624,0.0006916421,0.000032448657,0.0000050849058,0.00017051373,0.47654355,0.002765791,0.43685055,0.08076048,0.0004991564],"about_ca_topic_score_codex":0.00014910976,"about_ca_topic_score_gemma":0.00025147002,"teacher_disagreement_score":0.7490859,"about_ca_system_score_codex":0.000140751,"about_ca_system_score_gemma":0.0007154931,"threshold_uncertainty_score":0.9999328},"labels":[],"label_agreement":null},{"id":"W3123079277","doi":"","title":"Correlated Risks vs Contagion in Stochastic Transition Models","year":2012,"lang":"en","type":"preprint","venue":"RePEc: Research Papers in Economics","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Fondation du Risque; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; National Science Foundation","keywords":"Econometrics; Statistical physics; Limiting; Monte Carlo method; Economics; Sample (material); Correlation; Set (abstract data type); Mathematics; Statistics; Computer science; Physics; Engineering","score_opus":0.173916411347997,"score_gpt":0.4142851601206073,"score_spread":0.24036874877261027,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3123079277","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":[0.9236506,0.00058265403,0.015867416,0.00080330845,0.0020456575,0.0025407628,0.00016591963,0.00008230415,0.054261327],"genre_scores_gemma":[0.9891477,0.009124514,0.00041583568,0.00005821488,0.00015557065,0.00016529695,0.0001552598,0.000049498212,0.00072808017],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9944345,0.00096528494,0.001605657,0.0011720568,0.00093465904,0.0008878728],"domain_scores_gemma":[0.99636424,0.0013719031,0.00043287544,0.0012146484,0.0003390285,0.00027731052],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.008985459,0.0003419544,0.00079672696,0.0018118338,0.00013936718,0.0003302134,0.0010078624,0.0008824717,0.00016833172],"category_scores_gemma":[0.0010808067,0.00033443636,0.00020787207,0.00061504915,0.00020603383,0.0005646672,0.0004713419,0.0022069488,0.000083827625],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00027183926,0.00011940032,0.0019085027,0.0000066922935,0.000014409988,0.000011965953,0.001653492,0.8733619,0.000006392396,0.00027418954,0.00006157085,0.122309685],"study_design_scores_gemma":[0.0008726479,0.00005609553,0.006979233,0.00014169252,0.000009285249,0.00000906433,0.0010035771,0.9601027,0.000010018135,0.029854808,0.0005831922,0.00037769083],"about_ca_topic_score_codex":0.0006581866,"about_ca_topic_score_gemma":0.00081311463,"teacher_disagreement_score":0.12193199,"about_ca_system_score_codex":0.00085104385,"about_ca_system_score_gemma":0.0005624745,"threshold_uncertainty_score":0.9999108},"labels":[],"label_agreement":null},{"id":"W3123093843","doi":"10.1287/opre.2017.1716","title":"Quantile-Based Risk Sharing","year":2018,"lang":"en","type":"article","venue":"Operations Research","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":159,"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":"Eidgenössische Technische Hochschule Zürich; Swiss Finance Institute; Michigan State University","keywords":"Subadditivity; Quantile; Expected shortfall; Coherent risk measure; Value at risk; Risk measure; Risk management; Pareto principle; Arrow; Econometrics; Dynamic risk measure; Computer science; Actuarial science; Economics; Mathematics; Mathematical optimization; Financial economics; Finance","score_opus":0.39534550782819544,"score_gpt":0.5564986340027319,"score_spread":0.16115312617453648,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3123093843","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":[0.79842675,0.00006672988,0.13500164,0.0017785461,0.00035266555,0.00041032015,0.000028639735,0.00005970039,0.06387503],"genre_scores_gemma":[0.9772626,0.00007751673,0.008285423,0.00006206073,0.00029784153,0.000033006385,0.000012738695,0.0000106543575,0.013958159],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99662435,0.00047754403,0.0003758067,0.0004589392,0.0017395952,0.0003237831],"domain_scores_gemma":[0.9961407,0.00055732235,0.000027257984,0.0008795515,0.0022673132,0.00012782592],"candidate_categories":["sts","scholarly_communication","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.008074106,0.000070396905,0.0001073961,0.0006895638,0.001556299,0.0011620523,0.0007819976,0.00006608402,0.0032949038],"category_scores_gemma":[0.0055756536,0.00005185956,0.000047522448,0.0023289188,0.00029067197,0.00040968083,0.00014349593,0.00025550174,0.0063275495],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017279138,0.00041754404,0.26035503,0.0000027451367,0.000031373194,0.000016604019,0.0031450465,0.32602483,0.0036074505,0.062578104,0.1425091,0.20113936],"study_design_scores_gemma":[0.00022172155,0.00013922591,0.009517417,0.0000055363007,0.0000021863675,0.0000011804358,0.00027204747,0.9156995,0.0027948907,0.0027873062,0.068466894,0.00009210573],"about_ca_topic_score_codex":0.00070450007,"about_ca_topic_score_gemma":0.0012133358,"teacher_disagreement_score":0.58967465,"about_ca_system_score_codex":0.00003645208,"about_ca_system_score_gemma":0.00025718502,"threshold_uncertainty_score":0.99987483},"labels":[],"label_agreement":null},{"id":"W3123368827","doi":"10.48550/arxiv.1107.0183","title":"BSDEs in Utility Maximization with BMO Market Price of Risk","year":2011,"lang":"en","type":"preprint","venue":"RePEc: Research Papers in Economics","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Semimartingale; Utility maximization problem; Mathematics; Uniqueness; Square-integrable function; Bounded function; Quadratic equation; Exponential utility; Integrable system; Utility maximization; Brownian motion; Exponential function; Quadratic growth; Mathematical economics; Applied mathematics; Stochastic differential equation; Representation (politics); Duality (order theory); Quadratic variation; Maximization; Mathematical optimization; Pure mathematics; Mathematical analysis","score_opus":0.08560149987770302,"score_gpt":0.36549582987807133,"score_spread":0.27989433000036834,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3123368827","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":[0.6844087,0.000124278,0.0006240313,0.000056879497,0.00025449172,0.0009741455,0.00012310839,0.0000209937,0.31341335],"genre_scores_gemma":[0.9651317,0.02718848,0.0060050082,0.000010055766,0.000049476614,0.000079987694,0.000038353424,0.000035697703,0.0014612223],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.994633,0.0012950478,0.0014885411,0.0011899169,0.00085167354,0.0005417908],"domain_scores_gemma":[0.9945765,0.0020511248,0.0009201941,0.0017447036,0.00055675604,0.00015069879],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.013038683,0.00027028413,0.00073308416,0.0014833662,0.0000904401,0.00016437727,0.0012970765,0.0004512509,0.0007689914],"category_scores_gemma":[0.005168463,0.00022713092,0.00012604757,0.00077584997,0.00043138472,0.00027900803,0.00086195045,0.0012172459,0.000011316611],"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":[0.0006895588,0.00021415822,0.6156833,0.00003228446,0.000027614204,0.000011264245,0.0011995136,0.073777825,0.0000016450442,0.00018225,0.00014888251,0.30803168],"study_design_scores_gemma":[0.0008930499,0.00012175273,0.8192483,0.00019177647,0.0000110904975,0.0000049687515,0.0013788226,0.13785791,0.00013167433,0.034640074,0.0050687604,0.00045178414],"about_ca_topic_score_codex":0.0007355565,"about_ca_topic_score_gemma":0.002307558,"teacher_disagreement_score":0.31195214,"about_ca_system_score_codex":0.00028666205,"about_ca_system_score_gemma":0.0007914342,"threshold_uncertainty_score":0.926213},"labels":[],"label_agreement":null},{"id":"W3123374962","doi":"10.2139/ssrn.2693041","title":"Downside Risk Neutral Probabilities","year":2015,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Downside risk; Risk neutral; Economics; Business; Econometrics; Financial economics; Portfolio","score_opus":0.06317542579166709,"score_gpt":0.33874468640832417,"score_spread":0.2755692606166571,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3123374962","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":[0.9355153,0.0028246595,0.04595712,0.0015541313,0.0009217965,0.0001779867,0.0000051368743,0.000060379287,0.012983536],"genre_scores_gemma":[0.98506653,0.0044363844,0.00074022013,0.00007682502,0.00048034894,0.0000035720132,0.0000018142722,0.000014516377,0.009179784],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99608845,0.0004043265,0.0006132068,0.0002723205,0.00097090856,0.0016507678],"domain_scores_gemma":[0.99824876,0.00021390921,0.00040889965,0.00034270695,0.00055154634,0.00023417188],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.011922986,0.00014317638,0.0002225674,0.00024063661,0.00024558025,0.00033722058,0.0005827708,0.00007944564,0.000050160474],"category_scores_gemma":[0.0021922907,0.00009635725,0.0001484947,0.00052063947,0.00008215633,0.0006641131,0.0000475394,0.0012650475,0.00034115347],"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":[0.00034234047,0.00016670082,0.18071616,9.763061e-7,0.00014521705,0.000015081256,0.0034524791,0.058697626,0.000019610034,0.44561976,0.021747153,0.28907692],"study_design_scores_gemma":[0.0004959231,0.00029034633,0.0013748934,0.0000022029715,0.000017876273,0.00039788752,0.0047753244,0.0010799799,0.00003921281,0.9670181,0.024374394,0.00013383213],"about_ca_topic_score_codex":0.00015774227,"about_ca_topic_score_gemma":0.0012591607,"teacher_disagreement_score":0.52139837,"about_ca_system_score_codex":0.00045840515,"about_ca_system_score_gemma":0.0034295712,"threshold_uncertainty_score":0.6083912},"labels":[],"label_agreement":null},{"id":"W3123587043","doi":"10.1287/moor.1120.0577","title":"External Risk Measures and Basel Accords","year":2013,"lang":"en","type":"article","venue":"Mathematics of Operations Research","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":169,"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":"Hong Kong University of Science and Technology; Georgia State University; University of Alberta; National Science Foundation","keywords":"Basel II; Operational risk; Dynamic risk measure; Risk measure; Risk-weighted asset; Coherent risk measure; Capital requirement; Mathematics; Risk-adjusted return on capital; Robustness (evolution); Actuarial science; Measure (data warehouse); Econometrics; Risk analysis (engineering); Risk management; Value at risk; Economics; Computer science; Business; Finance; Data mining; Microeconomics; Profit (economics)","score_opus":0.2633710761696301,"score_gpt":0.4807259011762479,"score_spread":0.2173548250066178,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3123587043","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":[0.8699831,0.00020711788,0.12107403,0.00042768288,0.00004335893,0.000459838,0.00001465598,0.000012076605,0.007778154],"genre_scores_gemma":[0.9099837,0.0008993789,0.08481812,0.0000096310405,0.000038801598,0.00005704776,0.0000017278644,0.000009515116,0.004182098],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99690187,0.00033119865,0.00056524144,0.00021746261,0.0017877063,0.00019652434],"domain_scores_gemma":[0.99626666,0.0011049786,0.00006582599,0.0005414105,0.0019098988,0.00011123477],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0058876127,0.00007662812,0.00018313878,0.00046907298,0.0003712803,0.0006578127,0.00044613102,0.000055404358,0.00090479763],"category_scores_gemma":[0.006765632,0.000051739065,0.0000395135,0.00069329684,0.00020922752,0.000521102,0.00015426226,0.00018883921,0.000427991],"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":[0.000034105728,0.001384281,0.06529111,0.00005912543,0.00010328613,0.000006984447,0.020550113,0.06640042,0.025661623,0.121191904,0.055735853,0.6435812],"study_design_scores_gemma":[0.00038447106,0.00015068204,0.021476397,0.0000430559,0.000011787972,0.000014575093,0.004129693,0.8151718,0.0051964507,0.15133652,0.0019023734,0.00018221693],"about_ca_topic_score_codex":0.0005272504,"about_ca_topic_score_gemma":0.00010362946,"teacher_disagreement_score":0.74877137,"about_ca_system_score_codex":0.000012408984,"about_ca_system_score_gemma":0.00009594614,"threshold_uncertainty_score":0.99068993},"labels":[],"label_agreement":null},{"id":"W3123843897","doi":"10.1111/mafi.12323","title":"Ordering and inequalities for mixtures on risk aggregation","year":2021,"lang":"en","type":"article","venue":"Mathematical Finance","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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 Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Quantile; Copula (linguistics); Monotone polygon; Econometrics; Mathematics; Marginal distribution; Distribution (mathematics); Portfolio; Statistics; Economics; Random variable","score_opus":0.07771836551181885,"score_gpt":0.36250263177630054,"score_spread":0.2847842662644817,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3123843897","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":[0.5496453,0.00043755295,0.44441515,0.00073316187,0.00012290692,0.00016246845,0.000026060163,0.0000262637,0.0044311485],"genre_scores_gemma":[0.91311646,0.0011809302,0.07685037,0.0002317603,0.000098356984,0.000049020648,0.000007440082,0.000013901035,0.008451746],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9989447,0.00005839919,0.00032483073,0.00023923449,0.00031486378,0.00011800161],"domain_scores_gemma":[0.99768555,0.0017529626,0.00012862521,0.00024314412,0.00016207449,0.00002762515],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007273842,0.00007719535,0.00017782071,0.00004527951,0.00012618836,0.00014615624,0.000091975366,0.000048604994,0.00005539153],"category_scores_gemma":[0.0066798883,0.00005600654,0.00004392121,0.00020962974,0.00003926381,0.00012653878,0.00003199267,0.000053791915,0.00004578025],"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":[0.000049911552,0.00010696072,0.0018377147,0.000038636546,0.000010817012,0.0000072652115,0.0016777983,0.0036435162,0.0001076301,0.64404416,0.007582778,0.3408928],"study_design_scores_gemma":[0.0002557065,0.00005613593,0.0014597977,0.000058670696,0.000008620027,0.000007311846,0.00016100277,0.05308316,0.0052663144,0.91143095,0.02809143,0.00012089914],"about_ca_topic_score_codex":0.000002026375,"about_ca_topic_score_gemma":0.000003037757,"teacher_disagreement_score":0.3675648,"about_ca_system_score_codex":0.0000061545043,"about_ca_system_score_gemma":0.000022605605,"threshold_uncertainty_score":0.7996933},"labels":[],"label_agreement":null},{"id":"W3123924727","doi":"10.1287/moor.2019.1020","title":"Characterization, Robustness, and Aggregation of Signed Choquet Integrals","year":2020,"lang":"en","type":"article","venue":"Mathematics of Operations Research","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":66,"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":"Mathematics; Choquet integral; Monotonic function; Choquet theory; Additive function; Robustness (evolution); Class (philosophy); Regular polygon; Invariant (physics); Characterization (materials science); Pure mathematics; Applied mathematics; Mathematical economics; Discrete mathematics; Subderivative; Convex optimization; Mathematical analysis; Artificial intelligence; Computer science; Fuzzy logic","score_opus":0.2841848051385171,"score_gpt":0.45450103822292137,"score_spread":0.17031623308440424,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3123924727","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":[0.75836146,0.000048165126,0.23801194,0.0021807132,0.000021525397,0.00041437347,0.000038101556,0.000011529215,0.00091219705],"genre_scores_gemma":[0.97036606,0.00026597935,0.028683443,0.000023176972,0.000035887842,0.000020092693,0.00004568551,0.000010784123,0.0005488638],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99739534,0.0002325277,0.00083082874,0.00022072639,0.0011914528,0.00012911283],"domain_scores_gemma":[0.99682,0.0005271642,0.00013994997,0.00032702507,0.0020931016,0.00009276599],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002580665,0.00007990722,0.0002752097,0.00033962328,0.00015527624,0.00017942004,0.00036224833,0.00006628242,0.00029578028],"category_scores_gemma":[0.0064920085,0.000060218703,0.00003808419,0.0014406668,0.00020823683,0.00045602728,0.00011431295,0.000114616436,0.000022941535],"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":[0.000115219074,0.00082413456,0.005044374,0.00034290014,0.0000925359,0.000004278855,0.042375438,0.13899983,0.5288175,0.22441025,0.0043318514,0.054641664],"study_design_scores_gemma":[0.0003139163,0.00017756007,0.0011330793,0.000060056722,0.000009322902,0.000002608766,0.002162376,0.93024707,0.060554933,0.0047020847,0.00052623637,0.00011075105],"about_ca_topic_score_codex":0.000019662035,"about_ca_topic_score_gemma":0.000015042479,"teacher_disagreement_score":0.79124725,"about_ca_system_score_codex":0.000009143,"about_ca_system_score_gemma":0.00012577683,"threshold_uncertainty_score":0.77720094},"labels":[],"label_agreement":null},{"id":"W3123963739","doi":"10.3390/risks1010014","title":"Evaluating Risk Measures and Capital Allocations Based on Multi-Losses Driven by a Heavy-Tailed Background Risk: The Multivariate Pareto-II Model","year":2013,"lang":"en","type":"article","venue":"Risks","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":37,"is_retracted":false,"has_abstract":true,"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":"Multivariate statistics; Pareto principle; Expected shortfall; Econometrics; Value at risk; Capital allocation line; Tail risk; Generalized Pareto distribution; Extreme value theory; Multivariate normal distribution; Economics; Distortion (music); Actuarial science; Mathematics; Risk management; Statistics; Computer science; Microeconomics; Finance","score_opus":0.23695700104516426,"score_gpt":0.4285357680277035,"score_spread":0.19157876698253923,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3123963739","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":[0.80976313,0.0002926511,0.1879098,0.0005640066,0.00013508812,0.00082276255,0.00016489979,0.00006589942,0.0002817605],"genre_scores_gemma":[0.97892547,0.000620945,0.01938473,0.00018088234,0.00006087132,0.0001989857,0.000034349086,0.00003121678,0.0005625572],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99571264,0.0010406427,0.0007435611,0.00072592054,0.0013836967,0.00039352095],"domain_scores_gemma":[0.9955306,0.0019562454,0.00068684784,0.00090449996,0.0007191238,0.00020270636],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0032132133,0.00030229846,0.0003336055,0.00018281954,0.0016612716,0.00066689414,0.00057737855,0.00015738858,0.00015329696],"category_scores_gemma":[0.0039355825,0.00018313435,0.00013210572,0.0004917986,0.00020499786,0.00054748973,0.00012626107,0.00039099174,0.0002255818],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004891542,0.00017973655,0.09465414,0.0000010799845,0.000037011392,4.051214e-7,0.0023745995,0.85310686,0.00020244473,0.000051952866,0.0041666557,0.045176174],"study_design_scores_gemma":[0.0010080168,0.00013814971,0.09087264,0.000013562565,0.00007232336,8.899663e-7,0.0009307386,0.9041412,0.00010898866,0.0022370107,0.00024536392,0.00023111439],"about_ca_topic_score_codex":0.005372231,"about_ca_topic_score_gemma":0.0007293922,"teacher_disagreement_score":0.16916233,"about_ca_system_score_codex":0.00005391707,"about_ca_system_score_gemma":0.00016028712,"threshold_uncertainty_score":0.99963844},"labels":[],"label_agreement":null},{"id":"W3124334183","doi":"10.2139/ssrn.2957181","title":"Archimedean Copulas: Aggregation, Capital Allocation and Other Applications","year":2017,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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 Laval","funders":"","keywords":"Economics; Econometrics; Capital allocation line; Mathematical economics; Microeconomics; Incentive","score_opus":0.031757349255843496,"score_gpt":0.3473909316952535,"score_spread":0.31563358243941,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3124334183","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":[0.48257282,0.0026804616,0.4976353,0.00503039,0.0003449886,0.00050683843,0.000010379236,0.000046442903,0.011172371],"genre_scores_gemma":[0.99118304,0.0036202017,0.00057677546,0.000091853304,0.000328799,0.000014222955,0.0000035009746,0.000013826676,0.00416777],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99798363,0.00008657878,0.00040166546,0.00025669584,0.00055386464,0.0007175642],"domain_scores_gemma":[0.9984443,0.000091611924,0.0005948433,0.0005235225,0.00024051614,0.00010515994],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0032114894,0.00010717398,0.000137266,0.00016731808,0.0012282014,0.0007438843,0.00063948706,0.00005898562,0.000040795363],"category_scores_gemma":[0.00049424625,0.00008277831,0.000058255715,0.00011398757,0.0001358631,0.0005660417,0.000051955834,0.00047796202,0.000115190815],"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":[0.000022317543,0.000039990136,0.07231639,6.898304e-7,0.00004261949,6.1951266e-7,0.0005028144,0.0002710805,0.00008016085,0.40949044,0.0003748531,0.51685804],"study_design_scores_gemma":[0.00043360042,0.00007787471,0.015411884,0.000006450166,0.000019946603,0.00023491184,0.0012144294,0.00142118,0.00007600488,0.94237757,0.03857565,0.00015047706],"about_ca_topic_score_codex":0.00010709939,"about_ca_topic_score_gemma":0.001236335,"teacher_disagreement_score":0.53288716,"about_ca_system_score_codex":0.00012096838,"about_ca_system_score_gemma":0.0006376787,"threshold_uncertainty_score":0.94464564},"labels":[],"label_agreement":null},{"id":"W3124661448","doi":"10.1016/j.insmatheco.2013.05.005","title":"Optimal risk transfer under quantile-based risk measurers","year":2013,"lang":"en","type":"article","venue":"Insurance Mathematics and Economics","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":63,"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":"Reinsurance; Quantile; Computer science; Risk measure; Measure (data warehouse); Actuarial science; Risk analysis (engineering); Econometrics; Mathematical optimization; Data mining; Mathematics; Economics; Business; Finance","score_opus":0.051092301123409976,"score_gpt":0.27889477243345867,"score_spread":0.2278024713100487,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3124661448","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":[0.84100395,0.0001214099,0.15668175,0.00018571605,0.00018145128,0.000248087,0.000091383234,0.00002506443,0.001461174],"genre_scores_gemma":[0.96793497,0.0019247149,0.029803969,0.00011960834,0.000043222994,0.000025279403,0.0000043561326,0.000024210382,0.000119678705],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982868,0.00006312578,0.0007777766,0.0003793912,0.0002363835,0.00025656467],"domain_scores_gemma":[0.9982395,0.00064154604,0.00029900915,0.0005010665,0.00017203514,0.00014681013],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014456946,0.00020025784,0.00038587293,0.00015344181,0.00023951067,0.0005053606,0.00031413327,0.00011840726,0.00027352117],"category_scores_gemma":[0.0002971611,0.00015543442,0.00012967108,0.00017366053,0.00012362828,0.00042913904,0.000023424065,0.00015876102,0.0004012615],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003357911,0.00018094885,0.12287365,0.00001565071,0.00007226527,8.4356276e-7,0.0015357019,0.8100012,0.0000324236,0.0068576587,0.0012723696,0.05712372],"study_design_scores_gemma":[0.0008501312,0.00006223303,0.052301124,0.000015839112,0.000033089695,0.0000057006705,0.00084487617,0.8748512,0.0002455699,0.06823923,0.0021828942,0.00036807882],"about_ca_topic_score_codex":0.00014895962,"about_ca_topic_score_gemma":0.00005856348,"teacher_disagreement_score":0.126931,"about_ca_system_score_codex":0.00002277761,"about_ca_system_score_gemma":0.000050652015,"threshold_uncertainty_score":0.6338432},"labels":[],"label_agreement":null},{"id":"W3124686113","doi":"","title":"Measuring Risk When Expected Losses are Unbounded","year":2014,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"","keywords":"Expected shortfall; Expected value; Value at risk; Coherent risk measure; Conditional expectation; Conditional probability; Value (mathematics); Actuarial science; Pareto principle; Econometrics; Expected utility hypothesis; Mathematics; Mathematical economics; Economics; Risk management; Statistics; Finance","score_opus":0.049836891047605116,"score_gpt":0.29889702767498405,"score_spread":0.24906013662737894,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3124686113","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":[0.6034789,0.0020447322,0.3873961,0.0007751917,0.0006142197,0.00011638004,0.000002681002,0.000079711645,0.0054920805],"genre_scores_gemma":[0.98976326,0.005107277,0.00076101365,0.00007260333,0.00055793655,0.0000029348043,0.0000017347518,0.000023251328,0.0037099924],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99528575,0.00060498354,0.0006867046,0.0003676026,0.0013763573,0.0016785976],"domain_scores_gemma":[0.9975851,0.00040041417,0.000864681,0.00045810052,0.00053263776,0.00015903024],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008641322,0.00018725796,0.00031052573,0.0003761364,0.0006338844,0.0005538123,0.0007996875,0.00010170842,0.00017264926],"category_scores_gemma":[0.0036829507,0.00013652172,0.00017997515,0.00056709134,0.00006244486,0.0005591489,0.00005574617,0.0013359013,0.00029313107],"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":[0.00025633827,0.00018927017,0.44269437,0.0000022609943,0.00029246704,0.0000107997685,0.0017775646,0.0127536105,0.00020784348,0.086026944,0.005867693,0.44992083],"study_design_scores_gemma":[0.00073824794,0.00017106938,0.015550186,0.000014069279,0.000037790316,0.00024027454,0.0023400607,0.0031248163,0.00017377111,0.9552134,0.022145098,0.0002512114],"about_ca_topic_score_codex":0.00009359815,"about_ca_topic_score_gemma":0.0014991487,"teacher_disagreement_score":0.86918646,"about_ca_system_score_codex":0.00037095492,"about_ca_system_score_gemma":0.0008893284,"threshold_uncertainty_score":0.58039004},"labels":[],"label_agreement":null},{"id":"W3124740411","doi":"10.1111/j.1467-9965.2010.00453.x","title":"COMONOTONIC MEASURES OF MULTIVARIATE RISKS","year":2010,"lang":"en","type":"preprint","venue":"Mathematical Finance","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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; University of British Columbia","funders":"","keywords":"Subadditivity; Axiom; Extension (predicate logic); Quantile; Multivariate statistics; Risk measure; Coherence (philosophical gambling strategy); Mathematics; Interpretation (philosophy); Computation; Axiomatic system; Characterization (materials science); Mathematical economics; Property (philosophy); Econometrics; Applied mathematics; Discrete mathematics; Computer science; Economics; Algorithm; Statistics; Financial economics","score_opus":0.236900107926888,"score_gpt":0.42743418060845045,"score_spread":0.19053407268156244,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3124740411","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":[0.38128576,0.0003294389,0.59601927,0.0004136275,0.0010143223,0.00076372287,0.00008447256,0.00007133141,0.02001809],"genre_scores_gemma":[0.91125166,0.0007505866,0.08617463,0.000027827762,0.000075069795,0.00005175085,0.00000789708,0.000024453653,0.0016361041],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99605143,0.00017292914,0.001399578,0.00062755495,0.001482871,0.00026563785],"domain_scores_gemma":[0.99556714,0.001084383,0.0010503698,0.0016414211,0.0005799477,0.000076708384],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029811782,0.00028151702,0.0009127835,0.00018807512,0.00007647783,0.00014536438,0.0013438136,0.0005572891,0.00021832567],"category_scores_gemma":[0.0050642965,0.00020004156,0.00030038704,0.0003277152,0.00023903737,0.000093162205,0.0006721161,0.0008537593,0.0004073457],"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":[0.00015019185,0.0011822958,0.0036594525,0.0002048692,0.00012174066,0.000022763701,0.0043893713,0.08177169,0.00121528,0.4968413,0.0049966425,0.40544438],"study_design_scores_gemma":[0.00024051905,0.000030184281,0.006251674,0.00016716897,0.00003898137,0.0000039385645,0.000022492251,0.15303044,0.0034643859,0.81731105,0.019119103,0.000320088],"about_ca_topic_score_codex":0.000052969463,"about_ca_topic_score_gemma":0.000011456819,"teacher_disagreement_score":0.52996594,"about_ca_system_score_codex":0.00002006755,"about_ca_system_score_gemma":0.00017981265,"threshold_uncertainty_score":0.81574583},"labels":[],"label_agreement":null},{"id":"W3124817877","doi":"10.2478/demo-2013-0002","title":"Bounds on Capital Requirements For Bivariate Riskwith Given Marginals and Partial Information on theDependence","year":2013,"lang":"en","type":"article","venue":"Dependence Modeling","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":20,"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; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Chinese University of Hong Kong; University of Waterloo; Society of Actuaries","keywords":"Bivariate analysis; Mathematics; Capital (architecture); Capital requirement; Econometrics; Mathematical economics; Statistics; Economics; Geography; Microeconomics","score_opus":0.10790326513512208,"score_gpt":0.3621083930078092,"score_spread":0.2542051278726871,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3124817877","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":[0.5664958,0.000023534112,0.43068153,0.00048659864,0.00029306722,0.00057871186,0.000022015309,0.000031755673,0.001387033],"genre_scores_gemma":[0.99585444,0.00014296545,0.003157672,0.00039762078,0.00008939517,0.00011538602,0.000021181228,0.000012030771,0.00020933717],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997139,0.00008670874,0.00068455626,0.00044008228,0.0012921718,0.0003575218],"domain_scores_gemma":[0.9983078,0.0003142085,0.00031920138,0.00043282128,0.00048117153,0.00014477411],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017191224,0.0002020962,0.00023117801,0.0003045063,0.00038988068,0.0009064496,0.0004141456,0.00011648501,0.000118049036],"category_scores_gemma":[0.0008269524,0.00014980015,0.0000683341,0.00023630649,0.000041126394,0.0027118656,0.00007723281,0.00014434388,0.0004930303],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003546657,0.0000770322,0.0051307115,0.00001044164,0.000029494884,0.000002824612,0.0030348056,0.90766716,0.00024339344,0.011864192,0.0013259749,0.07025931],"study_design_scores_gemma":[0.00051186315,0.00024856688,0.001128843,0.000042899002,0.000010654005,0.000003996809,0.0005794909,0.9657882,0.0004114108,0.030597819,0.00046830854,0.00020794051],"about_ca_topic_score_codex":0.00033003822,"about_ca_topic_score_gemma":0.000014397793,"teacher_disagreement_score":0.42935863,"about_ca_system_score_codex":0.000043747717,"about_ca_system_score_gemma":0.000065206514,"threshold_uncertainty_score":0.87409145},"labels":[],"label_agreement":null},{"id":"W3124818850","doi":"","title":"A Neyman-Pearson Problem with Ambiguity and Nonlinear Pricing","year":2017,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Weighting; Mathematics; Mathematical economics; Ambiguity; Probability measure; Econometrics; Portfolio; Expected utility hypothesis; Hedge; Measure (data warehouse); Monotonic function; Economics; Computer science; Statistics; Financial economics","score_opus":0.03226067919549273,"score_gpt":0.3393013346622725,"score_spread":0.3070406554667798,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3124818850","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":[0.9386971,0.00090158696,0.048678763,0.0025033474,0.0001445179,0.00019562567,0.0000018513721,0.00002620532,0.008851017],"genre_scores_gemma":[0.9867648,0.0048769265,0.0037176546,0.000054331565,0.00024207204,0.0000018910736,8.033969e-7,0.000015277637,0.004326231],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9974132,0.000083788276,0.0003465488,0.00028770667,0.0007390701,0.0011296631],"domain_scores_gemma":[0.9985287,0.000063503256,0.00060199527,0.000441742,0.00024532567,0.00011872419],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0048075086,0.00013109365,0.00020940925,0.00014646848,0.0011408062,0.0010311732,0.0006071207,0.000057787234,0.000018295092],"category_scores_gemma":[0.00038444367,0.00008208366,0.000051079878,0.00014781693,0.00010475882,0.00080370164,0.000079302976,0.00089486624,0.000025793834],"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":[0.0002895163,0.00009725498,0.20396048,0.0000032166747,0.000116731855,0.0000318161,0.0008894011,0.0014102109,0.00015125956,0.031973545,0.00050543546,0.7605711],"study_design_scores_gemma":[0.003945536,0.0020887833,0.09627452,0.00010865724,0.00012156501,0.0036165,0.0034029447,0.02591786,0.00027167116,0.83077484,0.032666124,0.0008109732],"about_ca_topic_score_codex":0.00014895934,"about_ca_topic_score_gemma":0.001474662,"teacher_disagreement_score":0.7988013,"about_ca_system_score_codex":0.00012895257,"about_ca_system_score_gemma":0.0009987882,"threshold_uncertainty_score":0.99436265},"labels":[],"label_agreement":null},{"id":"W3124915494","doi":"10.2139/ssrn.3371642","title":"Is the Inf-convolution of Law-invariant Preferences Law-invariant?","year":2019,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Invariant (physics); Law; Mathematics; Political science; Pure mathematics; Mathematical physics","score_opus":0.03833585592938479,"score_gpt":0.31358140283955444,"score_spread":0.27524554691016967,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3124915494","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":[0.81237155,0.005166103,0.04776981,0.007496485,0.0020324313,0.00085572334,0.00003061999,0.000063526575,0.124213725],"genre_scores_gemma":[0.9915912,0.003881503,0.00017135534,0.0005194008,0.00020044325,0.0000033881818,0.00000317234,0.000013323162,0.003616191],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9958309,0.00040492896,0.00087949523,0.0003311495,0.0013223193,0.0012312429],"domain_scores_gemma":[0.99764866,0.00044012687,0.0007791119,0.00058629765,0.00046364372,0.000082171086],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007778757,0.00017899647,0.00032959096,0.00016162814,0.00035162215,0.00025842752,0.0010809444,0.00013292626,0.00049823854],"category_scores_gemma":[0.00024290702,0.00010137878,0.00021229144,0.0005615801,0.00016462301,0.0007198628,0.000084634805,0.0010571146,0.00036576157],"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":[0.00007114261,0.000042899854,0.0068062562,0.0000015055324,0.00008198178,7.2980606e-7,0.0007233257,0.0010518144,0.00027851833,0.98303497,0.00063941674,0.0072674123],"study_design_scores_gemma":[0.00064439746,0.00040664396,0.0027596066,0.00002212829,0.000044874207,0.00017026375,0.0019392262,0.0034383032,0.0007018971,0.97003347,0.019651921,0.00018724892],"about_ca_topic_score_codex":0.000741329,"about_ca_topic_score_gemma":0.0016466101,"teacher_disagreement_score":0.17921965,"about_ca_system_score_codex":0.00017540343,"about_ca_system_score_gemma":0.001455291,"threshold_uncertainty_score":0.5455363},"labels":[],"label_agreement":null},{"id":"W3124980628","doi":"","title":"How Long to Eat a Cake of Unknown Size? Optimal Time Horizon Under Uncertainty","year":2002,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Time horizon; Stock (firearms); Horizon; Economics; Mathematical economics; Marginal value; Mathematics; Econometrics; Mathematical optimization; Microeconomics; Engineering","score_opus":0.026986458425933658,"score_gpt":0.29309260627850764,"score_spread":0.26610614785257397,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3124980628","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":[0.80682933,0.002945027,0.17293943,0.011494119,0.00049704284,0.00035949572,0.000010462332,0.00005084574,0.004874258],"genre_scores_gemma":[0.94120383,0.002694665,0.0006858395,0.00009984359,0.00028167848,0.00000318315,0.0000016004076,0.000023599121,0.055005766],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9953813,0.00022584046,0.00063281186,0.0003825565,0.0016850274,0.001692439],"domain_scores_gemma":[0.99784416,0.00039041508,0.00042452573,0.00041749008,0.0006895892,0.0002338341],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003996637,0.00021783,0.00039868255,0.00034193246,0.00023693072,0.00034341638,0.0007610355,0.00012470743,0.0006451187],"category_scores_gemma":[0.0012858754,0.00015988061,0.00022959025,0.0011270645,0.00007170133,0.0004994035,0.000072349816,0.0008515398,0.0002769405],"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":[0.00038633717,0.00039415454,0.0036032016,0.0000035633495,0.00034931922,0.000034873687,0.0011920345,0.42383048,0.0020487504,0.04070841,0.026654981,0.5007939],"study_design_scores_gemma":[0.009227282,0.014276917,0.007345053,0.00019507186,0.00051774574,0.004513388,0.012548579,0.20860691,0.0028902844,0.49271852,0.24370609,0.0034541711],"about_ca_topic_score_codex":0.000036038127,"about_ca_topic_score_gemma":0.0004226761,"teacher_disagreement_score":0.49733973,"about_ca_system_score_codex":0.00059136364,"about_ca_system_score_gemma":0.00072751514,"threshold_uncertainty_score":0.70635974},"labels":[],"label_agreement":null},{"id":"W3125035203","doi":"","title":"Surplus-invariant risk measures","year":2017,"lang":"en","type":"preprint","venue":"RePEc: Research Papers in Economics","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Invariant (physics); Mathematics; Lattice (music); Pure mathematics; Dual (grammatical number); Mathematical economics; Econometrics","score_opus":0.14075726928433296,"score_gpt":0.4148802282821313,"score_spread":0.2741229589977983,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3125035203","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":[0.25664395,0.0008510109,0.00053372193,0.00147091,0.0042205984,0.0018507178,0.00045179605,0.000108914326,0.73386836],"genre_scores_gemma":[0.8281154,0.15190352,0.0019548514,0.0000489764,0.00074840744,0.00015987662,0.00006201498,0.000081618564,0.016925355],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99249667,0.0015444991,0.0015064058,0.0018181338,0.0016848061,0.0009494595],"domain_scores_gemma":[0.9910282,0.0023749368,0.0011588762,0.004335834,0.00073516933,0.0003669662],"candidate_categories":["metaresearch","metaepi_narrow","scholarly_communication","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.024101913,0.00040009434,0.00093482586,0.0013561883,0.0007469958,0.001885739,0.0037889571,0.00078104244,0.00024915556],"category_scores_gemma":[0.0189668,0.00034961497,0.00037771784,0.00022662418,0.00057128066,0.00034222135,0.002453417,0.0025015085,0.00022404538],"study_design_candidate":"design_other","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":[0.00010068321,0.00007759085,0.062093884,0.0000067046326,0.000059496622,0.000051863568,0.00043196263,0.0984465,0.0000056994254,0.00034930225,0.0016012348,0.83677506],"study_design_scores_gemma":[0.0012380026,0.00013433873,0.112852484,0.00019962175,0.000031979616,0.000022870025,0.0009245517,0.14138547,0.0001405197,0.1272427,0.6145652,0.001262267],"about_ca_topic_score_codex":0.00083021313,"about_ca_topic_score_gemma":0.00217063,"teacher_disagreement_score":0.8355128,"about_ca_system_score_codex":0.00053408655,"about_ca_system_score_gemma":0.0015679959,"threshold_uncertainty_score":0.9998956},"labels":[],"label_agreement":null},{"id":"W3125265748","doi":"10.1287/moor.2020.1072","title":"A Theory for Measures of Tail Risk","year":2021,"lang":"en","type":"article","venue":"Mathematics of Operations Research","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":63,"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":"Coherent risk measure; Dynamic risk measure; Spectral risk measure; Risk measure; Expected shortfall; Tail risk; Subadditivity; Value at risk; Mathematics; Time consistency; Risk management; Actuarial science; Econometrics; Measure (data warehouse); Convexity; Financial risk; Financial risk management; Economics; Computer science; Finance","score_opus":0.4231346017905099,"score_gpt":0.5173141037081926,"score_spread":0.09417950191768276,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3125265748","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":[0.23196386,0.00051401847,0.7532088,0.00043518882,0.00007788599,0.0006068512,0.00015674031,0.000009951982,0.013026665],"genre_scores_gemma":[0.81301254,0.00051894994,0.1767317,0.000006444482,0.000033668322,0.00006841627,0.00001336577,0.000012926938,0.009601995],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99684453,0.00054582855,0.0007199231,0.00019746921,0.0015312105,0.00016102598],"domain_scores_gemma":[0.99032396,0.003608553,0.00009923183,0.00069012603,0.0052271327,0.000050999726],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.012608446,0.000060313894,0.00024696882,0.0003466597,0.00024688276,0.00013469013,0.00042279076,0.00005811296,0.00038076192],"category_scores_gemma":[0.03259732,0.000043749267,0.000107928856,0.0011006109,0.00018107619,0.00015735389,0.00010263409,0.00011167101,0.00003345801],"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":[0.000063416075,0.00097167253,0.0013362155,0.00008473752,0.000106718835,0.0000024142382,0.011262551,0.09174784,0.016289962,0.815023,0.010665609,0.052445833],"study_design_scores_gemma":[0.00058434345,0.00018030852,0.00040419516,0.000058697256,0.00003217779,0.000006330847,0.011384821,0.22646183,0.11459457,0.63990635,0.0062502366,0.00013612701],"about_ca_topic_score_codex":0.000020357005,"about_ca_topic_score_gemma":0.00009478712,"teacher_disagreement_score":0.58104867,"about_ca_system_score_codex":0.000012149366,"about_ca_system_score_gemma":0.0003786857,"threshold_uncertainty_score":0.97555155},"labels":[],"label_agreement":null},{"id":"W3125444950","doi":"10.2139/ssrn.2920334","title":"Worst-Case Range Value-at-Risk with Partial Information","year":2017,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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; National Natural Science Foundation of China","keywords":"Range (aeronautics); Value (mathematics); Statistics; Econometrics; Economics; Mathematics; Engineering","score_opus":0.024270831382729043,"score_gpt":0.3155577806750789,"score_spread":0.29128694929234983,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3125444950","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":[0.796535,0.0003677144,0.19558379,0.0009995601,0.00048065462,0.00019443277,0.000010004265,0.000030637715,0.0057981913],"genre_scores_gemma":[0.9939594,0.0032968046,0.0005923163,0.0000714529,0.00031303882,0.000004863963,0.0000036722336,0.0000112403095,0.001747164],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9967269,0.00015139705,0.0005839339,0.00019261242,0.0010530452,0.0012921379],"domain_scores_gemma":[0.9974188,0.00012184276,0.0013286005,0.0006669846,0.0003086544,0.00015510422],"candidate_categories":["sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0066285185,0.00015740967,0.00021103394,0.0002237463,0.0022239059,0.0011214688,0.0007073462,0.00008807404,0.000106934436],"category_scores_gemma":[0.0010955619,0.00010054674,0.00011375662,0.00018652916,0.000104173865,0.0027477802,0.000087366985,0.0009499327,0.0004271223],"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":[0.0013144474,0.00008510193,0.27965385,0.0000019024563,0.00021800266,0.00022707887,0.0021020698,0.014944179,0.000007266903,0.092473105,0.0028638996,0.6061091],"study_design_scores_gemma":[0.009032728,0.0019708904,0.037245687,0.00005293281,0.00032345962,0.038174037,0.0063673183,0.048762742,0.00023373525,0.6862379,0.17037553,0.0012230631],"about_ca_topic_score_codex":0.00025617896,"about_ca_topic_score_gemma":0.001963676,"teacher_disagreement_score":0.60488605,"about_ca_system_score_codex":0.00033867697,"about_ca_system_score_gemma":0.0009939353,"threshold_uncertainty_score":0.9999155},"labels":[],"label_agreement":null},{"id":"W3125479692","doi":"","title":"Properties of a risk measure derived from the expected area in red","year":2014,"lang":"en","type":"article","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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; Actua","funders":"","keywords":"Risk measure; Limit (mathematics); Measure (data warehouse); Coherent risk measure; Expected shortfall; Order (exchange); Risk management; Capital allocation line; Mathematics; Capital (architecture); Spectral risk measure; Econometrics; Economics; Computer science; Financial economics; Finance; Microeconomics; Geography; Data mining; Mathematical analysis","score_opus":0.039527372391282754,"score_gpt":0.24709484880199006,"score_spread":0.2075674764107073,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3125479692","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":[0.8961295,0.0006380381,0.08747353,0.0043592546,0.00009003306,0.00023459099,0.000020383797,0.00005518203,0.010999528],"genre_scores_gemma":[0.99128574,0.00036071756,0.007625122,0.000051660656,0.00001219318,0.000019855745,0.000030652627,0.0000133698395,0.0006007106],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9889741,0.00888515,0.0006935916,0.0004322989,0.00081960467,0.00019526877],"domain_scores_gemma":[0.9912928,0.004180485,0.00060968375,0.0017138901,0.0021255347,0.00007765456],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.010639713,0.0001467992,0.00028078945,0.00015677305,0.00024394297,0.00022240942,0.0012286956,0.000097787946,0.000134167],"category_scores_gemma":[0.022810474,0.000095912255,0.0001081405,0.0010238575,0.00022845757,0.00021391142,0.0002111777,0.00020187303,0.00002708644],"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":[0.00018431986,0.0008060061,0.4793225,0.000007881428,0.00007345292,0.0000019505344,0.10799446,0.0019907246,0.056688372,0.013215873,0.003658167,0.3360563],"study_design_scores_gemma":[0.0020372411,0.0000012921072,0.5703742,0.0009336096,0.00005088703,0.0000035268206,0.003288853,0.13879439,0.24946098,0.020784728,0.01374567,0.0005246267],"about_ca_topic_score_codex":0.0029702496,"about_ca_topic_score_gemma":0.0081033865,"teacher_disagreement_score":0.33553165,"about_ca_system_score_codex":0.000025127329,"about_ca_system_score_gemma":0.00009226193,"threshold_uncertainty_score":0.9854208},"labels":[],"label_agreement":null},{"id":"W3125661116","doi":"10.1515/demo-2018-0012","title":"The strong Fatou property of risk measures","year":2018,"lang":"en","type":"article","venue":"Dependence Modeling","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Toronto Metropolitan University","funders":"","keywords":"Mathematics; Quasiconvex function; Converse; Invariant (physics); Property (philosophy); Pure mathematics; Philosophy; Epistemology; Geometry","score_opus":0.1433383166938065,"score_gpt":0.36872048681680203,"score_spread":0.22538217012299552,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3125661116","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":[0.26587892,0.00029130018,0.7288282,0.0001039325,0.0003196784,0.000115288,0.000005829037,0.000023633023,0.0044332393],"genre_scores_gemma":[0.9959077,0.0006923773,0.0028629077,0.00001700571,0.0001318602,0.0000044560584,6.204978e-7,0.000007773091,0.00037524692],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997402,0.00018570312,0.0005526729,0.00029787328,0.0013466969,0.0002150158],"domain_scores_gemma":[0.9977619,0.0003482548,0.00026344886,0.00059179985,0.0009760521,0.00005851523],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0037530442,0.00009343414,0.00014650746,0.00008656396,0.0005249771,0.0001847368,0.0007598168,0.000056961217,0.0000321855],"category_scores_gemma":[0.0027495564,0.00004132945,0.00007292669,0.0003868921,0.00014145623,0.000327212,0.000095334355,0.00012277445,0.00009787793],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007503817,0.000018823508,0.01948009,5.25243e-7,0.0000141550045,0.0000010730286,0.0015455929,0.5003153,0.00023322509,0.0016055679,0.0005781313,0.47613248],"study_design_scores_gemma":[0.00008373807,0.000027786542,0.00034302147,0.000007876471,0.0000088404995,0.0000023102364,0.0008258998,0.9835738,0.0010428872,0.012221642,0.0017883185,0.000073909774],"about_ca_topic_score_codex":0.00041005088,"about_ca_topic_score_gemma":0.0005239901,"teacher_disagreement_score":0.73002887,"about_ca_system_score_codex":0.000012186206,"about_ca_system_score_gemma":0.000090678346,"threshold_uncertainty_score":0.40377524},"labels":[],"label_agreement":null},{"id":"W3125804578","doi":"","title":"Modality for Scenario Analysis and Maximum Likelihood Allocation","year":2020,"lang":"en","type":"preprint","venue":"RePEc: Research Papers in Economics","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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 Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Econometrics; Conditional probability distribution; Capital allocation line; Conditional expectation; Distribution (mathematics); Set (abstract data type); Mathematics; Statistics; Economics; Computer science; Microeconomics","score_opus":0.09796591391454398,"score_gpt":0.4012534466663731,"score_spread":0.30328753275182907,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3125804578","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":[0.8765395,0.00055066496,0.04921807,0.019945018,0.0012143381,0.005233192,0.000791463,0.0001445868,0.046363156],"genre_scores_gemma":[0.97449267,0.013121131,0.010703031,0.000139892,0.00021805873,0.00025576935,0.00030045654,0.000036439284,0.0007325756],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99569756,0.00043772138,0.0011475306,0.0015197209,0.0006712594,0.000526185],"domain_scores_gemma":[0.9960514,0.0014834893,0.00042528304,0.0012154775,0.00052394567,0.00030044757],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007409024,0.00024968456,0.00077130407,0.0012983592,0.00020960972,0.0007096796,0.00093642407,0.00042580548,0.00006408237],"category_scores_gemma":[0.0034047782,0.00023823901,0.0003212396,0.0008842353,0.00020208141,0.00019610686,0.0009865683,0.0007532641,0.000011658052],"study_design_candidate":"design_other","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":[0.00018529387,0.00008245824,0.081685565,0.000034687528,0.0003026444,0.0000041149324,0.0007062584,0.0911565,0.000025940995,0.00047738393,0.00019977515,0.8251394],"study_design_scores_gemma":[0.0005499149,0.00008739011,0.06240466,0.000022966642,0.00010515547,0.0000013172454,0.0006915478,0.72146815,0.00008422512,0.20061223,0.0135622155,0.00041020455],"about_ca_topic_score_codex":0.00025594363,"about_ca_topic_score_gemma":0.0012872741,"teacher_disagreement_score":0.82472914,"about_ca_system_score_codex":0.0003221367,"about_ca_system_score_gemma":0.00063958834,"threshold_uncertainty_score":0.97151047},"labels":[],"label_agreement":null},{"id":"W3126359288","doi":"10.2139/ssrn.3711743","title":"Optimal Insurance under Maxmin Expected Utility","year":2020,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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 Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Indemnity; Expected utility hypothesis; Ambiguity; Ex-ante; Mathematical economics; Actuarial science; Prior probability; Subjective expected utility; Economics; Unobservable; Econometrics; Mathematics; Computer science; Statistics; Bayesian probability","score_opus":0.07313037717453885,"score_gpt":0.35751372645716367,"score_spread":0.2843833492826248,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3126359288","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":[0.4341052,0.006696532,0.54800946,0.004918712,0.0019281425,0.0004099296,0.000056033317,0.00013442202,0.0037415915],"genre_scores_gemma":[0.9802367,0.014398452,0.0018442096,0.0002137509,0.00080179144,0.000012258031,0.000034610664,0.000046513494,0.0024117075],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99227,0.00066871004,0.0014799568,0.0010515735,0.002157422,0.002372346],"domain_scores_gemma":[0.996507,0.00028436526,0.0012700135,0.00092126033,0.000714203,0.0003031467],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0060787853,0.00046863334,0.000772694,0.0003946717,0.00035828905,0.0007588941,0.0020780724,0.00046573777,0.0004072827],"category_scores_gemma":[0.0012714153,0.000375836,0.0005259789,0.00087309466,0.00012863008,0.00036803543,0.000616559,0.0068353475,0.0003055463],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0018155802,0.0006910264,0.078519545,0.00002837666,0.0015331157,0.00014747372,0.0037869592,0.35300577,0.0002490892,0.10422912,0.02256917,0.43342477],"study_design_scores_gemma":[0.00071151974,0.00018848295,0.022795402,0.00003213851,0.00005577859,0.00032346018,0.0024993196,0.013792031,0.00006842774,0.95143867,0.007493159,0.00060160586],"about_ca_topic_score_codex":0.0000797417,"about_ca_topic_score_gemma":0.00025527793,"teacher_disagreement_score":0.8472096,"about_ca_system_score_codex":0.0007362261,"about_ca_system_score_gemma":0.0071410746,"threshold_uncertainty_score":0.99986935},"labels":[],"label_agreement":null},{"id":"W3128002931","doi":"10.1111/mafi.12299","title":"Optimal dynamic risk sharing under the time‐consistent mean‐variance criterion","year":2021,"lang":"en","type":"article","venue":"Mathematical Finance","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":18,"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":"Research Grants Council, University Grants Committee; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Ambiguity; Variance (accounting); Lagrange multiplier; Economics; Time consistency; Pareto principle; Mathematics; Mathematical economics; Dynamic programming; Mathematical optimization; Econometrics; Computer science; Actuarial science","score_opus":0.04534721744414039,"score_gpt":0.33748335918302974,"score_spread":0.29213614173888935,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3128002931","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":[0.39499894,0.0006995655,0.5860804,0.0036283685,0.00029578753,0.00023303501,0.00002455537,0.00006993,0.013969443],"genre_scores_gemma":[0.89144903,0.00094665424,0.07597761,0.0005741324,0.00008279158,0.000037725007,0.000009845949,0.00003196953,0.03089022],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974428,0.00017422454,0.00068873103,0.00056315627,0.0008298042,0.00030131746],"domain_scores_gemma":[0.99734145,0.000997149,0.00025933108,0.0010702081,0.0002703805,0.00006145262],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0017541471,0.0001689122,0.00032467075,0.00005024621,0.00033871876,0.00044359095,0.0006414443,0.000087372595,0.0009590313],"category_scores_gemma":[0.0014752814,0.0001051129,0.00016401523,0.0006809601,0.00014019926,0.00025202686,0.00026750204,0.00022127894,0.0021849955],"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":[0.000103584905,0.0007315789,0.001513211,0.0000429302,0.00012032941,0.00021580646,0.004717199,0.26222575,0.0015899757,0.6117717,0.012226325,0.1047416],"study_design_scores_gemma":[0.0002098453,0.000023352299,0.0053011593,0.00006302249,0.0000339899,0.000081027254,0.00035208894,0.70284057,0.00030184243,0.28325737,0.007325325,0.00021037235],"about_ca_topic_score_codex":0.0000033798526,"about_ca_topic_score_gemma":0.0000043302625,"teacher_disagreement_score":0.5101028,"about_ca_system_score_codex":0.000039019564,"about_ca_system_score_gemma":0.0000674158,"threshold_uncertainty_score":0.9999542},"labels":[],"label_agreement":null},{"id":"W3128776309","doi":"10.2139/ssrn.3677078","title":"Robust Distortion Risk Measures","year":2020,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"","keywords":"Distortion (music); Computer science; Risk analysis (engineering); Business; Telecommunications","score_opus":0.08131184309446879,"score_gpt":0.306937773253136,"score_spread":0.2256259301586672,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3128776309","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":[0.09297035,0.0028862397,0.8972577,0.0036793167,0.00037013448,0.00009425931,0.00000432587,0.000049821632,0.002687871],"genre_scores_gemma":[0.97737724,0.020523718,0.00037221998,0.0002027304,0.0005813618,0.0000014364598,0.0000025090935,0.0000138948635,0.0009249202],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99662447,0.00029477914,0.00054395414,0.00028208032,0.0011718266,0.001082885],"domain_scores_gemma":[0.99876314,0.00010762248,0.0004738653,0.00018967326,0.00027740278,0.00018829679],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0048430134,0.000121265235,0.0001899332,0.00012279644,0.00036131727,0.00023348382,0.0005166961,0.00006697901,0.00011655521],"category_scores_gemma":[0.0020404141,0.00008669249,0.00016138372,0.00063910603,0.000036305362,0.00044899975,0.000032446842,0.001246268,0.00032820163],"study_design_candidate":"design_other","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":[0.00018637371,0.000048215235,0.072095186,4.6287434e-7,0.000092384165,0.00000648466,0.0009027317,0.07558001,0.00008816058,0.021891339,0.008790212,0.82031846],"study_design_scores_gemma":[0.0013374651,0.0008330119,0.013497985,0.0000067144574,0.000116957606,0.00030605248,0.0043527,0.027329503,0.00019689526,0.7741984,0.17730662,0.00051769795],"about_ca_topic_score_codex":0.000035639652,"about_ca_topic_score_gemma":0.00028667648,"teacher_disagreement_score":0.89688545,"about_ca_system_score_codex":0.0002519188,"about_ca_system_score_gemma":0.000957237,"threshold_uncertainty_score":0.5414484},"labels":[],"label_agreement":null},{"id":"W3134860850","doi":"10.3390/jrfm14050201","title":"Portfolio Optimization Constrained by Performance Attribution","year":2021,"lang":"en","type":"article","venue":"Journal of risk and financial management","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Sharpe ratio; Portfolio; Portfolio optimization; Econometrics; Drawdown (hydrology); Ranking (information retrieval); Post-modern portfolio theory; Selection (genetic algorithm); Benchmark (surveying); Capital asset pricing model; Asset allocation; Asset (computer security); Computer science; Economics; Financial economics; Replicating portfolio; Artificial intelligence; Engineering","score_opus":0.017366546396511636,"score_gpt":0.27235207497420283,"score_spread":0.2549855285776912,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3134860850","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":[0.14573741,0.0011870064,0.8501083,0.00015922768,0.00054107903,0.000076581244,0.000020398616,0.000006608138,0.0021633497],"genre_scores_gemma":[0.92874295,0.050781615,0.018977558,0.00015330692,0.00017222691,0.0000016747897,0.00001807984,0.000007221828,0.0011453743],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99815327,0.00009786764,0.000743444,0.00018290278,0.0006750139,0.0001474983],"domain_scores_gemma":[0.9984314,0.000079993726,0.0006731595,0.0001567368,0.0005677411,0.000090958885],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001548658,0.00010164813,0.00024504002,0.0002203896,0.00018407997,0.00015649936,0.00015258351,0.00006281475,0.00014042603],"category_scores_gemma":[0.000409207,0.000080365135,0.00008575699,0.0006681928,0.000051216637,0.0004300765,0.00006135903,0.00013238571,0.000007882657],"study_design_candidate":"design_other","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":[0.000096112344,0.000094676514,0.03561356,0.0000058520827,0.000015587568,0.00007200372,0.00015085032,0.061214898,0.000015431942,0.0025115386,0.019533133,0.8806763],"study_design_scores_gemma":[0.0026053698,0.00033595043,0.14226641,0.00008100936,0.00020391494,0.00021121843,0.00071391376,0.027235366,0.00062509207,0.0052088657,0.8201438,0.00036906224],"about_ca_topic_score_codex":0.0000024772069,"about_ca_topic_score_gemma":0.0000013616719,"teacher_disagreement_score":0.8803073,"about_ca_system_score_codex":0.00002632175,"about_ca_system_score_gemma":0.00006642119,"threshold_uncertainty_score":0.3277195},"labels":[],"label_agreement":null},{"id":"W3137982035","doi":"10.1287/moor.2020.1090","title":"Distributional Transforms, Probability Distortions, and Their Applications","year":2021,"lang":"en","type":"article","venue":"Mathematics of Operations Research","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"University of Waterloo","funders":"","keywords":"Probability measure; Convexity; Probability distribution; Mathematics; Applied probability; Coherence (philosophical gambling strategy); Probability theory; Construct (python library); Set (abstract data type); Distortion (music); Class (philosophy); Mathematical optimization; Focus (optics); Mathematical economics; Econometrics; Computer science; Statistics; Artificial intelligence; Finance","score_opus":0.25412315247916384,"score_gpt":0.47134959989459996,"score_spread":0.21722644741543612,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3137982035","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":[0.16830584,0.0005027853,0.82168543,0.002553479,0.000029957842,0.0007664457,0.00040017653,0.000020621232,0.0057352455],"genre_scores_gemma":[0.96341354,0.0003280588,0.034101784,0.000008227549,0.000032233114,0.00023310163,0.00015688616,0.0000074746654,0.0017186739],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99774814,0.00020465098,0.0006554885,0.00029358125,0.00092629314,0.00017187641],"domain_scores_gemma":[0.9960629,0.000872638,0.00004119504,0.00061317976,0.0023135159,0.00009661715],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0033652673,0.00007996697,0.00019643425,0.00017274467,0.00054167997,0.00024485128,0.00027923216,0.00006073471,0.00032681972],"category_scores_gemma":[0.0022225098,0.000055646295,0.00006317142,0.0012528056,0.00032956828,0.00026303227,0.0000964322,0.00015831493,0.000039704886],"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":[0.000008590741,0.0012594442,0.002478119,0.000064652435,0.000036015823,0.0000012886528,0.0033654105,0.0055390396,0.004985055,0.94803,0.0021837428,0.032048628],"study_design_scores_gemma":[0.00045054485,0.00008209946,0.004746691,0.000038679966,0.000015490972,0.000049493952,0.006279443,0.11169373,0.017862981,0.81662166,0.04188996,0.00026924323],"about_ca_topic_score_codex":0.000015606784,"about_ca_topic_score_gemma":0.00013173114,"teacher_disagreement_score":0.7951077,"about_ca_system_score_codex":0.0000411175,"about_ca_system_score_gemma":0.0003599488,"threshold_uncertainty_score":0.4166219},"labels":[],"label_agreement":null},{"id":"W3144753570","doi":"10.3905/jot.2006.628192","title":"Incorporating Trading Strategies in the Black-Litterman Framework","year":2006,"lang":"en","type":"article","venue":"The Journal of Trading","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Intertek (Canada)","funders":"","keywords":"Black–Litterman model; Portfolio; Portfolio optimization; Post-modern portfolio theory; Modern portfolio theory; Application portfolio management; Simplicity; Computer science; Momentum (technical analysis); Portfolio insurance; Black swan theory; Replicating portfolio; Economics; Project portfolio management; Financial economics; Mathematics; Management","score_opus":0.06682745858932787,"score_gpt":0.3569783572063802,"score_spread":0.29015089861705234,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3144753570","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":[0.93337095,0.000253643,0.05445773,0.0024800894,0.00026147845,0.000081062004,6.8765445e-7,0.0000060186117,0.009088313],"genre_scores_gemma":[0.99661356,0.00005965807,0.0025595562,0.00018921809,0.00053000223,4.5736942e-7,3.193408e-7,0.000007714575,0.00003952251],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9971403,0.0005664008,0.0009761496,0.00009295018,0.0010398629,0.00018435746],"domain_scores_gemma":[0.99646825,0.0022683684,0.00088566955,0.00025124088,0.00009645449,0.000029998197],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009389218,0.00010786326,0.00021968441,0.00026301682,0.00023557953,0.00058611535,0.0011007057,0.000056671764,0.000035279652],"category_scores_gemma":[0.0003758639,0.0000501753,0.0001054368,0.000898951,0.00011362336,0.00078207656,0.000018951952,0.0004935304,0.000006536122],"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":[0.00035683846,0.00035661922,0.17951244,0.000017355951,0.00008113317,0.0003815314,0.09543615,0.40435496,0.006278595,0.22156176,0.04668795,0.04497466],"study_design_scores_gemma":[0.00032279053,0.00011014032,0.060802117,0.00012618925,0.000039998857,0.00034537318,0.021077996,0.047981896,0.00031698006,0.8674636,0.0012568873,0.00015604956],"about_ca_topic_score_codex":0.00003905928,"about_ca_topic_score_gemma":0.000029755869,"teacher_disagreement_score":0.6459018,"about_ca_system_score_codex":0.000030829666,"about_ca_system_score_gemma":0.00007055834,"threshold_uncertainty_score":0.5651924},"labels":[],"label_agreement":null},{"id":"W3144971854","doi":"10.48550/arxiv.2103.16451","title":"Robustifying Conditional Portfolio Decisions via Optimal Transport","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":8,"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; Compute Canada; National Science Foundation","keywords":"Portfolio optimization; Portfolio; Mathematical optimization; Robustness (evolution); Ambiguity; Computer science; Conditional probability distribution; Modern portfolio theory; Conditional expectation; Probability distribution; Econometrics; Mathematics; Economics; Statistics; Finance","score_opus":0.20848213134250712,"score_gpt":0.26996074442325124,"score_spread":0.06147861308074412,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3144971854","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":[0.3720029,0.000092989794,0.6216624,0.00005410119,0.0007749638,0.00021254423,0.00012662949,0.00009280926,0.0049807066],"genre_scores_gemma":[0.98464775,0.0012160868,0.0062105632,0.00012371443,0.0001831376,0.0000020080001,0.0009167277,0.000039037874,0.0066609583],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9956308,0.00026951806,0.00090464007,0.0019355064,0.0007607264,0.0004988102],"domain_scores_gemma":[0.99535406,0.00076108036,0.0007301952,0.0015716302,0.0011491778,0.00043386515],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0012524945,0.00048577727,0.00075491925,0.0009008182,0.00044607793,0.00030636028,0.0016282265,0.0006331735,0.003615081],"category_scores_gemma":[0.00050130626,0.000508135,0.00073750137,0.0018249124,0.00024764286,0.00074971723,0.0006806483,0.00086436584,0.00029144032],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000059277223,0.000118959535,0.015909798,0.0000039704414,0.00008669062,0.0012405625,0.00015159279,0.9739554,0.0000123566915,0.0053330525,0.001769068,0.0013592604],"study_design_scores_gemma":[0.001014212,0.00005572154,0.039623667,0.00013450699,0.00038306144,0.000086647364,0.0012803773,0.89151186,0.00009199058,0.0576355,0.006973295,0.0012091594],"about_ca_topic_score_codex":0.00014626326,"about_ca_topic_score_gemma":0.0001098708,"teacher_disagreement_score":0.6154518,"about_ca_system_score_codex":0.00017382453,"about_ca_system_score_gemma":0.00075732346,"threshold_uncertainty_score":0.999737},"labels":[],"label_agreement":null},{"id":"W3147131168","doi":"10.2139/ssrn.3566708","title":"Distributionally Robust Optimization under Distorted Expectations","year":2020,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"University of Ottawa; University of Waterloo","funders":"","keywords":"Robust optimization; Mathematical optimization; Computer science; Mathematics","score_opus":0.058103395813330024,"score_gpt":0.31629346830347055,"score_spread":0.25819007249014053,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3147131168","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":[0.006546585,0.0007621727,0.981481,0.009491987,0.00020910842,0.00010795052,0.000015699325,0.000052700514,0.0013327817],"genre_scores_gemma":[0.9930051,0.0017767759,0.0032357313,0.00039254143,0.00042336443,0.0000054665406,0.0001029987,0.00001996975,0.0010380778],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99671453,0.00018531865,0.0006614421,0.0003302888,0.001070098,0.0010383362],"domain_scores_gemma":[0.9984949,0.00016948466,0.00039737634,0.00018822054,0.0005366996,0.00021328886],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013464574,0.00015071314,0.00019834324,0.00014613738,0.00047811813,0.00030118917,0.0005132593,0.00008063875,0.00037709658],"category_scores_gemma":[0.0010680582,0.00012123758,0.0001538366,0.0011301028,0.000055705765,0.0006358583,0.000040080427,0.0007912456,0.00018099864],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000456268,0.00003567578,0.0015492907,2.422299e-7,0.000042136333,0.0000017770706,0.00023695835,0.90007985,0.00001669936,0.091130264,0.0021477325,0.004713775],"study_design_scores_gemma":[0.0017778573,0.00048715138,0.0033414434,0.0000087109465,0.00010253967,0.00031091517,0.015484854,0.6133486,0.000042346088,0.35291123,0.011549532,0.00063480355],"about_ca_topic_score_codex":0.0000081032795,"about_ca_topic_score_gemma":0.00006576055,"teacher_disagreement_score":0.9864585,"about_ca_system_score_codex":0.0004620627,"about_ca_system_score_gemma":0.0017472379,"threshold_uncertainty_score":0.49439248},"labels":[],"label_agreement":null},{"id":"W3150771006","doi":"10.1080/14697688.2021.1879392","title":"Robust portfolio rebalancing with cardinality and diversification constraints","year":2021,"lang":"en","type":"article","venue":"Quantitative Finance","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"University of New Brunswick","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"CVAR; Portfolio; Computer science; Diversification (marketing strategy); Risk measure; Cardinality (data modeling); Portfolio optimization; Mathematical optimization; Expected shortfall; Econometrics; Value at risk; Economics; Risk management; Mathematics; Financial economics; Data mining; Finance; Business","score_opus":0.1469799693599546,"score_gpt":0.3534641680214585,"score_spread":0.20648419866150391,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3150771006","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":[0.786543,0.000812994,0.19627377,0.0005343182,0.00015445678,0.00013094937,0.000052697917,0.000026385602,0.015471431],"genre_scores_gemma":[0.9622586,0.00097626995,0.035210118,0.00007999142,0.000016294409,0.0000049887635,0.000018625913,0.0000060680313,0.0014290853],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9983901,0.00016035695,0.00030367236,0.00048723727,0.0004996921,0.0001589577],"domain_scores_gemma":[0.99822915,0.00038387277,0.00026244362,0.00032090792,0.0007550185,0.00004860526],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008924912,0.00010780583,0.00022894621,0.00007878745,0.0002003072,0.00013421047,0.00012759375,0.000045016775,0.000082845974],"category_scores_gemma":[0.00095471577,0.00008432492,0.00003628797,0.0007946689,0.00031084768,0.0004164124,0.000044614942,0.000088880275,0.00004711783],"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":[0.00017570957,0.00009897728,0.66532683,0.000010025191,0.000057336947,0.0002595874,0.003268975,0.021054268,0.00047692095,0.22535256,0.0077931033,0.076125726],"study_design_scores_gemma":[0.000689992,0.0001555556,0.9516639,0.000064948406,0.00003219441,0.000093273615,0.004440394,0.010984893,0.0012783615,0.0076282076,0.022630278,0.00033798933],"about_ca_topic_score_codex":0.00003146494,"about_ca_topic_score_gemma":0.00003320835,"teacher_disagreement_score":0.2863371,"about_ca_system_score_codex":0.000021168298,"about_ca_system_score_gemma":0.00013578795,"threshold_uncertainty_score":0.34386706},"labels":[],"label_agreement":null},{"id":"W3151835984","doi":"10.1287/ijoo.2021.0069","title":"Effective Budget of Uncertainty for Classes of Robust Optimization","year":2022,"lang":"en","type":"article","venue":"INFORMS Journal on Optimization","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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 Waterloo","funders":"","keywords":"Robust optimization; Robustness (evolution); Mathematical optimization; Conservatism; Computer science; Uncertainty analysis; Optimization problem; Sensitivity analysis; Set (abstract data type); Range (aeronautics); Mathematics; Engineering; Simulation","score_opus":0.041972657187275296,"score_gpt":0.33779397047857124,"score_spread":0.29582131329129596,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3151835984","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":[0.008815448,0.00005736868,0.98684245,0.00025938096,0.0008081765,0.00076782925,0.00011209571,0.000019807252,0.0023174405],"genre_scores_gemma":[0.8054335,0.000800907,0.19183742,0.00037811146,0.00023168472,0.00015908894,0.0003032032,0.000057229696,0.000798823],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9962317,0.00021664244,0.0015284171,0.00023826273,0.0015578821,0.00022713699],"domain_scores_gemma":[0.99438244,0.001065663,0.0023198267,0.00032807357,0.0018030447,0.000100926976],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0032736156,0.00018292756,0.00044932493,0.0009764624,0.0004683758,0.00012540177,0.00054884685,0.0000902391,0.0007832507],"category_scores_gemma":[0.0023449606,0.00013939067,0.0002597881,0.0014530877,0.00007981173,0.0009028415,0.00009219693,0.00024241021,0.000002046531],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006829706,0.00012378275,0.00072523305,0.0000066145117,0.000036105466,8.401835e-7,0.0004317082,0.98189723,0.000013304835,0.0011179263,0.0021667508,0.012797503],"study_design_scores_gemma":[0.0013675889,0.0013182703,0.000219823,0.000019688105,0.000033373337,0.00003097432,0.000816952,0.9901886,0.00036416488,0.0013415915,0.004140422,0.00015852478],"about_ca_topic_score_codex":0.000009365141,"about_ca_topic_score_gemma":0.0000014772959,"teacher_disagreement_score":0.7966181,"about_ca_system_score_codex":0.00016879114,"about_ca_system_score_gemma":0.0002465024,"threshold_uncertainty_score":0.8576046},"labels":[],"label_agreement":null},{"id":"W3153918522","doi":"","title":"A New Approach to Risk Attribution and its Application in Credit Risk Analysis","year":2020,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Context (archaeology); Credit risk; Risk analysis (engineering); Computer science; Affect (linguistics); Euler's formula; Decomposition; Actuarial science; Econometrics; Economics; Business; Mathematics; Psychology; Geography","score_opus":0.03436595786856393,"score_gpt":0.31737669491595566,"score_spread":0.2830107370473917,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3153918522","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":[0.19607769,0.0009947917,0.8013189,0.0010549278,0.000028108025,0.00018782307,0.0000098344535,0.000014410631,0.00031351956],"genre_scores_gemma":[0.98730606,0.010161601,0.0018013312,0.000089726804,0.00028621824,0.000007322166,0.000013065711,0.0000086584205,0.00032604244],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997083,0.00031646402,0.0005844743,0.00046706456,0.0007131571,0.0008358374],"domain_scores_gemma":[0.9987449,0.0001203891,0.0004616154,0.00019713675,0.00017980607,0.0002961746],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0052224756,0.00012989878,0.00029671387,0.0005244447,0.00020135326,0.00017710117,0.00038688138,0.00008861032,0.00002729195],"category_scores_gemma":[0.0011151714,0.000104665756,0.00012147716,0.0039146296,0.00001010745,0.00034829465,0.000053732798,0.0009961427,0.00012073345],"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":[0.00022560213,0.00006590033,0.3336891,9.613561e-7,0.00029960784,9.0377534e-7,0.0014990955,0.19375412,0.00007157765,0.023147242,0.0012806741,0.4459652],"study_design_scores_gemma":[0.001518885,0.0003810023,0.155527,0.0000034981372,0.00067220064,0.00005296316,0.0021080147,0.65662104,0.0000792406,0.16392422,0.018665085,0.00044687555],"about_ca_topic_score_codex":0.0003015423,"about_ca_topic_score_gemma":0.00076649623,"teacher_disagreement_score":0.7995176,"about_ca_system_score_codex":0.0002333786,"about_ca_system_score_gemma":0.0006252861,"threshold_uncertainty_score":0.43278},"labels":[],"label_agreement":null},{"id":"W3155825668","doi":"","title":"Moment Problem and Its Application to Tail Risk Assessment","year":2016,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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 Fredericton; University of New Brunswick; University of Manitoba","funders":"","keywords":"Moment (physics); Mathematics; Semidefinite programming; Random variable; Numerical analysis; Applied mathematics; Second moment of area; Moment-generating function; Upper and lower bounds; Variable (mathematics); Matching (statistics); Function (biology); Linear programming; Mathematical optimization; Combinatorics; Mathematical analysis; Statistics; Physics; Geometry","score_opus":0.01733980684808953,"score_gpt":0.3351083047700109,"score_spread":0.3177684979219214,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3155825668","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":[0.18407643,0.00060135714,0.806728,0.006325081,0.00008601878,0.0003372885,0.000005142,0.00002021093,0.0018204538],"genre_scores_gemma":[0.97940373,0.014208277,0.0013321129,0.00008007484,0.00013983641,0.000027947148,7.1276844e-7,0.000010932519,0.0047963713],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9973223,0.00015926872,0.00046041518,0.00031454384,0.0007911712,0.000952284],"domain_scores_gemma":[0.9988377,0.00013055326,0.0003449066,0.00021650452,0.00030435386,0.00016600204],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0059361947,0.000112282054,0.00015078203,0.00022037455,0.00025415322,0.00014851923,0.00032079272,0.000049492322,0.00004972958],"category_scores_gemma":[0.00017613271,0.00006262494,0.000049409085,0.0003768606,0.000015721309,0.0003934991,0.00005949587,0.00036521486,0.00018538762],"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":[0.000028306407,0.000037080266,0.018141346,3.6610976e-7,0.00002852882,4.5085324e-7,0.00010102989,0.0006494217,0.0016306954,0.1274041,0.00060760573,0.8513711],"study_design_scores_gemma":[0.00082709064,0.0005094141,0.0112129925,0.000013413113,0.000027345211,0.000116094314,0.00042344147,0.0020474992,0.0004103594,0.9180119,0.06617525,0.00022520947],"about_ca_topic_score_codex":0.000013733141,"about_ca_topic_score_gemma":0.00014506887,"teacher_disagreement_score":0.85114586,"about_ca_system_score_codex":0.0004700724,"about_ca_system_score_gemma":0.0007310191,"threshold_uncertainty_score":0.25537708},"labels":[],"label_agreement":null},{"id":"W3156214225","doi":"10.3390/jrfm14040182","title":"Analysis and Forecasting of Risk in Count Processes","year":2021,"lang":"en","type":"article","venue":"Journal of risk and financial management","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Count data; Estimation; Econometrics; Risk assessment; Event (particle physics); Statistics; Risk management; Series (stratigraphy); Computer science; Actuarial science; Mathematics; Economics; Finance","score_opus":0.028115560664748147,"score_gpt":0.2920469932634418,"score_spread":0.26393143259869367,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3156214225","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":[0.9166643,0.0024091778,0.08015371,0.000030221143,0.00008407182,0.00004398473,0.000013913354,0.0000011708252,0.00059945404],"genre_scores_gemma":[0.9557101,0.03827873,0.0058999057,0.000015636371,0.000037973987,6.654286e-7,8.180576e-7,0.000002567658,0.000053572374],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99832183,0.000098692086,0.0008039668,0.00015728669,0.00051984005,0.00009840452],"domain_scores_gemma":[0.99802476,0.00040172946,0.0009497137,0.000112036774,0.00046561498,0.000046123114],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022069362,0.00007259425,0.00036528736,0.0006461599,0.00007359223,0.000084185966,0.000108641696,0.000034880297,0.000015128922],"category_scores_gemma":[0.0025774955,0.00005339595,0.00007787956,0.0020048132,0.00003649771,0.00021061214,0.00007426391,0.000106218366,4.0061377e-7],"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":[0.000048579554,0.000036170393,0.6261281,0.000011534731,0.000028056606,0.000047136968,0.0005933195,0.0067495084,7.7353207e-7,0.0002350185,0.00005058837,0.36607122],"study_design_scores_gemma":[0.0005528937,0.00004930787,0.97725165,0.000041310257,0.00033235113,0.000009723664,0.0011884504,0.0021012917,0.00004632916,0.009981557,0.008370011,0.00007515398],"about_ca_topic_score_codex":0.0000666433,"about_ca_topic_score_gemma":0.0006682024,"teacher_disagreement_score":0.36599606,"about_ca_system_score_codex":0.000010622489,"about_ca_system_score_gemma":0.00005230855,"threshold_uncertainty_score":0.3085689},"labels":[],"label_agreement":null},{"id":"W3159931085","doi":"10.2139/ssrn.3805592","title":"Cardinality Constrained Risk Parity Portfolios","year":2020,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Parity (physics); Mathematics; Econometrics; Economics; Actuarial science; Physics; Particle physics","score_opus":0.046051429904500604,"score_gpt":0.328856010348776,"score_spread":0.28280458044427537,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3159931085","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":[0.5611036,0.0027645372,0.40902802,0.008798823,0.00063843385,0.0003191085,0.00004467925,0.00012941973,0.017173415],"genre_scores_gemma":[0.9893585,0.008710645,0.000366942,0.00042690974,0.00053947396,0.0000018843929,0.0000043855152,0.000014169108,0.0005771104],"study_design_codex":"observational","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99530894,0.00051984895,0.0008456399,0.0004212047,0.0012544631,0.0016499299],"domain_scores_gemma":[0.9980308,0.00023016952,0.0006770702,0.0003146512,0.0003987588,0.00034858505],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00848634,0.00018615164,0.0003816989,0.00012868544,0.00042089744,0.00029023094,0.00070550496,0.00010919919,0.00029848915],"category_scores_gemma":[0.0028488128,0.00013928552,0.0003074525,0.000872295,0.00010819322,0.00043841763,0.000066788205,0.001978001,0.00026969292],"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":[0.0003593002,0.00010937573,0.4476416,0.0000019883369,0.00035991057,0.00005177982,0.0010814652,0.0064315004,0.00009847756,0.13796408,0.010549492,0.39535102],"study_design_scores_gemma":[0.0015010422,0.00056865124,0.020233456,0.000004944652,0.00013229698,0.0006863104,0.004612208,0.008459308,0.00010289722,0.9039292,0.059306044,0.00046367932],"about_ca_topic_score_codex":0.00010467155,"about_ca_topic_score_gemma":0.00015247084,"teacher_disagreement_score":0.7659651,"about_ca_system_score_codex":0.00020884564,"about_ca_system_score_gemma":0.0023854596,"threshold_uncertainty_score":0.859354},"labels":[],"label_agreement":null},{"id":"W3159954713","doi":"10.1111/mafi.12313","title":"Bayes risk, elicitability, and the Expected Shortfall","year":2021,"lang":"en","type":"article","venue":"Mathematical Finance","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":26,"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; National Natural Science Foundation of China","keywords":"Bayes' theorem; Actuarial science; Risk management; Expected shortfall; Econometrics; Risk assessment; Risk measure; Risk analysis (engineering); Measure (data warehouse); Bayes' rule; Bayes factor; Economics; Computer science; Bayesian probability; Mathematics; Statistics; Business; Financial economics; Finance; Data mining","score_opus":0.03991192865554795,"score_gpt":0.33610049377836665,"score_spread":0.2961885651228187,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3159954713","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":[0.8055643,0.0014040441,0.1594403,0.0036895743,0.00014206288,0.00027244104,0.000013076684,0.000050563598,0.029423669],"genre_scores_gemma":[0.97982323,0.0014216327,0.015170423,0.00018485708,0.00004850485,0.00002704228,0.0000016984808,0.0000083848845,0.0033141975],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9980225,0.00032246838,0.0005211866,0.00035684122,0.0006070131,0.00017001398],"domain_scores_gemma":[0.99582255,0.003085588,0.00014020933,0.00066734554,0.00023881687,0.00004547633],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0021026926,0.00010458301,0.0003053353,0.00003112777,0.00019517825,0.00022730796,0.0002860811,0.000057965983,0.0003970892],"category_scores_gemma":[0.010523611,0.00005508515,0.000088242596,0.00054346595,0.00029223095,0.00013352364,0.00016089165,0.00013675696,0.00021535708],"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":[0.000095374235,0.000220025,0.01974752,0.0000125892275,0.000025029909,0.000028927387,0.004417574,0.0006670408,0.00003739058,0.8418668,0.008074229,0.12480748],"study_design_scores_gemma":[0.0005047086,0.000013064149,0.018708084,0.000014978689,0.000017441625,0.000029906703,0.00035234285,0.042270955,0.00027756777,0.92580634,0.011888649,0.00011595592],"about_ca_topic_score_codex":0.00000792637,"about_ca_topic_score_gemma":0.00001482328,"teacher_disagreement_score":0.17425899,"about_ca_system_score_codex":0.0000069980124,"about_ca_system_score_gemma":0.000035355748,"threshold_uncertainty_score":0.9978112},"labels":[],"label_agreement":null},{"id":"W3165351258","doi":"","title":"Bayes and Hurwicz Without Bernoulli","year":2020,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Ambiguity; Bernoulli's principle; Certainty; Expected utility hypothesis; Bayes' theorem; Decision theory; Mathematical economics; Decision maker; Subjective expected utility; Ranking (information retrieval); Mathematics; Bayesian inference; Bayesian probability; Computer science; Mathematical optimization; Artificial intelligence; Operations research; Engineering; Statistics","score_opus":0.043376001603239664,"score_gpt":0.33176583290948053,"score_spread":0.28838983130624085,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3165351258","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":[0.7481802,0.008469197,0.21285534,0.021414854,0.00036377448,0.0001998906,0.000004974623,0.00007464809,0.0084371455],"genre_scores_gemma":[0.9838253,0.0126760155,0.00046625172,0.00056989974,0.00036585174,9.899981e-7,0.0000011579234,0.000012938669,0.002081563],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99755037,0.00012134298,0.00039904885,0.00026897085,0.0006855756,0.0009747001],"domain_scores_gemma":[0.99918526,0.0001112321,0.00022183618,0.00013672555,0.00014745998,0.00019746769],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024543246,0.000114126124,0.00020078116,0.00010079266,0.00023733609,0.00028294756,0.0003581074,0.00005794254,0.0001182902],"category_scores_gemma":[0.0006639283,0.00008012573,0.00007111975,0.00039860266,0.000052480897,0.00038777868,0.000051898187,0.0008274366,0.00012699464],"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":[0.00023317816,0.000050677598,0.22451705,0.0000020933103,0.00014016025,0.000015282096,0.002492751,0.0021433632,0.0004018663,0.1355176,0.0065127998,0.6279732],"study_design_scores_gemma":[0.0013880084,0.00086916244,0.007857699,0.000009992608,0.0000574981,0.0008624801,0.0050238376,0.015096461,0.00014908364,0.8290817,0.1391806,0.00042347747],"about_ca_topic_score_codex":0.00001383107,"about_ca_topic_score_gemma":0.000097138625,"teacher_disagreement_score":0.6935641,"about_ca_system_score_codex":0.00006676625,"about_ca_system_score_gemma":0.00072666636,"threshold_uncertainty_score":0.35948464},"labels":[],"label_agreement":null},{"id":"W3166171467","doi":"10.1287/opre.2021.0243","title":"Robustifying Conditional Portfolio Decisions via Optimal Transport","year":2024,"lang":"en","type":"article","venue":"Operations Research","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Group for Research in Decision Analysis; HEC Montréal","funders":"","keywords":"Portfolio; Business; Computer science; Finance","score_opus":0.3166300958996918,"score_gpt":0.5139018419605486,"score_spread":0.19727174606085673,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3166171467","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":[0.076970376,0.0007628628,0.88833386,0.0027185266,0.00062763796,0.00051512447,0.00017205623,0.00014365926,0.029755905],"genre_scores_gemma":[0.94695777,0.0006107076,0.01914118,0.000062047206,0.00034715352,0.00012078361,0.00036484646,0.00002937591,0.03236614],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99438846,0.0003475449,0.00079281663,0.00075020606,0.0032273117,0.00049364276],"domain_scores_gemma":[0.99598193,0.0016138961,0.000018231067,0.0006400857,0.0014700259,0.00027579986],"candidate_categories":["scholarly_communication","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0061839498,0.00015362255,0.00020879805,0.0016191989,0.0012355844,0.0013796668,0.00069736416,0.00014338757,0.008873491],"category_scores_gemma":[0.0016503483,0.00011810182,0.00015686452,0.003889462,0.00026735198,0.0012331578,0.0000942236,0.000597196,0.003946908],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000020306234,0.00009384262,0.0007311126,0.0000022082886,0.000029250667,0.00016926297,0.0006165873,0.84811085,0.00075643265,0.043075886,0.0562766,0.050117668],"study_design_scores_gemma":[0.00015777585,0.00006876104,0.0035624388,0.00003256774,0.000010926293,0.00009217393,0.00046985626,0.8090302,0.0002904607,0.0074818046,0.17861225,0.00019078185],"about_ca_topic_score_codex":0.00014449412,"about_ca_topic_score_gemma":0.00020062242,"teacher_disagreement_score":0.86998737,"about_ca_system_score_codex":0.00009504969,"about_ca_system_score_gemma":0.00081279804,"threshold_uncertainty_score":0.999657},"labels":[],"label_agreement":null},{"id":"W3167396090","doi":"","title":"Risk-sensitive safety analysis via state-space augmentation.","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"CVAR; Mathematical optimization; State space; Probabilistic logic; Computer science; Expected shortfall; Monotonic function; Risk analysis (engineering); Mathematics; Risk management; Economics; Statistics","score_opus":0.08496736899224151,"score_gpt":0.2532671098573877,"score_spread":0.1682997408651462,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3167396090","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":[0.39710706,0.00004204293,0.5992751,0.000058972975,0.00035816265,0.00018777125,0.00019504277,0.000058957507,0.002716894],"genre_scores_gemma":[0.9846686,0.0032531472,0.0018544748,0.000066920555,0.000053466243,5.977988e-7,0.0004399328,0.000020496076,0.009642378],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9957024,0.0010450651,0.0006445024,0.0017349061,0.0005226662,0.00035048407],"domain_scores_gemma":[0.99482334,0.00082883734,0.0012524903,0.0015434612,0.0012858259,0.00026605593],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001571028,0.00038207296,0.0007825258,0.0012410722,0.00038414254,0.00041784323,0.0008627495,0.00030790278,0.00087055605],"category_scores_gemma":[0.00052015955,0.00039131005,0.00077662925,0.0050765243,0.00016411701,0.0005257855,0.0009060267,0.00059344195,0.00024525446],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000110148016,0.000057417197,0.06682311,0.000002487974,0.0007966612,0.0002450722,0.0008507498,0.92813814,0.0000061303385,0.0006237042,0.0004045077,0.0019418787],"study_design_scores_gemma":[0.00058038277,0.00003120283,0.093077146,0.000017182265,0.0019730073,0.0000023587688,0.0029056254,0.8834942,0.00023191441,0.015866699,0.0011657594,0.00065453345],"about_ca_topic_score_codex":0.0017030645,"about_ca_topic_score_gemma":0.0016725297,"teacher_disagreement_score":0.59742063,"about_ca_system_score_codex":0.00029619763,"about_ca_system_score_gemma":0.00029823033,"threshold_uncertainty_score":0.9998539},"labels":[],"label_agreement":null},{"id":"W3168615427","doi":"10.2139/ssrn.3155209","title":"On the Application of Fund Mapping Regressions to Segregated Funds Dynamic Hedging Schemes","year":2018,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Concordia University; Université Laval","funders":"","keywords":"Econometrics; Economics; Business; Actuarial science; Computer science; Finance","score_opus":0.05089501167417272,"score_gpt":0.371895513944529,"score_spread":0.3210005022703563,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3168615427","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":[0.5316507,0.00028730827,0.46271697,0.0029722096,0.0001614667,0.00017102106,0.0000019440222,0.00001884051,0.0020195472],"genre_scores_gemma":[0.99662954,0.0005166574,0.00064805074,0.00020954838,0.0001405717,0.000007279678,0.0000021591375,0.000013518038,0.001832686],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9972881,0.00017788736,0.0005510385,0.0002668594,0.00080127944,0.00091485557],"domain_scores_gemma":[0.9980692,0.0003958264,0.0004794822,0.00047895964,0.00048747822,0.000089036475],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0050344733,0.00012612886,0.00018050987,0.0003945624,0.0005725362,0.00012273957,0.00070329465,0.00006550132,0.00008508751],"category_scores_gemma":[0.00089952786,0.000072585455,0.00009545778,0.0014396079,0.0000938837,0.00018320969,0.00006478231,0.0006464527,0.00017786819],"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":[0.00018432076,0.00009750279,0.0062182676,0.0000015920295,0.00010853073,9.608847e-7,0.0012864157,0.0032228327,0.009028476,0.3935768,0.0021123195,0.584162],"study_design_scores_gemma":[0.00057912525,0.00071718165,0.0057165385,0.00010922953,0.000030982374,0.00014796341,0.0063888696,0.0777097,0.0021822513,0.8704821,0.035592932,0.00034313672],"about_ca_topic_score_codex":0.000021259355,"about_ca_topic_score_gemma":0.00023939332,"teacher_disagreement_score":0.58381885,"about_ca_system_score_codex":0.00024392066,"about_ca_system_score_gemma":0.00064357487,"threshold_uncertainty_score":0.44035435},"labels":[],"label_agreement":null},{"id":"W3171612222","doi":"10.1007/s11579-022-00313-9","title":"Law-Invariant Functionals that Collapse to the Mean: Beyond Convexity","year":2022,"lang":"en","type":"article","venue":"Mathematics and Financial Economics","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":16,"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":"Universität Bielefeld; University of Waterloo; Gottfried Wilhelm Leibniz Universität Hannover","keywords":"Quasiconvex function; Mathematical finance; Convexity; Invariant (physics); Quantile; Mathematics; Regular polygon; Mathematical economics; Applied mathematics; Convex analysis; Econometrics; Economics; Finance; Convex optimization","score_opus":0.06152520273270274,"score_gpt":0.27897634568154994,"score_spread":0.2174511429488472,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3171612222","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":[0.8399366,0.00026028324,0.061864633,0.016498983,0.003860002,0.0014739347,0.00070352154,0.00005610081,0.07534591],"genre_scores_gemma":[0.98179126,0.00026326245,0.008140352,0.006367437,0.0002823653,0.00013167039,0.00001746014,0.000022857852,0.0029833647],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99880797,0.000044723874,0.00044541975,0.00027571255,0.0002654035,0.00016075044],"domain_scores_gemma":[0.9987867,0.00041209313,0.0002482435,0.00039997027,0.00007023535,0.0000827483],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023129126,0.000109335844,0.00024638936,0.00007927006,0.00078951364,0.00026007925,0.00037964404,0.000038547085,0.00042768972],"category_scores_gemma":[0.00026206727,0.00007929204,0.00006914722,0.00022259564,0.000062354964,0.000115591414,0.00031532132,0.00010476117,0.000094056224],"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":[0.000020783647,0.00007413366,0.00026392916,0.0000020211953,0.0000074014997,0.0000017452799,0.0037253879,0.013382467,0.000001994079,0.95400697,0.02328483,0.0052283183],"study_design_scores_gemma":[0.00018254171,0.00007320092,0.0005668207,0.0000012748893,0.0000098850005,0.000012801107,0.0012312785,0.026465511,0.000020301462,0.2721416,0.6991598,0.00013501043],"about_ca_topic_score_codex":0.000040422176,"about_ca_topic_score_gemma":0.00023245215,"teacher_disagreement_score":0.6818654,"about_ca_system_score_codex":0.000041021285,"about_ca_system_score_gemma":0.00009682103,"threshold_uncertainty_score":0.607238},"labels":[],"label_agreement":null},{"id":"W3174830915","doi":"10.1287/opre.2022.2303","title":"Star-Shaped Risk Measures","year":2022,"lang":"en","type":"article","venue":"Operations Research","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Actua; University of Waterloo","funders":"","keywords":"Representation (politics); Computer science; Property (philosophy); Star (game theory); Coherent risk measure; Risk measure; Value (mathematics); Risk management; Risk analysis (engineering); Value at risk; Econometrics; Mathematics; Economics; Medicine; Machine learning; Financial economics; Epistemology; Philosophy","score_opus":0.35249811106854556,"score_gpt":0.5086021977632346,"score_spread":0.156104086694689,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3174830915","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":[0.8965083,0.00079283817,0.03432842,0.0065048933,0.00073210173,0.0012238843,0.0003417785,0.000106497224,0.059461292],"genre_scores_gemma":[0.9616681,0.00052550377,0.0023796763,0.000070201175,0.000093755,0.00020990503,0.000035124944,0.000011896933,0.035005882],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.9923831,0.002433319,0.00039705515,0.00041312852,0.0040546623,0.00031868942],"domain_scores_gemma":[0.9973106,0.00068326615,0.000031124066,0.0007038847,0.0011509405,0.000120197175],"candidate_categories":["sts","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.012931895,0.000070029295,0.00011959538,0.00070537935,0.0036699327,0.00068246434,0.00087110564,0.00002988084,0.00808239],"category_scores_gemma":[0.0044566463,0.00005606865,0.000059191843,0.0028686898,0.000106251486,0.0003319648,0.0004114057,0.00057282834,0.0010381059],"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":[0.00009298474,0.00025502426,0.01342816,4.2586387e-7,0.000023288942,0.000017957425,0.003218734,0.6815235,0.0015742371,0.0126219615,0.15998173,0.12726201],"study_design_scores_gemma":[0.00026109538,0.0001428333,0.0044691325,6.0404403e-7,0.000003048485,0.000006292086,0.0031733618,0.32351333,0.00015448547,0.0042751925,0.66389364,0.000106982654],"about_ca_topic_score_codex":0.00088461523,"about_ca_topic_score_gemma":0.00046847144,"teacher_disagreement_score":0.5039119,"about_ca_system_score_codex":0.00009882809,"about_ca_system_score_gemma":0.0004116652,"threshold_uncertainty_score":0.9997397},"labels":[],"label_agreement":null},{"id":"W3175466418","doi":"10.1080/14697688.2022.2092543","title":"Risk contributions of lambda quantiles*","year":2022,"lang":"en","type":"article","venue":"Quantitative Finance","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Quantile; Mathematics; Portfolio; Homogeneous function; Homogeneity (statistics); Econometrics; Value at risk; Mathematical economics; Homogeneous; Statistics; Economics; Risk management; Finance; Combinatorics","score_opus":0.0665885149120511,"score_gpt":0.40028698512282057,"score_spread":0.3336984702107695,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3175466418","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":[0.8818787,0.0013239504,0.10818742,0.0005077838,0.0006548246,0.00027262405,0.0019052412,0.00003181998,0.0052376403],"genre_scores_gemma":[0.9911983,0.0008006561,0.0059678196,0.000048206588,0.000013756413,0.000039048504,0.000024494013,0.0000075211306,0.0019001776],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99747616,0.00054414856,0.0006004742,0.0003244244,0.0008700631,0.00018473301],"domain_scores_gemma":[0.9969556,0.0012997864,0.0007564813,0.0004123532,0.00054560445,0.00003021585],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023288135,0.00009654674,0.0002813666,0.00021557494,0.00048960117,0.00003016148,0.0004858088,0.000026718963,0.0004249949],"category_scores_gemma":[0.0031320653,0.00008398763,0.00012214298,0.0015811931,0.00014819641,0.00021690218,0.00013521209,0.0001874586,0.00013810236],"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":[0.0001769309,0.00020958822,0.04371961,0.0000012120264,0.000024140656,0.000009634073,0.0023231457,0.14807002,0.0003943706,0.762387,0.029503604,0.013180756],"study_design_scores_gemma":[0.0009862018,0.00091924204,0.1380493,0.000009488242,0.000031228563,0.000008587292,0.0028922085,0.08981565,0.0023940604,0.11756364,0.6469822,0.0003481932],"about_ca_topic_score_codex":0.00014021082,"about_ca_topic_score_gemma":0.000020028638,"teacher_disagreement_score":0.6448233,"about_ca_system_score_codex":0.000032950386,"about_ca_system_score_gemma":0.00011596016,"threshold_uncertainty_score":0.46533963},"labels":[],"label_agreement":null},{"id":"W3175957685","doi":"10.1016/j.insmatheco.2021.06.005","title":"Optimal capital allocation principles considering capital shortfall and surplus risks in a hierarchical corporate structure","year":2021,"lang":"en","type":"article","venue":"Insurance Mathematics and Economics","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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 Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Expected shortfall; Capital structure; Economics; Capital (architecture); Capital allocation line; Risk-adjusted return on capital; Capital adequacy ratio; Economic capital; Microeconomics; Business; Financial capital; Risk management; Finance; Capital formation","score_opus":0.10466780872868729,"score_gpt":0.3078410151637969,"score_spread":0.2031732064351096,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3175957685","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":[0.9968479,0.00037607097,0.0018088154,0.00018478798,0.00015769072,0.00014560891,0.00007409661,0.000012538682,0.0003924541],"genre_scores_gemma":[0.9680263,0.0016765893,0.03012165,0.000037002552,0.00004018945,0.0000057874727,0.00001767392,0.000014287546,0.000060548653],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984956,0.00004009753,0.00071353535,0.00040464016,0.00014718313,0.00019893941],"domain_scores_gemma":[0.99882144,0.00033827627,0.00033958608,0.0002804164,0.000113820024,0.0001064707],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00061867776,0.00016190682,0.00036951556,0.00012606203,0.000114004324,0.00038813954,0.00012677099,0.00011364826,0.000026489906],"category_scores_gemma":[0.00039255846,0.00014628553,0.000036841982,0.00014602795,0.00013496942,0.00031526477,0.00012340683,0.00015683172,0.000005861443],"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":[0.000060198858,0.00021762184,0.617214,0.000073608004,0.00006462132,0.00010420942,0.016345793,0.2527417,0.00046293184,0.0884818,0.000031374664,0.024202138],"study_design_scores_gemma":[0.0012111152,0.000052797383,0.30081275,0.000058086876,0.000015810634,0.00040022863,0.0035640162,0.53317654,0.0008537316,0.15886915,0.00046745935,0.00051831506],"about_ca_topic_score_codex":0.000039393646,"about_ca_topic_score_gemma":0.0009356062,"teacher_disagreement_score":0.31640127,"about_ca_system_score_codex":0.000027712826,"about_ca_system_score_gemma":0.00009356457,"threshold_uncertainty_score":0.596535},"labels":[],"label_agreement":null},{"id":"W3176060828","doi":"10.15488/14685","title":"Law-Invariant Functionals that Collapse to the Mean: Beyond Convexity","year":2021,"lang":"en","type":"preprint","venue":"Zurich Open Repository and Archive (University of Zurich)","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":0,"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":"Universität Bielefeld; University of Waterloo","keywords":"Quasiconvex function; Convexity; Invariant (physics); Quantile; Regular polygon; Mathematics; Mathematical economics; Applied mathematics; Pure mathematics; Economics; Econometrics; Convex analysis; Finance; Convex optimization; Geometry; Mathematical physics","score_opus":0.06482790382696972,"score_gpt":0.28973198471499084,"score_spread":0.2249040808880211,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3176060828","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":[0.43654805,0.0015277617,0.2055109,0.013126446,0.0053868126,0.004444103,0.0009351644,0.00010924654,0.33241153],"genre_scores_gemma":[0.956032,0.0009700356,0.023177123,0.0008451602,0.00023792293,0.0000065863096,0.00017271788,0.000027240143,0.018531209],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99492484,0.002001133,0.00045793978,0.0011581712,0.0011796204,0.0002782791],"domain_scores_gemma":[0.99495196,0.0019595698,0.00081191224,0.0013524602,0.00061246887,0.00031162045],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0036541042,0.00031390335,0.0007439757,0.00025080072,0.0014660753,0.00096357544,0.0025191985,0.00021081859,0.00031449634],"category_scores_gemma":[0.0002205461,0.00026118263,0.0002594852,0.000573491,0.000433133,0.0005797568,0.0057781124,0.00048332813,0.000042164207],"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":[0.0021030188,0.001441293,0.04130229,0.00017510122,0.001724276,0.0015502482,0.08704298,0.044173744,0.0017945592,0.08165813,0.7250977,0.011936664],"study_design_scores_gemma":[0.0021419507,0.0005850317,0.14486311,0.00041897414,0.0007349095,0.0002061865,0.0502044,0.008914928,0.0011548288,0.06110879,0.727863,0.0018038548],"about_ca_topic_score_codex":0.0031880816,"about_ca_topic_score_gemma":0.0017419261,"teacher_disagreement_score":0.519484,"about_ca_system_score_codex":0.00004580916,"about_ca_system_score_gemma":0.00043642407,"threshold_uncertainty_score":0.999984},"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":"W3180712247","doi":"10.1080/03461238.2021.1944905","title":"A multivariate CVaR risk measure from the perspective of portfolio risk management","year":2021,"lang":"en","type":"article","venue":"Scandinavian Actuarial Journal","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Dynamic risk measure; Subadditivity; CVAR; Risk measure; Spectral risk measure; Expected shortfall; Coherent risk measure; Time consistency; Multivariate statistics; Econometrics; Portfolio; Value at risk; Risk management; Measure (data warehouse); Portfolio optimization; Downside risk; Actuarial science; Mathematics; Copula (linguistics); Economics; Computer science; Statistics; Financial economics; Finance; Data mining","score_opus":0.03735201553021493,"score_gpt":0.3344961316321783,"score_spread":0.29714411610196334,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3180712247","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":[0.5461447,0.0056003765,0.3140217,0.0039004853,0.01303194,0.0012569699,0.0013781874,0.00009952812,0.114566095],"genre_scores_gemma":[0.98535955,0.004641457,0.0076942397,0.000099565346,0.001211622,0.0000038644007,0.00001068499,0.000022837465,0.00095619727],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99469626,0.0013861879,0.0010261269,0.0005228835,0.0020119892,0.00035654483],"domain_scores_gemma":[0.99519694,0.00088547467,0.0016183559,0.0007991823,0.0012669925,0.00023307021],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0039450587,0.00024058903,0.0004592903,0.00022820222,0.0007837719,0.0006411238,0.0009136194,0.00013331856,0.0021920071],"category_scores_gemma":[0.003264635,0.00014432443,0.00043844688,0.001128604,0.00014565565,0.00042536037,0.00018936013,0.00072453555,0.000108675325],"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":[0.0022436872,0.00066184555,0.40995407,0.0000026440719,0.0024793278,0.0009823003,0.022536164,0.011537227,0.00034197277,0.016869338,0.060396004,0.47199544],"study_design_scores_gemma":[0.004917728,0.00018798781,0.5321322,0.00013873463,0.0009008172,0.00024450928,0.02571862,0.002165689,0.0007318707,0.41037637,0.021984527,0.0005009462],"about_ca_topic_score_codex":0.0016266613,"about_ca_topic_score_gemma":0.00017527094,"teacher_disagreement_score":0.4714945,"about_ca_system_score_codex":0.00015605602,"about_ca_system_score_gemma":0.00025106524,"threshold_uncertainty_score":0.9987201},"labels":[],"label_agreement":null},{"id":"W3181040298","doi":"10.1287/opre.2021.0393","title":"Technical Note—A Data-Driven Approach to Beating SAA Out of Sample","year":2023,"lang":"en","type":"article","venue":"Operations Research","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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 British Columbia","funders":"","keywords":"Robust optimization; Sample (material); Computer science; Mistake; Optimization problem; Mathematical optimization; Set (abstract data type); Task (project management); Data set; Artificial intelligence; Mathematics; Algorithm; Economics","score_opus":0.5361502316087712,"score_gpt":0.5619995598826671,"score_spread":0.025849328273895966,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3181040298","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":[0.02887111,0.000024324303,0.9239123,0.0033997642,0.000216743,0.0011892982,0.0006351154,0.00012455804,0.041626804],"genre_scores_gemma":[0.7065201,0.000118567594,0.284594,0.00004835779,0.00018872641,0.00012049368,0.0008322407,0.000021954827,0.0075556044],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9959319,0.00041352614,0.00056976476,0.0005733255,0.0021737071,0.0003377399],"domain_scores_gemma":[0.9955921,0.0016165434,0.000028845872,0.0015768515,0.0010379752,0.0001476583],"candidate_categories":["metaresearch","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00807095,0.00007548906,0.00019071407,0.00089210755,0.00053095305,0.00039575362,0.0016237983,0.00008253035,0.00017350246],"category_scores_gemma":[0.017807348,0.00005807632,0.000036475612,0.004329776,0.00012661953,0.0004300271,0.001061425,0.0002484459,0.0012061556],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000018917839,0.00015273654,0.0024186815,0.0000039699958,0.0000057581087,0.0000020356413,0.0020066001,0.78182405,0.0055352785,0.008400703,0.12932569,0.07030556],"study_design_scores_gemma":[0.000114826056,0.00006282612,0.0021045764,0.000007591671,0.0000025689062,0.0000011633646,0.0008763625,0.9536642,0.00038391328,0.0006464318,0.042045686,0.00008989256],"about_ca_topic_score_codex":0.00041341627,"about_ca_topic_score_gemma":0.00066604436,"teacher_disagreement_score":0.67764896,"about_ca_system_score_codex":0.000034995443,"about_ca_system_score_gemma":0.00028782853,"threshold_uncertainty_score":0.9995715},"labels":[],"label_agreement":null},{"id":"W3182352630","doi":"10.2139/ssrn.3430276","title":"Behavioral Portfolio Insurance Strategies","year":2019,"lang":"de","type":"article","venue":"SSRN Electronic Journal","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Toronto Metropolitan University","funders":"","keywords":"Actuarial science; Portfolio; Business; Economics; Finance","score_opus":0.022057600546077208,"score_gpt":0.3264588306550018,"score_spread":0.3044012301089246,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3182352630","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":[0.9341238,0.04000722,0.009543675,0.00049436593,0.0060893362,0.00041144498,0.000026059732,0.0000542612,0.009249835],"genre_scores_gemma":[0.87332153,0.105090275,0.00014479106,0.00008404124,0.00092397886,0.0000032347448,0.000014376664,0.000048422113,0.020369366],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9914779,0.00037273418,0.0013900902,0.00070188654,0.00197281,0.0040845843],"domain_scores_gemma":[0.9970816,0.00014700979,0.0011218549,0.00075532857,0.0006161601,0.0002780394],"candidate_categories":["metaepi_narrow","scholarly_communication","research_integrity","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.006155652,0.00045996023,0.0006476199,0.00058881706,0.0004540005,0.0014473124,0.0013697033,0.00033438258,0.0015962003],"category_scores_gemma":[0.00009930871,0.00037911083,0.00043511193,0.0012546828,0.00014325869,0.002144511,0.00010949546,0.0035325421,0.0055870074],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005627819,0.0007107795,0.33084625,0.000009717491,0.00048637966,0.00009232409,0.001527531,0.014511876,0.00031167906,0.27443147,0.004348255,0.37216097],"study_design_scores_gemma":[0.0037964443,0.0036044815,0.03175609,0.0001346418,0.0003354564,0.0014972789,0.03155058,0.0026454995,0.000112743204,0.72819716,0.19467694,0.0016926773],"about_ca_topic_score_codex":0.00019760568,"about_ca_topic_score_gemma":0.00030595763,"teacher_disagreement_score":0.45376572,"about_ca_system_score_codex":0.0007365273,"about_ca_system_score_gemma":0.010754928,"threshold_uncertainty_score":0.99986607},"labels":[],"label_agreement":null},{"id":"W3182943834","doi":"10.1111/itor.13031","title":"Balancing the profit and capacity under uncertainties: a target‐based distributionally robust knapsack problem","year":2021,"lang":"en","type":"article","venue":"International Transactions in Operational Research","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Memorial University of Newfoundland","funders":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Knapsack problem; Mathematical optimization; Continuous knapsack problem; Profit (economics); Piecewise; Conic section; Robust optimization; Computer science; Optimization problem; Mathematics; Economics","score_opus":0.2091509266967098,"score_gpt":0.4357661244904739,"score_spread":0.22661519779376413,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3182943834","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":[0.06069725,0.00021337683,0.86022174,0.065614,0.0005876189,0.00060612196,0.00038030677,0.000030925927,0.011648643],"genre_scores_gemma":[0.9764111,0.00018372251,0.015176925,0.00042741,0.00013601314,0.00019516959,0.00018531008,0.000012833981,0.007271507],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9947794,0.00066929945,0.0006601497,0.00057190395,0.002998264,0.00032100332],"domain_scores_gemma":[0.9939273,0.0024033613,0.000073199444,0.00031127039,0.003184813,0.0001000701],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0046935035,0.00014102572,0.00016485646,0.00042872518,0.0007482312,0.0008784719,0.0005546824,0.00010889494,0.0033924486],"category_scores_gemma":[0.0014779826,0.000103170205,0.00008711528,0.001485324,0.00036562613,0.0006447021,0.00004437576,0.0006322432,0.00007182509],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006520443,0.000121743615,0.015188296,0.0000037653647,0.000040215084,0.000011687265,0.00035808858,0.9229956,0.0003478657,0.05691647,0.0012828873,0.0026681456],"study_design_scores_gemma":[0.0012340817,0.000056939636,0.059195466,0.00007707289,0.000009096028,0.000090486,0.0026258011,0.808991,0.0020718302,0.0726856,0.052638244,0.0003243752],"about_ca_topic_score_codex":0.00026531302,"about_ca_topic_score_gemma":0.0008658052,"teacher_disagreement_score":0.91571385,"about_ca_system_score_codex":0.00034699263,"about_ca_system_score_gemma":0.0012328298,"threshold_uncertainty_score":0.9975186},"labels":[],"label_agreement":null},{"id":"W3183136893","doi":"10.1007/s11081-021-09659-3","title":"Norm induced polyhedral uncertainty sets for robust linear optimization","year":2021,"lang":"en","type":"article","venue":"Optimization and Engineering","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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 Alberta","funders":"","keywords":"Robust optimization; Intersection (aeronautics); Uncertain data; Mathematical optimization; Uncertainty theory; Constraint (computer-aided design); Mathematics; Set (abstract data type); Norm (philosophy); Uncertainty quantification; Uncertainty analysis; Computer science; Data mining","score_opus":0.06890294975717556,"score_gpt":0.3245371272210113,"score_spread":0.25563417746383577,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3183136893","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":[0.0076460605,0.00010967494,0.9903453,0.0005004586,0.00052282907,0.00021049783,0.000021196578,0.00010686617,0.00053714245],"genre_scores_gemma":[0.117644235,0.00070952845,0.8796879,0.0002408053,0.00022785427,0.000041347077,0.00040509884,0.000055191715,0.0009880654],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983656,0.000039055158,0.00050630583,0.00043975952,0.00038992925,0.0002593905],"domain_scores_gemma":[0.9985773,0.00025287658,0.00013769243,0.00029210284,0.0005866117,0.0001534165],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005411946,0.00018577793,0.0002524826,0.0002423372,0.00019276922,0.00028216193,0.00014753098,0.00015126752,0.00019687766],"category_scores_gemma":[0.0012399147,0.00017438679,0.000079013545,0.00087487063,0.00001593251,0.00049072097,0.000060115945,0.00009710586,0.000005869772],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012487435,0.000017890354,0.0002835049,0.0000059428994,0.000011332275,0.0000021430074,0.00014326663,0.99541897,0.00011099312,0.0003095119,0.00027835713,0.0034056047],"study_design_scores_gemma":[0.0005414446,0.000029709685,0.00023360668,0.000014201594,0.000016941092,0.000014647156,0.00013093498,0.99644494,0.0002905929,0.0000319011,0.0020369538,0.0002140994],"about_ca_topic_score_codex":0.000008994135,"about_ca_topic_score_gemma":0.00000556329,"teacher_disagreement_score":0.1106574,"about_ca_system_score_codex":0.00003674094,"about_ca_system_score_gemma":0.000082728555,"threshold_uncertainty_score":0.71112865},"labels":[],"label_agreement":null},{"id":"W3183952495","doi":"","title":"Classical Risk-Averse Control for Finite-Horizon Borel Models","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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 Toronto","funders":"","keywords":"Optimal control; Quadratic equation; Mathematics; Exponential function; Exponential utility; Mathematical economics; Mathematical optimization; Control (management); Measure (data warehouse); Controller (irrigation); Risk aversion (psychology); Expected utility hypothesis; Economics; Computer science","score_opus":0.176583731150855,"score_gpt":0.25879982742543817,"score_spread":0.08221609627458318,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3183952495","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":[0.123986684,0.00008185626,0.8696251,0.00012905314,0.0010843946,0.00061576354,0.0004177877,0.000099597346,0.0039597885],"genre_scores_gemma":[0.9896627,0.0016107635,0.0021553156,0.00009954575,0.00022683067,0.0000047118906,0.00015001379,0.00003641939,0.0060536973],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9963163,0.0004892938,0.00061655114,0.0017103496,0.00040692443,0.00046056698],"domain_scores_gemma":[0.99409807,0.002102665,0.00087934645,0.0015251028,0.0010811423,0.00031370582],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0014387553,0.0004092729,0.00075857085,0.0004886591,0.00033110636,0.00040021143,0.0013310312,0.00068725704,0.00020660882],"category_scores_gemma":[0.0016099969,0.00040069173,0.00077311863,0.0008799141,0.00017574025,0.00058017677,0.00059722346,0.000706283,0.000112368405],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026742986,0.00010502353,0.003255528,0.000007436047,0.00009731814,0.00009479376,0.00014080308,0.9635417,0.0000025437346,0.025812015,0.003907656,0.0027677615],"study_design_scores_gemma":[0.0013473447,0.00009740085,0.00032668005,0.000024689603,0.00024836365,0.0000010805234,0.0004713441,0.84949994,0.000014616461,0.1416228,0.005956012,0.00038970145],"about_ca_topic_score_codex":0.0001619614,"about_ca_topic_score_gemma":0.00015686057,"teacher_disagreement_score":0.8674697,"about_ca_system_score_codex":0.00015978898,"about_ca_system_score_gemma":0.0004866599,"threshold_uncertainty_score":0.9998445},"labels":[],"label_agreement":null},{"id":"W3185325703","doi":"10.1287/ijoc.2020.1044","title":"State-Variable Modeling for a Class of Two-Stage Stochastic Optimization Problems","year":2021,"lang":"en","type":"article","venue":"INFORMS journal on computing","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Concordia University","funders":"","keywords":"Mathematical optimization; Stochastic programming; State variable; Computer science; Scheduling (production processes); Integer programming; Integer (computer science); Stochastic optimization; Class (philosophy); Variable (mathematics); Optimization problem; Mathematics; Artificial intelligence","score_opus":0.08023899513477249,"score_gpt":0.3623664528531287,"score_spread":0.2821274577183562,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3185325703","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":[0.04392874,0.00006065496,0.95370185,0.000084355735,0.00058358035,0.00019031913,0.000012183378,0.000020302683,0.0014179805],"genre_scores_gemma":[0.78290206,0.00004747598,0.21592638,0.0002188272,0.00019173736,0.0000025306067,0.000018225335,0.00002155895,0.00067123567],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9970086,0.000060548016,0.0014442583,0.00022648589,0.000947674,0.00031242307],"domain_scores_gemma":[0.9960159,0.0006758528,0.0010137067,0.00025192814,0.0019120934,0.00013053737],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0030660918,0.00014741455,0.0003415069,0.0003433633,0.0003478873,0.00052333204,0.00032581785,0.00006223944,0.00006944071],"category_scores_gemma":[0.0018194333,0.000109534616,0.00014327173,0.0008301756,0.000022220374,0.0006643008,0.000083173065,0.00024738058,0.000007076324],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000041327734,0.000032116357,0.0001418274,0.000007056882,0.000018479259,0.0000028457828,0.0004949528,0.9770797,0.000037116308,0.0017545973,0.00009105386,0.020298915],"study_design_scores_gemma":[0.00097375625,0.00011720988,0.0000063281,0.0001217744,0.00001020288,0.000054201075,0.00033462103,0.9891188,0.00010438228,0.008341911,0.0006855923,0.00013122616],"about_ca_topic_score_codex":0.000007994762,"about_ca_topic_score_gemma":0.0000028460183,"teacher_disagreement_score":0.7389733,"about_ca_system_score_codex":0.00006211341,"about_ca_system_score_gemma":0.00040407464,"threshold_uncertainty_score":0.5046503},"labels":[],"label_agreement":null},{"id":"W3189195378","doi":"10.1007/s00780-024-00540-6","title":"Risk sharing under heterogeneous beliefs without convexity","year":2024,"lang":"en","type":"article","venue":"Finance and Stochastics","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":6,"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":"Convexity; Stochastic dominance; Mathematical economics; Mathematics; Mathematical finance; Pareto principle; Invariant (physics); Mathematical optimization; Argument (complex analysis); Simple (philosophy); Pareto optimal; Realm; Multi-objective optimization; Economics; Law","score_opus":0.05816895818961559,"score_gpt":0.35341466188429144,"score_spread":0.29524570369467584,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3189195378","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":[0.31049913,0.0023111643,0.68522024,0.0001319167,0.0006400681,0.00010520623,0.000049141017,0.000061908075,0.0009812221],"genre_scores_gemma":[0.99358207,0.0017234139,0.0018378276,0.00010552528,0.00015072289,0.000006495153,0.0000049551463,0.000012821564,0.0025761544],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99864715,0.000023691908,0.00031285253,0.00043503434,0.0003912118,0.00019006227],"domain_scores_gemma":[0.9992208,0.00024060164,0.00008549373,0.00030083553,0.000094983516,0.00005730832],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005566525,0.000121556404,0.00018166439,0.00011436139,0.0001920354,0.0003546252,0.0002020563,0.00007923934,0.000043907436],"category_scores_gemma":[0.00024124623,0.0000921329,0.000051524723,0.00034026857,0.00009005896,0.0001806135,0.000095013886,0.00015533286,0.00016942128],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008740802,0.00008592638,0.054373205,0.000026051515,0.00007836012,0.00011486697,0.0038156568,0.5384599,0.0000540544,0.09785145,0.007611574,0.29744157],"study_design_scores_gemma":[0.00021712123,0.00013676383,0.014690094,0.00006000745,0.000034429286,0.000055448516,0.00009286713,0.8191622,0.00008404427,0.13152952,0.033660546,0.0002769403],"about_ca_topic_score_codex":0.000026542173,"about_ca_topic_score_gemma":0.000019642579,"teacher_disagreement_score":0.6833824,"about_ca_system_score_codex":0.000013603867,"about_ca_system_score_gemma":0.000042120162,"threshold_uncertainty_score":0.37570706},"labels":[],"label_agreement":null},{"id":"W3189958970","doi":"10.1111/mafi.12417","title":"Risk concentration and the mean‐expected shortfall criterion","year":2023,"lang":"en","type":"article","venue":"Mathematical Finance","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Axiom; Spectral risk measure; Mathematical economics; Expected shortfall; Risk aversion (psychology); Portfolio; Economics; Monotonic function; Axiomatic system; Coherent risk measure; Mathematics; Risk measure; Expected utility hypothesis; Econometrics; Ambiguity aversion; Actuarial science; Computer science; Financial economics","score_opus":0.048105116447964884,"score_gpt":0.3426735529607346,"score_spread":0.2945684365127697,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3189958970","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":[0.8838025,0.00024338163,0.1018765,0.003800134,0.00022980143,0.00048030997,0.000015372414,0.00015027243,0.009401747],"genre_scores_gemma":[0.99355763,0.001978229,0.001784319,0.0001083473,0.000052412557,0.000031932614,0.000005499447,0.000008255433,0.0024733776],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99829584,0.00020295761,0.00046790525,0.00026540965,0.0005886701,0.00017919653],"domain_scores_gemma":[0.997782,0.0015235879,0.00016206985,0.0003729499,0.00012143059,0.00003798447],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021992375,0.000095201816,0.00022267505,0.00003761988,0.00022002193,0.00022384421,0.00023886464,0.000055001245,0.000091050795],"category_scores_gemma":[0.0029464725,0.000051139108,0.000056766396,0.0007733874,0.00022953723,0.00018560392,0.00006996392,0.0000973307,0.0006082531],"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":[0.00020154065,0.00007409497,0.007794552,0.000011946469,0.00002171013,0.000018090484,0.013418992,0.002298263,0.00011166074,0.8076309,0.033954583,0.13446364],"study_design_scores_gemma":[0.00064343767,0.00002521912,0.022026824,0.000018944302,0.000016984155,0.000008586753,0.00035138935,0.40576872,0.00011723257,0.55959594,0.011305398,0.00012132641],"about_ca_topic_score_codex":0.0000072314965,"about_ca_topic_score_gemma":0.000004113231,"teacher_disagreement_score":0.40347046,"about_ca_system_score_codex":0.0000072650587,"about_ca_system_score_gemma":0.000015061791,"threshold_uncertainty_score":0.7818066},"labels":[],"label_agreement":null},{"id":"W3192066814","doi":"10.1007/s11238-012-9293-8","title":"Pareto utility","year":2012,"lang":"en","type":"article","venue":"Theory and Decision","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Simon Fraser University","funders":"Japan Society for the Promotion of Science; Nederlandse Organisatie voor Wetenschappelijk Onderzoek","keywords":"Isoelastic utility; Expected utility hypothesis; Pareto principle; Von Neumann–Morgenstern utility theorem; Subjective expected utility; Class (philosophy); Mathematical economics; Risk aversion (psychology); Mathematical optimization; Function (biology); Exponential function; Exponential utility; Economics; Econometrics; Mathematics; Computer science; Artificial intelligence","score_opus":0.0801050099982059,"score_gpt":0.3952288934776774,"score_spread":0.3151238834794715,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3192066814","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":[0.74897414,0.0010466323,0.22921656,0.000026594711,0.00042735867,0.00006447064,0.000003834934,0.00002594971,0.020214431],"genre_scores_gemma":[0.9963236,0.0002561622,0.0024492927,0.00017673399,0.00011349866,0.0000019550055,0.000001867072,0.0000047286744,0.00067216053],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9984076,0.0003018596,0.000331141,0.0002127487,0.00055094174,0.00019570044],"domain_scores_gemma":[0.99629176,0.0029207687,0.000088146306,0.00043174793,0.000091026326,0.00017653858],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.010103222,0.00008490516,0.00014981099,0.00011455575,0.00017404874,0.00009827769,0.00019769324,0.000068240144,0.0009596011],"category_scores_gemma":[0.0025510779,0.00005292631,0.000047512232,0.0003164204,0.000081102924,0.00055250514,0.00009994625,0.00006481417,0.0005608167],"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":[0.00025342952,0.000032132357,0.048521698,2.434176e-7,0.0000019514498,6.621827e-7,0.0004371051,0.000017397302,0.00001721115,0.08033804,0.0038909432,0.8664892],"study_design_scores_gemma":[0.00015960357,0.000020520843,0.12124775,0.0000037637417,0.0000057536417,0.000010819485,0.00032163385,0.0004915466,0.00016988777,0.81339896,0.06408581,0.000083939056],"about_ca_topic_score_codex":0.0000019658194,"about_ca_topic_score_gemma":0.000001218094,"teacher_disagreement_score":0.86640525,"about_ca_system_score_codex":0.000004685093,"about_ca_system_score_gemma":0.000010340668,"threshold_uncertainty_score":0.9999536},"labels":[],"label_agreement":null},{"id":"W3194439889","doi":"10.2139/ssrn.3910498","title":"Robust Risk-Aware Reinforcement Learning","year":2021,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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 Toronto","funders":"","keywords":"Reinforcement learning; Reinforcement; Computer science; Artificial intelligence; Psychology; Social psychology","score_opus":0.04207220615069272,"score_gpt":0.312367694612759,"score_spread":0.27029548846206625,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3194439889","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":[0.08033089,0.0029966256,0.9051356,0.0009412238,0.0005750377,0.00007610138,9.888603e-7,0.00004346438,0.009900077],"genre_scores_gemma":[0.9239062,0.03294053,0.0004197744,0.00008146454,0.00031235858,0.0000017488696,0.000008622963,0.000015606507,0.04231367],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9960585,0.0003897732,0.00062077283,0.00030143323,0.0011462682,0.0014832553],"domain_scores_gemma":[0.99827003,0.00020661051,0.00052645634,0.0002905986,0.0005847757,0.00012152203],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006296268,0.00013251297,0.00020707739,0.0001868649,0.00061669317,0.00037715485,0.00039522594,0.000078344136,0.0006884672],"category_scores_gemma":[0.0016829359,0.00010226587,0.00017756883,0.0007554364,0.000026644539,0.0003995865,0.00007738017,0.0019908766,0.00031832687],"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":[0.000028302937,0.000024968536,0.02957073,4.180789e-7,0.00006861359,0.000021215397,0.00024370497,0.8084737,0.00002486849,0.01735802,0.001367434,0.142818],"study_design_scores_gemma":[0.0023873223,0.00079285644,0.0052257795,0.000036796384,0.00014471315,0.0026919097,0.024517909,0.18419318,0.0006664983,0.54910195,0.22941144,0.00082968624],"about_ca_topic_score_codex":0.00003419539,"about_ca_topic_score_gemma":0.00036528154,"teacher_disagreement_score":0.90471584,"about_ca_system_score_codex":0.00037253991,"about_ca_system_score_gemma":0.0025119262,"threshold_uncertainty_score":0.8649479},"labels":[],"label_agreement":null},{"id":"W3196976798","doi":"10.1016/j.insmatheco.2021.08.012","title":"Can a regulatory risk measure induce profit-maximizing risk capital allocations? The case of conditional tail expectation","year":2021,"lang":"en","type":"article","venue":"Insurance Mathematics and Economics","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"York University","funders":"","keywords":"Profitability index; Profit maximization; Economics; Microeconomics; Profit (economics); Popularity; Actuarial science; Mathematical economics; Finance; Political science","score_opus":0.04289601231242019,"score_gpt":0.2839924576385508,"score_spread":0.24109644532613062,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3196976798","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":[0.99252486,0.00036758443,0.0056653763,0.0003279075,0.00014229628,0.00018617716,0.00030311968,0.0000107927435,0.00047189646],"genre_scores_gemma":[0.99048877,0.0009142124,0.008376349,0.00003317138,0.0000508865,0.00002544417,0.000021910455,0.000012665885,0.000076614604],"study_design_codex":"observational","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9985502,0.00011738436,0.0007351342,0.00028380376,0.00018322624,0.00013028015],"domain_scores_gemma":[0.9973938,0.0007163121,0.0008890556,0.00047752014,0.00046095546,0.0000623811],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014713705,0.00012636116,0.00026280884,0.000091151065,0.00037476534,0.0001752103,0.00016635733,0.00007943926,0.00003478767],"category_scores_gemma":[0.001239733,0.000095699084,0.00008082602,0.00023034561,0.0001468686,0.00025366782,0.00004960151,0.00013948738,0.000009753934],"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":[0.00006968387,0.0004528066,0.33452734,0.00010198468,0.00047696193,0.00012214873,0.08579917,0.23271349,0.00047706577,0.2220661,0.0014756186,0.12171762],"study_design_scores_gemma":[0.0013659319,0.000067570174,0.15406434,0.000072148614,0.00013975993,0.0013455154,0.05222803,0.21640909,0.0034488714,0.56992835,0.00034011574,0.0005902682],"about_ca_topic_score_codex":0.00012474717,"about_ca_topic_score_gemma":0.00074350834,"teacher_disagreement_score":0.34786224,"about_ca_system_score_codex":0.00003256295,"about_ca_system_score_gemma":0.00017215192,"threshold_uncertainty_score":0.39024955},"labels":[],"label_agreement":null},{"id":"W3199502854","doi":"10.1109/compsac51774.2021.00261","title":"Portfolio Optimization Using Novel Intelligent Probabilistic Forecasts of Risk Measures","year":2021,"lang":"en","type":"article","venue":"","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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 Manitoba; Toronto Metropolitan University","funders":"","keywords":"Portfolio; Portfolio optimization; Volatility (finance); Econometrics; Stochastic volatility; Rate of return on a portfolio; Post-modern portfolio theory; EWMA chart; Expected shortfall; Value at risk; Computer science; Economics; Replicating portfolio; Financial economics; Risk management; Finance","score_opus":0.1624616552674316,"score_gpt":0.3699709830754542,"score_spread":0.2075093278080226,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3199502854","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":[0.049990326,0.00017720638,0.94144136,0.000038349583,0.0003558985,0.00016159606,0.00002410604,0.000027739998,0.0077833957],"genre_scores_gemma":[0.74765664,0.0005999697,0.25082335,0.00003989774,0.000060001305,0.0000033194194,0.000022100752,0.00001597647,0.0007787766],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99707115,0.00015102173,0.0009820925,0.00043814152,0.0011610979,0.00019647827],"domain_scores_gemma":[0.99657303,0.00045356067,0.00054702215,0.0005608765,0.001761223,0.00010430858],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0016172833,0.00014284784,0.00031169876,0.00027266366,0.00011255211,0.00013776698,0.00027210306,0.00009444855,0.0013556109],"category_scores_gemma":[0.00570801,0.00010804289,0.00014459249,0.0014977348,0.0000757463,0.00030926292,0.000102828446,0.00008608678,0.000017321563],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001734937,0.00011119515,0.00996771,0.0000016643403,0.000014711194,0.0000022746021,0.00022032855,0.95466274,0.00018034171,0.0025158431,0.00024432773,0.032061532],"study_design_scores_gemma":[0.00024308673,0.00002752578,0.0017480506,0.000017038237,0.000053120893,0.00003245134,0.00054036843,0.98057294,0.005842426,0.008405217,0.0023433946,0.00017440556],"about_ca_topic_score_codex":0.0001273416,"about_ca_topic_score_gemma":0.00009102174,"teacher_disagreement_score":0.6976663,"about_ca_system_score_codex":0.00004029751,"about_ca_system_score_gemma":0.00026878633,"threshold_uncertainty_score":0.99955726},"labels":[],"label_agreement":null},{"id":"W3200991750","doi":"10.1109/compsac51774.2021.00260","title":"Novel Data-Driven Resilient Portfolio Risk Measures Using Sign and Volatility Correlations","year":2021,"lang":"en","type":"article","venue":"","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Toronto Metropolitan University; University of Manitoba","funders":"","keywords":"Kurtosis; Portfolio; Portfolio optimization; Skewness; Econometrics; Rate of return on a portfolio; Expected shortfall; Value at risk; Modern portfolio theory; Post-modern portfolio theory; Sharpe ratio; Computer science; Efficient frontier; Mathematics; Actuarial science; Statistics; Replicating portfolio; Economics; Risk management; Financial economics; Finance","score_opus":0.21069143847019717,"score_gpt":0.39077437203004567,"score_spread":0.1800829335598485,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3200991750","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":[0.23625228,0.0003430124,0.75494355,0.00014767777,0.00031334994,0.00014145958,0.00018448434,0.000042315663,0.0076318644],"genre_scores_gemma":[0.92415756,0.0004966734,0.07212807,0.00009370919,0.0000747822,0.0000015268254,0.00009273313,0.0000107370615,0.0029442166],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99721336,0.00022765163,0.00065299874,0.0007215929,0.0009938142,0.00019056769],"domain_scores_gemma":[0.9969398,0.00065846206,0.00028641688,0.0013114956,0.000650807,0.00015305256],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002038366,0.0001307153,0.00023690978,0.00015703066,0.0003794017,0.0003338294,0.00038572858,0.000091215974,0.0006260565],"category_scores_gemma":[0.003915993,0.00010097729,0.000049848877,0.0009012877,0.00009446918,0.00075928034,0.00036528904,0.00014377128,0.00002999995],"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":[0.000032491833,0.00018159745,0.81250685,0.0000013035656,0.000047983744,0.000014945094,0.00041437423,0.09876221,0.0009785999,0.0015434857,0.019628664,0.06588748],"study_design_scores_gemma":[0.0002458614,0.000010736679,0.13799445,0.0000046899154,0.00004955517,0.000029661764,0.00040040442,0.8419493,0.00011763336,0.0022859874,0.016775336,0.0001363719],"about_ca_topic_score_codex":0.0004536545,"about_ca_topic_score_gemma":0.00059889746,"teacher_disagreement_score":0.74318707,"about_ca_system_score_codex":0.000023092955,"about_ca_system_score_gemma":0.00025922182,"threshold_uncertainty_score":0.685488},"labels":[],"label_agreement":null},{"id":"W3201964780","doi":"10.23952/asvao.3.2021.3.09","title":"A new view on risk measures associated with acceptance sets","year":2021,"lang":"en","type":"article","venue":"Applied Set-Valued Analysis and Optimization","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Risk analysis (engineering); Computer science; Psychology; Business","score_opus":0.0393773374280486,"score_gpt":0.315304575393988,"score_spread":0.2759272379659394,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3201964780","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":[0.020893194,0.00041152196,0.9722334,0.00021069568,0.000057169407,0.0002183826,0.000038707847,0.00007644572,0.005860492],"genre_scores_gemma":[0.9080248,0.004893133,0.08406315,0.0005075118,0.000070743015,0.000023148214,0.0006345331,0.00003770995,0.0017452681],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99636424,0.00036551562,0.0006808855,0.0008801989,0.0014309975,0.00027816725],"domain_scores_gemma":[0.9974514,0.00039696725,0.0006797195,0.00065564155,0.00060060574,0.00021564716],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015089938,0.0002756668,0.0006508989,0.00049979775,0.00040956933,0.000578183,0.0002524712,0.00016199388,0.00060529646],"category_scores_gemma":[0.00084931264,0.00020077816,0.00017642096,0.006050032,0.000050471535,0.00025637486,0.00005421497,0.00017845491,0.00002876171],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000055664514,0.000045639586,0.0094105955,6.8060046e-7,0.0004783643,0.0000043518535,0.00027352432,0.89656913,0.000009395111,0.000849332,0.0009891462,0.091314144],"study_design_scores_gemma":[0.0015807641,0.000105436346,0.03654215,0.00002558354,0.0027117718,0.00000297877,0.0006098085,0.95210296,0.00047277618,0.0027942494,0.0024304271,0.0006210847],"about_ca_topic_score_codex":0.00006399433,"about_ca_topic_score_gemma":0.00043133885,"teacher_disagreement_score":0.88817024,"about_ca_system_score_codex":0.000046755064,"about_ca_system_score_gemma":0.00015734154,"threshold_uncertainty_score":0.8187496},"labels":[],"label_agreement":null},{"id":"W3205581568","doi":"10.2139/ssrn.3943660","title":"A Framework for Measures of Risk under Uncertainty","year":2021,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Risk analysis (engineering); Computer science; Business; Econometrics; Economics","score_opus":0.06980704586656096,"score_gpt":0.3700424910319576,"score_spread":0.30023544516539663,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3205581568","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":[0.08058186,0.0040969704,0.9137756,0.00077834807,0.000345882,0.000071704264,0.000012554941,0.000008827752,0.00032822267],"genre_scores_gemma":[0.9748055,0.015437081,0.008505591,0.00009105661,0.0002558154,0.0000031799364,0.0000035448065,0.000013073069,0.0008852041],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9970798,0.00026979382,0.0005805197,0.00023449016,0.0008557079,0.0009796749],"domain_scores_gemma":[0.9968128,0.0012684701,0.000525728,0.00030864263,0.001008948,0.00007538838],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0062595992,0.00010311537,0.0002563244,0.00014650299,0.00022752126,0.0001164747,0.00037044138,0.00010953066,0.00009415225],"category_scores_gemma":[0.0059106634,0.00007480826,0.00025493014,0.00064702606,0.000046441488,0.00015987286,0.000028623037,0.00086073764,0.000013274726],"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":[0.00013084272,0.00006830691,0.006639887,8.516837e-7,0.0001326352,0.0000013622036,0.00033571717,0.074004516,0.00006957357,0.7165494,0.0005746483,0.20149225],"study_design_scores_gemma":[0.00035667143,0.00009685245,0.0009745858,0.000009713954,0.000039607516,0.00008209599,0.003647614,0.0016818456,0.00025718426,0.98749226,0.005273175,0.000088418084],"about_ca_topic_score_codex":0.00002992982,"about_ca_topic_score_gemma":0.00075049145,"teacher_disagreement_score":0.90527004,"about_ca_system_score_codex":0.00018119997,"about_ca_system_score_gemma":0.0029435619,"threshold_uncertainty_score":0.7076043},"labels":[],"label_agreement":null},{"id":"W3206750019","doi":"10.48550/arxiv.2110.06464","title":"Data-driven distributionally robust risk parity portfolio optimization","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Portfolio optimization; Mathematical optimization; Robust optimization; Risk measure; Minimax; Robustness (evolution); Portfolio; Computer science; Probability distribution; Optimization problem; Coherent risk measure; Ambiguity; Convex optimization; Convexity; Mathematics; Regular polygon; Economics; Finance; Statistics","score_opus":0.22703778842148437,"score_gpt":0.27247038701398285,"score_spread":0.045432598592498485,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3206750019","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":[0.10016952,0.00013533056,0.8919969,0.00008286188,0.0010050231,0.00033939176,0.0028805214,0.00014381988,0.0032466033],"genre_scores_gemma":[0.9662627,0.0063226717,0.013573345,0.000059857513,0.00023603778,0.0000012561841,0.010969578,0.000033960507,0.0025405763],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9947619,0.0007594745,0.0008455209,0.002511048,0.00068108673,0.00044098595],"domain_scores_gemma":[0.9924164,0.0005056912,0.0014449239,0.0038520547,0.0014477591,0.00033320748],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0020318727,0.0004573912,0.0006892715,0.00044800516,0.00048427045,0.00076408475,0.003370046,0.00061880774,0.0016937961],"category_scores_gemma":[0.00200425,0.0004767099,0.00033613556,0.0018809636,0.00022070427,0.0012706749,0.0040498087,0.0009144752,0.00014837494],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000044718618,0.00013276028,0.053972486,0.000005456926,0.00010162002,0.00019431819,0.000033661134,0.93488586,4.5373423e-7,0.002642232,0.0074546128,0.00053179584],"study_design_scores_gemma":[0.00039005658,0.000018528066,0.011179766,0.000034608453,0.00027401047,0.0000074681934,0.00023281576,0.97849,0.0000050411627,0.0056232843,0.0032328356,0.0005115832],"about_ca_topic_score_codex":0.00041785315,"about_ca_topic_score_gemma":0.00027818044,"teacher_disagreement_score":0.8784236,"about_ca_system_score_codex":0.00024230647,"about_ca_system_score_gemma":0.0008761854,"threshold_uncertainty_score":0.99976844},"labels":[],"label_agreement":null},{"id":"W3207460363","doi":"10.1007/s00780-024-00528-2","title":"A framework for measures of risk under uncertainty","year":2024,"lang":"en","type":"article","venue":"Finance and Stochastics","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"University of Waterloo","funders":"","keywords":"Axiom; Mathematical finance; Expected shortfall; Equivalence (formal languages); Risk measure; Risk management; Coherence (philosophical gambling strategy); Axiomatic system; Random variable; Measure (data warehouse); Set (abstract data type); Variable (mathematics); Coherent risk measure; Computer science; Econometrics; Contrast (vision); Mathematics; Risk analysis (engineering); Data mining; Statistics; Artificial intelligence; Economics; Discrete mathematics","score_opus":0.11767262058285978,"score_gpt":0.3893968674632683,"score_spread":0.2717242468804085,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3207460363","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":[0.029413655,0.0023856561,0.9671745,0.00016300815,0.0004407625,0.000115888935,0.00015343759,0.000016176862,0.00013697332],"genre_scores_gemma":[0.96680117,0.001459411,0.03130353,0.00003911998,0.00010800835,0.000011102459,0.000003972143,0.0000073577053,0.00026632345],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9990428,0.00002170429,0.00028194828,0.00022260363,0.0003158767,0.000115041374],"domain_scores_gemma":[0.9977457,0.001751474,0.000098298035,0.00018767694,0.00019074863,0.000026088734],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00067057845,0.00007482789,0.00016617833,0.000098860924,0.000083448496,0.00008723326,0.00012343528,0.00008096659,0.0000105853715],"category_scores_gemma":[0.0018488405,0.000052804782,0.000060991708,0.00036242374,0.00007514269,0.00008016372,0.00002204948,0.00008811006,0.0000088415245],"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":[0.000060282502,0.000015614061,0.0005200541,0.000008994155,0.000014337827,0.0000011330438,0.001141532,0.24477275,0.0000063666744,0.5121769,0.0039912076,0.23729087],"study_design_scores_gemma":[0.0000956877,0.00008116229,0.002152055,0.000061060906,0.000024553976,0.0000016478233,0.0002235015,0.21275757,0.000026472597,0.75403094,0.030461608,0.000083761144],"about_ca_topic_score_codex":0.0000137690795,"about_ca_topic_score_gemma":0.0000128568245,"teacher_disagreement_score":0.9373875,"about_ca_system_score_codex":0.00000636082,"about_ca_system_score_gemma":0.00006176576,"threshold_uncertainty_score":0.22133684},"labels":[],"label_agreement":null},{"id":"W3208746577","doi":"10.1287/moor.2022.0312","title":"Cash-Subadditive Risk Measures Without Quasi-Convexity","year":2025,"lang":"en","type":"article","venue":"Mathematics of Operations Research","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"University of Waterloo","funders":"","keywords":"Subadditivity; Convexity; Cash; Actuarial science; Econometrics; Mathematics; Business; Statistics; Combinatorics; Finance","score_opus":0.2647810528765891,"score_gpt":0.5222964876332598,"score_spread":0.25751543475667066,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3208746577","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":[0.26161158,0.0002498333,0.640022,0.0014530653,0.00021669356,0.0011200779,0.00015444736,0.00004728697,0.09512501],"genre_scores_gemma":[0.9433477,0.00056096003,0.040980753,0.00002498016,0.00003740635,0.000079824094,0.0000115103185,0.00001175057,0.014945102],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9954802,0.00079356827,0.00088411715,0.00035624436,0.0022043432,0.00028155942],"domain_scores_gemma":[0.99316496,0.0021492522,0.00010742012,0.0009591944,0.0035318655,0.00008733633],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.010749491,0.00012119048,0.0003521322,0.0009818358,0.0006884384,0.00041862408,0.0008042868,0.00010760277,0.00042819543],"category_scores_gemma":[0.019470517,0.000088409346,0.00010085324,0.0021900493,0.00039100705,0.0003368649,0.00019987996,0.0003568049,0.00030809033],"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":[0.00015409674,0.0033810935,0.05682065,0.00010025656,0.00038149988,0.000012830534,0.027664669,0.10773296,0.0022699856,0.52503234,0.1094544,0.16699523],"study_design_scores_gemma":[0.0010311657,0.0002835588,0.005607303,0.00017330128,0.00006333623,0.0000071082977,0.013859657,0.66376364,0.018365676,0.26671532,0.029751435,0.00037848222],"about_ca_topic_score_codex":0.00035280417,"about_ca_topic_score_gemma":0.0007278664,"teacher_disagreement_score":0.6817361,"about_ca_system_score_codex":0.000054383043,"about_ca_system_score_gemma":0.00049437216,"threshold_uncertainty_score":0.9887889},"labels":[],"label_agreement":null},{"id":"W3210901042","doi":"10.32920/ryerson.14662176.v1","title":"Rearrangement Algorithm in Risk Aggregation","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Measure (data warehouse); Aggregate (composite); Risk measure; Discretization; Expected shortfall; Exponential function; Portfolio; Econometrics; Investment (military); Mathematics; Actuarial science; Economics; Mathematical optimization; Computer science; Financial economics; Data mining","score_opus":0.07475718654506924,"score_gpt":0.3766298443416548,"score_spread":0.30187265779658556,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3210901042","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":[0.15592729,0.0012323887,0.79003835,0.00074366905,0.0027462232,0.00068867,0.000050727864,0.00008909916,0.048483606],"genre_scores_gemma":[0.44854963,0.03415237,0.4861591,0.00048736186,0.0007148606,0.00019344858,0.00070901134,0.00006364402,0.028970586],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9963451,0.00043043788,0.00090757303,0.00079085847,0.0013373942,0.00018864803],"domain_scores_gemma":[0.9977725,0.00025879228,0.0005221098,0.000949967,0.00042319886,0.00007344176],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0032042144,0.00018468946,0.00036469504,0.00051513565,0.00005913569,0.00061382446,0.00051425555,0.00027442005,0.0017533473],"category_scores_gemma":[0.0011134131,0.00014515949,0.00015243197,0.00075000536,0.000027310263,0.00019832898,0.0005552028,0.0004321428,0.00018647901],"study_design_candidate":"design_other","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":[0.0000039459414,0.000049038335,0.021855358,0.0000017094432,0.000010861398,0.000019141124,0.00065859983,0.05084055,0.0000022199924,0.00006138779,0.0048591974,0.921638],"study_design_scores_gemma":[0.00075568946,0.000039501378,0.11306641,0.00015142071,0.000046245284,0.0000058385303,0.0026350582,0.7797072,0.00058543147,0.06296697,0.039305698,0.0007345522],"about_ca_topic_score_codex":0.0015365926,"about_ca_topic_score_gemma":0.00096069404,"teacher_disagreement_score":0.92090344,"about_ca_system_score_codex":0.000081291335,"about_ca_system_score_gemma":0.00018583586,"threshold_uncertainty_score":0.99915916},"labels":[],"label_agreement":null},{"id":"W3213293004","doi":"10.5539/ibr.v14n12p82","title":"Diversifiable and Non-diversifiable Risk and the Advanced Choice under Ambiguous or Uncertain Conditions","year":2021,"lang":"en","type":"article","venue":"International Business Research","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Diversification (marketing strategy); Ambiguity; Economics; Modern portfolio theory; Systematic risk; Econometrics; Measure (data warehouse); Portfolio; Actuarial science; Mathematical economics; Financial economics; Computer science; Business; Marketing","score_opus":0.13414611393216325,"score_gpt":0.44894145938546004,"score_spread":0.3147953454532968,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3213293004","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":[0.9095882,0.00088710187,0.02021622,0.023476362,0.0013202212,0.0006682653,0.00036020545,0.000041731968,0.043441687],"genre_scores_gemma":[0.9603811,0.0054746163,0.0009285385,0.00023743663,0.00012839951,0.000030498659,0.00006107146,0.000009962807,0.03274836],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99697983,0.00035712624,0.0002896419,0.00046864504,0.0016485588,0.0002561764],"domain_scores_gemma":[0.99300534,0.003137854,0.00011677739,0.00033771148,0.0033052114,0.00009713401],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.002184331,0.00010410903,0.0001797764,0.00032835733,0.0008415101,0.000790012,0.00052015827,0.000065864944,0.0013097115],"category_scores_gemma":[0.00683957,0.000064099986,0.000036164456,0.0016184397,0.0006298802,0.0006819313,0.00055678905,0.0002588048,0.00010863474],"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":[0.004728846,0.000751023,0.36537537,0.000049989678,0.00068181945,0.0005785572,0.004419316,0.3384458,0.0018089495,0.02971239,0.1340072,0.11944073],"study_design_scores_gemma":[0.007401279,0.000059275546,0.67001355,0.000082502804,0.000041578267,0.00010986797,0.009806369,0.059039153,0.00041135476,0.06777346,0.18490314,0.00035844478],"about_ca_topic_score_codex":0.0022958661,"about_ca_topic_score_gemma":0.0003457855,"teacher_disagreement_score":0.3046382,"about_ca_system_score_codex":0.00006276772,"about_ca_system_score_gemma":0.00027026577,"threshold_uncertainty_score":0.9996032},"labels":[],"label_agreement":null},{"id":"W3215799339","doi":"10.2139/ssrn.3966204","title":"A theory of multivariate stress testing","year":2021,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Multivariate statistics; Stress (linguistics); Multivariate analysis; Stress testing (software); Econometrics; Mathematics; Statistics; Computer science; Linguistics; Philosophy","score_opus":0.05490709881204751,"score_gpt":0.34474653841804737,"score_spread":0.28983943960599984,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3215799339","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":[0.556189,0.004466906,0.42545348,0.00042147283,0.0005010213,0.000085120206,0.000007645925,0.000025670734,0.012849695],"genre_scores_gemma":[0.99166745,0.0012927554,0.0023386977,0.000031262003,0.00013039917,7.2177477e-7,0.000001635652,0.0000094433435,0.0045276624],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9973366,0.00040219844,0.00054048677,0.00019149396,0.00066215906,0.0008670157],"domain_scores_gemma":[0.9976459,0.0008643916,0.0004226346,0.00024230877,0.0007650422,0.000059689504],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006408029,0.000084739,0.00018998634,0.00013728853,0.00015029099,0.000100547906,0.00034313035,0.000049974824,0.00010600057],"category_scores_gemma":[0.0049311453,0.00006148417,0.00010082405,0.00078945945,0.000037683454,0.00023008975,0.000054161806,0.000654625,0.000021353757],"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":[0.000057267174,0.0001146613,0.033711575,0.0000013817136,0.00009031736,0.000021147343,0.0005076418,0.009784431,0.0023368904,0.41538826,0.00006590556,0.53792053],"study_design_scores_gemma":[0.00042405908,0.000094127776,0.005241427,0.000019708992,0.000021563366,0.0003835827,0.003410669,0.002956006,0.0020129692,0.984521,0.0008144901,0.00010040758],"about_ca_topic_score_codex":0.000018033867,"about_ca_topic_score_gemma":0.00012256092,"teacher_disagreement_score":0.56913275,"about_ca_system_score_codex":0.00009470079,"about_ca_system_score_gemma":0.0023673489,"threshold_uncertainty_score":0.5903398},"labels":[],"label_agreement":null},{"id":"W3216020594","doi":"10.2139/ssrn.3964775","title":"An impossibility theorem on capital allocation","year":2021,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Impossibility; Arrow's impossibility theorem; Mathematical economics; Economics; Capital (architecture); Mathematics; Political science; Geography; Law","score_opus":0.02512815560905579,"score_gpt":0.34873429441780096,"score_spread":0.32360613880874517,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3216020594","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":[0.94032824,0.00062998483,0.054026894,0.0010294572,0.00038442438,0.00006591093,0.0000026400833,0.00002512711,0.0035073045],"genre_scores_gemma":[0.9949387,0.0021981755,0.00026581966,0.00017105916,0.00029877602,0.0000016044729,0.000012105862,0.000011562402,0.002102243],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99661833,0.00048436312,0.0005051771,0.00038208498,0.0009653916,0.0010446333],"domain_scores_gemma":[0.9983291,0.0001825563,0.00024824546,0.00055202824,0.000555936,0.00013211719],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0071360003,0.00012163466,0.00016658686,0.00014121349,0.00030613385,0.0003717843,0.00045472124,0.000082053586,0.0002364573],"category_scores_gemma":[0.000862342,0.00008772116,0.00012543256,0.0005272723,0.00004647542,0.00057162694,0.000022528899,0.00088691205,0.00017205879],"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":[0.00012815508,0.00031515877,0.015286119,4.2841978e-7,0.00004536489,0.000014795564,0.00090462534,0.006448821,0.0013715326,0.6464785,0.00029944122,0.3287071],"study_design_scores_gemma":[0.0003481631,0.00038417173,0.009235244,0.0000033093932,0.000011583622,0.00034237618,0.0041424376,0.0026646522,0.0013139169,0.9803507,0.0010636775,0.00013976684],"about_ca_topic_score_codex":0.000018069666,"about_ca_topic_score_gemma":0.00061962695,"teacher_disagreement_score":0.33387223,"about_ca_system_score_codex":0.00038655644,"about_ca_system_score_gemma":0.0027120116,"threshold_uncertainty_score":0.48109922},"labels":[],"label_agreement":null},{"id":"W328947868","doi":"10.2139/ssrn.2589402","title":"The Design of an Optimal Retrospective Rating Plan","year":2015,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Plan (archaeology); Rating system; Computer science; Reliability engineering; Operations management; Statistics; Mathematics; Engineering; Economics; Geology; Environmental economics","score_opus":0.07813249374772635,"score_gpt":0.35077876196780566,"score_spread":0.2726462682200793,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W328947868","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":[0.2856158,0.0017520186,0.7094379,0.0004042907,0.00042919142,0.00017807582,0.000001954179,0.000019784064,0.0021609822],"genre_scores_gemma":[0.9936288,0.0013674919,0.0036088112,0.000017231136,0.00023978077,0.0000019327076,9.733747e-7,0.000010841021,0.0011241595],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9967082,0.000520855,0.00053039705,0.00017939381,0.0011044061,0.0009567193],"domain_scores_gemma":[0.99811643,0.00034467864,0.0005200184,0.00027525055,0.0006208747,0.00012275862],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.015542906,0.00009711003,0.00017334026,0.00011391558,0.0003596299,0.00023952415,0.00067348464,0.00005261852,0.000011604171],"category_scores_gemma":[0.0020184736,0.00005440835,0.000064280466,0.00044100688,0.00007920682,0.0004942292,0.000033269655,0.00074285344,0.000019172765],"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":[0.0016599254,0.00017640796,0.028706903,4.4839706e-7,0.00021027112,0.00001415905,0.0049072085,0.4554213,0.000458047,0.15427887,0.0047150673,0.3494514],"study_design_scores_gemma":[0.0010539974,0.0021556446,0.0021960435,0.000006247547,0.000026135445,0.00063430564,0.022487327,0.08323525,0.0006223312,0.88529485,0.0020793944,0.00020849155],"about_ca_topic_score_codex":0.000018915787,"about_ca_topic_score_gemma":0.000113290494,"teacher_disagreement_score":0.7310159,"about_ca_system_score_codex":0.0003035245,"about_ca_system_score_gemma":0.002357593,"threshold_uncertainty_score":0.5386892},"labels":[],"label_agreement":null},{"id":"W4205420919","doi":"10.1016/j.ejor.2021.12.045","title":"Cardinality-constrained risk parity portfolios","year":2022,"lang":"en","type":"article","venue":"European Journal of Operational Research","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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 Toronto; University of New Brunswick","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Heuristics; Mathematical optimization; Portfolio; Cardinality (data modeling); Quadratic equation; Computer science; Regular polygon; Quadratic programming; Portfolio optimization; Mathematics; Finance; Economics","score_opus":0.21229404515580744,"score_gpt":0.4494858854840021,"score_spread":0.23719184032819465,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4205420919","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":[0.82536864,0.00070712256,0.016788837,0.0055287,0.0011000821,0.0003344102,0.00016537702,0.000016656642,0.14999017],"genre_scores_gemma":[0.99315363,0.00026018138,0.0021664978,0.00013944898,0.0005156021,0.000002919581,0.000010757761,0.000016501486,0.0037344631],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.98133045,0.010615694,0.0012589353,0.0002914405,0.0061945864,0.0003088598],"domain_scores_gemma":[0.9946703,0.0012617295,0.0005100484,0.00038209345,0.002919363,0.00025649279],"candidate_categories":["metaresearch","sts","insufficient_payload"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.07101873,0.000101812526,0.00024918193,0.000834957,0.0014736811,0.0005074424,0.0012576313,0.000014719683,0.004307944],"category_scores_gemma":[0.008504469,0.00007784872,0.00020689268,0.0014675452,0.00019517854,0.0004874988,0.0004077842,0.0012485102,0.00023277405],"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":[0.00085925317,0.00055653276,0.13382654,0.0000025200275,0.0001760516,0.0022470073,0.002754999,0.29384917,0.00083765696,0.020157324,0.39391777,0.15081519],"study_design_scores_gemma":[0.0014220797,0.0011689095,0.17053646,0.0000087303115,0.000019648742,0.0010305425,0.003151205,0.0067717624,0.00012652569,0.008788814,0.8067424,0.00023287247],"about_ca_topic_score_codex":0.000022615866,"about_ca_topic_score_gemma":0.0000022344627,"teacher_disagreement_score":0.4128247,"about_ca_system_score_codex":0.00011492039,"about_ca_system_score_gemma":0.0007921062,"threshold_uncertainty_score":0.9998473},"labels":[],"label_agreement":null},{"id":"W4205842861","doi":"10.23952/jnva.5.2021.3.01","title":"Generic convergence of methods for solving stochastic feasibility problems","year":2021,"lang":"en","type":"article","venue":"Journal of Nonlinear and Variational Analysis","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Convergence (economics); Mathematical optimization; Computer science; Applied mathematics; Mathematics; Economics","score_opus":0.14614601502399688,"score_gpt":0.452535900534747,"score_spread":0.30638988551075014,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4205842861","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":[0.04521551,0.0006782077,0.9535255,0.00033048377,0.00014370048,0.0000503562,0.0000321882,0.0000013907623,0.000022669947],"genre_scores_gemma":[0.40735918,0.0002603215,0.59198695,0.00004561227,0.00014574477,0.0000012110895,0.00001766554,0.0000036561014,0.00017963881],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980237,0.00018815068,0.0010403504,0.0001817997,0.0004803934,0.00008562188],"domain_scores_gemma":[0.9944525,0.0015152871,0.0010026692,0.00015414193,0.002794282,0.00008116085],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0042267716,0.00007246104,0.00044173977,0.00036493747,0.00008352691,0.00006446377,0.00015524893,0.000048316047,0.00021515832],"category_scores_gemma":[0.0038413513,0.00005176429,0.0003741189,0.0016082905,0.000032742493,0.00021900816,0.000034119028,0.0000674089,6.4977013e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000099885394,0.00024898798,0.06655403,0.000013097392,0.0012019218,0.0000015727043,0.00045441807,0.9059827,0.0035188054,0.0015243666,0.00017352923,0.020226698],"study_design_scores_gemma":[0.00032092794,0.000060774404,0.051878512,0.0000055679516,0.0007655196,0.000015222629,0.00010528388,0.9329515,0.0002987137,0.013050696,0.00048117666,0.00006610425],"about_ca_topic_score_codex":0.000008462251,"about_ca_topic_score_gemma":0.000011101128,"teacher_disagreement_score":0.36214367,"about_ca_system_score_codex":0.000017323115,"about_ca_system_score_gemma":0.00026791703,"threshold_uncertainty_score":0.45987338},"labels":[],"label_agreement":null},{"id":"W4210368301","doi":"10.1287/moor.2021.1217","title":"Inf-Convolution, Optimal Allocations, and Model Uncertainty for Tail Risk Measures","year":2022,"lang":"en","type":"article","venue":"Mathematics of Operations Research","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"University of Waterloo","funders":"","keywords":"Risk measure; Coherent risk measure; Expected shortfall; Tail risk; Dynamic risk measure; Mathematics; Convexity; Measure (data warehouse); Quantile; Convolution (computer science); Econometrics; Value at risk; Risk management; Spectral risk measure; Pareto principle; Downside risk; Mathematical optimization; Computer science; Economics","score_opus":0.25711660572046824,"score_gpt":0.4669938152252159,"score_spread":0.20987720950474764,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4210368301","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":[0.29830995,0.00032599733,0.6975836,0.0012009977,0.000054227203,0.0011174835,0.0003459121,0.00002157282,0.0010402652],"genre_scores_gemma":[0.8488093,0.00043129752,0.1460033,0.000017948312,0.000024904639,0.0005673596,0.00004831249,0.00001544288,0.0040821247],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.996405,0.0004065568,0.0007405705,0.00031946212,0.0018829505,0.00024548062],"domain_scores_gemma":[0.99562836,0.0012860287,0.00011486121,0.0005893351,0.002292784,0.000088651555],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.011002618,0.00009602644,0.0002246803,0.00060388894,0.001761873,0.00025591036,0.00057061214,0.000045572793,0.00016206037],"category_scores_gemma":[0.006897496,0.000080538826,0.00006732335,0.0009984114,0.00022243535,0.00028672206,0.00029242688,0.00024296054,0.000011668971],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000018326346,0.00014839928,0.00028064003,0.000006633602,0.000015012944,1.2370896e-7,0.0030692883,0.93890387,0.0002602075,0.0474514,0.0065768077,0.0032693075],"study_design_scores_gemma":[0.00027666756,0.00010999622,0.000069769834,0.000003858073,0.00001145695,0.0000034227344,0.004585482,0.9484888,0.00018235332,0.042444088,0.0037418592,0.00008226169],"about_ca_topic_score_codex":0.00014619905,"about_ca_topic_score_gemma":0.00013148968,"teacher_disagreement_score":0.5515803,"about_ca_system_score_codex":0.000072222545,"about_ca_system_score_gemma":0.00046066748,"threshold_uncertainty_score":0.9995377},"labels":[],"label_agreement":null},{"id":"W4210550747","doi":"10.1017/s1748499521000269","title":"On RVaR-based optimal partial hedging","year":2022,"lang":"en","type":"article","venue":"Annals of Actuarial Science","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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 Alberta","funders":"","keywords":"CVAR; Constraint (computer-aided design); Distortion (music); Expected shortfall; Risk measure; Limiting; Value at risk; Range (aeronautics); Mathematical optimization; Economics; Econometrics; Measure (data warehouse); Risk management; Mathematical economics; Actuarial science; Computer science; Mathematics; Financial economics; Finance; Engineering","score_opus":0.19245187632015662,"score_gpt":0.4411353034129882,"score_spread":0.24868342709283156,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4210550747","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":[0.96771985,0.00002759513,0.014570152,0.004315136,0.0019171492,0.0002199828,0.000033480104,0.000041538067,0.01115511],"genre_scores_gemma":[0.9975252,0.000011411255,0.0010284777,0.001113554,0.00010456815,0.000009794757,0.0000028155202,0.0000052533364,0.00019892161],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99535495,0.00016720797,0.00049496576,0.00051111355,0.0030912242,0.00038056428],"domain_scores_gemma":[0.9979517,0.0006353426,0.00035915856,0.00053827977,0.00035706718,0.00015846179],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0067436686,0.00010492524,0.00020078715,0.00048392708,0.00086594536,0.00020717083,0.0014325115,0.000024966152,0.0015572574],"category_scores_gemma":[0.0029085989,0.00008382083,0.000114400646,0.0024695455,0.0004259539,0.00048655178,0.00025073954,0.00013941506,0.00005941647],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00041015103,0.00018518875,0.00061068806,5.1827254e-7,0.000003436969,0.00000775731,0.0005255931,0.9005031,0.0024417555,0.014784961,0.012630806,0.06789604],"study_design_scores_gemma":[0.0016346772,0.0031149727,0.006762271,0.000017818697,0.000016729446,0.000010863601,0.00085268164,0.58482724,0.14436916,0.041493088,0.2161129,0.00078760064],"about_ca_topic_score_codex":0.000067322195,"about_ca_topic_score_gemma":0.0000017306295,"teacher_disagreement_score":0.31567588,"about_ca_system_score_codex":0.000017777687,"about_ca_system_score_gemma":0.00055580656,"threshold_uncertainty_score":0.99935544},"labels":[],"label_agreement":null},{"id":"W4212773160","doi":"10.1007/s10479-022-04559-w","title":"Joint robust optimization of bed capacity, nurse staffing, and care access under uncertainty","year":2022,"lang":"en","type":"article","venue":"Annals of Operations Research","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Polytechnique Montréal","funders":"National Science Foundation","keywords":"Staffing; Robustness (evolution); Robust optimization; Computer science; Flexibility (engineering); Ellipsoid; Mathematical optimization; Health care; Resource allocation; Operations research; Operations management; Medicine; Mathematics; Economics; Statistics; Nursing","score_opus":0.556376839376147,"score_gpt":0.5228452868946046,"score_spread":0.03353155248154238,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4212773160","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":[0.96708024,0.0005448887,0.02316604,0.004574327,0.00013154269,0.00069738907,0.00025993914,0.000016023498,0.003529592],"genre_scores_gemma":[0.995554,0.00064798177,0.0029515184,0.00006564149,0.000025157944,0.000056312838,0.000089898625,0.000011573619,0.0005979458],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99568105,0.0009217835,0.00067374296,0.0003673113,0.0021047532,0.00025135736],"domain_scores_gemma":[0.9946792,0.00033704852,0.000121554345,0.0005206608,0.0042384756,0.00010303966],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.004121626,0.000093903036,0.00024879348,0.0009420872,0.00083919894,0.00031846322,0.0006288187,0.00005159634,0.0012476841],"category_scores_gemma":[0.0015535249,0.000080868645,0.0000658919,0.002064997,0.00029246925,0.0006426476,0.00042510335,0.00025911405,0.000003634884],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000032015865,0.000097236516,0.0012806663,0.0000059814365,0.000012531013,7.890821e-7,0.0029069323,0.9852222,0.00016462787,0.003511986,0.0042902306,0.0024747907],"study_design_scores_gemma":[0.00049686636,0.00047287974,0.0037124678,0.000020029742,0.000009164804,0.000005625806,0.029972535,0.9572747,0.004048,0.002053858,0.0017464742,0.00018740565],"about_ca_topic_score_codex":0.0014544072,"about_ca_topic_score_gemma":0.0004449055,"teacher_disagreement_score":0.028473713,"about_ca_system_score_codex":0.000037122867,"about_ca_system_score_gemma":0.0003779976,"threshold_uncertainty_score":0.9996653},"labels":[],"label_agreement":null},{"id":"W4212867121","doi":"10.1016/j.jmaa.2009.12.042","title":"Relevant mappings","year":2009,"lang":"en","type":"article","venue":"Journal of Mathematical Analysis and Applications","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Lakehead University; University of New Brunswick","funders":"","keywords":"Mathematics; Relevance (law); Class (philosophy); Pure mathematics; Mathematical economics; Arbitrage; Econometrics; Algebra over a field; Epistemology; Business; Finance; Political science; Philosophy","score_opus":0.03942049047572713,"score_gpt":0.3622978163826962,"score_spread":0.32287732590696905,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4212867121","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":[0.023986168,0.0001603254,0.96530586,0.004220589,0.000006711524,0.00006748277,0.0000018540154,0.000005543709,0.006245462],"genre_scores_gemma":[0.96386856,0.0004841179,0.03452087,0.00023056101,0.00010351637,0.0000027979922,0.0000010786283,0.00000239119,0.0007861083],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.998293,0.000037569636,0.0008725474,0.000119919685,0.0005948582,0.0000821229],"domain_scores_gemma":[0.99835336,0.00036506075,0.0005542211,0.00022909476,0.00037100338,0.00012725385],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015030274,0.000063844374,0.00034925068,0.0004244358,0.00009170289,0.00014498783,0.00023701153,0.00003596153,0.00020537236],"category_scores_gemma":[0.0003523674,0.000037264013,0.00023486727,0.0015309275,0.00003798241,0.00014864681,0.00001429788,0.00008402702,0.000044960037],"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":[0.000022458698,0.00061767636,0.0042650397,0.0000049385935,0.00047006606,0.0000068480313,0.00059039274,0.0028248138,0.0008247127,0.52850205,0.005982922,0.4558881],"study_design_scores_gemma":[0.00013708125,0.00006287637,0.015943011,0.000007075097,0.0006189482,0.000043393113,0.0002943242,0.013811878,0.00008738897,0.93675536,0.032150686,0.000087990265],"about_ca_topic_score_codex":4.3970573e-7,"about_ca_topic_score_gemma":4.8147234e-7,"teacher_disagreement_score":0.9398824,"about_ca_system_score_codex":0.000006927818,"about_ca_system_score_gemma":0.000016332384,"threshold_uncertainty_score":0.22486833},"labels":[],"label_agreement":null},{"id":"W4213236049","doi":"10.1287/ijoo.2021.0070","title":"Satisficing Models Under Uncertainty","year":2022,"lang":"en","type":"article","venue":"INFORMS Journal on Optimization","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":12,"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":"Satisficing; Mathematical optimization; Probabilistic logic; Mathematics; Representation (politics); Optimization problem; Decision problem; Function (biology); Probability distribution; Computer science; Algorithm; Artificial intelligence; Statistics","score_opus":0.08734465264683301,"score_gpt":0.34677902944996775,"score_spread":0.25943437680313475,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4213236049","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":[0.017565118,0.000057423335,0.9573426,0.0011115745,0.0011941429,0.00018549841,0.000014471148,0.000053415573,0.022475708],"genre_scores_gemma":[0.97789884,0.0005480379,0.01532019,0.0027705634,0.00020359833,0.000017175986,0.000047931004,0.000029171282,0.0031645144],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99601805,0.00018191693,0.001014249,0.00023559495,0.0022699006,0.00028028642],"domain_scores_gemma":[0.9978718,0.00035966004,0.00079471606,0.0003480848,0.0004652907,0.00016042095],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0030151831,0.00016158177,0.00022529555,0.00075284264,0.0012642959,0.00061294384,0.00057623535,0.0000577164,0.0024222748],"category_scores_gemma":[0.00046590672,0.00011805812,0.00014114176,0.0012348416,0.000032968423,0.0015845168,0.00012704103,0.00047801994,0.000054532135],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000087079745,0.00003069845,0.00019082692,2.498944e-7,0.000010432874,0.0000060570915,0.0004764438,0.96006745,0.0000015936071,0.0067801718,0.0048570554,0.027491927],"study_design_scores_gemma":[0.0005094509,0.00020298922,0.00009509105,0.0000048755596,0.0000076160363,0.00014451383,0.0013293392,0.9545402,0.0000057898374,0.030367747,0.012629713,0.00016266826],"about_ca_topic_score_codex":0.000015183748,"about_ca_topic_score_gemma":0.0000029661323,"teacher_disagreement_score":0.9603337,"about_ca_system_score_codex":0.00027642108,"about_ca_system_score_gemma":0.00023745059,"threshold_uncertainty_score":0.9984896},"labels":[],"label_agreement":null},{"id":"W4214845604","doi":"10.1007/s11009-022-09939-0","title":"Fraction-Degree Reference Dependent Stochastic Dominance","year":2022,"lang":"en","type":"article","venue":"Methodology And Computing In Applied Probability","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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 Alberta","funders":"National Natural Science Foundation of China","keywords":"Stochastic dominance; Mathematics; Generalization; Degree (music); Dominance (genetics); Mathematical economics; Scaling; Mathematical optimization; Mathematical analysis","score_opus":0.3300381240434885,"score_gpt":0.4149038424833973,"score_spread":0.08486571843990881,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4214845604","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":[0.47317976,0.000084222906,0.5231571,0.00011382895,0.00020519087,0.00028215567,0.000003303358,0.000028398492,0.0029460215],"genre_scores_gemma":[0.8649476,0.0000073516894,0.13475227,0.00013984335,0.000026142427,0.000053460666,0.0000035802316,0.0000050846934,0.000064660286],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99552876,0.0022324221,0.0006938823,0.00078745064,0.00048909296,0.00026841657],"domain_scores_gemma":[0.99334687,0.0057335827,0.00032752825,0.00047177292,0.000066687324,0.000053572498],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.018104635,0.00013394217,0.00038977806,0.00017135101,0.0004894851,0.000041951876,0.00042830643,0.00008964118,0.00018736767],"category_scores_gemma":[0.0018843035,0.00011939933,0.00003283373,0.0006316002,0.00015628932,0.000049675626,0.00049125106,0.0005525878,0.000012809183],"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":[0.00044139402,0.00015050264,0.045022592,0.0000061320634,0.000007679843,0.000004435513,0.0009663394,0.42807773,0.00017197982,0.038306896,0.00009362404,0.4867507],"study_design_scores_gemma":[0.0006764227,0.0000954367,0.1759223,0.0000031256059,0.000010079707,0.00004042532,0.00075565133,0.05028537,0.00007524702,0.77038354,0.0015067501,0.00024565653],"about_ca_topic_score_codex":0.00008100385,"about_ca_topic_score_gemma":0.000042901887,"teacher_disagreement_score":0.73207664,"about_ca_system_score_codex":0.00008123,"about_ca_system_score_gemma":0.000074891956,"threshold_uncertainty_score":0.6274742},"labels":[],"label_agreement":null},{"id":"W4221095861","doi":"10.1016/j.ejor.2022.03.020","title":"A marginal indemnity function approach to optimal reinsurance under the Vajda condition","year":2022,"lang":"en","type":"article","venue":"European Journal of Operational Research","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":20,"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; University of Calgary","keywords":"Reinsurance; Indemnity; Actuarial science; Pareto principle; Risk measure; Risk management; Value (mathematics); Distortion (music); Function (biology); Economics; Econometrics; Computer science; Mathematics; Statistics; Operations management; Finance","score_opus":0.23250272962125737,"score_gpt":0.4329393514668128,"score_spread":0.2004366218455554,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4221095861","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":[0.6558075,0.00023422582,0.2768798,0.008640981,0.0007575409,0.000462182,0.000040772327,0.00001123053,0.05716577],"genre_scores_gemma":[0.9918773,0.000032112905,0.0027754526,0.0007447915,0.0005190976,0.000012273094,0.000018115328,0.000016362965,0.004004496],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.9876349,0.0059690396,0.0007891315,0.00028106198,0.0050629964,0.00026289356],"domain_scores_gemma":[0.9963213,0.00078608,0.0002574081,0.00031807148,0.0021408617,0.00017622902],"candidate_categories":["metaresearch","sts","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.034513477,0.00009724661,0.00015741246,0.00058926357,0.0017081065,0.0006014382,0.0011001496,0.000014996984,0.0012282658],"category_scores_gemma":[0.0015065683,0.000062230516,0.000102871956,0.0016949024,0.00013459646,0.00054687855,0.00031691388,0.0009900344,0.0002452018],"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":[0.00075270975,0.00015704992,0.00053961255,7.2462274e-7,0.000024250961,0.00002866516,0.00088270195,0.88045806,0.00046447467,0.011376812,0.0994598,0.005855155],"study_design_scores_gemma":[0.0018316266,0.0026776895,0.30706915,0.000016107673,0.000020722551,0.0010041035,0.013852512,0.040248062,0.00013521535,0.005373854,0.62744343,0.00032749595],"about_ca_topic_score_codex":0.000005508123,"about_ca_topic_score_gemma":9.703108e-7,"teacher_disagreement_score":0.84020996,"about_ca_system_score_codex":0.00014434938,"about_ca_system_score_gemma":0.00044248902,"threshold_uncertainty_score":0.99968475},"labels":[],"label_agreement":null},{"id":"W4224295138","doi":"10.3390/jrfm15050190","title":"An Application of Portfolio Mean-Variance and Semi-Variance Optimization Techniques: A Case of Fiji","year":2022,"lang":"en","type":"article","venue":"Journal of risk and financial management","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":18,"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":"Portfolio; Sharpe ratio; Econometrics; Price variance; Portfolio optimization; Economics; Variance (accounting); Market portfolio; Stock exchange; Modern portfolio theory; Standard deviation; Stock market index; Market capitalization; Stock (firearms); Index (typography); Financial economics; Stock market; Mathematics; Statistics; Computer science; Finance; Geography","score_opus":0.013592711176172806,"score_gpt":0.29536418858641666,"score_spread":0.2817714774102439,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4224295138","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":[0.09488797,0.0005384723,0.90367484,0.00004029803,0.00012364874,0.0002607896,0.000039156712,0.0000065818367,0.00042822116],"genre_scores_gemma":[0.9392239,0.004910982,0.05567826,0.000040398343,0.00006218058,0.000013251661,0.0000036198346,0.000007693537,0.00005970042],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997977,0.00016725164,0.0009513862,0.00022373446,0.0005757842,0.00010481863],"domain_scores_gemma":[0.997557,0.00009570252,0.0016662355,0.00028289264,0.0003297442,0.000068462905],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027053047,0.00010335683,0.00032219902,0.0004565827,0.0002016303,0.000042017462,0.00024733416,0.00004504809,0.00003330348],"category_scores_gemma":[0.0001558746,0.00008959394,0.000061945044,0.0007918016,0.00006777279,0.0003679611,0.00011835662,0.00014232507,1.7779266e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000314806,0.00025240626,0.011304021,0.000024306797,0.000018369716,0.00017825466,0.0015408492,0.2168555,0.00007316429,0.0106053725,0.0007461288,0.7580868],"study_design_scores_gemma":[0.005972255,0.0049672555,0.08604722,0.00022301121,0.0010176062,0.003960502,0.013506368,0.5530112,0.0016348071,0.102494694,0.22584316,0.0013219764],"about_ca_topic_score_codex":0.00010358248,"about_ca_topic_score_gemma":0.000015230878,"teacher_disagreement_score":0.8479966,"about_ca_system_score_codex":0.000024941775,"about_ca_system_score_gemma":0.00004377803,"threshold_uncertainty_score":0.3653535},"labels":[],"label_agreement":null},{"id":"W4225482307","doi":"10.1111/mafi.12388","title":"Reinforcement learning with dynamic convex risk measures","year":2023,"lang":"en","type":"article","venue":"Mathematical Finance","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":23,"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":"Reinforcement learning; Computer science; Mathematical optimization; Flexibility (engineering); Q-learning; Dynamic programming; Artificial neural network; Obstacle; Dynamic risk measure; Convex optimization; Artificial intelligence; Value at risk; Regular polygon; Risk management; Mathematics; Economics; Finance","score_opus":0.048962392405897476,"score_gpt":0.3373794269646531,"score_spread":0.28841703455875567,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4225482307","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":[0.2550234,0.000057929414,0.7224036,0.0005033303,0.00009631117,0.00027145652,0.0000028946881,0.00022030289,0.021420803],"genre_scores_gemma":[0.97291046,0.0008718638,0.0044567315,0.000034429442,0.000016305405,0.00002794331,0.000005147719,0.00001450418,0.021662613],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99779326,0.0000931364,0.00045453903,0.00030289806,0.0010911003,0.0002650543],"domain_scores_gemma":[0.9985083,0.0006494469,0.00023382615,0.00039718588,0.0001557971,0.000055456894],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0018536067,0.00012278292,0.00025940032,0.00013967854,0.00021294814,0.00012999804,0.00030621022,0.00005362554,0.00017546058],"category_scores_gemma":[0.0020972425,0.00007627893,0.000057621524,0.0010808558,0.00009231528,0.0001619306,0.000065405016,0.000168291,0.003759647],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000070171875,0.00004595091,0.009729676,0.000011491575,0.000024829109,0.000039749204,0.0018704765,0.7935531,0.00003830041,0.042467374,0.0051780203,0.14697085],"study_design_scores_gemma":[0.00030877252,0.00013256706,0.008221028,0.0000454899,0.00001482867,0.000008856328,0.00028296735,0.8622267,0.00010708466,0.08223691,0.046219293,0.00019549567],"about_ca_topic_score_codex":0.0000045401994,"about_ca_topic_score_gemma":0.0000035914013,"teacher_disagreement_score":0.7179468,"about_ca_system_score_codex":0.000018453831,"about_ca_system_score_gemma":0.000035176276,"threshold_uncertainty_score":0.997016},"labels":[],"label_agreement":null},{"id":"W4226027375","doi":"10.2139/ssrn.4092939","title":"A Reverse Expected Shortfall Optimization Formula","year":2022,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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 Waterloo","funders":"","keywords":"Expected shortfall; Computer science; Economics; Risk management; Finance","score_opus":0.031535663852365683,"score_gpt":0.3189649422527673,"score_spread":0.2874292784004016,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4226027375","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":[0.089654535,0.001810821,0.8974846,0.0020007945,0.0011202431,0.00035627797,0.000012214186,0.00010359756,0.0074568954],"genre_scores_gemma":[0.9812757,0.00410768,0.0027809718,0.00032526147,0.00025240958,0.000023441578,0.000028207389,0.000031072173,0.011175268],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99547446,0.00040669212,0.000691696,0.00033318065,0.001625082,0.0014688927],"domain_scores_gemma":[0.9986414,0.00013193094,0.00046232907,0.00035823177,0.00029398655,0.00011214403],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.006336868,0.0001472967,0.00021737129,0.0004963115,0.000994048,0.00020766507,0.00078656105,0.000049111968,0.0015511342],"category_scores_gemma":[0.00051439187,0.00012384432,0.00017825169,0.0013546637,0.000027525326,0.0005664198,0.00014841615,0.0014025584,0.00004734328],"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":[0.00019826619,0.000100340985,0.0030228377,2.5368496e-7,0.000053036383,0.00001343088,0.0006601772,0.9166766,0.000032986554,0.031283766,0.0060438737,0.04191447],"study_design_scores_gemma":[0.0023426057,0.0016195506,0.0004999072,0.000005419142,0.0000832798,0.0031561907,0.027128736,0.2767251,0.000034369983,0.52370906,0.16396229,0.00073349575],"about_ca_topic_score_codex":0.000040098796,"about_ca_topic_score_gemma":0.00011850149,"teacher_disagreement_score":0.8947036,"about_ca_system_score_codex":0.00088812236,"about_ca_system_score_gemma":0.0017574097,"threshold_uncertainty_score":0.9993616},"labels":[],"label_agreement":null},{"id":"W4226439501","doi":"10.5539/ibr.v14n12p96","title":"Diversifiable and Non-diversifiable Risk and the Advanced Choice under Ambiguous or Uncertain Conditions","year":2021,"lang":"en","type":"article","venue":"International Business Research","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Diversification (marketing strategy); Ambiguity; Modern portfolio theory; Econometrics; Measure (data warehouse); Systematic risk; Economics; Risk measure; Portfolio; Mathematical economics; Actuarial science; Financial economics; Mathematics; Computer science; Business; Marketing","score_opus":0.13414611393216325,"score_gpt":0.44894145938546004,"score_spread":0.3147953454532968,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4226439501","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":[0.9095882,0.00088710187,0.02021622,0.023476362,0.0013202212,0.0006682653,0.00036020545,0.000041731968,0.043441687],"genre_scores_gemma":[0.9603811,0.0054746163,0.0009285385,0.00023743663,0.00012839951,0.000030498659,0.00006107146,0.000009962807,0.03274836],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99697983,0.00035712624,0.0002896419,0.00046864504,0.0016485588,0.0002561764],"domain_scores_gemma":[0.99300534,0.003137854,0.00011677739,0.00033771148,0.0033052114,0.00009713401],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.002184331,0.00010410903,0.0001797764,0.00032835733,0.0008415101,0.000790012,0.00052015827,0.000065864944,0.0013097115],"category_scores_gemma":[0.00683957,0.000064099986,0.000036164456,0.0016184397,0.0006298802,0.0006819313,0.00055678905,0.0002588048,0.00010863474],"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":[0.004728846,0.000751023,0.36537537,0.000049989678,0.00068181945,0.0005785572,0.004419316,0.3384458,0.0018089495,0.02971239,0.1340072,0.11944073],"study_design_scores_gemma":[0.007401279,0.000059275546,0.67001355,0.000082502804,0.000041578267,0.00010986797,0.009806369,0.059039153,0.00041135476,0.06777346,0.18490314,0.00035844478],"about_ca_topic_score_codex":0.0022958661,"about_ca_topic_score_gemma":0.0003457855,"teacher_disagreement_score":0.3046382,"about_ca_system_score_codex":0.00006276772,"about_ca_system_score_gemma":0.00027026577,"threshold_uncertainty_score":0.9996032},"labels":[],"label_agreement":null},{"id":"W4232458913","doi":"10.1017/s0021900200003302","title":"Characterizations of Conditional Comonotonicity","year":2007,"lang":"en","type":"article","venue":"Journal of Applied Probability","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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 Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mathematics; Generalization; Representation (politics); Conditional probability distribution; Conditional expectation; Field (mathematics); Conditional random field; Multivariate random variable; Mathematical economics; Discrete mathematics; Econometrics; Random variable; Pure mathematics; Statistics; Artificial intelligence; Computer science; Mathematical analysis; Law; Political science","score_opus":0.07150790968494311,"score_gpt":0.36495250390048833,"score_spread":0.29344459421554525,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4232458913","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":[0.84530854,0.000011708339,0.14405255,0.00019025344,0.00018391284,0.00017706587,0.00002152546,0.0000057026687,0.010048762],"genre_scores_gemma":[0.98577553,0.000020636086,0.013995205,0.000074012394,0.0000924156,0.0000011255397,0.00000694707,0.0000039980655,0.00003013123],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9971261,0.000053960383,0.0015234239,0.00014686077,0.0010176161,0.000132061],"domain_scores_gemma":[0.9963504,0.00069009536,0.001472171,0.00027345752,0.0011023122,0.00011156567],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007045297,0.00008187617,0.00032112806,0.00022275634,0.00007465687,0.00003519456,0.00036487562,0.00007630598,0.00030702862],"category_scores_gemma":[0.0007195857,0.000059079495,0.00013899826,0.0005863464,0.00016616507,0.00024201759,0.00003738298,0.00016312943,0.000013712156],"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":[0.0032018495,0.0027064316,0.40884167,0.00003885401,0.00018678453,0.00001835733,0.003527843,0.050947122,0.03353926,0.35322085,0.0047752773,0.13899569],"study_design_scores_gemma":[0.00065782963,0.00012751014,0.5066604,0.0000072481685,0.000025147103,0.000025499932,0.0001794049,0.0006368825,0.017115615,0.46327856,0.011167744,0.00011814435],"about_ca_topic_score_codex":0.0000016497314,"about_ca_topic_score_gemma":0.000005381559,"teacher_disagreement_score":0.140467,"about_ca_system_score_codex":0.000046054367,"about_ca_system_score_gemma":0.0001735038,"threshold_uncertainty_score":0.33617482},"labels":[],"label_agreement":null},{"id":"W4232665925","doi":"10.2139/ssrn.2827400","title":"Robust Empirical Optimization is Almost the Same As Mean-Variance Optimization","year":2015,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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 Toronto","funders":"","keywords":"Download; Computer science; Variance (accounting); Econometrics; World Wide Web; Mathematics; Economics","score_opus":0.10100097923731806,"score_gpt":0.36295371390523173,"score_spread":0.2619527346679137,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4232665925","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":[0.0031097368,0.0016178804,0.9745725,0.012599227,0.0007315021,0.00025046212,0.0000043992236,0.000052733805,0.0070615686],"genre_scores_gemma":[0.7863197,0.04469197,0.09569948,0.011135835,0.0038386274,0.000050981693,0.00010850538,0.00023549248,0.05791943],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9942505,0.0006011836,0.00095299026,0.00051145453,0.0020766917,0.0016072266],"domain_scores_gemma":[0.99677104,0.0003189764,0.0007246527,0.0006295231,0.0012759714,0.000279853],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009592634,0.0002665863,0.00031934943,0.0002852752,0.00064162276,0.0007924422,0.0011523019,0.00019252198,0.0004026379],"category_scores_gemma":[0.0021084687,0.00016820473,0.00017878729,0.0016758227,0.00012291723,0.0011293625,0.00010352369,0.0013094118,0.00032597463],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008799872,0.000047429927,0.0013888512,2.3666013e-7,0.000039477287,0.0000031294987,0.0011477474,0.97198915,0.0000010388981,0.008238912,0.009907586,0.007148462],"study_design_scores_gemma":[0.0010224137,0.00035579278,0.000095104915,0.000009917006,0.000060372047,0.00087024353,0.0049844543,0.8316642,0.000025087344,0.14020471,0.02038751,0.00032016682],"about_ca_topic_score_codex":0.00008784154,"about_ca_topic_score_gemma":0.00015122842,"teacher_disagreement_score":0.878873,"about_ca_system_score_codex":0.00069006835,"about_ca_system_score_gemma":0.003919896,"threshold_uncertainty_score":0.76415384},"labels":[],"label_agreement":null},{"id":"W4233037841","doi":"10.2143/ast.37.1.2020800","title":"Optimal Retention for a Stop-loss Reinsurance Under the VaR and CTE Risk Measures","year":2007,"lang":"en","type":"article","venue":"Astin Bulletin","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":176,"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":"Changjiang Scholar Program of Chinese Ministry of Education; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Reinsurance; Mathematics; Multivariate statistics; Poisson distribution; Distribution (mathematics); Binomial distribution; Binomial (polynomial); Multivariate normal distribution; Mathematical optimization; Applied mathematics; Statistics; Economics; Actuarial science","score_opus":0.056782277804696524,"score_gpt":0.3278571570165889,"score_spread":0.2710748792118924,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4233037841","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":[0.46202648,0.0006868978,0.5312,0.0036328465,0.00032098388,0.00037098714,0.000021962736,0.0000400067,0.0016998637],"genre_scores_gemma":[0.97941077,0.00054559845,0.01593558,0.00027157716,0.00015682967,0.000018235902,0.000005629513,0.000015558977,0.0036402438],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99797285,0.00018555007,0.0004832266,0.00039930848,0.00068123784,0.0002778454],"domain_scores_gemma":[0.9972163,0.0016836104,0.0003161183,0.00036511826,0.00035273403,0.00006613441],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0072222594,0.00013032703,0.00016835344,0.00009744967,0.00045215423,0.00019340661,0.00030212995,0.000086985725,0.00011948392],"category_scores_gemma":[0.0020108027,0.00008121469,0.00008808678,0.00031185328,0.00015013303,0.00007363931,0.000054534245,0.00013970211,0.00010072356],"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":[0.0016453151,0.000106030595,0.0542378,0.000008039596,0.00007974861,0.000016109248,0.0017122752,0.07641302,0.00044186818,0.007432536,0.092965305,0.76494193],"study_design_scores_gemma":[0.0009484937,0.00013843583,0.21789116,0.000019309302,0.000045125616,0.0000314676,0.000914831,0.0055249343,0.0007744487,0.006098203,0.76736385,0.00024977044],"about_ca_topic_score_codex":0.00006697594,"about_ca_topic_score_gemma":0.000023025734,"teacher_disagreement_score":0.7646922,"about_ca_system_score_codex":0.0000141473065,"about_ca_system_score_gemma":0.000022963071,"threshold_uncertainty_score":0.34776506},"labels":[],"label_agreement":null},{"id":"W4233276102","doi":"10.1287/opre.1120.1043","title":"Contributors","year":2012,"lang":"en","type":"article","venue":"Operations Research","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Computer science","score_opus":0.508454706314146,"score_gpt":0.5965897875985019,"score_spread":0.08813508128435588,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4233276102","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":[0.733701,0.001016056,0.05137827,0.0059967334,0.0010793994,0.00073228014,0.00003784271,0.00007008898,0.20598833],"genre_scores_gemma":[0.9680684,0.00012768038,0.0017316838,0.000055287815,0.00028675294,0.000033306398,0.000013603989,0.000005568856,0.029677723],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9970845,0.0005361579,0.00027509488,0.00016388259,0.0015432783,0.00039706661],"domain_scores_gemma":[0.9976484,0.0005887214,0.000011343925,0.00043636013,0.0011240523,0.00019109753],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.008960545,0.000047578967,0.00008753932,0.0003957329,0.00064473134,0.00047284254,0.00035538303,0.000052590378,0.0023003914],"category_scores_gemma":[0.005133777,0.000032938093,0.000033855766,0.0015349839,0.00009488463,0.00078482385,0.0000897335,0.00017332417,0.005719443],"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":[0.000030054915,0.00040898906,0.19208795,8.8316267e-7,0.000019669878,0.0000036676051,0.003838444,0.03109896,0.0020878573,0.28110504,0.36232954,0.12698896],"study_design_scores_gemma":[0.00023133286,0.000044498054,0.03158643,0.00000221747,0.0000020992113,0.000007056009,0.0010344437,0.029341005,0.001870977,0.0016813927,0.93408126,0.00011728196],"about_ca_topic_score_codex":0.000111162415,"about_ca_topic_score_gemma":0.00006645573,"teacher_disagreement_score":0.5717517,"about_ca_system_score_codex":0.000038157345,"about_ca_system_score_gemma":0.000109155735,"threshold_uncertainty_score":0.9986116},"labels":[],"label_agreement":null},{"id":"W4233559843","doi":"10.1142/9789814417358_0022","title":"Calculation of investment portfolios with risk free borrowing and lending","year":2013,"lang":"en","type":"book-chapter","venue":"World Scientific handbook in financial economic series","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"University of British Columbia","funders":"","keywords":"Investment (military); Portfolio; Investment strategy; Order (exchange); Investment performance; Economics; Financial risk; Finance; Dimension (graph theory); Financial economics; Actuarial science; Return on investment; Microeconomics; Mathematics; Profit (economics)","score_opus":0.029389255056015078,"score_gpt":0.25697604281448505,"score_spread":0.22758678775846997,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4233559843","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13245389,0.0074629337,0.0018802657,0.00034633806,0.0059131486,0.0025720624,0.00063151587,0.00010195261,0.8486379],"genre_scores_gemma":[0.03948011,0.001284819,0.0043002157,0.000059964157,0.00023519917,0.00004578219,0.00007582659,0.00006484168,0.95445323],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9966831,0.00005337569,0.0013778673,0.0009892316,0.000582507,0.00031386944],"domain_scores_gemma":[0.9971938,0.00020731815,0.001357303,0.0009339747,0.00017797484,0.00012964536],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001992686,0.0003683963,0.00079597306,0.0016524718,0.00034230505,0.0005206967,0.0005183932,0.00019703503,0.00083048956],"category_scores_gemma":[0.0002445279,0.00031256114,0.00012319327,0.00022994896,0.0008182051,0.0010189376,0.00022377368,0.0002620432,0.00013893301],"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":[0.0004295249,0.000057910423,0.035146933,0.00006206932,0.000103758706,0.000051791005,0.005492574,0.020280479,0.00009861054,0.7135862,0.12651916,0.098170996],"study_design_scores_gemma":[0.00092747714,0.0001272021,0.0071830014,0.00068980467,0.00007454426,0.000015275495,0.000102510385,0.0022120972,0.00058594946,0.23434128,0.7529581,0.00078271853],"about_ca_topic_score_codex":0.00020938892,"about_ca_topic_score_gemma":0.008974623,"teacher_disagreement_score":0.626439,"about_ca_system_score_codex":0.00017747178,"about_ca_system_score_gemma":0.0005120652,"threshold_uncertainty_score":0.99993265},"labels":[],"label_agreement":null},{"id":"W4233783080","doi":"10.1287/moor.2015.0755","title":"Joint Mixability","year":2016,"lang":"en","type":"article","venue":"Mathematics of Operations Research","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"University of Waterloo","funders":"","keywords":"Mathematics; Marginal distribution; Joint probability distribution; Random variable; Probabilistic logic; Monotone polygon; Applied mathematics; Generator (circuit theory); Combinatorics; Mathematical optimization; Statistics; Power (physics)","score_opus":0.5300525204133071,"score_gpt":0.5403228146903195,"score_spread":0.010270294277012426,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4233783080","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":[0.75430727,0.000042244632,0.21407904,0.0045622294,0.00009327615,0.00050445204,0.000032117307,0.00002313526,0.026356269],"genre_scores_gemma":[0.94843024,0.00009819037,0.04076837,0.000006890839,0.00003172663,0.00002912521,0.0000012402614,0.000007539546,0.010626678],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9966426,0.00030549022,0.00074501697,0.00023779865,0.0018652935,0.00020379959],"domain_scores_gemma":[0.9953964,0.0014752182,0.00004994616,0.0009230279,0.0020710933,0.00008436815],"candidate_categories":["metaresearch","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.009664,0.000062724364,0.00019359736,0.00043023744,0.00021214854,0.00014862716,0.00050628436,0.00005281461,0.0016236609],"category_scores_gemma":[0.014646981,0.000031874915,0.00006804122,0.0009047525,0.00028581018,0.0003129324,0.00015431846,0.00008126854,0.0008710479],"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":[0.000034287117,0.0017538702,0.007922292,0.000049020822,0.000051194114,0.0000058693413,0.008111841,0.010003602,0.12827837,0.6250137,0.05068293,0.16809301],"study_design_scores_gemma":[0.0011780492,0.00042931503,0.01074562,0.00017649132,0.00001265332,0.000018788855,0.00363869,0.13168184,0.13384531,0.6962941,0.021549542,0.00042954905],"about_ca_topic_score_codex":0.00003099816,"about_ca_topic_score_gemma":0.000053575048,"teacher_disagreement_score":0.194123,"about_ca_system_score_codex":0.00003515179,"about_ca_system_score_gemma":0.00015956968,"threshold_uncertainty_score":0.9999069},"labels":[],"label_agreement":null},{"id":"W4234387703","doi":"10.1007/0-306-48332-7_31","title":"Bilevel Programming","year":2001,"lang":"en","type":"book-chapter","venue":"Encyclopedia of Optimization","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Polytechnique Montréal; Université de Montréal","funders":"","keywords":"Bilevel optimization; Computer science; Algorithm","score_opus":0.05661161189794757,"score_gpt":0.32372416211595345,"score_spread":0.2671125502180059,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4234387703","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000051003276,0.00040415567,0.2531979,0.0000910515,0.0006510079,0.00043047505,0.00002553509,0.00006503331,0.7451297],"genre_scores_gemma":[0.00015217613,0.02320033,0.10225268,0.00004308851,0.0004665336,0.000017168713,0.00032342257,0.000096432865,0.8734482],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.99559623,0.000056734432,0.0015989523,0.00072284666,0.0017518705,0.00027335726],"domain_scores_gemma":[0.9959324,0.0003877896,0.0016175209,0.0008766268,0.0010399658,0.00014567995],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0011077444,0.00040615763,0.00068516337,0.0008644294,0.00012185083,0.00012957495,0.00068375503,0.0005388626,0.004174123],"category_scores_gemma":[0.00087294367,0.00034801828,0.0002755948,0.00040007432,0.00014247245,0.000447375,0.00012743864,0.00026309132,0.00026372814],"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":[0.000031484622,0.00004286249,0.0004750809,0.00000919112,0.000038240152,0.000015166511,0.0002343137,0.5254318,3.4419674e-7,0.020130644,0.017646026,0.43594486],"study_design_scores_gemma":[0.00024792503,0.000085676475,0.00004505203,0.00006851088,0.00007259011,0.000011689222,0.000021858381,0.022884198,0.0000030577019,0.017901761,0.9582519,0.0004057666],"about_ca_topic_score_codex":0.000012346678,"about_ca_topic_score_gemma":0.000008198835,"teacher_disagreement_score":0.9406059,"about_ca_system_score_codex":0.000055751665,"about_ca_system_score_gemma":0.0002242468,"threshold_uncertainty_score":0.9998972},"labels":[],"label_agreement":null},{"id":"W4237987460","doi":"10.1287/deca.1110.0214","title":"About the Authors","year":2011,"lang":"en","type":"article","venue":"Decision Analysis","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Mathematical economics; Computer science; Management science; Economics","score_opus":0.1872382633331073,"score_gpt":0.4056464698039651,"score_spread":0.21840820647085782,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4237987460","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":[0.19386837,0.00037329603,0.73122185,0.00034719528,0.0005439748,0.00012672678,0.00000816014,0.00006212252,0.073448285],"genre_scores_gemma":[0.98153734,0.0003342898,0.009217096,0.00053201336,0.00004836237,0.0000065425033,0.000006993533,0.000008078793,0.008309293],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9967327,0.00024049777,0.0007715622,0.0004463597,0.0016107039,0.00019813614],"domain_scores_gemma":[0.99677616,0.0009625457,0.00030228705,0.001348729,0.0004209632,0.0001893081],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0037135945,0.00012263765,0.00030983897,0.00096112123,0.00030561147,0.00026668105,0.0011855273,0.00008537653,0.006949958],"category_scores_gemma":[0.0030235155,0.000059886064,0.0005397376,0.0072466703,0.000087869324,0.0002345583,0.00012400415,0.00011457063,0.002122883],"study_design_candidate":"design_other","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":[0.00009312263,0.0000776742,0.21753486,1.18846835e-7,0.00037364213,0.000014990622,0.0040761656,0.006021166,0.000004596447,0.0067126728,0.058355656,0.7067353],"study_design_scores_gemma":[0.00026897065,0.000053006315,0.44273835,0.0000037614695,0.0008426501,0.000005376427,0.0022924303,0.11829969,0.00011396647,0.10250685,0.33255705,0.00031788243],"about_ca_topic_score_codex":0.00009821844,"about_ca_topic_score_gemma":0.00013979613,"teacher_disagreement_score":0.78766894,"about_ca_system_score_codex":0.000012229867,"about_ca_system_score_gemma":0.000035932244,"threshold_uncertainty_score":0.99865407},"labels":[],"label_agreement":null},{"id":"W4238560257","doi":"10.15353/rea.v4i1.1542","title":"A Note on the Generalised Measures of Risk Aversion","year":2012,"lang":"en","type":"article","venue":"Review of Economic Analysis","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Spectral risk measure; Measure (data warehouse); Risk aversion (psychology); Risk measure; Coherent risk measure; Econometrics; Dynamic risk measure; Economics; Scalar (mathematics); Mathematics; Expected utility hypothesis; Mathematical economics; Risk management; Financial economics; Computer science; Expected shortfall; Finance","score_opus":0.1118844901858132,"score_gpt":0.37236651084486794,"score_spread":0.26048202065905474,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4238560257","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":[0.77071416,0.15978427,0.03450357,0.0029240963,0.0006366551,0.00085387105,0.00027879764,0.000022592048,0.030281983],"genre_scores_gemma":[0.81690544,0.1822437,0.00045681247,0.00021761784,0.000046158977,0.000002664425,0.000007704578,0.0000032376518,0.000116674346],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.9982372,0.00039546855,0.000803369,0.00015615675,0.0003055172,0.000102292695],"domain_scores_gemma":[0.99726933,0.00080227194,0.0010930722,0.00066883536,0.00011507958,0.000051435567],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0053169536,0.00008178753,0.00056420814,0.00025221735,0.00005287828,0.000012290889,0.00036598725,0.000030846895,0.0017396233],"category_scores_gemma":[0.0011544677,0.000043752643,0.00062193896,0.0007066214,0.00004730607,0.00013385383,0.000033330958,0.000045203764,0.0002353095],"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":[0.00006491707,0.00019373998,0.46410143,0.00014710461,0.0020552343,2.709899e-7,0.0010867898,0.13673794,0.00006640965,0.014080603,0.05551082,0.32595474],"study_design_scores_gemma":[0.0009794276,0.00016731062,0.2443198,0.0015027132,0.018057564,0.00000272219,0.0006101906,0.20301501,0.0045363544,0.006595091,0.519141,0.0010728003],"about_ca_topic_score_codex":0.00015166201,"about_ca_topic_score_gemma":0.000027329756,"teacher_disagreement_score":0.4636302,"about_ca_system_score_codex":0.00002709326,"about_ca_system_score_gemma":0.000028667022,"threshold_uncertainty_score":0.9991729},"labels":[],"label_agreement":null},{"id":"W4239978806","doi":"10.1002/9781119483946.ch7","title":"Stochastic Modelling","year":2018,"lang":"en","type":"other","venue":"Mathematical Modelling","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"York University","funders":"","keywords":"Randomness; Stochastic modelling; Markov chain; Stochastic process; Continuous-time stochastic process; Computer science; Markov process; Set (abstract data type); Mathematics; Statistics; Machine learning","score_opus":0.1635304900893542,"score_gpt":0.3669370560261345,"score_spread":0.20340656593678033,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4239978806","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000067249007,0.00012490348,0.56421995,0.000025272368,0.00021320286,0.00019098898,0.000007826261,0.00015069482,0.43506047],"genre_scores_gemma":[0.0015126325,0.00011269786,0.2127269,0.000041407126,0.0007724046,0.00001956435,0.000012400375,0.000450628,0.78435135],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9960851,0.00009129431,0.0009595535,0.00077048165,0.0016672182,0.000426361],"domain_scores_gemma":[0.997447,0.0005968203,0.00048948644,0.0010741929,0.00018393809,0.00020852605],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00135938,0.0003891716,0.000751629,0.00061055116,0.00011099499,0.00031446147,0.0008265456,0.00046129705,0.012923594],"category_scores_gemma":[0.00029374156,0.00028452012,0.00022135915,0.00048393468,0.00014517925,0.000099272984,0.0001249303,0.00021755203,0.018604554],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000047930967,0.00003779396,3.9405342e-7,0.00001426172,0.000019152594,0.0000027974966,0.0001462005,0.83928627,1.3812151e-7,0.015410437,0.1439165,0.0011612892],"study_design_scores_gemma":[0.000067343986,0.000012373343,4.5019966e-9,0.00015710754,0.000029382487,0.0000039562005,0.000022663577,0.6528543,0.0000010240972,0.26940456,0.077230714,0.00021652755],"about_ca_topic_score_codex":0.000017860088,"about_ca_topic_score_gemma":0.0000022250065,"teacher_disagreement_score":0.351493,"about_ca_system_score_codex":0.000025244619,"about_ca_system_score_gemma":0.00005568823,"threshold_uncertainty_score":0.9999607},"labels":[],"label_agreement":null},{"id":"W4241968414","doi":"10.1017/s0001867800011678","title":"Asymptotic Bounds for the Distribution of the Sum of Dependent Random Variables","year":2014,"lang":"en","type":"article","venue":"Journal of Applied Probability","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":3,"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":"Mathematics; Random variable; Limit (mathematics); Mathematical proof; Combinatorics; Distribution (mathematics); Value (mathematics); Expected value; Marginal distribution; Discrete mathematics; Applied mathematics; Statistics; Mathematical analysis","score_opus":0.03345529852883366,"score_gpt":0.29882868617425523,"score_spread":0.26537338764542157,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4241968414","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":[0.5378249,0.00008154757,0.45915177,0.0006045206,0.0005281145,0.00070589833,0.000032185126,0.0000030913354,0.001067963],"genre_scores_gemma":[0.99798805,0.00003677833,0.001823427,0.00002313928,0.00008608539,0.000008593051,0.0000016210033,0.0000037418788,0.000028566166],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9973641,0.00019396296,0.0012114639,0.00013832982,0.00096990465,0.00012220773],"domain_scores_gemma":[0.99405205,0.0029075933,0.0016070071,0.0005100563,0.0008815577,0.00004174517],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.012510247,0.00008637209,0.0003605069,0.000037056532,0.0001499542,0.000055164048,0.0007097434,0.000068722526,0.00002700868],"category_scores_gemma":[0.0027078232,0.000037303438,0.0002543981,0.00034638194,0.00021068576,0.000103943465,0.00006470558,0.00012724179,8.070449e-7],"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":[0.006678011,0.0009944049,0.0895911,0.00011721205,0.0002757262,1.7018775e-7,0.0017581016,0.5822325,0.002756118,0.178184,0.006935826,0.13047685],"study_design_scores_gemma":[0.003559478,0.0002601696,0.076039225,0.000035563487,0.00023972895,0.000009711887,0.00030571097,0.027066441,0.012622844,0.8677561,0.011977353,0.00012767124],"about_ca_topic_score_codex":0.000009133949,"about_ca_topic_score_gemma":0.0000123450955,"teacher_disagreement_score":0.6895721,"about_ca_system_score_codex":0.000041593685,"about_ca_system_score_gemma":0.00016401375,"threshold_uncertainty_score":0.43358266},"labels":[],"label_agreement":null},{"id":"W4242534416","doi":"10.32920/ryerson.14662176","title":"Rearrangement Algorithm in Risk Aggregation","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Risk measure; Measure (data warehouse); Aggregate (composite); Discretization; Expected shortfall; Exponential function; Econometrics; Portfolio; Actuarial science; Investment (military); Coherent risk measure; Economics; Mathematics; Spectral risk measure; Computer science; Financial economics; Data mining","score_opus":0.07475718654506924,"score_gpt":0.3766298443416548,"score_spread":0.30187265779658556,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4242534416","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":[0.15592729,0.0012323887,0.79003835,0.00074366905,0.0027462232,0.00068867,0.000050727864,0.00008909916,0.048483606],"genre_scores_gemma":[0.44854963,0.03415237,0.4861591,0.00048736186,0.0007148606,0.00019344858,0.00070901134,0.00006364402,0.028970586],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9963451,0.00043043788,0.00090757303,0.00079085847,0.0013373942,0.00018864803],"domain_scores_gemma":[0.9977725,0.00025879228,0.0005221098,0.000949967,0.00042319886,0.00007344176],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0032042144,0.00018468946,0.00036469504,0.00051513565,0.00005913569,0.00061382446,0.00051425555,0.00027442005,0.0017533473],"category_scores_gemma":[0.0011134131,0.00014515949,0.00015243197,0.00075000536,0.000027310263,0.00019832898,0.0005552028,0.0004321428,0.00018647901],"study_design_candidate":"design_other","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":[0.0000039459414,0.000049038335,0.021855358,0.0000017094432,0.000010861398,0.000019141124,0.00065859983,0.05084055,0.0000022199924,0.00006138779,0.0048591974,0.921638],"study_design_scores_gemma":[0.00075568946,0.000039501378,0.11306641,0.00015142071,0.000046245284,0.0000058385303,0.0026350582,0.7797072,0.00058543147,0.06296697,0.039305698,0.0007345522],"about_ca_topic_score_codex":0.0015365926,"about_ca_topic_score_gemma":0.00096069404,"teacher_disagreement_score":0.92090344,"about_ca_system_score_codex":0.000081291335,"about_ca_system_score_gemma":0.00018583586,"threshold_uncertainty_score":0.99915916},"labels":[],"label_agreement":null},{"id":"W4243981739","doi":"10.1007/s10436-017-0300-5","title":"Quadratic minimization with portfolio and intertemporal wealth constraints","year":2017,"lang":"en","type":"article","venue":"Annals of Finance","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"University of Waterloo; Bank of Canada","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Portfolio; Mathematical optimization; Mathematical finance; Portfolio optimization; Stochastic control; Constraint (computer-aided design); Optimization problem; Mathematics; Quadratic programming; Mathematical economics; Duality (order theory); Optimal control; Economics; Finance","score_opus":0.15805202585467124,"score_gpt":0.4227842950046586,"score_spread":0.2647322691499874,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4243981739","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":[0.9528055,0.00035699207,0.012909072,0.003999936,0.00020590478,0.00027601575,0.0001230367,0.00001884094,0.029304672],"genre_scores_gemma":[0.9937271,0.0014835615,0.0029297387,0.00020165853,0.000023650799,0.000004395826,0.00000942037,0.000005939297,0.0016145743],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9987464,0.000042516327,0.00041940212,0.00026213418,0.00038302352,0.0001465342],"domain_scores_gemma":[0.9979639,0.00012682039,0.0009028428,0.0005689667,0.0003821883,0.000055268803],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00065574807,0.000095820986,0.00024552844,0.000105857674,0.00020967769,0.00017357396,0.00035238542,0.000048104608,0.00017699254],"category_scores_gemma":[0.0008692441,0.00006791602,0.00003182235,0.0001298219,0.00044392058,0.00060388376,0.000047786256,0.000051163788,0.000015718935],"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":[0.00020664168,0.00010529503,0.27644983,0.000012592199,0.00002436235,0.000033934477,0.00093736517,0.0007319367,0.000019033463,0.010385299,0.108514875,0.6025788],"study_design_scores_gemma":[0.00083336455,0.0005798732,0.90869606,0.00024104903,0.0000129131795,0.000044898363,0.0003490008,0.010634212,0.0013753279,0.01570208,0.06117184,0.00035936813],"about_ca_topic_score_codex":0.000060042265,"about_ca_topic_score_gemma":0.000057249053,"teacher_disagreement_score":0.63224626,"about_ca_system_score_codex":0.0000019341717,"about_ca_system_score_gemma":0.00010191221,"threshold_uncertainty_score":0.27695352},"labels":[],"label_agreement":null},{"id":"W4244871536","doi":"10.1002/9780470061602.eqf12012","title":"Optimization Methods","year":2010,"lang":"en","type":"other","venue":"Encyclopedia of Quantitative Finance","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Portfolio optimization; Project portfolio management; Quadratic programming; Stochastic programming; Portfolio; Computer science; Mathematical optimization; Calibration; Stochastic optimization; Sequential quadratic programming; Optimization problem; Finance; Mathematics; Economics; Project management; Management","score_opus":0.07064467658237668,"score_gpt":0.44227513853218947,"score_spread":0.3716304619498128,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4244871536","genre_codex":"other","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000010103307,0.0014201947,0.44763762,0.000051132803,0.0012558915,0.0002116126,0.00008230313,0.000043592452,0.54928756],"genre_scores_gemma":[0.000007823949,0.018510146,0.5194142,0.000019552446,0.00011095911,0.000015592943,0.000032134394,0.0001327804,0.4617568],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9968382,0.0004466131,0.00093299826,0.0006768151,0.0008644331,0.00024090255],"domain_scores_gemma":[0.9955962,0.0011396843,0.001888929,0.000890599,0.0004260732,0.000058555997],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0019155955,0.00031974816,0.0007711132,0.0007935366,0.00005868748,0.000046274563,0.0008412793,0.0004910408,0.0036444776],"category_scores_gemma":[0.0045825234,0.00025915477,0.00017852464,0.0012577282,0.00027686587,0.00019832296,0.00009745612,0.0003543738,0.00031307596],"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":[0.000025788302,0.000078629666,0.0004341081,0.000011445948,0.000030198651,0.0000045671613,0.00050160853,0.016870147,0.00001556085,0.027303798,0.80553234,0.14919181],"study_design_scores_gemma":[0.00016710866,0.00007822557,0.00027900413,0.000058157988,0.000022301889,0.0000012084622,0.0000585369,0.008665297,0.000053563806,0.003649339,0.9866818,0.00028545273],"about_ca_topic_score_codex":0.000112667454,"about_ca_topic_score_gemma":0.0001103756,"teacher_disagreement_score":0.18114947,"about_ca_system_score_codex":0.000010937141,"about_ca_system_score_gemma":0.0001819738,"threshold_uncertainty_score":0.99998605},"labels":[],"label_agreement":null},{"id":"W4249401458","doi":"10.2143/ast.37.2.2024072","title":"Quantifying and Correcting the Bias in Estimated Risk Measures","year":2007,"lang":"en","type":"article","venue":"Astin Bulletin","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Econometrics; Monte Carlo method; Expected shortfall; Value at risk; Statistics; Estimation; Computer science; Mathematics; Risk management; Economics","score_opus":0.2776417928637508,"score_gpt":0.40950337000740095,"score_spread":0.13186157714365015,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4249401458","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":[0.9616549,0.00059034413,0.032493927,0.0009611381,0.00035671284,0.00015453703,0.000001927036,0.000044689783,0.0037418287],"genre_scores_gemma":[0.9946458,0.0002027152,0.0046075378,0.00012374201,0.000055751294,0.0000026584412,0.0000012184361,0.000009828164,0.00035077854],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9979725,0.0002976601,0.0005585584,0.00030889385,0.0005997243,0.00026270165],"domain_scores_gemma":[0.994837,0.004415664,0.0002861933,0.0002559745,0.00014391643,0.000061250685],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.012369226,0.00011069045,0.00016512227,0.00020965839,0.0002874209,0.00021617977,0.00025470555,0.00006363352,0.00015266114],"category_scores_gemma":[0.014806069,0.00006768858,0.000034098925,0.0007386516,0.000086531436,0.000050431725,0.00007525985,0.00023062548,0.0001914405],"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":[0.00002733914,0.000008272108,0.624548,3.4843572e-7,0.0000020739153,0.000007765675,0.0006251003,0.004758269,0.000019752011,0.00005051185,0.0028618895,0.36709064],"study_design_scores_gemma":[0.00040176776,0.000034278655,0.9140463,0.00004175946,0.00001114873,0.00006159213,0.0025382994,0.020398498,0.0004999785,0.0006167269,0.061167274,0.00018238129],"about_ca_topic_score_codex":0.00088392285,"about_ca_topic_score_gemma":0.0007705384,"teacher_disagreement_score":0.36690825,"about_ca_system_score_codex":0.000012855976,"about_ca_system_score_gemma":0.000019641318,"threshold_uncertainty_score":0.99349266},"labels":[],"label_agreement":null},{"id":"W4250404544","doi":"10.32920/ryerson.14655180.v1","title":"A multi-period risk sharing supply chain contract under consideration of price and demand uncertainties","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Supply chain; Valuation (finance); Microeconomics; Procurement; Economics; Supply and demand; Geometric Brownian motion; Industrial organization; Business; Finance","score_opus":0.07463135586578183,"score_gpt":0.3528764878700397,"score_spread":0.2782451320042579,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4250404544","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":[0.69016296,0.001821199,0.3056184,0.00040764725,0.00039454308,0.0004650471,0.00004773582,0.000036914873,0.0010455655],"genre_scores_gemma":[0.96554244,0.0037435705,0.02909839,0.00010846745,0.000048236307,0.000021226924,0.000056020723,0.000016126336,0.0013655076],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9967245,0.00030316657,0.0011092214,0.0008721363,0.00077687093,0.00021413584],"domain_scores_gemma":[0.9965899,0.0008262231,0.0009419773,0.00069503277,0.00083694904,0.000109912806],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025259366,0.00027300432,0.0006762719,0.00032072814,0.00018843508,0.00102747,0.00032127212,0.0003299919,0.00064011145],"category_scores_gemma":[0.0018384694,0.00021048638,0.00014598157,0.0002708172,0.00016100399,0.00033023983,0.000580025,0.0003644518,0.0000058105293],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010966488,0.00021404037,0.46970567,0.00007144035,0.0002893048,0.000029248338,0.0126942,0.49457183,0.0010014958,0.003585512,0.0008943057,0.01683327],"study_design_scores_gemma":[0.0010521273,0.0000466788,0.21028483,0.0001385347,0.000107483305,0.000028087874,0.0076402063,0.76630354,0.0014451382,0.01221611,0.00024916203,0.00048807496],"about_ca_topic_score_codex":0.0015358945,"about_ca_topic_score_gemma":0.0011752882,"teacher_disagreement_score":0.27652,"about_ca_system_score_codex":0.00003497299,"about_ca_system_score_gemma":0.00030010284,"threshold_uncertainty_score":0.9907917},"labels":[],"label_agreement":null},{"id":"W4252333904","doi":"10.32920/ryerson.14655180","title":"A multi-period risk sharing supply chain contract under consideration of price and demand uncertainties","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Supply chain; Valuation (finance); Procurement; Microeconomics; Economics; Supply and demand; Geometric Brownian motion; Industrial organization; Business; Finance","score_opus":0.07463135586578183,"score_gpt":0.3528764878700397,"score_spread":0.2782451320042579,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4252333904","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":[0.69016296,0.001821199,0.3056184,0.00040764725,0.00039454308,0.0004650471,0.00004773582,0.000036914873,0.0010455655],"genre_scores_gemma":[0.96554244,0.0037435705,0.02909839,0.00010846745,0.000048236307,0.000021226924,0.000056020723,0.000016126336,0.0013655076],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9967245,0.00030316657,0.0011092214,0.0008721363,0.00077687093,0.00021413584],"domain_scores_gemma":[0.9965899,0.0008262231,0.0009419773,0.00069503277,0.00083694904,0.000109912806],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025259366,0.00027300432,0.0006762719,0.00032072814,0.00018843508,0.00102747,0.00032127212,0.0003299919,0.00064011145],"category_scores_gemma":[0.0018384694,0.00021048638,0.00014598157,0.0002708172,0.00016100399,0.00033023983,0.000580025,0.0003644518,0.0000058105293],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010966488,0.00021404037,0.46970567,0.00007144035,0.0002893048,0.000029248338,0.0126942,0.49457183,0.0010014958,0.003585512,0.0008943057,0.01683327],"study_design_scores_gemma":[0.0010521273,0.0000466788,0.21028483,0.0001385347,0.000107483305,0.000028087874,0.0076402063,0.76630354,0.0014451382,0.01221611,0.00024916203,0.00048807496],"about_ca_topic_score_codex":0.0015358945,"about_ca_topic_score_gemma":0.0011752882,"teacher_disagreement_score":0.27652,"about_ca_system_score_codex":0.00003497299,"about_ca_system_score_gemma":0.00030010284,"threshold_uncertainty_score":0.9907917},"labels":[],"label_agreement":null},{"id":"W4253121926","doi":"10.2139/ssrn.2044825","title":"Backtesting","year":2008,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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 Toronto","funders":"","keywords":"Political science","score_opus":0.07052102809816646,"score_gpt":0.3428157924890571,"score_spread":0.2722947643908906,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4253121926","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":[0.7777672,0.0023978387,0.18956424,0.0011626388,0.0005660562,0.00008361677,8.28778e-7,0.00005493459,0.028402634],"genre_scores_gemma":[0.9772774,0.008294453,0.00097429974,0.000101803096,0.0003506114,7.950067e-7,7.772437e-7,0.0000104385535,0.012989399],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9970073,0.00012385473,0.00045418795,0.00019001518,0.00085225014,0.0013723678],"domain_scores_gemma":[0.99893904,0.00023256283,0.0002597261,0.00021074143,0.0002649863,0.00009294519],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004693827,0.00009101983,0.00014502088,0.00020320805,0.0005155863,0.00009402578,0.00046298848,0.00004749469,0.00014322984],"category_scores_gemma":[0.0012991093,0.000064684406,0.00010280005,0.00060735334,0.000050154664,0.00039789907,0.000030603063,0.00089308893,0.0004881274],"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":[0.000072937,0.00011080799,0.27666306,4.311463e-7,0.000077853656,0.0000789441,0.0010693121,0.010370857,0.0002371883,0.15264876,0.012712664,0.5459572],"study_design_scores_gemma":[0.00062381773,0.00023929893,0.012724046,0.000005260137,0.000011701131,0.008819327,0.0014446187,0.003909337,0.00008200067,0.9295893,0.042302176,0.00024914145],"about_ca_topic_score_codex":0.000018038665,"about_ca_topic_score_gemma":0.00006980155,"teacher_disagreement_score":0.7769405,"about_ca_system_score_codex":0.00018746294,"about_ca_system_score_gemma":0.0016962425,"threshold_uncertainty_score":0.6274053},"labels":[],"label_agreement":null},{"id":"W4256364320","doi":"10.1142/9789814417358_0020","title":"Univariate and multivariate measures of risk aversion and risk premiums","year":2013,"lang":"en","type":"book-chapter","venue":"World Scientific handbook in financial economic series","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Univariate; Multivariate statistics; Risk aversion (psychology); Econometrics; Actuarial science; Multivariate analysis; Economics; Statistics; Mathematics; Expected utility hypothesis","score_opus":0.04043092323609568,"score_gpt":0.2593701442760788,"score_spread":0.21893922103998312,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4256364320","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.45817986,0.015850378,0.0030501483,0.00024262795,0.014823373,0.0037129268,0.003585757,0.0001699657,0.5003849],"genre_scores_gemma":[0.07259003,0.017412102,0.0036786105,0.000019739064,0.00025297297,0.000021311907,0.00006342015,0.00006983375,0.90589195],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9965382,0.00015529875,0.0013337987,0.0011718371,0.00048175539,0.0003191174],"domain_scores_gemma":[0.99667925,0.0006188964,0.0015641685,0.0007463608,0.00023309566,0.00015821248],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0035367927,0.00040868207,0.0009151064,0.0013636827,0.00047971742,0.0005889332,0.0004691046,0.00030416923,0.0007861781],"category_scores_gemma":[0.0007559239,0.00036061194,0.00013622525,0.00017491018,0.0011429858,0.00081606454,0.00034246914,0.00038658248,0.00022755803],"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":[0.0014839165,0.00010227403,0.074680574,0.00008421591,0.00021195925,0.000031125754,0.016311605,0.010684609,0.00037415646,0.30042985,0.057565693,0.53804004],"study_design_scores_gemma":[0.0012046583,0.00008290943,0.027486883,0.00040674067,0.0001213248,0.000007245921,0.00012574611,0.0030121598,0.0004911237,0.26786172,0.69839466,0.00080487004],"about_ca_topic_score_codex":0.00091631274,"about_ca_topic_score_gemma":0.011106313,"teacher_disagreement_score":0.6408289,"about_ca_system_score_codex":0.000102497695,"about_ca_system_score_gemma":0.0003435992,"threshold_uncertainty_score":0.9998846},"labels":[],"label_agreement":null},{"id":"W4281285722","doi":"10.1016/j.jbankfin.2022.106544","title":"The gradient allocation principle based on the higher moment risk measure","year":2022,"lang":"en","type":"article","venue":"Journal of Banking & Finance","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":10,"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":"University of Illinois at Urbana-Champaign; Universität Ulm; University of Waterloo; Departamento Administrativo de Ciencia, Tecnología e Innovación (COLCIENCIAS); Universidad Nacional de Colombia; York University; University of New South Wales; Australian Government","keywords":"Measure (data warehouse); Moment (physics); Risk measure; Mathematics; Economics; Physics; Computer science; Classical mechanics; Financial economics; Data mining","score_opus":0.049703135528830815,"score_gpt":0.3133121687905884,"score_spread":0.26360903326175755,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4281285722","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":[0.8601915,0.0023162812,0.08291358,0.041736543,0.005955255,0.0005867475,0.000019162355,0.00002202974,0.0062589017],"genre_scores_gemma":[0.9969413,0.0005137541,0.00070341065,0.00047138424,0.00013387298,0.000014935667,5.211513e-7,0.000007724772,0.0012130755],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.9964168,0.0005616439,0.0006259131,0.0001544043,0.0020793828,0.00016186274],"domain_scores_gemma":[0.9968337,0.0009410081,0.0013934106,0.00045870407,0.00034657953,0.00002662489],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008693417,0.00008897773,0.00014084482,0.00011224423,0.0012469077,0.00017389418,0.0008288503,0.000020532623,0.00016359119],"category_scores_gemma":[0.00053681195,0.000042738327,0.00014404219,0.00066332315,0.000045024277,0.00012057831,0.000063657266,0.00039549675,0.000012202164],"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":[0.00016958283,0.00009317907,0.0069302795,3.4287393e-7,0.00001458865,0.000009160324,0.0004622586,0.815132,0.000022187096,0.015315948,0.021887766,0.13996266],"study_design_scores_gemma":[0.00025860986,0.00023333386,0.06369395,0.000014874931,0.000014045677,0.000009320168,0.00008101599,0.027429814,0.000089049485,0.007948568,0.9001593,0.00006809934],"about_ca_topic_score_codex":0.000006980049,"about_ca_topic_score_gemma":0.0000046002406,"teacher_disagreement_score":0.8782716,"about_ca_system_score_codex":0.00014278799,"about_ca_system_score_gemma":0.00014801863,"threshold_uncertainty_score":0.9590332},"labels":[],"label_agreement":null},{"id":"W4281561301","doi":"10.48550/arxiv.2203.09612","title":"Risk-Averse Markov Decision Processes through a Distributional Lens","year":2022,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Natural Sciences and Engineering Research Council of Canada","keywords":"Markov decision process; Dynamic programming; Mathematical optimization; Limit (mathematics); Regular polygon; Markov process; Invariant (physics); Markov chain; Risk measure; Computer science; Econometrics; Order (exchange); Mathematics; Economics; Statistics; Machine learning","score_opus":0.14846909282494203,"score_gpt":0.27243172866020493,"score_spread":0.1239626358352629,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4281561301","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":[0.47984228,0.00021799387,0.50058746,0.00011342494,0.0015029907,0.00053153315,0.0017326651,0.00018162472,0.015290049],"genre_scores_gemma":[0.98415834,0.0071817855,0.0019853304,0.00008485831,0.00014015712,0.0000037783925,0.000455055,0.00002864588,0.0059620277],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9957636,0.0004849666,0.000643961,0.0018046697,0.00086962397,0.00043315286],"domain_scores_gemma":[0.9946888,0.001596355,0.001115707,0.0015052579,0.0009281711,0.0001657044],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0015386668,0.00042303285,0.0005394028,0.00042913153,0.0008233829,0.00028583815,0.0020489395,0.00035406218,0.0044281194],"category_scores_gemma":[0.0030451552,0.0004116915,0.00040662359,0.0025545633,0.00022275934,0.00086344103,0.0023965247,0.0009409865,0.00043634823],"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":[0.00039681824,0.00021481865,0.09621166,0.000016657266,0.00008443431,0.0002647888,0.00032748876,0.8616864,8.193339e-7,0.01424817,0.024282003,0.0022659146],"study_design_scores_gemma":[0.0014863656,0.00015528979,0.023538684,0.00008425426,0.00036639985,0.000019365827,0.0017572177,0.12709157,0.000033252447,0.5366469,0.3074993,0.001321384],"about_ca_topic_score_codex":0.00032242862,"about_ca_topic_score_gemma":0.00015009569,"teacher_disagreement_score":0.7345949,"about_ca_system_score_codex":0.00038084795,"about_ca_system_score_gemma":0.00086812576,"threshold_uncertainty_score":0.99983346},"labels":[],"label_agreement":null},{"id":"W4283737948","doi":"10.2139/ssrn.4149069","title":"Diversification Quotients: Quantifying Diversification via Risk Measures","year":2022,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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 Waterloo","funders":"","keywords":"Diversification (marketing strategy); Economics; Business; Geography; Econometrics; Marketing","score_opus":0.07673228782108325,"score_gpt":0.33023015336075356,"score_spread":0.2534978655396703,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4283737948","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":[0.45526198,0.0020811702,0.5389194,0.0009962001,0.0013008919,0.00026297302,0.000023448958,0.000065326814,0.0010886356],"genre_scores_gemma":[0.98952824,0.00859252,0.00021555978,0.00005529606,0.00013403897,0.000008784958,0.000023171144,0.000014810668,0.0014275634],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9947874,0.0008589809,0.00062764593,0.0004181932,0.0021588,0.001148991],"domain_scores_gemma":[0.997884,0.0002032875,0.0009728767,0.00041903972,0.0004203311,0.00010046149],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.011361397,0.00014599386,0.00018572655,0.00054427434,0.0024601414,0.00022483587,0.0010091169,0.000049157996,0.00025130666],"category_scores_gemma":[0.00070238777,0.00012841215,0.00018270565,0.0011702883,0.00004808061,0.0006921098,0.0001602871,0.0015022762,0.0001836284],"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":[0.00028034084,0.00022697117,0.25997612,6.1111075e-7,0.0001493456,0.0000035975781,0.002075164,0.053201787,0.00040967696,0.039794367,0.0022154145,0.6416666],"study_design_scores_gemma":[0.002093909,0.00085761806,0.059197314,0.0000055065934,0.0002168533,0.00045824007,0.044105712,0.026315281,0.00017162909,0.7442183,0.12162882,0.00073080143],"about_ca_topic_score_codex":0.0002283209,"about_ca_topic_score_gemma":0.00018772081,"teacher_disagreement_score":0.70442396,"about_ca_system_score_codex":0.0011400718,"about_ca_system_score_gemma":0.00077393296,"threshold_uncertainty_score":0.99883854},"labels":[],"label_agreement":null},{"id":"W4283795539","doi":"10.1080/23302674.2022.2092660","title":"Robust design of service systems with immobile servers, general arrival and service patterns, and demand uncertainty","year":2022,"lang":"en","type":"article","venue":"International Journal of Systems Science Operations & Logistics","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mathematical optimization; Computer science; Queueing theory; Server; Piecewise linear function; Piecewise; Quadratic equation; Robustness (evolution); Service (business); Quality of service; Nonlinear system; Mathematics; Computer network","score_opus":0.09633810518205585,"score_gpt":0.328939226678069,"score_spread":0.23260112149601317,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4283795539","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":[0.47976142,0.000456199,0.5161428,0.001087217,0.0018569648,0.0003674023,0.00025518343,0.0000074633567,0.00006535631],"genre_scores_gemma":[0.9937814,0.00021079976,0.0054528685,0.00019023751,0.00017578795,0.000015082474,0.00001009488,0.000008953213,0.00015476845],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99544036,0.00035648068,0.0010941774,0.0003240975,0.0026011697,0.00018368455],"domain_scores_gemma":[0.9935953,0.00035373858,0.0008103418,0.0002493776,0.0048331823,0.00015807981],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0042363196,0.00014956227,0.00033129306,0.0005722385,0.00052100956,0.00091932126,0.0011239527,0.0000385097,0.000028572607],"category_scores_gemma":[0.0004077098,0.00010666913,0.00002813387,0.0010085647,0.00022603382,0.0009234288,0.000264252,0.0001827251,0.0000015942711],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006881346,0.000044711633,0.011490648,0.000008622946,0.00003475275,0.000025681196,0.0005917364,0.98411685,0.0007898492,0.0024442384,0.0001926937,0.00019137208],"study_design_scores_gemma":[0.000614877,0.00029377374,0.0043261424,0.00007327621,0.000035155776,0.0013562356,0.005407368,0.98699355,0.000052321095,0.00012713434,0.0005733553,0.00014682818],"about_ca_topic_score_codex":0.002368062,"about_ca_topic_score_gemma":0.00028211882,"teacher_disagreement_score":0.51401997,"about_ca_system_score_codex":0.00016051205,"about_ca_system_score_gemma":0.0007117957,"threshold_uncertainty_score":0.8865036},"labels":[],"label_agreement":null},{"id":"W4284971611","doi":"10.1080/03461238.2022.2094718","title":"An impossibility theorem on capital allocation","year":2022,"lang":"en","type":"article","venue":"Scandinavian Actuarial Journal","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Impossibility; Subadditivity; Capital allocation line; Capital (architecture); Mathematical economics; Economics; Mathematics; Portfolio; Econometrics; Microeconomics; Financial economics; Discrete mathematics","score_opus":0.046906736419749726,"score_gpt":0.36987636474563823,"score_spread":0.3229696283258885,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4284971611","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":[0.98348254,0.000027639886,0.0071575437,0.00078863517,0.0034341672,0.00019360625,0.000038791186,0.000036128728,0.004840974],"genre_scores_gemma":[0.9976995,0.000027779863,0.00041931216,0.0002883875,0.0009726088,0.000008250896,0.000026669013,0.000015737607,0.0005417636],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99555415,0.0009864192,0.00071236066,0.0004276834,0.002003907,0.000315476],"domain_scores_gemma":[0.99808246,0.00025345632,0.00052014337,0.00060319074,0.00022828767,0.00031243492],"candidate_categories":["sts","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0057563856,0.0001601544,0.0002281712,0.00039823682,0.0014947221,0.00080552266,0.0009816547,0.00005993673,0.0046028225],"category_scores_gemma":[0.0005617977,0.00011930326,0.00016744722,0.0006370255,0.000083140825,0.0007189243,0.00008431777,0.0005807044,0.00011169248],"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":[0.0046444223,0.0016029946,0.12964252,0.0000015435268,0.00009400091,0.00022733885,0.019499986,0.09383118,0.0025580314,0.04108415,0.03199828,0.67481554],"study_design_scores_gemma":[0.004899926,0.006055692,0.2767797,0.00001740052,0.00007887378,0.0016662321,0.014683027,0.020107906,0.0012000436,0.64218384,0.031228052,0.0010992916],"about_ca_topic_score_codex":0.000054912256,"about_ca_topic_score_gemma":0.000016143835,"teacher_disagreement_score":0.67371625,"about_ca_system_score_codex":0.0002846928,"about_ca_system_score_gemma":0.00024044907,"threshold_uncertainty_score":0.9998052},"labels":[],"label_agreement":null},{"id":"W4285178771","doi":"10.2139/ssrn.4140716","title":"Integrated Order Acceptance and Resource Decisions Under Uncertainty: Robust and Stochastic Approaches","year":2022,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Western University; University of Windsor","funders":"","keywords":"Order (exchange); Resource (disambiguation); Computer science; Management science; Business; Economics","score_opus":0.08008786184820878,"score_gpt":0.30390013711307584,"score_spread":0.22381227526486708,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285178771","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":[0.36698714,0.006412349,0.6232559,0.0022549601,0.00016642587,0.00020368656,0.000011641624,0.000030361933,0.0006775237],"genre_scores_gemma":[0.9937657,0.0020210107,0.0010448557,0.00020082192,0.000075780976,0.00000953097,0.0000099434255,0.0000216941,0.002850648],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99655014,0.00041358848,0.0005133668,0.00044798027,0.0009751668,0.0010997445],"domain_scores_gemma":[0.998368,0.0007293319,0.00029882035,0.00027689245,0.00017609185,0.00015086411],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005370208,0.00018061862,0.0002615412,0.00038218327,0.0011672192,0.00035534627,0.0004935779,0.00005907853,0.00018910241],"category_scores_gemma":[0.0010959251,0.00013346258,0.000057971392,0.001278187,0.00013260946,0.00029208083,0.00023020079,0.0016641351,0.0000061256674],"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":[0.00019309536,0.000047761394,0.00096592994,2.55184e-7,0.00006529166,0.0000030305748,0.0005164749,0.7874576,0.000007389764,0.03324398,0.0009563225,0.17654286],"study_design_scores_gemma":[0.001398575,0.00050879834,0.001907996,0.00001174266,0.00006979321,0.002060455,0.068019465,0.265456,0.0000016703369,0.64282185,0.017311854,0.00043180728],"about_ca_topic_score_codex":0.00003663434,"about_ca_topic_score_gemma":0.00049128215,"teacher_disagreement_score":0.6267786,"about_ca_system_score_codex":0.00036129283,"about_ca_system_score_gemma":0.0012753224,"threshold_uncertainty_score":0.8977425},"labels":[],"label_agreement":null},{"id":"W4285250416","doi":"10.2139/ssrn.4148611","title":"Adaptive Robust Expansion Planning of Interdependent Electricity, Gas, and Heat Energy Systems","year":2022,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Interdependence; Electricity system; Electricity; Energy system; Energy (signal processing); Environmental science; Business; Electricity generation; Engineering; Thermodynamics; Physics","score_opus":0.039402191200986585,"score_gpt":0.2963473809550683,"score_spread":0.2569451897540817,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285250416","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":[0.35974145,0.024347683,0.61242276,0.00023475125,0.00073548715,0.00015276395,0.000008928075,0.000027494381,0.0023287002],"genre_scores_gemma":[0.99409246,0.0045350753,0.00008322094,0.00003201923,0.00012020998,0.000006310081,0.0000038733883,0.000015453517,0.0011113761],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9961095,0.0006971496,0.0006853593,0.00029338294,0.0012472572,0.0009673468],"domain_scores_gemma":[0.9988896,0.00021513151,0.00041460904,0.00019663648,0.00019436581,0.00008961677],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0060049375,0.00013891619,0.0002935286,0.0005223475,0.00048897666,0.00012367162,0.00047389953,0.000040675583,0.000044137578],"category_scores_gemma":[0.00011447898,0.00011165533,0.00009470613,0.0006227459,0.000034340814,0.00025949485,0.00019729546,0.0009455728,0.0000019969732],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004497806,0.00014201096,0.007440123,0.0000019606007,0.00014092597,0.000033300606,0.0012568671,0.8425392,0.0008064886,0.050023768,0.0015594228,0.09560616],"study_design_scores_gemma":[0.003080234,0.0064301654,0.0016530219,0.00009021311,0.00013689524,0.009795755,0.074700445,0.5991602,0.00095185166,0.29223475,0.010857487,0.000908995],"about_ca_topic_score_codex":0.00031207848,"about_ca_topic_score_gemma":0.00008426441,"teacher_disagreement_score":0.634351,"about_ca_system_score_codex":0.000477416,"about_ca_system_score_gemma":0.00074801,"threshold_uncertainty_score":0.45531723},"labels":[],"label_agreement":null},{"id":"W4286632234","doi":"10.1080/10920277.2022.2068611","title":"On a Risk Model With Dual Seasonalities","year":2022,"lang":"en","type":"article","venue":"North American Actuarial Journal","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Western University","funders":"Compute Canada","keywords":"Dual (grammatical number); Risk model; Mathematics; Econometrics; Philosophy; Linguistics","score_opus":0.03771163261746849,"score_gpt":0.31219760072675695,"score_spread":0.2744859681092885,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4286632234","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":[0.95503104,0.000014621998,0.041076954,0.00060084625,0.00043219564,0.00010331078,0.00014622764,0.000029818495,0.0025649955],"genre_scores_gemma":[0.99389523,0.00011210311,0.0034840887,0.000867526,0.00042356536,0.000012000927,0.000010922605,0.000020935795,0.0011736333],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99545765,0.0005524939,0.00050428783,0.00033586728,0.0027990788,0.00035060677],"domain_scores_gemma":[0.99754333,0.0005679955,0.0010214039,0.0003413632,0.00024793192,0.00027799266],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014736002,0.00017874941,0.00032813678,0.00033911873,0.0012296567,0.00039948436,0.00053288945,0.000014401792,0.0008989363],"category_scores_gemma":[0.00061113795,0.0001197948,0.00014198115,0.0011679634,0.0002077749,0.00028816116,0.000104526654,0.0007630513,0.00004409576],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010851333,0.00007461139,0.06455338,1.03075095e-7,0.000035635137,0.000059505503,0.0011427794,0.8341637,8.2797885e-7,0.00034017008,0.01807176,0.08047239],"study_design_scores_gemma":[0.005485503,0.008927844,0.20994513,0.000010224012,0.00024930527,0.0022840367,0.012268003,0.6082286,0.000016478694,0.031088335,0.119922355,0.0015741406],"about_ca_topic_score_codex":0.00016959016,"about_ca_topic_score_gemma":0.00019488006,"teacher_disagreement_score":0.22593507,"about_ca_system_score_codex":0.00011154908,"about_ca_system_score_gemma":0.0005924865,"threshold_uncertainty_score":0.98427224},"labels":[],"label_agreement":null},{"id":"W4287448280","doi":"10.1016/j.cor.2022.105965","title":"Robust design of service systems with immobile servers under demand uncertainty","year":2022,"lang":"en","type":"article","venue":"Computers & Operations Research","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"","keywords":"Server; Computer science; Service (business); Distributed computing; On demand; Mathematical optimization; Database; Computer network; Mathematics; Business","score_opus":0.3101979093184881,"score_gpt":0.4141470132605036,"score_spread":0.10394910394201551,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4287448280","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":[0.086145066,0.00022894048,0.91039175,0.0013988606,0.0003116604,0.0010316384,0.000033063872,0.000030531406,0.00042848673],"genre_scores_gemma":[0.9867304,0.000054632597,0.011293361,0.00015003026,0.000052912073,0.0001990105,0.00005241567,0.000017525035,0.0014496901],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9942666,0.0020383531,0.0005146943,0.00047157163,0.0023881309,0.00032063885],"domain_scores_gemma":[0.99607646,0.0011111811,0.00007234017,0.0007132902,0.0019039544,0.0001227527],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005226346,0.00011483901,0.00024295048,0.0006288311,0.0012478771,0.000489335,0.0010298752,0.000040864466,0.00030238164],"category_scores_gemma":[0.000119905046,0.00008660093,0.00003912125,0.0036644714,0.00012708141,0.00036147694,0.00040594372,0.00030900244,0.000047560363],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000673846,0.00007925039,0.00031018394,0.0000042909887,0.000022777682,0.000005828503,0.0008770834,0.9871734,0.000120068726,0.0014113493,0.009344359,0.00058404636],"study_design_scores_gemma":[0.00040940265,0.0003150909,0.00042802718,0.000012388276,0.0000055070946,0.000021068574,0.004879577,0.9913039,0.000055111017,0.00015995961,0.0023024403,0.0001074941],"about_ca_topic_score_codex":0.0015865049,"about_ca_topic_score_gemma":0.0002692837,"teacher_disagreement_score":0.90058535,"about_ca_system_score_codex":0.00015663252,"about_ca_system_score_gemma":0.000654338,"threshold_uncertainty_score":0.9597788},"labels":[],"label_agreement":null},{"id":"W4288061410","doi":"10.1017/9781009089470.022","title":"Probability and Statistics Review","year":2022,"lang":"en","type":"other","venue":"","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Statistics; Computer science; Econometrics; Mathematics","score_opus":0.127659315994659,"score_gpt":0.39376807794345003,"score_spread":0.2661087619487911,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4288061410","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[1.2228864e-7,0.01951356,0.01850982,0.00026728527,0.00016520449,0.000387412,0.00024289177,0.000049293183,0.9608644],"genre_scores_gemma":[2.364783e-7,0.09712998,0.046071783,0.00060784037,0.00002910063,0.000014969561,0.000062975814,0.000046960882,0.8560362],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9984698,0.00015332193,0.0003078221,0.00032730537,0.0006673008,0.0000744365],"domain_scores_gemma":[0.9990162,0.00018056204,0.00023077008,0.00047278983,0.000050097347,0.00004952772],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0012087096,0.00009458498,0.00026896826,0.00009980981,0.000036915204,0.00004777091,0.0002197483,0.000048357026,0.24481425],"category_scores_gemma":[0.001028889,0.000061559615,0.000025423811,0.00027329073,0.000043950175,0.000023026496,0.00011661692,0.000079464284,0.0002465459],"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":[4.034805e-7,0.000005990575,0.00066230464,0.000037656464,0.0000026292773,0.0000011513346,0.0000032479625,0.0000014806188,3.961083e-9,0.0050718742,0.933952,0.060261276],"study_design_scores_gemma":[0.00002707854,0.000011355351,0.000114368515,0.000035099994,0.000012498471,0.0000021494511,0.0000051175775,0.000054090266,1.6820794e-8,0.014433176,0.98522645,0.0000785852],"about_ca_topic_score_codex":0.00010332836,"about_ca_topic_score_gemma":0.0001570538,"teacher_disagreement_score":0.24456769,"about_ca_system_score_codex":0.000010095765,"about_ca_system_score_gemma":0.000051287105,"threshold_uncertainty_score":0.7558761},"labels":[],"label_agreement":null},{"id":"W4288372734","doi":"10.48550/arxiv.1904.07381","title":"Approximation Algorithms for Distributionally Robust Stochastic\\n Optimization with Black-Box Distributions","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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 Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mathematical optimization; Vertex cover; Robust optimization; Approximation algorithm; Rounding; Probability distribution; Computer science; Optimization problem; Stochastic optimization; Set cover problem; Stochastic programming; Algorithm; Mathematics; Set (abstract data type)","score_opus":0.13909731530125033,"score_gpt":0.25537053871625354,"score_spread":0.11627322341500321,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4288372734","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":[0.009175916,0.00002217692,0.98526543,0.00022419008,0.0005368768,0.0015749349,0.0023461003,0.00013413672,0.00072026],"genre_scores_gemma":[0.9551302,0.00012473617,0.034277875,0.00002897278,0.00018916317,0.0000148946765,0.006901576,0.000042143743,0.0032904057],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99678236,0.0001759372,0.00061515305,0.0014868444,0.0005051047,0.00043457415],"domain_scores_gemma":[0.9949397,0.00063360046,0.001001447,0.0011810322,0.0020343082,0.00020995656],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0010100092,0.00044041866,0.00055309845,0.0004320681,0.00040761643,0.0004113572,0.0010466896,0.00046460386,0.00015160427],"category_scores_gemma":[0.0006612429,0.00040283005,0.0003028549,0.0013126223,0.00026937894,0.00066086213,0.00041803264,0.00039256405,0.00011176024],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019069819,0.00011456604,0.0007187519,0.000016266938,0.00007171537,0.0000074842665,0.000057810576,0.96380883,8.1599705e-7,0.03302492,0.0016487706,0.00033936667],"study_design_scores_gemma":[0.0009619669,0.00010515554,0.0006693579,0.00005840955,0.00021210808,0.000003710836,0.00021379035,0.9746459,0.000013448129,0.022096947,0.0005192008,0.0004999728],"about_ca_topic_score_codex":0.000035276622,"about_ca_topic_score_gemma":0.000020175019,"teacher_disagreement_score":0.9509875,"about_ca_system_score_codex":0.00038307105,"about_ca_system_score_gemma":0.00056864094,"threshold_uncertainty_score":0.99984235},"labels":[],"label_agreement":null},{"id":"W4288469095","doi":"10.2139/ssrn.4168264","title":"A General Wasserstein Framework for Data-driven Distributionally Robust Optimization: Tractability and Applications","year":2022,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"University of Ottawa","funders":"","keywords":"Robust optimization; Mathematical optimization; Computer science; Mathematics","score_opus":0.062118223788169544,"score_gpt":0.35373175952618663,"score_spread":0.29161353573801707,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4288469095","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":[0.0057795844,0.0009441754,0.98966724,0.002538569,0.00015485326,0.00044717107,0.00033214776,0.000023519004,0.000112725334],"genre_scores_gemma":[0.7486423,0.005847199,0.23929662,0.00040460774,0.0015036135,0.00046626577,0.0019309234,0.000056401084,0.0018520455],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9968654,0.00024211497,0.0006192358,0.00052101945,0.00084709283,0.0009051557],"domain_scores_gemma":[0.9979321,0.0005685328,0.00042976014,0.00063002144,0.00032175254,0.00011788358],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.005911235,0.00013115635,0.00021373236,0.000140624,0.0014019595,0.0002824434,0.001068995,0.0000651171,0.0002256597],"category_scores_gemma":[0.00073141925,0.00011747568,0.00009145658,0.00068525213,0.000067482135,0.0005218618,0.0002561409,0.001000036,0.000003453896],"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":[0.000065775246,0.00008733373,0.0026020505,7.869303e-7,0.000038485156,3.721013e-7,0.00004097794,0.6869451,0.0000026488349,0.28508645,0.0006200825,0.024509965],"study_design_scores_gemma":[0.00038503122,0.00014698133,0.00037228907,0.0000012403359,0.00003975104,0.00018884639,0.0009896195,0.42551267,0.0000011946875,0.54025877,0.03195325,0.00015035957],"about_ca_topic_score_codex":0.0000139336635,"about_ca_topic_score_gemma":0.00006244841,"teacher_disagreement_score":0.7503706,"about_ca_system_score_codex":0.0004760397,"about_ca_system_score_gemma":0.0017402273,"threshold_uncertainty_score":0.9998981},"labels":[],"label_agreement":null},{"id":"W4289823579","doi":"10.1109/tac.2022.3195381","title":"On Optimizing the Conditional Value-at-Risk of a Maximum Cost for Risk-Averse Safety Analysis","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Automatic Control","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"University of Toronto","funders":"","keywords":"CVAR; Mathematical optimization; Markov decision process; State space; Computer science; Dynamic programming; Constraint (computer-aided design); Markov process; Mathematics; Risk management; Expected shortfall; Statistics","score_opus":0.024470880897066654,"score_gpt":0.30347796427952556,"score_spread":0.2790070833824589,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4289823579","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":[0.021901326,0.000017983577,0.96972,0.0006158838,0.00056776195,0.0012812692,0.005386499,0.00007096938,0.0004382917],"genre_scores_gemma":[0.9953475,0.000057636364,0.0026696783,0.00034647936,0.000019650948,0.00055446295,0.000042147327,0.0000192735,0.00094316335],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9956809,0.0010121039,0.0010484492,0.00045392226,0.001532216,0.00027237108],"domain_scores_gemma":[0.9900546,0.007863936,0.0009720227,0.0007775113,0.00023382338,0.00009809729],"candidate_categories":["sts","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0036477088,0.00021122578,0.00055596046,0.0007483445,0.0018387651,0.00007981153,0.0005396066,0.000063152,0.004782163],"category_scores_gemma":[0.0003395834,0.0001494872,0.0008621077,0.0016762818,0.00012328231,0.00013252645,0.000004477013,0.00026200133,0.00006309115],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005080335,0.00022019503,0.00014363819,0.0000018395787,0.0007762298,0.0000014174833,0.00046823788,0.968674,0.000019933155,0.00038801096,0.0010165463,0.027781893],"study_design_scores_gemma":[0.0028389087,0.0002822907,0.0014915143,0.0000040974815,0.0018139868,0.0000067272085,0.00062004547,0.9851215,0.00013019012,0.005112506,0.0024171034,0.00016117119],"about_ca_topic_score_codex":0.00010880873,"about_ca_topic_score_gemma":0.00009647557,"teacher_disagreement_score":0.9734462,"about_ca_system_score_codex":0.00021072237,"about_ca_system_score_gemma":0.00012582449,"threshold_uncertainty_score":0.9994607},"labels":[],"label_agreement":null},{"id":"W4290791833","doi":"10.1016/j.ejor.2022.08.003","title":"Bowley vs. Pareto optima in reinsurance contracting","year":2022,"lang":"en","type":"article","venue":"European Journal of Operational Research","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"University of Waterloo","funders":"","keywords":"Reinsurance; Pareto principle; Mathematical economics; Pareto optimal; Competitive equilibrium; Pareto efficiency; Economics; Status quo; Mathematical optimization; Popularity; Microeconomics; Computer science; Multi-objective optimization; Mathematics; Actuarial science","score_opus":0.24601652644715927,"score_gpt":0.46849259681837346,"score_spread":0.2224760703712142,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4290791833","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":[0.9269237,0.000723283,0.0033224418,0.007948355,0.00062014326,0.00022083758,0.000018651275,0.0000064767446,0.060216106],"genre_scores_gemma":[0.994461,0.00021166533,0.002377815,0.00019333948,0.00028418828,0.0000032399048,0.0000042423835,0.000015315,0.0024492226],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.98852956,0.0054349448,0.001190402,0.000246898,0.004306416,0.00029177556],"domain_scores_gemma":[0.9958902,0.0016586365,0.00032627018,0.00023326887,0.001753981,0.00013767184],"candidate_categories":["metaresearch","insufficient_payload"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.046300046,0.00007817273,0.0002028356,0.00083151367,0.00063662214,0.00043567026,0.0010066967,0.000011470126,0.0013521858],"category_scores_gemma":[0.008554524,0.00006230357,0.000079984165,0.0013799918,0.00008009915,0.0006694757,0.00024304404,0.0010123783,0.0001455979],"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":[0.00097130804,0.00025115395,0.06700902,0.0000015689485,0.000019548183,0.0019272759,0.00216651,0.8107483,0.0013933299,0.0037899453,0.060277097,0.051444963],"study_design_scores_gemma":[0.0028668556,0.0014729999,0.38263658,0.000051899715,0.0000041535823,0.00087525736,0.004179414,0.048915107,0.00033708612,0.0021647145,0.5561935,0.00030242212],"about_ca_topic_score_codex":0.000011570067,"about_ca_topic_score_gemma":0.0000055609958,"teacher_disagreement_score":0.7618332,"about_ca_system_score_codex":0.00013742107,"about_ca_system_score_gemma":0.00048250266,"threshold_uncertainty_score":0.99979687},"labels":[],"label_agreement":null},{"id":"W4293232070","doi":"10.48550/arxiv.2203.02599","title":"A reverse ES (CVaR) optimization formula","year":2022,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"CVAR; Mathematics; Simple (philosophy); Function (biology); Applied mathematics; Mathematical optimization; Convex function; Optimization problem; Regular polygon; Expected shortfall; Finance; Risk management; Economics","score_opus":0.18829881148676214,"score_gpt":0.266905202768457,"score_spread":0.07860639128169483,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4293232070","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":[0.13060611,0.00010087966,0.8372167,0.0002275449,0.0018175839,0.000686645,0.00015402502,0.00024914028,0.02894138],"genre_scores_gemma":[0.9664615,0.0020359573,0.004761066,0.0002018166,0.00012302834,0.0000031594445,0.00023023924,0.000039322375,0.026143903],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99677575,0.0004292798,0.00055275945,0.0013851364,0.000508738,0.00034832297],"domain_scores_gemma":[0.9966094,0.0003516807,0.00080078334,0.0015908352,0.00044712465,0.00020020867],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0014069655,0.00034109902,0.0004730017,0.000834515,0.00045259253,0.00026582784,0.0017428987,0.000321814,0.0049292515],"category_scores_gemma":[0.0005725974,0.00035472243,0.00038419792,0.0018054992,0.00010956633,0.0005958598,0.0018590166,0.00061704044,0.00023868552],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007579203,0.000060792114,0.0052571916,0.0000055598885,0.000034584868,0.00011545089,0.00021430993,0.9696034,0.0000010706539,0.016491009,0.007391704,0.0007490877],"study_design_scores_gemma":[0.00044646728,0.000049897986,0.00048163117,0.000016422304,0.00009934647,0.000004341164,0.00069272035,0.92033774,0.000008227732,0.037464768,0.039943177,0.00045524357],"about_ca_topic_score_codex":0.00018120944,"about_ca_topic_score_gemma":0.000036650894,"teacher_disagreement_score":0.8358554,"about_ca_system_score_codex":0.0002608368,"about_ca_system_score_gemma":0.00030344565,"threshold_uncertainty_score":0.99989045},"labels":[],"label_agreement":null},{"id":"W4296709748","doi":"10.1111/poms.13877","title":"Constrained optimization of objective functions determined from random forests","year":2022,"lang":"en","type":"article","venue":"Production and Operations Management","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"McGill University","funders":"","keywords":"Mathematical optimization; Heuristics; Computer science; Random forest; Set (abstract data type); Tree (set theory); Sensitivity (control systems); Sampling (signal processing); Optimization problem; Mathematics; Artificial intelligence","score_opus":0.03030334422869278,"score_gpt":0.29814640814168486,"score_spread":0.26784306391299206,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4296709748","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":[0.122183196,0.00018217631,0.8647458,0.0022781338,0.0016582778,0.0013887982,0.00011013873,0.00007741053,0.0073760506],"genre_scores_gemma":[0.9788992,0.0001834562,0.01348795,0.000073166426,0.00007607389,0.00021462329,0.00014675964,0.000008489907,0.006910277],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981822,0.00022686544,0.00050321384,0.00043344815,0.00055440835,0.00009991212],"domain_scores_gemma":[0.9991288,0.00007993432,0.00014807787,0.00034441336,0.00025549598,0.000043278975],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0009061259,0.0000996428,0.00016861627,0.00040175358,0.0007288782,0.00011804906,0.00014508057,0.000019296614,0.0010109994],"category_scores_gemma":[0.0003575442,0.00008911686,0.000050418574,0.0008352943,0.00007895258,0.00035991616,0.0001360484,0.000071317416,0.000009493553],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009228692,0.00008910678,0.0026406723,0.0000014302734,0.000037486203,8.862774e-7,0.0005740678,0.9750404,0.00004630566,0.0011720558,0.0020574238,0.018247886],"study_design_scores_gemma":[0.002493099,0.00023467666,0.018856175,0.000011110118,0.00018884626,0.000018562714,0.011095336,0.94424194,0.00044223288,0.0035354553,0.018527377,0.0003552061],"about_ca_topic_score_codex":0.00006910694,"about_ca_topic_score_gemma":0.0001468375,"teacher_disagreement_score":0.85671604,"about_ca_system_score_codex":0.000037479884,"about_ca_system_score_gemma":0.00003223527,"threshold_uncertainty_score":0.9999022},"labels":[],"label_agreement":null},{"id":"W4296912309","doi":"10.1109/adconip55568.2022.9894158","title":"Distributionally Robust Chance-Constrained Optimization with Deep Kernel Ambiguity Set","year":2022,"lang":"en","type":"article","venue":"","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Kernel (algebra); Mathematical optimization; Ambiguity; Computer science; Nonlinear programming; Robust optimization; Kernel method; Mathematics; Artificial intelligence; Algorithm; Nonlinear system; Support vector machine","score_opus":0.06346209957747093,"score_gpt":0.31253697408445263,"score_spread":0.2490748745069817,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4296912309","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":[0.014276857,0.00003103844,0.9664159,0.0012274523,0.00023693877,0.0002606395,0.00019004314,0.00010285897,0.017258294],"genre_scores_gemma":[0.95435864,0.00004374764,0.038445827,0.0004933006,0.000078282304,0.000079935184,0.0007248726,0.000016145807,0.0057592257],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99693465,0.00019786642,0.0004824733,0.0005010427,0.0016205289,0.00026342162],"domain_scores_gemma":[0.99855506,0.00019588476,0.00028260853,0.0004309966,0.0004154968,0.00011994755],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0014586565,0.000152096,0.0002081588,0.0001782857,0.00075031206,0.0002096707,0.0005153122,0.00004220223,0.00944072],"category_scores_gemma":[0.00031090475,0.000112234506,0.00007047751,0.0014565034,0.00011608726,0.0003904028,0.00017053403,0.0001543069,0.000053713113],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007326632,0.000055467837,0.007337286,4.1855043e-7,0.000010292138,0.0000075878556,0.00013239015,0.9794048,0.0000040627415,0.004488856,0.0045445077,0.0039410414],"study_design_scores_gemma":[0.0007572086,0.00017369755,0.0071052033,0.0000019348674,0.000016095848,0.00006444985,0.001317731,0.9771778,0.000048514,0.0021860858,0.010875158,0.00027611523],"about_ca_topic_score_codex":0.000053603548,"about_ca_topic_score_gemma":0.00005547996,"teacher_disagreement_score":0.94008183,"about_ca_system_score_codex":0.00009489116,"about_ca_system_score_gemma":0.00018489489,"threshold_uncertainty_score":0.9914648},"labels":[],"label_agreement":null},{"id":"W4297141705","doi":"10.1016/j.insmatheco.2022.09.006","title":"Bilateral risk sharing in a comonotone market with rank-dependent utilities","year":2022,"lang":"en","type":"article","venue":"Insurance Mathematics and Economics","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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; University of Calgary","keywords":"Rank (graph theory); Business; Economics; Actuarial science; Econometrics; Mathematics; Combinatorics","score_opus":0.03624562839216128,"score_gpt":0.27320694856929184,"score_spread":0.23696132017713056,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4297141705","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":[0.9914582,0.000106436055,0.0009242481,0.00009237252,0.00011372443,0.00019850099,0.00008959657,0.000014033185,0.007002937],"genre_scores_gemma":[0.9946185,0.0009638619,0.0033439898,0.00005173368,0.000014643501,0.00004644374,0.000003009574,0.000013824368,0.00094398885],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986948,0.000041711082,0.0005728832,0.00031541902,0.00019426412,0.00018097422],"domain_scores_gemma":[0.99897623,0.00027375363,0.0003158018,0.00035523815,0.000030374356,0.00004861871],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019122533,0.000119756805,0.0002986797,0.0001925723,0.00021516507,0.00022885166,0.0003112295,0.000026818318,0.00024282974],"category_scores_gemma":[0.00007329175,0.00009638247,0.000034702924,0.00017482614,0.000052298896,0.00023612555,0.00018891966,0.00015126774,0.000008436801],"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":[0.00016715987,0.00013055363,0.8638143,0.000013033004,0.000022935006,0.000012579756,0.0061030234,0.11003799,0.0000026585471,0.0040552025,0.00016166373,0.015478891],"study_design_scores_gemma":[0.0015254061,0.00012283197,0.14516854,0.000020523661,0.000010557184,0.0000895988,0.0053506047,0.71185505,0.000027535825,0.13187982,0.0035865302,0.00036295748],"about_ca_topic_score_codex":0.00012553496,"about_ca_topic_score_gemma":0.00021502358,"teacher_disagreement_score":0.71864575,"about_ca_system_score_codex":0.000046556655,"about_ca_system_score_gemma":0.000027089796,"threshold_uncertainty_score":0.3930363},"labels":[],"label_agreement":null},{"id":"W4298167153","doi":"10.48550/arxiv.1905.12095","title":"On Linear Programming for Constrained and Unconstrained Average-Cost Markov Decision Processes with Countable Action Spaces and Strictly Unbounded Costs","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Alberta Innovates; Alberta Innovates - Technology Futures; DeepMind; Alberta Machine Intelligence Institute","keywords":"Mathematics; Countable set; Markov decision process; Action (physics); Duality (order theory); State space; Mathematical optimization; Markov kernel; Discrete mathematics; Markov chain; Applied mathematics; Markov process; Markov model; Variable-order Markov model; Statistics","score_opus":0.10955146812047985,"score_gpt":0.2775900175842334,"score_spread":0.16803854946375357,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4298167153","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":[0.6206557,0.000066497654,0.37580755,0.000056700585,0.00026565243,0.0018400672,0.00015405218,0.000073350624,0.0010803981],"genre_scores_gemma":[0.99033624,0.0010556956,0.006601551,0.000053857493,0.000055493158,0.000008799229,0.00010579277,0.000030698386,0.0017518987],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9973453,0.00011017833,0.00040123437,0.0013921359,0.0003796838,0.0003714158],"domain_scores_gemma":[0.99484694,0.0027038788,0.0007004334,0.0005601128,0.0009712671,0.00021738255],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0009591537,0.00042465932,0.00056985946,0.0005822205,0.00037741644,0.00073583005,0.0004049794,0.00036544263,0.00003667234],"category_scores_gemma":[0.0012083284,0.00035157794,0.00008045566,0.0009546536,0.00033939135,0.0005883082,0.00023224558,0.0003419285,0.000011659631],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.007747031,0.00040160233,0.07230164,0.00052077376,0.00034252735,0.0001436852,0.0006710465,0.77906805,0.0000313688,0.012352733,0.0013868388,0.1250327],"study_design_scores_gemma":[0.01483274,0.0027450921,0.005699578,0.0019235035,0.00080292474,0.000112137735,0.0060415585,0.88705677,0.0005081412,0.05383803,0.023545377,0.0028941587],"about_ca_topic_score_codex":0.00011744596,"about_ca_topic_score_gemma":0.00064778473,"teacher_disagreement_score":0.36968046,"about_ca_system_score_codex":0.00016319579,"about_ca_system_score_gemma":0.00080304046,"threshold_uncertainty_score":0.9998936},"labels":[],"label_agreement":null},{"id":"W4298359184","doi":"10.1016/j.spa.2017.01.011","title":"Arbitrage theory for non convex financial market models","year":2017,"lang":"en","type":"article","venue":"Stochastic Processes and their Applications","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Arbitrage; Financial market; Regular polygon; Economics; Financial economics; Business; Finance; Mathematics","score_opus":0.04799805968195127,"score_gpt":0.3368354241211522,"score_spread":0.28883736443920094,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4298359184","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":[0.0006301604,0.00021512968,0.9826264,0.0004196658,0.00006676773,0.0008333386,0.00015219107,0.000026873808,0.015029502],"genre_scores_gemma":[0.9952656,0.00006370049,0.001907845,0.00012762447,0.00017451272,0.0008941092,0.000014135499,0.000013248122,0.0015392037],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99891585,0.000014466188,0.000294416,0.00040073716,0.00018587988,0.0001886303],"domain_scores_gemma":[0.99766636,0.0008174987,0.00032012034,0.00065425405,0.00044581122,0.00009597562],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00088533957,0.00013900737,0.00021465772,0.000079392485,0.0011562657,0.00040686826,0.0006262452,0.00007559248,0.000033821278],"category_scores_gemma":[0.0011723202,0.000096031006,0.00005254658,0.00014622296,0.00020397834,0.00038947136,0.00008842629,0.000076608056,0.000011049391],"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":[0.00020702761,0.00013367466,0.000038611033,0.000062295774,0.000026747555,2.4196092e-7,0.0013714529,0.0013847075,0.00008087525,0.78903484,0.0037084266,0.20395112],"study_design_scores_gemma":[0.00027038855,0.000030034684,0.00016977898,0.00001251375,0.000015557904,0.0000027530314,0.00025349695,0.064722136,0.00006735886,0.9307557,0.0035691138,0.00013120504],"about_ca_topic_score_codex":0.0000068149916,"about_ca_topic_score_gemma":0.000017639453,"teacher_disagreement_score":0.99463546,"about_ca_system_score_codex":0.0000059826257,"about_ca_system_score_gemma":0.00020807481,"threshold_uncertainty_score":0.8893178},"labels":[],"label_agreement":null},{"id":"W4306359307","doi":"10.1007/s10479-022-04993-w","title":"Robust multivariate adaptive regression splines under cross-polytope uncertainty: an application in a natural gas market","year":2022,"lang":"en","type":"article","venue":"Annals of Operations Research","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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 Calgary","funders":"","keywords":"Robustification; Multivariate adaptive regression splines; Computer science; Robust optimization; Mathematical optimization; Curse of dimensionality; Interpretability; Uncertainty quantification; Mars Exploration Program; Univariate; Multivariate statistics; Exploit; Nonparametric regression; Regression analysis; Machine learning; Mathematics; Artificial intelligence; Outlier","score_opus":0.4183602756074331,"score_gpt":0.5421433808573288,"score_spread":0.12378310524989572,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4306359307","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":[0.9803088,0.00046568664,0.009454942,0.005605661,0.00018771974,0.0010570948,0.00012285734,0.000027978238,0.0027692884],"genre_scores_gemma":[0.9914051,0.00028253844,0.0022747118,0.00013670692,0.0000727643,0.00026430658,0.00012941408,0.000016208623,0.0054182243],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9941353,0.0017997038,0.000787994,0.00063739624,0.0022399956,0.0003995995],"domain_scores_gemma":[0.9959175,0.0007437966,0.00011998159,0.0007911515,0.0023068364,0.0001207165],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009927142,0.00013632553,0.0002546232,0.0011197458,0.0010330998,0.00034913418,0.0009164354,0.00007408554,0.0008571007],"category_scores_gemma":[0.001593566,0.0001056358,0.00007504711,0.0031367696,0.00022997057,0.0009414436,0.0004243393,0.0005525182,0.000030998955],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00044867903,0.00027799123,0.0040282696,0.0000013889959,0.000008313987,0.0000043411633,0.00087011117,0.96368,0.0011084233,0.0051037045,0.004167893,0.020300847],"study_design_scores_gemma":[0.00040414237,0.00021847757,0.034833882,0.000011204924,0.0000014801991,0.000004570777,0.0030078113,0.95266926,0.0005989648,0.0051041273,0.0030117987,0.00013426834],"about_ca_topic_score_codex":0.003430735,"about_ca_topic_score_gemma":0.002354318,"teacher_disagreement_score":0.030805614,"about_ca_system_score_codex":0.000070859016,"about_ca_system_score_gemma":0.0003861922,"threshold_uncertainty_score":0.9384652},"labels":[],"label_agreement":null},{"id":"W4306746472","doi":"10.3233/jifs-222041","title":"Uncertain optimal control problem with the first hitting time objective and application to a portfolio selection model","year":2022,"lang":"en","type":"article","venue":"Journal of Intelligent & Fuzzy Systems","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Optimal control; Mathematical optimization; Portfolio; Computer science; Control (management); Selection (genetic algorithm); Bellman equation; Path (computing); Mathematics; Control theory (sociology); Economics","score_opus":0.01904060300330759,"score_gpt":0.2884834127110631,"score_spread":0.2694428097077555,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4306746472","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":[0.11069723,0.0004912434,0.8834408,0.001964923,0.00019881522,0.001347144,0.000015132151,0.000021928818,0.0018227634],"genre_scores_gemma":[0.9966823,0.00003439908,0.0016697308,0.00021260073,0.00018386735,0.00010985146,0.0000015052697,0.000017705826,0.0010880682],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9967704,0.00028550831,0.0009929652,0.00028809352,0.0014466429,0.00021639808],"domain_scores_gemma":[0.99692297,0.00040619663,0.0013587663,0.00021037823,0.0009691412,0.0001325276],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0049252356,0.00015937867,0.00037000005,0.00041412184,0.0005948738,0.00030475433,0.00044402765,0.00004203649,0.000023542823],"category_scores_gemma":[0.00014801286,0.00009206545,0.00009443897,0.0010536463,0.000037253325,0.00028458686,0.00006948524,0.0002981406,0.00001660479],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003852193,0.000045666755,0.0028286525,0.000003257923,0.000056991474,0.000004545714,0.00208595,0.9842401,0.00017019265,0.00038978353,0.006223139,0.0035664833],"study_design_scores_gemma":[0.00038142866,0.0008301255,0.0003022652,0.0000315717,0.00005955393,0.0006374857,0.0050788205,0.9731732,0.00008854383,0.00041893413,0.018842861,0.000155201],"about_ca_topic_score_codex":0.000094815674,"about_ca_topic_score_gemma":0.000019174659,"teacher_disagreement_score":0.885985,"about_ca_system_score_codex":0.00024209962,"about_ca_system_score_gemma":0.00020757403,"threshold_uncertainty_score":0.45753482},"labels":[],"label_agreement":null},{"id":"W4310251704","doi":"10.1287/opre.2022.2387","title":"Data-Driven Optimization with Distributionally Robust Second Order Stochastic Dominance Constraints","year":2022,"lang":"en","type":"article","venue":"Operations Research","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Group for Research in Decision Analysis; HEC Montréal","funders":"","keywords":"Mathematical optimization; Bounding overwatch; Exploit; Computer science; Ambiguity; Robust optimization; Stochastic programming; Stochastic dominance; Stochastic optimization; Optimization problem; Adaptability; Empirical distribution function; Mathematics","score_opus":0.21635385931483805,"score_gpt":0.4358256818899646,"score_spread":0.21947182257512657,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4310251704","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":[0.012437824,0.0000838126,0.9770969,0.002302244,0.00015602939,0.0007460145,0.0029275478,0.0000306361,0.0042189467],"genre_scores_gemma":[0.89712703,0.000059994203,0.08316737,0.000106077,0.00012682642,0.00036666996,0.005119057,0.000029201376,0.0138978],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9947566,0.00080526306,0.00050249975,0.0007625478,0.002789434,0.0003836468],"domain_scores_gemma":[0.995526,0.0006604213,0.000069922484,0.0014153278,0.002192537,0.00013576816],"candidate_categories":["sts","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0045656734,0.00012550398,0.00018809468,0.00044134376,0.0024955673,0.0007390688,0.0018089855,0.00004477503,0.01767002],"category_scores_gemma":[0.0030178137,0.0001004664,0.000023753155,0.0032696964,0.00046878716,0.00091255497,0.0010870286,0.0004826588,0.00016570321],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000053161813,0.0001028529,0.00017558753,6.9612895e-7,0.000012058916,0.00000946742,0.00013267019,0.9737263,0.000019830532,0.002686732,0.020321911,0.0027587754],"study_design_scores_gemma":[0.0004925899,0.0001316333,0.00035342108,0.00000418441,0.0000056509857,0.00004803925,0.0009765774,0.98198825,0.000006566771,0.00017134796,0.015682362,0.00013939505],"about_ca_topic_score_codex":0.00005391154,"about_ca_topic_score_gemma":0.00057480624,"teacher_disagreement_score":0.8939296,"about_ca_system_score_codex":0.00013701156,"about_ca_system_score_gemma":0.0013135915,"threshold_uncertainty_score":0.998803},"labels":[],"label_agreement":null},{"id":"W4310822182","doi":"10.48550/arxiv.2212.01533","title":"Bounds on Choquet Risk Measures in Finite Product Spaces with Ambiguous Marginals","year":2022,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Mathematics; Choquet integral; Generalization; Measure (data warehouse); Mathematical optimization; Applied mathematics; Space (punctuation); Dual polyhedron; Product (mathematics); Risk measure; Marginal distribution; Computer science; Economics; Pure mathematics; Random variable; Statistics; Mathematical analysis; Artificial intelligence","score_opus":0.1615751888564097,"score_gpt":0.25719341730185996,"score_spread":0.09561822844545026,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4310822182","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":[0.9719326,0.0002068399,0.009108008,0.00023635781,0.0006354189,0.0006533375,0.00013127938,0.00010630425,0.016989892],"genre_scores_gemma":[0.9841726,0.0034775129,0.00025440098,0.000077054916,0.00008845406,0.000005026167,0.000048087943,0.000033585162,0.011843289],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9955353,0.0009719908,0.00045667356,0.0018403723,0.0007690365,0.0004266212],"domain_scores_gemma":[0.9964362,0.00043859798,0.0008804684,0.001739678,0.00033782737,0.00016727674],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0026917476,0.0004270589,0.0006114889,0.0013691183,0.00035658112,0.00039924358,0.0015430863,0.00019594417,0.00054609607],"category_scores_gemma":[0.00091118796,0.0003712688,0.00019419852,0.0023760824,0.00019832523,0.00034234315,0.0007150786,0.0011851774,0.0001466252],"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":[0.0004428186,0.00012951644,0.167003,0.000005021426,0.000043465643,0.00033520427,0.00035641633,0.8265331,8.424319e-7,0.0014567622,0.0010672555,0.002626614],"study_design_scores_gemma":[0.004403113,0.0015284223,0.4540398,0.0003564685,0.0005170458,0.000021678625,0.0043826806,0.20518996,0.00021190323,0.14667007,0.17910661,0.0035722593],"about_ca_topic_score_codex":0.0021788976,"about_ca_topic_score_gemma":0.00117632,"teacher_disagreement_score":0.62134314,"about_ca_system_score_codex":0.00026923328,"about_ca_system_score_gemma":0.0004127184,"threshold_uncertainty_score":0.99987394},"labels":[],"label_agreement":null},{"id":"W4311858761","doi":"10.1515/strm-2021-0033","title":"Minkowski deviation measures","year":2022,"lang":"en","type":"article","venue":"Statistics & Risk Modeling","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Mathematics; Converse; Minkowski space; Measure (data warehouse); Bounded function; Large deviations theory; Minkowski addition; Absolute deviation; Set (abstract data type); Regular polygon; Applied mathematics; Mathematical analysis; Statistics; Computer science; Data mining","score_opus":0.11108072689744433,"score_gpt":0.36783754404152613,"score_spread":0.25675681714408183,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4311858761","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":[0.027526462,0.0002929847,0.968813,0.00007813367,0.0004932273,0.00014361339,0.0006136689,0.000047836376,0.0019910787],"genre_scores_gemma":[0.88664216,0.00044144772,0.111849606,0.00009772756,0.00006973876,0.000027324633,0.00010690611,0.00001921652,0.00074585073],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9963373,0.00041971228,0.0006829625,0.0004074122,0.0019130463,0.00023957419],"domain_scores_gemma":[0.998048,0.00050805445,0.0004889018,0.00041654464,0.00044896195,0.00008955386],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0031234631,0.00012543616,0.00019462174,0.00027503146,0.00091506843,0.00019572901,0.00042920726,0.000033241668,0.0005190836],"category_scores_gemma":[0.002600416,0.0001152374,0.000057960777,0.00060774974,0.000022949864,0.00017754182,0.00015157113,0.00025536687,0.000107403175],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019670526,0.000019887593,0.0037542908,4.3997485e-7,0.0000073055494,0.0000039268957,0.00064728584,0.8779754,0.0000097781785,0.010611956,0.0056630024,0.10128706],"study_design_scores_gemma":[0.0001437204,0.0000323828,0.0002306369,9.116663e-7,0.000021127187,0.0000033287988,0.00046019707,0.7801052,0.000006277798,0.20775858,0.011119829,0.00011784408],"about_ca_topic_score_codex":0.00029093865,"about_ca_topic_score_gemma":0.000088688095,"teacher_disagreement_score":0.8591157,"about_ca_system_score_codex":0.000076349934,"about_ca_system_score_gemma":0.00013156886,"threshold_uncertainty_score":0.7038059},"labels":[],"label_agreement":null},{"id":"W4313420380","doi":"10.1016/j.compchemeng.2022.108122","title":"Scenario reduction and scenario tree generation for stochastic programming using Sinkhorn distance","year":2022,"lang":"en","type":"article","venue":"Computers & Chemical Engineering","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Reduction (mathematics); Computer science; Tree (set theory); Stochastic programming; Mathematical optimization; Algorithm; Mathematics; Combinatorics","score_opus":0.05620759789295142,"score_gpt":0.29681804109688387,"score_spread":0.24061044320393243,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313420380","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":[0.22733438,0.00015846052,0.7714376,0.000075476884,0.0007001823,0.00022590942,0.0000042209863,0.00006049017,0.0000032813166],"genre_scores_gemma":[0.8881485,0.0000026273694,0.111363016,0.000016798767,0.00034255386,0.00003789057,0.000039308536,0.000019454452,0.000029859226],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985604,0.000019986823,0.00035755325,0.00042406545,0.00040727635,0.00023067788],"domain_scores_gemma":[0.99938977,0.00012961295,0.00011344125,0.00019595756,0.00007886843,0.00009235302],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046671662,0.00013609442,0.00018604557,0.00014990708,0.00025184217,0.00020526488,0.00019416439,0.00004265301,0.000004879775],"category_scores_gemma":[0.0001749669,0.00013928897,0.00006375313,0.0004420977,0.000029015262,0.00020987606,0.00012319976,0.00014563695,5.638267e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016147058,0.000018169949,0.000024344088,0.000004381852,0.0000071344994,9.447444e-7,0.00029997516,0.8786064,0.044580378,0.00019425206,0.00026331513,0.07598459],"study_design_scores_gemma":[0.0002632197,0.00003097309,0.000014942344,0.000009953048,0.000013381651,0.000043174226,0.00004713564,0.993739,0.0019676085,0.000104071114,0.003603858,0.00016267548],"about_ca_topic_score_codex":0.0000066983166,"about_ca_topic_score_gemma":4.1641763e-7,"teacher_disagreement_score":0.6608141,"about_ca_system_score_codex":0.00015220205,"about_ca_system_score_gemma":0.00003331505,"threshold_uncertainty_score":0.5680039},"labels":[],"label_agreement":null},{"id":"W4315864698","doi":"10.1016/j.cie.2023.109009","title":"Robust optimization for energy-aware cryptocurrency farm location with renewable energy","year":2023,"lang":"en","type":"article","venue":"Computers & Industrial Engineering","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Université de Montréal; Group for Research in Decision Analysis","funders":"","keywords":"Profit (economics); Renewable energy; Cryptocurrency; Mathematical optimization; Econometrics; Microeconomics; Business; Economics; Computer science; Mathematics; Engineering; Electrical engineering","score_opus":0.1051429632606319,"score_gpt":0.2810745906430107,"score_spread":0.17593162738237883,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4315864698","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":[0.00045083652,0.000047443027,0.9966994,0.00016581391,0.0019702236,0.00019278571,0.000013176759,0.00032872113,0.00013163412],"genre_scores_gemma":[0.8421123,0.0005899985,0.14310476,0.00032598153,0.006278509,0.00057256786,0.0020283128,0.000295703,0.0046918746],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979176,0.000043536922,0.0005580815,0.0005074597,0.00060227804,0.0003710693],"domain_scores_gemma":[0.99834114,0.00047636838,0.00022150856,0.00039147184,0.0004292052,0.00014029289],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00061709934,0.00023001696,0.00029245456,0.0006594247,0.00017504694,0.00031497036,0.000439895,0.00020124849,0.000023878769],"category_scores_gemma":[0.00027845197,0.00019982325,0.00007900794,0.002867281,0.00002112083,0.00036772277,0.00007819504,0.00009883319,0.000006350739],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000033430526,0.000011890153,0.00009080838,0.0000025730128,0.000016874656,0.0000022194574,0.000046825517,0.9183683,0.000009439124,0.00062955124,0.014318304,0.06646981],"study_design_scores_gemma":[0.00086961803,0.00010423153,0.000024617928,0.00005556496,0.000015703628,0.0000034503312,0.000041414016,0.9475819,0.0002776266,0.00014193592,0.050640583,0.0002433623],"about_ca_topic_score_codex":0.00019972872,"about_ca_topic_score_gemma":0.000030200952,"teacher_disagreement_score":0.8535946,"about_ca_system_score_codex":0.000076568846,"about_ca_system_score_gemma":0.0001528592,"threshold_uncertainty_score":0.8148556},"labels":[],"label_agreement":null},{"id":"W4318479566","doi":"10.1287/opre.2022.2429","title":"Technical Note—Risk-Averse Regret Minimization in Multistage Stochastic Programs","year":2023,"lang":"en","type":"article","venue":"Operations Research","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Group for Research in Decision Analysis; HEC Montréal","funders":"","keywords":"Regret; Minification; Mathematical optimization; Computer science; Stochastic programming; Mathematics; Machine learning","score_opus":0.247145376137846,"score_gpt":0.510228524390415,"score_spread":0.26308314825256895,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4318479566","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":[0.31723922,0.00011597847,0.6568306,0.0058166157,0.00071168464,0.0050298846,0.0001738421,0.00063822506,0.01344393],"genre_scores_gemma":[0.9692381,0.00029622784,0.013680079,0.00001688124,0.00009207371,0.00029764563,0.0002605537,0.000024251884,0.016094167],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9952347,0.0008879166,0.0006626886,0.00059778325,0.0021149477,0.0005019139],"domain_scores_gemma":[0.99717087,0.0009677087,0.000048143895,0.00073332817,0.00093082373,0.0001491333],"candidate_categories":["metaresearch","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00731458,0.000117126714,0.00019114121,0.0016540859,0.00062399573,0.0006955197,0.00060548633,0.00016971953,0.00047560595],"category_scores_gemma":[0.011468893,0.000094635834,0.00007126898,0.008063276,0.00022206189,0.00047390728,0.00021893988,0.0005175072,0.0032335347],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004721192,0.00017430219,0.0069630984,0.0000019151835,0.0000026498585,0.000034986704,0.0013360132,0.8355046,0.00034828737,0.002330526,0.011873326,0.14138311],"study_design_scores_gemma":[0.00045423623,0.00009851023,0.010710402,0.000017388102,0.0000028307832,0.000004121966,0.000978209,0.97770566,0.000044232613,0.0009589501,0.00889371,0.00013175323],"about_ca_topic_score_codex":0.0005544746,"about_ca_topic_score_gemma":0.004669443,"teacher_disagreement_score":0.65199894,"about_ca_system_score_codex":0.00012368208,"about_ca_system_score_gemma":0.0002959628,"threshold_uncertainty_score":0.99754256},"labels":[],"label_agreement":null},{"id":"W4319791779","doi":"10.1017/s0269964822000079","title":"Pareto-optimal reinsurance with default risk and solvency regulation","year":2023,"lang":"en","type":"article","venue":"Probability in the Engineering and Informational Sciences","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; University of Calgary","keywords":"Reinsurance; Indemnity; Solvency; Actuarial science; Pareto principle; Constraint (computer-aided design); Asset (computer security); Investment (military); Economics; Finance; Computer science; Mathematics","score_opus":0.03902347184049652,"score_gpt":0.2883907585299058,"score_spread":0.2493672866894093,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4319791779","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":[0.99509054,0.00006068229,0.0033439363,0.0005873121,0.000047351667,0.00016828981,0.0000073322367,0.000039197836,0.0006553623],"genre_scores_gemma":[0.99774414,0.0001737318,0.0019963572,0.000023775776,0.000017777968,0.000020134768,0.000005169453,0.0000014654364,0.000017445029],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99859864,0.000055248318,0.00032595845,0.00016928528,0.00069843174,0.00015245688],"domain_scores_gemma":[0.99894154,0.00071002234,0.00010608953,0.00013091379,0.00007867726,0.000032763575],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0054157553,0.00007568406,0.00009005338,0.00017883939,0.00027375555,0.00024852192,0.00021915948,0.000030439296,0.0000053218073],"category_scores_gemma":[0.0011917566,0.000040920928,0.000014030103,0.0013518689,0.00018843544,0.0009910435,0.000037391444,0.000088705856,0.000009705631],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000059197837,0.0000034478019,0.037103277,0.000004284633,9.023534e-7,1.3237846e-7,0.002173771,0.9479495,0.0000023716955,0.004108074,0.00006519406,0.008583106],"study_design_scores_gemma":[0.00007515766,0.00003731585,0.26583266,0.000008776379,0.0000012910443,0.000007137397,0.00038617273,0.72067577,0.000005697999,0.012056997,0.0008564782,0.000056530866],"about_ca_topic_score_codex":0.0000309683,"about_ca_topic_score_gemma":0.00002144762,"teacher_disagreement_score":0.22872938,"about_ca_system_score_codex":0.000009731946,"about_ca_system_score_gemma":0.000034154193,"threshold_uncertainty_score":0.23965026},"labels":[],"label_agreement":null},{"id":"W4319865077","doi":"10.2139/ssrn.4329890","title":"Expected Utility Optimization with Stochastically Ordered Returns","year":2023,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Economics; Financial economics; Econometrics; Business; Actuarial science","score_opus":0.040133877486520045,"score_gpt":0.3301071877368257,"score_spread":0.28997331025030565,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4319865077","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":[0.07565887,0.00018072288,0.92005837,0.00136488,0.0002371823,0.00019061682,0.0000046281657,0.00015929181,0.0021454429],"genre_scores_gemma":[0.99053794,0.0021795281,0.0029965553,0.000056959238,0.00019369354,0.000007832996,0.000025977195,0.000028794359,0.003972694],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.995553,0.00025078873,0.0006357012,0.00041023508,0.0014137955,0.0017364381],"domain_scores_gemma":[0.99802256,0.0003096172,0.00035635385,0.00040580836,0.0007438796,0.00016175932],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0049305283,0.00018134766,0.00026351,0.0004783504,0.0004095321,0.00031413263,0.00055538095,0.000107650536,0.00032517337],"category_scores_gemma":[0.0015623731,0.000121233956,0.00009547927,0.002687212,0.00008449559,0.0004934452,0.000053258933,0.0009924627,0.00018270384],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008440591,0.00014597166,0.014355693,0.0000017849221,0.00019023185,0.000038445,0.0011249081,0.8599787,0.000069718866,0.039716125,0.004027874,0.07950649],"study_design_scores_gemma":[0.0021538658,0.0010244783,0.008535566,0.000023172557,0.0000713075,0.0007112082,0.00877936,0.6264415,0.000040127386,0.34864637,0.0030153885,0.00055767497],"about_ca_topic_score_codex":0.000018275834,"about_ca_topic_score_gemma":0.0003965438,"teacher_disagreement_score":0.9170618,"about_ca_system_score_codex":0.00021675824,"about_ca_system_score_gemma":0.0020435904,"threshold_uncertainty_score":0.4943777},"labels":[],"label_agreement":null},{"id":"W4321853897","doi":"10.48550/arxiv.2302.11701","title":"Pairwise counter-monotonicity","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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; Canada Research Chairs","keywords":"Monotonic function; Pairwise comparison; Representation (politics); Property (philosophy); Mathematics; Quantile; Mathematical economics; Econometrics; Statistics; Mathematical analysis; Law","score_opus":0.2880842283291713,"score_gpt":0.2799569230244341,"score_spread":0.008127305304737209,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4321853897","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":[0.74559855,0.00006221427,0.23555362,0.00033564857,0.0027476153,0.0005634719,0.00018049109,0.0005467133,0.014411649],"genre_scores_gemma":[0.96261096,0.0014142287,0.00020278776,0.00009827764,0.00012832759,0.0000011981946,0.000054142805,0.000032720884,0.03545733],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9969384,0.0002989323,0.0005022398,0.0014727982,0.000410798,0.00037683532],"domain_scores_gemma":[0.9963623,0.0005769829,0.0005739935,0.0017395377,0.00052396517,0.00022318726],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00170449,0.00032673083,0.0004958254,0.0006417006,0.00021740307,0.00030146947,0.0018539493,0.0004394871,0.00038057598],"category_scores_gemma":[0.0006006685,0.0003189063,0.00039099963,0.0014043372,0.000167126,0.00033370126,0.0014890123,0.0005626685,0.0030001495],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007217462,0.00006344551,0.048609998,0.0000081845865,0.00005715779,0.00032410407,0.00021119992,0.91616,0.000002865951,0.01172765,0.021471594,0.0012916514],"study_design_scores_gemma":[0.00048532014,0.000046705896,0.017304394,0.000052002546,0.00010123974,0.0000027756598,0.0004230159,0.7286913,0.000034916924,0.22397268,0.028256068,0.0006296053],"about_ca_topic_score_codex":0.00044028927,"about_ca_topic_score_gemma":0.00028383938,"teacher_disagreement_score":0.23535083,"about_ca_system_score_codex":0.00017955698,"about_ca_system_score_gemma":0.0002859647,"threshold_uncertainty_score":0.9999263},"labels":[],"label_agreement":null},{"id":"W4322720630","doi":"10.1007/978-3-031-23876-5_2","title":"Single-Objective Optimization","year":2023,"lang":"en","type":"book-chapter","venue":"International series in management science/operations research/International series in operations research & management science","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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 New Brunswick","funders":"","keywords":"Set (abstract data type); Context (archaeology); Best practice; Best value; Statement (logic); Value (mathematics); Action (physics); Measure (data warehouse); Computer science; Problem statement; Operations research; Mathematical optimization; Mathematics; Management science; Engineering; Machine learning; Data mining; Political science; Law; Business; Geography; Marketing; Programming language","score_opus":0.17656993944075378,"score_gpt":0.47191578407399004,"score_spread":0.29534584463323627,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4322720630","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00093438383,0.00006865213,0.007153828,0.017719071,0.005799101,0.00443282,0.0002526102,0.00021974539,0.9634198],"genre_scores_gemma":[0.054559007,0.013898603,0.026395315,0.00018716036,0.00055835303,0.0015096922,0.0005863744,0.00015697701,0.90214854],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.96360666,0.0007080781,0.0031246867,0.0044058557,0.025783112,0.0023715852],"domain_scores_gemma":[0.98570025,0.00075486174,0.00026506212,0.0027468544,0.009977054,0.00055591576],"candidate_categories":["metaresearch","metaepi_narrow","bibliometrics","sts","scholarly_communication","open_science","insufficient_payload"],"consensus_categories":["sts","scholarly_communication","open_science","insufficient_payload"],"category_scores_codex":[0.043212056,0.0008660633,0.00076621707,0.032046806,0.004583532,0.011872162,0.014138955,0.0003620333,0.003979572],"category_scores_gemma":[0.00719507,0.00085003575,0.0002491815,0.01580658,0.011271101,0.0140830865,0.008725281,0.0021505354,0.0022289203],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001406551,0.00021491272,0.00015175747,0.000012549316,0.000064824286,0.00021487575,0.00069160614,0.49521402,0.00009633533,0.4944488,0.0022793524,0.006470288],"study_design_scores_gemma":[0.0019311185,0.0005189705,0.0041633327,0.0010208262,0.000031638436,0.00006846564,0.013387024,0.5287826,0.0003965811,0.14341007,0.30434832,0.0019410643],"about_ca_topic_score_codex":0.0007375454,"about_ca_topic_score_gemma":0.008260352,"teacher_disagreement_score":0.35103875,"about_ca_system_score_codex":0.0066779107,"about_ca_system_score_gemma":0.0012997543,"threshold_uncertainty_score":0.99970645},"labels":[],"label_agreement":null},{"id":"W4323058863","doi":"10.2139/ssrn.4376779","title":"Evolutionary multi-objective optimisation for large-scale portfolio selection with both random and uncertain returns","year":2023,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Queen's University","funders":"Basic and Applied Basic Research Foundation of Guangdong Province","keywords":"Portfolio; Selection (genetic algorithm); Scale (ratio); Portfolio optimization; Econometrics; Computer science; Economics; Mathematical optimization; Financial economics; Mathematics; Machine learning; Geography","score_opus":0.028035882616294323,"score_gpt":0.3237959155933546,"score_spread":0.29576003297706027,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4323058863","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":[0.21756475,0.000918679,0.779106,0.00082907523,0.0002899444,0.000751096,0.000026437796,0.00012470638,0.00038930128],"genre_scores_gemma":[0.9729124,0.007611216,0.008480801,0.00007536649,0.00040547154,0.000064754735,0.00006265148,0.000043134314,0.010344241],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9965391,0.00022084461,0.00053914427,0.0004612464,0.00077721564,0.001462444],"domain_scores_gemma":[0.99828947,0.00040895146,0.00043753983,0.00017100779,0.00056522206,0.00012783804],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006317255,0.0001992966,0.00029599588,0.0005944823,0.0007771023,0.00019898584,0.00021922935,0.00012919318,0.000035144316],"category_scores_gemma":[0.0005511426,0.00014358091,0.00012226406,0.0014165699,0.000052978783,0.00068893057,0.000031453535,0.00066567765,0.000018778632],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.012325193,0.00082567445,0.3019826,0.000025949364,0.0012379065,0.00003331117,0.011652733,0.430049,0.0012219885,0.06118543,0.033065178,0.14639504],"study_design_scores_gemma":[0.012531945,0.0016583378,0.03908271,0.000038006685,0.00016273708,0.0012504703,0.019654337,0.66831297,0.00013760253,0.24567485,0.010857642,0.00063839986],"about_ca_topic_score_codex":0.00004580912,"about_ca_topic_score_gemma":0.0013082677,"teacher_disagreement_score":0.77062523,"about_ca_system_score_codex":0.00047959934,"about_ca_system_score_gemma":0.0015649006,"threshold_uncertainty_score":0.59769213},"labels":[],"label_agreement":null},{"id":"W4323059266","doi":"10.2139/ssrn.4372148","title":"Wasserstein-Kelly Portfolios: A Robust Data-Driven Solution to Optimize Portfolio Growth","year":2023,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"University of Ottawa","funders":"","keywords":"Portfolio; Computer science; Econometrics; Economics; Business; Financial economics","score_opus":0.09284523609444259,"score_gpt":0.35196066419571853,"score_spread":0.25911542810127597,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4323059266","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":[0.17831604,0.0017021032,0.78592885,0.0130879395,0.003415201,0.0012126449,0.0001270703,0.0007422841,0.015467853],"genre_scores_gemma":[0.9401679,0.02591288,0.0077088526,0.000659858,0.0014379035,0.000024112353,0.000280786,0.00012298331,0.023684742],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9912425,0.00033981563,0.0014208661,0.0010418337,0.0023893574,0.0035656088],"domain_scores_gemma":[0.99626565,0.00030086606,0.000722102,0.0014250247,0.0007904587,0.00049592153],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.012908865,0.00037581366,0.0005632935,0.0016350189,0.0007141718,0.00066947547,0.002935732,0.00020048853,0.00028179653],"category_scores_gemma":[0.002094668,0.00030514624,0.00025356506,0.004564426,0.00007310775,0.0016940633,0.0006177605,0.0014785716,0.0021313825],"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":[0.0005288172,0.00026825306,0.01941133,0.0000056281156,0.0004743515,0.0002491559,0.0008172228,0.4454913,0.00045445055,0.06871199,0.35069594,0.11289155],"study_design_scores_gemma":[0.003808581,0.0016544255,0.012172893,0.00009856466,0.00037275098,0.002988967,0.011021183,0.40209386,0.00016176693,0.4698786,0.093547806,0.0022005776],"about_ca_topic_score_codex":0.00017667512,"about_ca_topic_score_gemma":0.0006941296,"teacher_disagreement_score":0.77822,"about_ca_system_score_codex":0.00063329533,"about_ca_system_score_gemma":0.0031590892,"threshold_uncertainty_score":0.99994004},"labels":[],"label_agreement":null},{"id":"W4324344186","doi":"10.1016/j.cor.2023.106219","title":"Risk-allocation-based index tracking","year":2023,"lang":"en","type":"article","venue":"Computers & Operations Research","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"University of Toronto; University of New Brunswick","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Benchmark (surveying); Computer science; Replicate; Portfolio; Mathematical optimization; Heuristic; Cardinality (data modeling); Matching (statistics); Mathematics; Artificial intelligence; Statistics; Data mining; Finance","score_opus":0.2675152343798474,"score_gpt":0.49898783072142117,"score_spread":0.23147259634157374,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4324344186","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":[0.2337692,0.000052232223,0.75852793,0.0043200264,0.000604423,0.00050903735,0.000017849408,0.00021693516,0.0019823655],"genre_scores_gemma":[0.991283,0.00014977729,0.005278856,0.00011966525,0.00020263171,0.000060175047,0.00008445971,0.000017699254,0.0028037243],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.995243,0.0009925432,0.00053828437,0.0005462711,0.00224522,0.00043469697],"domain_scores_gemma":[0.99481374,0.0022796837,0.000048834958,0.0008198272,0.001869278,0.00016861988],"candidate_categories":["sts","scholarly_communication","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.007899055,0.00011133497,0.00016490693,0.0017120705,0.00142931,0.0014526034,0.00096298265,0.0000927515,0.00024963522],"category_scores_gemma":[0.0036073164,0.000092620896,0.00008161207,0.006460799,0.00016674392,0.0004939954,0.00016853045,0.0004021606,0.0031477623],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000070758447,0.000032297252,0.014707819,7.811266e-7,0.0000057117322,0.000005538906,0.00048900454,0.8501717,0.000053722662,0.0010110352,0.031100007,0.10241532],"study_design_scores_gemma":[0.00024389717,0.000041211577,0.04575543,0.000008564701,0.0000018964,0.0000010275166,0.00032185158,0.9360413,0.000203939,0.001256677,0.016021673,0.00010251069],"about_ca_topic_score_codex":0.00028746173,"about_ca_topic_score_gemma":0.00031454477,"teacher_disagreement_score":0.7575138,"about_ca_system_score_codex":0.000071925366,"about_ca_system_score_gemma":0.00046318752,"threshold_uncertainty_score":0.9998707},"labels":[],"label_agreement":null},{"id":"W4362587963","doi":"10.1287/mnsc.2023.4748","title":"Supermodularity in Two-Stage Distributionally Robust Optimization","year":2023,"lang":"en","type":"article","venue":"Management Science","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Wilfrid Laurier University","funders":"","keywords":"Newsvendor model; Computer science; Mathematical optimization; Robust optimization; Optimization problem; Exploit; Scheduling (production processes); Property (philosophy); Class (philosophy); Affine transformation; Mathematical economics; Supply chain; Mathematics; Artificial intelligence","score_opus":0.09140125065034992,"score_gpt":0.39131194519671275,"score_spread":0.2999106945463628,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4362587963","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":[0.18195023,0.00001447975,0.7540188,0.0022738902,0.00080222485,0.0005951102,0.000035616787,0.00022772865,0.060081944],"genre_scores_gemma":[0.9792018,0.00019349418,0.012756664,0.00014712797,0.000029129102,0.000026800048,0.0000630511,0.0000068965533,0.0075750616],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9962737,0.000076479344,0.00046041553,0.0006939004,0.0020770123,0.00041850438],"domain_scores_gemma":[0.9989331,0.00010548369,0.00011031059,0.0005725296,0.00017961513,0.000098986246],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006437745,0.00010909078,0.00013273768,0.0011275773,0.00036722864,0.00052450685,0.0011453199,0.000021839523,0.00030204214],"category_scores_gemma":[0.000645455,0.00009300214,0.000041714855,0.010109407,0.00027124997,0.0011107892,0.00046235998,0.0000756145,0.00047880548],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000045237207,0.000024303463,0.035282124,0.000001283317,0.0000011196016,0.000020927522,0.000058119596,0.92711335,0.000016617569,0.030488707,0.0009755485,0.006013364],"study_design_scores_gemma":[0.00026082114,0.0000075076396,0.17593536,0.0000054335233,0.000002231647,6.131982e-7,0.0002785214,0.8145721,0.000033303826,0.0047773668,0.004011072,0.00011567633],"about_ca_topic_score_codex":0.000051821757,"about_ca_topic_score_gemma":0.00004253305,"teacher_disagreement_score":0.7972516,"about_ca_system_score_codex":0.00011024056,"about_ca_system_score_gemma":0.00005311367,"threshold_uncertainty_score":0.61542356},"labels":[],"label_agreement":null},{"id":"W4362635539","doi":"10.1016/j.cor.2023.106243","title":"A double-oracle, logic-based Benders decomposition approach to solve the <mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" altimg=\"si421.svg\" display=\"inline\" id=\"d1e4126\"><mml:mi>K</mml:mi></mml:math>-adaptability problem","year":2023,"lang":"en","type":"article","venue":"Computers & Operations Research","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Group for Research in Decision Analysis; HEC Montréal; Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Oracle; Mathematical optimization; Computer science; Benchmark (surveying); Knapsack problem; Adaptability; Mathematics; Algorithm","score_opus":0.10967688610944475,"score_gpt":0.37500656782604136,"score_spread":0.2653296817165966,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4362635539","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":[0.74819124,0.000065400876,0.24458781,0.0035618239,0.0004118669,0.0004478208,0.0000634296,0.00016453527,0.0025060596],"genre_scores_gemma":[0.9451654,0.00011696412,0.052273095,0.000496892,0.00031029317,0.0007584992,0.0006235808,0.000060642265,0.00019466582],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9945896,0.00046136978,0.00086683204,0.0010008394,0.002297374,0.0007839701],"domain_scores_gemma":[0.9961509,0.0013725499,0.00015839085,0.0013736571,0.0006146444,0.00032985603],"candidate_categories":["sts","scholarly_communication","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.005662949,0.00026166035,0.00022245805,0.0005264123,0.002137182,0.0019274176,0.0014308799,0.00028251248,0.00004989253],"category_scores_gemma":[0.00057605153,0.0002259979,0.00026860178,0.0026045411,0.0004584681,0.00070645893,0.0007609519,0.00063877413,0.001659646],"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":[0.00013322668,0.00016265128,0.000017882086,0.000018860832,0.000033801025,0.000015107849,0.00122334,0.37929294,0.00013778813,0.61007416,0.005162778,0.0037274377],"study_design_scores_gemma":[0.00052556535,0.0002988573,0.00030314445,0.0000470342,0.0000240504,0.000026686634,0.0012140753,0.9918132,0.00060338044,0.0010327756,0.003888292,0.00022297895],"about_ca_topic_score_codex":0.0005938347,"about_ca_topic_score_gemma":0.00046217442,"teacher_disagreement_score":0.6125202,"about_ca_system_score_codex":0.00005200416,"about_ca_system_score_gemma":0.000733796,"threshold_uncertainty_score":0.9991619},"labels":[],"label_agreement":null},{"id":"W4367020731","doi":"10.2139/ssrn.4424759","title":"Worst-case Risk Measures of Stop-Loss and Limited Loss Random Variables Under Distribution Uncertainty With Applications to Robust Reinsurance","year":2023,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Reinsurance; Random variable; Variable (mathematics); Mathematics; Distortion (music); Distribution (mathematics); Statistics; Econometrics; Computer science; Actuarial science; Economics","score_opus":0.04016458523133271,"score_gpt":0.3044448538224763,"score_spread":0.2642802685911436,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4367020731","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":[0.33425203,0.0004961118,0.6641866,0.00059676694,0.000045310357,0.00027926243,0.00006697969,0.00003079916,0.00004613993],"genre_scores_gemma":[0.9874607,0.011225441,0.0007184724,0.000025334242,0.000098294615,0.000032945594,0.000033592427,0.000015911277,0.00038931615],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9968994,0.00029490204,0.0006169545,0.00037649355,0.0008517565,0.00096045283],"domain_scores_gemma":[0.99759847,0.000653528,0.0005097625,0.00036549533,0.0007071363,0.00016561073],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006666997,0.00017253087,0.00032336375,0.00029263183,0.00056868274,0.00016758771,0.00031848744,0.00008820643,0.000007987166],"category_scores_gemma":[0.00071203726,0.00011862912,0.000077840676,0.0024099806,0.0001312466,0.00027780532,0.00004546218,0.0006426003,0.000017933988],"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":[0.0009670508,0.000069803675,0.021597875,0.0000029098862,0.00015733117,0.00002964006,0.00031257072,0.85852236,0.000053947206,0.05020779,0.0003817674,0.06769695],"study_design_scores_gemma":[0.008770077,0.0010728316,0.026202416,0.000106624844,0.00038958216,0.006257244,0.01027851,0.049486354,0.0002067946,0.8805846,0.015706439,0.0009385016],"about_ca_topic_score_codex":0.00022688514,"about_ca_topic_score_gemma":0.001276942,"teacher_disagreement_score":0.8303768,"about_ca_system_score_codex":0.00022598158,"about_ca_system_score_gemma":0.00079979876,"threshold_uncertainty_score":0.4837555},"labels":[],"label_agreement":null},{"id":"W4367680256","doi":"10.1017/asb.2023.17","title":"Estimating the VaR-induced Euler allocation rule","year":2023,"lang":"en","type":"article","venue":"Astin Bulletin","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Western University; York University","funders":"","keywords":"Estimator; Conditional expectation; Consistency (knowledge bases); Econometrics; Function (biology); Statistical inference; Euler's formula; Cumulative distribution function; Quantile; Asymptotic distribution; Mathematics; Quantile regression; Computer science; Applied mathematics; Statistics; Probability density function; Discrete mathematics","score_opus":0.08185915201194853,"score_gpt":0.3534434590786965,"score_spread":0.27158430706674797,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4367680256","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":[0.85641944,0.00004865695,0.05450392,0.051407117,0.0027226699,0.00046240582,0.000007956655,0.0004881702,0.03393966],"genre_scores_gemma":[0.9792813,0.000015657013,0.009817356,0.00046616452,0.00037511875,0.00003658197,0.000021717467,0.000019946543,0.009966122],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978623,0.00021080724,0.00046829705,0.000343428,0.0008601761,0.00025501638],"domain_scores_gemma":[0.99737865,0.0016064792,0.00021470858,0.00051417557,0.00022414884,0.00006184013],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0033404117,0.00011329628,0.00013623165,0.00014984712,0.000375692,0.00026715847,0.00055120996,0.00006306867,0.0014667589],"category_scores_gemma":[0.005235653,0.000070531685,0.000064187494,0.0012099794,0.00004235057,0.00007131348,0.0001261556,0.0001319773,0.015192046],"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":[0.000017262144,0.00002766317,0.008093977,0.000002291484,0.000013697032,0.000010719315,0.0013125314,0.11998444,0.0010255919,0.0012509335,0.51047176,0.35778913],"study_design_scores_gemma":[0.00043118518,0.00005580111,0.10578435,0.00003030125,0.00002093405,0.000016817421,0.0007954036,0.4592133,0.0010945906,0.010167515,0.42204446,0.00034535333],"about_ca_topic_score_codex":0.00007383107,"about_ca_topic_score_gemma":0.0000051069032,"teacher_disagreement_score":0.35744378,"about_ca_system_score_codex":0.000013223055,"about_ca_system_score_gemma":0.000040428826,"threshold_uncertainty_score":0.99944603},"labels":[],"label_agreement":null},{"id":"W4367840406","doi":"10.1016/j.insmatheco.2023.04.002","title":"Assessing the difference between integrated quantiles and integrated cumulative distribution functions","year":2023,"lang":"en","type":"article","venue":"Insurance Mathematics and Economics","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Western University; York University; Carleton University","funders":"","keywords":"Quantile; Cumulative distribution function; Quantile function; Statistical inference; Econometrics; Inference; Function (biology); Probability density function; Probability distribution; Distribution (mathematics); Computer science; Mathematics; Mathematical optimization; Statistics; Artificial intelligence","score_opus":0.13505060069763905,"score_gpt":0.3707758129705439,"score_spread":0.23572521227290485,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4367840406","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":[0.9288187,0.000039520204,0.069939755,0.00036052655,0.00014180143,0.00013364738,0.00024680197,0.000047388447,0.00027185454],"genre_scores_gemma":[0.99774677,0.00078179216,0.00096569007,0.000018002333,0.000035561094,0.00001072185,0.00012710695,0.000009441756,0.00030489892],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9988673,0.000053972217,0.00053393695,0.00025810735,0.00012608379,0.00016065325],"domain_scores_gemma":[0.9978624,0.0013833598,0.000292924,0.00024673453,0.00015663062,0.00005795148],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011731313,0.00013586241,0.00026657459,0.0001028962,0.00039275215,0.00091362745,0.0001754504,0.0000719779,0.000009902607],"category_scores_gemma":[0.00080889766,0.000081540275,0.000038489547,0.00054915104,0.00017251018,0.0004016252,0.000075211174,0.00012954562,0.000041943164],"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":[0.000012054497,0.000052277577,0.5870361,0.000019713372,0.00007906076,0.000001675587,0.0047531393,0.005374046,0.00007796719,0.017212573,0.001217027,0.3841644],"study_design_scores_gemma":[0.00016412867,0.000020521406,0.54757977,0.00003743953,0.000017100709,0.000005014581,0.006158784,0.38718313,0.000057084515,0.055885505,0.0027379864,0.000153514],"about_ca_topic_score_codex":0.000053904347,"about_ca_topic_score_gemma":0.000044204357,"teacher_disagreement_score":0.38401088,"about_ca_system_score_codex":0.000021934004,"about_ca_system_score_gemma":0.000037165035,"threshold_uncertainty_score":0.88101304},"labels":[],"label_agreement":null},{"id":"W4376115427","doi":"10.3390/jrfm16050266","title":"Modeling Risk for CVaR-Based Decisions in Risk Aggregation","year":2023,"lang":"en","type":"article","venue":"Journal of risk and financial management","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Shunde Polytechnic","keywords":"CVAR; Expected shortfall; Econometrics; Probabilistic logic; Diversification (marketing strategy); Portfolio; Computer science; Coherent risk measure; Risk analysis (engineering); Value at risk; Actuarial science; Risk management; Mathematics; Economics; Business","score_opus":0.04864796575980966,"score_gpt":0.33022917382080774,"score_spread":0.28158120806099807,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4376115427","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":[0.4258425,0.00020580301,0.5732207,0.00006361507,0.0003705929,0.00018339777,0.000032644602,0.000008458152,0.00007229446],"genre_scores_gemma":[0.9419875,0.036111478,0.021558393,0.000038887345,0.00017439693,0.00001238093,0.000004950938,0.0000114017,0.000100595455],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975605,0.00017505973,0.0010426629,0.00025143978,0.00074752705,0.000222836],"domain_scores_gemma":[0.9976336,0.00094221684,0.0007987298,0.00022083074,0.00031354668,0.0000911196],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0061719217,0.00012510925,0.0003057564,0.0012667574,0.00030908865,0.00015124849,0.00028360996,0.00007909405,0.000008008097],"category_scores_gemma":[0.004653382,0.00009429309,0.00017766179,0.0013684917,0.000025539652,0.000300379,0.00006340707,0.00021433896,0.000018161663],"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":[0.00014034171,0.000026189058,0.029330904,0.0000012704551,0.0000039521105,0.000010838127,0.00020320651,0.46069133,3.2914232e-7,0.00031735987,0.0009955877,0.50827867],"study_design_scores_gemma":[0.0017216386,0.00014746052,0.10861155,0.00006301903,0.000085342304,0.0000015928183,0.000462406,0.75169265,0.0000069692865,0.10872126,0.028357329,0.00012878202],"about_ca_topic_score_codex":0.00008311788,"about_ca_topic_score_gemma":0.00019459384,"teacher_disagreement_score":0.5516623,"about_ca_system_score_codex":0.00003581715,"about_ca_system_score_gemma":0.00005477109,"threshold_uncertainty_score":0.55708694},"labels":[],"label_agreement":null},{"id":"W4376167462","doi":"10.48550/arxiv.2305.06190","title":"Two-stage and Lagrangian Dual Decision Rules for Multistage Adaptive Robust Optimization","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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 Toronto; HEC Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; University of Toronto; Agence Nationale de la Recherche; Government of Ontario","keywords":"Mathematical optimization; Dual (grammatical number); Cutting-plane method; Newsvendor model; Stochastic programming; Robust optimization; Linear programming; Bounding overwatch; Mathematics; Integer programming; Computer science","score_opus":0.2421683157101788,"score_gpt":0.2861749890756911,"score_spread":0.04400667336551231,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4376167462","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":[0.104467995,0.00007106091,0.89213985,0.000048944607,0.00076722255,0.00083049963,0.0007210816,0.00017349448,0.00077984796],"genre_scores_gemma":[0.8918728,0.0019612499,0.088204,0.000048607697,0.00019204836,0.0000069011307,0.00045897215,0.000080303194,0.017175071],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9968371,0.00021469493,0.0005727982,0.0016219605,0.00036498648,0.00038847062],"domain_scores_gemma":[0.9958586,0.001601817,0.0006667102,0.00091692776,0.0007104492,0.0002454913],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001493006,0.0004021718,0.00054680405,0.00089216756,0.00041744017,0.00040796114,0.00073059223,0.00041873366,0.000115278206],"category_scores_gemma":[0.0009236852,0.00039594393,0.00027821513,0.0008942588,0.00019204467,0.00053773273,0.00092049694,0.00033101256,0.00010992372],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00029660625,0.000038025515,0.0021786252,0.000011049957,0.00004472537,0.00008538016,0.00019617527,0.98117894,0.0000029902005,0.012897282,0.0010007242,0.0020695005],"study_design_scores_gemma":[0.0012297003,0.00007226535,0.0016470773,0.000067630055,0.000100835394,0.00000205491,0.0009968732,0.9745591,0.000014153333,0.019913865,0.0009557432,0.0004406853],"about_ca_topic_score_codex":0.00032543385,"about_ca_topic_score_gemma":0.0004563291,"teacher_disagreement_score":0.8039359,"about_ca_system_score_codex":0.0001203145,"about_ca_system_score_gemma":0.00015495218,"threshold_uncertainty_score":0.99984926},"labels":[],"label_agreement":null},{"id":"W4376274500","doi":"10.1016/j.insmatheco.2023.05.001","title":"Asymptotic properties of generalized shortfall risk measures for heavy-tailed risks","year":2023,"lang":"en","type":"article","venue":"Insurance Mathematics and Economics","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Agence Nationale de la Recherche","keywords":"Expected shortfall; Risk measure; Estimator; Measure (data warehouse); Quantile; Econometrics; Coherent risk measure; Asymptotic analysis; Mathematics; Dynamic risk measure; Spectral risk measure; Statistics; Computer science; Risk management; Economics; Data mining","score_opus":0.17901757132703372,"score_gpt":0.339343853408245,"score_spread":0.1603262820812113,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4376274500","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":[0.98816,0.0003072771,0.010296542,0.00010857153,0.00022520618,0.00040031475,0.00011500767,0.0000385264,0.00034852952],"genre_scores_gemma":[0.98314035,0.006302345,0.010112649,0.000024171177,0.00005379396,0.00005251296,0.00000594569,0.000021884674,0.0002863548],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984179,0.000036542904,0.00087089266,0.00028110133,0.00018520362,0.00020833564],"domain_scores_gemma":[0.9984436,0.00036829917,0.00051805674,0.00038744605,0.00021420394,0.00006838178],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020919817,0.00014431015,0.00046920005,0.00019431142,0.00015435708,0.00015504047,0.00027012304,0.00008175221,0.000007698261],"category_scores_gemma":[0.00094675954,0.00010601206,0.00012308192,0.00022781617,0.00009313863,0.00018414525,0.00005590703,0.000053780743,0.000033214215],"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":[0.00049608975,0.00033362387,0.36560628,0.00026754412,0.0003527031,0.000001896629,0.011004471,0.3650863,0.0010893607,0.034395173,0.0038933703,0.21747321],"study_design_scores_gemma":[0.0012935761,0.00011702931,0.022648659,0.00006593132,0.000053974272,0.0000062086096,0.0009841382,0.7430047,0.0038034795,0.22216535,0.005453121,0.00040385354],"about_ca_topic_score_codex":0.00003655454,"about_ca_topic_score_gemma":0.00006184088,"teacher_disagreement_score":0.3779184,"about_ca_system_score_codex":0.000013238645,"about_ca_system_score_gemma":0.000044320517,"threshold_uncertainty_score":0.43230462},"labels":[],"label_agreement":null},{"id":"W4376455127","doi":"10.2139/ssrn.4438479","title":"Bowley-Optimal Convex Premium Principles under Exponential Utility","year":2023,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Exponential function; Regular polygon; Mathematics; Mathematical economics; Economics; Mathematical optimization; Mathematical analysis; Geometry","score_opus":0.08254431847615737,"score_gpt":0.3562057865496834,"score_spread":0.273661468073526,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4376455127","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":[0.8374766,0.0008846573,0.1527695,0.0018750805,0.0013450095,0.00022622134,0.0000090729045,0.00017941925,0.005234399],"genre_scores_gemma":[0.9763284,0.005191059,0.0002116301,0.000072271934,0.00048995024,0.000005948969,0.000013318698,0.00002448955,0.017662957],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99442846,0.00033949543,0.000882344,0.00047767625,0.0015535074,0.0023185299],"domain_scores_gemma":[0.99823326,0.00031288838,0.00042513397,0.000478657,0.00035925375,0.00019080707],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.010171599,0.00020681883,0.0003136506,0.00043976898,0.0005174828,0.0003733274,0.0008649742,0.00014543922,0.0005147792],"category_scores_gemma":[0.0007526292,0.0001574922,0.0002465488,0.0012294205,0.00011711035,0.00058412575,0.00015484955,0.0012747461,0.0010374093],"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":[0.00084997725,0.00047985124,0.10967473,0.000007928917,0.00064600277,0.00007782965,0.002501952,0.12112003,0.0013844204,0.42018425,0.031882383,0.31119063],"study_design_scores_gemma":[0.0017853775,0.00044850036,0.05425132,0.000016991733,0.00007663495,0.00063125236,0.009148189,0.08293012,0.00066815206,0.73593974,0.11343475,0.00066899846],"about_ca_topic_score_codex":0.00002845904,"about_ca_topic_score_gemma":0.00020664827,"teacher_disagreement_score":0.31575546,"about_ca_system_score_codex":0.00029147312,"about_ca_system_score_gemma":0.0022585948,"threshold_uncertainty_score":0.9997404},"labels":[],"label_agreement":null},{"id":"W4377834536","doi":"10.2139/ssrn.4452742","title":"Risk Budgeting Allocation for Dynamic Risk Measures","year":2023,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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 Toronto","funders":"","keywords":"Actuarial science; Business; Economics; Risk analysis (engineering)","score_opus":0.03077382725685985,"score_gpt":0.3478135359005271,"score_spread":0.31703970864366726,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4377834536","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":[0.3553804,0.0013127737,0.6409238,0.000914131,0.00063738984,0.00026755352,0.000018792298,0.00010758588,0.00043755362],"genre_scores_gemma":[0.94046396,0.054596566,0.0009536263,0.00002711709,0.00026828155,0.000015848544,0.000017170181,0.000027094817,0.0036303343],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9959985,0.00037817488,0.0006720374,0.00034281716,0.0009954271,0.0016130607],"domain_scores_gemma":[0.99748415,0.00079014583,0.00079159567,0.00029361522,0.0005461439,0.00009435299],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02085868,0.0001472043,0.0002104239,0.0005002025,0.00086462474,0.00028685038,0.0005524365,0.00009648299,0.0000148504305],"category_scores_gemma":[0.005724864,0.00011197255,0.00021510596,0.0011672942,0.000035342076,0.00037758154,0.00003619746,0.00097584986,0.0002743784],"study_design_candidate":"design_other","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":[0.00008695674,0.000022764463,0.026296357,6.956859e-7,0.00009429954,8.7734963e-7,0.00040420843,0.055486687,0.00012435515,0.0080052335,0.0022911245,0.90718645],"study_design_scores_gemma":[0.0006335772,0.00020280122,0.01159797,0.000005696284,0.000066370456,0.000051398256,0.0026488747,0.14350216,0.000050235103,0.8252699,0.015779473,0.00019156364],"about_ca_topic_score_codex":0.00007492557,"about_ca_topic_score_gemma":0.0012725103,"teacher_disagreement_score":0.9069949,"about_ca_system_score_codex":0.000354223,"about_ca_system_score_gemma":0.0010772839,"threshold_uncertainty_score":0.7229244},"labels":[],"label_agreement":null},{"id":"W4378530332","doi":"10.1016/j.ejor.2023.05.025","title":"Distortion risk measure under parametric ambiguity","year":2023,"lang":"en","type":"article","venue":"European Journal of Operational Research","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"CVAR; Distortion (music); Ambiguity; Measure (data warehouse); Mathematical optimization; Parametric statistics; Mathematics; Risk measure; Coherent risk measure; Computer science; Applied mathematics; Risk management; Expected shortfall; Data mining; Statistics","score_opus":0.3988349360587256,"score_gpt":0.49286997261592497,"score_spread":0.09403503655719936,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4378530332","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":[0.9042259,0.00038747583,0.065048896,0.0038057964,0.00085728115,0.00019302801,0.00002490278,0.000029569634,0.025427142],"genre_scores_gemma":[0.9926393,0.000978337,0.0008488716,0.0000530726,0.000521928,0.0000010437896,0.000009963786,0.000017759756,0.004929719],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.9883558,0.0047352198,0.0009813339,0.0002517706,0.005390153,0.00028568282],"domain_scores_gemma":[0.99341774,0.0016295628,0.0003629608,0.00031592208,0.0040444583,0.0002293417],"candidate_categories":["metaresearch","insufficient_payload"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.051362287,0.00009075821,0.00018177854,0.0016723844,0.00059448835,0.0006209621,0.0007887326,0.00003000673,0.00045859703],"category_scores_gemma":[0.021095432,0.000062263156,0.00013945739,0.004452422,0.00013563437,0.00062222336,0.00013256872,0.00065128173,0.002667974],"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":[0.0002903781,0.00017824864,0.053050872,0.0000021579744,0.000069981324,0.00033129155,0.0007717316,0.3496725,0.00058809394,0.0036067278,0.37024578,0.22119223],"study_design_scores_gemma":[0.00072305085,0.0004147448,0.8901201,0.000022307508,0.000010683091,0.00006617233,0.00063574436,0.012473328,0.00018246994,0.008441912,0.086763605,0.00014590852],"about_ca_topic_score_codex":0.000011407152,"about_ca_topic_score_gemma":0.000005761816,"teacher_disagreement_score":0.8370692,"about_ca_system_score_codex":0.000097496944,"about_ca_system_score_gemma":0.00035888777,"threshold_uncertainty_score":0.99810857},"labels":[],"label_agreement":null},{"id":"W4378674802","doi":"10.1016/j.insmatheco.2023.05.006","title":"Pairwise counter-monotonicity","year":2023,"lang":"en","type":"article","venue":"Insurance Mathematics and Economics","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":"Actua; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Monotonic function; Pairwise comparison; Mathematics; Representation (politics); Property (philosophy); Quantile; Mathematical economics; Applied mathematics; Econometrics; Statistics; Mathematical analysis","score_opus":0.0736198519584988,"score_gpt":0.3266547097669433,"score_spread":0.2530348578084445,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4378674802","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":[0.9894744,0.00004474723,0.0023443778,0.00035239558,0.0002540475,0.000113799746,0.000041380845,0.000061255785,0.007313577],"genre_scores_gemma":[0.99226344,0.0031704248,0.0032995304,0.00012132433,0.000049810275,0.000011564049,0.000005310068,0.000013389202,0.001065237],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99894774,0.000013182082,0.00049064687,0.00024060775,0.00013593282,0.00017187398],"domain_scores_gemma":[0.99891585,0.00038544802,0.00020385241,0.0003553393,0.00006868153,0.00007082934],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013053499,0.00009946111,0.00023454748,0.00013770018,0.00012135707,0.00025886495,0.0002336932,0.000056216548,0.000043945816],"category_scores_gemma":[0.00027423727,0.000080440164,0.000053709115,0.00026185156,0.000058088433,0.00023746103,0.00007598307,0.00005350712,0.00063858315],"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":[0.00006507815,0.00028623722,0.3236721,0.00007929864,0.000101374484,0.000025789974,0.011771312,0.09071494,0.000117401425,0.15157005,0.033315618,0.3882808],"study_design_scores_gemma":[0.0003809031,0.00003172084,0.044083744,0.000015862663,0.0000058374194,0.000014451212,0.0007908644,0.65175337,0.00011062968,0.25051585,0.052034732,0.00026206468],"about_ca_topic_score_codex":0.000008603663,"about_ca_topic_score_gemma":0.000021531097,"teacher_disagreement_score":0.5610384,"about_ca_system_score_codex":0.000014327281,"about_ca_system_score_gemma":0.000023399216,"threshold_uncertainty_score":0.82079077},"labels":[],"label_agreement":null},{"id":"W4378976224","doi":"10.1137/22m1531567","title":"One Axiom to Rule Them All: A Minimalist Axiomatization of Quantiles","year":2023,"lang":"en","type":"article","venue":"SIAM Journal on Financial Mathematics","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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 Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Quantile; Mathematics; Monotonic function; Axiom; Monotone polygon; Axiom of choice; Axiom independence; Interval (graph theory); Mathematical economics; Set (abstract data type); Discrete mathematics; Applied mathematics; Econometrics; Set theory; Computer science; Combinatorics; Mathematical analysis","score_opus":0.18264647037069687,"score_gpt":0.38915405010992954,"score_spread":0.20650757973923267,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4378976224","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":[0.92505985,0.000023006593,0.067329854,0.0017448596,0.00069052016,0.0002940945,0.00004025693,0.000069866896,0.004747662],"genre_scores_gemma":[0.95756495,0.0004294514,0.03974821,0.0003765054,0.0003556492,0.000011627775,0.000012089545,0.000044400815,0.0014570996],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9963304,0.00010773526,0.0013332086,0.00022858389,0.0017035577,0.00029652606],"domain_scores_gemma":[0.99727863,0.0006970283,0.0008879924,0.00040527107,0.0005566745,0.00017437473],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0028071199,0.00016904628,0.00048197384,0.0006779951,0.00019354161,0.00022107217,0.0006119896,0.00011215543,0.00019855746],"category_scores_gemma":[0.005615727,0.00013289481,0.00018161089,0.002115849,0.000050138635,0.00024237281,0.00008432158,0.00015353058,0.0014250057],"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":[0.000578323,0.0023871488,0.0058049094,0.00018817133,0.00015742246,0.00017299646,0.03592193,0.042904846,0.009326448,0.53059196,0.18576168,0.18620417],"study_design_scores_gemma":[0.0014210488,0.0010735382,0.048746783,0.0008412411,0.000107265696,0.00014921893,0.002606481,0.035061475,0.011443454,0.8647754,0.032908797,0.00086525135],"about_ca_topic_score_codex":0.0000039711454,"about_ca_topic_score_gemma":0.000006647655,"teacher_disagreement_score":0.33418348,"about_ca_system_score_codex":0.00003521785,"about_ca_system_score_gemma":0.00014005699,"threshold_uncertainty_score":0.9993525},"labels":[],"label_agreement":null},{"id":"W4379618761","doi":"10.1016/j.ejor.2023.06.003","title":"Risk budgeting portfolios from simulations","year":2023,"lang":"en","type":"article","venue":"European Journal of Operational Research","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro; Conselho Nacional de Desenvolvimento Científico e Tecnológico","keywords":"Portfolio; Computer science; Stochastic gradient descent; Portfolio optimization; Asset (computer security); Econometrics; Expected shortfall; Distortion (music); Project portfolio management; Asset allocation; Mathematical optimization; Finance; Economics; Mathematics; Machine learning; Project management","score_opus":0.25663820760260064,"score_gpt":0.4776061459422727,"score_spread":0.22096793833967204,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4379618761","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":[0.9603494,0.00016074556,0.012505555,0.0025448357,0.0005045597,0.0001313661,0.00009976011,0.00002441149,0.023679359],"genre_scores_gemma":[0.99135524,0.0005080955,0.0032749735,0.00006632685,0.0010294531,5.708665e-7,0.000037957107,0.000022268103,0.00370511],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9911782,0.003311689,0.0011602239,0.00025500494,0.003813202,0.00028169443],"domain_scores_gemma":[0.99161685,0.0039657354,0.00040630417,0.00032129473,0.003471289,0.00021853566],"candidate_categories":["metaresearch","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.025466872,0.000092062015,0.00018511483,0.0012060493,0.000753725,0.00070631347,0.0008382743,0.000026129106,0.0015509512],"category_scores_gemma":[0.021322286,0.000066516164,0.000120837656,0.0023968061,0.00011196331,0.0006702632,0.0001860219,0.0005817282,0.002909701],"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":[0.00011335905,0.00007444665,0.07663373,5.926122e-7,0.000058826998,0.00045327345,0.0016457854,0.6574943,0.0020985892,0.001361524,0.18994354,0.07012205],"study_design_scores_gemma":[0.0010495887,0.0002966157,0.5740465,0.00003962251,0.000015519441,0.000037300004,0.0013425326,0.15143839,0.0004196008,0.015039725,0.2560611,0.00021350803],"about_ca_topic_score_codex":0.000021567272,"about_ca_topic_score_gemma":0.000006345645,"teacher_disagreement_score":0.5060559,"about_ca_system_score_codex":0.00003839286,"about_ca_system_score_gemma":0.00030937043,"threshold_uncertainty_score":0.99936175},"labels":[],"label_agreement":null},{"id":"W4381995674","doi":"10.1080/01605682.2023.2224823","title":"Enhancing an existing algorithm for small-cardinality constrained portfolio optimisation","year":2023,"lang":"en","type":"article","venue":"Journal of the Operational Research Society","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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; Western University","funders":"","keywords":"Cardinality (data modeling); JavaScript; Computer science; Heuristics; Zoom; Executable; Portfolio; Algorithm; Mathematical optimization; Programming language; Mathematics; Data mining; Operating system","score_opus":0.37129724908971173,"score_gpt":0.5060031720084774,"score_spread":0.1347059229187657,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4381995674","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":[0.42498055,0.00011752093,0.5608202,0.008880435,0.0014897838,0.00108175,0.00011537736,0.000041196003,0.00247316],"genre_scores_gemma":[0.59733886,0.00065154827,0.38464266,0.0005712196,0.003242908,0.000057617883,0.00007510232,0.000045719386,0.013374381],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9947517,0.0007015192,0.0009876521,0.0002558099,0.0029556113,0.00034771365],"domain_scores_gemma":[0.99152595,0.0025736056,0.00045434965,0.00031670902,0.004953768,0.00017562519],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.035539463,0.00010448372,0.000240037,0.0001899551,0.0012429248,0.0006373671,0.0008064042,0.00010466039,0.00010857855],"category_scores_gemma":[0.008281178,0.00006448567,0.0004605022,0.0015653111,0.00021309026,0.0007237448,0.00014045187,0.00042816566,0.000015583999],"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":[0.00019298418,0.00032222056,0.0073944298,0.000023739,0.0003887743,0.000019815652,0.009131502,0.29042596,0.026679797,0.0123411855,0.18290387,0.4701757],"study_design_scores_gemma":[0.0013764423,0.00036822486,0.017814472,0.00007259579,0.000036081863,0.00009991374,0.009640222,0.9018979,0.006436391,0.03500746,0.026999498,0.00025080415],"about_ca_topic_score_codex":0.00002112552,"about_ca_topic_score_gemma":0.000010088063,"teacher_disagreement_score":0.61147195,"about_ca_system_score_codex":0.00017290977,"about_ca_system_score_gemma":0.0012307357,"threshold_uncertainty_score":0.99311507},"labels":[],"label_agreement":null},{"id":"W4382365949","doi":"10.2139/ssrn.4494381","title":"Integrated Order Acceptance and Resource Decisions Under Uncertainty: Robust and Stochastic Approaches","year":2023,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Western University; University of Windsor","funders":"","keywords":"Order (exchange); Resource (disambiguation); Computer science; Management science; Risk analysis (engineering); Business; Economics","score_opus":0.1496347318893025,"score_gpt":0.33831829015690934,"score_spread":0.18868355826760683,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4382365949","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":[0.11928002,0.0079963785,0.8684643,0.0028548597,0.00051550416,0.00041724072,0.000032043918,0.00009209167,0.0003475651],"genre_scores_gemma":[0.9559705,0.030714124,0.003763885,0.00014925521,0.00042540662,0.00002387206,0.0000762211,0.00010728732,0.008769419],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9942883,0.00047070265,0.0010730127,0.001065301,0.0013131805,0.0017894825],"domain_scores_gemma":[0.9961869,0.00153704,0.0007908449,0.00068281835,0.00052867073,0.00027373817],"candidate_categories":["metaepi_narrow","scholarly_communication","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.007967063,0.00047086083,0.0006841706,0.00083723303,0.0006018564,0.0011907,0.0009883002,0.00046194447,0.000044310378],"category_scores_gemma":[0.0037293201,0.00033947223,0.0001511487,0.0012168961,0.00026901945,0.00027263738,0.0007747369,0.0045752944,0.000034577857],"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":[0.00013806006,0.00003180543,0.0006120657,0.0000023029872,0.00020534576,0.000005041827,0.00043057784,0.86668724,0.0000015465026,0.0124787865,0.0012723873,0.11813481],"study_design_scores_gemma":[0.0005595123,0.00011576082,0.0016086937,0.00011719323,0.00011381344,0.0003652828,0.012638875,0.20024867,6.561676e-7,0.78226525,0.0015065763,0.00045974748],"about_ca_topic_score_codex":0.00012523509,"about_ca_topic_score_gemma":0.0026198463,"teacher_disagreement_score":0.86470044,"about_ca_system_score_codex":0.0004977408,"about_ca_system_score_gemma":0.0033899033,"threshold_uncertainty_score":0.9999057},"labels":[],"label_agreement":null},{"id":"W4382406632","doi":"10.2139/ssrn.4493844","title":"Tail Mean-Variance Portfolio Selection with Estimation Risk","year":2023,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Portfolio; Selection (genetic algorithm); Estimation; Variance (accounting); Econometrics; Portfolio optimization; Statistics; Economics; Computer science; Mathematics; Financial economics; Artificial intelligence","score_opus":0.0373846709587307,"score_gpt":0.3309834291158094,"score_spread":0.2935987581570787,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4382406632","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":[0.08415639,0.0010565511,0.91050047,0.00077652733,0.0014940652,0.0004806641,0.000025789042,0.00024665464,0.0012629193],"genre_scores_gemma":[0.9132178,0.04633637,0.00981259,0.00006341932,0.0013805035,0.00005683725,0.000101490936,0.00015156054,0.028879415],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99207264,0.00062132266,0.0013833245,0.0010322796,0.002570311,0.0023201054],"domain_scores_gemma":[0.99476206,0.0003569792,0.0027502987,0.00076431595,0.0011695166,0.00019684443],"candidate_categories":["metaepi_narrow","scholarly_communication","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.013675603,0.0004975421,0.00065834884,0.0010864389,0.00072304066,0.0010553084,0.0011802734,0.0004708694,0.00013352327],"category_scores_gemma":[0.0014434679,0.00036874023,0.00028118296,0.0017725521,0.00008799329,0.0006823825,0.00023196827,0.0061319456,0.00047972868],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022302887,0.00006781724,0.02855811,0.0000051761467,0.00035389082,0.000015958156,0.00036930997,0.81918156,0.0000054424545,0.011979898,0.004026433,0.13521339],"study_design_scores_gemma":[0.0006173562,0.0003686255,0.006124817,0.00007852453,0.00022106896,0.0006186864,0.00075981463,0.13392925,0.000031738837,0.854492,0.0022157715,0.0005423149],"about_ca_topic_score_codex":0.00051894353,"about_ca_topic_score_gemma":0.004283345,"teacher_disagreement_score":0.9006879,"about_ca_system_score_codex":0.0013348402,"about_ca_system_score_gemma":0.007694392,"threshold_uncertainty_score":0.9999817},"labels":[],"label_agreement":null},{"id":"W4382541589","doi":"10.1080/10920277.2023.2211648","title":"Calibrating Distribution Models from PELVE","year":2023,"lang":"en","type":"article","venue":"North American Actuarial Journal","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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 Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Converse; Calibration; Monotonic function; Constraint (computer-aided design); Value at risk; Point (geometry); Econometrics; Computer science; Distribution (mathematics); Mathematical optimization; Convergence (economics); Applied mathematics; Bridge (graph theory); Value (mathematics); Mathematics; Statistics; Risk management; Economics; Finance; Mathematical analysis","score_opus":0.08994618169083735,"score_gpt":0.3546876808717615,"score_spread":0.26474149918092416,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4382541589","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":[0.7526256,0.0000127559315,0.24458332,0.0006896085,0.0010387773,0.00007171501,0.00019989867,0.00008656997,0.0006917924],"genre_scores_gemma":[0.9955036,0.00033243612,0.0019634361,0.00020047913,0.0015268764,0.0000028065074,0.00027354798,0.000016172724,0.00018063454],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9967871,0.00027341102,0.0007828165,0.00033414562,0.0014388895,0.00038366302],"domain_scores_gemma":[0.99761677,0.0006702038,0.0007956131,0.00031298815,0.00029935094,0.00030509912],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010531814,0.00015735364,0.0003310014,0.00022269205,0.00046743653,0.0007462955,0.0005453535,0.000042795877,0.0002399322],"category_scores_gemma":[0.0012851255,0.00011700181,0.00017320544,0.002307313,0.00013752135,0.00091269385,0.00009207163,0.00034399302,0.000394153],"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":[0.00015067098,0.000031278065,0.11045994,2.1718387e-7,0.000041704967,0.000080399346,0.0012297946,0.22936022,0.00004817905,0.00018631562,0.061384644,0.59702665],"study_design_scores_gemma":[0.0009465256,0.00023475202,0.330593,0.0000107058,0.000052770345,0.00006395771,0.0022814777,0.60459596,0.0000798798,0.028314449,0.032326914,0.00049964624],"about_ca_topic_score_codex":0.0005515748,"about_ca_topic_score_gemma":0.00022369395,"teacher_disagreement_score":0.596527,"about_ca_system_score_codex":0.000058841397,"about_ca_system_score_gemma":0.0002242089,"threshold_uncertainty_score":0.71965444},"labels":[],"label_agreement":null},{"id":"W4383896183","doi":"10.1002/aic.18177","title":"Distributionally robust chance‐constrained optimization with Sinkhorn ambiguity set","year":2023,"lang":"en","type":"article","venue":"AIChE Journal","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Ambiguity; Mathematical optimization; Discretization; Robust optimization; Probability distribution; Mathematics; Computer science; Set (abstract data type)","score_opus":0.122008880550641,"score_gpt":0.36201635387072284,"score_spread":0.24000747332008182,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4383896183","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":[0.081630245,0.000060590017,0.9086275,0.004783094,0.00070210994,0.00017066344,0.00011251104,0.00013584834,0.003777418],"genre_scores_gemma":[0.9698857,0.0009579415,0.023229724,0.0003874284,0.000763289,0.000010392233,0.00033813575,0.000031374093,0.0043960134],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99710137,0.00018495672,0.00061593263,0.00030175198,0.0014422096,0.00035376736],"domain_scores_gemma":[0.9978628,0.00026320998,0.00046385592,0.00029123918,0.0008996998,0.00021920822],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029757053,0.0001590487,0.0002324756,0.00033751453,0.00054438476,0.0005467124,0.00043833762,0.000101865204,0.0007794305],"category_scores_gemma":[0.0010293756,0.000107783104,0.00009899649,0.0021330588,0.000116129115,0.00067501504,0.000058020505,0.00029442646,0.00027490244],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000063875275,0.0000244792,0.010151311,7.2613955e-7,0.000025579084,0.00004966922,0.00016742863,0.9471171,0.0000307934,0.00037220863,0.033386923,0.008609892],"study_design_scores_gemma":[0.002100477,0.0003791733,0.08277294,0.00006272389,0.00008006407,0.0011120603,0.0014905563,0.8682691,0.00027949584,0.009587415,0.033234384,0.00063161284],"about_ca_topic_score_codex":0.0000072387775,"about_ca_topic_score_gemma":0.00001545929,"teacher_disagreement_score":0.8882555,"about_ca_system_score_codex":0.00006237774,"about_ca_system_score_gemma":0.00029079153,"threshold_uncertainty_score":0.85342175},"labels":[],"label_agreement":null},{"id":"W4384574991","doi":"10.23952/jnva.7.2023.4.06","title":"Absolute value equations with data uncertainty in the $l_1$ and $l_\\infty$ norm balls","year":2023,"lang":"en","type":"article","venue":"Journal of Nonlinear and Variational Analysis","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Natural Science Foundation of Tianjin City; Ministry of Science and Technology, Taiwan; National Natural Science Foundation of China","keywords":"Norm (philosophy); Jump; Linear programming; Mathematics; Applied mathematics; Value (mathematics); Mathematical optimization; Range (aeronautics); Mathematical economics; Statistics; Law; Physics; Engineering","score_opus":0.11407182978435482,"score_gpt":0.3913802885440648,"score_spread":0.27730845875971,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4384574991","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":[0.5964921,0.000523801,0.37038845,0.02997642,0.00024595895,0.00038592968,0.0006489547,0.000020984526,0.0013174],"genre_scores_gemma":[0.98442537,0.0011435952,0.013031183,0.0004315223,0.00033171897,0.0000026569587,0.00026187362,0.0000066504986,0.00036542083],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977412,0.00019166399,0.0006574528,0.00020477692,0.001091629,0.00011323934],"domain_scores_gemma":[0.99691004,0.0018648576,0.00043820418,0.0003076957,0.00041292937,0.00006629925],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0051526697,0.00008512174,0.00026557504,0.0008160696,0.00016445274,0.00025990396,0.00045838172,0.000041895182,0.00006306737],"category_scores_gemma":[0.0010747674,0.00004426787,0.00007323662,0.0034463622,0.000047068057,0.0005271984,0.00008296507,0.0001400868,0.000008668644],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000085993335,0.000092182854,0.090044074,0.0000018271917,0.00057425565,0.000026721691,0.0012859479,0.89136004,0.0000068064173,0.005268152,0.0016940765,0.009559892],"study_design_scores_gemma":[0.0002701752,0.000037013586,0.26345423,0.000004047726,0.00030770208,0.0000144573,0.0003988151,0.7285788,2.2142466e-7,0.0033503904,0.0035328988,0.000051267718],"about_ca_topic_score_codex":0.00014049649,"about_ca_topic_score_gemma":0.0004947399,"teacher_disagreement_score":0.38793328,"about_ca_system_score_codex":0.000012621614,"about_ca_system_score_gemma":0.00014391297,"threshold_uncertainty_score":0.250626},"labels":[],"label_agreement":null},{"id":"W4385309477","doi":"10.1080/03461238.2023.2239533","title":"Pareto-optimal insurance with an upper limit on the insurer's exposure","year":2023,"lang":"en","type":"article","venue":"Scandinavian Actuarial Journal","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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 Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Deductible; Limit (mathematics); Economics; Arrow; Pareto principle; Distortion (music); Variable (mathematics); Actuarial science; Insurance policy; Ex-ante; Mathematical economics; Econometrics; Mathematics; Computer science; Operations management","score_opus":0.07473076207641781,"score_gpt":0.33729453409187504,"score_spread":0.2625637720154572,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385309477","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":[0.99009734,0.000034691013,0.0012132119,0.0033140525,0.00157579,0.00022501517,0.000036152112,0.00008217022,0.0034215879],"genre_scores_gemma":[0.99611306,0.00028690588,0.00044346089,0.00050965743,0.0013850575,0.0000107862725,0.000012356517,0.000032728338,0.0012059801],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99578804,0.0005401698,0.00065378385,0.00044496328,0.002042954,0.00053006364],"domain_scores_gemma":[0.9974166,0.00069491524,0.00047705605,0.0006936454,0.0003709301,0.00034682077],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0034247695,0.000263257,0.0003250736,0.00044970174,0.0010488975,0.0013831793,0.0011089371,0.00014736211,0.0006785021],"category_scores_gemma":[0.00089913217,0.00013019377,0.000157552,0.0016661581,0.00017131364,0.0008694288,0.00006193053,0.000685194,0.00064802315],"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":[0.0035530166,0.0002348075,0.6944967,0.0000018830827,0.0001519808,0.0004956257,0.007146574,0.04661405,0.00013269157,0.0040032165,0.06790782,0.17526168],"study_design_scores_gemma":[0.0023415016,0.0027964995,0.9330978,0.00010621591,0.000038272807,0.00056377804,0.0033295415,0.0031987482,0.00025009352,0.012020349,0.04167665,0.0005805689],"about_ca_topic_score_codex":0.000019366049,"about_ca_topic_score_gemma":0.000035031273,"teacher_disagreement_score":0.23860112,"about_ca_system_score_codex":0.000058153983,"about_ca_system_score_gemma":0.00019289865,"threshold_uncertainty_score":0.99965346},"labels":[],"label_agreement":null},{"id":"W4385386470","doi":"10.1111/mafi.12414","title":"Robust distortion risk measures","year":2023,"lang":"en","type":"article","venue":"Mathematical Finance","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":52,"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; Fonds Wetenschappelijk Onderzoek","keywords":"Robustness (evolution); Coherent risk measure; Spectral risk measure; Mathematics; Portfolio; Econometrics; Distortion (music); Model risk; Risk measure; Value at risk; Mathematical optimization; Computer science; Statistics; Expected shortfall; Risk management; Economics","score_opus":0.1718758265551399,"score_gpt":0.35496769870630285,"score_spread":0.18309187215116296,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385386470","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":[0.25730136,0.000098168726,0.7207029,0.0007137136,0.00034768865,0.00019883919,0.000019763651,0.0002442164,0.020373361],"genre_scores_gemma":[0.9748059,0.0013170104,0.009212274,0.00004371807,0.00009703889,0.000031512693,0.0000071583686,0.000016117843,0.014469253],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9979345,0.000097929835,0.00049114606,0.00030502473,0.00095389783,0.00021746327],"domain_scores_gemma":[0.99853903,0.0005976718,0.00018920773,0.0004871257,0.00013414085,0.000052799398],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.002194851,0.000098026554,0.00020912688,0.00013777225,0.00017582599,0.000115970346,0.00033504245,0.00006541843,0.00018463306],"category_scores_gemma":[0.004579706,0.00006785969,0.00009171234,0.0012448913,0.00006878439,0.00019378288,0.000059909496,0.0000989072,0.00781543],"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":[0.000033870172,0.00014634151,0.017526284,0.000008649562,0.000013602229,0.000028898316,0.0013967318,0.1025358,0.000047302703,0.10789971,0.14569211,0.6246707],"study_design_scores_gemma":[0.00017356926,0.00003325543,0.04496515,0.00002093425,0.000012911888,0.000005467675,0.00009973093,0.18181434,0.00021420103,0.6604903,0.11197571,0.00019444978],"about_ca_topic_score_codex":0.0000048496167,"about_ca_topic_score_gemma":0.0000041458015,"teacher_disagreement_score":0.71750456,"about_ca_system_score_codex":0.00001736159,"about_ca_system_score_gemma":0.000020347672,"threshold_uncertainty_score":0.9929571},"labels":[],"label_agreement":null},{"id":"W4385608630","doi":"10.2139/ssrn.4532243","title":"Dynamic Growth-Optimal Portfolio Choice Under Risk Control","year":2023,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Portfolio; Control (management); Risk Control; Replicating portfolio; Economics; Portfolio optimization; Actuarial science; Computer science; Financial economics; Risk management; Finance","score_opus":0.031528690153254386,"score_gpt":0.34061505329229974,"score_spread":0.30908636313904536,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385608630","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":[0.18621272,0.0060518635,0.79695207,0.003172573,0.0048314906,0.00072884804,0.00016797996,0.00031526957,0.001567189],"genre_scores_gemma":[0.91016895,0.06751976,0.00069805066,0.00017401748,0.0009230516,0.00003334677,0.00006597072,0.00014736858,0.020269478],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.98873395,0.0009612839,0.002099353,0.001316904,0.0029200488,0.003968446],"domain_scores_gemma":[0.9930493,0.0013394713,0.0028006106,0.0012103374,0.0012275863,0.00037266882],"candidate_categories":["metaepi_narrow","scholarly_communication","research_integrity","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0164727,0.000704002,0.0010893887,0.0014391486,0.00071149046,0.001164821,0.0026365505,0.0007686185,0.00027267722],"category_scores_gemma":[0.0034340415,0.0005643178,0.00095076056,0.001184721,0.00015973538,0.000513649,0.00054641726,0.009770381,0.0010895005],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003526876,0.00023185289,0.05571518,0.0000100932375,0.0018068281,0.000093923176,0.00029046115,0.84492236,0.000021740356,0.037248097,0.011544934,0.047761805],"study_design_scores_gemma":[0.0014592657,0.00022307686,0.020396598,0.00003907863,0.00034593543,0.0004266189,0.00094660907,0.065813474,0.0000033405927,0.9071112,0.0025197014,0.00071508985],"about_ca_topic_score_codex":0.0008134931,"about_ca_topic_score_gemma":0.0023482728,"teacher_disagreement_score":0.8698631,"about_ca_system_score_codex":0.0016905221,"about_ca_system_score_gemma":0.0075481506,"threshold_uncertainty_score":0.9998721},"labels":[],"label_agreement":null},{"id":"W4385664847","doi":"10.1080/14697688.2023.2236148","title":"Distributionally robust end-to-end portfolio construction","year":2023,"lang":"en","type":"article","venue":"Quantitative Finance","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":20,"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":"Portfolio; Computer science; Robustness (evolution); Ambiguity; Minimax; Portfolio optimization; Robust optimization; Mathematical optimization; Modern portfolio theory; End-to-end principle; Operations research; Artificial intelligence; Economics; Mathematics; Finance","score_opus":0.11825431369144535,"score_gpt":0.38583090632485734,"score_spread":0.267576592633412,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385664847","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":[0.6065867,0.00017263945,0.37040323,0.0029461617,0.0017849704,0.0004298477,0.0007597054,0.00024189842,0.016674815],"genre_scores_gemma":[0.9366847,0.001010999,0.050066136,0.00024835204,0.0001359661,0.00006271768,0.00033173605,0.000024323694,0.0114350235],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.9972698,0.00013561819,0.0006098088,0.0005847706,0.0010592599,0.00034076205],"domain_scores_gemma":[0.99777967,0.0007454834,0.00031021843,0.0003978066,0.0006710776,0.00009574896],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001527064,0.00016549492,0.000272709,0.00044384866,0.00030272434,0.0001639816,0.0004054546,0.00007729098,0.0005264952],"category_scores_gemma":[0.002564583,0.00014228154,0.00010459067,0.003908953,0.00020377418,0.0005022876,0.000092946604,0.00012339001,0.0047302563],"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":[0.00009874145,0.000042806532,0.027072128,0.0000019269291,0.00001824433,0.000045431192,0.0005385009,0.1431603,0.00020088928,0.61879593,0.13592868,0.07409641],"study_design_scores_gemma":[0.00040986665,0.00023308836,0.4521818,0.000033096934,0.000012884977,0.00002364723,0.00090757105,0.036162153,0.00061565224,0.06379507,0.4451873,0.00043786172],"about_ca_topic_score_codex":0.00003612124,"about_ca_topic_score_gemma":0.000024354973,"teacher_disagreement_score":0.5550009,"about_ca_system_score_codex":0.00004748841,"about_ca_system_score_gemma":0.00015623629,"threshold_uncertainty_score":0.9960447},"labels":[],"label_agreement":null},{"id":"W4385759488","doi":"10.3390/jrfm16080369","title":"Tensors Associated with Mean Quadratic Differences Explaining the Riskiness of Portfolios of Financial Assets","year":2023,"lang":"en","type":"article","venue":"Journal of risk and financial management","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Portfolio; Asset (computer security); Economics; Joint probability distribution; Quadratic equation; Econometrics; Aggregate (composite); Set (abstract data type); Mathematical economics; Multilinear map; Actuarial science; Mathematics; Computer science; Financial economics; Statistics","score_opus":0.03386244459504665,"score_gpt":0.28821758447060847,"score_spread":0.2543551398755618,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385759488","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":[0.98705447,0.00016732365,0.011853048,0.000085345564,0.00026514192,0.0001708215,0.000021800797,0.000008000337,0.0003740781],"genre_scores_gemma":[0.9961406,0.0031721052,0.0004071701,0.00002082386,0.00007770673,0.000003688345,0.0000020393513,0.000008120282,0.00016774771],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9969273,0.000251792,0.0011476558,0.00018470151,0.001282665,0.00020585152],"domain_scores_gemma":[0.9962421,0.0007726221,0.0021722892,0.00022968036,0.00052111514,0.000062207255],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004083865,0.00015261055,0.0005575632,0.0005845427,0.00020411308,0.00006610268,0.00044760737,0.00007058262,0.000014938969],"category_scores_gemma":[0.0022228619,0.000080908576,0.00013624429,0.0017381659,0.00015768573,0.00023491468,0.000098981676,0.00018699392,0.0000021079811],"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":[0.00043422237,0.00018772329,0.7145046,0.00002209681,0.00008760653,0.00013185474,0.0062410156,0.009892582,0.0000124956805,0.00472151,0.0048493007,0.25891498],"study_design_scores_gemma":[0.00075761677,0.00034973788,0.98509204,0.00015079859,0.00015155342,0.0000075169487,0.0018870755,0.0012207717,0.000035852987,0.009054166,0.0011812717,0.000111618014],"about_ca_topic_score_codex":0.000045299377,"about_ca_topic_score_gemma":0.00011010793,"teacher_disagreement_score":0.27058738,"about_ca_system_score_codex":0.000013688593,"about_ca_system_score_gemma":0.00008315091,"threshold_uncertainty_score":0.3299356},"labels":[],"label_agreement":null},{"id":"W4386204995","doi":"10.2139/ssrn.4551069","title":"Uncertainty Propagation and Dynamic Robust Risk Measures","year":2023,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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; Concordia University","funders":"","keywords":"Econometrics; Risk analysis (engineering); Economics; Computer science; Business","score_opus":0.03439337730346152,"score_gpt":0.3189017666755554,"score_spread":0.2845083893720939,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386204995","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":[0.81412435,0.0017030227,0.18118888,0.0014778132,0.00039471863,0.00019755603,0.0000068082245,0.00010049127,0.0008063461],"genre_scores_gemma":[0.9470081,0.0493768,0.00014768272,0.000022625576,0.00010309418,0.0000037695224,0.000006869476,0.000013140528,0.0033179352],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9969061,0.00030422735,0.00044075213,0.0002793088,0.00094783504,0.0011217823],"domain_scores_gemma":[0.99885625,0.00021426202,0.00034844727,0.00020289168,0.0002858967,0.00009223146],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009998942,0.00011917642,0.0001670712,0.00040606802,0.00048677935,0.00028296566,0.00029719342,0.00007253471,0.000019547622],"category_scores_gemma":[0.001369103,0.00008274608,0.0000750198,0.0010771599,0.000058241076,0.00037708078,0.000041360217,0.00086144684,0.00015175689],"study_design_candidate":"design_other","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":[0.00006358057,0.000017893033,0.021835001,6.5630826e-7,0.000053559033,0.0000044545504,0.00037694626,0.18988673,0.00008915872,0.011145964,0.00097174395,0.7755543],"study_design_scores_gemma":[0.00055534317,0.00020060796,0.021401538,0.000007570918,0.000035626617,0.00023353653,0.002972273,0.23258057,0.0000151327595,0.7361784,0.0056242966,0.00019510991],"about_ca_topic_score_codex":0.000069029615,"about_ca_topic_score_gemma":0.001544611,"teacher_disagreement_score":0.7753592,"about_ca_system_score_codex":0.00025834824,"about_ca_system_score_gemma":0.00089097733,"threshold_uncertainty_score":0.37439623},"labels":[],"label_agreement":null},{"id":"W4386229360","doi":"10.5705/ss.202022.0282","title":"Differentially Private Regularized Stochastic Convex Optimization with Heavy-Tailed Data","year":2023,"lang":"en","type":"article","venue":"Statistica Sinica","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Queen's University; University of Alberta","funders":"","keywords":"Regular polygon; Mathematics; Convex optimization; Econometrics; Computer science; Mathematical optimization","score_opus":0.13856239532222742,"score_gpt":0.39415158567922504,"score_spread":0.2555891903569976,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386229360","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":[0.003606812,0.000015261856,0.9924795,0.0009958058,0.00040617934,0.00048815514,0.0007649266,0.00025450607,0.0009888593],"genre_scores_gemma":[0.7401359,0.00023483165,0.2500096,0.00023126266,0.0001842651,0.000047037855,0.004359797,0.00008877083,0.0047084913],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9956766,0.00033971117,0.00094588485,0.0010007162,0.001586586,0.00045051984],"domain_scores_gemma":[0.99456155,0.0022274153,0.00046633757,0.0020918366,0.0004215102,0.0002313515],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0017318919,0.00024972795,0.0005110803,0.00034576413,0.00030683787,0.0005015512,0.0013451829,0.00010788294,0.0011717095],"category_scores_gemma":[0.0052550784,0.00017778424,0.000043257067,0.0017916363,0.0002724264,0.00047465463,0.00044876628,0.0001709833,0.0006836029],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0020346888,0.0002873691,0.0027158922,0.00002044208,0.00023627651,0.00015320799,0.00070289103,0.79703563,0.00013965022,0.031640526,0.10753691,0.05749651],"study_design_scores_gemma":[0.0014445483,0.00018671648,0.0070002875,0.000029810803,0.00008938298,0.00000898291,0.00009321192,0.9749349,0.0000105673425,0.0109207155,0.0049639936,0.00031689295],"about_ca_topic_score_codex":0.000017626082,"about_ca_topic_score_gemma":0.00003539411,"teacher_disagreement_score":0.7424699,"about_ca_system_score_codex":0.00002321487,"about_ca_system_score_gemma":0.00026134416,"threshold_uncertainty_score":0.9997414},"labels":[],"label_agreement":null},{"id":"W4386376541","doi":"10.1016/j.insmatheco.2023.08.006","title":"Diversification quotients based on VaR and ES","year":2023,"lang":"en","type":"article","venue":"Insurance Mathematics and Economics","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Actua; University of Waterloo","funders":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China; National University's Basic Research Foundation of China; Society of Actuaries; Nankai University; Natural Sciences and Engineering Research Council of Canada","keywords":"Diversification (marketing strategy); Value at risk; Econometrics; Multivariate statistics; Risk measure; Portfolio; Axiom; Mathematics; Portfolio optimization; Economics; Actuarial science; Financial economics; Mathematical economics; Risk management; Statistics; Business; Finance","score_opus":0.07420728896613092,"score_gpt":0.30644309160426236,"score_spread":0.23223580263813143,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386376541","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":[0.9954982,0.000014039365,0.001632842,0.000320193,0.00014324614,0.0000937127,0.000032344127,0.000026062871,0.0022393505],"genre_scores_gemma":[0.9962935,0.00089788734,0.0024580166,0.00009465539,0.00001885426,0.000004735066,0.00000745626,0.000006960097,0.00021795805],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992978,0.0000117440995,0.00027101298,0.00020632462,0.00011520832,0.00009793845],"domain_scores_gemma":[0.9991683,0.00036081192,0.00015173886,0.00022620977,0.00004193994,0.000051038285],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00065210456,0.00007401215,0.00013894249,0.00015972569,0.0001261171,0.00016821064,0.000115022376,0.00003994994,0.000014370293],"category_scores_gemma":[0.00020372227,0.000060611066,0.000022935483,0.00017275606,0.000043299617,0.00014473782,0.000033019813,0.000032138312,0.00015394494],"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":[0.00007729152,0.00023953857,0.4450123,0.000062409636,0.000033337124,0.000004835906,0.0053300457,0.16699047,0.000052400137,0.087750226,0.004478181,0.28996894],"study_design_scores_gemma":[0.0003473033,0.000041731808,0.11365363,0.00001619338,0.0000044981894,0.0000014105793,0.0004359768,0.8204893,0.000074055446,0.060551774,0.0042492454,0.00013492248],"about_ca_topic_score_codex":0.0000033783401,"about_ca_topic_score_gemma":0.0000031890143,"teacher_disagreement_score":0.65349877,"about_ca_system_score_codex":0.00000857853,"about_ca_system_score_gemma":0.000010367903,"threshold_uncertainty_score":0.24716476},"labels":[],"label_agreement":null},{"id":"W4386558723","doi":"10.2139/ssrn.4566214","title":"A Note on the Induction of Comonotonic Additive Risk Measures from Acceptance Sets","year":2023,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Concordia University; University of Waterloo","funders":"","keywords":"Econometrics; Mathematics; Economics","score_opus":0.07346190272059058,"score_gpt":0.3573415463938666,"score_spread":0.283879643673276,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386558723","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":[0.8543976,0.0036598307,0.129644,0.00413112,0.004720319,0.0010138477,0.00084059377,0.000109607805,0.0014831064],"genre_scores_gemma":[0.81588954,0.18262047,0.00020049821,0.000051965166,0.00048601232,0.000024117868,0.000038560123,0.000037079237,0.00065179483],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99337256,0.0012698936,0.0011285872,0.0006669806,0.0023328592,0.0012291145],"domain_scores_gemma":[0.99378866,0.0017599709,0.0026070548,0.00096996117,0.0007877376,0.000086622065],"candidate_categories":["research_integrity"],"consensus_categories":[],"category_scores_codex":[0.010871625,0.00034202004,0.00056963926,0.00046519077,0.00044138439,0.00030587084,0.001695856,0.00038414978,0.000081298414],"category_scores_gemma":[0.0034311304,0.00021115397,0.00045438646,0.0007190898,0.00014727237,0.00021526827,0.00030246415,0.006429484,0.00023773595],"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":[0.0003603419,0.000093541894,0.0032355315,6.3730266e-7,0.00054937025,0.000004967951,0.001892211,0.05966298,0.00005173551,0.008490102,0.004668246,0.92099035],"study_design_scores_gemma":[0.0003648422,0.00021211967,0.016442137,0.00007768507,0.00012889288,0.000019845807,0.002761206,0.0055958712,0.0005655537,0.9674296,0.006118974,0.0002833106],"about_ca_topic_score_codex":0.00084999256,"about_ca_topic_score_gemma":0.0036886202,"teacher_disagreement_score":0.95893943,"about_ca_system_score_codex":0.000773638,"about_ca_system_score_gemma":0.0036273177,"threshold_uncertainty_score":0.9958627},"labels":[],"label_agreement":null},{"id":"W4386714771","doi":"10.3390/math11183883","title":"Multistage Adaptive Robust Binary Optimization: Uncertainty Set Lifting versus Partitioning through Breakpoints Optimization","year":2023,"lang":"en","type":"article","venue":"Mathematics","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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","keywords":"Adaptability; Binary number; Partition (number theory); Mathematical optimization; Set (abstract data type); Mathematics; Algorithm; Computer science; Combinatorics","score_opus":0.23866695480840433,"score_gpt":0.38957215507002374,"score_spread":0.1509052002616194,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386714771","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":[0.005491923,0.00003822056,0.98145974,0.00039950776,0.0008133997,0.0004366101,0.000082454644,0.0004524458,0.010825725],"genre_scores_gemma":[0.09857388,0.00055250863,0.8962267,0.00013359085,0.00030978798,0.00008485609,0.0006152985,0.00009671902,0.0034066741],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99636936,0.00019852204,0.0011052666,0.0005798845,0.0012923017,0.00045465288],"domain_scores_gemma":[0.9962446,0.001636633,0.0006868281,0.0007020207,0.0006040345,0.00012584649],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0018410471,0.00028803753,0.00041878788,0.00032917308,0.0006296663,0.00046588975,0.0005062514,0.00017583939,0.0010677949],"category_scores_gemma":[0.0028199842,0.00025073555,0.00014361719,0.0027777173,0.000120660436,0.00095394906,0.00022204759,0.00017971768,0.0006929279],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004901224,0.000055673143,0.00013686166,0.000010024912,0.000026009766,0.00001949411,0.003781709,0.98578334,0.0000035499716,0.002015477,0.0074513378,0.00066751905],"study_design_scores_gemma":[0.0008077216,0.0000885515,0.000051323776,0.000066065164,0.00003683621,0.000007743806,0.005782313,0.9889447,0.00003481155,0.0029782858,0.00091245817,0.00028919324],"about_ca_topic_score_codex":0.000043177723,"about_ca_topic_score_gemma":0.00001611277,"teacher_disagreement_score":0.09308196,"about_ca_system_score_codex":0.00009774006,"about_ca_system_score_gemma":0.000095006304,"threshold_uncertainty_score":0.9999945},"labels":[],"label_agreement":null},{"id":"W4387122153","doi":"10.1142/13656","title":"Selected Works of William T Ziemba","year":2023,"lang":"en","type":"book","venue":"World Scientific series in finance","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Art","score_opus":0.04884069837694191,"score_gpt":0.31712966684446015,"score_spread":0.26828896846751826,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387122153","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005670059,0.005341478,0.0011143497,0.00065800047,0.027092218,0.0014530411,0.0004371925,0.00035090264,0.95788276],"genre_scores_gemma":[0.0026277984,0.0012969746,0.0013721331,0.000023712262,0.00013204607,0.00003023727,0.00018795843,0.00005023084,0.9942789],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9942888,0.00013819878,0.0016153654,0.0012982558,0.0021452252,0.00051416754],"domain_scores_gemma":[0.9953894,0.0006670386,0.0011343391,0.0016932467,0.0010484232,0.00006752954],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0035525656,0.0003542818,0.00087465794,0.002313402,0.0002581914,0.0005691073,0.0017470573,0.00028971507,0.00033298787],"category_scores_gemma":[0.0015303211,0.00031681833,0.00018057102,0.011052286,0.0010646917,0.0006759575,0.00030845456,0.00050475105,0.0007269188],"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":[0.000059439248,0.000033838267,0.0012874003,0.00001413383,0.000009747837,0.00003223544,0.000619438,0.008248689,0.000016943914,0.0069795884,0.95159495,0.031103596],"study_design_scores_gemma":[0.00015873105,0.000024997587,0.0037108746,0.00045751382,0.000009180799,0.0000027038977,0.00003516296,0.00065463246,0.00013348136,0.047845207,0.94663817,0.00032936534],"about_ca_topic_score_codex":0.0000075120306,"about_ca_topic_score_gemma":0.0030229257,"teacher_disagreement_score":0.04086562,"about_ca_system_score_codex":0.00014502871,"about_ca_system_score_gemma":0.0010859839,"threshold_uncertainty_score":0.9999284},"labels":[],"label_agreement":null},{"id":"W4387495437","doi":"10.1287/opre.2020.0685","title":"Distributionally Robust Optimization Under Distorted Expectations","year":2023,"lang":"en","type":"article","venue":"Operations Research","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"University of Ottawa; University of Waterloo","funders":"","keywords":"Ambiguity; Cumulative prospect theory; Robust optimization; Distortion (music); Expected utility hypothesis; Computer science; Mathematical optimization; Risk aversion (psychology); Prospect theory; Class (philosophy); Optimal decision; Convex optimization; Subjective expected utility; Economics; Econometrics; Regular polygon; Mathematical economics; Microeconomics; Mathematics; Artificial intelligence","score_opus":0.4007544570497615,"score_gpt":0.5133885084234063,"score_spread":0.11263405137364485,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387495437","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":[0.037399102,0.00008186531,0.9363899,0.009530981,0.00040569555,0.00065392634,0.00019985254,0.00025467816,0.015084001],"genre_scores_gemma":[0.9458433,0.00033544793,0.011656899,0.00005582984,0.00022140586,0.000283184,0.002317121,0.000028430393,0.039258424],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9953891,0.00060856104,0.00058080285,0.00052205665,0.0024711762,0.00042827506],"domain_scores_gemma":[0.99572915,0.00090131693,0.000037189227,0.0006483548,0.0025096487,0.00017432084],"candidate_categories":["sts","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0033337146,0.00011323573,0.00014935494,0.0011449332,0.0017709282,0.0009679023,0.00057779497,0.00010340931,0.0018436785],"category_scores_gemma":[0.004070227,0.000094397,0.00007555421,0.0077835754,0.000203026,0.0007648517,0.00015526477,0.00025123058,0.004011167],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000070600545,0.00004629738,0.0005619682,3.9125388e-7,0.000007380585,0.0000030695674,0.0003070805,0.9352306,0.00007931993,0.021034312,0.041413795,0.0013086965],"study_design_scores_gemma":[0.00021217085,0.000033129876,0.007318534,0.000004339642,0.0000036833037,0.000003287957,0.0028371527,0.9775846,0.00006575769,0.0024479271,0.009360823,0.00012860732],"about_ca_topic_score_codex":0.00010487094,"about_ca_topic_score_gemma":0.00030416535,"teacher_disagreement_score":0.924733,"about_ca_system_score_codex":0.00015057434,"about_ca_system_score_gemma":0.00043094208,"threshold_uncertainty_score":0.99952865},"labels":[],"label_agreement":null},{"id":"W4387639549","doi":"10.1007/s11081-023-09856-2","title":"Distributionally robust optimization using optimal transport for Gaussian mixture models","year":2023,"lang":"en","type":"article","venue":"Optimization and Engineering","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"University of Alberta","funders":"","keywords":"Ambiguity; Robust optimization; Mathematical optimization; Mixture model; Gaussian; Probability distribution; Mathematics; Set (abstract data type); Portfolio; Computer science; Portfolio optimization; Optimization problem; Statistics","score_opus":0.06790315755395122,"score_gpt":0.301513552620018,"score_spread":0.2336103950660668,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387639549","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":[0.0047760387,0.000063017345,0.99360937,0.00029695433,0.0003297877,0.00033899056,0.00012911164,0.0002430684,0.000213676],"genre_scores_gemma":[0.17457578,0.00080120104,0.82194173,0.000061513645,0.00022403235,0.000059255977,0.0014707434,0.00007578829,0.00078992517],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99808955,0.000024796273,0.00056709576,0.00046494757,0.0005151817,0.00033844725],"domain_scores_gemma":[0.99895316,0.00016937961,0.00014495513,0.00023742454,0.00033034696,0.00016470163],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008016687,0.00021984984,0.00026486636,0.0004545622,0.00028227933,0.00020621892,0.00019842034,0.00016995665,0.00007714689],"category_scores_gemma":[0.0002528968,0.00020469453,0.00010159726,0.0014078862,0.00003010156,0.0008414406,0.00003342166,0.00009209806,0.0000041480503],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019916972,0.000011207789,0.0002694246,0.000008622359,0.000013307865,0.0000020179125,0.00014648959,0.9972147,0.000030033669,0.001512168,0.00036228,0.00040986083],"study_design_scores_gemma":[0.00042659408,0.000020760868,0.00030399382,0.00001967175,0.000027229511,0.000009879016,0.00009167474,0.9975857,0.000025891819,0.00013065485,0.0011121414,0.00024580306],"about_ca_topic_score_codex":0.0000054270818,"about_ca_topic_score_gemma":9.661043e-7,"teacher_disagreement_score":0.17166759,"about_ca_system_score_codex":0.000042869484,"about_ca_system_score_gemma":0.00005911451,"threshold_uncertainty_score":0.8347201},"labels":[],"label_agreement":null},{"id":"W4387737735","doi":"10.3390/jrfm16100450","title":"Derivative of Reduced Cumulative Distribution Function and Applications","year":2023,"lang":"en","type":"article","venue":"Journal of risk and financial management","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Cumulative distribution function; CVAR; Mathematics; Probability density function; Derivative (finance); Applied mathematics; Distribution (mathematics); Empirical distribution function; Inverse; Sensitivity (control systems); Half-normal distribution; Function (biology); Conditional probability distribution; Probability distribution; Statistics; Mathematical analysis; Expected shortfall; Asymptotic distribution; Risk management","score_opus":0.030775037833415078,"score_gpt":0.3163528894856493,"score_spread":0.28557785165223426,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387737735","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":[0.41823894,0.00018802164,0.58076626,0.000088575944,0.00015392339,0.00016609124,0.000030214034,0.0000062649183,0.00036168762],"genre_scores_gemma":[0.993571,0.0054186303,0.0007580751,0.000011419799,0.00009195279,0.000005540487,0.000008490852,0.000002818926,0.00013211409],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99887806,0.000061701096,0.00048847974,0.000120462515,0.0003745277,0.000076777666],"domain_scores_gemma":[0.998802,0.00017473895,0.0006117391,0.00009417601,0.0002732623,0.000044077784],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011883229,0.000060950566,0.000168967,0.00025441565,0.00013030315,0.000037874222,0.000082499886,0.000033928223,0.0000055632268],"category_scores_gemma":[0.00032535286,0.000045418175,0.000045634824,0.0008871051,0.000058568075,0.00020292164,0.000056700694,0.00007070881,0.000004582483],"study_design_candidate":"design_other","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":[0.00016944714,0.000042930194,0.023546249,0.0000072371467,0.000019340678,0.0000032677,0.0005965692,0.0032232634,0.00004166213,0.026193839,0.0031822145,0.942974],"study_design_scores_gemma":[0.0004521518,0.00013916547,0.8227197,0.000016656031,0.00006418729,0.0000025125844,0.0008495952,0.0009919927,0.000057058103,0.106679946,0.06796686,0.000060160975],"about_ca_topic_score_codex":0.0000067062797,"about_ca_topic_score_gemma":0.0000025014785,"teacher_disagreement_score":0.94291383,"about_ca_system_score_codex":0.000012366739,"about_ca_system_score_gemma":0.000015323305,"threshold_uncertainty_score":0.18520994},"labels":[],"label_agreement":null},{"id":"W4387757992","doi":"10.2139/ssrn.4580211","title":"Risk-Constrained Portfolio Choice under Rank-Dependent Utility","year":2023,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Portfolio; Rank (graph theory); Econometrics; Actuarial science; Economics; Mathematics; Business; Financial economics; Combinatorics","score_opus":0.04167031074946069,"score_gpt":0.34493226266519633,"score_spread":0.30326195191573563,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387757992","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":[0.8435058,0.0013114883,0.13580416,0.002342754,0.0015288529,0.00034079154,0.000030433785,0.00027486737,0.014860861],"genre_scores_gemma":[0.9721381,0.012965725,0.00009048129,0.00012806254,0.00041483558,0.0000050861872,0.000011415754,0.000025994948,0.014220265],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9936319,0.0004937116,0.001008321,0.00053492514,0.00182662,0.0025045509],"domain_scores_gemma":[0.9973665,0.0007066636,0.00070910837,0.00057734456,0.00040169476,0.00023870841],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.016204922,0.00023977549,0.00036051145,0.00064675364,0.00064008415,0.00036866468,0.00091381,0.00015841426,0.0007882621],"category_scores_gemma":[0.0021070447,0.00018065899,0.0002960184,0.0018868212,0.00011309097,0.0005688747,0.00009886558,0.0019194112,0.0011517142],"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":[0.0002530474,0.00020420738,0.2530364,0.0000020052846,0.00044018807,0.000042198888,0.00044615977,0.024211286,0.00015350511,0.08303495,0.013595053,0.624581],"study_design_scores_gemma":[0.0014098245,0.00016807958,0.083714575,0.0000059730733,0.00006751006,0.0004320921,0.0034183036,0.008922151,0.00006699525,0.88614553,0.015314729,0.0003342391],"about_ca_topic_score_codex":0.00021733974,"about_ca_topic_score_gemma":0.0012293883,"teacher_disagreement_score":0.8031106,"about_ca_system_score_codex":0.00034013978,"about_ca_system_score_gemma":0.002514081,"threshold_uncertainty_score":0.999626},"labels":[],"label_agreement":null},{"id":"W4387769669","doi":"10.1080/10920277.2023.2249524","title":"A Reverse ES (CVaR) Optimization Formula","year":2023,"lang":"en","type":"article","venue":"North American Actuarial Journal","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"CVAR; Mathematics; Function (biology); Simple (philosophy); Expected shortfall; Applied mathematics; Mathematical optimization; Mathematical economics; Risk management; Economics; Finance","score_opus":0.06297167681046371,"score_gpt":0.3637619066771326,"score_spread":0.30079022986666887,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387769669","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":[0.64298874,0.000020234093,0.3477456,0.0024030213,0.0028274944,0.00028231204,0.00003589712,0.000235211,0.003461487],"genre_scores_gemma":[0.96934104,0.0020102952,0.024248127,0.0008665667,0.001832002,0.000007835361,0.00005916973,0.000041712334,0.0015932239],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99655795,0.0002507196,0.00085462356,0.0003433906,0.0015412127,0.00045207565],"domain_scores_gemma":[0.99740005,0.00044593585,0.00092251564,0.00039120344,0.0005022617,0.00033801876],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016588396,0.00018658795,0.00037027273,0.00079257035,0.0004992517,0.000637357,0.00064456626,0.000049355094,0.0006370531],"category_scores_gemma":[0.002364139,0.00013843992,0.00020743757,0.0039318656,0.00016398681,0.000824034,0.00009183581,0.0003117679,0.00073715573],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015073219,0.000027618262,0.044288013,4.2235874e-7,0.00002656024,0.00006320932,0.00061125396,0.61131936,0.0000107558,0.000062319355,0.06875755,0.27468222],"study_design_scores_gemma":[0.0023859167,0.00081924425,0.17662711,0.000020103424,0.00011008534,0.00047544172,0.002847279,0.46133634,0.0000566316,0.0025565375,0.35181722,0.0009481061],"about_ca_topic_score_codex":0.00008192393,"about_ca_topic_score_gemma":0.00010090087,"teacher_disagreement_score":0.32635233,"about_ca_system_score_codex":0.000059818307,"about_ca_system_score_gemma":0.00025717376,"threshold_uncertainty_score":0.94748914},"labels":[],"label_agreement":null},{"id":"W4388127715","doi":"10.1515/strm-2023-0006","title":"Bounds on Choquet risk measures in finite product spaces with ambiguous marginals","year":2023,"lang":"en","type":"article","venue":"Statistics & Risk Modeling","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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 Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mathematics; Generalization; Measure (data warehouse); Choquet integral; Space (punctuation); Dual polyhedron; Mathematical optimization; Applied mathematics; Product (mathematics); Risk measure; Pure mathematics; Computer science; Mathematical analysis; Economics","score_opus":0.09100956003382547,"score_gpt":0.3607627536017664,"score_spread":0.2697531935679409,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388127715","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":[0.36959448,0.00018462075,0.6277927,0.00014995324,0.00024379099,0.00029404782,0.00065725663,0.000109108885,0.00097404286],"genre_scores_gemma":[0.9504803,0.0072308183,0.041014545,0.00004622418,0.00011168523,0.000032848868,0.00008993055,0.000046550813,0.0009471177],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9955795,0.00045478274,0.00079080014,0.00082625257,0.0018238295,0.0005248665],"domain_scores_gemma":[0.99703217,0.0009494437,0.0006099586,0.000731521,0.000540888,0.00013604146],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0043274527,0.00028120226,0.0004209973,0.00086082274,0.00039204213,0.00056266825,0.00043220675,0.00007886909,0.000038674578],"category_scores_gemma":[0.0064108353,0.00020700603,0.000051076982,0.0019283338,0.000090021866,0.00026245098,0.00006771371,0.0004401611,0.00038576793],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014541585,0.00003409006,0.035684757,0.000002656766,0.000015757621,0.000040552295,0.00095243665,0.88696814,0.000003050294,0.0008648504,0.0020639447,0.07322434],"study_design_scores_gemma":[0.0004469329,0.00015673133,0.01114476,0.00004011412,0.00003487034,0.0000021003098,0.0004287589,0.9043396,0.00002509958,0.081095025,0.0020006704,0.00028535127],"about_ca_topic_score_codex":0.0026390173,"about_ca_topic_score_gemma":0.0017469579,"teacher_disagreement_score":0.58677816,"about_ca_system_score_codex":0.000053737465,"about_ca_system_score_gemma":0.00018195601,"threshold_uncertainty_score":0.8441461},"labels":[],"label_agreement":null},{"id":"W4388282681","doi":"10.1016/j.cor.2023.106459","title":"An asynchronous parallel benders decomposition method for stochastic network design problems","year":2023,"lang":"en","type":"article","venue":"Computers & Operations Research","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Polytechnique Montréal; Université du Québec à Montréal","funders":"","keywords":"Asynchronous communication; Computer science; Benchmark (surveying); Benders' decomposition; Mathematical optimization; Synchronization (alternating current); Decomposition; Parallel computing; Disjoint sets; Integer (computer science); Parallel algorithm; Acceleration; Algorithm; Mathematics","score_opus":0.30545842645085114,"score_gpt":0.5291444966876263,"score_spread":0.22368607023677517,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388282681","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":[0.0013411967,0.00006943497,0.99460167,0.0013933326,0.0004602978,0.0018710339,0.000014185378,0.00015733771,0.00009152175],"genre_scores_gemma":[0.14623302,0.00008606812,0.8516816,0.000120245924,0.0004699863,0.0005979302,0.00029934762,0.00003517546,0.00047665532],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99493974,0.001689921,0.0006112789,0.00074442395,0.001307076,0.0007075538],"domain_scores_gemma":[0.99423647,0.0034340546,0.00005301372,0.00071722834,0.0013088703,0.0002503802],"candidate_categories":["sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.011805767,0.0001611169,0.0002608483,0.0008559383,0.0017363136,0.0013439684,0.0009276517,0.000118849675,0.000059677604],"category_scores_gemma":[0.000605945,0.0001381676,0.00009488734,0.0030717766,0.000112575086,0.0006859943,0.00013863687,0.00024199711,0.00033663673],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003744799,0.000053995525,0.000024904253,0.0000020569075,0.000016153093,0.0000022068518,0.0007891833,0.90423495,0.00013755151,0.0034137808,0.03591937,0.055368435],"study_design_scores_gemma":[0.00041388738,0.00044050225,0.00037933674,0.000015431164,0.000006845544,0.000006602108,0.00027221246,0.97936994,0.000028091335,0.017530348,0.0013775073,0.00015928464],"about_ca_topic_score_codex":0.0000794518,"about_ca_topic_score_gemma":0.00012565144,"teacher_disagreement_score":0.14489183,"about_ca_system_score_codex":0.00010137287,"about_ca_system_score_gemma":0.00036183797,"threshold_uncertainty_score":0.99969274},"labels":[],"label_agreement":null},{"id":"W4388296094","doi":"10.1137/22m1496803","title":"Portfolio Optimization within a Wasserstein Ball","year":2023,"lang":"en","type":"article","venue":"SIAM Journal on Financial Mathematics","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Portfolio optimization; Portfolio; Ball (mathematics); Mathematics; Financial economics; Economics; Econometrics; Mathematical optimization; Computer science; Mathematical analysis","score_opus":0.08279237500958224,"score_gpt":0.35821798084470147,"score_spread":0.2754256058351192,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388296094","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":[0.35823968,0.000112495945,0.5836996,0.0026551676,0.005762944,0.0007825761,0.000036210535,0.0004591852,0.048252188],"genre_scores_gemma":[0.7173666,0.0030341286,0.23223732,0.0018896495,0.0029504003,0.000048091475,0.00006370729,0.00024103883,0.042169057],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9957644,0.0001384583,0.0014480241,0.00031599234,0.0019379077,0.0003952421],"domain_scores_gemma":[0.99714506,0.0005021673,0.0010874149,0.00049381563,0.0005392185,0.0002323059],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0045195026,0.0002366586,0.0004446423,0.00091807876,0.0004312406,0.0005399458,0.00067673624,0.00017913335,0.0004584769],"category_scores_gemma":[0.0059980685,0.00017226896,0.00022719218,0.002547727,0.000054736123,0.00047994428,0.00007853874,0.00038821818,0.0015067122],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000053816082,0.0001764392,0.000488771,0.00000852561,0.000016376398,0.00020678563,0.0018272168,0.88119346,0.000044298245,0.036697593,0.0714919,0.0077948193],"study_design_scores_gemma":[0.0012097033,0.0005588773,0.0019653572,0.00022322951,0.000054804626,0.0003861908,0.0014936336,0.6929316,0.00038990105,0.27317244,0.026955623,0.00065862003],"about_ca_topic_score_codex":0.0000014322613,"about_ca_topic_score_gemma":0.0000036328033,"teacher_disagreement_score":0.35912693,"about_ca_system_score_codex":0.00006914326,"about_ca_system_score_gemma":0.00028631848,"threshold_uncertainty_score":0.99927074},"labels":[],"label_agreement":null},{"id":"W4388470173","doi":"10.2139/ssrn.4598167","title":"End-to-End, Decision-based, Cardinality-Constrained Portfolio Optimization","year":2023,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Portfolio; Cardinality (data modeling); Portfolio optimization; Mathematical optimization; Computer science; End-to-end principle; Business; Economics; Mathematics; Financial economics; Artificial intelligence; Data mining","score_opus":0.03086196698304805,"score_gpt":0.33764278500772615,"score_spread":0.3067808180246781,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388470173","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":[0.062078446,0.00034250936,0.92781514,0.002446111,0.0010779947,0.0003171074,0.000025692978,0.00018210032,0.0057149213],"genre_scores_gemma":[0.982393,0.0030111598,0.0075739436,0.00047690212,0.0005371584,0.000018160445,0.000053320415,0.000053932305,0.00588241],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99266785,0.0003483954,0.0012572708,0.0006594856,0.0024975166,0.0025694882],"domain_scores_gemma":[0.99664795,0.0009650351,0.0005077109,0.00067578565,0.0007985863,0.00040492442],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.014473827,0.00030304698,0.00046904507,0.0016644709,0.0006398929,0.00056121056,0.0010596736,0.0001732943,0.0010151145],"category_scores_gemma":[0.0029761074,0.0002422097,0.000381291,0.004477289,0.00009149671,0.0005613208,0.00010178016,0.0011274792,0.001103924],"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":[0.000116127674,0.00003178564,0.006485454,5.4336004e-7,0.00004721272,0.000026950725,0.000077794975,0.74889046,0.000019281999,0.011625859,0.0036197638,0.22905879],"study_design_scores_gemma":[0.0032150191,0.000965085,0.0075740097,0.000066948865,0.00015453239,0.0010652994,0.004875533,0.36841354,0.00016741901,0.5431942,0.0690556,0.0012528622],"about_ca_topic_score_codex":0.000053264783,"about_ca_topic_score_gemma":0.00022708531,"teacher_disagreement_score":0.92031455,"about_ca_system_score_codex":0.0005860681,"about_ca_system_score_gemma":0.0041524717,"threshold_uncertainty_score":0.9998981},"labels":[],"label_agreement":null},{"id":"W4388661513","doi":"10.2139/ssrn.4619113","title":"Causality in Empirical Analyses With Emphasis on Asymmetric Information and Risk Management","year":2023,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"","keywords":"Emphasis (telecommunications); Causality (physics); Information asymmetry; Risk management; Risk analysis (engineering); Business; Computer science; Economics; Management; Finance","score_opus":0.07140725501465153,"score_gpt":0.411059825009772,"score_spread":0.3396525699951205,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388661513","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":[0.97219735,0.00025533675,0.022919197,0.00054533116,0.000073407646,0.00013726669,0.000003805774,0.000030782357,0.0038374986],"genre_scores_gemma":[0.9617151,0.03748969,0.0002343541,0.00007495227,0.000032195625,0.0000047736303,0.000006461277,0.0000057058223,0.0004367846],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99719083,0.00024264016,0.00052956026,0.00019318912,0.0009918626,0.00085190753],"domain_scores_gemma":[0.99903023,0.00026123607,0.00031660267,0.00020013226,0.000114712486,0.000077099285],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0065053427,0.000114641865,0.00018385329,0.001857422,0.00019693447,0.00024740517,0.00022055283,0.0000507129,0.000011154055],"category_scores_gemma":[0.0004399937,0.00007288175,0.0000482425,0.0042789085,0.000030929874,0.0006679258,0.000041461164,0.0006258708,0.00012302007],"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":[0.0001110217,0.000027162647,0.4597911,9.1549606e-7,0.00006735163,0.000007976317,0.00021678008,0.015793048,1.5423774e-7,0.0033310244,0.00062569045,0.5200278],"study_design_scores_gemma":[0.0013504982,0.0005465455,0.8698623,0.000012967145,0.00006426123,0.00008923566,0.0050791586,0.010246787,0.000011079239,0.10192556,0.010606561,0.00020500364],"about_ca_topic_score_codex":0.0001206267,"about_ca_topic_score_gemma":0.000772279,"teacher_disagreement_score":0.5198228,"about_ca_system_score_codex":0.00027413928,"about_ca_system_score_gemma":0.00029597562,"threshold_uncertainty_score":0.29720312},"labels":[],"label_agreement":null},{"id":"W4388894529","doi":"10.1016/j.insmatheco.2023.11.002","title":"Stressing dynamic loss models","year":2023,"lang":"en","type":"article","venue":"Insurance Mathematics and Economics","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"University of Toronto","funders":"Canadian Statistical Sciences Institute; Natural Sciences and Engineering Research Council of Canada","keywords":"Stress testing (software); Computer science; Applied mathematics; Time horizon; Poisson distribution; Process (computing); Gamma process; Divergence (linguistics); Stress (linguistics); Mathematics; Stochastic process; Compound Poisson process; Mathematical optimization; Econometrics; Poisson process; Statistics","score_opus":0.08729140369060752,"score_gpt":0.3377092539411797,"score_spread":0.25041785025057217,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388894529","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":[0.97510356,0.000081058046,0.01633017,0.0002553219,0.00020112013,0.00008004561,0.000039474464,0.000052572366,0.007856684],"genre_scores_gemma":[0.98574966,0.0034739866,0.0098958565,0.000042117816,0.000019275021,0.0000054918937,0.000006439013,0.000014558317,0.00079262065],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99899083,0.000010623399,0.00046401043,0.00023388142,0.0001297213,0.00017093524],"domain_scores_gemma":[0.99908763,0.00027803562,0.00020329631,0.00031405484,0.000055366883,0.00006161716],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00078901235,0.00009696716,0.0002171651,0.00015970232,0.000137078,0.00031301042,0.00021500306,0.000054553733,0.000015856138],"category_scores_gemma":[0.00011347847,0.00007986877,0.00004501818,0.00025398895,0.000057374735,0.00038706523,0.00007259668,0.000051496543,0.00018748178],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009529487,0.000047697307,0.011264952,0.000023674753,0.000021867307,0.000008732195,0.0033990121,0.73857516,0.000015238456,0.07213732,0.0008277281,0.1736691],"study_design_scores_gemma":[0.00008523019,0.0000042559423,0.0014880368,0.000008574115,0.0000015916381,0.000005396701,0.00026814622,0.65211105,0.0000063239127,0.34538952,0.0005602277,0.000071654445],"about_ca_topic_score_codex":0.000004736886,"about_ca_topic_score_gemma":0.000018514113,"teacher_disagreement_score":0.2732522,"about_ca_system_score_codex":0.000013753874,"about_ca_system_score_gemma":0.000020931458,"threshold_uncertainty_score":0.3256954},"labels":[],"label_agreement":null},{"id":"W4388929300","doi":"10.1007/s43069-023-00277-6","title":"ChatGPT-Based Investment Portfolio Selection","year":2023,"lang":"en","type":"article","venue":"Operations Research Forum","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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 Toronto; University of New Brunswick","funders":"","keywords":"Portfolio; Portfolio optimization; Selection (genetic algorithm); Investment strategy; Black–Litterman model; Computer science; Stock (firearms); Stock market; Econometrics; Economics; Artificial intelligence; Replicating portfolio; Financial economics; Microeconomics; Engineering; Context (archaeology)","score_opus":0.22780718902306915,"score_gpt":0.4938378116246731,"score_spread":0.2660306226016039,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388929300","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":[0.6004482,0.00028676275,0.071941204,0.076167345,0.0019828505,0.0041276976,0.00016361756,0.0012225221,0.24365981],"genre_scores_gemma":[0.9257899,0.00025931414,0.002446877,0.00073233695,0.00016547722,0.0003359189,0.00021018351,0.000028528613,0.07003145],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99536574,0.0005192712,0.00049677875,0.000494049,0.0024928583,0.000631303],"domain_scores_gemma":[0.9974728,0.00040873795,0.000034214103,0.0005681656,0.0012965259,0.00021952468],"candidate_categories":["sts","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0061098,0.000112540394,0.0001522955,0.0018773822,0.0013376069,0.00084536226,0.0004943343,0.000097846256,0.001379756],"category_scores_gemma":[0.002717878,0.00008799882,0.00008131851,0.0073696156,0.000135702,0.00059423264,0.00013299382,0.00026591463,0.005655841],"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":[0.000023287374,0.000098798766,0.0107226735,0.0000013851235,0.00001139332,0.000011328558,0.0002547049,0.40086225,0.0012124387,0.035180125,0.53646326,0.015158336],"study_design_scores_gemma":[0.0002702723,0.00015091045,0.004837465,0.0000060047214,0.000002199941,0.0000030752133,0.0006966968,0.63143355,0.0021656363,0.005442811,0.3548687,0.00012266837],"about_ca_topic_score_codex":0.00029675622,"about_ca_topic_score_gemma":0.0009253775,"teacher_disagreement_score":0.3253417,"about_ca_system_score_codex":0.00010894721,"about_ca_system_score_gemma":0.0006003122,"threshold_uncertainty_score":0.9999625},"labels":[],"label_agreement":null},{"id":"W4388941598","doi":"10.1287/moor.2022.0156","title":"Extreme-Case Distortion Risk Measures: A Unification and Generalization of Closed-Form Solutions","year":2023,"lang":"en","type":"article","venue":"Mathematics of Operations Research","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Simon Fraser University","funders":"","keywords":"Generalization; Mathematics; Extreme point; Extreme value theory; Distortion (music); Regular polygon; Risk measure; Risk management; Econometrics; Operations research; Computer science; Mathematical economics; Statistics; Combinatorics; Economics","score_opus":0.4734195131576592,"score_gpt":0.47380776710063716,"score_spread":0.00038825394297797855,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388941598","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":[0.7388003,0.000129877,0.25896144,0.00029005724,0.000059706326,0.0005449314,0.00009014369,0.000035815814,0.001087747],"genre_scores_gemma":[0.9830004,0.0023427147,0.01304669,0.0000019285048,0.000030472685,0.00005993433,0.00007148811,0.000014444816,0.0014319239],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99645317,0.000439937,0.0010017536,0.0002786612,0.0015965799,0.00022989634],"domain_scores_gemma":[0.99596584,0.0007646735,0.00020648609,0.00066408527,0.0023127818,0.00008611064],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009195009,0.000096222655,0.00022953298,0.001195504,0.0006992676,0.00015883529,0.00027114796,0.000093293565,0.000064416745],"category_scores_gemma":[0.0063952454,0.0000779401,0.00006181766,0.0030434285,0.0002693634,0.00044180363,0.00012543563,0.00013931513,0.000062498686],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000060708473,0.0011953,0.011782962,0.000177559,0.00013249952,0.000027204771,0.044405386,0.41344908,0.031487536,0.31126404,0.0130553255,0.1729624],"study_design_scores_gemma":[0.00029901604,0.000095023854,0.003882314,0.000034046916,0.000028111095,0.000034572444,0.0051788124,0.95243937,0.002525369,0.03447807,0.0008830834,0.00012219456],"about_ca_topic_score_codex":0.00030061722,"about_ca_topic_score_gemma":0.00064086524,"teacher_disagreement_score":0.5389903,"about_ca_system_score_codex":0.00004246417,"about_ca_system_score_gemma":0.0001532705,"threshold_uncertainty_score":0.76561683},"labels":[],"label_agreement":null},{"id":"W4389265763","doi":"10.1016/j.ejor.2023.11.030","title":"A framework for fair decision-making over time with time-invariant utilities","year":2023,"lang":"en","type":"article","venue":"European Journal of Operational Research","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":9,"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":"Canada Excellence Research Chairs, Government of Canada; National University of Singapore; Polytechnique Montréal","keywords":"Computer science; Mathematical optimization; Abstraction; Function (biology); Relaxation (psychology); Decision maker; Quality (philosophy); Contrast (vision); Operations research; Artificial intelligence; Mathematics","score_opus":0.17608458030095545,"score_gpt":0.4690230156366712,"score_spread":0.29293843533571573,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389265763","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":[0.3184356,0.00026667624,0.6417005,0.005709139,0.000513069,0.0007453504,0.000106766005,0.000057867877,0.032465015],"genre_scores_gemma":[0.8852898,0.00014896694,0.10006321,0.00024123795,0.0011163092,0.000006809166,0.000014959301,0.00005876582,0.013059926],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9935146,0.0011370004,0.00089906354,0.00030239552,0.0037766898,0.00037025908],"domain_scores_gemma":[0.9855351,0.010446628,0.00025878026,0.0003372807,0.00324628,0.00017593597],"candidate_categories":["metaresearch","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.02416322,0.00012445405,0.00025783514,0.0010185579,0.0005838985,0.0009815061,0.00089460326,0.000039558614,0.0025742438],"category_scores_gemma":[0.019599399,0.000077533245,0.00013068515,0.0017139983,0.00015963241,0.0006974743,0.00016758572,0.00043247835,0.001801524],"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":[0.0037610268,0.0002114218,0.0040057558,0.0000085338625,0.00018638372,0.0011393407,0.0039028479,0.12849084,0.0008404044,0.04321871,0.65988225,0.15435249],"study_design_scores_gemma":[0.0027864734,0.0035376977,0.069748215,0.0011363686,0.0000339447,0.00046266062,0.0019174101,0.38407305,0.00021168194,0.24450418,0.29092735,0.0006609506],"about_ca_topic_score_codex":8.8216e-7,"about_ca_topic_score_gemma":8.1286777e-7,"teacher_disagreement_score":0.5668542,"about_ca_system_score_codex":0.00005212373,"about_ca_system_score_gemma":0.000560494,"threshold_uncertainty_score":0.9989757},"labels":[],"label_agreement":null},{"id":"W4389363251","doi":"10.48550/arxiv.2312.01034","title":"Monotonic mean-deviation risk measures","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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 Natural Science Foundation of China","keywords":"Monotonic function; Mathematics; Standard deviation; Rate function; Convexity; Estimator; Coherent risk measure; Weighting; Statistics; Risk measure; Consistency (knowledge bases); Absolute deviation; Large deviations theory; Applied mathematics; Generalized mean; Mathematical analysis; Discrete mathematics; Economics","score_opus":0.28973611082975437,"score_gpt":0.2729487471731944,"score_spread":0.016787363656559973,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389363251","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":[0.4242961,0.00016418159,0.5629616,0.00019489562,0.0023044357,0.0006477185,0.0001430889,0.0005254778,0.008762491],"genre_scores_gemma":[0.97737557,0.008235008,0.00031187432,0.000038920112,0.00015235026,0.0000017281943,0.00005145889,0.000037487644,0.013795591],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99644643,0.0006059491,0.0005644096,0.0014684249,0.00056132633,0.00035346785],"domain_scores_gemma":[0.9956107,0.0008723178,0.0009973263,0.0016284208,0.0006958005,0.00019543234],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0026545431,0.0003371316,0.00048195096,0.0008847512,0.0003184941,0.00030716872,0.0015360821,0.00046622276,0.00017753591],"category_scores_gemma":[0.0017186152,0.0003277447,0.00040348564,0.0017183569,0.00010994959,0.00039265063,0.0008812051,0.0006483122,0.0023978772],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000460314,0.000030848027,0.047568962,0.000003400473,0.00006971903,0.00005417817,0.00032157195,0.9361007,0.0000027355238,0.004184068,0.006980408,0.0046373494],"study_design_scores_gemma":[0.00036580427,0.000029953824,0.02457347,0.000029720635,0.00017487993,7.9957215e-7,0.00040300254,0.6846093,0.000049888687,0.28058213,0.0087092575,0.00047178974],"about_ca_topic_score_codex":0.00061088224,"about_ca_topic_score_gemma":0.0007353205,"teacher_disagreement_score":0.5626497,"about_ca_system_score_codex":0.00015624745,"about_ca_system_score_gemma":0.00028042393,"threshold_uncertainty_score":0.99991745},"labels":[],"label_agreement":null},{"id":"W4389495430","doi":"10.2139/ssrn.4658490","title":"Robust Optimization of a Capital-Constrained Newsvendor Model Under the Robust Conditional Value-at-Risk Decision Criterion","year":2023,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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 Windsor","funders":"","keywords":"Newsvendor model; Robust optimization; Value (mathematics); Economics; Econometrics; Expected shortfall; Mathematical optimization; Mathematics; Risk management; Statistics; Business; Finance; Supply chain; Marketing","score_opus":0.07630833068445403,"score_gpt":0.32854565372390004,"score_spread":0.252237323039446,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389495430","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":[0.08927729,0.0012857246,0.9057507,0.0014410776,0.00108326,0.0005428229,0.00028944353,0.00006661213,0.00026307823],"genre_scores_gemma":[0.9261244,0.037044406,0.030118227,0.00016577015,0.0006866382,0.000054144202,0.0005398475,0.00014875836,0.0051178085],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9911996,0.00078472326,0.0022573133,0.0009767141,0.0029868952,0.00179474],"domain_scores_gemma":[0.9927637,0.0017635862,0.0028630707,0.0010697456,0.0013404484,0.00019947095],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0114091085,0.00055085524,0.0008023473,0.00096472644,0.0009612154,0.00055420626,0.0017901985,0.00056778884,0.0002895728],"category_scores_gemma":[0.002173174,0.00037781653,0.0007581357,0.0009203337,0.00033782443,0.00049883896,0.00076706405,0.003513482,0.00007993269],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019592047,0.00007061983,0.00037977606,0.0000039446163,0.00019100535,0.0000036085999,0.00035373267,0.97162414,0.000016380056,0.021722985,0.0016315146,0.0038063924],"study_design_scores_gemma":[0.00055291987,0.00008333587,0.00045336355,0.000047865444,0.000122389,0.00015874796,0.0012791668,0.5477882,0.000010898744,0.44925183,0.00002803545,0.00022322056],"about_ca_topic_score_codex":0.00022461744,"about_ca_topic_score_gemma":0.0010033328,"teacher_disagreement_score":0.87563246,"about_ca_system_score_codex":0.0013557427,"about_ca_system_score_gemma":0.005804459,"threshold_uncertainty_score":0.9998674},"labels":[],"label_agreement":null},{"id":"W4389765011","doi":"10.2139/ssrn.4648630","title":"A Comparison of Range Value at Risk (RVaR) Forecasting Models","year":2023,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Range (aeronautics); Econometrics; Value (mathematics); Value at risk; Statistics; Economics; Actuarial science; Mathematics; Risk management; Engineering; Finance","score_opus":0.13394640574056516,"score_gpt":0.3880937069613758,"score_spread":0.25414730122081064,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389765011","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":[0.8627773,0.0020954825,0.13134791,0.00020830086,0.00032780418,0.00013341363,0.000009816682,0.000047235797,0.003052749],"genre_scores_gemma":[0.9866982,0.00944253,0.00050681777,0.000013023925,0.00015047182,0.000002913645,0.000004826852,0.000019310472,0.0031618972],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9957375,0.00031369328,0.0009450157,0.00026977435,0.0012660902,0.0014679062],"domain_scores_gemma":[0.99778444,0.0005902734,0.0009308236,0.00029382497,0.00029915327,0.00010150634],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009537049,0.0001405934,0.0003822856,0.00047151462,0.00043164467,0.00008743122,0.00057002186,0.000089869245,0.000056074605],"category_scores_gemma":[0.00092988013,0.000105067185,0.00021844002,0.001380063,0.000051527848,0.00040046475,0.000111854744,0.000895204,0.00015844182],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001513544,0.00005312997,0.13371857,0.0000017446141,0.00008179081,0.0000041767034,0.0016988441,0.6632366,0.000067801004,0.024750251,0.0026411456,0.17359458],"study_design_scores_gemma":[0.00041186565,0.00015295237,0.0012126922,0.000008431077,0.000027328819,0.000073913725,0.0013848373,0.55681056,0.00008325545,0.43867746,0.0010594882,0.00009718372],"about_ca_topic_score_codex":0.00008330229,"about_ca_topic_score_gemma":0.0004270106,"teacher_disagreement_score":0.41392723,"about_ca_system_score_codex":0.00027760267,"about_ca_system_score_gemma":0.000692077,"threshold_uncertainty_score":0.42845154},"labels":[],"label_agreement":null},{"id":"W4389893099","doi":"10.32920/24625149","title":"An Analysis of the Haezendonck-Goovaerts Risk Measure","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Expected shortfall; Risk measure; Spectral risk measure; Measure (data warehouse); Portfolio; Mathematics; Dynamic risk measure; Risk model; Coherent risk measure; Focus (optics); Econometrics; Actuarial science; Statistics; Economics; Computer science; Finance; Physics","score_opus":0.1463173783844417,"score_gpt":0.4078202485055461,"score_spread":0.2615028701211044,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389893099","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":[0.6870009,0.00036005446,0.26707038,0.0015261045,0.00422925,0.0012080956,0.0010350787,0.00041390205,0.037156247],"genre_scores_gemma":[0.9881471,0.00060243113,0.0017433035,0.000060530663,0.0000782934,0.000013773566,0.00005986716,0.00001954552,0.009275192],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99449563,0.0008129211,0.0010836512,0.00082771713,0.0025867952,0.0001932755],"domain_scores_gemma":[0.99393123,0.0006468868,0.0012738286,0.003160426,0.00088512764,0.00010252646],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0052343737,0.00022303082,0.0007394919,0.0010569276,0.00016167988,0.00027989675,0.002281068,0.00031201198,0.00063444657],"category_scores_gemma":[0.0023616431,0.00011609354,0.00090651395,0.004385704,0.00010875638,0.00012683302,0.00073425024,0.00039406476,0.00009568848],"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":[0.000007464521,0.000026935772,0.37776002,9.800448e-7,0.0003719188,8.3827285e-7,0.00038624936,0.60532594,0.0000053491067,0.0002828372,0.0060890582,0.009742409],"study_design_scores_gemma":[0.00008186407,0.000014065278,0.62210226,0.000011699958,0.001500993,3.0552377e-7,0.00039665133,0.3576467,0.00019932452,0.01522742,0.0026186525,0.0002000843],"about_ca_topic_score_codex":0.0031156961,"about_ca_topic_score_gemma":0.0044975663,"teacher_disagreement_score":0.30114618,"about_ca_system_score_codex":0.000029520157,"about_ca_system_score_gemma":0.00020451428,"threshold_uncertainty_score":0.6946745},"labels":[],"label_agreement":null},{"id":"W4389914618","doi":"10.32920/24625149.v1","title":"An Analysis of the Haezendonck-Goovaerts Risk Measure","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Expected shortfall; Risk measure; Spectral risk measure; Measure (data warehouse); Portfolio; Dynamic risk measure; Coherent risk measure; Mathematics; Risk model; Focus (optics); Econometrics; Actuarial science; Statistics; Economics; Computer science; Finance; Physics; Data mining","score_opus":0.1463173783844417,"score_gpt":0.4078202485055461,"score_spread":0.2615028701211044,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389914618","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":[0.6870009,0.00036005446,0.26707038,0.0015261045,0.00422925,0.0012080956,0.0010350787,0.00041390205,0.037156247],"genre_scores_gemma":[0.9881471,0.00060243113,0.0017433035,0.000060530663,0.0000782934,0.000013773566,0.00005986716,0.00001954552,0.009275192],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99449563,0.0008129211,0.0010836512,0.00082771713,0.0025867952,0.0001932755],"domain_scores_gemma":[0.99393123,0.0006468868,0.0012738286,0.003160426,0.00088512764,0.00010252646],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0052343737,0.00022303082,0.0007394919,0.0010569276,0.00016167988,0.00027989675,0.002281068,0.00031201198,0.00063444657],"category_scores_gemma":[0.0023616431,0.00011609354,0.00090651395,0.004385704,0.00010875638,0.00012683302,0.00073425024,0.00039406476,0.00009568848],"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":[0.000007464521,0.000026935772,0.37776002,9.800448e-7,0.0003719188,8.3827285e-7,0.00038624936,0.60532594,0.0000053491067,0.0002828372,0.0060890582,0.009742409],"study_design_scores_gemma":[0.00008186407,0.000014065278,0.62210226,0.000011699958,0.001500993,3.0552377e-7,0.00039665133,0.3576467,0.00019932452,0.01522742,0.0026186525,0.0002000843],"about_ca_topic_score_codex":0.0031156961,"about_ca_topic_score_gemma":0.0044975663,"teacher_disagreement_score":0.30114618,"about_ca_system_score_codex":0.000029520157,"about_ca_system_score_gemma":0.00020451428,"threshold_uncertainty_score":0.6946745},"labels":[],"label_agreement":null},{"id":"W4389986755","doi":"10.1137/1.9781611977806.ch24","title":"Chapter 24: Strong Convexity","year":2023,"lang":"en","type":"book-chapter","venue":"Society for Industrial and Applied Mathematics eBooks","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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 British Columbia, Okanagan Campus; University of British Columbia","funders":"","keywords":"Convexity; Computer science; Economics; Financial economics","score_opus":0.28445928921428393,"score_gpt":0.339195880277138,"score_spread":0.054736591062854056,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389986755","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00019274928,0.000037817954,0.011358892,0.00018674375,0.0010306236,0.0021084596,0.00051192223,0.0001938907,0.9843789],"genre_scores_gemma":[0.0008813369,0.00018283342,0.010327525,0.00014739622,0.0012668788,0.00011064396,0.00009819897,0.000151227,0.98683393],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9968275,0.0000048514585,0.0011114273,0.00068393024,0.0010196831,0.00035261654],"domain_scores_gemma":[0.9972018,0.00097095076,0.0008868392,0.0005602348,0.00019617201,0.00018401646],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0018176822,0.00047115923,0.0008748452,0.00011074457,0.00039667566,0.00032140664,0.00043495162,0.0010700886,0.00015476436],"category_scores_gemma":[0.00012284069,0.00037017482,0.00073174667,0.00003988061,0.00033791587,0.000047555262,0.00023817214,0.0005590504,0.00015323043],"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":[0.000022703493,0.0000082929855,6.098352e-7,0.000024492814,0.00014106969,5.625874e-7,0.0008492115,0.000016604237,0.000013190035,0.9246836,0.025057381,0.04918229],"study_design_scores_gemma":[0.0009101532,0.00005138118,1.8356023e-7,0.00006578898,0.00017277866,0.0000023038067,0.0005908129,0.0011545032,0.0000919387,0.73414487,0.2623982,0.00041707896],"about_ca_topic_score_codex":0.0000021683977,"about_ca_topic_score_gemma":0.000003808244,"teacher_disagreement_score":0.23734084,"about_ca_system_score_codex":0.00003101579,"about_ca_system_score_gemma":0.00011200666,"threshold_uncertainty_score":0.999875},"labels":[],"label_agreement":null},{"id":"W4390079318","doi":"10.1016/j.ejor.2023.12.021","title":"Leveraging decision diagrams to solve two-stage stochastic programs with binary recourse and logical linking constraints","year":2023,"lang":"en","type":"article","venue":"European Journal of Operational Research","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"University of Toronto","funders":"","keywords":"Binary decision diagram; Computer science; Mathematical optimization; Binary number; Stochastic programming; Stage (stratigraphy); Set (abstract data type); Class (philosophy); Mathematics; Algorithm; Artificial intelligence; Programming language","score_opus":0.29401581664398335,"score_gpt":0.46408445923681996,"score_spread":0.1700686425928366,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390079318","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":[0.80848384,0.000062649364,0.18739146,0.0018720399,0.00017410617,0.00027952783,0.000004765882,0.000016972275,0.0017146551],"genre_scores_gemma":[0.9777261,0.000103426515,0.020982968,0.00010077028,0.0003198314,0.0000034313387,0.0000111305735,0.000023237215,0.0007291155],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99423903,0.0008931114,0.00080273004,0.00037765698,0.0032904106,0.00039708227],"domain_scores_gemma":[0.9951386,0.0019350481,0.0001877982,0.00022444918,0.0020891167,0.00042500097],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.02208751,0.00013368756,0.00023781903,0.0009701444,0.00054376194,0.0011341014,0.0005675439,0.000025886162,0.00019756239],"category_scores_gemma":[0.003999024,0.00008317744,0.00006022607,0.0017665398,0.00031633867,0.0004881422,0.0002654466,0.0005599451,0.00035917395],"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":[0.0006147108,0.00012244046,0.010812607,0.0000029872965,0.0000333747,0.0019304991,0.002466514,0.33982748,0.0006105071,0.0015323469,0.0038105173,0.638236],"study_design_scores_gemma":[0.012282556,0.016226772,0.5019175,0.0028071376,0.00006794257,0.0036155966,0.023714412,0.3646943,0.00023997977,0.014113459,0.05846894,0.001851398],"about_ca_topic_score_codex":0.0000025135641,"about_ca_topic_score_gemma":0.0000052482565,"teacher_disagreement_score":0.6363846,"about_ca_system_score_codex":0.00004566186,"about_ca_system_score_gemma":0.00032531907,"threshold_uncertainty_score":0.99990284},"labels":[],"label_agreement":null},{"id":"W4390274257","doi":"10.21203/rs.3.rs-3765572/v1","title":"Hybrid simplicial-randomized approximate stochastic dynamic programming for multireservoir optimization","year":2023,"lang":"en","type":"preprint","venue":"Research Square","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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; Laurentian University","funders":"","keywords":"Convexity; Bellman equation; Mathematics; Mathematical optimization; Grid; Upper and lower bounds; Curvature; Function (biology); Stochastic programming; Dynamic programming; Applied mathematics; Computer science; Mathematical analysis","score_opus":0.1945129989454447,"score_gpt":0.4935135314944872,"score_spread":0.29900053254904246,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390274257","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":[0.00489783,0.00038303735,0.97842693,0.0014267958,0.0010779473,0.012543988,0.0006020086,0.00045520926,0.00018625113],"genre_scores_gemma":[0.7668909,0.002821696,0.1898112,0.00005193224,0.0012112148,0.019438067,0.0068645724,0.0005825737,0.012327831],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.98760116,0.0022251036,0.0019554265,0.0019881949,0.004836632,0.0013934722],"domain_scores_gemma":[0.97985995,0.01234179,0.0008443248,0.0019302845,0.00463533,0.00038834987],"candidate_categories":["metaresearch","metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.02784227,0.00053629716,0.0015443712,0.002488918,0.00093852676,0.0021817265,0.0021320134,0.00049971597,0.000103226266],"category_scores_gemma":[0.045525424,0.00042680732,0.0008459648,0.0017877095,0.0005116931,0.00034636725,0.002053977,0.0012517282,0.00021175269],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0049200677,0.00010812551,0.000038336955,0.00020512479,0.00007505936,0.000011527559,0.00043933257,0.9604527,0.0000029259227,0.00048919226,0.002550838,0.030706815],"study_design_scores_gemma":[0.01173048,0.00009432851,0.000037839818,0.00031338577,0.00004353236,0.00000257839,0.00039379124,0.915471,0.0000118262515,0.07066964,0.0008340663,0.00039753524],"about_ca_topic_score_codex":0.00033438898,"about_ca_topic_score_gemma":0.00009673992,"teacher_disagreement_score":0.7886157,"about_ca_system_score_codex":0.0003483649,"about_ca_system_score_gemma":0.0008442167,"threshold_uncertainty_score":0.9998184},"labels":[],"label_agreement":null},{"id":"W4390539045","doi":"10.1287/ijoc.2022.0010","title":"A Column Generation Scheme for Distributionally Robust Multi-Item Newsvendor Problems","year":2024,"lang":"en","type":"article","venue":"INFORMS journal on computing","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Group for Research in Decision Analysis; HEC Montréal","funders":"","keywords":"Solver; Mathematical optimization; Computer science; Ambiguity; Newsvendor model; Column generation; Linear programming; Event (particle physics); Operations research; Mathematics","score_opus":0.2094707748557991,"score_gpt":0.398550081017716,"score_spread":0.1890793061619169,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390539045","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":[0.081285894,0.00025369227,0.91483116,0.00074914715,0.0020059424,0.00033305652,0.000032657226,0.0000756376,0.0004327916],"genre_scores_gemma":[0.8658884,0.00013843544,0.12799276,0.0005591864,0.002824463,0.000015579231,0.00014248693,0.00003513082,0.0024035599],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99724036,0.00004248147,0.0011423568,0.00028353836,0.0009657539,0.0003255053],"domain_scores_gemma":[0.9977705,0.0007461622,0.0004250163,0.00017522436,0.00071907823,0.00016403392],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0031902685,0.00016695367,0.00022386744,0.00035654346,0.0006426337,0.0023678122,0.00036712358,0.00009745277,0.0000612907],"category_scores_gemma":[0.001514349,0.000114798684,0.00023062274,0.0006911007,0.000031801086,0.0009016037,0.00005776219,0.0003308434,0.00012564925],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000028740626,0.000046736935,0.0023343526,0.000009801818,0.0000400982,0.000014763132,0.0003869639,0.6716043,0.00018766576,0.0042403336,0.02795973,0.29314652],"study_design_scores_gemma":[0.0004617504,0.0001319259,0.0009663682,0.00009337271,0.000007573156,0.00015047328,0.000062396655,0.8776655,0.00009474928,0.0010015725,0.119219474,0.0001448371],"about_ca_topic_score_codex":0.0000039446886,"about_ca_topic_score_gemma":0.000008053255,"teacher_disagreement_score":0.7868384,"about_ca_system_score_codex":0.00014947912,"about_ca_system_score_gemma":0.000281205,"threshold_uncertainty_score":0.99866784},"labels":[],"label_agreement":null},{"id":"W4390990651","doi":"10.1016/j.spl.2024.110044","title":"A note on the induction of comonotonic additive risk measures from acceptance sets","year":2024,"lang":"en","type":"article","venue":"Statistics & Probability Letters","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Concordia University; University of Waterloo","funders":"Conselho Nacional de Desenvolvimento Científico e Tecnológico; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior","keywords":"Mathematics; Equivalence (formal languages); Complement (music); Regular polygon; Random variable; Set (abstract data type); Measure (data warehouse); Statistics; Probability measure; Econometrics; Discrete mathematics; Combinatorics; Data mining; Computer science","score_opus":0.0625179178194037,"score_gpt":0.34593882856916586,"score_spread":0.28342091074976217,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390990651","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":[0.32196045,0.00007575219,0.66979456,0.0030170493,0.0007456378,0.0004702073,0.0036032167,0.000045072276,0.0002880394],"genre_scores_gemma":[0.9246729,0.0006954884,0.073770136,0.00058246317,0.00010049948,0.000044363227,0.00008584072,0.000016432487,0.000031856503],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9967604,0.00068753556,0.0006223201,0.00055353076,0.0011850465,0.00019116748],"domain_scores_gemma":[0.9942735,0.004409122,0.00029327694,0.0006797819,0.00029051286,0.000053820237],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021451714,0.00016531593,0.0002378468,0.00011765447,0.00016539599,0.00022645845,0.00042346254,0.000069066875,0.00018176663],"category_scores_gemma":[0.0039020947,0.00010345253,0.00009212291,0.00060000655,0.00035977422,0.00020331334,0.00005353571,0.0003570309,0.0001534707],"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":[0.00014288779,0.00008306892,0.004597057,0.000004618158,0.00008375688,0.000010305547,0.004526457,0.003241079,0.0007711593,0.041774336,0.07238594,0.87237936],"study_design_scores_gemma":[0.00018034,0.00010897726,0.06371804,0.00004577218,0.0000806246,0.0000013582325,0.00008472116,0.022747973,0.002063573,0.876526,0.034205437,0.00023719021],"about_ca_topic_score_codex":0.00037931936,"about_ca_topic_score_gemma":0.00031276993,"teacher_disagreement_score":0.87214214,"about_ca_system_score_codex":0.00010370161,"about_ca_system_score_gemma":0.000115416544,"threshold_uncertainty_score":0.46714535},"labels":[],"label_agreement":null},{"id":"W4391020856","doi":"10.1109/cdc49753.2023.10383306","title":"Inner Approximations of Stochastic Programs for Data-Driven Stochastic Barrier Function Design","year":2023,"lang":"en","type":"article","venue":"","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Transport Canada","funders":"","keywords":"Computer science; Logarithm; Linear programming; Affine transformation; Mathematical optimization; Stochastic programming; Reduction (mathematics); Noise (video); Function (biology); Stochastic optimization; Algorithm; Mathematics; Artificial intelligence","score_opus":0.25088503128029427,"score_gpt":0.3918886703690864,"score_spread":0.14100363908879215,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391020856","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":[0.0017757366,0.000015225583,0.99592084,0.00015287181,0.0004429357,0.0011545343,0.000072473216,0.00014778861,0.000317577],"genre_scores_gemma":[0.88268834,0.0000065107374,0.109265156,0.00005731964,0.00016185644,0.00034536928,0.0009222306,0.000034108383,0.0065191137],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99774384,0.00008877483,0.0006773629,0.00047595444,0.00077013776,0.00024392782],"domain_scores_gemma":[0.99710155,0.0011179716,0.0002650703,0.00087717373,0.0005421001,0.00009615593],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022131149,0.00012522559,0.00023799585,0.00043980448,0.00017325651,0.00013891941,0.00062840676,0.00008136237,0.000185387],"category_scores_gemma":[0.002581751,0.00008961843,0.000070140966,0.001762236,0.00008121138,0.0005825873,0.00014452999,0.00005665629,0.000238546],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008894137,0.000060569604,0.00008431042,0.0000044415638,0.000029233548,2.2055329e-7,0.00027331628,0.8274629,0.0000921831,0.0033959998,0.040947534,0.12756032],"study_design_scores_gemma":[0.00033304078,0.00014404637,0.0002860612,0.000007961822,0.000045704448,0.0000011236481,0.0004892447,0.9728487,0.000013967971,0.02417616,0.0015443612,0.0001096226],"about_ca_topic_score_codex":0.000014334262,"about_ca_topic_score_gemma":0.000017281971,"teacher_disagreement_score":0.8866557,"about_ca_system_score_codex":0.00001372097,"about_ca_system_score_gemma":0.00014174952,"threshold_uncertainty_score":0.36545333},"labels":[],"label_agreement":null},{"id":"W4391021824","doi":"10.1109/cdc49753.2023.10384249","title":"Risk-Constrained Control of Mean-Field Linear Quadratic Systems","year":2023,"lang":"en","type":"article","venue":"","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Bounding overwatch; Control theory (sociology); Constraint (computer-aided design); Controller (irrigation); Linear system; Optimal control; Mathematical optimization; Mathematics; Quadratic equation; State (computer science); Affine transformation; Computer science; Applied mathematics; Control (management); Algorithm","score_opus":0.05369625523866135,"score_gpt":0.3527917013040446,"score_spread":0.29909544606538324,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391021824","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":[0.19094685,0.00023698968,0.7579325,0.00084235915,0.0013099437,0.00059587206,0.00005855092,0.00026597953,0.047810987],"genre_scores_gemma":[0.99400353,0.00019823633,0.00072962634,0.00007348638,0.0000749533,0.000008445912,0.000004145544,0.000006620181,0.004900957],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978547,0.00019626498,0.00075011526,0.00022974958,0.00079822756,0.00017093284],"domain_scores_gemma":[0.9966052,0.0022347027,0.00035264992,0.0004177484,0.00031219225,0.000077489],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.002688015,0.00009006293,0.00029886587,0.00029663826,0.00007809129,0.000077696954,0.00031075336,0.00008210177,0.0003102246],"category_scores_gemma":[0.0028530334,0.000058331203,0.00010043323,0.0011233271,0.000044861605,0.00016726421,0.000025250762,0.00007489741,0.00080634246],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024901459,0.00014603662,0.19182561,0.000025928292,0.00022808807,0.000038427475,0.0029492737,0.54791296,0.0012121872,0.061545733,0.087498955,0.10636777],"study_design_scores_gemma":[0.0007479649,0.00014099668,0.0026020147,0.000011095408,0.000030858107,0.0000026669145,0.0019236068,0.98459995,0.00069017854,0.0031396481,0.0059812483,0.00012979208],"about_ca_topic_score_codex":0.0003562362,"about_ca_topic_score_gemma":0.00005164042,"teacher_disagreement_score":0.80305666,"about_ca_system_score_codex":0.000005143002,"about_ca_system_score_gemma":0.000060306636,"threshold_uncertainty_score":0.9999716},"labels":[],"label_agreement":null},{"id":"W4391314142","doi":"10.3982/ecta18930","title":"“Near” Weighted Utilitarian Characterizations of Pareto Optima","year":2024,"lang":"en","type":"article","venue":"Econometrica","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Japan Society for the Promotion of Science; Natural Sciences and Engineering Research Council of Canada; Seoul National University; University of British Columbia; UBC Sauder School of Business; National Research Foundation of Korea; Ministry of Education; Carnegie Mellon University; Institute of Economic Research, Hitotsubashi University; Northwestern University; Japan Society for the Promotion of Science London; Stanford University; Korea Advanced Institute of Science and Technology; National Science Foundation","keywords":"Pareto principle; Mathematics; Mathematical economics; Pareto optimal; Mathematical optimization; Economics; Multi-objective optimization","score_opus":0.08391305937807016,"score_gpt":0.36995705926512834,"score_spread":0.2860439998870582,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391314142","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":[0.69829583,0.003696729,0.09858154,0.0039845044,0.0031723625,0.0005726137,0.0005189396,0.00029627618,0.19088122],"genre_scores_gemma":[0.9915016,0.00037717167,0.0045331586,0.000045442448,0.00007327668,0.000009690487,0.000047041813,0.000012211524,0.0034003837],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99841964,0.000055080618,0.00068184204,0.00036710332,0.00031708385,0.00015923513],"domain_scores_gemma":[0.9985018,0.0006763248,0.00013934505,0.00043277358,0.00015113212,0.00009866576],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0008383792,0.00009686587,0.00024198585,0.0013951268,0.00009205396,0.0003580765,0.00036603538,0.00006674306,0.0036040568],"category_scores_gemma":[0.0007884644,0.000077016804,0.000119885335,0.005219057,0.00007303008,0.0005313604,0.000058847534,0.000075991244,0.0010844298],"study_design_candidate":"design_other","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":[0.000040229086,0.00026989324,0.13954051,0.000028298378,0.00018830788,0.000031717693,0.0027231895,0.003703449,0.00012399594,0.059396368,0.049869943,0.7440841],"study_design_scores_gemma":[0.00013458781,0.00008303282,0.10160334,0.000014173551,0.000019833267,0.0000059186727,0.000121892444,0.21847694,0.00016779221,0.007878345,0.67130613,0.0001880229],"about_ca_topic_score_codex":0.000020064115,"about_ca_topic_score_gemma":0.00000244725,"teacher_disagreement_score":0.74389607,"about_ca_system_score_codex":0.000028305349,"about_ca_system_score_gemma":0.00011750974,"threshold_uncertainty_score":0.99969333},"labels":[],"label_agreement":null},{"id":"W4391368157","doi":"10.1080/1350486x.2023.2301354","title":"Robust Risk-Aware Option Hedging","year":2023,"lang":"en","type":"article","venue":"Applied Mathematical Finance","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":8,"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 Toronto","keywords":"Computer science; Actuarial science; Risk analysis (engineering); Econometrics; Economics; Financial economics; Business","score_opus":0.12200367765718867,"score_gpt":0.34473437732558754,"score_spread":0.22273069966839887,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391368157","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":[0.17032064,0.000035530513,0.79323345,0.00035061588,0.00018769159,0.0003497381,0.000013973615,0.00031406627,0.035194308],"genre_scores_gemma":[0.9711759,0.00071788504,0.024635363,0.00006347734,0.0001051548,0.00007716771,0.000011616927,0.000024116169,0.003189283],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9977269,0.000042968823,0.00058645185,0.00046581068,0.00085637765,0.00032150175],"domain_scores_gemma":[0.9981004,0.00084971567,0.00026059666,0.0006220864,0.00009887277,0.00006830537],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001974144,0.00014751666,0.0002891193,0.0001905301,0.00023855249,0.00019212018,0.00043956286,0.00010185901,0.00020876642],"category_scores_gemma":[0.0008870506,0.00011166251,0.0000831036,0.0016333481,0.00007465582,0.0001688613,0.00012315933,0.00015742921,0.010480737],"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":[0.000021300762,0.00006632262,0.00058374373,0.000011812764,0.00000803949,0.00001130083,0.00072785356,0.11757675,0.000083763625,0.6538742,0.019737026,0.20729788],"study_design_scores_gemma":[0.00021544633,0.0000140259435,0.0029579836,0.00002098328,0.000010786905,0.000004190239,0.00031731368,0.22413401,0.00027312728,0.7592302,0.0126217585,0.00020014093],"about_ca_topic_score_codex":0.0000014671738,"about_ca_topic_score_gemma":7.6549696e-7,"teacher_disagreement_score":0.8008553,"about_ca_system_score_codex":0.00001885005,"about_ca_system_score_gemma":0.000026678219,"threshold_uncertainty_score":0.9902897},"labels":[],"label_agreement":null},{"id":"W4391542539","doi":"10.3390/risks12020033","title":"Robust Portfolio Optimization with Environmental, Social, and Corporate Governance Preference","year":2024,"lang":"en","type":"article","venue":"Risks","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Western University","funders":"","keywords":"Corporate governance; Preference; Portfolio; Portfolio optimization; Business; Robust optimization; Economics; Microeconomics; Mathematical optimization; Finance; Mathematics","score_opus":0.28417230034948526,"score_gpt":0.33974692865011247,"score_spread":0.055574628300627205,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391542539","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":[0.5047137,0.0050329017,0.46929336,0.0009652603,0.0005608846,0.0005821203,0.00020538094,0.00025448014,0.018391887],"genre_scores_gemma":[0.9836764,0.0054867114,0.0067266426,0.000068921225,0.00008730117,0.000013193603,0.000030983196,0.000020752565,0.0038891493],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99825543,0.00006684706,0.00031712095,0.00049263716,0.000693175,0.00017477515],"domain_scores_gemma":[0.99929416,0.000106976055,0.00025737155,0.0002249444,0.000039424755,0.00007710768],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005027947,0.00014834164,0.00017398469,0.0000909413,0.00019327478,0.00049709715,0.00019067642,0.000083986335,0.000620709],"category_scores_gemma":[0.00004809946,0.00010384144,0.000033121065,0.00050776865,0.00014289771,0.00062147924,0.00005934257,0.00012517377,0.000112272086],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000101021215,0.000079834026,0.07939467,0.000011992496,0.00005171083,0.00008637497,0.0009673189,0.7290001,0.00007359623,0.0059168953,0.035223566,0.14909293],"study_design_scores_gemma":[0.0005352012,0.0002016083,0.1873981,0.000065776585,0.00008401962,0.000073331255,0.00042011202,0.75855255,0.00021951192,0.0050439,0.04682683,0.0005790612],"about_ca_topic_score_codex":0.000046627945,"about_ca_topic_score_gemma":0.000014209908,"teacher_disagreement_score":0.47896263,"about_ca_system_score_codex":0.000036413978,"about_ca_system_score_gemma":0.00006038633,"threshold_uncertainty_score":0.6796328},"labels":[],"label_agreement":null},{"id":"W4391578166","doi":"10.1080/10920277.2023.2285976","title":"Measuring Discrete Risks on Infinite Domains: Theoretical Foundations, Conditional Five Number Summaries, and Data Analyses","year":2024,"lang":"en","type":"article","venue":"North American Actuarial Journal","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Western University","funders":"Mitacs","keywords":"Quantile; Estimator; Truncation (statistics); Mathematics; Inference; Poisson distribution; Applied mathematics; Computer science; Econometrics; Statistics; Artificial intelligence","score_opus":0.2329493930711319,"score_gpt":0.4694442616772288,"score_spread":0.2364948686060969,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391578166","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":[0.7450903,0.0003620189,0.2352549,0.0047714827,0.0025227119,0.0003721766,0.0022076946,0.00014636145,0.0092723835],"genre_scores_gemma":[0.9928048,0.001330125,0.003590648,0.00037798195,0.0012708958,0.0000039004026,0.00051480636,0.00002140787,0.000085383435],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9960926,0.0004879195,0.00080846524,0.0006092214,0.0016754023,0.0003263853],"domain_scores_gemma":[0.99589485,0.00249604,0.00039853132,0.00059732987,0.0002796763,0.00033355443],"candidate_categories":["scholarly_communication","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0019196803,0.00023191761,0.00038725697,0.0004057442,0.00062085805,0.0024714482,0.0007477092,0.000052049625,0.0012079825],"category_scores_gemma":[0.0034442171,0.00015769423,0.00012012458,0.001153453,0.0010806618,0.0011611406,0.00023250535,0.00059645093,0.00022767847],"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":[0.0015262299,0.0001540347,0.24671781,0.0000074454106,0.0009194354,0.0004896375,0.0018487329,0.018963527,0.000030060177,0.07330856,0.06116254,0.594872],"study_design_scores_gemma":[0.0018994935,0.0006764178,0.43093297,0.00013706426,0.0006915342,0.0012510134,0.0023116819,0.11942181,0.000036525063,0.15694842,0.28440997,0.0012831195],"about_ca_topic_score_codex":0.00018792621,"about_ca_topic_score_gemma":0.00020481867,"teacher_disagreement_score":0.5935889,"about_ca_system_score_codex":0.00006271649,"about_ca_system_score_gemma":0.00043271962,"threshold_uncertainty_score":0.9997051},"labels":[],"label_agreement":null},{"id":"W4391656247","doi":"10.1016/j.ejor.2024.02.002","title":"Robust insurance design with distortion risk measures","year":2024,"lang":"en","type":"article","venue":"European Journal of Operational Research","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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 Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; University of Calgary","keywords":"Computer science; Risk management; Actuarial science; Distortion (music); Risk measure; Risk analysis (engineering); Econometrics; Mathematics; Business; Finance","score_opus":0.378339752836445,"score_gpt":0.4313619118091266,"score_spread":0.05302215897268159,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391656247","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":[0.08676463,0.0021440415,0.8982281,0.0014548112,0.0003943806,0.00015501335,0.000013116835,0.000015276928,0.010830623],"genre_scores_gemma":[0.9813358,0.0017177016,0.013659839,0.000024678571,0.0005392548,0.0000015258147,0.0000022431118,0.00002217023,0.0026967684],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.99088156,0.0039802627,0.0007175033,0.00025501018,0.003966518,0.00019912061],"domain_scores_gemma":[0.9951653,0.00133846,0.00016978156,0.00021553435,0.0029508315,0.00016013082],"candidate_categories":["metaresearch","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.03274034,0.00009458672,0.00014848646,0.0006846075,0.0003834113,0.0012407477,0.0005626924,0.00001896647,0.00022594424],"category_scores_gemma":[0.0041409796,0.000054247437,0.00007477442,0.001250688,0.00015656643,0.000977563,0.00004337444,0.00061343814,0.0004585302],"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":[0.0004613236,0.00006663301,0.011421566,0.0000024763099,0.000057620597,0.0006279689,0.0010279499,0.705187,0.00041298583,0.0013683967,0.06614159,0.21322446],"study_design_scores_gemma":[0.0014747492,0.003130366,0.36633635,0.00040990405,0.00003954047,0.0010933478,0.00081184995,0.09053201,0.0015228549,0.0048294924,0.52933,0.0004895151],"about_ca_topic_score_codex":0.0000064266037,"about_ca_topic_score_gemma":0.0000051598518,"teacher_disagreement_score":0.8945712,"about_ca_system_score_codex":0.00007563123,"about_ca_system_score_gemma":0.00056902296,"threshold_uncertainty_score":0.99979603},"labels":[],"label_agreement":null},{"id":"W4391922577","doi":"10.48550/arxiv.2402.11981","title":"Universal generalization guarantees for Wasserstein distributionally robust models","year":2024,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Discovery Air (Canada)","funders":"","keywords":"Generalization; Mathematical optimization; Computer science; Mathematics; Mathematical economics","score_opus":0.23101895994518673,"score_gpt":0.26522391445537435,"score_spread":0.034204954510187624,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391922577","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":[0.11007161,0.00020546066,0.88346785,0.0003041083,0.0011471012,0.0005519665,0.0008387588,0.00016883964,0.0032443071],"genre_scores_gemma":[0.98074746,0.0008330994,0.0018639852,0.000048085316,0.00021992835,0.0000028688653,0.00090791524,0.00003724056,0.015339424],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9971538,0.00017625045,0.00051177264,0.0014331392,0.00037399156,0.00035103553],"domain_scores_gemma":[0.99724275,0.00032231017,0.00042341495,0.00087222864,0.0009814672,0.00015785785],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00095210224,0.00036518893,0.0004455723,0.00063156994,0.00026796307,0.00042064823,0.0011879808,0.00046078788,0.00011672594],"category_scores_gemma":[0.00020888548,0.0003604492,0.000485365,0.0012151927,0.00013182982,0.00049057795,0.00090020173,0.00035952157,0.00012660617],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007613868,0.000029168392,0.00026673268,0.000015558053,0.000058189748,0.000031575193,0.000083955514,0.6794629,0.0000071410177,0.31261167,0.0071039745,0.00025297902],"study_design_scores_gemma":[0.00025341148,0.000023706212,0.00004924411,0.000035629982,0.00012514781,0.0000014753981,0.00017823526,0.6167809,0.000025457171,0.3797789,0.002492755,0.00025517988],"about_ca_topic_score_codex":0.00013179133,"about_ca_topic_score_gemma":0.000115232535,"teacher_disagreement_score":0.8816039,"about_ca_system_score_codex":0.00033049338,"about_ca_system_score_gemma":0.0004881524,"threshold_uncertainty_score":0.9998847},"labels":[],"label_agreement":null},{"id":"W4392373677","doi":"10.54254/2755-2721/44/20230249","title":"Distributionally Robust Optimization methods on robust medical diagnosis systems","year":2024,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Robust optimization; Computer science; Robustness (evolution); Outlier; Machine learning; Artificial intelligence; Domain (mathematical analysis); Partition (number theory); Optimization problem; Data mining; Mathematical optimization; Algorithm; Mathematics","score_opus":0.047100498649426246,"score_gpt":0.3401294191023163,"score_spread":0.2930289204528901,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392373677","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":[0.0012013449,0.0006620521,0.9954272,0.0005305933,0.0006075625,0.00014057099,0.000026654914,0.00015767217,0.0012463608],"genre_scores_gemma":[0.78182787,0.00038188722,0.21662429,0.00013863016,0.0004330683,0.00014309389,0.00028781593,0.000033013934,0.00013032327],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978726,0.00004650732,0.0004524102,0.00040548755,0.0010624246,0.00016059297],"domain_scores_gemma":[0.99739057,0.0021716487,0.00005362851,0.00011593471,0.00011640283,0.00015183116],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013729448,0.00015290728,0.00019257874,0.00026229734,0.00011317587,0.0004712074,0.0001767214,0.000108302156,0.00013354793],"category_scores_gemma":[0.00040712606,0.00012493656,0.000049941955,0.000669553,0.000030141253,0.00015179437,0.000051128445,0.00016356393,0.00004166463],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000042559354,0.000012881714,0.000043717766,0.000008141009,0.000017592783,0.000005060472,0.00003081491,0.81342447,0.0000014816949,0.16457519,0.0008849507,0.020991432],"study_design_scores_gemma":[0.00012066334,0.000015123537,0.0006227203,0.00004775912,0.00001032715,0.000019740022,0.000027272135,0.98902094,0.0000072320818,0.0020880997,0.007879053,0.00014104876],"about_ca_topic_score_codex":0.0000045097677,"about_ca_topic_score_gemma":1.7397956e-7,"teacher_disagreement_score":0.78062654,"about_ca_system_score_codex":0.000042469495,"about_ca_system_score_gemma":0.00007980736,"threshold_uncertainty_score":0.5094765},"labels":[],"label_agreement":null},{"id":"W4392697139","doi":"10.1016/j.insmatheco.2024.03.001","title":"Tail mean-variance portfolio selection with estimation risk","year":2024,"lang":"en","type":"article","venue":"Insurance Mathematics and Economics","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Actua; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; University of Waterloo; Nanyang Technological University","keywords":"Portfolio; Selection (genetic algorithm); Estimation; Statistics; Econometrics; Variance (accounting); Portfolio optimization; Economics; Mathematics; Computer science; Financial economics; Artificial intelligence","score_opus":0.027333830550158974,"score_gpt":0.2869549881814635,"score_spread":0.2596211576313045,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392697139","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":[0.769734,0.00029517646,0.22479208,0.00013088586,0.00024557186,0.00015574716,0.000033394805,0.00007443362,0.004538682],"genre_scores_gemma":[0.9338501,0.0026133817,0.062430777,0.000033950455,0.00008828611,0.00001660791,0.000006143827,0.000023265608,0.00093747734],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99872595,0.000021710774,0.00053397234,0.0003711868,0.00018589229,0.00016129232],"domain_scores_gemma":[0.9989837,0.0003182948,0.0002697434,0.0002560854,0.00010326208,0.00006894662],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001129327,0.00014884082,0.00024294245,0.00018692647,0.00016500929,0.00075113575,0.0001466199,0.000073135685,0.00006929464],"category_scores_gemma":[0.00016174042,0.00010879194,0.000041784406,0.00038596624,0.00005892903,0.00066528283,0.000024224944,0.000120310986,0.00014098677],"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":[0.000053056378,0.00010196813,0.051136088,0.000081259874,0.0001174382,0.000010525114,0.0040597264,0.3843793,0.000023563658,0.090890706,0.0026323325,0.46651405],"study_design_scores_gemma":[0.00014716988,0.000047877424,0.0046464903,0.00004417445,0.000020899013,0.00006758158,0.00016102876,0.88179415,0.00008822534,0.10710208,0.0057097673,0.00017056362],"about_ca_topic_score_codex":0.000025025522,"about_ca_topic_score_gemma":0.000070509224,"teacher_disagreement_score":0.49741486,"about_ca_system_score_codex":0.000034302535,"about_ca_system_score_gemma":0.00006824213,"threshold_uncertainty_score":0.72432196},"labels":[],"label_agreement":null},{"id":"W4392697211","doi":"10.1016/j.ejor.2024.03.016","title":"Worst-case risk measures of stop-loss and limited loss random variables under distribution uncertainty with applications to robust reinsurance","year":2024,"lang":"en","type":"article","venue":"European Journal of Operational Research","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Reinsurance; Random variable; Variable (mathematics); Mathematics; Distribution (mathematics); Statistics; Econometrics; Actuarial science; Economics","score_opus":0.1696763661460953,"score_gpt":0.4025862293248418,"score_spread":0.23290986317874648,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392697211","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":[0.21265364,0.0009637905,0.7828013,0.0020956143,0.00007825408,0.00034350294,0.00018019616,0.0000092386135,0.0008744844],"genre_scores_gemma":[0.9918282,0.0012746295,0.006094096,0.000030071087,0.00024087049,0.000008874508,0.000024460689,0.000017680984,0.00048114022],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.99463713,0.00178707,0.0008530567,0.00032828128,0.0021810199,0.00021343678],"domain_scores_gemma":[0.9933681,0.0021235358,0.0002108905,0.00028232613,0.0037743475,0.00024083274],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01740459,0.00012145947,0.0002432987,0.0004911113,0.00046450712,0.0006791964,0.00038921615,0.000030368086,0.00006583527],"category_scores_gemma":[0.0028016309,0.00007654612,0.00006624904,0.0018415124,0.000276995,0.0004975779,0.000084869156,0.0004598464,0.00003682991],"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":[0.0011900334,0.00009966507,0.0030919402,0.000010117293,0.00010007042,0.00065815035,0.00069056946,0.9315757,0.0003005038,0.01755088,0.0051151956,0.039617136],"study_design_scores_gemma":[0.014059077,0.0051474646,0.13478957,0.0017468558,0.00037439866,0.016369177,0.0073956377,0.28328237,0.0020036753,0.03168871,0.50149345,0.001649596],"about_ca_topic_score_codex":0.000033266882,"about_ca_topic_score_gemma":0.000024690074,"teacher_disagreement_score":0.77917457,"about_ca_system_score_codex":0.000071821676,"about_ca_system_score_gemma":0.0004283658,"threshold_uncertainty_score":0.6549506},"labels":[],"label_agreement":null},{"id":"W4392868640","doi":"10.1016/j.ejor.2024.03.020","title":"A survey of contextual optimization methods for decision-making under uncertainty","year":2024,"lang":"en","type":"article","venue":"European Journal of Operational Research","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":122,"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; Group for Research in Decision Analysis; Université de Montréal","funders":"Fonds de recherche du Québec – Nature et technologies; Canada First Research Excellence Fund; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Computer science; Variety (cybernetics); Stochastic programming; Stochastic optimization; Machine learning; Field (mathematics); Artificial intelligence; Terminology; Optimization problem; Reinforcement learning; Management science; Operations research; Mathematical optimization; Mathematics","score_opus":0.369427914832771,"score_gpt":0.5759235784793887,"score_spread":0.2064956636466177,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392868640","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":[0.007402139,0.0015164178,0.9876135,0.0004898262,0.0005686052,0.00020674834,0.00004613188,0.0000059084396,0.0021507523],"genre_scores_gemma":[0.75042313,0.00024575702,0.24848075,0.00005252215,0.0002190973,0.000001910918,0.000013712505,0.000023722516,0.0005393653],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.989949,0.005763729,0.0015334189,0.00029876863,0.0022247029,0.00023037539],"domain_scores_gemma":[0.96407926,0.026682258,0.00027851382,0.00024377825,0.008593755,0.00012245003],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.08121918,0.00010721632,0.00029206838,0.0010588093,0.00024814202,0.00076309853,0.00074296043,0.000037891572,0.0007345286],"category_scores_gemma":[0.049936518,0.000072252915,0.00016224581,0.0016592743,0.00016457893,0.0006359196,0.00011681211,0.00034051097,0.00003665455],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002898971,0.000032060067,0.00019285076,0.0000030020412,0.00003453687,0.000011906919,0.00026873473,0.74511534,0.00013537533,0.0031580164,0.013362379,0.23739591],"study_design_scores_gemma":[0.00041594674,0.0005276947,0.009296055,0.00023312744,0.000010985578,0.000050019305,0.00038717088,0.96716154,0.000068966256,0.004914017,0.016823908,0.00011056962],"about_ca_topic_score_codex":0.0000107893065,"about_ca_topic_score_gemma":0.000012903353,"teacher_disagreement_score":0.743021,"about_ca_system_score_codex":0.00007839719,"about_ca_system_score_gemma":0.0009468156,"threshold_uncertainty_score":0.9580663},"labels":[],"label_agreement":null},{"id":"W4392897245","doi":"10.1007/s11579-024-00358-y","title":"The perturbation method applied to a robust optimization problem with constraint","year":2024,"lang":"en","type":"article","venue":"Mathematics and Financial Economics","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"National Natural Science Foundation of China","keywords":"Convexity; Mathematics; Ambiguity; Mathematical optimization; Mathematical finance; Constraint (computer-aided design); Quadratic equation; Perturbation (astronomy); Bounded function; Optimization problem; Quadratic programming; Applied mathematics; Converse; Computer science; Mathematical analysis","score_opus":0.03403789333889145,"score_gpt":0.2877360348378424,"score_spread":0.2536981414989509,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392897245","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":[0.003885133,0.00006610316,0.9799785,0.0011036762,0.00012635505,0.00039889617,0.000011953298,0.00003137852,0.014398018],"genre_scores_gemma":[0.032616317,0.00054618134,0.9651558,0.00022417483,0.00012499162,0.00007875031,0.000008176284,0.000024920302,0.0012206819],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989746,0.000015423626,0.00044858395,0.00028323827,0.00013642023,0.00014170262],"domain_scores_gemma":[0.99896127,0.00055160775,0.00011991966,0.0002140338,0.000086764725,0.00006640854],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015446285,0.00011426658,0.00018026998,0.00010968713,0.00024531764,0.00091692933,0.00016053669,0.00006115849,0.000019035817],"category_scores_gemma":[0.00020325193,0.000066621586,0.000031984513,0.00028546783,0.000054738583,0.00013403902,0.000045022156,0.00007188539,0.000028310224],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000107123815,0.000008298228,0.000005473037,0.0000055878454,0.0000049139585,4.6797587e-7,0.0013290354,0.45903656,0.0000019681204,0.43263194,0.0004193039,0.10654574],"study_design_scores_gemma":[0.00009246211,0.000045028217,0.000022375949,0.000016861206,0.000013370335,0.000013933854,0.00042453766,0.8616272,0.000021512042,0.10456177,0.03304275,0.00011817274],"about_ca_topic_score_codex":0.0000030715944,"about_ca_topic_score_gemma":0.000028333,"teacher_disagreement_score":0.40259066,"about_ca_system_score_codex":0.0000293864,"about_ca_system_score_gemma":0.00011967265,"threshold_uncertainty_score":0.88419706},"labels":[],"label_agreement":null},{"id":"W4392943351","doi":"10.1109/icmla58977.2023.00048","title":"Preferential Proximal Policy Optimization","year":2023,"lang":"en","type":"article","venue":"","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Ontario Tech University","funders":"","keywords":"Computer science","score_opus":0.11613256734532813,"score_gpt":0.4136075758443717,"score_spread":0.29747500849904357,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392943351","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":[0.036377143,0.0000094004035,0.7449472,0.0035459548,0.0006279095,0.00034856432,0.000012113679,0.0006407603,0.21349096],"genre_scores_gemma":[0.91457415,0.00021473676,0.013108807,0.00022310273,0.00045109467,0.00001960359,0.000074321426,0.000016699269,0.0713175],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982924,0.00007378729,0.00035498446,0.00028211533,0.00079030526,0.0002063955],"domain_scores_gemma":[0.9991697,0.00012857206,0.000094956755,0.00031636155,0.000209439,0.00008098464],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0007951681,0.00007900534,0.00011170081,0.0006377672,0.000124441,0.00024719688,0.00030801343,0.00006160674,0.0015498186],"category_scores_gemma":[0.0010293623,0.000054809818,0.000057259742,0.0027993973,0.00002860687,0.00037880757,0.00009260121,0.000044716726,0.0022262472],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016345582,0.000019468784,0.004047904,4.9945805e-7,0.000004567424,0.0000011963319,0.00018806056,0.8902717,0.000050947194,0.011239317,0.056408864,0.037751112],"study_design_scores_gemma":[0.00027093105,0.0000371011,0.008224244,0.000001662939,0.000003989888,0.0000025921183,0.00014792517,0.9441412,0.00046440415,0.009111733,0.03746267,0.00013154055],"about_ca_topic_score_codex":0.000046799247,"about_ca_topic_score_gemma":0.00000959878,"teacher_disagreement_score":0.878197,"about_ca_system_score_codex":0.000014611658,"about_ca_system_score_gemma":0.00010403474,"threshold_uncertainty_score":0.9993629},"labels":[],"label_agreement":null},{"id":"W4393222532","doi":"10.1287/moor.2023.0015","title":"Uniqueness of Convex-Ranged Probabilities and Applications to Risk Measures and Games","year":2024,"lang":"en","type":"article","venue":"Mathematics of Operations Research","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Università Bocconi","keywords":"Mathematics; Uniqueness; Regular polygon; Mathematical economics; Mathematical optimization; Applied mathematics; Calculus (dental); Mathematical analysis; Geometry","score_opus":0.23452325936549379,"score_gpt":0.4890808803450211,"score_spread":0.25455762097952733,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393222532","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":[0.765898,0.0021786047,0.22541441,0.0010793809,0.00003299995,0.0016754048,0.00013430891,0.000031359315,0.0035555374],"genre_scores_gemma":[0.9605148,0.0016435398,0.035993382,0.0000043870164,0.000021260465,0.00023842152,0.000003435149,0.000010512429,0.0015702681],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99796057,0.00023575847,0.0005215986,0.0002434836,0.0009173639,0.000121232566],"domain_scores_gemma":[0.9965985,0.0016441304,0.000033087894,0.00040559986,0.0012391746,0.00007953366],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0052118115,0.00007057896,0.00021680811,0.00059591694,0.0001892748,0.00032042456,0.00023242051,0.000048107035,0.00004396436],"category_scores_gemma":[0.0026989055,0.000050103536,0.000026894815,0.0010576366,0.00034702496,0.00019971252,0.000121029545,0.00011542946,0.000015067822],"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":[0.00002926166,0.0003609552,0.005297189,0.00059204106,0.0001031806,0.0000015787504,0.04553088,0.023953207,0.0081190495,0.7352041,0.0026841897,0.17812438],"study_design_scores_gemma":[0.0004362172,0.0005066365,0.004415569,0.00043783567,0.000076805416,0.0000235535,0.02574443,0.34890932,0.027922977,0.5571186,0.033985637,0.00042240086],"about_ca_topic_score_codex":0.00010600848,"about_ca_topic_score_gemma":0.00014712705,"teacher_disagreement_score":0.32495612,"about_ca_system_score_codex":0.000011743502,"about_ca_system_score_gemma":0.00014941736,"threshold_uncertainty_score":0.3231037},"labels":[],"label_agreement":null},{"id":"W4393955038","doi":"10.2139/ssrn.4750381","title":"Worst-Case Reinsurance Strategy with Likelihood Ratio Uncertainty","year":2024,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Reinsurance; Econometrics; Actuarial science; Statistics; Economics; Business; Mathematics","score_opus":0.024859477388502037,"score_gpt":0.32181918632873024,"score_spread":0.2969597089402282,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393955038","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":[0.29540446,0.01551567,0.6761613,0.002293465,0.00088889,0.0003035562,0.000015182537,0.0001610817,0.009256366],"genre_scores_gemma":[0.98862535,0.005785676,0.0002868846,0.00006367926,0.00042131465,0.0000055758114,0.0000039486063,0.000025124007,0.004782437],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99621946,0.00017581327,0.00059028435,0.00042881275,0.0009893836,0.0015962266],"domain_scores_gemma":[0.9987179,0.00026658693,0.0001948428,0.00033103404,0.0003336155,0.00015602585],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0052141272,0.00019821947,0.00023028922,0.0003252808,0.00036480586,0.0010424897,0.0004072205,0.000085288506,0.00018570734],"category_scores_gemma":[0.00018754166,0.00012119323,0.00012540413,0.0013261192,0.00006745802,0.0008371672,0.000022804446,0.0015224628,0.00022271306],"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":[0.00018285123,0.000049142698,0.00094734674,0.00000293902,0.0001572366,0.0012945442,0.00043199936,0.06423895,0.00005139396,0.21609293,0.0016955058,0.71485513],"study_design_scores_gemma":[0.00068491336,0.0009323319,0.00026262048,0.000044981298,0.000062696876,0.032500368,0.005124239,0.051397365,0.00006662379,0.89511925,0.013407148,0.00039747584],"about_ca_topic_score_codex":0.00009718423,"about_ca_topic_score_gemma":0.002714631,"teacher_disagreement_score":0.7144577,"about_ca_system_score_codex":0.00037122585,"about_ca_system_score_gemma":0.004087501,"threshold_uncertainty_score":0.9999945},"labels":[],"label_agreement":null},{"id":"W4394007771","doi":"","title":"Matrix-variate, vector-variate and univariate risk measures and related aspects","year":2021,"lang":"en","type":"preprint","venue":"Thèses en ligne de l'Université Toulouse III (Université Toulouse III)","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Universidad de Medellín; McMaster University; Indian National Science Academy","keywords":"Random variate; Univariate; Computer science; Statistics; Multivariate statistics; Artificial intelligence; Mathematics; Random variable","score_opus":0.0141539162407204,"score_gpt":0.25668139654875805,"score_spread":0.24252748030803764,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394007771","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":[0.9543382,0.005304614,0.015734378,0.0018056504,0.0016116962,0.0014203787,0.00045819726,0.0009717359,0.018355181],"genre_scores_gemma":[0.94594437,0.039395783,0.004356962,0.00013365272,0.00032309678,0.0000067779924,0.00022437454,0.00019432184,0.009420634],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.9896621,0.0022199778,0.001422109,0.003098839,0.0021870981,0.0014098483],"domain_scores_gemma":[0.9911502,0.001700677,0.002131885,0.0026288438,0.0012459976,0.0011424158],"candidate_categories":["metaepi_narrow","sts","scholarly_communication","research_integrity","insufficient_payload"],"consensus_categories":["metaepi_narrow","research_integrity"],"category_scores_codex":[0.0033244162,0.0015040148,0.0022512292,0.002228749,0.0017579179,0.0015804886,0.002507684,0.0020616127,0.0011214914],"category_scores_gemma":[0.0007550035,0.0016159702,0.00070569216,0.0025584016,0.00052915176,0.0018231292,0.0047952197,0.0023532608,0.00009834248],"study_design_candidate":"qualitative","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":[0.00872838,0.0023560158,0.048302557,0.0005385392,0.0131261125,0.031562105,0.42438698,0.20351616,0.0087301135,0.06764452,0.05164819,0.13946034],"study_design_scores_gemma":[0.048088524,0.002617352,0.40189743,0.002588114,0.01804443,0.0047636726,0.16094668,0.13778384,0.0034852792,0.09111246,0.11060468,0.01806754],"about_ca_topic_score_codex":0.010038356,"about_ca_topic_score_gemma":0.0018787674,"teacher_disagreement_score":0.35359487,"about_ca_system_score_codex":0.000934108,"about_ca_system_score_gemma":0.001210498,"threshold_uncertainty_score":0.9999483},"labels":[],"label_agreement":null},{"id":"W4394576543","doi":"10.3390/jrfm17040148","title":"A Solvency II Partial Internal Model Considering Reinsurance and Counterparty Default Risk","year":2024,"lang":"en","type":"article","venue":"Journal of risk and financial management","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Solvency; Reinsurance; Credit risk; Internal model; Counterparty; Business; Risk model; Actuarial science; Economics; Finance; Market liquidity","score_opus":0.023674683939007183,"score_gpt":0.3001439446213971,"score_spread":0.2764692606823899,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394576543","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":[0.5212068,0.0035728351,0.4736226,0.00011175013,0.0007255755,0.000080919956,0.000022706028,0.000010898621,0.00064592523],"genre_scores_gemma":[0.96567893,0.027876047,0.005787296,0.00006028877,0.00021697338,0.0000024252665,3.569056e-7,0.000008506418,0.0003691534],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980918,0.000078374585,0.00077459746,0.00026518267,0.00061039155,0.0001796511],"domain_scores_gemma":[0.999009,0.00017170988,0.00041655422,0.00014914926,0.00014466845,0.00010891185],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022081945,0.00013760499,0.00028453706,0.00032018145,0.00027246203,0.00035785075,0.00018403628,0.000055870092,0.00001701905],"category_scores_gemma":[0.00042973133,0.000097353535,0.00011239389,0.0002676211,0.00008354979,0.0005156586,0.00017204687,0.00028347233,0.000006752412],"study_design_candidate":"design_other","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":[0.00030155643,0.000062140796,0.034454096,0.00002410829,0.000050888277,0.00030305286,0.0037509312,0.036203492,0.000013477986,0.006655425,0.009274026,0.9089068],"study_design_scores_gemma":[0.001326356,0.00039031426,0.038271658,0.0003539236,0.0002735668,0.00019158756,0.00053425255,0.5712579,0.000049848084,0.12765788,0.25935495,0.00033777623],"about_ca_topic_score_codex":0.00003078503,"about_ca_topic_score_gemma":0.000037878686,"teacher_disagreement_score":0.90856904,"about_ca_system_score_codex":0.000021937512,"about_ca_system_score_gemma":0.000049959584,"threshold_uncertainty_score":0.3969962},"labels":[],"label_agreement":null},{"id":"W4394912726","doi":"10.1016/j.compchemeng.2024.108703","title":"Distributionally robust chance-constrained optimization with Gaussian mixture ambiguity set","year":2024,"lang":"en","type":"article","venue":"Computers & Chemical Engineering","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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 Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Ambiguity; Robust optimization; Mathematical optimization; Set (abstract data type); Gaussian; Optimization problem; Computer science; Mathematics; Chemistry","score_opus":0.025246637369100346,"score_gpt":0.26986226971734034,"score_spread":0.24461563234824,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394912726","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":[0.012023195,0.00018060117,0.9857684,0.0007079275,0.0005638616,0.00011833382,0.00004894092,0.00029955505,0.00028918998],"genre_scores_gemma":[0.86562073,0.000033826633,0.1334465,0.00006133799,0.0003782265,0.00001290521,0.00031588002,0.000027880033,0.00010268735],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998234,0.00001703463,0.00036809777,0.00049497886,0.00060934975,0.00027656773],"domain_scores_gemma":[0.99912256,0.00024144838,0.000058090878,0.00027598205,0.00013572192,0.00016620026],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032210952,0.000208997,0.00022880871,0.0001610378,0.000057776946,0.0004093731,0.0003534948,0.000118649,0.000081636405],"category_scores_gemma":[0.00016488365,0.00015909992,0.0000895129,0.0010120206,0.000056589648,0.0003333354,0.00007425498,0.00023236574,0.000025625484],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001036831,0.000010908943,0.000038423477,0.000011392995,0.000022828557,0.000025896887,0.00009967988,0.9895122,0.00081015495,0.0013547167,0.0026187212,0.0054847063],"study_design_scores_gemma":[0.00016898567,0.000018479392,0.0000997518,0.00009843199,0.000013660166,0.000054513115,0.000011280471,0.99346983,0.0018273195,0.00012814584,0.0038917891,0.0002177933],"about_ca_topic_score_codex":0.0000026418813,"about_ca_topic_score_gemma":2.469272e-7,"teacher_disagreement_score":0.8535976,"about_ca_system_score_codex":0.00008052944,"about_ca_system_score_gemma":0.000070917995,"threshold_uncertainty_score":0.64879066},"labels":[],"label_agreement":null},{"id":"W4395043963","doi":"10.1007/s10463-024-00904-x","title":"Assessing the coverage probabilities of fixed-margin confidence intervals for the tail conditional allocation","year":2024,"lang":"en","type":"article","venue":"Annals of the Institute of Statistical Mathematics","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Western University","funders":"","keywords":"Resampling; Margin (machine learning); Confidence interval; Coverage probability; Econometrics; Mathematics; Conditional probability; Statistics; Computer science; Machine learning","score_opus":0.20062472180127758,"score_gpt":0.4594255929366521,"score_spread":0.25880087113537453,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4395043963","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":[0.015826194,0.00028858488,0.97612214,0.004999693,0.00046764995,0.0005885291,0.00054529746,0.000009132337,0.0011527973],"genre_scores_gemma":[0.97953856,0.00015726037,0.019717457,0.00013911632,0.0000403029,0.000033420692,0.000015238384,0.000007982984,0.00035066807],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9976404,0.000108614186,0.0010450024,0.00016146417,0.0009221178,0.00012242924],"domain_scores_gemma":[0.9884783,0.009673558,0.00051795016,0.00047573808,0.0008284865,0.000025938582],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.003183249,0.00010952988,0.00028745376,0.00006154184,0.00015112385,0.00019782314,0.000729326,0.000047198173,0.000088613146],"category_scores_gemma":[0.009581611,0.000048043974,0.0001570895,0.00033629933,0.0010588797,0.00043325886,0.000105010615,0.00009557479,0.000005471983],"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":[0.000010852663,0.00006563154,0.000038211543,0.00023260766,0.00005976406,3.7088319e-7,0.0010098898,0.012385358,0.00016506095,0.9700522,0.009087572,0.006892509],"study_design_scores_gemma":[0.00007036846,0.00004979328,0.00076879596,0.00031463752,0.000059311045,0.0000050119615,0.00067787955,0.10131498,0.0031527001,0.88924444,0.00428223,0.0000598379],"about_ca_topic_score_codex":0.000030515084,"about_ca_topic_score_gemma":0.00001271535,"teacher_disagreement_score":0.96371233,"about_ca_system_score_codex":0.000008647874,"about_ca_system_score_gemma":0.0002378827,"threshold_uncertainty_score":0.9987611},"labels":[],"label_agreement":null},{"id":"W4395704223","doi":"10.1016/j.ejor.2024.04.031","title":"Determinism versus uncertainty: Examining the worst-case expected performance of data-driven policies","year":2024,"lang":"en","type":"article","venue":"European Journal of Operational Research","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Hong Kong Polytechnic University; Research Grants Council, University Grants Committee; National Natural Science Foundation of China","keywords":"Determinism; Computer science; Econometrics; Economics; Epistemology; Philosophy","score_opus":0.5115057049763427,"score_gpt":0.5065944696810811,"score_spread":0.004911235295261607,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4395704223","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":[0.98698384,0.0006740023,0.0015322267,0.0015054543,0.00055645505,0.000107305954,0.00003356765,0.000007895156,0.008599264],"genre_scores_gemma":[0.99575204,0.0006020824,0.0019183701,0.00003561528,0.0006216451,9.739724e-7,0.000011296428,0.000016423786,0.0010415522],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99419045,0.0018836332,0.0009435107,0.00024998415,0.0025096138,0.00022279104],"domain_scores_gemma":[0.99383867,0.0036141544,0.00019623314,0.00056742836,0.0016720415,0.00011146619],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.017130144,0.000095995965,0.00017420486,0.00062446314,0.00041293728,0.0008036367,0.0015627843,0.000020075731,0.00031767137],"category_scores_gemma":[0.0040267105,0.000053060015,0.000058510443,0.0012071162,0.00029930408,0.001135623,0.00041619194,0.0005017516,0.0001295074],"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":[0.0015784743,0.00015207804,0.003387008,0.000023702407,0.00029004941,0.006447091,0.019176545,0.34265134,0.0022340456,0.0048073656,0.09760452,0.5216478],"study_design_scores_gemma":[0.0017728474,0.0029460152,0.021515723,0.00036296094,0.000055008953,0.004370713,0.011739607,0.75612986,0.00055473903,0.0002410602,0.19997399,0.00033746017],"about_ca_topic_score_codex":0.000013082695,"about_ca_topic_score_gemma":0.000010119952,"teacher_disagreement_score":0.5213103,"about_ca_system_score_codex":0.000040364335,"about_ca_system_score_gemma":0.0005832094,"threshold_uncertainty_score":0.7749487},"labels":[],"label_agreement":null},{"id":"W4396629565","doi":"10.2139/ssrn.4816584","title":"Pareto Efficiency and Financial Fairness Under Limited Expected Loss Constraint","year":2024,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Pareto principle; Constraint (computer-aided design); Economics; Pareto optimal; Pareto efficiency; Microeconomics; Finance; Multi-objective optimization; Mathematical optimization; Mathematics; Operations management","score_opus":0.031069013998786445,"score_gpt":0.3253062649164938,"score_spread":0.29423725091770736,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396629565","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":[0.69690394,0.018698169,0.27565217,0.002649922,0.0032292814,0.0004842607,0.000051532494,0.00014625103,0.0021844571],"genre_scores_gemma":[0.98082525,0.01643733,0.00018147577,0.000115696785,0.00065814436,0.00001444168,0.000020973552,0.0000427531,0.0017039307],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99369067,0.00037169416,0.001259052,0.0009976295,0.0015427874,0.0021381474],"domain_scores_gemma":[0.9975176,0.00036607205,0.0006779496,0.0005725172,0.0006300375,0.00023584475],"candidate_categories":["metaepi_narrow","scholarly_communication","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.005670503,0.00047385742,0.00066749024,0.00086004165,0.00037506656,0.0011835875,0.0009808537,0.000545654,0.00010385577],"category_scores_gemma":[0.0011909541,0.0003570053,0.0003267758,0.0009713447,0.000318529,0.00016099469,0.00072961964,0.0055876435,0.00010724494],"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":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00033115773,0.0003084896,0.005773718,0.000042964642,0.00045122468,0.00026890342,0.00365451,0.05265525,0.00007564397,0.65658385,0.0035438,0.27631047],"study_design_scores_gemma":[0.00045423603,0.00016851403,0.0011819723,0.00010150009,0.000100737794,0.0011039266,0.0027131124,0.006316238,0.000022956743,0.98583907,0.0015239763,0.0004737756],"about_ca_topic_score_codex":0.00006109956,"about_ca_topic_score_gemma":0.00054535136,"teacher_disagreement_score":0.3292552,"about_ca_system_score_codex":0.00071187783,"about_ca_system_score_gemma":0.00909129,"threshold_uncertainty_score":0.9998882},"labels":[],"label_agreement":null},{"id":"W4397031293","doi":"10.1016/j.ejor.2024.05.034","title":"Worst-case Conditional Value at Risk for asset liability management: A framework for general loss functions","year":2024,"lang":"en","type":"article","venue":"European Journal of Operational Research","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"","keywords":"Liability; Actuarial science; Value (mathematics); Risk management; Asset (computer security); Value at risk; Business; Computer science; Econometrics; Economics; Mathematics; Finance; Statistics; Computer security","score_opus":0.20360037111206564,"score_gpt":0.48741996012274114,"score_spread":0.2838195890106755,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4397031293","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":[0.08056865,0.00037870646,0.9097917,0.003290945,0.0010014961,0.00063633866,0.00094903284,0.000015057562,0.0033680403],"genre_scores_gemma":[0.76876986,0.00038392158,0.20852783,0.00020303015,0.0030945046,0.00006846922,0.00023317717,0.000058107587,0.018661093],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9946126,0.0014376447,0.0010512378,0.00044581413,0.0021165512,0.00033616132],"domain_scores_gemma":[0.99055284,0.005899008,0.0002030781,0.0003185681,0.0027832163,0.00024331182],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.026326057,0.0001383539,0.00020795787,0.0007341874,0.0011988851,0.0011596741,0.0005194098,0.000049516893,0.00085503067],"category_scores_gemma":[0.006892547,0.00010124305,0.00032576054,0.00089413795,0.000201976,0.00065746106,0.00015255342,0.00051871384,0.00035571464],"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":[0.0008061744,0.00019788662,0.0014788201,0.000031175867,0.00025519717,0.0012411001,0.00044669077,0.10356861,0.00005705333,0.5737603,0.29417726,0.023979768],"study_design_scores_gemma":[0.00092227367,0.0006436242,0.0066008456,0.00007128455,0.00006003507,0.0011411133,0.0003105855,0.09223331,0.000047772,0.2579656,0.6398186,0.00018495774],"about_ca_topic_score_codex":0.0000022367133,"about_ca_topic_score_gemma":0.0000034985292,"teacher_disagreement_score":0.7012639,"about_ca_system_score_codex":0.00018242354,"about_ca_system_score_gemma":0.00029890068,"threshold_uncertainty_score":0.9998772},"labels":[],"label_agreement":null},{"id":"W4399099816","doi":"10.1111/mafi.12435","title":"Distortion risk measures: Prudence, coherence, and the expected shortfall","year":2024,"lang":"en","type":"article","venue":"Mathematical Finance","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Prudence; Expected shortfall; Mathematics; Coherent risk measure; Econometrics; Regular polygon; Superadditivity; Mathematical economics; Economics; Risk management; Finance","score_opus":0.043436269875950936,"score_gpt":0.3330253895629994,"score_spread":0.2895891196870484,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399099816","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":[0.22940023,0.0057743755,0.7329378,0.0013607888,0.00051980175,0.0007084089,0.000022901542,0.00020039685,0.029075254],"genre_scores_gemma":[0.9884279,0.0027254636,0.00306839,0.000044127035,0.00007514021,0.00007321142,0.0000017640984,0.000011221775,0.005572797],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9977365,0.0002050045,0.0005628584,0.00039533098,0.00091899704,0.00018131465],"domain_scores_gemma":[0.9975764,0.001674906,0.000121119345,0.00045139436,0.00012572292,0.000050454008],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025485738,0.00013359889,0.00026899684,0.00009154499,0.00019932256,0.00048474557,0.00035038972,0.00007263591,0.0001226418],"category_scores_gemma":[0.0028857067,0.000066530934,0.000091819995,0.0007858931,0.00036583023,0.00027028663,0.00006819592,0.00018675119,0.0004377731],"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":[0.00010593701,0.000076756434,0.0030485713,0.000020299572,0.00003157094,0.000023650624,0.004727605,0.0006166123,0.000018772826,0.5619942,0.020746998,0.408589],"study_design_scores_gemma":[0.00031024127,0.000033716748,0.0048175473,0.000102830105,0.000044637974,0.000029069544,0.0002001751,0.12032268,0.000064941356,0.7899922,0.08390142,0.00018052117],"about_ca_topic_score_codex":0.000011287156,"about_ca_topic_score_gemma":0.000010343933,"teacher_disagreement_score":0.75902766,"about_ca_system_score_codex":0.000017525801,"about_ca_system_score_gemma":0.00004275617,"threshold_uncertainty_score":0.56268334},"labels":[],"label_agreement":null},{"id":"W4399122827","doi":"10.1080/14697688.2024.2352542","title":"Risk management under weighted limited expected loss","year":2024,"lang":"en","type":"article","venue":"Quantitative Finance","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval; Actua","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Risk management; Economics; Actuarial science; Econometrics; Business; Finance","score_opus":0.06875653254617435,"score_gpt":0.38205780574904663,"score_spread":0.31330127320287227,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399122827","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":[0.3631396,0.006509148,0.5909108,0.0010339773,0.0016573919,0.00044005504,0.0001137187,0.00031908057,0.035876226],"genre_scores_gemma":[0.9236193,0.011784356,0.045849655,0.00014070136,0.00006300143,0.000046267643,0.000031102696,0.000029796958,0.018435836],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99745065,0.0002462154,0.000538074,0.00068719115,0.00080412783,0.0002737622],"domain_scores_gemma":[0.99810904,0.00088625576,0.00018890372,0.00048672027,0.00027865815,0.000050414812],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0009073714,0.00018755294,0.00023788976,0.00046928748,0.00019342772,0.0003499424,0.00041709226,0.000072184834,0.00025902013],"category_scores_gemma":[0.0002790392,0.0001396307,0.000121611054,0.002830174,0.00013546378,0.000515219,0.000078505924,0.00017485944,0.0027253397],"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":[0.00008136659,0.00007673712,0.0039448086,0.000007198291,0.00009369695,0.00017955193,0.0017572291,0.009502961,0.000016950546,0.84890103,0.04209148,0.09334699],"study_design_scores_gemma":[0.0004415384,0.00018997822,0.08572662,0.00011167745,0.000055665227,0.00000826936,0.0012092984,0.20192948,0.00025297896,0.2719339,0.43768874,0.0004518158],"about_ca_topic_score_codex":0.000025122734,"about_ca_topic_score_gemma":0.000017465678,"teacher_disagreement_score":0.5769671,"about_ca_system_score_codex":0.000044092332,"about_ca_system_score_gemma":0.000040267947,"threshold_uncertainty_score":0.99805117},"labels":[],"label_agreement":null},{"id":"W4399668851","doi":"10.3390/risks12060095","title":"Expected Utility Optimization with Convolutional Stochastically Ordered Returns","year":2024,"lang":"en","type":"article","venue":"Risks","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Convolution (computer science); Expected utility hypothesis; Exponential function; Mathematical optimization; Lévy process; Computer science; Heuristic; Poisson distribution; Production (economics); Order (exchange); Mathematics; Mathematical economics; Applied mathematics; Economics; Microeconomics","score_opus":0.1647134270609593,"score_gpt":0.41459848685524864,"score_spread":0.24988505979428935,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399668851","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":[0.016710445,0.00044031872,0.97255015,0.0006396229,0.00043690746,0.00022723414,0.00005238873,0.00022880803,0.008714142],"genre_scores_gemma":[0.9713358,0.00008872575,0.026780503,0.00006257236,0.00014057853,0.000018371551,0.00007584566,0.000017554608,0.0014800401],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99754065,0.00013690357,0.00046078203,0.00054467114,0.0010901392,0.00022686896],"domain_scores_gemma":[0.9983119,0.0005412078,0.00009546104,0.00040007682,0.0005216785,0.00012969802],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00084414636,0.00014920789,0.00019529778,0.00024219562,0.0001586381,0.00041301583,0.0002654601,0.0001132278,0.0023307214],"category_scores_gemma":[0.0008941151,0.00009923077,0.000067541616,0.0012928604,0.0001407108,0.00040535195,0.00004556331,0.0001790849,0.00026003004],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00034841566,0.00012907037,0.011437028,0.000008478982,0.000081495244,0.000060374983,0.0011065456,0.90784985,0.000045218436,0.015871774,0.028641885,0.034419853],"study_design_scores_gemma":[0.00026084905,0.00008661174,0.012427165,0.000023517916,0.000026709962,0.000025703197,0.0001989226,0.9725471,0.000030539242,0.0048012077,0.009400263,0.00017139113],"about_ca_topic_score_codex":0.00005512677,"about_ca_topic_score_gemma":0.000042244323,"teacher_disagreement_score":0.95462537,"about_ca_system_score_codex":0.00003910112,"about_ca_system_score_gemma":0.000262143,"threshold_uncertainty_score":0.9985813},"labels":[],"label_agreement":null},{"id":"W4399828400","doi":"10.32920/26052682","title":"Statistical Consistency for Risk Measures With the Lebesgue Property","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Property (philosophy); Consistency (knowledge bases); Mathematics; Lebesgue integration; Computer science; Econometrics; Discrete mathematics; Philosophy; Epistemology","score_opus":0.11900922398625097,"score_gpt":0.37774346611900256,"score_spread":0.2587342421327516,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399828400","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":[0.0014578236,0.00073622196,0.9209444,0.0066864593,0.0009888117,0.0017332364,0.00075398904,0.00012818915,0.066570856],"genre_scores_gemma":[0.75318754,0.0019326167,0.12532419,0.00074535765,0.00065255084,0.0008581357,0.00014347034,0.00009994405,0.11705619],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99667,0.0002891953,0.00059617235,0.00083174155,0.0013734588,0.00023944132],"domain_scores_gemma":[0.9959356,0.0018654439,0.00027847005,0.0009455752,0.0008765251,0.00009835049],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0034824465,0.0002605127,0.00039764066,0.00014187755,0.00027149578,0.0010226568,0.000761282,0.00018557443,0.000289649],"category_scores_gemma":[0.00228026,0.000081677295,0.00015593096,0.0002756325,0.00028246405,0.00005144599,0.00047269426,0.0005561058,0.00025804128],"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":[0.00015571954,0.000043310523,0.0020171837,0.000022567581,0.00014671686,0.000009965005,0.0006308913,0.011237987,0.0000013847925,0.03721958,0.72601205,0.22250266],"study_design_scores_gemma":[0.00026723943,0.00017191653,0.0015580009,0.00005255871,0.00034144497,0.000014477655,0.000673383,0.0688401,0.00004452499,0.39334676,0.5342827,0.00040693898],"about_ca_topic_score_codex":0.00058382394,"about_ca_topic_score_gemma":0.0009410667,"teacher_disagreement_score":0.7956202,"about_ca_system_score_codex":0.000026953485,"about_ca_system_score_gemma":0.0006833377,"threshold_uncertainty_score":0.98615026},"labels":[],"label_agreement":null},{"id":"W4399828406","doi":"10.32920/26052682.v1","title":"Statistical Consistency for Risk Measures With the Lebesgue Property","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Property (philosophy); Consistency (knowledge bases); Lebesgue integration; Mathematics; Econometrics; Pure mathematics; Discrete mathematics; Epistemology; Philosophy","score_opus":0.11900922398625097,"score_gpt":0.37774346611900256,"score_spread":0.2587342421327516,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399828406","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":[0.0014578236,0.00073622196,0.9209444,0.0066864593,0.0009888117,0.0017332364,0.00075398904,0.00012818915,0.066570856],"genre_scores_gemma":[0.75318754,0.0019326167,0.12532419,0.00074535765,0.00065255084,0.0008581357,0.00014347034,0.00009994405,0.11705619],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99667,0.0002891953,0.00059617235,0.00083174155,0.0013734588,0.00023944132],"domain_scores_gemma":[0.9959356,0.0018654439,0.00027847005,0.0009455752,0.0008765251,0.00009835049],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0034824465,0.0002605127,0.00039764066,0.00014187755,0.00027149578,0.0010226568,0.000761282,0.00018557443,0.000289649],"category_scores_gemma":[0.00228026,0.000081677295,0.00015593096,0.0002756325,0.00028246405,0.00005144599,0.00047269426,0.0005561058,0.00025804128],"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":[0.00015571954,0.000043310523,0.0020171837,0.000022567581,0.00014671686,0.000009965005,0.0006308913,0.011237987,0.0000013847925,0.03721958,0.72601205,0.22250266],"study_design_scores_gemma":[0.00026723943,0.00017191653,0.0015580009,0.00005255871,0.00034144497,0.000014477655,0.000673383,0.0688401,0.00004452499,0.39334676,0.5342827,0.00040693898],"about_ca_topic_score_codex":0.00058382394,"about_ca_topic_score_gemma":0.0009410667,"teacher_disagreement_score":0.7956202,"about_ca_system_score_codex":0.000026953485,"about_ca_system_score_gemma":0.0006833377,"threshold_uncertainty_score":0.98615026},"labels":[],"label_agreement":null},{"id":"W4399828878","doi":"10.1142/9789811285530_0003","title":"Bounds on the Expectation of a Convex Function of a Random Variable: With Applications to Stochastic Programming","year":2024,"lang":"en","type":"book-chapter","venue":"World Scientific series in finance","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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; Memorial University of Newfoundland","funders":"","keywords":"Mathematics; Regular polygon; Mathematical optimization; Stochastic programming; Computer science; Applied mathematics; Geometry","score_opus":0.04024615125682853,"score_gpt":0.2968698768504999,"score_spread":0.2566237255936714,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399828878","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0033790946,0.0023237006,0.4908258,0.0018225944,0.00541018,0.00849771,0.00040918862,0.00013818558,0.48719355],"genre_scores_gemma":[0.15715256,0.000030715022,0.0040753107,0.00004260048,0.00006804221,0.00056135334,0.0000507416,0.000034594977,0.8379841],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9970262,0.000035065117,0.0008561234,0.00070434005,0.0011978139,0.00018046098],"domain_scores_gemma":[0.9971943,0.00060736906,0.0006488284,0.00090720004,0.0006086184,0.00003367028],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018645535,0.00021446796,0.00044210817,0.0010214176,0.00020555445,0.00028254974,0.0005100753,0.00008058125,0.00015363561],"category_scores_gemma":[0.00022207512,0.00013839628,0.00007767076,0.0024452936,0.00062016083,0.00018395027,0.000073334304,0.00021125612,0.000070998714],"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":[0.0006432451,0.00004036089,0.000028453313,0.000027188564,0.000022970122,0.0000014891107,0.0014757015,0.035827443,0.000049229984,0.93424433,0.0055549904,0.02208459],"study_design_scores_gemma":[0.00033119146,0.00021978786,0.0001066441,0.00070647587,0.000055144104,0.000002866713,0.0003695348,0.0014830527,0.00011028294,0.09237167,0.9039977,0.00024561648],"about_ca_topic_score_codex":0.000007415241,"about_ca_topic_score_gemma":0.0004596763,"teacher_disagreement_score":0.89844275,"about_ca_system_score_codex":0.00005249623,"about_ca_system_score_gemma":0.0002501672,"threshold_uncertainty_score":0.56436366},"labels":[],"label_agreement":null},{"id":"W4399890910","doi":"10.3390/math12131921","title":"A Fuzzy Entropy Approach for Portfolio Selection","year":2024,"lang":"en","type":"article","venue":"Mathematics","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Western University","funders":"Agencia Nacional de Investigación y Desarrollo; Canada Research Chairs","keywords":"Ambiguity; Entropy (arrow of time); Portfolio optimization; Mathematical optimization; Econometrics; Portfolio; Computer science; Fuzzy logic; Mathematics; Economics; Artificial intelligence; Finance","score_opus":0.0827752260520779,"score_gpt":0.373609531856183,"score_spread":0.2908343058041051,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399890910","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":[0.0048245676,0.00019011126,0.94835633,0.0001263467,0.00034371784,0.00039110958,0.000012888671,0.00016246963,0.045592476],"genre_scores_gemma":[0.30957055,0.00016464893,0.6542079,0.00010002028,0.00053576304,0.00015303926,0.000044891498,0.000053847783,0.035169322],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99842316,0.000025652016,0.0004900576,0.00029560208,0.0005813221,0.00018422007],"domain_scores_gemma":[0.9990037,0.00040965504,0.000106123036,0.00025038756,0.00017018293,0.000059964023],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001394301,0.0001116663,0.00019317181,0.0002640963,0.00010113904,0.00048104083,0.00022545639,0.00007650282,0.00013686989],"category_scores_gemma":[0.0005594615,0.00007725518,0.0001364492,0.00082049024,0.00002370735,0.00021936638,0.000026486496,0.00007142242,0.0002035564],"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":[0.000021140757,0.00029177874,0.0005330894,0.00015325552,0.000075460994,0.0000050344797,0.0032869747,0.011538621,0.00057025766,0.6338843,0.28532997,0.064310074],"study_design_scores_gemma":[0.00009694831,0.00004084122,0.00002717389,0.0000117387735,0.000030743362,0.000030892857,0.00031082364,0.6333168,0.0003378795,0.29968116,0.066003576,0.0001113929],"about_ca_topic_score_codex":0.000002119989,"about_ca_topic_score_gemma":7.941224e-7,"teacher_disagreement_score":0.6217782,"about_ca_system_score_codex":0.000028217602,"about_ca_system_score_gemma":0.000060526607,"threshold_uncertainty_score":0.4638688},"labels":[],"label_agreement":null},{"id":"W4400732905","doi":"10.1287/moor.2023.0211","title":"Risk-Averse Markov Decision Processes Through a Distributional Lens","year":2024,"lang":"en","type":"article","venue":"Mathematics of Operations Research","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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 Toronto","funders":"","keywords":"Markov decision process; Mathematics; Partially observable Markov decision process; Mathematical optimization; Mathematical economics; Decision theory; Through-the-lens metering; Markov chain; Lens (geology); Markov process; Statistics","score_opus":0.22862792256408282,"score_gpt":0.5019646536223583,"score_spread":0.2733367310582755,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400732905","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":[0.2309448,0.0010877526,0.74561584,0.0011791728,0.00028647334,0.00074028264,0.00062028866,0.00008138386,0.019444017],"genre_scores_gemma":[0.8735773,0.0026792607,0.11889966,0.00000938516,0.000106471714,0.00006958624,0.00006916944,0.000021439722,0.0045677223],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99533415,0.00023423729,0.0008512337,0.0004098183,0.002886926,0.00028362006],"domain_scores_gemma":[0.99222875,0.004367286,0.00005868383,0.00063557865,0.0026396448,0.00007003669],"candidate_categories":["metaresearch","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0060359775,0.00012281664,0.00023632628,0.00052174757,0.00054001785,0.0008832877,0.0006390593,0.00009764711,0.0013202693],"category_scores_gemma":[0.018859139,0.00008427719,0.000098360535,0.00324061,0.0002660858,0.0010164127,0.00020280851,0.0003181223,0.00096349465],"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":[0.000110885274,0.0016178562,0.002630486,0.0004325899,0.00018472862,0.00006014962,0.019058166,0.12130285,0.0011672325,0.5539454,0.23593129,0.06355833],"study_design_scores_gemma":[0.00033169342,0.00018892386,0.00054713606,0.00034256314,0.00003461723,0.000039420433,0.0033067425,0.6122842,0.0034022892,0.3015893,0.077655,0.00027810133],"about_ca_topic_score_codex":0.0000731878,"about_ca_topic_score_gemma":0.0001318597,"teacher_disagreement_score":0.6426325,"about_ca_system_score_codex":0.00006443247,"about_ca_system_score_gemma":0.00070755294,"threshold_uncertainty_score":0.9998144},"labels":[],"label_agreement":null},{"id":"W4401134013","doi":"10.1007/s10898-024-01422-z","title":"Robust bilevel optimization for near-optimal lower-level solutions","year":2024,"lang":"en","type":"article","venue":"Journal of Global Optimization","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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; HEC Montréal","funders":"","keywords":"Bilevel optimization; Mathematical optimization; Mathematics; Robustness (evolution); Robust optimization; Heuristic; Optimization problem; Constraint (computer-aided design); Regular polygon; Upper and lower bounds; Convex optimization; Duality (order theory)","score_opus":0.15662085209633478,"score_gpt":0.3719881802762845,"score_spread":0.21536732817994972,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401134013","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":[0.0011065007,0.0012015065,0.9897679,0.0022481643,0.0038663556,0.0003887726,0.00025338505,0.00007602698,0.0010913581],"genre_scores_gemma":[0.08928784,0.00095311855,0.90781915,0.00024366363,0.00082427193,0.0000142383815,0.000076174736,0.00004561247,0.000735903],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99563086,0.00017294608,0.0018109968,0.0004689476,0.0014737768,0.00044248026],"domain_scores_gemma":[0.9953871,0.00051372446,0.0009021395,0.00036282922,0.0025748,0.00025942287],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.002968053,0.00028750673,0.00048232562,0.00038225087,0.00044308807,0.0015563767,0.00064254325,0.00025712058,0.0005061839],"category_scores_gemma":[0.0023308622,0.0002258,0.0004949467,0.0024213635,0.00012708873,0.0022174136,0.000084413594,0.00021071399,0.000045372584],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017612397,0.00009974003,0.00025725266,0.000007713542,0.00006290577,0.000015218832,0.00007776266,0.9412065,0.00000468754,0.002866331,0.035050366,0.020175373],"study_design_scores_gemma":[0.00070358877,0.0003546543,0.00026488162,0.00007890534,0.00013504383,0.00019210522,0.00010603922,0.9788789,0.000009101132,0.0017896651,0.017244989,0.00024208892],"about_ca_topic_score_codex":0.00001026076,"about_ca_topic_score_gemma":0.000007653452,"teacher_disagreement_score":0.08818134,"about_ca_system_score_codex":0.00036657316,"about_ca_system_score_gemma":0.00088757067,"threshold_uncertainty_score":0.9994801},"labels":[],"label_agreement":null},{"id":"W4401285306","doi":"10.1007/s10203-024-00469-7","title":"The limitations of comonotonic additive risk measures: a literature review","year":2024,"lang":"en","type":"review","venue":"Decisions in Economics and Finance","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Conselho Nacional de Desenvolvimento Científico e Tecnológico; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior","keywords":"Axiom; Consistency (knowledge bases); Additive function; Mathematical economics; Mathematics; Econometrics; Computer science; Discrete mathematics","score_opus":0.13359154529370348,"score_gpt":0.38324610724662883,"score_spread":0.24965456195292535,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401285306","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000076975875,0.9960907,0.00022158332,0.0001228681,0.00050092104,0.000697979,0.0011066636,0.0000053064246,0.0012462457],"genre_scores_gemma":[0.0000111696,0.9979995,0.0008594351,0.0000418358,0.000042614283,0.0001691182,0.00005824389,0.000024155364,0.00079395686],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9966455,0.00030971403,0.001962726,0.00064917107,0.00023211847,0.00020074775],"domain_scores_gemma":[0.9905063,0.007004169,0.0011982864,0.0009950799,0.0002546035,0.00004159505],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0033377102,0.00030399614,0.0014423693,0.0004315157,0.00021492496,0.00034356368,0.0008446651,0.00023478043,4.6295034e-7],"category_scores_gemma":[0.0049910303,0.00017000333,0.00047958657,0.0014843298,0.00016820575,0.00018949053,0.00020323387,0.0005548852,0.00011275737],"study_design_candidate":"design_other","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":[0.0000024087396,0.000010947617,0.0000016579621,0.000015466458,0.000023028548,0.0000036620966,0.00008096651,0.000091969516,1.8880611e-10,0.007832255,0.0053559956,0.9865816],"study_design_scores_gemma":[0.000053631175,0.000021056905,0.000015139663,0.012670324,0.00018804737,0.000021730444,0.000019494779,0.0010846388,1.5569563e-8,0.014223282,0.97153676,0.00016589095],"about_ca_topic_score_codex":0.000002964779,"about_ca_topic_score_gemma":0.00016303509,"teacher_disagreement_score":0.98641574,"about_ca_system_score_codex":0.000054961605,"about_ca_system_score_gemma":0.0003642961,"threshold_uncertainty_score":0.69325346},"labels":[{"model":"gemma","categories":[],"domain":null,"study_design":"not_applicable","genre":"review","about_ca_system":false,"about_ca_topic":false,"confidence":"low"},{"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":"W4401457094","doi":"10.1287/moor.2023.0267","title":"Uncertainty Propagation and Dynamic Robust Risk Measures","year":2024,"lang":"en","type":"article","venue":"Mathematics of Operations Research","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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 Toronto; Concordia University","funders":"","keywords":"Risk measure; Ambiguity; Mathematics; Uncertainty quantification; Infimum and supremum; Consistency (knowledge bases); Mathematical optimization; Convexity; Time horizon; Coherent risk measure; Sensitivity analysis; Set (abstract data type); Measure (data warehouse); Computer science; Uncertainty analysis; Data mining; Statistics; Economics; Discrete mathematics","score_opus":0.2246702037594537,"score_gpt":0.4762460642136392,"score_spread":0.25157586045418545,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401457094","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":[0.53912234,0.0014051667,0.4507382,0.0013393862,0.00015338582,0.00084854127,0.00007159189,0.00006394935,0.0062574614],"genre_scores_gemma":[0.9672469,0.0017883034,0.027252732,0.000002930172,0.000026917038,0.00004119125,0.000012423091,0.000013336021,0.0036152778],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9969512,0.00034125306,0.0005665188,0.00029752476,0.0016680051,0.00017548153],"domain_scores_gemma":[0.9971461,0.0011360527,0.00003892893,0.00042973537,0.0011796199,0.00006952837],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0083859665,0.00008398126,0.0001709343,0.000701307,0.00035994334,0.0008217945,0.00029565566,0.00006618144,0.00012983993],"category_scores_gemma":[0.005073976,0.000056407425,0.000044689466,0.0013761927,0.00022158951,0.00039257327,0.0000991388,0.00024005199,0.00013178257],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012047943,0.00016510328,0.0004282599,0.00007575035,0.000047329057,0.000005543098,0.008168754,0.7726888,0.0022728122,0.07494724,0.0034723135,0.13771603],"study_design_scores_gemma":[0.00006485375,0.000051656272,0.00026163692,0.000046616842,0.000009265223,0.000006670444,0.0012785894,0.96658725,0.000340132,0.029799884,0.0014913909,0.000062079205],"about_ca_topic_score_codex":0.00011926344,"about_ca_topic_score_gemma":0.0003640022,"teacher_disagreement_score":0.42812455,"about_ca_system_score_codex":0.000040868887,"about_ca_system_score_gemma":0.00020731943,"threshold_uncertainty_score":0.7924583},"labels":[],"label_agreement":null},{"id":"W4401586637","doi":"10.1080/03155986.2024.2386494","title":"Robust portfolio optimization model for electronic coupon allocation","year":2024,"lang":"en","type":"article","venue":"INFOR Information Systems and Operational Research","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Coupon; Portfolio optimization; Portfolio; Computer science; Black–Litterman model; Robust optimization; Mathematical optimization; Business; Economics; Replicating portfolio; Financial economics; Mathematics; Finance","score_opus":0.1813693457854003,"score_gpt":0.4232369303827695,"score_spread":0.2418675845973692,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401586637","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":[0.0014894303,0.00060074223,0.98664623,0.00076224917,0.00038134633,0.0013447286,0.00011582802,0.00007894578,0.0085805245],"genre_scores_gemma":[0.97710156,0.0013997018,0.005265511,0.00021970674,0.0003222096,0.00071667315,0.0011726163,0.000022788296,0.01377922],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99576986,0.00010652956,0.0011911199,0.00029716545,0.0022117947,0.0004235533],"domain_scores_gemma":[0.99543744,0.0007818411,0.00014196221,0.00028413004,0.0032116591,0.00014294763],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.008890165,0.0001558805,0.00020240014,0.0012531753,0.0006341538,0.0040028524,0.00029498004,0.00017584271,0.00007629657],"category_scores_gemma":[0.001338966,0.00012067801,0.00007033496,0.0011956438,0.000079022626,0.005999618,0.00006586589,0.00024173124,0.00018908767],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003288038,0.0000055181094,0.00004255822,0.000024618676,0.000011164442,1.4216738e-7,0.0005153207,0.72293115,0.0000054162097,0.25450873,0.014523212,0.0073992936],"study_design_scores_gemma":[0.00022419712,0.00006681227,0.000031741518,0.00002954271,0.000004799052,0.0000142700455,0.0004930667,0.8382489,0.000009147232,0.0015166971,0.15923968,0.00012113971],"about_ca_topic_score_codex":0.00008327902,"about_ca_topic_score_gemma":0.000015573482,"teacher_disagreement_score":0.9813807,"about_ca_system_score_codex":0.00017121703,"about_ca_system_score_gemma":0.001094805,"threshold_uncertainty_score":0.9970311},"labels":[],"label_agreement":null},{"id":"W4401693081","doi":"10.1109/isit57864.2024.10619153","title":"On the Success Probability of the $L_{0}$-regularized Box-constrained Babai Point","year":2024,"lang":"en","type":"article","venue":"","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Point (geometry); Computer science; Mathematics","score_opus":0.05498239474840573,"score_gpt":0.33408728676787014,"score_spread":0.2791048920194644,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401693081","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":[0.8064662,0.00009551637,0.02953752,0.033458225,0.001337757,0.0011242866,0.000036313413,0.00012536319,0.12781881],"genre_scores_gemma":[0.99217546,0.0000135134605,0.0008051781,0.00037018018,0.00003395964,0.0000146037555,0.0000012547628,0.0000068988397,0.0065789665],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99751574,0.00035612253,0.0005706377,0.00036122196,0.001038841,0.00015746604],"domain_scores_gemma":[0.9966961,0.0019929186,0.00011543216,0.0009408195,0.00021097074,0.000043732074],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0037867061,0.00011823301,0.00018370176,0.000087772336,0.00015287915,0.00030031177,0.0008144744,0.00006587601,0.0025417926],"category_scores_gemma":[0.0021552115,0.000042383683,0.00022433975,0.0011284511,0.00032148886,0.00019438978,0.00011904853,0.00015157372,0.0001190201],"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":[0.00006281579,0.000106221894,0.0038302888,0.000008737444,0.000037519152,0.0000027777994,0.001179422,0.0034116467,0.0005882203,0.92763925,0.030680157,0.032452967],"study_design_scores_gemma":[0.00029851493,0.00007437229,0.008813522,0.00005317107,0.000027837616,0.000010512745,0.0004966437,0.064572304,0.011525666,0.90465164,0.0093006315,0.00017520481],"about_ca_topic_score_codex":0.000057127316,"about_ca_topic_score_gemma":0.00007561547,"teacher_disagreement_score":0.18570922,"about_ca_system_score_codex":0.000020291745,"about_ca_system_score_gemma":0.0001709455,"threshold_uncertainty_score":0.99837},"labels":[],"label_agreement":null},{"id":"W4401734216","doi":"10.2139/ssrn.4899487","title":"The distribution of out-of-sample returns of estimated optimal portfolios","year":2024,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Distribution (mathematics); Sample (material); Econometrics; Mathematics; Statistics; Economics; Physics; Mathematical analysis","score_opus":0.04986697603103621,"score_gpt":0.36809954955869617,"score_spread":0.31823257352766,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401734216","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":[0.74391234,0.020240305,0.22916903,0.00093803334,0.003529685,0.0005756105,0.00071959937,0.00004234072,0.0008730298],"genre_scores_gemma":[0.9751048,0.023199523,0.0006786085,0.000002461371,0.00016997164,0.000005310874,0.0001105886,0.00002471465,0.00070403976],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9943782,0.0002863958,0.0021321066,0.0003962294,0.0016740793,0.0011329823],"domain_scores_gemma":[0.99443567,0.00085230084,0.0025752005,0.00075202744,0.0012991908,0.0000856176],"candidate_categories":["research_integrity"],"consensus_categories":[],"category_scores_codex":[0.010099249,0.00027024638,0.0006812419,0.00030974782,0.00015981494,0.00016102048,0.0013462715,0.0003017147,0.000040012033],"category_scores_gemma":[0.0024536739,0.0001664798,0.0005542816,0.0007148395,0.00025498285,0.00010974899,0.0005349357,0.0027168966,0.0000109680195],"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":[0.0009951575,0.00036245942,0.0129145775,0.000113375136,0.0018378339,0.000014647365,0.002215945,0.28982145,0.0004279352,0.390665,0.007018908,0.29361272],"study_design_scores_gemma":[0.0002719594,0.00030554662,0.0008413771,0.00019309825,0.00023578775,0.00011873167,0.0013224506,0.03290031,0.0012511854,0.96089065,0.001452177,0.00021671248],"about_ca_topic_score_codex":0.0001841643,"about_ca_topic_score_gemma":0.000255452,"teacher_disagreement_score":0.57022566,"about_ca_system_score_codex":0.00033931693,"about_ca_system_score_gemma":0.005306813,"threshold_uncertainty_score":0.9995839},"labels":[],"label_agreement":null},{"id":"W4402118037","doi":"10.1016/j.ejor.2024.08.030","title":"End-to-end, decision-based, cardinality-constrained portfolio optimization","year":2024,"lang":"en","type":"article","venue":"European Journal of Operational Research","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"University of Toronto; University of New Brunswick","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Cardinality (data modeling); Computer science; Portfolio; End-to-end principle; Mathematical optimization; Portfolio optimization; Operations research; Branch and bound; Business; Mathematics; Algorithm; Artificial intelligence; Data mining; Finance","score_opus":0.14955321770607521,"score_gpt":0.4483302913334653,"score_spread":0.2987770736273901,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402118037","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":[0.024653273,0.0008149778,0.9030114,0.006672477,0.0013040038,0.0003087538,0.00006116904,0.00003231808,0.06314163],"genre_scores_gemma":[0.9290251,0.00023357957,0.06552546,0.00035449502,0.0010834108,0.0000036357421,0.00002582272,0.000044746146,0.0037037092],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.988996,0.0024561293,0.0016553154,0.00050648075,0.0059803706,0.0004056866],"domain_scores_gemma":[0.9906459,0.0033961316,0.00018762569,0.0004585051,0.004795882,0.0005159994],"candidate_categories":["metaresearch","scholarly_communication","insufficient_payload"],"consensus_categories":["metaresearch","insufficient_payload"],"category_scores_codex":[0.037447184,0.00018519694,0.00032211456,0.002411415,0.00045050756,0.002243495,0.0011291718,0.00004868127,0.005564064],"category_scores_gemma":[0.010457462,0.00013199603,0.0002739761,0.0030613584,0.0002046234,0.00094481243,0.0001573363,0.00070160825,0.001311183],"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":[0.00017706622,0.00005262776,0.00068608276,0.0000027636518,0.00003307453,0.0007569973,0.00021893893,0.80369455,0.00018680663,0.003858053,0.052453306,0.13787973],"study_design_scores_gemma":[0.0011261817,0.0011046707,0.008434458,0.00033840272,0.000034331824,0.0006520059,0.00059240736,0.40341008,0.00045029886,0.0028280076,0.5805998,0.00042934812],"about_ca_topic_score_codex":0.000006975129,"about_ca_topic_score_gemma":0.0000029503965,"teacher_disagreement_score":0.90437186,"about_ca_system_score_codex":0.00015486065,"about_ca_system_score_gemma":0.0017196974,"threshold_uncertainty_score":0.9994664},"labels":[],"label_agreement":null},{"id":"W4402264749","doi":"10.23919/acc60939.2024.10644521","title":"Large Decentralized Continuous-Time Convex Stochastic Teams and Their Mean-Field Limits","year":2024,"lang":"en","type":"article","venue":"","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Queen's University; Royal Military College of Canada","funders":"","keywords":"Regular polygon; Computer science; Field (mathematics); Mathematical optimization; Mathematics; Geometry","score_opus":0.02551113262583963,"score_gpt":0.321980411884685,"score_spread":0.29646927925884536,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402264749","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":[0.22350566,0.0040443763,0.72742,0.0029737449,0.0012231949,0.00055745075,0.00004874048,0.00045351067,0.03977338],"genre_scores_gemma":[0.98008746,0.0004379784,0.00052112,0.00068764755,0.00007296248,0.000007239245,0.000008576894,0.000015765941,0.01816125],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99825907,0.00009204345,0.0004414473,0.000448593,0.00045208546,0.00030675047],"domain_scores_gemma":[0.99768513,0.0016483874,0.000064787215,0.00030610102,0.00013816211,0.00015740535],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0012615708,0.00016507469,0.00030308342,0.00019171769,0.000115189265,0.0006331552,0.0002563987,0.000108936816,0.0030725326],"category_scores_gemma":[0.0007469205,0.000096464144,0.00008982463,0.00047106,0.00004498695,0.0003664074,0.000067370864,0.000121698424,0.0009903439],"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":[0.00028422658,0.00024571823,0.005554498,0.000019054518,0.00023701551,0.00007508261,0.01575089,0.0013766753,0.0038057521,0.07701815,0.40713146,0.48850146],"study_design_scores_gemma":[0.0014390377,0.00031435213,0.0008165111,0.0000858488,0.00005886157,0.00007538431,0.0042973445,0.5932276,0.0047908784,0.038165357,0.35607126,0.00065757026],"about_ca_topic_score_codex":0.000025820453,"about_ca_topic_score_gemma":0.000035372206,"teacher_disagreement_score":0.7565818,"about_ca_system_score_codex":0.0000117844875,"about_ca_system_score_gemma":0.000065941254,"threshold_uncertainty_score":0.9997875},"labels":[],"label_agreement":null},{"id":"W4403330231","doi":"10.1016/j.insmatheco.2024.09.005","title":"A new characterization of second-order stochastic dominance","year":2024,"lang":"en","type":"article","venue":"Insurance Mathematics and Economics","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Actua; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Stochastic dominance; Characterization (materials science); Dominance (genetics); Order (exchange); Mathematical economics; Mathematics; Econometrics; Economics; Biology; Materials science; Nanotechnology","score_opus":0.03260314650943892,"score_gpt":0.2912589807282775,"score_spread":0.2586558342188386,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403330231","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":[0.76571834,0.00020409215,0.23236068,0.000121701174,0.00033812516,0.000103916005,0.00005125368,0.00001538309,0.0010865041],"genre_scores_gemma":[0.98196393,0.00076063036,0.014830122,0.000033953565,0.00008391852,0.000005336826,0.0000070535984,0.000015643018,0.0022993907],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989855,0.000008174464,0.0005760259,0.00022307309,0.000104484316,0.00010273822],"domain_scores_gemma":[0.9991945,0.00023025094,0.00021117776,0.00023323452,0.00007676979,0.000054039047],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047783245,0.000098071345,0.00025165756,0.00013818039,0.000038081147,0.00022273192,0.00015188889,0.000053899486,0.00017502402],"category_scores_gemma":[0.0001276404,0.00007999477,0.000043789663,0.00024793387,0.000036053985,0.00035216432,0.000033500462,0.000050511324,0.000064439635],"study_design_candidate":"design_other","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":[0.000056476292,0.00011631926,0.002757779,0.0002664825,0.000098962424,0.000005081933,0.012698454,0.017204665,0.0062561086,0.20458758,0.0015801754,0.75437194],"study_design_scores_gemma":[0.00052688614,0.0000787,0.018003771,0.00021894688,0.000025907311,0.000052735682,0.00029383646,0.7281828,0.0012577034,0.22884011,0.022094732,0.00042390684],"about_ca_topic_score_codex":0.0000048570864,"about_ca_topic_score_gemma":0.000013804167,"teacher_disagreement_score":0.75394803,"about_ca_system_score_codex":0.000010282619,"about_ca_system_score_gemma":0.00007659726,"threshold_uncertainty_score":0.3262092},"labels":[],"label_agreement":null},{"id":"W4403613117","doi":"10.1007/s00199-024-01610-8","title":"Probabilistic risk aversion for generalized rank-dependent functions","year":2024,"lang":"en","type":"article","venue":"Economic Theory","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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; Canada Research Chairs","keywords":"Public finance; Rank (graph theory); Probabilistic logic; Econometrics; Mathematical economics; Risk aversion (psychology); Mathematics; Economics; Statistics; Expected utility hypothesis; Combinatorics","score_opus":0.03844385911622488,"score_gpt":0.32639604723680715,"score_spread":0.28795218812058226,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403613117","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":[0.47279736,0.0016375094,0.48926768,0.00069228484,0.009891539,0.0011010887,0.0006289881,0.00030743488,0.023676122],"genre_scores_gemma":[0.93177277,0.00047388946,0.0014217101,0.00009998671,0.0006104649,0.00009453881,0.000051130297,0.000030425339,0.06544511],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99857634,0.00020895382,0.0004453125,0.00046707518,0.00013330186,0.00016901371],"domain_scores_gemma":[0.99774706,0.0016207914,0.00011911356,0.0003925464,0.000047244637,0.00007324712],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0036639296,0.000112441456,0.0001782265,0.00022524717,0.00021010693,0.00031266047,0.00026061473,0.00007090256,0.002826513],"category_scores_gemma":[0.000575953,0.00008565548,0.00018984551,0.0001092,0.000056418576,0.00029405966,0.000045572746,0.00008294519,0.0034149038],"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":[0.00043278336,0.000040256436,0.00062666804,0.000011857945,0.00010469763,0.0000036930421,0.0009024986,0.23664375,0.00006624902,0.385189,0.12476605,0.2512125],"study_design_scores_gemma":[0.00044980826,0.000046438952,0.00023191594,0.0000064525807,0.00007256723,0.000006358867,0.00024950592,0.13568065,0.00008715238,0.5963086,0.26671204,0.00014851824],"about_ca_topic_score_codex":0.000021893147,"about_ca_topic_score_gemma":0.000025535763,"teacher_disagreement_score":0.48784596,"about_ca_system_score_codex":0.00011238746,"about_ca_system_score_gemma":0.00012098593,"threshold_uncertainty_score":0.998085},"labels":[],"label_agreement":null},{"id":"W4403818725","doi":"10.1007/s00199-024-01614-4","title":"Approximate optimality and the risk/reward tradeoff given repeated gambles","year":2024,"lang":"en","type":"article","venue":"Economic Theory","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Key Technologies Research and Development Program; Taishan Scholar Foundation of Shandong Province; Natural Science Foundation of Shandong Province","keywords":"Public finance; Economics; Mathematical economics; Econometrics; Mathematics; Mathematical optimization; Applied mathematics; Computer science","score_opus":0.03189356464810656,"score_gpt":0.31722113143327046,"score_spread":0.2853275667851639,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403818725","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":[0.9108801,0.0060846354,0.025226967,0.0031125783,0.0015737062,0.0005467272,0.00015400589,0.00025929254,0.052162003],"genre_scores_gemma":[0.9923936,0.00208348,0.00079205516,0.000090357746,0.00015945613,0.000017579525,0.000006397504,0.000016616892,0.004440435],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99796426,0.00071794103,0.00052124157,0.00047925502,0.00013981134,0.00017748504],"domain_scores_gemma":[0.99702567,0.0022019672,0.0001667077,0.0005163396,0.000021777661,0.000067559486],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009468549,0.00013494653,0.0002617206,0.00012654612,0.00019428902,0.00056568434,0.00037842683,0.00006892065,0.00066467404],"category_scores_gemma":[0.00047344572,0.000075812466,0.0001352706,0.00014002861,0.0003788043,0.00034065262,0.00009047715,0.00015820606,0.0004993503],"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":[0.0007299225,0.000022788361,0.0067270948,0.000011312413,0.00019466017,0.000015204063,0.00752728,0.023705581,0.0000143472735,0.6929003,0.022707812,0.24544369],"study_design_scores_gemma":[0.00063896837,0.000022596045,0.005210348,0.00001302965,0.000058061076,0.000032682197,0.00092089333,0.15878081,0.000118030104,0.7732162,0.060797565,0.00019081072],"about_ca_topic_score_codex":0.00005969672,"about_ca_topic_score_gemma":0.000010657283,"teacher_disagreement_score":0.24525288,"about_ca_system_score_codex":0.00003279659,"about_ca_system_score_gemma":0.000058550628,"threshold_uncertainty_score":0.72777146},"labels":[],"label_agreement":null},{"id":"W4403820105","doi":"10.1101/2024.10.28.620618","title":"Should I stay or should I go? Generalized marginal value theorem with temporal discounting","year":2024,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"York University","funders":"","keywords":"Discounting; Value (mathematics); Economics; Mathematics; Mathematical economics; Econometrics; Marginal value; Statistics; Microeconomics","score_opus":0.07030821192634928,"score_gpt":0.3250155901043962,"score_spread":0.2547073781780469,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403820105","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":[0.957411,0.0022097137,0.029583722,0.0019829527,0.00463985,0.0018694538,0.0007791649,0.0010993154,0.00042480946],"genre_scores_gemma":[0.9620816,0.0005809836,0.034301564,0.0004779556,0.001470462,0.00022816093,0.0000022727538,0.00031171294,0.00054528227],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.9908653,0.00060183526,0.0017194316,0.002601478,0.0031696775,0.0010422836],"domain_scores_gemma":[0.9938226,0.00045313284,0.0011832883,0.0028011198,0.0012311391,0.0005087712],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.005390868,0.0011566891,0.0013444141,0.0010455579,0.00042270083,0.0030226565,0.0020264802,0.0008856399,0.000573551],"category_scores_gemma":[0.0010154622,0.00075605523,0.00037766335,0.002639609,0.00037792884,0.0005427412,0.0014944655,0.0017753245,0.00046190666],"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":[0.0077224206,0.002243829,0.4185686,0.002316057,0.004657115,0.008110686,0.0012102636,0.12648071,0.10648916,0.23199251,0.08948964,0.00071897707],"study_design_scores_gemma":[0.00840723,0.0013294414,0.09207665,0.0049594594,0.0030274512,0.0000025294573,0.0002580537,0.18512599,0.10058349,0.0045281905,0.58568907,0.014012456],"about_ca_topic_score_codex":0.00021621784,"about_ca_topic_score_gemma":0.000046176974,"teacher_disagreement_score":0.4961994,"about_ca_system_score_codex":0.0003190309,"about_ca_system_score_gemma":0.0020673973,"threshold_uncertainty_score":0.99948907},"labels":[],"label_agreement":null},{"id":"W4403901707","doi":"10.1016/j.risk.2024.100003","title":"Infinite-mean models in risk management: Discussions and recent advances","year":2024,"lang":"en","type":"article","venue":"Risk sciences.","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"University of Waterloo","funders":"","keywords":"Risk management; Computer science; Economics; Management","score_opus":0.06010482769198154,"score_gpt":0.370776200196426,"score_spread":0.31067137250444443,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403901707","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":[0.39456034,0.08364097,0.16619007,0.005173282,0.004701807,0.0016870154,0.00029417902,0.00052724144,0.3432251],"genre_scores_gemma":[0.7257133,0.26581487,0.006695494,0.000057425794,0.000046537003,0.00001808749,0.0000026513378,0.000008580639,0.0016430689],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9968893,0.0002438582,0.0005558851,0.0008259339,0.0011421983,0.00034285345],"domain_scores_gemma":[0.998745,0.00054329383,0.00016151775,0.00035198094,0.00005816305,0.00013999586],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004577007,0.00015544091,0.00019640342,0.000882998,0.0004601042,0.00080085435,0.0005859795,0.000056529927,0.00015164366],"category_scores_gemma":[0.00038356517,0.00009209834,0.00005526276,0.003694405,0.00031891413,0.0018549102,0.0002101009,0.00021733242,0.00010995784],"study_design_candidate":"design_other","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":[0.000005534612,0.000022828248,0.030405955,0.0000032927699,0.0000043168952,0.000013491432,0.0014303846,0.11135812,7.607299e-7,0.020875381,0.0013942409,0.8344857],"study_design_scores_gemma":[0.00015435669,0.000036350317,0.009983529,0.00004963128,0.000016170685,0.0000036966362,0.0017459373,0.39799643,0.0000056523563,0.34257913,0.24725077,0.00017836857],"about_ca_topic_score_codex":0.000109355984,"about_ca_topic_score_gemma":0.00040349702,"teacher_disagreement_score":0.8343073,"about_ca_system_score_codex":0.000030803854,"about_ca_system_score_gemma":0.000056558696,"threshold_uncertainty_score":0.77226573},"labels":[],"label_agreement":null},{"id":"W4404164467","doi":"10.1007/978-3-031-73758-9_5","title":"Lagrangian Adaptation","year":2024,"lang":"en","type":"book-chapter","venue":"SpringerBriefs in mathematics","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"McGill University","funders":"","keywords":"Adaptation (eye); Computer science; Psychology; Neuroscience","score_opus":0.09154031797478444,"score_gpt":0.3385983098201315,"score_spread":0.24705799184534705,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404164467","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00006306879,0.001153626,0.01115559,0.00024178778,0.00087101286,0.0003513853,0.000027218279,0.00011109025,0.9860252],"genre_scores_gemma":[0.0019067139,0.0012119845,0.018480659,0.000056593155,0.00026599108,0.000011279909,0.000020177748,0.000106157946,0.97794044],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9963462,0.000016601101,0.0013268341,0.0005936603,0.0014715417,0.00024521656],"domain_scores_gemma":[0.99779856,0.000530957,0.0004885481,0.0008893954,0.00020240416,0.00009012076],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.001874126,0.00035345706,0.0005895711,0.00088856474,0.000053441996,0.0003748232,0.0006225885,0.00041006942,0.0010428628],"category_scores_gemma":[0.00049150485,0.00029150877,0.00024359128,0.00026466057,0.00008774383,0.00017220076,0.00016349091,0.00047711856,0.005331413],"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":[0.0000038659537,0.000019597179,0.000013557218,0.000059903567,0.000024205765,0.00006765193,0.001947027,0.0021941126,0.0000022711577,0.9711717,0.0038656932,0.020630427],"study_design_scores_gemma":[0.00006571517,0.000013968116,0.000010248263,0.00030091192,0.00003188755,0.000008661985,0.0001084259,0.018914621,0.0000058517776,0.67665315,0.30365223,0.00023432824],"about_ca_topic_score_codex":0.000011748206,"about_ca_topic_score_gemma":0.00014368142,"teacher_disagreement_score":0.29978654,"about_ca_system_score_codex":0.00009954386,"about_ca_system_score_gemma":0.00009933904,"threshold_uncertainty_score":0.9999537},"labels":[],"label_agreement":null},{"id":"W4404351472","doi":"10.1145/3677052.3698668","title":"EX-DRL: Hedging Against Heavy Losses with EXtreme Distributional Reinforcement Learning","year":2024,"lang":"en","type":"article","venue":"","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"York University; University of Toronto","funders":"","keywords":"Reinforcement learning; Computer science; Reinforcement; Environmental science; Artificial intelligence; Engineering; Structural engineering","score_opus":0.07769634120264451,"score_gpt":0.3399878389204604,"score_spread":0.2622914977178159,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404351472","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":[0.035719834,0.0004495655,0.87783515,0.0014224859,0.00036029928,0.00014956565,0.0000058336996,0.00025379457,0.08380346],"genre_scores_gemma":[0.96653646,0.00028292031,0.0017904233,0.00014649225,0.00013791864,0.000007461029,0.00008731562,0.00001198196,0.030999023],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.9976704,0.000057580968,0.00044114064,0.0004150353,0.001160516,0.0002552981],"domain_scores_gemma":[0.9990023,0.00035409577,0.00008197531,0.00024123806,0.00021102872,0.00010935929],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0010120474,0.00014153961,0.00015613984,0.00020910951,0.00024217897,0.00077002577,0.00022414424,0.000046086407,0.001477684],"category_scores_gemma":[0.000308748,0.000086607964,0.000073565985,0.0009190902,0.00007289797,0.0005918065,0.00006946302,0.0001595358,0.00064786704],"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":[0.00007257393,0.000029492578,0.020725818,0.000008759819,0.000043935383,0.00008556883,0.00042857125,0.8113446,0.00014823079,0.040464688,0.03203055,0.09461719],"study_design_scores_gemma":[0.00021888198,0.000112531045,0.0016471116,0.0000773771,0.00001387126,0.000026203463,0.0006992795,0.49681854,0.0009188312,0.0016739845,0.49752355,0.00026982249],"about_ca_topic_score_codex":0.0000238342,"about_ca_topic_score_gemma":0.000012835492,"teacher_disagreement_score":0.93081665,"about_ca_system_score_codex":0.00006051962,"about_ca_system_score_gemma":0.00015075921,"threshold_uncertainty_score":0.9994351},"labels":[],"label_agreement":null},{"id":"W4404387046","doi":"10.1016/j.insmatheco.2024.11.003","title":"Distributionally robust insurance under the Wasserstein distance","year":2024,"lang":"en","type":"article","venue":"Insurance Mathematics and Economics","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Alberta Innovates","keywords":"Mathematical optimization; Computer science; Mathematics","score_opus":0.056519712898669694,"score_gpt":0.29880916552937,"score_spread":0.24228945263070029,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404387046","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":[0.83071613,0.0019677859,0.15881942,0.0028379324,0.0006348513,0.00017960394,0.00030203242,0.000052081283,0.0044901296],"genre_scores_gemma":[0.99302346,0.002945883,0.0027368192,0.00014679525,0.00010604663,0.000017100865,0.000012038863,0.000016610931,0.0009952489],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9985928,0.000026009839,0.0006074814,0.00035230734,0.00022424628,0.00019716063],"domain_scores_gemma":[0.99841434,0.00082253426,0.00016388888,0.00044149254,0.000092133596,0.00006562458],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001219102,0.00015449671,0.00023459297,0.00006527075,0.00024326607,0.0009370216,0.0003937478,0.00006638415,0.000045396962],"category_scores_gemma":[0.00015189369,0.000096197306,0.00008905372,0.00030993792,0.0001609206,0.00041383717,0.00006723727,0.00013333878,0.0001458359],"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":[0.000014713104,0.000054860953,0.011506701,0.000037324106,0.000051731924,0.000004988829,0.0009029885,0.10353055,0.000014938588,0.8530168,0.003322425,0.02754201],"study_design_scores_gemma":[0.0002141412,0.000023375702,0.037573904,0.000084300154,0.000013876826,0.000051739957,0.001005053,0.43430075,0.000052390566,0.4551513,0.0711938,0.00033538675],"about_ca_topic_score_codex":0.000007407508,"about_ca_topic_score_gemma":0.00004780407,"teacher_disagreement_score":0.39786547,"about_ca_system_score_codex":0.000042993423,"about_ca_system_score_gemma":0.000062142266,"threshold_uncertainty_score":0.9035721},"labels":[],"label_agreement":null},{"id":"W4405205029","doi":"10.1007/978-3-662-70074-7_4","title":"Probabilities","year":2024,"lang":"en","type":"book-chapter","venue":"","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Continental (Canada)","funders":"","keywords":"Computer science","score_opus":0.11971803522992266,"score_gpt":0.3618590022591336,"score_spread":0.24214096702921092,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405205029","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000024243493,0.00086620526,0.0007734408,0.00056718185,0.0009116453,0.00015516843,0.000024004548,0.00011797451,0.996582],"genre_scores_gemma":[0.00021502322,0.0006543097,0.0007882447,0.00011682322,0.00024412092,0.0000037983248,0.000013036803,0.000027199228,0.99793744],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9978859,0.0000066247007,0.0005288268,0.00046895977,0.0010036405,0.00010605128],"domain_scores_gemma":[0.9987814,0.00025655018,0.00011349845,0.00056075124,0.00022798669,0.00005981142],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0006608952,0.0001767454,0.0002585149,0.0003219609,0.000040258295,0.0003707452,0.0003601108,0.00021779114,0.017058754],"category_scores_gemma":[0.00013212417,0.00010795306,0.00019100888,0.00005986335,0.00008527014,0.00011160784,0.000109990186,0.0001681752,0.0207477],"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":[0.0000014573822,0.0000010214001,0.0000059147437,0.0000020507757,0.0000080397085,0.0000056654694,0.000055856333,0.00005325882,7.4259326e-8,0.8133383,0.16624197,0.020286413],"study_design_scores_gemma":[0.000008711221,0.000009228932,9.1461135e-7,0.000010332951,0.0000076028277,0.0000028119891,0.000011988717,0.00024272944,0.0000012986899,0.49172008,0.50791043,0.000073883086],"about_ca_topic_score_codex":0.000006014527,"about_ca_topic_score_gemma":0.00002671683,"teacher_disagreement_score":0.34166846,"about_ca_system_score_codex":0.000023500008,"about_ca_system_score_gemma":0.00008197347,"threshold_uncertainty_score":0.9838398},"labels":[],"label_agreement":null},{"id":"W4405377144","doi":"10.1007/s00780-024-00555-z","title":"Risk-constrained portfolio choice under rank-dependent utility","year":2024,"lang":"en","type":"article","venue":"Finance and Stochastics","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Portfolio; Constraint (computer-aided design); Spectral risk measure; Mathematical optimization; Budget constraint; Mathematical finance; Rank (graph theory); Risk measure; Ranking (information retrieval); Econometrics; Expected shortfall; Monotonic function; Distortion (music); Arbitrage; Portfolio optimization; Computer science; Mathematical economics; Measure (data warehouse); Selection (genetic algorithm); Mathematics; Economics; Microeconomics; Financial economics","score_opus":0.047916844298719036,"score_gpt":0.3438712018818748,"score_spread":0.2959543575831558,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405377144","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":[0.16691619,0.0026615448,0.8209633,0.00038473879,0.0011979464,0.00022290302,0.00018169828,0.00010244706,0.0073691877],"genre_scores_gemma":[0.9924229,0.0015958317,0.0014061284,0.00011749306,0.00018900722,0.000009265205,0.000010846455,0.000012810597,0.0042357165],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979803,0.00006147328,0.0005072428,0.0005447548,0.0006448689,0.00026136573],"domain_scores_gemma":[0.9984427,0.0007697679,0.00014071382,0.00039663777,0.0001623881,0.00008775967],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011628137,0.00017031364,0.00025219374,0.00018136583,0.00021015383,0.00035049062,0.0002353423,0.000119466815,0.00023021516],"category_scores_gemma":[0.0008313988,0.00012969674,0.00008267798,0.0005822779,0.00015538996,0.0002901073,0.00007176945,0.00023792601,0.0001716476],"study_design_candidate":"design_other","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":[0.00011242253,0.00012520942,0.02256577,0.000021523487,0.0000764106,0.000088144894,0.0011944136,0.038413245,0.000042221833,0.08808014,0.029857336,0.81942314],"study_design_scores_gemma":[0.0008595543,0.00019670828,0.14757964,0.00008222787,0.000119886776,0.00008251744,0.00047494125,0.5136163,0.0000917895,0.1411114,0.19516513,0.00061984896],"about_ca_topic_score_codex":0.000067880734,"about_ca_topic_score_gemma":0.000044452503,"teacher_disagreement_score":0.8255067,"about_ca_system_score_codex":0.000016056783,"about_ca_system_score_gemma":0.00013449401,"threshold_uncertainty_score":0.5288879},"labels":[],"label_agreement":null},{"id":"W4405451631","doi":"10.1017/asb.2024.33","title":"A note on continuity and asymptotic consistency of measures of risk and variability","year":2024,"lang":"en","type":"article","venue":"Astin Bulletin","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Consistency (knowledge bases); Mathematics; Time consistency; Econometrics; Statistics; Actuarial science; Economics; Discrete mathematics","score_opus":0.03451599643508236,"score_gpt":0.3118294910568939,"score_spread":0.27731349462181154,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405451631","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":[0.96148103,0.0012410273,0.022252494,0.0010173307,0.00032019065,0.00026516969,0.00012017001,0.000037852027,0.0132647455],"genre_scores_gemma":[0.99673116,0.00033753685,0.0026879306,0.00002205579,0.000021554732,0.000002256951,0.0000011369539,0.0000056513654,0.00019071113],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99816173,0.00035262227,0.0005336727,0.00034779502,0.00050247775,0.00010170572],"domain_scores_gemma":[0.9950529,0.0041968366,0.00018891663,0.0002732862,0.00022825104,0.00005985545],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.004034551,0.00009725921,0.00028726194,0.00013350978,0.00004986187,0.000062688174,0.00011271861,0.00006714019,0.00021804222],"category_scores_gemma":[0.009627514,0.00006957638,0.000052873846,0.0002685987,0.0003340506,0.00003208584,0.00006197284,0.00010924287,0.000024407123],"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":[0.00023324629,0.00012387239,0.31078482,0.00006927239,0.00004362219,0.000009013285,0.002634389,0.0007680698,0.00052310555,0.018260388,0.007174733,0.6593755],"study_design_scores_gemma":[0.0009283633,0.00046152354,0.831999,0.00031021718,0.00017025463,0.00002602167,0.00037406766,0.009761894,0.002182963,0.064162605,0.08927224,0.000350844],"about_ca_topic_score_codex":0.00014101311,"about_ca_topic_score_gemma":0.000017629263,"teacher_disagreement_score":0.65902466,"about_ca_system_score_codex":0.0000060597704,"about_ca_system_score_gemma":0.000048051737,"threshold_uncertainty_score":0.9987148},"labels":[],"label_agreement":null},{"id":"W4405706555","doi":"10.1287/opre.2023.0299","title":"Risk Budgeting Allocation for Dynamic Risk Measures","year":2024,"lang":"en","type":"article","venue":"Operations Research","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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 Toronto","funders":"","keywords":"Computer science; Risk management; Actuarial science; Economics; Econometrics; Finance","score_opus":0.19552886579973786,"score_gpt":0.5204258886954735,"score_spread":0.32489702289573563,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405706555","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":[0.13931473,0.0020547437,0.84734756,0.0031138433,0.00066882756,0.0012002632,0.0002193043,0.0001341357,0.0059466194],"genre_scores_gemma":[0.964162,0.004415122,0.015833335,0.0000149375155,0.0001999143,0.0002750459,0.0000801438,0.00002435218,0.014995179],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99602026,0.0009284069,0.0005059503,0.00056831486,0.0016381067,0.00033893582],"domain_scores_gemma":[0.9951019,0.0023495432,0.000033168555,0.0005349202,0.0018758572,0.00010457689],"candidate_categories":["metaresearch","sts","scholarly_communication","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.015870033,0.00009997766,0.00013104893,0.0008453644,0.0015106974,0.002166831,0.00047397372,0.00009556785,0.00018812527],"category_scores_gemma":[0.014035603,0.00007426285,0.00009962533,0.002064247,0.000109548135,0.00062537944,0.000075755896,0.00039724534,0.0009769375],"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":[0.00002467238,0.00004348169,0.0018575101,0.000004452615,0.000032049244,0.0000019095971,0.001547852,0.3084355,0.0010313047,0.008112275,0.029168703,0.6497403],"study_design_scores_gemma":[0.000102278056,0.0000608763,0.0015531183,0.000011523995,0.0000112043,0.0000019937058,0.00058214675,0.85534126,0.00036281117,0.008715614,0.13316956,0.00008762485],"about_ca_topic_score_codex":0.00053067855,"about_ca_topic_score_gemma":0.0011958185,"teacher_disagreement_score":0.8315142,"about_ca_system_score_codex":0.00010369428,"about_ca_system_score_gemma":0.00038671226,"threshold_uncertainty_score":0.9998009},"labels":[],"label_agreement":null},{"id":"W4406437627","doi":"10.1287/mnsc.2023.00513","title":"Diversification Quotients: Quantifying Diversification via Risk Measures","year":2025,"lang":"en","type":"article","venue":"Management Science","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"University of Waterloo","funders":"","keywords":"Diversification (marketing strategy); Risk management; Business; Economics; Actuarial science; Econometrics; Finance; Marketing","score_opus":0.10613461962601571,"score_gpt":0.37386861348688316,"score_spread":0.26773399386086744,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406437627","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":[0.081626624,0.00008727351,0.8456998,0.0009417551,0.0013889937,0.0004894123,0.0000053899776,0.000106534906,0.06965425],"genre_scores_gemma":[0.99059075,0.00080165506,0.0041901893,0.00015863887,0.000015169931,0.000012421745,0.000005127185,0.000003271962,0.00422276],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9963162,0.00012745433,0.00045008666,0.0008121019,0.001999543,0.0002946255],"domain_scores_gemma":[0.99811333,0.00013342609,0.0003392344,0.0009031909,0.0004323454,0.00007845541],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0052062287,0.00012247938,0.00013047947,0.0012768444,0.00131522,0.0006129907,0.0016550618,0.00003538364,0.00005560451],"category_scores_gemma":[0.0007784134,0.00010231388,0.000064698645,0.0051295683,0.00035899918,0.0012384206,0.0004305545,0.00008129864,0.0004935072],"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":[0.000025210547,0.000084068546,0.2640774,0.0000039715815,0.000017887201,0.000001630879,0.00047997126,0.010484045,0.00031753627,0.040196247,0.0050045224,0.6793075],"study_design_scores_gemma":[0.00045398684,0.000026642076,0.81555396,0.000024261639,0.00007598255,3.802861e-7,0.002524744,0.064516395,0.0010255376,0.03139216,0.08414768,0.00025823893],"about_ca_topic_score_codex":0.00010535392,"about_ca_topic_score_gemma":0.00001573163,"teacher_disagreement_score":0.90896416,"about_ca_system_score_codex":0.0001349143,"about_ca_system_score_gemma":0.000037458733,"threshold_uncertainty_score":0.9999849},"labels":[],"label_agreement":null},{"id":"W4406685128","doi":"10.1080/03610918.2025.2450725","title":"A simulation analysis of returns-risk portfolio optimization models","year":2025,"lang":"en","type":"article","venue":"Communications in Statistics - Simulation and Computation","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Schwartz/Reisman Emergency Medicine Institute; Kelowna General Hospital; Workplace Safety & Insurance Board; Ministry of Education and Child Care; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"","keywords":"Portfolio optimization; Portfolio; Econometrics; Computer science; Modern portfolio theory; Economics; Financial economics; Actuarial science","score_opus":0.1878558533025497,"score_gpt":0.4920745988936804,"score_spread":0.3042187455911307,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406685128","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":[0.015505284,0.00032889025,0.98103136,0.00009720515,0.00009067159,0.0003822276,0.00017765531,0.00003882878,0.0023478554],"genre_scores_gemma":[0.8432088,0.0006956281,0.15517347,0.00005493309,0.0000054471047,0.00001393173,0.00071880256,0.000009028797,0.00011996488],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99654377,0.00068454957,0.001641477,0.00040280432,0.0005783088,0.00014908153],"domain_scores_gemma":[0.9898433,0.0066985493,0.0009922191,0.0010179051,0.0013927774,0.000055215212],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018139193,0.00017158214,0.0004670102,0.0026593807,0.0002853528,0.00018199044,0.00047198156,0.00014120019,0.000041522635],"category_scores_gemma":[0.0025220965,0.00017521084,0.0000864043,0.0057582087,0.00017395611,0.00055107137,0.0001901283,0.00018258391,0.0000023633372],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003546297,0.000089855435,0.02262751,0.0000039804213,0.000090171816,1.9039129e-7,0.000731811,0.89974594,8.8394256e-7,0.023409538,0.000048820883,0.05321581],"study_design_scores_gemma":[0.0004318415,0.000020324162,0.042446505,0.000017361755,0.00032070838,8.256658e-8,0.00027828244,0.87786144,0.0000011818631,0.07834286,0.00015344657,0.0001259774],"about_ca_topic_score_codex":0.00012594256,"about_ca_topic_score_gemma":0.00021251653,"teacher_disagreement_score":0.82770354,"about_ca_system_score_codex":0.00007598102,"about_ca_system_score_gemma":0.00010379357,"threshold_uncertainty_score":0.71448904},"labels":[],"label_agreement":null},{"id":"W4406853036","doi":"10.1016/j.insmatheco.2025.01.006","title":"Bowley-optimal convex-loaded premium principles","year":2025,"lang":"en","type":"article","venue":"Insurance Mathematics and Economics","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":3,"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":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China; Society of Actuaries","keywords":"Regular polygon; Mathematics; Geometry","score_opus":0.05839372610501883,"score_gpt":0.31678328665059974,"score_spread":0.25838956054558093,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406853036","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":[0.9408752,0.00028110453,0.020648172,0.000472141,0.00040040977,0.00019811344,0.000028170722,0.00003361248,0.03706308],"genre_scores_gemma":[0.97170335,0.0014144739,0.021571195,0.00022136382,0.00004946188,0.00001741111,0.0000039894767,0.000011987584,0.0050067655],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99859345,0.000018979083,0.0007512737,0.00032287586,0.00012352444,0.00018990895],"domain_scores_gemma":[0.998652,0.000424808,0.00029063053,0.00045057773,0.00011410403,0.0000679076],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009429404,0.00014356879,0.0003556073,0.00016586563,0.00015060412,0.00035036847,0.00033843552,0.00009505035,0.000052700852],"category_scores_gemma":[0.0004241164,0.000118172225,0.00006845189,0.00019814823,0.00010231929,0.0002517658,0.0001155059,0.00008543876,0.00008235466],"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":[0.00008154077,0.00037363288,0.1451738,0.00012868564,0.00016400898,0.000005859128,0.0049365307,0.08113284,0.0002665834,0.6431959,0.007245791,0.117294826],"study_design_scores_gemma":[0.0012098756,0.000059856597,0.03347075,0.00010085749,0.000034679095,0.000022594279,0.0014756008,0.5584852,0.0031852701,0.22517611,0.17620096,0.0005782296],"about_ca_topic_score_codex":0.000005898564,"about_ca_topic_score_gemma":0.000010102965,"teacher_disagreement_score":0.47735238,"about_ca_system_score_codex":0.000023340242,"about_ca_system_score_gemma":0.000069341404,"threshold_uncertainty_score":0.48189235},"labels":[],"label_agreement":null},{"id":"W4406934403","doi":"10.1007/s11269-024-04078-2","title":"Reallocating Shared Groundwater Resources Using a Participatory Two- level Weighted Bankruptcy Framework","year":2025,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Western University","funders":"","keywords":"Hydrogeology; Bankruptcy; Groundwater; Citizen journalism; Groundwater resources; Business; Water resource management; Environmental science; Environmental economics; Environmental resource management; Computer science; Geology; Aquifer; Economics; Finance; Geotechnical engineering","score_opus":0.1381075411341681,"score_gpt":0.385644720885507,"score_spread":0.2475371797513389,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406934403","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":[0.8474214,0.00022275565,0.11894331,0.001110138,0.00058644556,0.0006237041,0.000008400317,0.00019765645,0.030886175],"genre_scores_gemma":[0.9477393,0.000048988335,0.027948264,0.0011024802,0.00019907345,0.00008066385,0.000022748756,0.000040593583,0.022817908],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9950195,0.0004859134,0.0012449949,0.0010260929,0.0014137882,0.0008097094],"domain_scores_gemma":[0.99781626,0.00024212434,0.00027862674,0.0012972843,0.00021018255,0.00015554218],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0028322728,0.00036849175,0.00046610917,0.00096908247,0.0006999969,0.001292191,0.0012423221,0.00014551486,0.00069017283],"category_scores_gemma":[0.00014059433,0.00024285968,0.00018956492,0.001213363,0.00015234809,0.00044052725,0.00095235376,0.00025621743,0.00035793506],"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":[0.0014457351,0.0015792188,0.21089467,0.00043002793,0.0021570928,0.0007098144,0.14196156,0.17838266,0.0019035616,0.033137295,0.02935348,0.39804488],"study_design_scores_gemma":[0.0015650055,0.0000950513,0.020335149,0.0005345396,0.0003323227,0.000006485431,0.005704531,0.1052477,0.002124169,0.06871359,0.79436934,0.00097213197],"about_ca_topic_score_codex":0.00038465162,"about_ca_topic_score_gemma":0.00004821102,"teacher_disagreement_score":0.76501584,"about_ca_system_score_codex":0.0001243758,"about_ca_system_score_gemma":0.000013286852,"threshold_uncertainty_score":0.99974453},"labels":[],"label_agreement":null},{"id":"W4407098379","doi":"10.23919/isita60732.2024.10858253","title":"Bounding Excess Minimum Risk via Rényi's Divergence","year":2024,"lang":"en","type":"article","venue":"","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Queen's University","funders":"","keywords":"Bounding overwatch; Divergence (linguistics); Computer science; Mathematics; Artificial intelligence; Philosophy","score_opus":0.06296064653329107,"score_gpt":0.3836824357851577,"score_spread":0.3207217892518666,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407098379","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":[0.2420564,0.0009899337,0.69870704,0.00051477103,0.003995126,0.00012683809,0.0000140394595,0.00023363395,0.05336222],"genre_scores_gemma":[0.9737218,0.0009944091,0.0028234466,0.00007198021,0.00019875426,0.000005146098,0.0000032999149,0.00001007088,0.02217107],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978715,0.00010268673,0.00043151577,0.0004662324,0.0009118328,0.00021622366],"domain_scores_gemma":[0.9986872,0.00062405237,0.000084668914,0.00037369772,0.00012166644,0.0001087189],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0017485144,0.00011066063,0.00014447345,0.00028433485,0.00023054176,0.0008334056,0.0004772774,0.00006387795,0.003719517],"category_scores_gemma":[0.0005393243,0.000072329785,0.000102222184,0.0012724802,0.000047008558,0.0006815778,0.000113601396,0.000114014576,0.0034623747],"study_design_candidate":"design_other","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":[0.000023893097,0.000046018453,0.12534985,0.0000069888106,0.000042227664,0.00008494579,0.0024404125,0.0093538705,0.0004347971,0.012429087,0.16404787,0.68574005],"study_design_scores_gemma":[0.00011847597,0.00005027661,0.010922073,0.00002089855,0.00003249517,0.000018697852,0.0004966537,0.4852363,0.0009944882,0.10473547,0.39705545,0.0003187477],"about_ca_topic_score_codex":0.00017047428,"about_ca_topic_score_gemma":0.0000699924,"teacher_disagreement_score":0.73166543,"about_ca_system_score_codex":0.000021138787,"about_ca_system_score_gemma":0.00005205328,"threshold_uncertainty_score":0.99731356},"labels":[],"label_agreement":null},{"id":"W4407157190","doi":"10.3390/jrfm18020088","title":"Editorial Board Members’ Collection Series: Journal of Risk and Financial Management","year":2025,"lang":"en","type":"paratext","venue":"Journal of risk and financial management","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"University of Guelph","funders":"","keywords":"Series (stratigraphy); Business; Editorial board; Risk management; Finance; Accounting; Computer science; Library science; Geology","score_opus":0.011737103145999387,"score_gpt":0.2810065943166139,"score_spread":0.2692694911706145,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407157190","genre_codex":"editorial","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013315403,0.01754309,0.32559922,0.00036981935,0.5906081,0.0016985511,0.00057203526,0.000022371385,0.05027139],"genre_scores_gemma":[0.008317624,0.7979999,0.01588128,0.00016949672,0.088329986,0.000019357407,0.000021399988,0.00006349311,0.08919745],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9932946,0.00050185475,0.002807967,0.0005218939,0.0024692102,0.0004045044],"domain_scores_gemma":[0.9923641,0.0004520652,0.005095118,0.000395271,0.0014470483,0.0002464046],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.005398135,0.00051285175,0.0014303188,0.002396844,0.0006304709,0.0006312164,0.00077696773,0.0005316291,0.00025570937],"category_scores_gemma":[0.001519972,0.00040084863,0.00050847646,0.0015627504,0.00016990832,0.0007328338,0.00040815584,0.0011768169,0.00004173763],"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":[0.0013042835,0.000109979475,0.00053677184,0.000066866116,0.00010646642,0.000082368155,0.0008088019,0.0021629564,2.3994625e-7,0.00032688,0.7733286,0.22116578],"study_design_scores_gemma":[0.0026073856,0.00065872556,0.0054884967,0.0004002411,0.0009425012,0.000051022333,0.0009254294,0.00005100875,0.0000057216876,0.0071695643,0.98135036,0.00034956128],"about_ca_topic_score_codex":0.00009219691,"about_ca_topic_score_gemma":0.000085606174,"teacher_disagreement_score":0.78045684,"about_ca_system_score_codex":0.0001811107,"about_ca_system_score_gemma":0.00037093734,"threshold_uncertainty_score":0.9998443},"labels":[],"label_agreement":null},{"id":"W4407196798","doi":"10.1016/j.ejor.2025.01.039","title":"Diversification for infinite-mean Pareto models without risk aversion","year":2025,"lang":"en","type":"article","venue":"European Journal of Operational Research","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Actua; University of Waterloo","funders":"Fundamental Research Funds for the Central Universities; China Postdoctoral Science Foundation; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Diversification (marketing strategy); Pareto principle; Risk aversion (psychology); Economics; Econometrics; Mathematics; Mathematical economics; Expected utility hypothesis; Operations management; Business; Marketing","score_opus":0.28008259968338517,"score_gpt":0.46949841627973804,"score_spread":0.18941581659635287,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407196798","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":[0.17863704,0.0002654917,0.7726084,0.0032269459,0.0005662154,0.00044158637,0.00005308761,0.000010617289,0.044190586],"genre_scores_gemma":[0.98277056,0.0007179824,0.010125979,0.00011450222,0.00024267515,0.000002499936,0.00001670755,0.000010892215,0.0059981816],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9946656,0.0019224142,0.00086374674,0.00025941498,0.002078212,0.0002105875],"domain_scores_gemma":[0.99210024,0.0016897791,0.0003441666,0.00030151336,0.0054317247,0.00013260008],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.022748198,0.00009188075,0.00018280321,0.0010181804,0.0007345068,0.00052355474,0.000836978,0.000031958803,0.000118258526],"category_scores_gemma":[0.006239142,0.00006696218,0.00013249369,0.0009502437,0.00011811667,0.00089021795,0.0001367225,0.0003669857,0.00013339563],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0016044595,0.0002514008,0.040978465,0.000008662032,0.000116715695,0.000029987053,0.003677863,0.5286595,0.000929644,0.042847015,0.2518588,0.12903747],"study_design_scores_gemma":[0.0036303862,0.0010042528,0.054263663,0.0001442073,0.000052340238,0.00002284437,0.003111049,0.4540673,0.00096103817,0.058549594,0.42389792,0.0002954201],"about_ca_topic_score_codex":0.000008583797,"about_ca_topic_score_gemma":0.0000041318194,"teacher_disagreement_score":0.80413353,"about_ca_system_score_codex":0.00008383164,"about_ca_system_score_gemma":0.0004461515,"threshold_uncertainty_score":0.7884117},"labels":[],"label_agreement":null},{"id":"W4407795009","doi":"10.1017/asb.2025.1","title":"Worst-case reinsurance strategy with likelihood ratio uncertainty","year":2025,"lang":"en","type":"article","venue":"Astin Bulletin","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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 Waterloo","funders":"","keywords":"Reinsurance; Econometrics; Economics; Statistics; Mathematics; Actuarial science","score_opus":0.029732318876131845,"score_gpt":0.3240783556594435,"score_spread":0.2943460367833116,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407795009","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":[0.25364962,0.0008879457,0.48681024,0.016219039,0.00095357216,0.0009941305,0.000059482496,0.0003026762,0.24012329],"genre_scores_gemma":[0.9789486,0.00006548586,0.0051650465,0.0005907621,0.00007942889,0.000027963819,0.000010731555,0.000011768405,0.015100227],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9977874,0.00017821229,0.0005667345,0.0005518976,0.0006070128,0.00030870078],"domain_scores_gemma":[0.99787194,0.000749327,0.00021543814,0.0006094971,0.00044978273,0.000104014696],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0013660007,0.00018516026,0.0002619722,0.0002112169,0.00028070816,0.00033291965,0.00038365013,0.00008823894,0.0015134591],"category_scores_gemma":[0.000952803,0.00012808711,0.00006196333,0.0011827804,0.0001072366,0.000093993796,0.000062209845,0.00018214951,0.000657249],"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":[0.0004685156,0.00014304067,0.01952034,0.000008826862,0.000051171282,0.0009895167,0.00033888753,0.20260179,0.000059681584,0.013988481,0.2632135,0.49861625],"study_design_scores_gemma":[0.0032636356,0.0005386367,0.02759494,0.00016607507,0.000082955055,0.00066907203,0.003214223,0.0377759,0.00086084986,0.023363762,0.9014979,0.0009720625],"about_ca_topic_score_codex":0.00026281903,"about_ca_topic_score_gemma":0.0002333405,"teacher_disagreement_score":0.72529894,"about_ca_system_score_codex":0.000026149644,"about_ca_system_score_gemma":0.00019046834,"threshold_uncertainty_score":0.9993993},"labels":[],"label_agreement":null},{"id":"W4407816190","doi":"10.28924/2291-8639-23-2025-51","title":"\\(L\\)-Mild Normality and \\(L_2\\)-Mild Normality","year":2025,"lang":"en","type":"article","venue":"International Journal of Analysis and Applications","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Normality; Mathematics; Statistics","score_opus":0.03543086188743504,"score_gpt":0.409277678677323,"score_spread":0.37384681678988796,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407816190","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":[0.2999324,0.000995191,0.6775999,0.008406555,0.00025093995,0.0001632609,0.00007879972,0.00001342018,0.012559529],"genre_scores_gemma":[0.9939681,0.0020637328,0.0024451257,0.0004849734,0.00014947129,0.0000077745335,0.00001395226,0.0000026976231,0.00086416904],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9978353,0.00008786606,0.000968793,0.00023467469,0.0007759246,0.000097468714],"domain_scores_gemma":[0.9972463,0.00037281303,0.00063686556,0.00024952774,0.0013872248,0.00010721522],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00173817,0.000100605226,0.0003088687,0.0009615509,0.00016272573,0.0003617133,0.0005303897,0.000057758378,0.00010500991],"category_scores_gemma":[0.00022212365,0.00007545426,0.00021759837,0.0013783044,0.00011176487,0.0003893216,0.00012249989,0.00013906034,0.000006411922],"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":[0.00007517922,0.00019833099,0.78889173,0.0000028534873,0.0016548793,0.0000037020272,0.00023119402,0.004421332,0.00013710397,0.05611243,0.0032795428,0.14499171],"study_design_scores_gemma":[0.0005527718,0.000025255546,0.82915545,0.000013259927,0.0007253255,0.000020256162,0.00047880298,0.008572081,0.0003287641,0.036431175,0.12354573,0.00015116087],"about_ca_topic_score_codex":0.00009511996,"about_ca_topic_score_gemma":0.00009769661,"teacher_disagreement_score":0.6940357,"about_ca_system_score_codex":0.000029639923,"about_ca_system_score_gemma":0.000072966825,"threshold_uncertainty_score":0.34880096},"labels":[],"label_agreement":null},{"id":"W4407821405","doi":"10.1016/j.segan.2025.101664","title":"A fully adaptive distributionally robust multistage framework based on mixed decision rules for wind-thermal system operation under uncertainty","year":2025,"lang":"en","type":"article","venue":"Sustainable Energy Grids and Networks","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Guangxi Key Research and Development Program; Natural Science Foundation of Guangxi Province; National Natural Science Foundation of China","keywords":"Mathematical optimization; Robust optimization; Thermal; Computer science; Decision rule; Operations research; Engineering; Mathematics; Meteorology; Artificial intelligence; Physics","score_opus":0.017807865095367425,"score_gpt":0.28778322259903155,"score_spread":0.26997535750366414,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407821405","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":[0.01418802,0.00057063537,0.98228794,0.00041155866,0.0006466245,0.00044123872,0.0000626053,0.00006947324,0.0013218903],"genre_scores_gemma":[0.98650503,0.000101082995,0.009020775,0.0004443576,0.0003100276,0.000117163385,0.0003335899,0.00002201749,0.003145956],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9970589,0.0002546677,0.0007006556,0.0007638916,0.00064841344,0.00057346927],"domain_scores_gemma":[0.99540883,0.0026694036,0.00024583645,0.0004917028,0.0010328086,0.00015138807],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018362646,0.00031101896,0.00042368475,0.00033467537,0.0010260256,0.00060144096,0.0003604593,0.00038891303,0.00003196457],"category_scores_gemma":[0.00066105556,0.00023760823,0.00016428171,0.0008696135,0.00011948513,0.00030593,0.00011401069,0.0002065556,0.0000021496153],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007352737,0.000042616455,0.0002493327,0.0000071280256,0.000018926576,0.000008860996,0.000013921451,0.6422493,9.426483e-7,0.33479217,0.002985033,0.01889649],"study_design_scores_gemma":[0.001015639,0.00021565288,0.002998332,0.0001439242,0.000037814832,0.0000012107965,0.0021189665,0.95982236,0.000015843376,0.018181892,0.015189093,0.00025927823],"about_ca_topic_score_codex":0.00022509009,"about_ca_topic_score_gemma":0.00008077493,"teacher_disagreement_score":0.9732672,"about_ca_system_score_codex":0.00033206402,"about_ca_system_score_gemma":0.000357651,"threshold_uncertainty_score":0.96893823},"labels":[],"label_agreement":null},{"id":"W4408019669","doi":"10.1016/j.insmatheco.2025.02.006","title":"Efficient evaluation of risk allocations","year":2025,"lang":"en","type":"article","venue":"Insurance Mathematics and Economics","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Université Laval; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Risk analysis (engineering); Computer science; Business","score_opus":0.06330511799435296,"score_gpt":0.3531745612312664,"score_spread":0.28986944323691344,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408019669","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":[0.96453655,0.000201883,0.024974585,0.0000951411,0.00017597308,0.00015493028,0.000031974996,0.000006090372,0.009822887],"genre_scores_gemma":[0.9910437,0.0006152327,0.008222018,0.000016815267,0.000009463124,0.000009949477,0.0000019829633,0.0000031794934,0.00007768458],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989989,0.000038386625,0.0005513301,0.00015751766,0.00018497069,0.00006894222],"domain_scores_gemma":[0.99846005,0.00050525845,0.0003394709,0.00031675646,0.00035555847,0.000022915223],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00304639,0.000061404375,0.00017589543,0.00016511953,0.00008678678,0.000066465946,0.00014732938,0.000038790233,0.00002591594],"category_scores_gemma":[0.0010732202,0.00005067876,0.00003965962,0.00022985593,0.000054146003,0.000046558645,0.000034009183,0.000035542776,0.000015112172],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000049490077,0.00009335565,0.028045895,0.000007721214,0.000020165817,2.4347685e-8,0.001343614,0.75698084,0.00001154244,0.071569055,0.0001800149,0.14174281],"study_design_scores_gemma":[0.00020757272,0.0000039712563,0.043423966,0.000011380486,0.000022749718,4.3995246e-7,0.0003561491,0.7638872,0.000091064474,0.19165608,0.0002968666,0.000042532633],"about_ca_topic_score_codex":0.000012040336,"about_ca_topic_score_gemma":0.000025520649,"teacher_disagreement_score":0.14170028,"about_ca_system_score_codex":0.000019969266,"about_ca_system_score_gemma":0.00007474019,"threshold_uncertainty_score":0.20666198},"labels":[],"label_agreement":null},{"id":"W4408062133","doi":"10.23952/jnva.9.2025.3.07","title":"Characterizing robust $\\varepsilon$-optimal solution sets for uncertain convex optimization problems","year":2025,"lang":"en","type":"article","venue":"Journal of Nonlinear and Variational Analysis","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Centre Scientifique et Technique du Bâtiment; Natural Science Foundation of Chongqing; Chongqing Technology and Business University","keywords":"Mathematical optimization; Regular polygon; Robust optimization; Optimization problem; Convex optimization; Mathematics; Computer science; Geometry","score_opus":0.06824840180215758,"score_gpt":0.36274975072953053,"score_spread":0.29450134892737295,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408062133","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":[0.014214217,0.00017087383,0.9815902,0.003389064,0.00024732164,0.00014360729,0.000068694615,0.0000078380135,0.00016816487],"genre_scores_gemma":[0.3910345,0.00085345854,0.6049754,0.0006829533,0.00048760735,0.000013770672,0.00040438422,0.000015451795,0.001532438],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997577,0.00013782387,0.0011850363,0.00025768048,0.00068704376,0.00015539702],"domain_scores_gemma":[0.99614364,0.00066676165,0.0010784707,0.00015263459,0.0018718533,0.00008661231],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028323212,0.0001328288,0.00047404566,0.0012159972,0.00026919207,0.0002893542,0.00022226447,0.00010842577,0.00012844727],"category_scores_gemma":[0.001157681,0.00010052069,0.00033916038,0.0019034483,0.00003677059,0.0006002029,0.00003828352,0.000117102325,0.000001932291],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011841954,0.0000754863,0.010244997,0.000005390221,0.00053496746,9.792977e-7,0.00016456645,0.9842127,0.000077560704,0.0007472456,0.00051252457,0.003305192],"study_design_scores_gemma":[0.0007119351,0.000072672476,0.015013844,0.00001667108,0.00077730836,0.000007700017,0.00009946747,0.97822315,0.00002608823,0.0015839408,0.0033675246,0.00009971301],"about_ca_topic_score_codex":0.00002005322,"about_ca_topic_score_gemma":0.000011184817,"teacher_disagreement_score":0.3768203,"about_ca_system_score_codex":0.000054203207,"about_ca_system_score_gemma":0.00023068942,"threshold_uncertainty_score":0.40991145},"labels":[],"label_agreement":null},{"id":"W4408315831","doi":"10.1017/asb.2025.6","title":"Pareto-optimal peer-to-peer risk sharing with robust distortion risk measures","year":2025,"lang":"en","type":"article","venue":"Astin Bulletin","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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 Waterloo","funders":"","keywords":"Distortion (music); Risk aversion (psychology); Aggregate (composite); Econometrics; Pareto principle; Probabilistic logic; Time consistency; Computer science; Mathematical optimization; Economics; Expected utility hypothesis; Actuarial science; Microeconomics; Mathematical economics; Mathematics","score_opus":0.04015971537353784,"score_gpt":0.3112490315686342,"score_spread":0.27108931619509635,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408315831","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":[0.27621755,0.00019033819,0.6941976,0.007633087,0.0006391142,0.00043084528,0.000067224886,0.00017027227,0.020453969],"genre_scores_gemma":[0.9189145,0.00016002885,0.029346604,0.00028166905,0.00017773299,0.00005584058,0.000029001527,0.000029887904,0.05100472],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.99552834,0.0002742259,0.00073086045,0.00089982955,0.0021504222,0.00041632232],"domain_scores_gemma":[0.9966145,0.0006186107,0.00043750094,0.00081208325,0.001324945,0.00019236181],"candidate_categories":["metaresearch","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0048583704,0.00026675695,0.00035502505,0.00040952026,0.0005424139,0.00048205143,0.000747172,0.00012984488,0.0008361919],"category_scores_gemma":[0.009018566,0.00019633547,0.00011516314,0.0011828673,0.000092030314,0.00012043545,0.0002031107,0.00037360142,0.0011001189],"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":[0.00017168256,0.000035738347,0.38282523,0.0000012782735,0.000026623278,0.0000064363844,0.00033300256,0.35919207,0.000004539718,0.00012696002,0.19216247,0.065113984],"study_design_scores_gemma":[0.00060561515,0.00013061872,0.23591885,0.00005409916,0.00009872839,0.000005194823,0.000541786,0.013305181,0.00017344211,0.0006543619,0.74816453,0.00034761513],"about_ca_topic_score_codex":0.00094372575,"about_ca_topic_score_gemma":0.0002972465,"teacher_disagreement_score":0.664851,"about_ca_system_score_codex":0.00007724496,"about_ca_system_score_gemma":0.0000857132,"threshold_uncertainty_score":0.99967766},"labels":[],"label_agreement":null},{"id":"W4408611054","doi":"10.61091/jcmcc124-41","title":"Capital allocation and risk aversion strategies for optimizing multinational firms’ investment portfolios via linear programming algorithms","year":2025,"lang":"en","type":"article","venue":"Journal of Combinatorial Mathematics and Combinatorial Computing","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Multinational corporation; Risk aversion (psychology); Linear programming; Investment (military); Capital allocation line; Business; Economics; Mathematical optimization; Computer science; Microeconomics; Financial economics; Algorithm; Finance; Mathematics; Expected utility hypothesis","score_opus":0.02494072706858487,"score_gpt":0.3200841610927241,"score_spread":0.29514343402413923,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408611054","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":[0.544872,0.00036786136,0.44612262,0.00019527992,0.007483543,0.0006313433,0.000005180143,0.000028285598,0.00029392538],"genre_scores_gemma":[0.8753305,0.00022178661,0.12369812,0.000032730855,0.0006612303,0.0000068881914,0.0000054345355,0.000019418436,0.000023882396],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9965405,0.00016635927,0.0016490773,0.0003360044,0.00102216,0.00028592287],"domain_scores_gemma":[0.9939405,0.0020718274,0.0019218695,0.00021490997,0.0016857822,0.00016511352],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004711093,0.00027182026,0.00065591634,0.0005171715,0.0006113915,0.0006941887,0.000368463,0.00018790652,0.0000032470396],"category_scores_gemma":[0.0020620804,0.00022437323,0.00017736564,0.0005324471,0.00011592715,0.00061895576,0.00020460163,0.0003211466,8.5067984e-7],"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":[0.00027728576,0.0007376164,0.001988587,0.00013904166,0.00029409252,0.000011239608,0.0052825147,0.0069216737,0.0002492901,0.91649556,0.00060888095,0.0669942],"study_design_scores_gemma":[0.0040535876,0.00058024446,0.00035633717,0.0001802801,0.00016197408,0.00003396836,0.002760815,0.28728235,0.00022543671,0.70193833,0.002189142,0.00023755687],"about_ca_topic_score_codex":0.000019739387,"about_ca_topic_score_gemma":9.3685935e-7,"teacher_disagreement_score":0.33045852,"about_ca_system_score_codex":0.000085122636,"about_ca_system_score_gemma":0.00030579313,"threshold_uncertainty_score":0.91496754},"labels":[],"label_agreement":null},{"id":"W4408960189","doi":"10.1016/j.ejor.2025.03.020","title":"Pareto-optimal insurance under robust distortion risk measures","year":2025,"lang":"en","type":"article","venue":"European Journal of Operational Research","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":2,"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":"Natural Sciences and Engineering Research Council of Canada; Alberta Innovates","keywords":"Distortion (music); Pareto principle; Actuarial science; Econometrics; Computer science; Risk management; Pareto optimal; Economics; Mathematical optimization; Multi-objective optimization; Mathematics; Finance","score_opus":0.2285720716164106,"score_gpt":0.4435861146798621,"score_spread":0.21501404306345148,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408960189","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":[0.45999435,0.0013617721,0.4912242,0.0038612692,0.0008001223,0.00017898936,0.000025696327,0.000011862395,0.04254176],"genre_scores_gemma":[0.98879325,0.0015538634,0.0038135294,0.0001261799,0.0003433332,0.0000014566662,0.0000050823937,0.000012477134,0.0053508505],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99099034,0.004001179,0.0011122623,0.00027535303,0.0033678564,0.00025302096],"domain_scores_gemma":[0.9935358,0.0011168257,0.00034727907,0.0003209827,0.004528109,0.00015099155],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.026226513,0.00010934211,0.00021224137,0.0008443258,0.0006517787,0.0006698572,0.0008626539,0.000033484954,0.00024312176],"category_scores_gemma":[0.0093135135,0.00007654512,0.00013220348,0.0013385951,0.00020267008,0.0007131426,0.00011950259,0.00069680274,0.00024322115],"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":[0.00033456463,0.00012226512,0.07382379,0.0000014954909,0.000055056353,0.00006558499,0.00035234363,0.7469425,0.0003237379,0.0049296143,0.08004088,0.093008146],"study_design_scores_gemma":[0.0010099392,0.00031032428,0.8124523,0.00006789154,0.00001324174,0.000044701635,0.00067410676,0.009278195,0.00041328534,0.004009155,0.17156756,0.00015927068],"about_ca_topic_score_codex":0.000011334131,"about_ca_topic_score_gemma":0.000013733566,"teacher_disagreement_score":0.73862857,"about_ca_system_score_codex":0.000114933115,"about_ca_system_score_gemma":0.00056891405,"threshold_uncertainty_score":0.9990315},"labels":[],"label_agreement":null},{"id":"W4408962963","doi":"10.61091/jcmcc125-25","title":"Algorithmic design of a conditional value-at-risk optimization model based on implied volatility for multi-period portfolio adjustment","year":2025,"lang":"en","type":"article","venue":"Journal of Combinatorial Mathematics and Combinatorial Computing","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Portfolio; Econometrics; Portfolio optimization; Volatility (finance); Value at risk; Implied volatility; Economics; Period (music); Value (mathematics); Financial economics; Computer science; Mathematics; Risk management; Statistics","score_opus":0.048855825829892854,"score_gpt":0.3402111267827095,"score_spread":0.29135530095281664,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408962963","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":[0.024281878,0.00010764995,0.9694101,0.00007010895,0.0050636414,0.0008161474,0.000039581064,0.000017698261,0.00019320357],"genre_scores_gemma":[0.7293265,0.000043630724,0.27026954,0.00003598294,0.0002564441,0.000009604507,0.0000105148665,0.000021804102,0.000026009415],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9953712,0.00030126914,0.0023399966,0.00036422515,0.0013382137,0.0002850897],"domain_scores_gemma":[0.9920502,0.0025914481,0.0029103225,0.0003883512,0.0019045959,0.00015510619],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.005630773,0.00031247135,0.0009875257,0.00061488483,0.00043333136,0.00017107496,0.0005091823,0.00022178351,0.000014722838],"category_scores_gemma":[0.0031719832,0.0002568286,0.000319327,0.0006072943,0.00011090726,0.00020264431,0.0001358509,0.00027719443,6.4255744e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006456974,0.00084352243,0.00024339113,0.000041178504,0.00008253267,0.0000015685191,0.00028161833,0.8498316,0.000063375956,0.14544491,0.0006538014,0.0018668015],"study_design_scores_gemma":[0.0056324615,0.0005330922,0.00011616802,0.00010348173,0.0001268914,0.000004729914,0.00008172269,0.69816977,0.00024560993,0.294804,0.000037359823,0.00014471672],"about_ca_topic_score_codex":0.0000043224804,"about_ca_topic_score_gemma":1.2831016e-7,"teacher_disagreement_score":0.70504457,"about_ca_system_score_codex":0.00018634432,"about_ca_system_score_gemma":0.00055040844,"threshold_uncertainty_score":0.9999884},"labels":[],"label_agreement":null},{"id":"W4409364512","doi":"10.1609/aaai.v39i16.33808","title":"Designing Ambiguity Sets for Distributionally Robust Optimization Using Structural Causal Optimal Transport","year":2025,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Mila - Quebec Artificial Intelligence Institute","funders":"","keywords":"Ambiguity; Robust optimization; Mathematical optimization; Computer science; Optimal design; Operations research; Mathematics; Machine learning","score_opus":0.20700598772163933,"score_gpt":0.40282897794249023,"score_spread":0.1958229902208509,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409364512","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":[0.20870724,0.000013244505,0.78815675,0.0008038804,0.0004929606,0.0005856076,0.00007372601,0.000033651613,0.0011329668],"genre_scores_gemma":[0.94351596,0.000020366195,0.056012254,0.000077145596,0.000052137217,0.000022145497,0.000013875558,0.000010987839,0.0002751236],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99714255,0.000030900315,0.0010734608,0.00057203544,0.0008449388,0.00033612762],"domain_scores_gemma":[0.99640185,0.00028117857,0.00064009905,0.0002275297,0.0023734574,0.000075868804],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015038722,0.00023801629,0.00035234247,0.0002436833,0.00054591923,0.00029707176,0.0010946605,0.00014683041,0.00011879819],"category_scores_gemma":[0.0018608078,0.00017411675,0.00019542809,0.0012167919,0.00029680794,0.0005096838,0.00009989839,0.0001960878,0.0000046923547],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003247286,0.000049191964,0.0037488923,0.0000140916045,0.000021505448,1.8853163e-7,0.00030131702,0.84277266,0.0043252283,0.13604951,0.00017854628,0.012214124],"study_design_scores_gemma":[0.00005511588,0.00007081578,0.001000411,0.0001026911,0.000047197085,0.000002167226,0.00057107705,0.8401487,0.09149187,0.06629205,0.000039403956,0.0001785327],"about_ca_topic_score_codex":0.000048072103,"about_ca_topic_score_gemma":0.000011472507,"teacher_disagreement_score":0.73480874,"about_ca_system_score_codex":0.00008783424,"about_ca_system_score_gemma":0.00031239036,"threshold_uncertainty_score":0.7100275},"labels":[],"label_agreement":null},{"id":"W4409566021","doi":"10.3390/jrfm18040218","title":"Multi-Period Portfolio Optimization Model with Cone Constraints and Discrete Decisions","year":2025,"lang":"en","type":"article","venue":"Journal of risk and financial management","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Cone (formal languages); Period (music); Portfolio; Portfolio optimization; Computer science; Mathematical optimization; Mathematics; Economics; Algorithm; Physics; Financial economics","score_opus":0.023160883064091326,"score_gpt":0.30327257889412584,"score_spread":0.28011169583003453,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409566021","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":[0.033591226,0.0005302974,0.96364,0.00018993468,0.00016972721,0.00017587666,0.000016675513,0.000006253016,0.0016800264],"genre_scores_gemma":[0.7459027,0.014770411,0.23818336,0.00013812317,0.000031860156,0.000003399427,0.0000020596278,0.0000069317575,0.0009611403],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983381,0.000061979394,0.0007078356,0.0002396015,0.00050160335,0.00015087145],"domain_scores_gemma":[0.99870616,0.00014760873,0.00052285014,0.00018616123,0.00032941313,0.00010782731],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011980662,0.00013851919,0.0003258403,0.0005702883,0.00025587543,0.00022516493,0.00018947915,0.00006309314,0.000021010203],"category_scores_gemma":[0.000559499,0.00009066683,0.000059752925,0.0005620297,0.00019088853,0.0003562087,0.0001030231,0.00014708549,0.00000123797],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003026783,0.00007801233,0.020569388,0.000004167116,0.00003357463,0.00007044843,0.00045798134,0.5342746,0.0000023774194,0.006590296,0.0016269916,0.43598953],"study_design_scores_gemma":[0.005822461,0.00033379867,0.1219642,0.00029841202,0.00043427278,0.00010907394,0.002248703,0.8303271,0.000010848114,0.019137904,0.01891151,0.00040174223],"about_ca_topic_score_codex":0.0000058231217,"about_ca_topic_score_gemma":0.000014129059,"teacher_disagreement_score":0.7254566,"about_ca_system_score_codex":0.000017262177,"about_ca_system_score_gemma":0.00008373665,"threshold_uncertainty_score":0.3697286},"labels":[],"label_agreement":null},{"id":"W4409837082","doi":"10.1016/j.jebo.2025.107001","title":"On the optimality of straight deductibles under smooth ambiguity aversion","year":2025,"lang":"en","type":"article","venue":"Journal of Economic Behavior & Organization","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":3,"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":"University of Waterloo; University of Illinois at Urbana-Champaign; National Natural Science Foundation of China; Ministry of Education - Singapore; California State University","keywords":"Ambiguity; Ambiguity aversion; Mathematics; Mathematical economics; Computer science","score_opus":0.060472860707403,"score_gpt":0.36256867202111215,"score_spread":0.30209581131370916,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409837082","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":[0.9856167,0.000020553318,0.010714685,0.0013848308,0.0009355342,0.00012585704,0.000008241457,0.000006126764,0.0011874747],"genre_scores_gemma":[0.9987539,0.00012449568,0.0003613538,0.000117574695,0.000060748058,5.7580564e-7,0.0000055911064,0.000009011891,0.0005667394],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99824315,0.00016172796,0.0009908439,0.0001676743,0.00032710526,0.00010946743],"domain_scores_gemma":[0.9978245,0.000301701,0.0008554146,0.0003297674,0.00064098625,0.000047636633],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0018105259,0.00010554225,0.00026557397,0.00034976605,0.00016159833,0.0001306494,0.00049703097,0.0000909431,0.001996855],"category_scores_gemma":[0.0004919489,0.00006204767,0.00010156683,0.00046622465,0.000081579354,0.00041715076,0.00005848799,0.00014442446,0.00004433995],"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":[0.0003116823,0.0012002564,0.45547935,0.000012335161,0.00016630697,0.000010156677,0.0017155419,0.24676782,0.03034232,0.17975962,0.055391684,0.028842933],"study_design_scores_gemma":[0.0013306671,0.00036052222,0.7859186,0.000075321215,0.00035487392,0.000036072888,0.0021654952,0.0014911931,0.16920388,0.03678909,0.0019638643,0.00031043025],"about_ca_topic_score_codex":0.000019989875,"about_ca_topic_score_gemma":0.000009942725,"teacher_disagreement_score":0.33043927,"about_ca_system_score_codex":0.00017723872,"about_ca_system_score_gemma":0.00024564395,"threshold_uncertainty_score":0.99891543},"labels":[],"label_agreement":null},{"id":"W4410111358","doi":"10.2139/ssrn.5210128","title":"Dimension Reduction of Distributionally Robust Optimization Problems","year":2025,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Reduction (mathematics); Dimension (graph theory); Dimensionality reduction; Mathematical optimization; Robust optimization; Mathematics; Computer science; Combinatorics; Artificial intelligence; Geometry","score_opus":0.03697233086191422,"score_gpt":0.319694011604348,"score_spread":0.2827216807424338,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410111358","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":[0.013228249,0.0027828289,0.978665,0.0011688625,0.0019220853,0.00045996095,0.00008101065,0.000041176885,0.0016508581],"genre_scores_gemma":[0.8900797,0.079430856,0.013517496,0.00003471609,0.00087258,0.00003587044,0.0008018378,0.000044801305,0.015182146],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9943453,0.0004342913,0.0016689709,0.0006371327,0.0017106291,0.0012036778],"domain_scores_gemma":[0.9950933,0.00015681228,0.002026689,0.0006238299,0.0020048355,0.00009456888],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0077673886,0.0003141206,0.00056988903,0.0007682972,0.0003087223,0.00024223576,0.0009290829,0.00043749553,0.000095625954],"category_scores_gemma":[0.0009708565,0.0002566811,0.00037317764,0.0009779253,0.00009844313,0.00036453115,0.00036911687,0.0026376876,0.000010863435],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000069118214,0.00008786528,0.0005429305,0.000008987327,0.00010163464,4.6573408e-7,0.00009343279,0.96094245,0.000031615877,0.024269296,0.0007023628,0.013149869],"study_design_scores_gemma":[0.0007006116,0.00023972371,0.00032916214,0.00028426768,0.0001969837,0.00023732871,0.0007986106,0.22352423,0.000235494,0.7713135,0.0016889394,0.00045116825],"about_ca_topic_score_codex":0.000075595315,"about_ca_topic_score_gemma":0.00005858492,"teacher_disagreement_score":0.9651475,"about_ca_system_score_codex":0.001078089,"about_ca_system_score_gemma":0.0069982996,"threshold_uncertainty_score":0.99998856},"labels":[],"label_agreement":null},{"id":"W4410112292","doi":"10.2139/ssrn.5203042","title":"Model Ambiguity in Risk Sharing with Monotone Mean-Variance","year":2025,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Ambiguity; Monotone polygon; Variance (accounting); Mathematics; Econometrics; Statistics; Computer science; Economics","score_opus":0.04813271783399219,"score_gpt":0.34945965675416635,"score_spread":0.30132693892017415,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410112292","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":[0.22658938,0.0033413977,0.7631977,0.00043363162,0.00038996947,0.00037636491,0.000031001742,0.000042396372,0.005598135],"genre_scores_gemma":[0.9223426,0.061647188,0.0040867245,0.00007097368,0.0001864449,0.000027293452,0.00000950822,0.000027543272,0.011601706],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9938934,0.00030925614,0.0012364947,0.0010206946,0.001449847,0.0020903272],"domain_scores_gemma":[0.99686486,0.00018108088,0.0012722969,0.0010538587,0.00049843924,0.0001294401],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.012057342,0.0003982392,0.0007055031,0.00087588985,0.000295804,0.00042885877,0.0020353636,0.00036289485,0.00002644001],"category_scores_gemma":[0.0006101697,0.0003007775,0.00022716528,0.0009796149,0.00006532352,0.0004112609,0.0006398941,0.007299874,0.000022262837],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016964387,0.0000620935,0.03541102,0.0000034000836,0.000091135014,0.000007886963,0.00046522322,0.91332364,0.0000024447966,0.020438006,0.0001185346,0.029906968],"study_design_scores_gemma":[0.0004561745,0.0000458365,0.0015973699,0.000081615326,0.000042316344,0.000034860535,0.0002777613,0.40407512,0.0000087506005,0.59301496,0.00013782349,0.0002274023],"about_ca_topic_score_codex":0.0012703091,"about_ca_topic_score_gemma":0.015546047,"teacher_disagreement_score":0.759111,"about_ca_system_score_codex":0.001380402,"about_ca_system_score_gemma":0.008476952,"threshold_uncertainty_score":0.99994445},"labels":[],"label_agreement":null},{"id":"W4411005531","doi":"10.1016/j.eswa.2025.128384","title":"E-CARGO Based distributionally robust chance-constrained optimization under severe weather conditions","year":2025,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Nipissing University","funders":"National Natural Science Foundation of China","keywords":"Robust optimization; Computer science; Mathematical optimization; Operations research; Mathematics","score_opus":0.03485903491674016,"score_gpt":0.3284380142646913,"score_spread":0.2935789793479512,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411005531","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":[0.000182505,0.00038372478,0.96783507,0.0028826797,0.0002209039,0.0014232759,0.00032502261,0.00017931248,0.02656751],"genre_scores_gemma":[0.953533,0.000097236865,0.028087907,0.00093323627,0.0002454996,0.004320542,0.0013975926,0.000035611283,0.011349401],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9973701,0.0001586127,0.00074035913,0.00066176726,0.0007824496,0.0002867009],"domain_scores_gemma":[0.9971313,0.0005014754,0.00035297635,0.0009150973,0.00095993513,0.00013920452],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005640992,0.00024287356,0.00033407737,0.00037178837,0.0006969417,0.0003694773,0.00050970196,0.00015887409,0.0004319898],"category_scores_gemma":[0.0001148464,0.00018588292,0.00009648636,0.0020685073,0.00019415427,0.00032961776,0.000035960762,0.000120166784,0.00014642012],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000020184041,0.000106390966,0.0011814646,0.000004004571,0.000035600762,6.024759e-7,0.00005735031,0.88645965,0.0000493876,0.095702365,0.016003227,0.00037974727],"study_design_scores_gemma":[0.0020217597,0.00006364168,0.0030845474,0.00016316785,0.00006809041,0.00003653412,0.003027373,0.76274586,0.00015564935,0.003623477,0.22430642,0.00070346764],"about_ca_topic_score_codex":0.00007199416,"about_ca_topic_score_gemma":0.000024652105,"teacher_disagreement_score":0.9533505,"about_ca_system_score_codex":0.00014577834,"about_ca_system_score_gemma":0.0004948181,"threshold_uncertainty_score":0.75800854},"labels":[],"label_agreement":null},{"id":"W4411034033","doi":"10.1007/978-3-031-90606-0_24","title":"Evaluating the Influence of Diversification Strategies on Portfolio Optimization: A Case Study in Vietnam","year":2025,"lang":"en","type":"book-chapter","venue":"Lecture notes in networks and systems","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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 de Montréal","funders":"","keywords":"Diversification (marketing strategy); Portfolio; Business; Marketing; Finance","score_opus":0.08514295911242081,"score_gpt":0.37926948118023823,"score_spread":0.2941265220678174,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411034033","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":[0.4272024,0.020861935,0.45324758,0.00042866264,0.0033654668,0.012730181,0.00007680381,0.00010891186,0.08197806],"genre_scores_gemma":[0.9970361,0.0008197434,0.00014046414,0.000042780033,0.00011923702,0.000037412607,0.00001371779,0.0000146435605,0.0017759097],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99671155,0.0003757784,0.0013043318,0.0005972291,0.0008312915,0.00017981249],"domain_scores_gemma":[0.99588555,0.002116746,0.000900322,0.0006745718,0.00038710443,0.000035723893],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0032683027,0.00029456418,0.000620997,0.0005235039,0.00017254749,0.00032381582,0.00036358344,0.00035702236,0.000021719157],"category_scores_gemma":[0.0005455277,0.00018905819,0.00006909576,0.0005594929,0.00009621157,0.00016588147,0.00009675804,0.0004692524,0.0000010167138],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000051495976,0.000024008441,0.0058846367,0.000012920532,0.000019583807,0.00010665378,0.0009876217,0.97646344,1.8517481e-7,0.0016851585,0.000036335856,0.014727968],"study_design_scores_gemma":[0.0004132285,0.0003160187,0.0012244276,0.0005159494,0.000050999686,0.00007492351,0.0011661861,0.99377763,1.0202165e-7,0.0020830508,0.00016096854,0.00021651258],"about_ca_topic_score_codex":0.0006049136,"about_ca_topic_score_gemma":0.00078041706,"teacher_disagreement_score":0.5698337,"about_ca_system_score_codex":0.00005617945,"about_ca_system_score_gemma":0.00014990965,"threshold_uncertainty_score":0.77095693},"labels":[],"label_agreement":null},{"id":"W4411193925","doi":"10.1016/j.cor.2025.107162","title":"Optimal interventions in robust optimization with time-dependent uncertainties","year":2025,"lang":"en","type":"article","venue":"Computers & Operations Research","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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 British Columbia, Okanagan Campus; Kelowna General Hospital","funders":"","keywords":"Computer science; Mathematical optimization; Robust optimization; Psychological intervention; Mathematics; Medicine","score_opus":0.15559557217603054,"score_gpt":0.4470110162983257,"score_spread":0.29141544412229514,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411193925","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":[0.024376793,0.00019351595,0.9664017,0.0026865553,0.0002286591,0.00064734765,0.000009206527,0.00005108011,0.005405145],"genre_scores_gemma":[0.69518393,0.00042289437,0.25040123,0.0001760655,0.00011047557,0.0002582173,0.00016448201,0.00003171677,0.053250957],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9962006,0.0008122598,0.00074120687,0.000596632,0.0012630221,0.0003862283],"domain_scores_gemma":[0.9970176,0.00065481407,0.000045388988,0.00065325503,0.0015379367,0.000090994174],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0039047096,0.00014727339,0.00025753846,0.0022204898,0.0006391557,0.0013081697,0.00085935736,0.00009374989,0.00048415668],"category_scores_gemma":[0.0007842646,0.0001149675,0.000087264634,0.0039823586,0.00023914632,0.00071957574,0.00032599753,0.00037305563,0.00020726053],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004266222,0.0001435156,0.0015709869,0.000004167558,0.000017001528,0.000008146687,0.0004107328,0.98051953,0.000011421386,0.0020298993,0.008008149,0.007233776],"study_design_scores_gemma":[0.0005193418,0.00012226994,0.0012405975,0.00013041648,0.0000059585313,0.0000042230813,0.0006442754,0.9956145,0.00006831192,0.0001924046,0.0013369625,0.000120737604],"about_ca_topic_score_codex":0.000336891,"about_ca_topic_score_gemma":0.0009553283,"teacher_disagreement_score":0.7160005,"about_ca_system_score_codex":0.00019128021,"about_ca_system_score_gemma":0.00045363462,"threshold_uncertainty_score":0.99972856},"labels":[],"label_agreement":null},{"id":"W4411435126","doi":"10.2139/ssrn.5166709","title":"Constructing elicitable risk measures","year":2025,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Business; Economics","score_opus":0.04160647327909968,"score_gpt":0.35003812824623903,"score_spread":0.30843165496713937,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411435126","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":[0.0719353,0.0219919,0.81581163,0.0014879269,0.0057593775,0.00053872785,0.00012807094,0.0001476707,0.082199365],"genre_scores_gemma":[0.8004225,0.161799,0.0044984487,0.0001317932,0.0013629093,0.000015928745,0.000025624831,0.000040438128,0.031703394],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9929323,0.00079037505,0.001361547,0.0007061167,0.0018244276,0.0023852119],"domain_scores_gemma":[0.99560946,0.0006334347,0.0017512616,0.00079174014,0.0010737063,0.0001404162],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.017988313,0.00036109288,0.0006642086,0.0008410984,0.0006204906,0.0009217661,0.0016800726,0.0004107893,0.000091694805],"category_scores_gemma":[0.004447179,0.0002814017,0.00045998013,0.00074635376,0.000094371266,0.00027484255,0.000519348,0.0073431884,0.000085287065],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000055061908,0.00003083864,0.04889646,0.0000028573131,0.0003317015,0.000004105381,0.00022305867,0.03442866,0.0000028900981,0.088405184,0.0025749584,0.8250442],"study_design_scores_gemma":[0.0003434921,0.00005030486,0.00023913369,0.00005666912,0.00013070785,0.00021113237,0.0034826067,0.004904257,0.0000459996,0.97460264,0.015623129,0.00030991997],"about_ca_topic_score_codex":0.00046586368,"about_ca_topic_score_gemma":0.0021087558,"teacher_disagreement_score":0.88619745,"about_ca_system_score_codex":0.0012371748,"about_ca_system_score_gemma":0.010734045,"threshold_uncertainty_score":0.9999638},"labels":[],"label_agreement":null},{"id":"W4411576260","doi":"10.1080/03461238.2025.2522201","title":"Counter-monotonic risk allocations and distortion risk measures","year":2025,"lang":"en","type":"article","venue":"Scandinavian Actuarial Journal","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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 Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Monotonic function; Actuarial science; Distortion (music); Econometrics; Mathematics; Computer science; Mathematical economics; Statistics; Economics; Telecommunications; Mathematical analysis","score_opus":0.024874059452541335,"score_gpt":0.34928114194723164,"score_spread":0.3244070824946903,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411576260","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":[0.50383735,0.0013786507,0.47749588,0.0014444856,0.0047526336,0.00036725812,0.00007719175,0.000057708312,0.010588865],"genre_scores_gemma":[0.9885661,0.008320846,0.0009060594,0.000086063636,0.00039938785,0.000006492857,0.000005591533,0.000009698802,0.0016997855],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9972237,0.0005093556,0.0007866187,0.00035320694,0.0008566929,0.00027045232],"domain_scores_gemma":[0.9978206,0.00043135547,0.0007362061,0.00036769503,0.00043990125,0.00020422133],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0034023917,0.00017345625,0.0002809311,0.0005519961,0.0011989208,0.0010542198,0.0004229567,0.00012897048,0.00016251516],"category_scores_gemma":[0.0026152413,0.00012859165,0.00014500033,0.0006993264,0.00014739539,0.0006090345,0.00006285546,0.00049208914,0.00005491571],"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":[0.00028914868,0.00007214054,0.3966277,0.0000010984501,0.00010640462,0.000007853122,0.0010458296,0.0022410958,0.00005498614,0.001151073,0.025747955,0.5726547],"study_design_scores_gemma":[0.002451949,0.0002081784,0.69806343,0.00007982609,0.00034869323,0.00013102102,0.0009769922,0.0071921544,0.0001819166,0.124609314,0.1653645,0.0003920285],"about_ca_topic_score_codex":0.00025286456,"about_ca_topic_score_gemma":0.00027110372,"teacher_disagreement_score":0.5722627,"about_ca_system_score_codex":0.0001616391,"about_ca_system_score_gemma":0.0002454084,"threshold_uncertainty_score":0.9999828},"labels":[],"label_agreement":null},{"id":"W4411605756","doi":"10.5220/0013604500003970","title":"Multi-Objective Evolutionary Computation for the Portfolio Optimization Problem with Respect to Environmental, Social, and Governance Criteria","year":2025,"lang":"en","type":"article","venue":"","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Evolutionary computation; Corporate governance; Computation; Portfolio; Computer science; Portfolio optimization; Evolutionary algorithm; Mathematical optimization; Artificial intelligence; Business; Mathematics; Finance; Algorithm","score_opus":0.040935609740232894,"score_gpt":0.3526728965945062,"score_spread":0.31173728685427327,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411605756","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":[0.0051118773,0.00023081295,0.98758805,0.0033628175,0.00011292551,0.0011499976,0.000050668,0.000033230095,0.0023596236],"genre_scores_gemma":[0.6809443,0.00032905437,0.31079745,0.0007573511,0.000052416366,0.00017070619,0.00003120702,0.000013709071,0.0069037545],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99858725,0.00007978333,0.00034566966,0.0004178391,0.0004160609,0.0001533867],"domain_scores_gemma":[0.9989805,0.00048301416,0.00017971102,0.00016331434,0.00015430871,0.000039109564],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006112627,0.00012664357,0.0001576686,0.00011849741,0.0005773347,0.00021824754,0.00018410152,0.000053558713,0.00009307736],"category_scores_gemma":[0.00016110427,0.00007750721,0.00003865919,0.0005906522,0.000102257785,0.00035267617,0.0000747848,0.000050019622,0.000007126903],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00056113285,0.00014773197,0.011969126,0.0000054967154,0.00006346787,0.0000012763891,0.0013571557,0.84107834,0.00018014408,0.004448509,0.103517294,0.03667034],"study_design_scores_gemma":[0.0009362652,0.00014880249,0.21167566,0.00001532376,0.000038779373,0.0000044748635,0.0013905247,0.7701197,0.00009015142,0.0024377564,0.012963189,0.00017935304],"about_ca_topic_score_codex":0.000063222105,"about_ca_topic_score_gemma":0.00005061041,"teacher_disagreement_score":0.6767906,"about_ca_system_score_codex":0.00008520398,"about_ca_system_score_gemma":0.000071801805,"threshold_uncertainty_score":0.444045},"labels":[],"label_agreement":null},{"id":"W4412464337","doi":"10.1017/asb.2025.10055","title":"Multi-asset return risk measures","year":2025,"lang":"en","type":"article","venue":"Astin Bulletin","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Actuarial science; Econometrics; Economics; Business; Financial economics","score_opus":0.05846956612984249,"score_gpt":0.35879010996285127,"score_spread":0.30032054383300877,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412464337","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":[0.15533869,0.0027922334,0.6565134,0.026277993,0.0042036255,0.0009797832,0.00013508808,0.000516927,0.15324226],"genre_scores_gemma":[0.8960287,0.00087503146,0.039221276,0.0009928561,0.0001326927,0.00002363479,0.000013744687,0.000017132998,0.06269494],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99764854,0.00036812117,0.00055776956,0.00046105156,0.00072357553,0.00024091761],"domain_scores_gemma":[0.9978011,0.0009358953,0.00023519772,0.0005876538,0.0003607069,0.000079422614],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0028645643,0.00014232725,0.00022739531,0.00026760853,0.00025105439,0.00023035677,0.00057251914,0.00010763975,0.0011885996],"category_scores_gemma":[0.008015211,0.00010659755,0.000107362815,0.0007708435,0.000072161005,0.000051220883,0.00011969241,0.00020308976,0.0019545904],"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":[0.00003578354,0.00004854394,0.19915907,8.9957916e-7,0.000016325877,0.0000060395714,0.00014922977,0.0018776048,0.000099373814,0.00026602813,0.6563399,0.14200124],"study_design_scores_gemma":[0.00039875766,0.000017330027,0.09762246,0.00001368498,0.00001674414,0.0000019079807,0.00012941574,0.004767652,0.00031533823,0.0015152832,0.8950816,0.000119850636],"about_ca_topic_score_codex":0.00018764069,"about_ca_topic_score_gemma":0.000064272936,"teacher_disagreement_score":0.74069,"about_ca_system_score_codex":0.00002002631,"about_ca_system_score_gemma":0.000069306174,"threshold_uncertainty_score":0.99972445},"labels":[],"label_agreement":null},{"id":"W4413049767","doi":"10.1016/j.jfds.2025.100165","title":"Catastrophic-risk-aware reinforcement learning with extreme-value-theory-based policy gradients☆","year":2025,"lang":"en","type":"article","venue":"The Journal of Finance and Data Science","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Reinforcement learning; Extreme value theory; Reinforcement; Computer science; Risk analysis (engineering); Business; Artificial intelligence; Mathematics; Psychology; Social psychology; Statistics","score_opus":0.05512067823412605,"score_gpt":0.3696084126283005,"score_spread":0.3144877343941745,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413049767","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":[0.3791559,0.0006614051,0.6159969,0.0025837498,0.00022971534,0.00015407382,0.0000340929,0.000008700766,0.0011754796],"genre_scores_gemma":[0.9935565,0.0029178625,0.0025298677,0.00027471152,0.00005030878,5.553601e-7,0.0000033582373,0.0000031763852,0.00066365174],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975861,0.00020841874,0.00051273673,0.00027516828,0.0011628901,0.00025472097],"domain_scores_gemma":[0.99722654,0.0004891892,0.0008301476,0.0009211466,0.00045794493,0.00007506422],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009989196,0.00010902211,0.00019170211,0.00050441327,0.00085041125,0.0002739952,0.0023504396,0.000022181997,0.000012048815],"category_scores_gemma":[0.0023684152,0.000054410975,0.00002441628,0.002412552,0.000769506,0.0014863678,0.00032097189,0.00025774326,0.0000055994797],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00061864825,0.000045452645,0.02892606,0.000004629909,0.000015817215,0.000013509471,0.00081935787,0.7268498,0.00022556282,0.022462066,0.002604533,0.21741457],"study_design_scores_gemma":[0.0034470973,0.0018081653,0.17295882,0.000627902,0.00025893,0.00023295294,0.0032214825,0.64760053,0.0022491831,0.030824263,0.13616177,0.00060890894],"about_ca_topic_score_codex":0.00009688802,"about_ca_topic_score_gemma":0.000014294354,"teacher_disagreement_score":0.6144006,"about_ca_system_score_codex":0.000036720074,"about_ca_system_score_gemma":0.00088950316,"threshold_uncertainty_score":0.6540762},"labels":[],"label_agreement":null},{"id":"W4413119066","doi":"10.1016/j.ejor.2025.07.057","title":"Worst-case values of target semi-variances with applications to robust portfolio selection","year":2025,"lang":"en","type":"article","venue":"European Journal of Operational Research","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Actua; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Selection (genetic algorithm); Portfolio; Computer science; Econometrics; Mathematical optimization; Mathematics; Statistics; Economics; Artificial intelligence; Financial economics","score_opus":0.14466597772328801,"score_gpt":0.4422429272366033,"score_spread":0.29757694951331526,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413119066","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":[0.05317756,0.000292953,0.88769156,0.0024364947,0.00014057671,0.00041595867,0.000015791411,0.0000089400955,0.05582017],"genre_scores_gemma":[0.9154616,0.00013348016,0.076256506,0.000134314,0.00031780227,0.000009899898,0.0000058755477,0.000014825657,0.0076656984],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.9953434,0.0011150254,0.0010088291,0.00027519028,0.0020452894,0.00021229054],"domain_scores_gemma":[0.9926734,0.0007570396,0.00030722353,0.00026668987,0.005819575,0.00017604881],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.012940877,0.00010400442,0.00023393329,0.0013226954,0.00046493913,0.00037712176,0.0006129198,0.00002586258,0.0003837635],"category_scores_gemma":[0.001982517,0.00007069533,0.00007167196,0.0033207764,0.00014033611,0.00052081817,0.00009784144,0.00035093914,0.00007536728],"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":[0.0005990867,0.00031299147,0.017066747,0.000009473249,0.00011590501,0.00038900145,0.00075794064,0.82926625,0.001480736,0.036072962,0.08849659,0.025432331],"study_design_scores_gemma":[0.0041548493,0.0046006106,0.098263055,0.0006599783,0.00012255082,0.0050606825,0.0076040816,0.04842064,0.012687708,0.02657444,0.7909337,0.0009176885],"about_ca_topic_score_codex":0.00001400525,"about_ca_topic_score_gemma":0.000011567644,"teacher_disagreement_score":0.86228406,"about_ca_system_score_codex":0.000060272854,"about_ca_system_score_gemma":0.0009450194,"threshold_uncertainty_score":0.44850755},"labels":[],"label_agreement":null},{"id":"W4413122761","doi":"10.3390/info16080676","title":"An Approximate Algorithm for Sparse Distributionally Robust Optimization","year":2025,"lang":"en","type":"article","venue":"Information","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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; University of Alberta","keywords":"Robust optimization; Computer science; Algorithm; Mathematical optimization; Optimization algorithm; Mathematics","score_opus":0.04056409178548765,"score_gpt":0.34445253107084367,"score_spread":0.303888439285356,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413122761","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":[0.00045204812,0.000015285694,0.9923898,0.0003394836,0.00046660533,0.00046378406,0.00021318119,0.00008501498,0.0055748047],"genre_scores_gemma":[0.055102084,0.00013665149,0.93487793,0.0011410005,0.0001740164,0.0002337906,0.007058103,0.000011815368,0.0012646173],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99838275,0.000046772813,0.00074163266,0.00015986379,0.00050150295,0.00016745545],"domain_scores_gemma":[0.9981843,0.00016121144,0.0003054265,0.0003334688,0.0009568909,0.000058704776],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013830721,0.00010597496,0.00013944285,0.00036723458,0.00026717462,0.0005822357,0.000323441,0.00010177445,0.00007857809],"category_scores_gemma":[0.00059179607,0.00008915864,0.000064708816,0.0007977554,0.000032500117,0.0038649226,0.000028165137,0.00005134723,0.00005636302],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001650346,0.000019075405,0.00018087176,0.000002022025,0.0000041503436,4.4053316e-8,0.000094416464,0.6825483,0.0000017556113,0.006149361,0.0049901525,0.30599335],"study_design_scores_gemma":[0.00040823195,0.000031986132,0.0009927356,0.0000060593748,0.0000099265535,0.0000011805391,0.00018999047,0.9405934,0.00011283695,0.0059564756,0.05160283,0.00009435419],"about_ca_topic_score_codex":0.000007179163,"about_ca_topic_score_gemma":0.0000020345278,"teacher_disagreement_score":0.305899,"about_ca_system_score_codex":0.000058619877,"about_ca_system_score_gemma":0.0001245554,"threshold_uncertainty_score":0.5614512},"labels":[],"label_agreement":null},{"id":"W4413134662","doi":"10.1007/s11579-025-00398-y","title":"Max- and min-stability under first-order stochastic dominance","year":2025,"lang":"en","type":"article","venue":"Mathematics and Financial Economics","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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 Waterloo; Western University","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Mathematics; Stochastic dominance; Stability (learning theory); Infimum and supremum; Stochastic ordering; Characterization (materials science); Mathematical finance; Bivariate analysis; Order (exchange); Axiom; Mathematical economics; Applied mathematics; Combinatorics; Mathematical optimization; Statistics; Computer science; Economics; Finance","score_opus":0.03727865838197264,"score_gpt":0.29051550576264445,"score_spread":0.2532368473806718,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413134662","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":[0.6679175,0.00026838933,0.3268289,0.0007770861,0.0002654896,0.00021238167,0.00001973172,0.00001161264,0.0036989187],"genre_scores_gemma":[0.9732173,0.0010053299,0.023878971,0.00039221375,0.000053949476,0.000021581194,0.0000029977912,0.000011792526,0.0014158352],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9988592,0.00001216713,0.0005506536,0.00033576935,0.00008273039,0.00015948237],"domain_scores_gemma":[0.9987837,0.0005464802,0.00018320075,0.00032978397,0.00010138808,0.00005543847],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00084028253,0.00013185352,0.00032730406,0.00011304707,0.00020729742,0.00020684145,0.0001642592,0.00010198716,0.000038304494],"category_scores_gemma":[0.0008635218,0.00011257289,0.000039797345,0.00021667845,0.00014462203,0.00015272204,0.00012374464,0.0000731899,0.000016055299],"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":[0.00007445956,0.00023506761,0.005328698,0.0000846528,0.0000219788,0.0000016919224,0.0036298833,0.012631577,0.000011557081,0.8831505,0.0026192528,0.09221067],"study_design_scores_gemma":[0.00058656186,0.00003708841,0.0117038125,0.000033596574,0.000026128071,0.0000060207617,0.00060133974,0.22972886,0.000032542885,0.73795766,0.019038945,0.0002474236],"about_ca_topic_score_codex":0.000017782064,"about_ca_topic_score_gemma":0.00022337928,"teacher_disagreement_score":0.30529985,"about_ca_system_score_codex":0.000025000387,"about_ca_system_score_gemma":0.00009197065,"threshold_uncertainty_score":0.4590589},"labels":[],"label_agreement":null},{"id":"W4413237434","doi":"10.1007/s10589-025-00704-w","title":"Risk-averse constrained blackbox optimization under mixed aleatory/epistemic uncertainties","year":2025,"lang":"en","type":"article","venue":"Computational Optimization and Applications","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"McGill University; Polytechnique Montréal; Group for Research in Decision Analysis","funders":"","keywords":"Uncertainty quantification; Mathematics; Mathematical optimization; Robust optimization; Mathematical economics; Epistemology; Applied mathematics; Statistics; Philosophy","score_opus":0.027412392189510863,"score_gpt":0.3305064136275489,"score_spread":0.30309402143803804,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413237434","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":[0.0010238102,0.0002159226,0.9862269,0.0023154153,0.00020381465,0.0007750368,0.000099761,0.00017257313,0.008966748],"genre_scores_gemma":[0.68227243,0.0013918851,0.3088034,0.0015268512,0.00014825455,0.0003442786,0.0011394514,0.00004105518,0.0043323906],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997117,0.0002678462,0.0009899585,0.0007228367,0.0006621619,0.00024022802],"domain_scores_gemma":[0.9964994,0.0011472184,0.00057082676,0.00044984714,0.0011775797,0.00015511499],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008555712,0.00026698867,0.00033223955,0.000682969,0.0008522761,0.00046434256,0.0003733029,0.00017233862,0.00034823],"category_scores_gemma":[0.0004287807,0.00025260652,0.00011266664,0.0019464502,0.00038748473,0.000446734,0.00010442412,0.0001727554,0.000068426816],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016517071,0.00006276186,0.0019099985,0.0000043340387,0.00003518331,2.4836257e-7,0.00007856993,0.8625524,0.0000021012281,0.12686676,0.0025090277,0.0059620827],"study_design_scores_gemma":[0.0007663913,0.000017450962,0.0020081452,0.000014622333,0.00006267434,0.0000056193485,0.00092381804,0.9353393,0.000009761966,0.0531181,0.0075000613,0.0002340886],"about_ca_topic_score_codex":0.000016982074,"about_ca_topic_score_gemma":0.000007836011,"teacher_disagreement_score":0.6812486,"about_ca_system_score_codex":0.000088107045,"about_ca_system_score_gemma":0.00030762912,"threshold_uncertainty_score":0.9999926},"labels":[],"label_agreement":null},{"id":"W4413289142","doi":"10.1002/sam.70038","title":"Distributionally Conservative Stochastic Dominance via Subsampling","year":2025,"lang":"en","type":"article","venue":"Statistical Analysis and Data Mining The ASA Data Science Journal","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Queen's University","funders":"","keywords":"Stochastic dominance; Dominance (genetics); Econometrics; Statistics; Mathematics; Computer science; Economics; Mathematical optimization; Biology","score_opus":0.14582943694385603,"score_gpt":0.4572373035182113,"score_spread":0.31140786657435526,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413289142","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":[0.015072787,0.0002048321,0.97732174,0.0022815287,0.00021645929,0.00006689362,0.0046903966,0.0000068775194,0.00013847851],"genre_scores_gemma":[0.87983966,0.00027778637,0.1178821,0.00035755668,0.000087484164,0.0000016501856,0.0014162733,0.0000036086635,0.00013385773],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99580467,0.0002226315,0.0009113569,0.0009977409,0.0016787658,0.00038481923],"domain_scores_gemma":[0.99151975,0.004557347,0.00050676905,0.0024639573,0.0007308873,0.00022129259],"candidate_categories":["metaresearch","sts","scholarly_communication","open_science"],"consensus_categories":[],"category_scores_codex":[0.017363546,0.00015127892,0.00039269283,0.0005202839,0.002013274,0.0022496693,0.005821222,0.00003478034,0.00016493844],"category_scores_gemma":[0.022477955,0.00008494105,0.000039430535,0.005674806,0.0017255241,0.0024344376,0.0026808234,0.00027630114,0.000013354232],"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":[0.00032122168,0.0002269856,0.20927307,0.000005665837,0.0010571302,0.000068195746,0.00085952895,0.015631648,0.0001840186,0.092156835,0.09351248,0.58670324],"study_design_scores_gemma":[0.00016643603,0.000019041736,0.18048581,0.000016362199,0.0005259099,0.00003495087,0.0007997002,0.79580075,0.0000029786424,0.019220771,0.002805837,0.00012145941],"about_ca_topic_score_codex":0.00011595843,"about_ca_topic_score_gemma":0.00018373772,"teacher_disagreement_score":0.8647669,"about_ca_system_score_codex":0.00003474141,"about_ca_system_score_gemma":0.00066521025,"threshold_uncertainty_score":0.99955773},"labels":[],"label_agreement":null},{"id":"W4413411476","doi":"10.1007/s10479-025-06781-8","title":"Set-valued $$\\Phi $$-coherent risk measures for sustainable decision-making","year":2025,"lang":"en","type":"article","venue":"Annals of Operations Research","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Acadia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Theory of computation; Set (abstract data type); Computer science; Algorithm","score_opus":0.326874455026115,"score_gpt":0.5719149145548164,"score_spread":0.24504045952870135,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413411476","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":[0.3696378,0.0022930414,0.5981827,0.0042282944,0.0002842316,0.0026331504,0.00010980682,0.000045992412,0.022585046],"genre_scores_gemma":[0.97451526,0.0015372717,0.0079914825,0.00011567474,0.000065007946,0.00020490099,0.000012776878,0.00001244972,0.015545188],"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9952696,0.0007603043,0.00089836784,0.000508594,0.002004939,0.00055818865],"domain_scores_gemma":[0.98411405,0.0037730888,0.00009263507,0.00087453926,0.011047866,0.00009780009],"candidate_categories":["metaresearch","sts"],"consensus_categories":[],"category_scores_codex":[0.016654314,0.00012623165,0.00030557756,0.0016142145,0.0013899269,0.0008210254,0.0009379373,0.00011908801,0.00024094763],"category_scores_gemma":[0.042509545,0.00009626637,0.00017281604,0.0032154312,0.00018940115,0.000654903,0.0002545195,0.0002509082,0.000056919755],"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":[0.00056401594,0.0001989426,0.0043513607,0.000017125249,0.000088328066,0.000005564099,0.0014011662,0.29104745,0.00012116865,0.12145935,0.30397797,0.27676755],"study_design_scores_gemma":[0.0009286546,0.0004427379,0.009235831,0.00016889404,0.000026842932,0.0000017201157,0.012661727,0.33887446,0.005730911,0.34434354,0.28726703,0.00031762916],"about_ca_topic_score_codex":0.00033861952,"about_ca_topic_score_gemma":0.00034121957,"teacher_disagreement_score":0.6048775,"about_ca_system_score_codex":0.00003866194,"about_ca_system_score_gemma":0.0008144921,"threshold_uncertainty_score":0.9999101},"labels":[],"label_agreement":null},{"id":"W4413902410","doi":"10.1111/mafi.70008","title":"Upper Comonotonicity and Risk Aggregation Under Dependence Uncertainty","year":2025,"lang":"en","type":"article","venue":"Mathematical Finance","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Deutsche Forschungsgemeinschaft","keywords":"Economics; Econometrics; Actuarial science; Financial economics","score_opus":0.032490470543397335,"score_gpt":0.34475889776678725,"score_spread":0.31226842722338993,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413902410","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":[0.5080991,0.00025873774,0.47992256,0.00074200996,0.00009670921,0.00018625055,0.000007643121,0.000031655618,0.010655325],"genre_scores_gemma":[0.9809715,0.0015932199,0.013033034,0.00020092144,0.000013427959,0.000017934699,0.0000015615781,0.0000049897826,0.0041634133],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99838144,0.00012444089,0.0004753665,0.00037128656,0.00046692305,0.0001805443],"domain_scores_gemma":[0.9979803,0.0011971788,0.0001777863,0.00044514047,0.00015690817,0.0000426934],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013440755,0.000118408585,0.00024794397,0.000108951026,0.00020646965,0.00016929006,0.00028831154,0.0000946081,0.00009910047],"category_scores_gemma":[0.0021725006,0.00008455343,0.000055806348,0.0005810643,0.00014607023,0.00022487606,0.00010545413,0.00014399426,0.00018153565],"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":[0.000039799583,0.00010738945,0.051045135,0.0000119079,0.000017047494,0.0000030902545,0.00043090817,0.018596236,0.000017364418,0.71828043,0.0020822953,0.20936838],"study_design_scores_gemma":[0.0001923575,0.000015376918,0.030590482,0.000044893703,0.00001554385,0.0000035493495,0.00010051814,0.1907251,0.0002375898,0.77323353,0.0047409707,0.00010004884],"about_ca_topic_score_codex":0.00003414041,"about_ca_topic_score_gemma":0.000023530647,"teacher_disagreement_score":0.47287238,"about_ca_system_score_codex":0.000027736389,"about_ca_system_score_gemma":0.00005367592,"threshold_uncertainty_score":0.3447989},"labels":[],"label_agreement":null},{"id":"W4414434407","doi":"10.1287/mnsc.2023.01659","title":"E-backtesting","year":2025,"lang":"en","type":"article","venue":"Management Science","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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 Waterloo","funders":"","keywords":"Risk measure; Measure (data warehouse); Basel II; Risk management; Expected shortfall; Identification (biology); Basel III; Market risk; Financial market","score_opus":0.06099032342716247,"score_gpt":0.4177832286398843,"score_spread":0.35679290521272183,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414434407","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":[0.019163962,0.000029181067,0.15886794,0.001395274,0.00065425073,0.00013911181,2.9942197e-7,0.000056963494,0.819693],"genre_scores_gemma":[0.93363416,0.000052097097,0.014457059,0.0007350251,0.000014838275,0.0000072151583,3.0170185e-7,0.0000018029233,0.05109751],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9979664,0.000025121786,0.00027057354,0.00043969386,0.00107556,0.00022265829],"domain_scores_gemma":[0.99907166,0.00012846709,0.000076685676,0.000535046,0.00014189693,0.000046217647],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0038630662,0.00005919677,0.000075452415,0.00072776614,0.0004247791,0.000555799,0.0012094275,0.000011610666,0.000099397614],"category_scores_gemma":[0.0008210024,0.000044315384,0.000026139716,0.0054419595,0.00024415748,0.00048204217,0.00038466885,0.000035675264,0.0003995718],"study_design_candidate":"design_other","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":[0.000003110268,0.00002230956,0.041832525,0.0000020074974,0.0000030109663,0.000006299795,0.000075162774,0.007079059,0.00010544695,0.26476717,0.020217348,0.6658866],"study_design_scores_gemma":[0.00033431902,0.000020113146,0.2811368,0.00003515779,0.000015689026,0.000001414305,0.0008860993,0.08255609,0.0009574586,0.2427782,0.39105237,0.0002262773],"about_ca_topic_score_codex":0.0000064014666,"about_ca_topic_score_gemma":0.0000019195118,"teacher_disagreement_score":0.9144702,"about_ca_system_score_codex":0.00003177048,"about_ca_system_score_gemma":0.000037010243,"threshold_uncertainty_score":0.5359583},"labels":[],"label_agreement":null},{"id":"W4414521604","doi":"10.1111/mafi.70007","title":"Risk Sharing, Measuring Variability, and Distortion Riskmetrics","year":2025,"lang":"en","type":"article","venue":"Mathematical Finance","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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; Memorial University of Newfoundland","funders":"","keywords":"Pairwise comparison; Distortion (music); Class (philosophy); Monotone polygon; Portfolio; Set (abstract data type)","score_opus":0.06025857197653624,"score_gpt":0.3462322986647374,"score_spread":0.2859737266882012,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414521604","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":[0.3277444,0.00011547116,0.6450853,0.00017415902,0.00014951703,0.00014485607,0.000005303494,0.000038154893,0.026542852],"genre_scores_gemma":[0.9777522,0.0005694741,0.013595501,0.00003815257,0.000022266711,0.000015341991,9.083878e-7,0.000005795656,0.008000373],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99820167,0.00009883713,0.00057283026,0.00043336282,0.0005264763,0.00016685393],"domain_scores_gemma":[0.9978917,0.0011577549,0.0001915113,0.00055762444,0.00015692024,0.00004447322],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0038952995,0.00010604837,0.0002505992,0.00020671863,0.00018667521,0.00019471423,0.0003328778,0.00008054702,0.000058290636],"category_scores_gemma":[0.013178319,0.00007039067,0.000057796064,0.001160198,0.000088978,0.00020159631,0.00019173062,0.00013852632,0.0001000313],"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":[0.000021371705,0.0001665303,0.29849857,0.00003052569,0.0000130443,0.0000027560907,0.00042420652,0.0018732713,0.000008951221,0.36131746,0.0025421295,0.3351012],"study_design_scores_gemma":[0.00013118627,0.000012172717,0.073134415,0.000029327543,0.000018389796,0.0000016982331,0.000016370144,0.07485525,0.00007253811,0.8438721,0.0077653644,0.00009119315],"about_ca_topic_score_codex":0.00001104379,"about_ca_topic_score_gemma":0.0000020244404,"teacher_disagreement_score":0.6500078,"about_ca_system_score_codex":0.000035756806,"about_ca_system_score_gemma":0.000024210038,"threshold_uncertainty_score":0.9951341},"labels":[],"label_agreement":null},{"id":"W4414592730","doi":"10.48550/arxiv.2509.09865","title":"Linear fractional relative risk aversion","year":2025,"lang":"en","type":"preprint","venue":"ArXiv.org","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Japan Society for the Promotion of Science; Social Sciences and Humanities Research Council of Canada","keywords":"Monopolistic competition; Constant (computer programming); Risk aversion (psychology); Aggregate (composite); Isoelastic utility; Marginal utility; Competition (biology); Measure (data warehouse)","score_opus":0.11917783337482041,"score_gpt":0.3950923346295649,"score_spread":0.2759145012547445,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414592730","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":[0.77760935,0.0005575895,0.15752259,0.0015661563,0.0058215912,0.00055158994,0.00037308218,0.00017465942,0.05582338],"genre_scores_gemma":[0.89584374,0.0055083,0.011734642,0.00047777596,0.00094215147,0.000037715807,0.00031257683,0.00002731881,0.08511578],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9963901,0.0004119431,0.0008091887,0.00095671986,0.0012003261,0.00023173893],"domain_scores_gemma":[0.9958991,0.0013965846,0.00092343934,0.0010134814,0.00064702425,0.00012037959],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0017442452,0.0002810705,0.00042331495,0.00050460134,0.000363994,0.00011423709,0.0007843846,0.00054849114,0.0012716268],"category_scores_gemma":[0.0037289062,0.00022845493,0.00033154982,0.0006945428,0.00009509208,0.00039409113,0.0008698424,0.0012145711,0.0028104659],"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":[0.00005804181,0.0000558168,0.89118487,0.000004051132,0.0000742156,0.000007959942,0.0003861794,0.077461846,0.0000056096387,0.0003516906,0.024711434,0.0056982753],"study_design_scores_gemma":[0.00047695398,0.000050363902,0.6098516,0.00011757896,0.00016060432,0.0000028128009,0.0002504253,0.049258534,0.00036388592,0.045982983,0.2929718,0.0005124949],"about_ca_topic_score_codex":0.00023448482,"about_ca_topic_score_gemma":0.000019914312,"teacher_disagreement_score":0.2813333,"about_ca_system_score_codex":0.00010383412,"about_ca_system_score_gemma":0.00035645976,"threshold_uncertainty_score":0.99964136},"labels":[],"label_agreement":null},{"id":"W4414638325","doi":"10.1007/978-3-032-05832-4_12","title":"Integrating AI and Social Responsibility in Portfolio Optimization: A Study with Evolutionary Algorithms","year":2025,"lang":"en","type":"book-chapter","venue":"Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"McGill University","funders":"","keywords":"Evolutionary algorithm; Portfolio; Evolutionary computation; Portfolio optimization; Computational finance; Randomness; Consistency (knowledge bases); Stability (learning theory); Application portfolio management","score_opus":0.03428737450944834,"score_gpt":0.30961911542660897,"score_spread":0.2753317409171606,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414638325","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":[0.0008336466,0.0002232493,0.9943546,0.00096462696,0.0002176766,0.0009115196,0.000067182955,0.0000275219,0.0023999796],"genre_scores_gemma":[0.04048816,0.00010095284,0.95886904,0.00016157347,0.00008417183,0.00003328163,0.0000444446,0.00001231039,0.00020607183],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980993,0.000042923224,0.0009710137,0.00023272123,0.00048595775,0.00016804477],"domain_scores_gemma":[0.99795103,0.0006236486,0.0005509198,0.00042488836,0.0004202865,0.000029222943],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013228277,0.0002440999,0.00044779546,0.000526206,0.00095855835,0.00027751038,0.00091695844,0.00018146881,0.0000022869185],"category_scores_gemma":[0.0002500141,0.0001705086,0.00009653031,0.00061374245,0.00046329727,0.00042853915,0.0005103578,0.0004157122,1.1157188e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012086211,0.000024959827,0.00034416217,0.000024427402,0.000032517273,1.541185e-7,0.0023349647,0.9704328,1.203718e-7,0.007429351,0.00007689592,0.019287564],"study_design_scores_gemma":[0.00034040975,0.000117839205,0.0009290152,0.00011061539,0.000037614125,0.0000066787934,0.00008863328,0.99297166,0.0000012698696,0.0015934722,0.0035890592,0.00021376168],"about_ca_topic_score_codex":0.000059342252,"about_ca_topic_score_gemma":0.00026581553,"teacher_disagreement_score":0.039654516,"about_ca_system_score_codex":0.00007196539,"about_ca_system_score_gemma":0.0003623399,"threshold_uncertainty_score":0.7372553},"labels":[],"label_agreement":null},{"id":"W4414683292","doi":"10.48550/arxiv.2506.19155","title":"Relative Explanations for Contextual Problems with Endogenous Uncertainty: An Application to Competitive Facility Location","year":2025,"lang":"en","type":"preprint","venue":"ArXiv.org","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Counterfactual thinking; Covariate; Computation; Minification; Facility location problem; Regularization (linguistics); Optimization problem; Work (physics)","score_opus":0.16589647401297833,"score_gpt":0.37087919964136284,"score_spread":0.20498272562838452,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414683292","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":[0.11105739,0.00008571467,0.8778062,0.001049702,0.00026863534,0.004426594,0.001488823,0.0001263645,0.0036905732],"genre_scores_gemma":[0.9879541,0.000061757826,0.0049479245,0.00029154864,0.00009806027,0.0022095123,0.0026639486,0.0000151704235,0.0017579803],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.99642146,0.0002989048,0.0009022308,0.0013149384,0.0007613435,0.00030114103],"domain_scores_gemma":[0.99412173,0.0010420375,0.00067232206,0.0011656061,0.0028120133,0.00018631233],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0015364282,0.00034816522,0.000525264,0.00044025606,0.00041475432,0.00019156895,0.0007715908,0.00028796063,0.00003486977],"category_scores_gemma":[0.0012481033,0.00028266697,0.00011112773,0.0009878682,0.00013110842,0.0004207062,0.00021741856,0.0003385724,0.00013714122],"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":[0.00038111024,0.0003161717,0.10879907,0.000049517083,0.00014315014,0.000001398506,0.010958598,0.8264743,0.00010626076,0.018150967,0.0007061191,0.033913318],"study_design_scores_gemma":[0.0065007657,0.0034222805,0.30228662,0.0010225022,0.0009785221,0.000027606126,0.029965209,0.23075135,0.0032186504,0.090170614,0.32702833,0.0046275402],"about_ca_topic_score_codex":0.0006340511,"about_ca_topic_score_gemma":0.0024408887,"teacher_disagreement_score":0.8768967,"about_ca_system_score_codex":0.00024588231,"about_ca_system_score_gemma":0.00063945045,"threshold_uncertainty_score":0.99996257},"labels":[],"label_agreement":null},{"id":"W4415009434","doi":"10.1515/dema-2025-0141","title":"Norm constrained empirical portfolio optimization with stochastic dominance: Robust optimization non-asymptotics","year":2025,"lang":"en","type":"article","venue":"Demonstratio Mathematica","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Queen's University","funders":"","keywords":"Stochastic dominance; Independent and identically distributed random variables; Robust optimization; Dual (grammatical number); Stochastic optimization; Optimization problem; Norm (philosophy); Random variable; Portfolio","score_opus":0.041905185787907334,"score_gpt":0.3390720348412047,"score_spread":0.2971668490532974,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415009434","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":[0.004383079,0.000038010206,0.9411365,0.00097367645,0.00026144003,0.000970039,0.000022311551,0.0001451881,0.052069787],"genre_scores_gemma":[0.478362,0.000068620815,0.5178145,0.00036193704,0.00008459141,0.00009720882,0.00009585896,0.000042513526,0.003072772],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9954906,0.00014766473,0.0016680926,0.00082473416,0.0013662066,0.00050271006],"domain_scores_gemma":[0.995975,0.0010610366,0.00075749017,0.00097526313,0.001021709,0.00020948845],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0014755081,0.0004507726,0.00074376684,0.00072132796,0.00047617164,0.00069921085,0.00065774354,0.0002900835,0.00074822054],"category_scores_gemma":[0.0016005973,0.00033867353,0.0001542216,0.0026846614,0.00042644228,0.0007785229,0.0000887768,0.00026627118,0.00009010493],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007957819,0.00019699604,0.0010042835,0.000016872998,0.000050673454,0.000011322996,0.0003143985,0.9846694,0.0000073256147,0.008280374,0.0034154116,0.001953366],"study_design_scores_gemma":[0.001102944,0.00012886256,0.0003210829,0.00014228963,0.00014702328,0.00006698482,0.0006790932,0.99036753,0.00008069766,0.0064559537,0.00012738902,0.0003801443],"about_ca_topic_score_codex":0.0000050747904,"about_ca_topic_score_gemma":0.000013304228,"teacher_disagreement_score":0.4739789,"about_ca_system_score_codex":0.00009347688,"about_ca_system_score_gemma":0.00077068893,"threshold_uncertainty_score":0.99990654},"labels":[],"label_agreement":null},{"id":"W4415181289","doi":"10.1080/10920277.2025.2558685","title":"Optimal Reinsurance Design under Ambiguity and Value-at-Risk Preference with Wasserstein and <i> L <sup>k</sup> </i> Distance Metrics","year":2025,"lang":"en","type":"article","venue":"North American Actuarial Journal","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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 Calgary","funders":"Humanities and Social Sciences Youth Foundation, Ministry of Education of the People's Republic of China; Natural Sciences and Engineering Research Council of Canada","keywords":"Ambiguity; Preference; Reinsurance; Measure (data warehouse); Key (lock)","score_opus":0.04200987717252893,"score_gpt":0.2986934481745265,"score_spread":0.25668357100199757,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415181289","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":[0.54998064,0.0002787548,0.4487537,0.00027004062,0.000110625755,0.00016522469,0.000023181043,0.000019690162,0.00039813877],"genre_scores_gemma":[0.954544,0.0046289093,0.03981456,0.0003468937,0.00012482279,0.0000066525263,0.0000038069256,0.000017404336,0.00051296805],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99617106,0.00068447035,0.0007729108,0.0006685013,0.0012412912,0.00046174487],"domain_scores_gemma":[0.9963552,0.0013724965,0.0009795728,0.00045961395,0.00046423092,0.0003689381],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019279955,0.0003150095,0.0006010297,0.00046097927,0.00084351393,0.00080539857,0.0005364359,0.00007196297,0.000027630274],"category_scores_gemma":[0.0014104958,0.00021705485,0.00007587107,0.0026569478,0.0006693867,0.0006465346,0.00015462251,0.00055679446,0.000006639801],"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":[0.0016323507,0.000058046197,0.4714198,0.0000031076072,0.000098911565,0.000028278972,0.0005764909,0.36627093,0.000012119632,0.00020244317,0.0021333774,0.15756413],"study_design_scores_gemma":[0.0039359545,0.0015161554,0.81911725,0.00007630346,0.000336258,0.0003135142,0.0017812573,0.15638639,0.00019688255,0.0027581789,0.012667819,0.00091407174],"about_ca_topic_score_codex":0.00025105767,"about_ca_topic_score_gemma":0.00024363771,"teacher_disagreement_score":0.40893915,"about_ca_system_score_codex":0.0001247242,"about_ca_system_score_gemma":0.0003837939,"threshold_uncertainty_score":0.88512397},"labels":[],"label_agreement":null},{"id":"W4415349339","doi":"10.1017/asb.2025.10074","title":"Worst-case distortion risk measures of transformed losses with uncertain distributions lying in Wasserstein balls","year":2025,"lang":"en","type":"article","venue":"Astin Bulletin","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Reinsurance; Distortion (music); Risk measure; Measure (data warehouse); Dimension (graph theory); Distribution (mathematics); Probability distribution","score_opus":0.053978469706911184,"score_gpt":0.3328406259281257,"score_spread":0.2788621562212145,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415349339","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":[0.4730144,0.00029459005,0.5207279,0.0016289207,0.00013652352,0.00037550897,0.00009524163,0.000035700865,0.0036912465],"genre_scores_gemma":[0.9955555,0.00024145147,0.0033581478,0.000029570956,0.000016936869,0.000035807552,0.000029818175,0.000008342415,0.0007243954],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9975818,0.0002861241,0.00083089963,0.00040136965,0.00063907716,0.00026073205],"domain_scores_gemma":[0.99787664,0.0010334074,0.00031940205,0.00037171747,0.00032800547,0.00007081707],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020579903,0.00016819764,0.00033155773,0.00039589382,0.00021266562,0.00008975004,0.00027758043,0.00009438512,0.00018477939],"category_scores_gemma":[0.002410991,0.00012414376,0.00009110643,0.0016441848,0.00015393205,0.00010798687,0.000029366149,0.00018205239,0.00002959128],"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":[0.0009901663,0.0004914083,0.58931255,0.000034942517,0.00009586405,0.00032804618,0.0017526789,0.09421676,0.00030643484,0.005658911,0.015094471,0.29171777],"study_design_scores_gemma":[0.011166639,0.0010299524,0.47600055,0.0017149084,0.0006559093,0.0003700909,0.018804662,0.024083337,0.01624322,0.02808511,0.41962978,0.0022158476],"about_ca_topic_score_codex":0.0013529791,"about_ca_topic_score_gemma":0.0021477456,"teacher_disagreement_score":0.5225411,"about_ca_system_score_codex":0.00008454036,"about_ca_system_score_gemma":0.00015126089,"threshold_uncertainty_score":0.5062435},"labels":[],"label_agreement":null},{"id":"W4415722129","doi":"10.3390/jrfm18110610","title":"From Penalties to Protection: The Continuous Time Sustainable Efficiency Frontier","year":2025,"lang":"en","type":"article","venue":"Journal of risk and financial management","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Efficient frontier; Portfolio; Portfolio optimization; Ambiguity; Equivalence (formal languages); Post-modern portfolio theory; Point (geometry); Robust optimization","score_opus":0.0092002990201594,"score_gpt":0.2644770743117687,"score_spread":0.2552767752916093,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415722129","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":[0.245738,0.0015580321,0.74178493,0.002025605,0.0009674082,0.00055325817,0.0000064052624,0.000009677758,0.007356673],"genre_scores_gemma":[0.94439876,0.0020812443,0.004161799,0.0005360772,0.00050192507,0.00001860994,6.5501575e-7,0.000007776799,0.048293144],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9984314,0.00012604763,0.00056063494,0.00017533971,0.000535143,0.00017142596],"domain_scores_gemma":[0.9988263,0.00015132812,0.0003558823,0.00022042697,0.00039363012,0.0000524168],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021734694,0.000096104435,0.0002468337,0.00038574036,0.00037789578,0.00031821392,0.0003907601,0.000040596045,0.00006672729],"category_scores_gemma":[0.0008600851,0.000054994955,0.000090195805,0.0007425505,0.000049205308,0.00020337051,0.00016247081,0.00014795439,0.000032638854],"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":[0.00041491637,0.00009234106,0.006398707,0.000005803555,0.000032778436,0.000062879015,0.0024718973,0.0051802485,0.000005794513,0.0061949766,0.11870139,0.8604383],"study_design_scores_gemma":[0.000473969,0.00014106347,0.062120106,0.00003004567,0.00007334763,0.000002995262,0.0035727315,0.00068673474,0.000032182837,0.053945217,0.8788365,0.000085091444],"about_ca_topic_score_codex":0.00014596354,"about_ca_topic_score_gemma":0.000008538859,"teacher_disagreement_score":0.8603532,"about_ca_system_score_codex":0.000032389977,"about_ca_system_score_gemma":0.00005287282,"threshold_uncertainty_score":0.30685443},"labels":[],"label_agreement":null},{"id":"W4415873354","doi":"10.1287/mnsc.2023.03895","title":"On Generalization and Regularization via Wasserstein Distributionally Robust Optimization","year":2025,"lang":"en","type":"article","venue":"Management Science","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"University of Ottawa","funders":"","keywords":"Regularization (linguistics); Curse of dimensionality; Robust optimization; Affine transformation; Equivalence (formal languages); Generalization; Regular polygon","score_opus":0.025212661970183885,"score_gpt":0.31449022122880493,"score_spread":0.28927755925862103,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415873354","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":[0.0053768717,0.000025537416,0.9619057,0.0016000052,0.00037983907,0.00031453426,0.0000037177285,0.00005216014,0.030341603],"genre_scores_gemma":[0.9317524,0.00040499933,0.048403442,0.0010890864,0.000036554287,0.0000346854,0.00011302547,0.000010072423,0.018155754],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99732614,0.00007934144,0.00041491602,0.0006768214,0.0012797425,0.00022302],"domain_scores_gemma":[0.9988526,0.000092556445,0.00017683841,0.00047425972,0.00033302334,0.00007071438],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022168937,0.00012581096,0.00012297322,0.00088072114,0.000668377,0.000671051,0.00054029905,0.0000452865,0.00007255037],"category_scores_gemma":[0.00050292286,0.00010573411,0.000028236314,0.0041482984,0.000249436,0.0008077544,0.00021289963,0.000046949604,0.000025003423],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010619647,0.000024381256,0.0015289219,0.0000022323532,0.0000035805667,8.6768495e-7,0.000018727229,0.7005857,0.000044793745,0.28017312,0.0015583073,0.0160487],"study_design_scores_gemma":[0.00027126787,0.000026927315,0.014812578,0.000019581563,0.000015939237,7.255204e-7,0.000060695438,0.9243174,0.00028008263,0.057805512,0.002263582,0.00012567644],"about_ca_topic_score_codex":0.000005886756,"about_ca_topic_score_gemma":0.0000028198479,"teacher_disagreement_score":0.9263755,"about_ca_system_score_codex":0.00010798298,"about_ca_system_score_gemma":0.000044310316,"threshold_uncertainty_score":0.647096},"labels":[],"label_agreement":null},{"id":"W4415944292","doi":"10.1111/mafi.70016","title":"Elicitability and Identifiability of Tail Risk Measures","year":2025,"lang":"en","type":"article","venue":"Mathematical Finance","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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 Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Identifiability; Tail risk; Expected shortfall; Quantile; Tail dependence; Class (philosophy); Quantile regression; Range (aeronautics); Joint probability distribution","score_opus":0.04206371272763961,"score_gpt":0.3628689958445079,"score_spread":0.3208052831168683,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415944292","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":[0.7963151,0.00025244564,0.19336368,0.0002654247,0.00007580055,0.00016421782,0.000012767857,0.000015299021,0.009535312],"genre_scores_gemma":[0.99010086,0.0003474354,0.008152045,0.000022206885,0.000006374264,0.000009791654,3.6022738e-7,0.0000026102011,0.0013583408],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9981495,0.00018001598,0.00071266165,0.00032406027,0.0005107112,0.0001230364],"domain_scores_gemma":[0.9973601,0.0014811298,0.00020087618,0.00063937873,0.00028848017,0.000030043033],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0037751591,0.00008644554,0.0003234594,0.00008275007,0.000086953456,0.00006157525,0.0002757904,0.00006464035,0.00006638165],"category_scores_gemma":[0.012535199,0.00005998664,0.00007305027,0.00059607136,0.00026915097,0.00014088908,0.00010078115,0.00008643915,0.000039297116],"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":[0.000066837114,0.00036504117,0.3156891,0.00007396987,0.000020610729,0.0000010596438,0.0009111455,0.000933154,0.0002271768,0.27435443,0.003343895,0.4040136],"study_design_scores_gemma":[0.000119918775,0.000013858609,0.15938847,0.000023065884,0.000013161574,5.695946e-7,0.000063106614,0.0098763,0.0011016668,0.82547176,0.00387104,0.000057063],"about_ca_topic_score_codex":0.000020624246,"about_ca_topic_score_gemma":0.000008689354,"teacher_disagreement_score":0.55111736,"about_ca_system_score_codex":0.000011020745,"about_ca_system_score_gemma":0.000039475624,"threshold_uncertainty_score":0.9957826},"labels":[],"label_agreement":null},{"id":"W4415962524","doi":"10.1287/mnsc.2024.06518","title":"Partial Law Invariance and Risk Measures","year":2025,"lang":"en","type":"article","venue":"Management Science","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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 Waterloo","funders":"","keywords":"Probabilistic logic; Representation (politics); Sophistication; Mathematical finance; Financial market; Risk management; Decision theory; Bridge (graph theory)","score_opus":0.04564881445061315,"score_gpt":0.35309521465352584,"score_spread":0.3074464002029127,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415962524","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":[0.06491818,0.00027020875,0.2928337,0.0018157939,0.0010778434,0.00039685253,0.0000037136092,0.00007712067,0.6386066],"genre_scores_gemma":[0.9872555,0.0013166429,0.004671605,0.0005920869,0.000017809625,0.00001114484,2.6307228e-7,0.000001970411,0.0061329342],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99784267,0.000072982766,0.0002700149,0.0005363581,0.0010562154,0.00022176116],"domain_scores_gemma":[0.9991112,0.00010781554,0.000106579486,0.00049641455,0.00011005237,0.00006796011],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00449506,0.000077635,0.00010377983,0.00031620666,0.00065221026,0.00070778653,0.0007673577,0.000019113504,0.000031080177],"category_scores_gemma":[0.00046023805,0.00005786699,0.000021882664,0.0021355434,0.00060403784,0.0006160528,0.00037223005,0.000054228043,0.000080138765],"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":[0.000011233335,0.000019724812,0.049668394,0.0000015334153,0.000007786133,0.0000038821636,0.00012841025,0.003279206,0.00003195893,0.65036625,0.004283844,0.29219776],"study_design_scores_gemma":[0.00041249685,0.000026307966,0.16872285,0.000018411925,0.000038227998,9.642298e-7,0.0003834229,0.036662225,0.00089372584,0.20337945,0.5892654,0.00019651075],"about_ca_topic_score_codex":0.00011198805,"about_ca_topic_score_gemma":0.000064894506,"teacher_disagreement_score":0.92233735,"about_ca_system_score_codex":0.000018969144,"about_ca_system_score_gemma":0.000027116703,"threshold_uncertainty_score":0.6825202},"labels":[],"label_agreement":null},{"id":"W4416011069","doi":"10.1287/mnsc.2023.03523","title":"Model Aggregation for Risk Evaluation and Robust Optimization","year":2025,"lang":"en","type":"article","venue":"Management Science","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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 Waterloo","funders":"","keywords":"Expected shortfall; Axiom; Equivalence (formal languages); Axiomatic system; Robust optimization; Value at risk; Risk management; Portfolio; Risk measure","score_opus":0.09926344080492287,"score_gpt":0.39835802755531274,"score_spread":0.29909458675038986,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416011069","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":[0.0097505925,0.000067642744,0.96484,0.0005673061,0.00027226674,0.0009067129,0.000004375337,0.00003022158,0.02356088],"genre_scores_gemma":[0.66660935,0.00079520146,0.3269757,0.0002489318,0.000019826857,0.00014900968,0.0000124567205,0.0000055782434,0.0051839612],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976429,0.000061938445,0.00034367305,0.00057610177,0.0011928035,0.00018261696],"domain_scores_gemma":[0.9986182,0.0001380064,0.00021231206,0.00038924898,0.00059621583,0.0000460077],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008335336,0.00008660083,0.00009798349,0.000765705,0.0006640925,0.0005519187,0.0004331483,0.000029984596,0.000020726473],"category_scores_gemma":[0.0014652781,0.00007173425,0.00002876588,0.0020914606,0.00017353523,0.0009384048,0.00014631382,0.000032828728,0.000006179865],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008055028,0.000009659876,0.0016762066,0.0000020215705,0.000002509544,3.6236482e-8,0.000058890535,0.76658815,0.000004582853,0.013475953,0.0010614195,0.21711254],"study_design_scores_gemma":[0.00035728666,0.000012103075,0.0038810533,0.0000095773285,0.000042965912,1.1166247e-7,0.00015709738,0.92595625,0.00005483065,0.06880048,0.00065678946,0.000071487666],"about_ca_topic_score_codex":0.000006540464,"about_ca_topic_score_gemma":0.0000074870472,"teacher_disagreement_score":0.65685874,"about_ca_system_score_codex":0.00007327785,"about_ca_system_score_gemma":0.00007427196,"threshold_uncertainty_score":0.5322165},"labels":[],"label_agreement":null},{"id":"W4416026126","doi":"10.1111/sjos.70033","title":"Estimation of generalized tail distortion risk measures with applications in reinsurance","year":2025,"lang":"en","type":"article","venue":"Scandinavian Journal of Statistics","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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 Waterloo; Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada; Society of Actuaries","keywords":"Reinsurance; Estimator; Measure (data warehouse); Distortion (music); Risk measure; Risk model; Estimation; Dynamic risk measure","score_opus":0.02587118153890233,"score_gpt":0.3376242466109248,"score_spread":0.3117530650720225,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416026126","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":[0.13001186,0.00029287513,0.86894464,0.00005861238,0.00011236577,0.0001323453,0.00010848831,0.000002941411,0.00033585491],"genre_scores_gemma":[0.913919,0.00083281024,0.08497332,0.000008533755,0.000017907341,0.0000046564155,0.000012017221,0.0000049384284,0.00022684856],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99788135,0.00017627377,0.0009985662,0.00013042273,0.0007102866,0.00010311305],"domain_scores_gemma":[0.997305,0.00035972524,0.0013057609,0.00021133522,0.0007624964,0.000055714176],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013443453,0.000090279136,0.00030280306,0.00049832073,0.000088074776,0.00005501864,0.0002446388,0.000042178497,0.000015169483],"category_scores_gemma":[0.00095303426,0.00006266407,0.000042329528,0.0010807613,0.00010594619,0.00021653029,0.000010835318,0.0001378653,0.0000017985393],"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":[0.0002373543,0.00007653793,0.47563422,0.000008832753,0.000025866007,0.0000063111183,0.00040208557,0.22004741,0.00003219298,0.010230103,0.0016529779,0.29164612],"study_design_scores_gemma":[0.0020237041,0.00030199185,0.79740584,0.0002459888,0.00013959437,0.000019041014,0.0005098243,0.042449553,0.00086087384,0.1532768,0.0025802378,0.00018656252],"about_ca_topic_score_codex":0.00005969025,"about_ca_topic_score_gemma":0.00009997926,"teacher_disagreement_score":0.78397137,"about_ca_system_score_codex":0.00007072045,"about_ca_system_score_gemma":0.0001648765,"threshold_uncertainty_score":0.25553665},"labels":[],"label_agreement":null},{"id":"W4416052765","doi":"10.48550/arxiv.2504.11412","title":"Measures of Variability for Risk-averse Policy Gradient","year":2025,"lang":"en","type":"preprint","venue":"ArXiv.org","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Alliance de recherche numérique du Canada; Government of Canada; Canadian Institute for Advanced Research","keywords":"CVAR; Standard deviation; Randomness; Gini coefficient; Quantile; Work (physics); Expected shortfall; Empirical research","score_opus":0.19636261645845485,"score_gpt":0.4111475165118763,"score_spread":0.21478490005342146,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416052765","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":[0.7722212,0.00020732675,0.20860194,0.0009007091,0.002573765,0.00150907,0.0011639011,0.00009120575,0.012730895],"genre_scores_gemma":[0.99031574,0.0011001369,0.0047202,0.00011527463,0.00030711695,0.00009449575,0.00005342099,0.000014322929,0.0032792762],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99597543,0.00064790726,0.0012307455,0.0009394459,0.0009026057,0.00030385228],"domain_scores_gemma":[0.993799,0.002027496,0.0011543212,0.0017800916,0.0011093193,0.00012978794],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.007407903,0.0002836026,0.0006916772,0.00059552793,0.00016497786,0.00007670966,0.0011387723,0.00037386335,0.00009141268],"category_scores_gemma":[0.022260249,0.00022478294,0.00051990367,0.00079349213,0.00015801268,0.00011221324,0.0006399526,0.00035882797,0.000042566193],"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":[0.00009793615,0.00013961416,0.93226373,0.0000426888,0.000086449625,6.5469715e-7,0.0007693375,0.027978033,0.000017059407,0.00277587,0.006364957,0.029463686],"study_design_scores_gemma":[0.0008114838,0.00010301793,0.65365225,0.000105437546,0.00026116837,0.0000010385447,0.00023694083,0.014033117,0.0015697578,0.22015572,0.108523734,0.00054632727],"about_ca_topic_score_codex":0.0015129362,"about_ca_topic_score_gemma":0.00012394282,"teacher_disagreement_score":0.27861145,"about_ca_system_score_codex":0.00011551727,"about_ca_system_score_gemma":0.0008099096,"threshold_uncertainty_score":0.9859757},"labels":[],"label_agreement":null},{"id":"W4416273650","doi":"10.1016/j.insmatheco.2025.103183","title":"A one-step approach for determining the optimal aggregate capital reserve and allocation","year":2025,"lang":"en","type":"article","venue":"Insurance Mathematics and Economics","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Actua; Royal Bank of Canada; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Capital allocation line; Aggregate (composite); Capital (architecture); Function (biology); Optimal allocation; Yield (engineering)","score_opus":0.06640886120401635,"score_gpt":0.3158241150233445,"score_spread":0.24941525381932816,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416273650","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":[0.88132936,0.00019061701,0.115818344,0.00034021045,0.000078308105,0.0003271706,0.00002171253,0.000008763895,0.0018855218],"genre_scores_gemma":[0.8913394,0.00084721565,0.10722881,0.00011474471,0.00003074199,0.000053099022,0.000007824483,0.000008610492,0.00036951771],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991011,0.000016421067,0.00043478195,0.00024289344,0.00007667302,0.00012814318],"domain_scores_gemma":[0.9988108,0.0005381631,0.00023116617,0.00027389475,0.00011281045,0.000033111948],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011083601,0.00009303677,0.00020702746,0.00008177605,0.00022887792,0.0003863483,0.00021586491,0.000057859015,0.0000015075601],"category_scores_gemma":[0.00039356115,0.000067948866,0.000040028004,0.00010006852,0.000087375396,0.00022220147,0.000075534066,0.000051175302,0.0000018261387],"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":[0.00017819925,0.00038458442,0.094012246,0.0002606245,0.0001991551,4.5708606e-7,0.013868896,0.06617504,0.000049265756,0.24330592,0.0016995905,0.579866],"study_design_scores_gemma":[0.00050919445,0.000031782874,0.011427591,0.000020245423,0.000016926022,0.0000024942115,0.0011323071,0.94623834,0.00006420823,0.039113756,0.0013245932,0.000118570075],"about_ca_topic_score_codex":0.000008245606,"about_ca_topic_score_gemma":0.000019092731,"teacher_disagreement_score":0.8800633,"about_ca_system_score_codex":0.000012165513,"about_ca_system_score_gemma":0.000028794588,"threshold_uncertainty_score":0.37255657},"labels":[],"label_agreement":null},{"id":"W4416398624","doi":"10.1016/j.ejor.2025.11.015","title":"Credit selection in collateralized loan obligation: Efficient approximation through linearization and clustering","year":2025,"lang":"en","type":"article","venue":"European Journal of Operational Research","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"HEC Montréal; Belgian Federal Science Policy Office; Fonds De La Recherche Scientifique - FNRS","keywords":"Loan; Cluster analysis; Optimization problem; Collateralized debt obligation; Securitization; Bailout; Piecewise linear function; Linearization","score_opus":0.14211217299464257,"score_gpt":0.4449334478302602,"score_spread":0.3028212748356176,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416398624","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":[0.56048024,0.00024026912,0.41925088,0.004914002,0.00037368128,0.0003620915,0.0000029944504,0.000009802778,0.01436605],"genre_scores_gemma":[0.9872796,0.00023370689,0.010810824,0.00009314826,0.00021501101,0.0000027240874,0.000008332972,0.000008913776,0.001347729],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.995246,0.0017999992,0.0010340408,0.00023349597,0.0015157437,0.00017073484],"domain_scores_gemma":[0.996807,0.0005467494,0.00023224678,0.00010654404,0.0022476043,0.000059867874],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.012664121,0.000086593274,0.0001767716,0.0010341074,0.00032706812,0.0007336109,0.00026888272,0.00003592637,0.00008780105],"category_scores_gemma":[0.0033025742,0.00006856942,0.00003871002,0.0021519596,0.00008579344,0.00066245045,0.00010090127,0.0003173701,0.00002472851],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004468536,0.0001425674,0.015700223,0.000012563294,0.000022109709,0.000028132841,0.0025659262,0.93791664,0.0052164495,0.012684103,0.0054303263,0.019834083],"study_design_scores_gemma":[0.0019876978,0.00026056104,0.11906256,0.0001608049,0.0000069842754,0.0000611548,0.0006008766,0.85824853,0.001426986,0.0051698782,0.012886403,0.0001275915],"about_ca_topic_score_codex":0.000013159862,"about_ca_topic_score_gemma":0.000012447948,"teacher_disagreement_score":0.4267994,"about_ca_system_score_codex":0.00011611036,"about_ca_system_score_gemma":0.0003005416,"threshold_uncertainty_score":0.7074227},"labels":[],"label_agreement":null},{"id":"W4416411423","doi":"10.1007/s00362-025-01775-x","title":"Generalized FGM dependence: geometrical representation and convex bounds on sums","year":2025,"lang":"en","type":"article","venue":"Statistical Papers","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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 Laval","funders":"Ministero dell’Istruzione, dell’Università e della Ricerca; Politecnico di Torino","keywords":"Bernoulli's principle; Regular polygon; Multivariate statistics; Independent and identically distributed random variables; Class (philosophy); Copula (linguistics); Representation (politics); Aggregate (composite); Bernoulli distribution","score_opus":0.05833186546979928,"score_gpt":0.4156883007651458,"score_spread":0.3573564352953465,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416411423","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":[0.29625195,0.00047112373,0.4114623,0.0042940006,0.0019353504,0.00084877963,0.00023722877,0.00015955376,0.2843397],"genre_scores_gemma":[0.9827697,0.0003585336,0.0076625445,0.001086886,0.000043210162,0.000014481311,0.000041503547,0.0000073985657,0.008015743],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.99762315,0.00021836045,0.0004937617,0.0005249033,0.00091861165,0.0002212146],"domain_scores_gemma":[0.99650156,0.0027954841,0.000091146256,0.00031271426,0.00014844717,0.00015065864],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0008124982,0.00012239408,0.00026143325,0.0003953621,0.000198354,0.00030030232,0.0002017051,0.00009720937,0.00093720894],"category_scores_gemma":[0.0068222326,0.00009154058,0.000043041815,0.0012485731,0.00022355332,0.00011933235,0.00006018694,0.00013805773,0.00013152033],"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":[0.00031782396,0.000087406755,0.046721816,0.000007039814,0.000047723654,0.000054870405,0.00018716493,0.0012886004,0.00030044199,0.675286,0.048151284,0.22754984],"study_design_scores_gemma":[0.0025978344,0.00035331983,0.5229049,0.000031105523,0.00009827961,0.000015428597,0.0007853569,0.044485603,0.00042566436,0.34638628,0.08139071,0.0005255525],"about_ca_topic_score_codex":0.00011049292,"about_ca_topic_score_gemma":0.000025541929,"teacher_disagreement_score":0.6865178,"about_ca_system_score_codex":0.00003464673,"about_ca_system_score_gemma":0.00007751874,"threshold_uncertainty_score":0.9999761},"labels":[],"label_agreement":null},{"id":"W4416436962","doi":"10.1016/j.jeconom.2025.106127","title":"Statistical inference for systemic risk-driven portfolio selection","year":2025,"lang":"en","type":"article","venue":"Journal of Econometrics","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Portfolio; Portfolio optimization; Post-modern portfolio theory; Nonparametric statistics; Selection (genetic algorithm); Benchmark (surveying); Expected shortfall; Replicating portfolio; Efficient frontier; Black–Litterman model","score_opus":0.16165597337704246,"score_gpt":0.3849055748599709,"score_spread":0.22324960148292844,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416436962","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":[0.12835898,0.00033090194,0.86841077,0.000121387115,0.00080910017,0.00013578705,0.000028028157,0.0000064913897,0.0017985911],"genre_scores_gemma":[0.97663575,0.0014907559,0.02063358,0.00007182024,0.00014175753,0.000003485819,0.0000027972021,0.000005674024,0.0010143796],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978956,0.0001001871,0.0013100189,0.00017577283,0.00036226414,0.00015615155],"domain_scores_gemma":[0.9933728,0.0036208776,0.0014743056,0.00015456007,0.0012748075,0.000102625374],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0031079522,0.00009050607,0.0003706015,0.0026616307,0.00011893858,0.0002466177,0.00037361393,0.00008998226,0.00015163826],"category_scores_gemma":[0.0118910475,0.000069640766,0.00014119617,0.0028148748,0.000028232293,0.0004267796,0.000024870897,0.00017437116,0.000022419421],"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":[0.0001279549,0.000099699675,0.70382375,0.000012977705,0.00009864292,0.000006360482,0.000100242054,0.088537835,0.000012922887,0.020511229,0.060231537,0.12643687],"study_design_scores_gemma":[0.003113085,0.0013256037,0.3085341,0.000111525405,0.00037682895,0.0003186561,0.0009299626,0.31983933,0.00023117177,0.1690661,0.19565639,0.00049723877],"about_ca_topic_score_codex":0.000008814189,"about_ca_topic_score_gemma":0.000005422463,"teacher_disagreement_score":0.8482768,"about_ca_system_score_codex":0.00012579952,"about_ca_system_score_gemma":0.00035060325,"threshold_uncertainty_score":0.9964322},"labels":[],"label_agreement":null},{"id":"W4416767403","doi":"10.1002/cfp2.70018","title":"A Hybrid Lifecycle Net Worth Optimization Model","year":2025,"lang":"en","type":"article","venue":"Financial Planning Review","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Probity Medical Research","funders":"","keywords":"Portfolio; Financial modeling; Portfolio optimization; System lifecycle; Net (polyhedron); Net present value; Advice (programming); Financial services","score_opus":0.06375020386347784,"score_gpt":0.38886411381591135,"score_spread":0.3251139099524335,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416767403","genre_codex":"methods","genre_gemma":"review","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":[0.0017784003,0.05898923,0.9184084,0.0011014988,0.00050244515,0.00047509518,0.000024125471,0.00009036319,0.01863043],"genre_scores_gemma":[0.27880198,0.34236544,0.23494583,0.063519254,0.0013277823,0.0005140257,0.0009666389,0.00015001991,0.07740902],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979056,0.000105622574,0.0007946576,0.00042377182,0.0005352666,0.00023509041],"domain_scores_gemma":[0.9986814,0.00019479722,0.0003024739,0.00049070985,0.00025980678,0.0000708136],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015269127,0.0001622148,0.00045651427,0.00021753479,0.00020684313,0.00013710033,0.0004654814,0.000062299885,0.00011219407],"category_scores_gemma":[0.0037244894,0.00013048881,0.00013647084,0.001121362,0.000030682048,0.00032236762,0.00009132864,0.00014484453,0.00012489963],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009101311,0.000015148493,0.0026705984,0.000045171546,0.0000027136514,0.000005742873,0.00002219047,0.7464163,3.5201003e-7,0.0007914736,0.1983332,0.051688023],"study_design_scores_gemma":[0.00018293102,0.00001877797,0.002021351,0.0020622322,0.000045346573,0.0000047385347,0.0000027882038,0.74531555,0.0000058957235,0.0059754476,0.24416691,0.00019803121],"about_ca_topic_score_codex":0.000008075665,"about_ca_topic_score_gemma":0.0000014487931,"teacher_disagreement_score":0.68346256,"about_ca_system_score_codex":0.000025539232,"about_ca_system_score_gemma":0.00029468237,"threshold_uncertainty_score":0.53211796},"labels":[],"label_agreement":null},{"id":"W4416770164","doi":"10.48550/arxiv.2511.20126","title":"Scaling limits of multi-period distributionally robust optimization problems","year":2025,"lang":"","type":"preprint","venue":"arXiv (Cornell University)","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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; Ministry of Education, India; National Research Foundation of Korea; Nanyang Technological University; National Research Foundation","keywords":"Scaling; Semigroup; Parametric statistics; Monotone polygon; Optimization problem; Limit (mathematics); Scaling limit; Robust optimization; Robustness (evolution)","score_opus":0.1786755741143489,"score_gpt":0.2573033714947035,"score_spread":0.07862779738035461,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416770164","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":[0.025467478,0.00035485195,0.96676266,0.00014309901,0.0015757659,0.0011430744,0.00081403856,0.000096862546,0.0036421935],"genre_scores_gemma":[0.95632106,0.009731013,0.020028371,0.00003855172,0.000101908,0.0000034557095,0.0006388524,0.000031667063,0.013105105],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.993768,0.0006768933,0.0017924949,0.002422305,0.0006556382,0.00068468944],"domain_scores_gemma":[0.9918158,0.0007329395,0.00224073,0.0017246717,0.0031129774,0.00037289085],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0019696485,0.0007725689,0.0011828991,0.0012019615,0.0007104156,0.0003374137,0.0021534942,0.0010257674,0.0007480609],"category_scores_gemma":[0.00150882,0.0008569549,0.0007628027,0.004394813,0.0006094707,0.0009568175,0.0012310938,0.00080364075,0.000084248335],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019992546,0.0005218323,0.011387493,0.00010298119,0.00018264484,0.000036008805,0.00044190531,0.9728465,0.000013946564,0.011742557,0.00017457955,0.0023496216],"study_design_scores_gemma":[0.0014588508,0.00007813697,0.0013949103,0.00058217393,0.00038306598,0.000004755467,0.0006711792,0.9911442,0.00013851054,0.0023813786,0.0010208162,0.00074202666],"about_ca_topic_score_codex":0.00021111578,"about_ca_topic_score_gemma":0.000061902254,"teacher_disagreement_score":0.94673425,"about_ca_system_score_codex":0.00042056793,"about_ca_system_score_gemma":0.0014738141,"threshold_uncertainty_score":0.9993881},"labels":[],"label_agreement":null},{"id":"W4416892297","doi":"10.1016/j.jet.2025.106125","title":"Optimal screening with securities","year":2025,"lang":"en","type":"article","venue":"Journal of Economic Theory","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Agencia Nacional de Investigación y Desarrollo","keywords":"Issuer; Debt; Private information retrieval; Private placement; Asset (computer security); Information asymmetry; Trading strategy; Economic rent","score_opus":0.030458987843079127,"score_gpt":0.3388775844214065,"score_spread":0.3084185965783274,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416892297","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":[0.73476684,0.0009544082,0.21786392,0.00056772673,0.0006525185,0.000048040176,0.0000039380525,0.000007279264,0.04513532],"genre_scores_gemma":[0.9824835,0.0002292864,0.01064074,0.00020644312,0.00016842895,5.3406166e-7,3.1374805e-7,0.000005559892,0.0062651737],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9989443,0.00012157898,0.0005853528,0.00010179475,0.00014753835,0.00009948226],"domain_scores_gemma":[0.99843675,0.00070878386,0.0005059897,0.00016734793,0.00013361701,0.000047542057],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0031063708,0.00006901611,0.00022581818,0.00037272534,0.00007536282,0.0001689434,0.0003658449,0.00003552138,0.00041850613],"category_scores_gemma":[0.00021930443,0.000044585504,0.00009536411,0.00011130697,0.00007879261,0.00047804072,0.000033668177,0.00011645547,0.000026558138],"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":[0.0010063038,0.00003557584,0.025328077,0.00000264747,0.00019280636,0.000032052278,0.0011680919,0.508597,0.000019534591,0.29998085,0.026059993,0.13757704],"study_design_scores_gemma":[0.0035472687,0.0005655011,0.02574194,0.00027825098,0.00016094487,0.00047393105,0.016327722,0.019299828,0.0016466378,0.66270566,0.26877165,0.00048070398],"about_ca_topic_score_codex":0.000002081119,"about_ca_topic_score_gemma":0.0000035143219,"teacher_disagreement_score":0.4892972,"about_ca_system_score_codex":0.000030566902,"about_ca_system_score_gemma":0.00017104269,"threshold_uncertainty_score":0.45823488},"labels":[],"label_agreement":null},{"id":"W4416940087","doi":"10.1007/s00245-025-10352-5","title":"A Parametric Approach to the Estimation of Convex Risk Functionals Based on Wasserstein Distance","year":2025,"lang":"en","type":"article","venue":"Applied Mathematics & Optimization","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Deutsche Forschungsgemeinschaft","keywords":"Parametric statistics; Nonparametric statistics; Probabilistic logic; Context (archaeology); Martingale (probability theory); Regular polygon; Margin (machine learning); Probability distribution; Parametrization (atmospheric modeling)","score_opus":0.032649893277587876,"score_gpt":0.3160187944073,"score_spread":0.28336890112971214,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416940087","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":[0.0011305514,0.00002571192,0.95222455,0.0003808106,0.00014801304,0.0010766394,0.000025441177,0.00005209125,0.044936173],"genre_scores_gemma":[0.5501063,0.000036073903,0.44884703,0.0002911495,0.000017740316,0.00016435992,0.000059107915,0.00001547177,0.00046277873],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99700904,0.00010827691,0.0010126462,0.00045740473,0.0012184243,0.0001942197],"domain_scores_gemma":[0.9957271,0.0020471024,0.0007761535,0.00097579043,0.0004099956,0.00006382616],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026628375,0.00021058894,0.0003918007,0.0007527765,0.0002713721,0.00019458485,0.0005565971,0.00011806058,0.000060280036],"category_scores_gemma":[0.002705275,0.00014052696,0.00010297612,0.0042953957,0.00007175452,0.00012560471,0.000055930548,0.00013757078,0.00006754604],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005463361,0.00024323729,0.00007677764,0.000017711847,0.000016014119,4.549608e-8,0.00024778428,0.9141314,0.000004393495,0.074316695,0.0029550449,0.007936266],"study_design_scores_gemma":[0.00037256026,0.000035047065,0.00020093216,0.00002809286,0.0000629082,2.5693868e-7,0.00038457877,0.9796323,0.0002694963,0.01813185,0.00075227267,0.00012966363],"about_ca_topic_score_codex":0.0000067200344,"about_ca_topic_score_gemma":0.0000016694128,"teacher_disagreement_score":0.54897577,"about_ca_system_score_codex":0.00006367538,"about_ca_system_score_gemma":0.00010789908,"threshold_uncertainty_score":0.5730523},"labels":[],"label_agreement":null},{"id":"W4416976827","doi":"10.1017/s0022109025102457","title":"Optimal Portfolio Size Under Parameter Uncertainty","year":2025,"lang":"en","type":"article","venue":"Journal of Financial and Quantitative Analysis","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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","funders":"","keywords":"Portfolio; Portfolio optimization; Robustness (evolution); Limiting; Sample size determination; Dimension (graph theory); Flexibility (engineering); Post-modern portfolio theory","score_opus":0.049671518993620475,"score_gpt":0.3819882875345286,"score_spread":0.33231676854090814,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416976827","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":[0.75623024,0.0008473119,0.24057095,0.0008915073,0.00020044988,0.000040772175,0.000009470227,0.0000033460562,0.0012059325],"genre_scores_gemma":[0.9812161,0.0010034796,0.015003259,0.0005629558,0.000052087275,0.00000103654,0.0000016476704,0.0000033075958,0.0021561722],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9977173,0.00020440112,0.0010400715,0.00023156447,0.00064209953,0.00016452154],"domain_scores_gemma":[0.9957505,0.002043201,0.0008091803,0.00018174486,0.0011221594,0.000093231494],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021534134,0.00013316875,0.00065972086,0.0010917429,0.00016288564,0.0001803155,0.00026631536,0.00008608273,0.00020554781],"category_scores_gemma":[0.0052094767,0.00008696761,0.0004902088,0.003663813,0.00013778872,0.00039194396,0.000047082154,0.00017506992,0.000008011344],"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":[0.0011565484,0.0003183659,0.24103731,0.0000067730434,0.0023998774,0.00011372032,0.0015549944,0.5559146,0.00025215087,0.101317264,0.027466418,0.068462014],"study_design_scores_gemma":[0.0009624456,0.0004814667,0.831576,0.000034361823,0.0022214055,0.000013025153,0.0019017135,0.04864283,0.00013885299,0.09223174,0.021505218,0.0002909622],"about_ca_topic_score_codex":0.00006376976,"about_ca_topic_score_gemma":0.00008547091,"teacher_disagreement_score":0.5905387,"about_ca_system_score_codex":0.000025822497,"about_ca_system_score_gemma":0.00022797234,"threshold_uncertainty_score":0.6236606},"labels":[],"label_agreement":null},{"id":"W4416981161","doi":"10.1002/cfp2.70019","title":"Solving the Net Worth Optimization Problem","year":2025,"lang":"en","type":"article","venue":"Financial Planning Review","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Probity Medical Research","funders":"","keywords":"Portfolio; Portfolio optimization; Balance sheet; Asset (computer security); Quadratic programming; Quadratic equation; Net worth; Asset allocation; Capital asset; Capital asset pricing model","score_opus":0.05402477124959699,"score_gpt":0.37992673543877625,"score_spread":0.32590196418917927,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416981161","genre_codex":"methods","genre_gemma":"review","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":[0.00073294155,0.26889843,0.6331533,0.0086750835,0.001400944,0.0015873517,0.000012948188,0.00014944024,0.08538955],"genre_scores_gemma":[0.1376388,0.5096286,0.171608,0.08113696,0.002395877,0.0009195526,0.0005136536,0.00014191076,0.096016616],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9981014,0.00016059319,0.0007150626,0.00031878127,0.0004936646,0.00021049018],"domain_scores_gemma":[0.99850845,0.00042142608,0.00032797919,0.00046153364,0.0002451743,0.000035426103],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002637893,0.0001324725,0.00032163624,0.00012283152,0.00038067985,0.00020379067,0.0005761401,0.00006474079,0.00018042432],"category_scores_gemma":[0.003446472,0.00007837257,0.000107946034,0.0018029895,0.000041812065,0.00024616267,0.000097829194,0.00016541859,0.00007381297],"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":[0.000009840048,0.000016771284,0.015429859,0.000087906425,0.000006503084,0.000005755809,0.00015267917,0.24833533,6.8620835e-7,0.004269888,0.47833216,0.2533526],"study_design_scores_gemma":[0.0001066509,0.00001681752,0.008609581,0.0027377233,0.000042221134,0.000004186687,0.000014145131,0.018122558,0.0000028949314,0.003478629,0.9667257,0.0001388861],"about_ca_topic_score_codex":0.000012610727,"about_ca_topic_score_gemma":0.0000051502207,"teacher_disagreement_score":0.48839355,"about_ca_system_score_codex":0.000019774983,"about_ca_system_score_gemma":0.00020827162,"threshold_uncertainty_score":0.4125998},"labels":[],"label_agreement":null},{"id":"W4417252829","doi":"10.48550/arxiv.2506.06776","title":"Testing the Solvability of Systems of Linear Inequalities","year":2025,"lang":"en","type":"preprint","venue":"ArXiv.org","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Social Sciences and Humanities Research Council of Canada","keywords":"Pointwise; Inference; Fiducial inference; Constraint (computer-aided design); Set (abstract data type); Function (biology); Value (mathematics); Moment (physics)","score_opus":0.30916730516276647,"score_gpt":0.40783691001843325,"score_spread":0.09866960485566678,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417252829","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":[0.98601836,0.00077997195,0.0050158,0.00012776948,0.0012823859,0.00041314453,0.00013865085,0.000031465053,0.006192423],"genre_scores_gemma":[0.99702114,0.00012709438,0.00097192975,0.000026485099,0.00014396835,0.000022882476,0.000012853721,0.0000071144,0.0016665285],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99629134,0.0005594023,0.0016445782,0.00045463364,0.0008873395,0.00016268752],"domain_scores_gemma":[0.9916439,0.004061646,0.0013966248,0.0015320261,0.00132862,0.000037188456],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0052359393,0.00018364524,0.00063943095,0.00019492304,0.000107244276,0.000054844826,0.0011419796,0.00021560573,0.0000334318],"category_scores_gemma":[0.011492306,0.00011090505,0.00018909173,0.0007124794,0.00023447665,0.000086557884,0.0008113557,0.00032329097,0.000014778048],"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":[0.000010862194,0.000031658983,0.9204149,0.00012270639,0.000024768082,3.9724154e-7,0.0010516795,0.07604713,0.000061504135,0.0005017757,0.00039587083,0.0013367416],"study_design_scores_gemma":[0.00024636756,0.00008096066,0.91245544,0.0006956328,0.000109526016,0.0000018829322,0.004011186,0.06712578,0.002004698,0.0091226725,0.0038029368,0.00034290098],"about_ca_topic_score_codex":0.0019972834,"about_ca_topic_score_gemma":0.00002609978,"teacher_disagreement_score":0.011002754,"about_ca_system_score_codex":0.000023075447,"about_ca_system_score_gemma":0.00037477651,"threshold_uncertainty_score":0.99683434},"labels":[],"label_agreement":null},{"id":"W4417272590","doi":"10.28924/2291-8639-23-2025-332","title":"TVaR-Based Capital Allocation under Liouville Copulas","year":2025,"lang":"","type":"article","venue":"International Journal of Analysis and Applications","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Solvency; Parametric statistics; Aggregate (composite); Generator (circuit theory); Multivariate statistics; Class (philosophy); Capital (architecture); Distribution (mathematics)","score_opus":0.01803194642821497,"score_gpt":0.3688293376915387,"score_spread":0.3507973912633237,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417272590","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":[0.041239407,0.0020225528,0.9408135,0.011037581,0.0006296269,0.00021994262,0.000070420625,0.000009173307,0.0039577866],"genre_scores_gemma":[0.9885734,0.004580241,0.0034705717,0.0008271416,0.0003412785,0.000019673813,0.000065944245,0.000009047894,0.002112748],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9958169,0.00016735995,0.0019323305,0.00043115337,0.0014662564,0.00018599174],"domain_scores_gemma":[0.9932821,0.00062139623,0.0016462406,0.00042716746,0.0038387796,0.0001843338],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015558612,0.00022954737,0.00056212745,0.0031396658,0.0002918507,0.000826404,0.0010985931,0.00016091454,0.00052773027],"category_scores_gemma":[0.00024090955,0.00019576217,0.0006380835,0.003622065,0.00021671952,0.00049224705,0.00011110631,0.0002562225,0.000034939058],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00028973454,0.0013985499,0.08585919,0.000010835431,0.009315446,0.00001366787,0.0004833955,0.47510034,0.0006671435,0.15979408,0.005717266,0.26135036],"study_design_scores_gemma":[0.004427504,0.00031602322,0.22019109,0.00024292457,0.01182747,0.000057180114,0.0063428045,0.33672708,0.0028057972,0.08804196,0.32797828,0.0010418636],"about_ca_topic_score_codex":0.00012571368,"about_ca_topic_score_gemma":0.00013402765,"teacher_disagreement_score":0.94733393,"about_ca_system_score_codex":0.00015137375,"about_ca_system_score_gemma":0.0005954127,"threshold_uncertainty_score":0.79829496},"labels":[],"label_agreement":null},{"id":"W4417289907","doi":"10.48550/arxiv.2512.09208","title":"Risk-insurance parity","year":2025,"lang":"","type":"preprint","venue":"ArXiv.org","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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; Canada Research Chairs","keywords":"Indemnity; Parity (physics); Risk aversion (psychology); Insurance policy; Property insurance; Underwriting","score_opus":0.08770740902810224,"score_gpt":0.366270454743612,"score_spread":0.2785630457155097,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417289907","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":[0.90853024,0.003296791,0.031919237,0.0010346652,0.010283985,0.0010951811,0.00076133996,0.00018885259,0.04288971],"genre_scores_gemma":[0.8966719,0.0681258,0.0022444658,0.00048270714,0.00061064423,0.00008337439,0.000078728095,0.00003385098,0.03166852],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9896321,0.0014079595,0.0028739912,0.002962036,0.0021193807,0.0010044872],"domain_scores_gemma":[0.98960066,0.0019489053,0.002653439,0.0040031835,0.0013238085,0.00046998967],"candidate_categories":["metaepi_narrow","research_integrity","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.005368426,0.0009889128,0.001561511,0.00071473775,0.0012026484,0.0007043203,0.0030248126,0.0012782685,0.0019094361],"category_scores_gemma":[0.0062120813,0.0008891753,0.0009296459,0.0025035357,0.00050729496,0.00069251296,0.0021834986,0.0023790218,0.0049973046],"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":[0.00010517477,0.00020121253,0.89316905,0.000029802553,0.00011281741,0.00001907422,0.0006431085,0.024098385,0.0000059260965,0.00019992163,0.0070207897,0.07439472],"study_design_scores_gemma":[0.0005484096,0.00005281085,0.84153557,0.00020713553,0.00014680508,0.0000038349403,0.00015774404,0.010145872,0.0003284988,0.0062982454,0.13979702,0.0007780917],"about_ca_topic_score_codex":0.0015003815,"about_ca_topic_score_gemma":0.0002437122,"teacher_disagreement_score":0.13277622,"about_ca_system_score_codex":0.00016383157,"about_ca_system_score_gemma":0.0010731785,"threshold_uncertainty_score":0.9999225},"labels":[],"label_agreement":null},{"id":"W4417422057","doi":"10.5267/j.dsl.2025.9.002","title":"An empirical application of Markowitz mean-variance theory in evaluating portfolio performance: Evidence from Vietnam’s VN30 equity constituents","year":2025,"lang":"en","type":"article","venue":"Decision Science Letters","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Sharpe ratio; Portfolio; Portfolio optimization; Investment strategy; Equity (law); Post-modern portfolio theory; Constraint (computer-aided design); Efficient frontier; Stock (firearms)","score_opus":0.1110730965420253,"score_gpt":0.48559629104752644,"score_spread":0.37452319450550114,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417422057","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":[0.6188031,0.00009988548,0.37903252,0.00046311674,0.00047191398,0.0003172114,0.000006882701,0.000021481308,0.00078387494],"genre_scores_gemma":[0.9700227,0.00014096934,0.027629288,0.0020721855,0.000044032306,0.000035886053,0.000005720686,0.000007672237,0.0000415939],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99130553,0.00065816665,0.0017375695,0.0014489071,0.0043319166,0.00051788095],"domain_scores_gemma":[0.992587,0.003994539,0.00072232005,0.0017667022,0.0007393111,0.00019017328],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.029186508,0.00024306083,0.00049894175,0.0014108116,0.00048137596,0.00044431424,0.003083778,0.00012671803,0.0001717415],"category_scores_gemma":[0.010085314,0.0001911097,0.00010452501,0.0068294955,0.0011191636,0.0029231117,0.00054781104,0.00032288718,0.00008865021],"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":[0.00024016008,0.00007117546,0.46033272,0.0000025346292,0.0000032997275,0.0000032980056,0.00065365114,0.02995167,0.036899418,0.00036874585,0.0007683068,0.470705],"study_design_scores_gemma":[0.0005429943,0.0000669638,0.6545117,0.00025670452,0.000017400713,0.0000028483091,0.00043886175,0.32346478,0.0039961617,0.016188672,0.00027519997,0.00023771329],"about_ca_topic_score_codex":0.00010628507,"about_ca_topic_score_gemma":0.00003593438,"teacher_disagreement_score":0.4704673,"about_ca_system_score_codex":0.00022356672,"about_ca_system_score_gemma":0.00065238215,"threshold_uncertainty_score":0.9996568},"labels":[],"label_agreement":null},{"id":"W4417465829","doi":"10.1016/j.insmatheco.2025.103201","title":"The ultimate drawdown insurance and its state-dependent premium","year":2025,"lang":"en","type":"article","venue":"Insurance Mathematics and Economics","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada; Western University","keywords":"Drawdown (hydrology); Liquidity premium; Reinsurance; Constant (computer programming); Risk premium; Surrender; Insurance policy","score_opus":0.027639689939751664,"score_gpt":0.3043641314776989,"score_spread":0.27672444153794723,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417465829","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":[0.9877631,0.0015760631,0.00224217,0.0005908994,0.00038557313,0.0002637446,0.00006410768,0.000023301005,0.007091014],"genre_scores_gemma":[0.9775312,0.01801663,0.0015541246,0.000121173594,0.00002022651,0.000019889105,0.0000011984978,0.00001050621,0.0027250277],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985947,0.00002968482,0.0006928485,0.00031644944,0.00014828017,0.00021803564],"domain_scores_gemma":[0.99828297,0.0008255845,0.00030321663,0.0003862561,0.00013564184,0.000066323555],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014071713,0.00015184336,0.00027945475,0.000089113666,0.00041793846,0.0006488946,0.00033686354,0.000060446375,0.000006215174],"category_scores_gemma":[0.0004393278,0.000100155485,0.00003943599,0.00016126475,0.0001030273,0.0002941417,0.0001315883,0.00009900576,0.000029230014],"study_design_candidate":"design_other","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":[0.0001916035,0.0002338791,0.19580773,0.00017497056,0.00019252526,0.0000069392236,0.0071395515,0.029792206,0.00023831596,0.14999259,0.0028967066,0.613333],"study_design_scores_gemma":[0.0012246583,0.00005038124,0.10308525,0.000100766774,0.000019042898,0.000029905643,0.0009812341,0.43868202,0.0014554766,0.4273633,0.026504206,0.0005037652],"about_ca_topic_score_codex":0.000011253574,"about_ca_topic_score_gemma":0.000075655415,"teacher_disagreement_score":0.6128292,"about_ca_system_score_codex":0.000019756617,"about_ca_system_score_gemma":0.00004600404,"threshold_uncertainty_score":0.6257306},"labels":[],"label_agreement":null},{"id":"W4417497027","doi":"10.3390/jrfm19010004","title":"Robust Multi-Objective Optimization Model for Reserve and Credit Fund Allocation in Banking Under Conditional Value-at-Risk Constraints","year":2025,"lang":"en","type":"article","venue":"Journal of risk and financial management","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Direktorat Riset dan Pengabdian Masyarakat; Universitas Padjadjaran; Lembaga Pengelola Dana Pendidikan; Ministrstvo za visoko šolstvo, znanost in tehnologijo","keywords":"CVAR; Profitability index; Credit risk; Context (archaeology); Optimization problem; Asset allocation","score_opus":0.06753357054351906,"score_gpt":0.3299451361276101,"score_spread":0.26241156558409107,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417497027","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":[0.03471158,0.0004103126,0.96359736,0.00018078585,0.00025723086,0.0003541235,0.000050183542,0.0000043007954,0.00043411902],"genre_scores_gemma":[0.8795372,0.005770165,0.1140915,0.00011833625,0.000073571086,0.000015523547,0.000016641508,0.0000071717827,0.0003698566],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982754,0.00013171631,0.000745419,0.00026946882,0.0004228465,0.00015512973],"domain_scores_gemma":[0.99831694,0.00043123512,0.0006618225,0.0001160379,0.00041952234,0.000054438322],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024589596,0.00012300684,0.0002606558,0.0006840917,0.0003003654,0.00013600191,0.00014832246,0.000092720904,0.000013369423],"category_scores_gemma":[0.0008090658,0.000104598694,0.00007130941,0.00043691095,0.00011473733,0.00040874034,0.000094815536,0.00015074549,5.580073e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023219806,0.000060924976,0.017999055,0.000007775686,0.000019323494,0.0000033033873,0.00042685558,0.92748773,8.400035e-7,0.016662842,0.00054431136,0.03655487],"study_design_scores_gemma":[0.0019748716,0.000054970307,0.10993822,0.000048401296,0.00007883876,0.0000035800854,0.00066204,0.8244268,0.0000034162044,0.06224991,0.0004682538,0.000090708716],"about_ca_topic_score_codex":0.0000260353,"about_ca_topic_score_gemma":0.00016851941,"teacher_disagreement_score":0.84950584,"about_ca_system_score_codex":0.00010312565,"about_ca_system_score_gemma":0.0000790696,"threshold_uncertainty_score":0.4265411},"labels":[],"label_agreement":null},{"id":"W48523452","doi":"10.1023/a:1004649211111","title":"Convergence Properties of Two-Stage Stochastic Programming","year":2000,"lang":"en","type":"article","venue":"Journal of Optimization Theory and Applications","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Science North","funders":"","keywords":"Mathematics; Proofs of convergence of random variables; Convergence (economics); Mathematical optimization; Stochastic programming; Exponential function; Rate of convergence; Theory of computation; Convergence tests; Compact convergence; Applied mathematics; Stochastic optimization; Normal convergence; Convergence of random variables; Quadratic programming; Random variable; Computer science; Algorithm; Statistics; Algebra of random variables; Mathematical analysis","score_opus":0.04315506297365662,"score_gpt":0.3381747733330435,"score_spread":0.2950197103593869,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W48523452","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":[0.037820008,0.0005703586,0.9602161,0.00010958962,0.000042210417,0.00025724468,0.000005084446,0.000009831268,0.0009695686],"genre_scores_gemma":[0.97723764,0.0006878833,0.02001929,0.000047049507,0.00008160818,0.000019850238,0.0000023960172,0.000008805656,0.0018954491],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998341,0.00018622879,0.00081998087,0.00014034295,0.00041570197,0.000096708696],"domain_scores_gemma":[0.9981724,0.00025516748,0.0006793473,0.00020845188,0.0005981524,0.0000864726],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020876655,0.000086761116,0.00021085056,0.00018923842,0.00015757271,0.00008639016,0.0002686349,0.00003966622,0.0007494816],"category_scores_gemma":[0.00028594868,0.000060936447,0.00006733108,0.00059188105,0.00017646923,0.0004421014,0.00001727095,0.00008602375,0.000010382226],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009355441,0.00006722574,0.00017839661,0.0000036612046,0.00001283512,3.3019623e-7,0.00037904704,0.9017427,0.00016515523,0.028956398,0.000028799685,0.06837187],"study_design_scores_gemma":[0.005121205,0.0008778351,0.0009654233,0.0003604628,0.0005026456,0.00044194123,0.010161635,0.7562362,0.009400138,0.1445143,0.0702021,0.0012161395],"about_ca_topic_score_codex":0.0000017243618,"about_ca_topic_score_gemma":4.0216216e-7,"teacher_disagreement_score":0.9401968,"about_ca_system_score_codex":0.000008444211,"about_ca_system_score_gemma":0.00006995024,"threshold_uncertainty_score":0.82062984},"labels":[],"label_agreement":null},{"id":"W48561159","doi":"10.69645/ykis3274","title":"Risk decomposition (and risk budgeting)","year":2007,"lang":"en","type":"article","venue":"˜The œbusiness & management collection.","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Alpha Technologies (Canada)","funders":"","keywords":"Decomposition; Business; Economics; Risk analysis (engineering); Actuarial science; Chemistry","score_opus":0.017851041791136273,"score_gpt":0.3118433882990267,"score_spread":0.2939923465078904,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W48561159","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":[0.3050685,0.00031206178,0.64759624,0.0005016314,0.0015091744,0.0007675203,0.0000079842375,0.0001515732,0.04408531],"genre_scores_gemma":[0.96985114,0.0056698774,0.0056697135,0.00018944482,0.00027351378,0.00003676609,0.000010972852,0.000024813315,0.018273758],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9974112,0.00024425125,0.000630085,0.0005050131,0.00090245146,0.00030699917],"domain_scores_gemma":[0.99777627,0.00059326884,0.00053727604,0.0005310313,0.00048138647,0.00008073823],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.004951107,0.00018729734,0.00019457113,0.00063292176,0.0016184638,0.0005428884,0.00037245525,0.00007475571,0.00015180014],"category_scores_gemma":[0.00022865049,0.00012955732,0.00007408825,0.0039844783,0.00010692684,0.0003481054,0.00017798926,0.00016306997,0.00017314481],"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":[0.00037367266,0.00018588146,0.2095689,0.000010358903,0.00018795575,0.000024545907,0.00068766123,0.037987214,0.000017099272,0.0015156389,0.07976331,0.66967773],"study_design_scores_gemma":[0.0006291428,0.000025278825,0.8001767,0.000009022955,0.00015823252,0.000015371079,0.000578498,0.01581479,0.0000236536,0.010662472,0.17169803,0.0002088184],"about_ca_topic_score_codex":0.0006116737,"about_ca_topic_score_gemma":0.0009469991,"teacher_disagreement_score":0.66946894,"about_ca_system_score_codex":0.00006797253,"about_ca_system_score_gemma":0.000018064418,"threshold_uncertainty_score":0.9996813},"labels":[],"label_agreement":null},{"id":"W54143233","doi":"","title":"MONOTONICITY OF RATIOS INVOLVING INCOMPLETE GAMMA FUNCTIONS WITH ACTUARIAL APPLICATIONS","year":2008,"lang":"en","type":"article","venue":"","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Western University; York University","funders":"","keywords":"Monotonic function; Mathematics; Probabilistic logic; Mathematical economics; Gamma function; Calculus (dental); Applied mathematics; Conditional probability; Econometrics; Pure mathematics; Statistics; Mathematical analysis; Medicine","score_opus":0.08991675392504822,"score_gpt":0.3238771379795721,"score_spread":0.2339603840545239,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W54143233","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":[0.22120015,0.0000214044,0.74736375,0.00020492387,0.000077525576,0.00041057402,0.000013033424,0.000054900196,0.030653745],"genre_scores_gemma":[0.9804716,0.0000849103,0.016354883,0.000071471026,0.00008531471,0.000060066483,0.000015785758,0.0000071771806,0.002848809],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.998408,0.00006202668,0.00049100176,0.00027326087,0.00063892594,0.00012673798],"domain_scores_gemma":[0.99832624,0.00037535143,0.00025394929,0.0005013029,0.00046800074,0.00007515454],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041372815,0.00009281242,0.00019255678,0.0002042917,0.00032879776,0.00004471262,0.00027908813,0.0000504642,0.0005407702],"category_scores_gemma":[0.00017974696,0.00006138455,0.00005856993,0.0009660185,0.00014395107,0.00036956155,0.000045545305,0.00007255466,0.00012044002],"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":[0.0004581017,0.00065954647,0.7388816,0.0000069516277,0.00012668726,0.000008418647,0.0037979872,0.10288007,0.004582589,0.057208255,0.04772851,0.043661315],"study_design_scores_gemma":[0.0046261107,0.0009434749,0.39732653,0.000026257001,0.00014244595,0.00016071986,0.0051116617,0.20294113,0.012927929,0.025031403,0.34940264,0.0013596885],"about_ca_topic_score_codex":0.00017249586,"about_ca_topic_score_gemma":0.00019836621,"teacher_disagreement_score":0.75927144,"about_ca_system_score_codex":0.000016935106,"about_ca_system_score_gemma":0.00016309638,"threshold_uncertainty_score":0.59210545},"labels":[],"label_agreement":null},{"id":"W586461037","doi":"10.1016/j.orl.2015.06.006","title":"Robust binary optimization using a safe tractable approximation","year":2015,"lang":"en","type":"article","venue":"Operations Research Letters","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Marriott International (Canada)","funders":"","keywords":"Robust optimization; Mathematical optimization; Binary number; Optimization problem; Mathematics; Nonlinear programming; Linear programming; Nonlinear system; Computer science; Algorithm","score_opus":0.4841275584469856,"score_gpt":0.4660255832628508,"score_spread":0.018101975184134755,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W586461037","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":[0.22105344,0.00007101121,0.7660603,0.0072393483,0.00028306627,0.0005902818,0.0000122234005,0.000046947993,0.0046434244],"genre_scores_gemma":[0.5898868,0.00013251862,0.40471593,0.00077267585,0.00047568048,0.000114296185,0.00024105163,0.000050734827,0.0036103504],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9948594,0.001013656,0.00062050734,0.00051209616,0.0025508252,0.00044350978],"domain_scores_gemma":[0.99698824,0.0002895359,0.0000679564,0.00064121315,0.0017405157,0.0002725483],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0075385524,0.00013710553,0.00019127292,0.0011023242,0.00082273094,0.0013664503,0.0005344909,0.00009796511,0.00040109587],"category_scores_gemma":[0.0033373367,0.00011433961,0.000058715897,0.0029601955,0.00016990678,0.0023118656,0.00012219274,0.00030108888,0.00036948305],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000024166238,0.00006165358,0.000513314,9.606737e-7,0.000005577911,0.000006331146,0.0006432175,0.9716787,0.0021076717,0.0003746247,0.023121282,0.0014625143],"study_design_scores_gemma":[0.0003599552,0.000049286933,0.00014223391,0.000008595813,0.000004919024,0.000012222282,0.00094539695,0.9921953,0.00026989155,0.00013401287,0.0057419315,0.00013622567],"about_ca_topic_score_codex":0.0004151563,"about_ca_topic_score_gemma":0.00005650728,"teacher_disagreement_score":0.36883336,"about_ca_system_score_codex":0.00026108485,"about_ca_system_score_gemma":0.00044165304,"threshold_uncertainty_score":0.9996702},"labels":[],"label_agreement":null},{"id":"W6889985338","doi":"10.32920/24088725","title":"The strong Fatou property of risk measures","year":2023,"lang":"en","type":"article","venue":"","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Converse; Quasiconvex function; Property (philosophy); Invariant (physics); Range (aeronautics); Dual (grammatical number)","score_opus":0.1404676586068999,"score_gpt":0.37757367672817893,"score_spread":0.23710601812127904,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6889985338","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":[0.52899563,0.00055511633,0.08950533,0.0048193177,0.0018604228,0.00080639235,0.000051507897,0.00044221393,0.37296405],"genre_scores_gemma":[0.97554547,0.0014637754,0.00035714553,0.000013133251,0.00003178718,0.0000042087795,0.0000012800812,0.000004193924,0.022579007],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9984237,0.00014060173,0.00031857853,0.00014025213,0.00085547543,0.00012139027],"domain_scores_gemma":[0.9984367,0.0007793382,0.00013786412,0.00036439084,0.00024899637,0.000032703294],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028689972,0.000046307316,0.00008870791,0.00008418907,0.00020326013,0.00010172494,0.0003823267,0.000025295854,0.00009284551],"category_scores_gemma":[0.002544535,0.000015656495,0.000055506174,0.00087882,0.00006680031,0.000111784946,0.000058383146,0.000045702862,0.00040651124],"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":[0.000019669642,0.000009901538,0.083123766,2.3298817e-7,0.000010525821,7.177544e-7,0.00047301844,0.013037359,0.000049110757,0.004825447,0.11982729,0.778623],"study_design_scores_gemma":[0.00028981248,0.000055534205,0.17472076,0.0000045462593,0.000014727468,0.00000128686,0.0045414562,0.09744805,0.0027841066,0.031636972,0.6883513,0.00015146835],"about_ca_topic_score_codex":0.00016894113,"about_ca_topic_score_gemma":0.00021721883,"teacher_disagreement_score":0.77847147,"about_ca_system_score_codex":0.0000034537666,"about_ca_system_score_gemma":0.000036681897,"threshold_uncertainty_score":0.5225015},"labels":[],"label_agreement":null},{"id":"W6891313238","doi":"10.3929/ethz-b-000745346","title":"Risk exchange under infinite-mean Pareto models","year":2025,"lang":"en","type":"other","venue":"Repository for Publications and Research Data (ETH Zurich)","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":0,"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":"Natural Sciences and Engineering Research Council of Canada","keywords":"Portfolio; Pareto principle; Diversification (marketing strategy); Class (philosophy); Pareto optimal; Focus (optics)","score_opus":0.3533217382188891,"score_gpt":0.48597964079733763,"score_spread":0.1326579025784485,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6891313238","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000013998178,0.0070576495,0.19161358,0.001953346,0.00059429667,0.0018716026,0.007631057,0.00021570001,0.7890488],"genre_scores_gemma":[0.0009842039,0.020783335,0.00698016,0.00006330545,0.00070036063,0.0003597725,0.0037582451,0.00016479757,0.96620584],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99437255,0.0009148861,0.00077159016,0.001568005,0.0018176486,0.00055529806],"domain_scores_gemma":[0.9900214,0.002756253,0.0005584521,0.0046899156,0.0016375874,0.0003364137],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.007759738,0.0002780499,0.00043686974,0.0026508255,0.0011073535,0.0018124821,0.0029992086,0.000512496,0.00037733823],"category_scores_gemma":[0.003458839,0.00021062838,0.00009847173,0.002352199,0.00037986503,0.00094008236,0.0014949332,0.0005849947,0.000064218046],"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":[0.000011727187,0.00008495964,0.00044984755,0.000030274528,0.000078997924,7.9064387e-7,0.00008826841,0.00014708059,0.000001494159,0.015855616,0.96563977,0.017611172],"study_design_scores_gemma":[0.00023781473,0.000045199133,0.00038509545,0.000050869843,0.000046338144,0.0000035679896,0.0002360609,0.018601656,0.000004302665,0.018926144,0.9612359,0.00022708921],"about_ca_topic_score_codex":0.0018059025,"about_ca_topic_score_gemma":0.0005185283,"teacher_disagreement_score":0.18463342,"about_ca_system_score_codex":0.00006302394,"about_ca_system_score_gemma":0.00074890355,"threshold_uncertainty_score":0.9992237},"labels":[],"label_agreement":null},{"id":"W6911297781","doi":"10.5267/j.ac.2025.2.002","title":"Fuzzy portfolio optimization using conditional drawdown at risk: Empirical evidence on selective companies in the Tehran Stock Exchang","year":2025,"lang":"en","type":"article","venue":"Accounting","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Portfolio; Stock (firearms); Certainty; Portfolio optimization; Stock market; Fuzzy logic; Drawdown (hydrology); Investment strategy; Fuzzy set; Stock exchange","score_opus":0.157008421462587,"score_gpt":0.4246864463985188,"score_spread":0.26767802493593185,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6911297781","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":[0.921898,0.00027906068,0.06587987,0.0007389795,0.00035100427,0.0004919765,0.00001682874,0.000049225928,0.010295053],"genre_scores_gemma":[0.9951552,0.00012585237,0.003139318,0.001099049,0.0001440682,0.000024003673,0.000026055766,0.000011278906,0.00027513388],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.996642,0.0005100358,0.00074236543,0.0005557631,0.0012184676,0.00033140267],"domain_scores_gemma":[0.99526083,0.0033022093,0.0005332423,0.0004177422,0.00045181185,0.000034130946],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003545123,0.00019910882,0.00028939883,0.00067437295,0.00087060424,0.00049610954,0.00060124765,0.00012249826,0.00014849436],"category_scores_gemma":[0.0039872588,0.00014047904,0.00010033366,0.0026835871,0.00011515433,0.0009414448,0.0001474425,0.00031317532,0.00004519119],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000051771927,0.00003710378,0.4508138,0.0000018982223,0.000009553997,0.0000041042226,0.00092886295,0.54151624,0.000023207487,0.00042052023,0.0050912434,0.0011016765],"study_design_scores_gemma":[0.0005256776,0.000050287257,0.4386071,0.0001762225,0.000052506737,0.000018447558,0.0016942478,0.54617137,0.00014730568,0.010618207,0.0016487195,0.00028988443],"about_ca_topic_score_codex":0.0002586756,"about_ca_topic_score_gemma":0.00018699172,"teacher_disagreement_score":0.07325724,"about_ca_system_score_codex":0.00021358243,"about_ca_system_score_gemma":0.00018076222,"threshold_uncertainty_score":0.66960716},"labels":[],"label_agreement":null},{"id":"W6923346793","doi":"10.14288/1.0229309","title":"Your journey through the Canadian Pacific Rockies : what you see and where","year":2016,"lang":"en","type":"article","venue":"Open Collections","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Work (physics); Feature (linguistics); Government (linguistics)","score_opus":0.09911103275461741,"score_gpt":0.35667058532316637,"score_spread":0.25755955256854896,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6923346793","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0012888574,0.0010612843,0.0037853287,0.050849613,0.0014811418,0.0007637592,0.00007805439,0.000028315271,0.94066364],"genre_scores_gemma":[0.115814194,0.00659366,0.0005554677,0.00027309908,0.0001084405,0.0000531476,0.0000015544297,0.0000120737295,0.87658834],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9986737,0.00015967024,0.00027849776,0.00029567917,0.00036272337,0.00022974637],"domain_scores_gemma":[0.99865955,0.00033606315,0.00011435994,0.00043810316,0.0002923967,0.00015954561],"candidate_categories":["sts","scholarly_communication","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00075971015,0.00010149687,0.00014338076,0.00010143201,0.0059620827,0.01076014,0.00059849676,0.000072557894,0.002569374],"category_scores_gemma":[0.00043137488,0.000049320275,0.000043656757,0.0014265121,0.00011560158,0.0021530245,0.000118809374,0.000086793705,0.00014596921],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008216854,0.000008724635,0.0014160308,1.7430645e-7,0.0000133172125,0.000002148306,0.0014394084,0.00013593749,0.0000029567377,0.0005598974,0.97820455,0.018208653],"study_design_scores_gemma":[0.00022945918,0.00002930758,0.0011014276,0.000019299143,0.000010712328,0.00003955636,0.010311612,0.00016037095,0.000018066572,0.034503285,0.9534752,0.000101723715],"about_ca_topic_score_codex":0.2297785,"about_ca_topic_score_gemma":0.8278961,"teacher_disagreement_score":0.59811765,"about_ca_system_score_codex":0.00008985938,"about_ca_system_score_gemma":0.00053037703,"threshold_uncertainty_score":0.9983424},"labels":[],"label_agreement":null},{"id":"W6930411370","doi":"10.5281/zenodo.11851184","title":"Soda pdf key","year":2024,"lang":"en","type":"other","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Key (lock); Password; Upload; License; File Transfer Protocol; Download","score_opus":0.08198313988006813,"score_gpt":0.3301538412254809,"score_spread":0.2481707013454128,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6930411370","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000008987961,0.0006994259,0.006326574,0.0006027493,0.00067742955,0.00039287048,0.00038012874,0.001462888,0.98944896],"genre_scores_gemma":[0.0007724507,0.0010405268,0.00054468104,0.00014472306,0.0006824478,3.4524753e-8,0.0013791082,0.009337488,0.9860985],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99628705,0.00045311832,0.00051750103,0.0009175354,0.0014252663,0.00039953875],"domain_scores_gemma":[0.9977928,0.00004372439,0.0003038902,0.0010915863,0.00052835234,0.00023968074],"candidate_categories":["scholarly_communication","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0017639344,0.00026871992,0.0003215194,0.0012228156,0.0007610084,0.0030472775,0.0022124934,0.00024806557,0.35856104],"category_scores_gemma":[0.0016567664,0.0002331974,0.00014079723,0.0015527207,0.00018963165,0.00018286794,0.0014055397,0.000425262,0.579782],"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":[0.000009749276,0.00003202647,5.6798115e-7,0.000017901877,0.000035969828,0.000023317187,0.00029202792,0.000030481837,0.000018408404,0.0030887346,0.90515405,0.091296785],"study_design_scores_gemma":[0.00016535478,0.000073641546,0.000011803903,0.00006694259,0.00002432033,0.000059898885,0.00015018703,0.00031963777,0.000011931393,0.0024434885,0.99641573,0.00025707704],"about_ca_topic_score_codex":0.000035450033,"about_ca_topic_score_gemma":0.0000015531207,"teacher_disagreement_score":0.22122099,"about_ca_system_score_codex":0.000085615444,"about_ca_system_score_gemma":0.0000078908115,"threshold_uncertainty_score":0.9979876},"labels":[],"label_agreement":null},{"id":"W6931593549","doi":"10.5281/zenodo.7589701","title":"A possible coupling between the Arctic freshwater, the Arctic sea ice cover and the North Atlantic drift. A case study","year":2003,"lang":"en","type":"report","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Arctic dipole anomaly; Arctic sea ice decline; Arctic; Sea ice; Arctic ice pack; Arctic geoengineering; North Atlantic Deep Water; Ocean gyre; Thermohaline circulation","score_opus":0.10192820074916267,"score_gpt":0.33103596315006084,"score_spread":0.22910776240089817,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6931593549","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":[0.9051258,0.0025645597,0.006113638,0.004656794,0.0014743224,0.010222162,0.0011110768,0.00073859066,0.06799306],"genre_scores_gemma":[0.98828834,0.0022162737,0.000036365484,0.00016890235,0.00046311747,7.172847e-7,0.00056861126,0.0009787589,0.007278891],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9921177,0.0022359844,0.0010878716,0.00096222205,0.0030146365,0.00058156566],"domain_scores_gemma":[0.9926912,0.0012817892,0.00085803337,0.001909594,0.0030452567,0.00021411749],"candidate_categories":["metaresearch","sts","scholarly_communication","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.012983887,0.0004045412,0.0005746967,0.00036837225,0.0073195696,0.005153032,0.0025721067,0.00015048604,0.0037572023],"category_scores_gemma":[0.008415339,0.00019789522,0.00017794708,0.0019814954,0.00082574843,0.0003288802,0.0018183797,0.0011326723,0.0026621972],"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":[0.0005005661,0.0007231316,0.07273769,0.00025809623,0.0015789084,0.00207637,0.030796763,0.012385991,0.0000037311245,0.00051760074,0.8267981,0.051623054],"study_design_scores_gemma":[0.0011537082,0.000221883,0.006761744,0.000053196505,0.0003724666,0.0039481567,0.0036430138,0.0038381799,0.0000010422433,0.0002914177,0.9793876,0.000327628],"about_ca_topic_score_codex":0.0028207826,"about_ca_topic_score_gemma":0.00016745095,"teacher_disagreement_score":0.15258946,"about_ca_system_score_codex":0.00020943285,"about_ca_system_score_gemma":0.000076149845,"threshold_uncertainty_score":0.9999372},"labels":[],"label_agreement":null},{"id":"W69357263","doi":"10.1007/978-1-4419-1642-6_4","title":"Stochastic Decomposition and Extensions","year":2010,"lang":"en","type":"book-chapter","venue":"International series in management science/operations research/International series in operations research & management science","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Decomposition; Computer science; Mathematical optimization; Quality (philosophy); Theoretical computer science; Algorithm; Mathematics; Chemistry","score_opus":0.11314490445595228,"score_gpt":0.4892856962576819,"score_spread":0.37614079180172966,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W69357263","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012575817,0.0001898237,0.008572126,0.032561246,0.006098971,0.005640574,0.00028268056,0.00015330069,0.93392545],"genre_scores_gemma":[0.36486375,0.012441462,0.03496601,0.00027663572,0.00053530565,0.0012876127,0.000399194,0.0001134154,0.5851166],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.97391695,0.00045305444,0.0024250092,0.003616211,0.01777158,0.0018171692],"domain_scores_gemma":[0.9894229,0.0006335752,0.0001908878,0.0022612335,0.006890643,0.0006007404],"candidate_categories":["metaresearch","metaepi_narrow","bibliometrics","sts","scholarly_communication","open_science","research_integrity","insufficient_payload"],"consensus_categories":["sts","insufficient_payload"],"category_scores_codex":[0.033174433,0.0007099758,0.0006318785,0.021702405,0.0051952098,0.009652797,0.009319674,0.00033516256,0.0031266066],"category_scores_gemma":[0.004355158,0.0006766369,0.00015693258,0.006870717,0.013428433,0.010587323,0.007445042,0.0025656186,0.00079892477],"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":[0.00017385437,0.00024299485,0.00018606363,0.000014847588,0.000057021065,0.00020729307,0.0007470569,0.13974003,0.00085162814,0.84536743,0.0010922725,0.0113194855],"study_design_scores_gemma":[0.0024477935,0.0005008881,0.017106542,0.0011716944,0.00004496293,0.00024762138,0.009345474,0.28572997,0.0005024241,0.33876976,0.34189424,0.0022386264],"about_ca_topic_score_codex":0.000608424,"about_ca_topic_score_gemma":0.009733229,"teacher_disagreement_score":0.5065977,"about_ca_system_score_codex":0.0025778434,"about_ca_system_score_gemma":0.00093849155,"threshold_uncertainty_score":0.9999791},"labels":[],"label_agreement":null},{"id":"W6957912237","doi":"10.60692/1nfky-8xb60","title":"Asymptotic properties of generalized shortfall risk measures for heavy-tailed risks","year":2023,"lang":"en","type":"article","venue":"Greater South Information System","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Expected shortfall; Estimator; Measure (data warehouse); Risk measure; Coherent risk measure; Asymptotic analysis; Spectral risk measure","score_opus":0.21689413905482974,"score_gpt":0.3176599600652221,"score_spread":0.10076582101039239,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6957912237","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":[0.86496186,0.000014076453,0.13191143,0.000046203604,0.00067952566,0.0009821663,0.00023829253,0.00027366643,0.0008927708],"genre_scores_gemma":[0.9983924,0.000006601231,0.00075596565,0.000033715514,0.000081321436,0.00017926171,0.000032535303,0.000014071776,0.0005041132],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99635136,0.0002429089,0.0016573699,0.00023532634,0.0012108724,0.00030215422],"domain_scores_gemma":[0.9970774,0.00005623408,0.0010301706,0.00060791476,0.0011253388,0.000102919505],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.003644339,0.00019786762,0.000467197,0.0008336977,0.0002578728,0.00034033242,0.00043312868,0.00014072393,0.000015381725],"category_scores_gemma":[0.0007578181,0.00012838442,0.00021731152,0.00105716,0.000051905,0.0009382457,0.00006815072,0.00006832725,0.0008103533],"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":[0.0007053014,0.0000055423275,0.7882677,0.00017777107,0.00014090895,0.0000010012769,0.08238926,0.11177649,0.000018050925,0.0005661174,0.004075286,0.011876606],"study_design_scores_gemma":[0.0059406795,0.0003381701,0.2504075,0.00036981402,0.00026067186,0.000026083178,0.048018314,0.6700213,0.011745411,0.0002754181,0.011531391,0.0010652406],"about_ca_topic_score_codex":0.00003793234,"about_ca_topic_score_gemma":0.0000016070588,"teacher_disagreement_score":0.5582448,"about_ca_system_score_codex":0.000047483616,"about_ca_system_score_gemma":0.00008136867,"threshold_uncertainty_score":0.99996763},"labels":[],"label_agreement":null},{"id":"W6957934693","doi":"10.6084/m9.figshare.26143643","title":"Additional file 1 of Using community-based participatory research methods to build the foundation for an equitable integrated health data system within a Canadian urban context","year":2024,"lang":"en","type":"article","venue":"Figshare","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"William Osler Health System; University of Toronto; Trillium Health Centre","funders":"","keywords":"Context (archaeology); Foundation (evidence); Participatory action research; Health data; Citizen journalism; Government (linguistics); Community-based participatory research","score_opus":0.8083251689209744,"score_gpt":0.5919454345260516,"score_spread":0.21637973439492275,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6957934693","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000029092631,0.00007535262,0.0036950363,0.00024999044,0.00009337727,0.0006889391,0.9947799,0.000034128032,0.00035420095],"genre_scores_gemma":[0.01926947,1.4143352e-7,0.027489334,0.0004212456,0.00008806283,0.0007349902,0.951676,0.000020314701,0.00030041387],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99532175,0.0028009247,0.00054926024,0.00031948072,0.00064623327,0.00036237904],"domain_scores_gemma":[0.9831813,0.014052563,0.0001743187,0.0011243218,0.0011857215,0.0002817788],"candidate_categories":["metaresearch","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.008561227,0.00009779333,0.00020026925,0.00047647886,0.001015093,0.0005417993,0.0011960004,0.00006850446,0.53171164],"category_scores_gemma":[0.023399875,0.000067966415,0.000040246512,0.0016282838,0.000031197083,0.0004822877,0.00017363083,0.00031307247,0.00035235874],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000121170315,0.000011406642,0.0000019173888,0.000057331556,0.000007706456,5.629023e-7,0.0008612321,0.00242169,0.0000024562676,0.00012006202,0.98400307,0.012500436],"study_design_scores_gemma":[0.00003122978,0.00006961961,0.000019864532,0.0011936123,0.0000023736236,0.0000011894848,0.0030625034,0.38841563,0.000015736698,0.000081861996,0.6070599,0.000046495286],"about_ca_topic_score_codex":0.032804042,"about_ca_topic_score_gemma":0.12494298,"teacher_disagreement_score":0.53135926,"about_ca_system_score_codex":0.00031254155,"about_ca_system_score_gemma":0.005119451,"threshold_uncertainty_score":0.98482645},"labels":[],"label_agreement":null},{"id":"W6958800895","doi":"10.6084/m9.figshare.27221717","title":"Additional file 1 of Exploring the effects of COVID-19 outbreak control policies on services offered to people experiencing homelessness","year":2024,"lang":"en","type":"article","venue":"Figshare","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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; St. Francis Xavier University; Dalhousie University","funders":"","keywords":"Control (management); Government (linguistics); Qualitative research; Context (archaeology); Public policy","score_opus":0.06875492505624577,"score_gpt":0.33544463610720915,"score_spread":0.2666897110509634,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6958800895","genre_codex":"dataset","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011179432,0.00009605328,0.000024807472,0.00014426844,0.00014205786,0.00028772655,0.98746425,0.000046335976,0.00061506045],"genre_scores_gemma":[0.8102431,0.0000069709663,0.000057709534,0.0005888877,0.00018435642,0.0033008186,0.18514048,0.000020103023,0.00045755669],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9982953,0.000105211366,0.0003611802,0.0002714378,0.0008039678,0.00016291135],"domain_scores_gemma":[0.9836938,0.015453408,0.00016559161,0.0003414192,0.00022977595,0.00011600541],"candidate_categories":["metaresearch","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00012494973,0.0001246399,0.00026656428,0.0002716703,0.0001210399,0.00014427978,0.00051452965,0.000042905045,0.818982],"category_scores_gemma":[0.011080976,0.00007813817,0.000113490576,0.000966654,0.000010959592,0.00027389362,0.000102885824,0.000070554284,0.0007878022],"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":[0.000019745796,0.000014716163,0.000020867266,0.00016744758,0.000011830568,0.0000036460488,0.012466559,0.0030785287,0.000024618419,0.000034118788,0.98015475,0.004003144],"study_design_scores_gemma":[0.00020539458,0.00015118891,0.012277909,0.0028673362,0.000009091332,0.0000035755572,0.007891051,0.0043917424,0.0013821325,0.0002228576,0.97041684,0.0001808881],"about_ca_topic_score_codex":0.0000738861,"about_ca_topic_score_gemma":0.00015350503,"teacher_disagreement_score":0.8181942,"about_ca_system_score_codex":0.00003092182,"about_ca_system_score_gemma":0.00017113455,"threshold_uncertainty_score":0.9999902},"labels":[],"label_agreement":null},{"id":"W6980163407","doi":"","title":"Bayesian optimization to solve black box problems with hidden constraints","year":2024,"lang":"en","type":"article","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"","keywords":"Black box; Bayesian probability; Bayesian optimization; Constraint (computer-aided design); Optimization problem; Constrained optimization","score_opus":0.021645823487459085,"score_gpt":0.27055442883374564,"score_spread":0.24890860534628656,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6980163407","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":[0.004954883,0.00020868803,0.89228743,0.010879893,0.0001583939,0.00049146434,0.000030348947,0.0002614799,0.090727426],"genre_scores_gemma":[0.6994763,0.00031153057,0.2848932,0.00022353101,0.000035259774,0.000047652346,0.0001359576,0.000052084684,0.014824491],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9955778,0.001409803,0.00067998806,0.00088109134,0.0010781895,0.00037311247],"domain_scores_gemma":[0.99421906,0.001636166,0.00022922116,0.0013519472,0.0022504018,0.0003131872],"candidate_categories":["scholarly_communication","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0070940084,0.00025134106,0.00028062373,0.0004866885,0.00031469407,0.0015944961,0.0009618951,0.00013022304,0.0009954657],"category_scores_gemma":[0.0018753215,0.00020063386,0.00011430367,0.0021395942,0.00035455963,0.0005848676,0.0002188891,0.00022470434,0.00044731083],"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":[0.000055756194,0.0005222253,0.0052958024,0.00006260362,0.0001418806,0.00004495313,0.042116232,0.16609333,0.0010673637,0.23015796,0.02819413,0.52624774],"study_design_scores_gemma":[0.00072939345,0.0000045417205,0.0011847158,0.0013202716,0.0000638488,0.00006140434,0.00093423703,0.8891949,0.009038368,0.01148281,0.08519858,0.00078692357],"about_ca_topic_score_codex":0.00015681541,"about_ca_topic_score_gemma":0.0005677639,"teacher_disagreement_score":0.72310156,"about_ca_system_score_codex":0.00007369814,"about_ca_system_score_gemma":0.00030919904,"threshold_uncertainty_score":0.99991775},"labels":[],"label_agreement":null},{"id":"W6980407927","doi":"","title":"Canadian Oil Sands Major CO2 Producer","year":2006,"lang":"en","type":"other","venue":"Bulletin of Miscellaneous Information (Royal Gardens Kew)","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Oil sands; Petroleum; Synthetic crude; Greenhouse gas; Fossil fuel; Fugitive emissions","score_opus":0.010440414647209833,"score_gpt":0.22443901432923746,"score_spread":0.21399859968202764,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6980407927","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000028020886,0.0003946372,0.0000010382226,0.0005680747,0.00067983544,0.00024024332,0.0003796298,0.00011177652,0.99759674],"genre_scores_gemma":[0.00042397724,0.00038995245,0.0011258958,0.00035015677,0.0003532385,0.000014303283,0.00041126955,0.00011900501,0.9968122],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9966362,0.000116633164,0.001171193,0.0003830572,0.0012525552,0.0004403222],"domain_scores_gemma":[0.99732494,0.00014823208,0.0009896643,0.0007904282,0.00046074422,0.00028597013],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0008412786,0.0003770057,0.0005427658,0.00043466742,0.00012889791,0.00023496832,0.0007454728,0.00054702174,0.45322374],"category_scores_gemma":[0.0004988143,0.00032243982,0.00019766012,0.00004149083,0.000121114004,4.490686e-7,0.00006584455,0.00024616678,0.031393085],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002106866,0.000018130477,0.00003487902,0.000037370613,0.000023443175,0.00001618749,0.00006234888,0.0013526607,3.492467e-8,0.000025415617,0.9869343,0.011474152],"study_design_scores_gemma":[0.0003357326,0.000044164168,0.00003489836,0.000074411095,0.00003613119,0.00003820631,0.00004851079,0.000056553334,0.0000035595908,0.00003402768,0.99893355,0.00036025018],"about_ca_topic_score_codex":0.4337856,"about_ca_topic_score_gemma":0.5310304,"teacher_disagreement_score":0.42183065,"about_ca_system_score_codex":0.00010621725,"about_ca_system_score_gemma":0.00026609196,"threshold_uncertainty_score":0.99992275},"labels":[],"label_agreement":null},{"id":"W6986235658","doi":"","title":"Pasado, presente y futuro del cine centroamericano en el Festival Internacional de Cine de Toronto (TIFF)","year":2022,"lang":"es","type":"article","venue":"Dialnet (Universidad de la Rioja)","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Context (archaeology); Coronavirus disease 2019 (COVID-19); Somali","score_opus":0.012193946326464774,"score_gpt":0.31294630307227583,"score_spread":0.3007523567458111,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6986235658","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":[0.9268579,0.0019351338,0.028624887,0.007686029,0.0015802959,0.0005754424,0.00055718125,0.00017199198,0.032011162],"genre_scores_gemma":[0.9814491,0.0035515726,0.0033776942,0.0011860435,0.000811222,0.000021480728,0.00014962253,0.00006402993,0.009389196],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.99410236,0.0019927667,0.00054882426,0.00086544687,0.0015041842,0.0009864155],"domain_scores_gemma":[0.99641865,0.0014904508,0.0005633123,0.00070804247,0.00021324678,0.0006063019],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0020469122,0.0004379575,0.0005886496,0.00044065394,0.00075864705,0.00034665235,0.0016188446,0.00029852169,0.0064376495],"category_scores_gemma":[0.0006466276,0.00046570864,0.00036298955,0.0010205923,0.00034555048,0.0007769881,0.00096077274,0.00086947996,0.00008095836],"study_design_candidate":"not_applicable","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":[0.0031743953,0.0013429046,0.3814639,0.000035483965,0.0005995651,0.0024615289,0.023833575,0.07920282,0.0034167673,0.045797706,0.3232541,0.13541727],"study_design_scores_gemma":[0.0026652354,0.00049738697,0.26332852,0.00004471338,0.00021564313,0.00032323322,0.011649914,0.07069848,0.00012410904,0.0019421559,0.64782697,0.0006836294],"about_ca_topic_score_codex":0.007580368,"about_ca_topic_score_gemma":0.00034150144,"teacher_disagreement_score":0.32457286,"about_ca_system_score_codex":0.0016674725,"about_ca_system_score_gemma":0.0010258968,"threshold_uncertainty_score":0.99977946},"labels":[],"label_agreement":null},{"id":"W6986736394","doi":"","title":"On randomized algorithms and their applications in robust optimization. Algoritmos aleatorios y aplicaciones en optimización robusta.","year":2014,"lang":"es","type":"article","venue":"","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Go/no go","score_opus":0.01886566426549658,"score_gpt":0.2846968101748296,"score_spread":0.26583114590933304,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6986736394","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":[0.0018186964,0.0007636335,0.98361284,0.002033183,0.00038074967,0.0029519547,0.00007321092,0.00016821868,0.008197529],"genre_scores_gemma":[0.41898388,0.031081924,0.5365912,0.0013730049,0.0010722707,0.0015343744,0.00046705402,0.00027966115,0.008616683],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9929208,0.0017142111,0.0020280895,0.0016241956,0.0009989989,0.000713662],"domain_scores_gemma":[0.98806185,0.008911097,0.00072070427,0.001324341,0.0005487373,0.00043329684],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.005475574,0.000829423,0.0017990369,0.0010890356,0.0005436632,0.0010306139,0.0009473281,0.00060518895,0.0006641349],"category_scores_gemma":[0.0023346327,0.00057308696,0.00035360386,0.0021603198,0.00046714663,0.0007609178,0.00027101947,0.0004982812,0.00014665641],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014176986,0.00028483736,0.00048129362,0.000012796438,0.00005493577,0.0000013881798,0.00092621904,0.91098547,0.0000022776142,0.045175392,0.00071520667,0.03994246],"study_design_scores_gemma":[0.0296773,0.000121384925,0.00016616807,0.0000727209,0.00007133971,0.000010192295,0.00086009636,0.9564436,0.00006249874,0.0051226164,0.0067088758,0.0006831964],"about_ca_topic_score_codex":0.00015197929,"about_ca_topic_score_gemma":0.000021909449,"teacher_disagreement_score":0.44702166,"about_ca_system_score_codex":0.000098026845,"about_ca_system_score_gemma":0.00014349198,"threshold_uncertainty_score":0.99967206},"labels":[],"label_agreement":null},{"id":"W6996423005","doi":"","title":"Risk management with reinsurance policies","year":2020,"lang":"en","type":"dissertation","venue":"Mspace (University of Manitoba)","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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 Winnipeg; University of Manitoba","funders":"","keywords":"Reinsurance; Risk management; Stochastic control; Asset (computer security); Risk measure; Control (management); Markov process; Risk Control","score_opus":0.024978541972279777,"score_gpt":0.2583719599083338,"score_spread":0.233393417936054,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6996423005","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":[0.6699724,0.00042738524,0.012403744,0.0014533196,0.00066050235,0.0007498663,0.00012639233,0.00014232284,0.31406403],"genre_scores_gemma":[0.8919397,0.015051457,0.024816955,0.00009841176,0.00016031951,0.0000013626636,0.000525814,0.00007526517,0.06733074],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9977371,0.00012160274,0.00019456887,0.00051230786,0.00124378,0.00019062319],"domain_scores_gemma":[0.99803376,0.000103415936,0.0008689622,0.00052896864,0.00035983842,0.00010507664],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003305839,0.00021602506,0.00042265037,0.00042665508,0.00030557768,0.000077621145,0.0008777486,0.00015335101,0.000034240344],"category_scores_gemma":[0.000064509535,0.00020970871,0.00014765684,0.0010120275,0.000087175475,0.00028963663,0.00007746279,0.00022318317,0.00026717756],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.004457456,0.00033561882,0.5376129,0.0003775002,0.0012627749,0.00080004864,0.015335416,0.02197174,0.00005734872,0.019245064,0.22062355,0.17792057],"study_design_scores_gemma":[0.0007200581,0.0001947,0.7238814,0.0001154112,0.00028986807,0.0000024056694,0.096236266,0.0014352752,0.000067156456,0.0013812616,0.17525406,0.00042209047],"about_ca_topic_score_codex":0.0015006455,"about_ca_topic_score_gemma":0.15394902,"teacher_disagreement_score":0.24673328,"about_ca_system_score_codex":0.000044864257,"about_ca_system_score_gemma":0.000060111746,"threshold_uncertainty_score":0.8614892},"labels":[],"label_agreement":null},{"id":"W7008550336","doi":"","title":"Characterizing, optimizing and backtesting metrics of risk","year":2023,"lang":"en","type":"dissertation","venue":"UWSpace (University of Waterloo)","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Context (archaeology); Risk management; Portfolio; Axiom; Risk assessment; Choquet integral; Risk measure","score_opus":0.04131359158376261,"score_gpt":0.27946605067446073,"score_spread":0.23815245909069813,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7008550336","genre_codex":"empirical","genre_gemma":"other","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":[0.9982184,0.00015289786,0.00014859586,0.00007867437,0.0004357653,0.0001446472,0.00009020856,0.000044897955,0.00068588665],"genre_scores_gemma":[0.37758893,0.013347402,0.04064352,0.000011578241,0.00010863036,5.078887e-7,0.000904518,0.00009530635,0.5672996],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.997978,0.00013756598,0.00037396615,0.00046939845,0.0008318785,0.00020924798],"domain_scores_gemma":[0.99671775,0.0007193918,0.0014729048,0.00038639025,0.00060335326,0.00010023671],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012546132,0.00019928429,0.00059785566,0.0013343858,0.00022955821,0.00006291763,0.00051802857,0.0002724566,0.0001064666],"category_scores_gemma":[0.0010993065,0.00020438562,0.00016490193,0.0014745512,0.00009022054,0.0003707295,0.00012186517,0.00022853848,0.000051722312],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005171596,0.00013786076,0.09764285,0.00031605724,0.00032339428,0.00009142608,0.62424463,0.0032861128,0.005229763,0.00030270763,0.0047410727,0.26316693],"study_design_scores_gemma":[0.0012157641,0.00023794691,0.2589444,0.00052386656,0.0005883025,0.0000053891836,0.71235895,0.018734496,0.0036355348,0.0016623165,0.0012346327,0.0008583992],"about_ca_topic_score_codex":0.015873712,"about_ca_topic_score_gemma":0.007260446,"teacher_disagreement_score":0.6206295,"about_ca_system_score_codex":0.00002172463,"about_ca_system_score_gemma":0.000071528906,"threshold_uncertainty_score":0.9906797},"labels":[],"label_agreement":null},{"id":"W7025069439","doi":"","title":"19th Century Portable Desktop With Kat","year":2017,"lang":"en","type":"other","venue":"Bulletin of Miscellaneous Information (Royal Gardens Kew)","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Laptop; Desk; Key (lock); Mobile device","score_opus":0.014556447868214526,"score_gpt":0.23900114020847085,"score_spread":0.22444469234025632,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7025069439","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000016865511,0.00058094034,0.000008651983,0.000112080364,0.00048666826,0.00043225902,0.00015102047,0.00012314941,0.99808836],"genre_scores_gemma":[0.00040099848,0.0020919305,0.0025185803,0.00015236714,0.00018876536,0.000011228435,0.00017944312,0.00010717439,0.99434954],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9968233,0.00009091007,0.00086702907,0.00037202312,0.0014827583,0.00036394282],"domain_scores_gemma":[0.9958644,0.00017074725,0.0021167216,0.0012169731,0.00044463214,0.00018652511],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0008150911,0.0003910561,0.0006337174,0.00020037997,0.00018455222,0.0003378637,0.0010211455,0.00043463687,0.4051652],"category_scores_gemma":[0.0004866263,0.0002876964,0.00016948732,0.00001751244,0.00022257777,8.7092104e-7,0.00011797382,0.00025212741,0.017923063],"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":[0.00008008018,0.000027644992,0.000047286594,0.0000389801,0.00004071185,0.000026629177,0.00007986889,0.0009939689,2.7468188e-8,0.000032909953,0.98442423,0.014207686],"study_design_scores_gemma":[0.00045192937,0.00011546764,0.00003609922,0.00018373602,0.00004910898,0.00007116428,0.000097161465,0.00004648788,0.000003487228,0.000027761456,0.9985602,0.00035740828],"about_ca_topic_score_codex":0.004714419,"about_ca_topic_score_gemma":0.002775908,"teacher_disagreement_score":0.38724214,"about_ca_system_score_codex":0.000031285184,"about_ca_system_score_gemma":0.00011215572,"threshold_uncertainty_score":0.9999575},"labels":[],"label_agreement":null},{"id":"W7027438324","doi":"","title":"Conditional Value at Risk Asset Allocation&#13;\\nA Copula Based Approach","year":2013,"lang":"en","type":"dissertation","venue":"Spectrum Research Repository (Concordia University)","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"","keywords":"Bootstrapping (finance); Copula (linguistics); Asset allocation; Value at risk; Expected shortfall; Asset (computer security); Basis risk; Financial asset","score_opus":0.0540633790272885,"score_gpt":0.3352700841366758,"score_spread":0.2812067051093873,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7027438324","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":[0.6801916,0.00018196244,0.0040762466,0.00021896021,0.0014820192,0.0014471322,0.00025329285,0.00016214843,0.31198663],"genre_scores_gemma":[0.7365315,0.0002973087,0.00028787187,0.000017631877,0.00037359342,0.000023146593,0.0038244282,0.0000637419,0.25858077],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9882698,0.003053522,0.00094507786,0.0018276473,0.004920283,0.0009836878],"domain_scores_gemma":[0.9924386,0.0019159347,0.001126459,0.0016850126,0.0021422124,0.0006917636],"candidate_categories":["metaepi_narrow","sts","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0033666634,0.00054789905,0.0007709538,0.0035141446,0.0024742885,0.00089185237,0.0023382928,0.00084898324,0.001099661],"category_scores_gemma":[0.0012475831,0.0005416044,0.00052435196,0.0035823775,0.0005005408,0.00094527163,0.00026252863,0.0016207113,0.0011854471],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0032422869,0.0014019088,0.5547468,0.0002028984,0.00095102633,0.00099283,0.0016373174,0.066155516,0.0033494818,0.032057386,0.3309296,0.0043329056],"study_design_scores_gemma":[0.002877153,0.00066551944,0.665984,0.00011845893,0.00033689948,0.000055405915,0.005613367,0.094155416,0.011586983,0.010306257,0.20638464,0.0019159326],"about_ca_topic_score_codex":0.01682965,"about_ca_topic_score_gemma":0.005395053,"teacher_disagreement_score":0.12454496,"about_ca_system_score_codex":0.0013943431,"about_ca_system_score_gemma":0.0024935114,"threshold_uncertainty_score":0.9998135},"labels":[],"label_agreement":null},{"id":"W7028515644","doi":"","title":"Et les lâches s'agenouillent = Cowards bend the knee","year":2007,"lang":"fr","type":"other","venue":"","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Power (physics); Perspective (graphical); Subject (documents)","score_opus":0.08831749385815504,"score_gpt":0.380067768890509,"score_spread":0.29175027503235396,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7028515644","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00025224214,0.01024953,0.08650184,0.006181898,0.002221309,0.0005423932,0.000046200308,0.00006855747,0.89393604],"genre_scores_gemma":[0.0036926449,0.016952537,0.0065080244,0.0017587956,0.0006484899,0.000013735093,0.000028652463,0.00012800704,0.9702691],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99565494,0.0005037236,0.0009121149,0.0007198583,0.0016760062,0.000533344],"domain_scores_gemma":[0.99631,0.0015648447,0.0005338517,0.0011012424,0.00029788315,0.00019217753],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0042910893,0.00048274934,0.00052057736,0.00059191015,0.00041862027,0.0005365443,0.0011164249,0.0007094787,0.18846992],"category_scores_gemma":[0.0006287044,0.00025772752,0.00033482278,0.0009750771,0.0004972046,0.00015841382,0.00023720319,0.0005123594,0.008657138],"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":[0.000017047641,0.00007932266,0.0016048877,0.000005276405,0.00007065397,0.000016041817,0.0011745382,0.0022762143,0.0000027991744,0.10753929,0.24784075,0.6393732],"study_design_scores_gemma":[0.00021495561,0.00008805521,0.003409779,0.000048632053,0.00006343965,0.00001934742,0.0017440267,0.002068925,0.000042012623,0.0028497402,0.98908705,0.00036402597],"about_ca_topic_score_codex":0.001383009,"about_ca_topic_score_gemma":0.001985186,"teacher_disagreement_score":0.74124634,"about_ca_system_score_codex":0.000058860052,"about_ca_system_score_gemma":0.00032072238,"threshold_uncertainty_score":0.9999875},"labels":[],"label_agreement":null},{"id":"W7038789100","doi":"","title":"İskandinav bestecilerinin 21.yy. piyano müziğindeki yeri","year":2023,"lang":"tr","type":"dissertation","venue":"Trakya University's Institutional Open Access System (Trakya University)","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Subject (documents); Quarter (Canadian coin); Latvian; Work (physics)","score_opus":0.1353419642062014,"score_gpt":0.3779194381392294,"score_spread":0.242577473933028,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7038789100","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22229451,0.00023080567,0.008985491,0.0006954619,0.012091555,0.0055714045,0.0031970798,0.0011521099,0.7457816],"genre_scores_gemma":[0.65216786,0.00286523,0.0008051675,0.000065937624,0.00044612464,0.000006223203,0.0034509911,0.00015648532,0.340036],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.98739237,0.0011296704,0.0019964036,0.0038188836,0.0039667445,0.0016959409],"domain_scores_gemma":[0.9901591,0.0009049678,0.0029298835,0.0019124551,0.0027197269,0.0013738547],"candidate_categories":["metaepi_narrow","sts","scholarly_communication","open_science","research_integrity","insufficient_payload"],"consensus_categories":["metaepi_narrow","scholarly_communication","insufficient_payload"],"category_scores_codex":[0.0023530575,0.001828654,0.0026400874,0.007177531,0.0055357954,0.0051523503,0.018632418,0.0020222177,0.0017101498],"category_scores_gemma":[0.00030319224,0.0021215861,0.0012317965,0.013279225,0.0011680487,0.015104653,0.003954305,0.0016219444,0.0052154777],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":true,"about_ca_system_consensus":true,"study_design_scores_codex":[0.009338692,0.0016507326,0.018632995,0.0012013452,0.0027257933,0.017888784,0.0054421723,0.069345504,0.00017606287,0.8212281,0.03931013,0.013059731],"study_design_scores_gemma":[0.008641473,0.0004895359,0.03502004,0.0033405225,0.0018308096,0.00024897742,0.07111784,0.00627711,0.0002202872,0.00037727592,0.86831135,0.004124771],"about_ca_topic_score_codex":0.006991331,"about_ca_topic_score_gemma":0.004707712,"teacher_disagreement_score":0.82900125,"about_ca_system_score_codex":0.004000174,"about_ca_system_score_gemma":0.006076575,"threshold_uncertainty_score":0.9998233},"labels":[],"label_agreement":null},{"id":"W7039667115","doi":"","title":"McGee, Mildred Interview 1","year":2007,"lang":"en","type":"article","venue":"DigitalResearch@Fordham (Fordham University)","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Miami; German; Graduation (instrument); Class (philosophy); Girl; Quarter (Canadian coin)","score_opus":0.18770589533908355,"score_gpt":0.3910077825254314,"score_spread":0.20330188718634784,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7039667115","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":[0.22696596,0.0002013642,0.13033992,0.0009147196,0.00055335206,0.0007532616,0.000113040776,0.0003288702,0.6398295],"genre_scores_gemma":[0.8991768,0.0006268537,0.001330225,0.00023768924,0.00014755288,0.0000012209978,0.00007805553,0.000047672165,0.098353945],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9947067,0.0002591439,0.0007462849,0.00089145795,0.0022954172,0.0011009807],"domain_scores_gemma":[0.99573207,0.0011249783,0.00028899126,0.0011306467,0.0010276424,0.0006956795],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0047163027,0.00032392977,0.00046753424,0.0024268616,0.00048714742,0.00061480334,0.0021302088,0.00020915293,0.0009490213],"category_scores_gemma":[0.0015233215,0.00028831753,0.00037722194,0.005045653,0.00041110197,0.002386785,0.0007634932,0.0004009944,0.001354117],"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":[0.00069921196,0.00056804996,0.04152064,0.000022491522,0.00016924775,0.0018705748,0.0008639021,0.0007744431,0.00017810753,0.036602113,0.05158284,0.86514837],"study_design_scores_gemma":[0.0010610321,0.00025542337,0.017674306,0.00003538987,0.00001679169,0.000044334196,0.003122789,0.00081830414,0.00058691687,0.0062632756,0.96965975,0.00046168314],"about_ca_topic_score_codex":0.00009277604,"about_ca_topic_score_gemma":0.00025078724,"teacher_disagreement_score":0.91807693,"about_ca_system_score_codex":0.00022402784,"about_ca_system_score_gemma":0.00024700718,"threshold_uncertainty_score":0.99996424},"labels":[],"label_agreement":null},{"id":"W7066821381","doi":"","title":"Los contactos interpersonales en situación intercultural en el barrio de Montreal Norte de la Región Metropolitana de Montreal, Quebec, Canadá","year":2012,"lang":"es","type":"other","venue":"Universidad de Chile","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Context (archaeology); Indigenous; Metropolitan area","score_opus":0.008383118611870695,"score_gpt":0.30302606601105253,"score_spread":0.29464294739918184,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7066821381","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":[0.3798007,0.004002333,0.010653286,0.0009246244,0.0006930631,0.0009343585,0.0014280378,0.00024909133,0.6013145],"genre_scores_gemma":[0.7542418,0.0048555406,0.0017504448,0.00042376632,0.00092772295,0.00001105152,0.00021100118,0.00018362966,0.237395],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9946491,0.0016061058,0.0006506273,0.00089788553,0.0007984025,0.0013978884],"domain_scores_gemma":[0.9956445,0.0014702033,0.0007001165,0.0008258111,0.00023350265,0.0011258735],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0012516592,0.00082356634,0.000983389,0.0011413548,0.00036644586,0.0004961477,0.0016320436,0.0011650602,0.0050602625],"category_scores_gemma":[0.0007373954,0.00072890706,0.00057232333,0.0006053507,0.0004473395,0.0006840694,0.00026702462,0.00092149613,0.0002827247],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00078079285,0.0004881483,0.1537512,0.00009239078,0.0015433771,0.0014035157,0.09274196,0.0020661342,0.0008460057,0.008010552,0.69718915,0.041086793],"study_design_scores_gemma":[0.0027886736,0.00023456775,0.202929,0.00079731026,0.001131738,0.00054754975,0.06353553,0.009052529,0.00047108336,0.0008971672,0.71563894,0.0019759105],"about_ca_topic_score_codex":0.57721585,"about_ca_topic_score_gemma":0.5488044,"teacher_disagreement_score":0.37444112,"about_ca_system_score_codex":0.00426726,"about_ca_system_score_gemma":0.0019697493,"threshold_uncertainty_score":0.9995552},"labels":[],"label_agreement":null},{"id":"W7071380563","doi":"","title":"Sensitivity Testing Using Expectiles with Applications in Extremes","year":2022,"lang":"en","type":"dissertation","venue":"Spectrum Research Repository (Concordia University)","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Nucleofection; Proteogenomics; TSG101; Fusible alloy; Gestational period; Dysgeusia","score_opus":0.10931897618777932,"score_gpt":0.36975024887053376,"score_spread":0.26043127268275446,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7071380563","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":[0.8786633,0.00013944332,0.0007295345,0.000036436697,0.00028238483,0.0008176044,0.000017821878,0.00007629485,0.119237155],"genre_scores_gemma":[0.963675,0.00008146495,0.00038678123,0.0000021105075,0.00017856182,0.000014312103,0.00010582,0.00004903595,0.03550691],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99325126,0.0017940578,0.00051768956,0.0012441654,0.0025196031,0.0006732294],"domain_scores_gemma":[0.994994,0.0024696281,0.0005225695,0.0009725094,0.00081265846,0.0002286572],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.00246154,0.00031828746,0.00053206185,0.0038552429,0.001718545,0.00041177816,0.0008849527,0.0002301504,0.00013303166],"category_scores_gemma":[0.0006270337,0.00030788427,0.00014214583,0.007897617,0.00022828019,0.0006038071,0.00023437137,0.0012936408,0.0000128226575],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00077859574,0.0003087197,0.9615813,0.000039223596,0.00009238699,0.0029069318,0.0024740207,0.017576303,0.005055092,0.002623987,0.00038145878,0.0061819972],"study_design_scores_gemma":[0.0014094353,0.00063701574,0.7725403,0.0002371231,0.00014999812,0.0002045068,0.13266715,0.018329881,0.008744264,0.0034413445,0.059938118,0.0017008969],"about_ca_topic_score_codex":0.035793304,"about_ca_topic_score_gemma":0.069609754,"teacher_disagreement_score":0.18904103,"about_ca_system_score_codex":0.0010399511,"about_ca_system_score_gemma":0.0019859516,"threshold_uncertainty_score":0.9999373},"labels":[],"label_agreement":null},{"id":"W7084070081","doi":"10.1109/infocom55648.2025.11044570","title":"Constrained Over-the-Air Model Updating for Wireless Online Federated Learning with Delayed Information","year":2025,"lang":"en","type":"article","venue":"","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Ontario Tech University; University of Toronto","funders":"","keywords":"Constraint (computer-aided design); Wireless; Queue; Wireless network; Channel (broadcasting); Fading; Sequence (biology); Server; Power (physics); Power control","score_opus":0.029527474618409027,"score_gpt":0.3379955882180535,"score_spread":0.30846811359964443,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7084070081","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":[0.2300606,0.0000045099623,0.7577032,0.0008727279,0.0000530351,0.00030020654,0.000017533675,0.00007232715,0.010915857],"genre_scores_gemma":[0.97267574,0.000017948563,0.022539672,0.0011358116,0.000015032998,0.00002118963,0.00018089451,0.000005246247,0.0034084853],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986159,0.000056724344,0.0005681154,0.00017053784,0.00041641243,0.00017228437],"domain_scores_gemma":[0.99826664,0.0005650408,0.00027583123,0.00016742987,0.0006879468,0.00003714094],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00091895723,0.0001125869,0.0001661517,0.000187065,0.0005429093,0.0003809761,0.00021576972,0.00006540965,0.000039450377],"category_scores_gemma":[0.0006505644,0.00006290413,0.00004610858,0.0007729292,0.000055850345,0.000844938,0.000038890856,0.00011878962,0.000008556319],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014158766,0.000018110473,0.0015285022,0.0000024996596,0.000022676522,1.6236072e-7,0.0003120508,0.87989205,0.00004792382,0.012957119,0.003947968,0.10112934],"study_design_scores_gemma":[0.00071935286,0.000051972296,0.00074865314,0.0000118045145,0.000013116967,0.0000014075491,0.0023373608,0.98905265,0.00011570247,0.0017836803,0.0050732377,0.000091037546],"about_ca_topic_score_codex":0.00004467485,"about_ca_topic_score_gemma":0.00019362847,"teacher_disagreement_score":0.7426151,"about_ca_system_score_codex":0.000021792757,"about_ca_system_score_gemma":0.0002449582,"threshold_uncertainty_score":0.41756743},"labels":[],"label_agreement":null},{"id":"W7095972278","doi":"","title":"Urbanites Creating New Ruralities: Reflections on Social Action and Struggle in the Greater Toronto Area.&amp;quot; The Great Lakes Geographer 7","year":2000,"lang":"en","type":"article","venue":"","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Rurality; Geographer; Politics; Metropolitan area; Variety (cybernetics); Restructuring; Rural area; Action (physics)","score_opus":0.18851215981341699,"score_gpt":0.4055608299141724,"score_spread":0.21704867010075543,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7095972278","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":[0.7736022,0.00015347992,0.00026273145,0.001981249,0.000107150816,0.00024029888,0.000008528714,0.000049974915,0.2235944],"genre_scores_gemma":[0.90216386,0.0005555989,0.00037114034,0.0009577105,0.00034694528,0.000027196289,0.0000121817475,0.000011658496,0.0955537],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9983094,0.0002738888,0.0003510006,0.00028567246,0.0005659836,0.00021407398],"domain_scores_gemma":[0.9988676,0.0006294234,0.000091101894,0.00030331215,0.00006571042,0.00004286835],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0007564167,0.00014686235,0.00014848587,0.00008437919,0.0007482755,0.0006184428,0.00026577106,0.00008289291,0.008714204],"category_scores_gemma":[0.00012187427,0.000068531685,0.000080940175,0.0005095835,0.00009162603,0.00055614643,0.000016179625,0.00011970336,0.00006237926],"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":[0.00033590553,0.00012986641,0.10001195,0.0000030507967,0.00006224646,0.0000037252153,0.11262702,0.0031849453,0.000098300654,0.006925122,0.23723084,0.53938705],"study_design_scores_gemma":[0.00095054775,0.00022110585,0.34436572,0.000028460332,0.000064916276,0.00004507671,0.08692641,0.001380193,0.000076280696,0.023868583,0.5415771,0.0004956043],"about_ca_topic_score_codex":0.0076154927,"about_ca_topic_score_gemma":0.036545955,"teacher_disagreement_score":0.53889143,"about_ca_system_score_codex":0.000035528257,"about_ca_system_score_gemma":0.000027020633,"threshold_uncertainty_score":0.99899286},"labels":[],"label_agreement":null},{"id":"W7096749754","doi":"","title":"Mathematical Finance Company – Canadian Parameters – September 1999 Stochastic Asset Generators for Investment Portfolios of Bonds, Stocks, Real Estate, Commodities, and Foreign Assets Based on the Double Mean Reverting Process™ and the Vector Autoregress","year":2011,"lang":"en","type":"article","venue":"","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Stochastic investment model; Asset allocation; Interest rate; Bond; Stock (firearms); Rate of return; Equity (law); Capital asset pricing model; Mean reversion; Arbitrage","score_opus":0.08285069067754365,"score_gpt":0.3105351742607664,"score_spread":0.22768448358322274,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7096749754","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":[0.95158345,0.00006680099,0.015169157,0.00053426926,0.00011826826,0.0027220878,0.00021164688,0.00003815454,0.029556148],"genre_scores_gemma":[0.991926,0.000027305736,0.0070933015,0.000512281,0.000015460279,0.00021482844,0.000021118825,0.000021041013,0.00016865385],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977115,0.0001870283,0.00072022976,0.0003924154,0.0006341878,0.0003546837],"domain_scores_gemma":[0.99693555,0.0016260301,0.0004677737,0.0005313413,0.00024443556,0.00019486659],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025021525,0.00024069192,0.00044709386,0.0001751442,0.0003957334,0.00016689998,0.0003825616,0.00008894331,0.00006494356],"category_scores_gemma":[0.00039010562,0.00011959769,0.000069293295,0.00033430773,0.00040128914,0.00019652699,0.000040251532,0.00012490977,0.0000024056897],"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":[0.0024045028,0.00046511076,0.029775845,0.00018012288,0.000282668,0.000025409401,0.019909034,0.066325024,0.000015810821,0.7494041,0.12100426,0.010208164],"study_design_scores_gemma":[0.0029411188,0.0003790258,0.006493307,0.00012754624,0.000119117576,0.000020979434,0.002694513,0.928199,0.0005350864,0.057315815,0.00074307265,0.00043142858],"about_ca_topic_score_codex":0.010288656,"about_ca_topic_score_gemma":0.012852152,"teacher_disagreement_score":0.861874,"about_ca_system_score_codex":0.000044039913,"about_ca_system_score_gemma":0.00033744375,"threshold_uncertainty_score":0.9963019},"labels":[],"label_agreement":null},{"id":"W7097465033","doi":"","title":"A proposed stochastic simulation framework for the government of canada’s debt strategy problem. Bank of Canada: Working Paper 2003-10","year":2003,"lang":"en","type":"article","venue":"","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Debt; Government (linguistics); Stochastic modelling; Bad debt; Government debt","score_opus":0.047450173693235634,"score_gpt":0.30326134662343374,"score_spread":0.2558111729301981,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7097465033","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":[0.0043305494,0.00022116741,0.9697655,0.00065786694,0.00037300092,0.0013767093,0.00003621395,0.000007658131,0.023231335],"genre_scores_gemma":[0.98450947,0.000008043345,0.010285358,0.0001358666,0.000021821092,0.000015390695,0.0000016806156,0.000010003132,0.005012384],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9967599,0.0000870793,0.00070897676,0.00023668598,0.0019535068,0.00025385778],"domain_scores_gemma":[0.99658173,0.0019024431,0.00047712983,0.0003636942,0.00059911393,0.00007588631],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011465695,0.00012393376,0.0002298376,0.000027306045,0.00016937575,0.000041441297,0.00027149275,0.0000666359,0.00080611417],"category_scores_gemma":[0.0030172258,0.00007250367,0.00003894845,0.0006106804,0.000036459252,0.00009612947,0.000017060185,0.00007499202,2.913211e-7],"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":[0.00005275867,0.000020698251,0.00057446375,0.0000037057803,0.000018097364,3.595218e-7,0.00009117886,0.9494051,0.000016400361,0.034746464,0.0071733287,0.007897457],"study_design_scores_gemma":[0.0009152317,0.00020787452,0.006833874,0.000102772596,0.000087309425,0.0000020315802,0.0035951813,0.8795074,0.001628303,0.05367775,0.05300969,0.00043257608],"about_ca_topic_score_codex":0.44200101,"about_ca_topic_score_gemma":0.9586621,"teacher_disagreement_score":0.9801789,"about_ca_system_score_codex":0.00019024285,"about_ca_system_score_gemma":0.0049136654,"threshold_uncertainty_score":0.88263845},"labels":[],"label_agreement":null},{"id":"W7100185944","doi":"","title":"On Portfolio Separation Theorems with Heterogeneous Beliefs and Attitudes towards Risk","year":2008,"lang":"en","type":"article","venue":"","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Portfolio; Separation (statistics); Empirical research; Mutual fund separation theorem","score_opus":0.050116515261919806,"score_gpt":0.3509335784180551,"score_spread":0.3008170631561353,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7100185944","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":[0.9304915,0.00012761298,0.02671755,0.000104412415,0.00009321735,0.00018102555,0.000008218705,0.00006727474,0.042209204],"genre_scores_gemma":[0.9909942,0.0015137533,0.0024572266,0.0002495236,0.00006174386,0.0000078599405,0.00000766393,0.000011777565,0.004696249],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9980585,0.000105937084,0.00034117754,0.00042746548,0.00089045655,0.00017647655],"domain_scores_gemma":[0.99888635,0.00021368184,0.00019307081,0.0003992529,0.0001888231,0.00011879691],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006638298,0.00015367976,0.00020379292,0.00019593323,0.0003238075,0.00013495113,0.00018320551,0.00007074785,0.0004703575],"category_scores_gemma":[0.00021175854,0.00008732054,0.000046155987,0.0003998629,0.00015181243,0.00027443125,0.000035303245,0.000086506145,0.00015399393],"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":[0.0006372372,0.0003847465,0.64462596,0.0000026783327,0.00011149089,0.00020555763,0.0026737275,0.15678018,0.00007143881,0.025307525,0.026922062,0.14227737],"study_design_scores_gemma":[0.0024601307,0.00282367,0.87484294,0.000029780469,0.00006553763,0.0012152627,0.00022690596,0.034298625,0.005909515,0.051621776,0.025360914,0.001144933],"about_ca_topic_score_codex":0.00012993801,"about_ca_topic_score_gemma":0.000093862545,"teacher_disagreement_score":0.23021697,"about_ca_system_score_codex":0.000013563717,"about_ca_system_score_gemma":0.00005985917,"threshold_uncertainty_score":0.5150085},"labels":[],"label_agreement":null},{"id":"W7112476317","doi":"","title":"Portfolio risk dynamics: Managing value-at-risk across stocks and bonds","year":2025,"lang":"en","type":"dissertation","venue":"Repositório do ISCTE-IUL","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Portfolio; Bond; Metric (unit); Equity (law); Investment strategy; Risk management; Asset allocation; Asset (computer security); Portfolio investment; Value at risk","score_opus":0.01600368584396347,"score_gpt":0.3429648592506483,"score_spread":0.32696117340668485,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7112476317","genre_codex":"empirical","genre_gemma":"other","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":[0.81095403,0.0076981424,0.0076288003,0.00019863043,0.0034796088,0.0013093314,0.00083898514,0.00032415686,0.16756833],"genre_scores_gemma":[0.4171495,0.030497428,0.003140516,0.00027798148,0.0005114914,0.00016483365,0.004117727,0.00023460659,0.5439059],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9911089,0.0005728503,0.0023701603,0.0023026508,0.0025295115,0.0011159166],"domain_scores_gemma":[0.9921478,0.00093283446,0.0031557907,0.0021586837,0.00092618226,0.0006787082],"candidate_categories":["metaepi_narrow","sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.004397273,0.00092023355,0.0013689452,0.0010953228,0.001866005,0.0019092507,0.0012023986,0.0010381918,0.0001945046],"category_scores_gemma":[0.0040936274,0.00080889213,0.0004989062,0.0019100879,0.00012542047,0.00064849295,0.00046338415,0.0011773595,0.00019046493],"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":[0.001194755,0.00029505373,0.29377586,0.0002185348,0.0009707908,0.00056809065,0.014129152,0.02763551,0.000064110376,0.0041625877,0.06386643,0.59311914],"study_design_scores_gemma":[0.0042456794,0.0007890688,0.35643756,0.0017663476,0.003385067,0.0005161367,0.02521528,0.35601977,0.0008712403,0.06964032,0.17518696,0.0059265606],"about_ca_topic_score_codex":0.0019397874,"about_ca_topic_score_gemma":0.003989305,"teacher_disagreement_score":0.5871926,"about_ca_system_score_codex":0.00053136336,"about_ca_system_score_gemma":0.00068270444,"threshold_uncertainty_score":0.9994362},"labels":[],"label_agreement":null},{"id":"W7113901295","doi":"10.52843/cassyni.g78kyf","title":"The Role of Relief Payments and Premium Subsidies in Optimal Insurance Contracting","year":2025,"lang":"","type":"article","venue":"","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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 Calgary","funders":"","keywords":"Subsidy; Payment; Bridging (networking); Multidisciplinary approach; Risk premium; Risk management; Insurance policy; Payment by Results","score_opus":0.014305739922841464,"score_gpt":0.31569057350992025,"score_spread":0.3013848335870788,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7113901295","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":[0.89792794,0.02246437,0.0015124143,0.0009606556,0.0006096735,0.00048016675,0.0000107574215,0.000011888949,0.07602212],"genre_scores_gemma":[0.9627055,0.031244867,0.00083155395,0.000058720434,0.00001684625,0.0000102400845,6.982106e-7,0.000005105852,0.005126478],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.997218,0.00024335775,0.0012373106,0.00042226329,0.00055918033,0.0003198618],"domain_scores_gemma":[0.99618775,0.002567353,0.00046449745,0.00041256406,0.00031574396,0.00005209076],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027315198,0.00017215154,0.00035974456,0.0002217059,0.0003577281,0.00031846954,0.00043388715,0.0001245766,0.000042334326],"category_scores_gemma":[0.00197869,0.00010806209,0.00006184001,0.0010258361,0.00029255787,0.0005101788,0.00019888175,0.0002002921,0.0000063090947],"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":[0.00025766806,0.00008822844,0.7003657,0.0000058436085,0.000026695792,0.0000014528694,0.001338073,0.005050952,0.00034518517,0.0061280844,0.000279913,0.2861122],"study_design_scores_gemma":[0.0018684571,0.00015915134,0.74522513,0.0003127341,0.000035827605,0.000004239794,0.01927604,0.15350388,0.017720588,0.014953095,0.046605412,0.00033547514],"about_ca_topic_score_codex":0.00029860568,"about_ca_topic_score_gemma":0.0002697749,"teacher_disagreement_score":0.2857767,"about_ca_system_score_codex":0.000023018787,"about_ca_system_score_gemma":0.000168854,"threshold_uncertainty_score":0.44066444},"labels":[],"label_agreement":null},{"id":"W7116295801","doi":"10.1016/j.insmatheco.2025.103202","title":"Optimal reinsurance design under convex premium principles and distortion risk measures","year":2025,"lang":"en","type":"article","venue":"Insurance Mathematics and Economics","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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 Calgary","funders":"Shenzhen Science and Technology Innovation Program; Alberta Innovates; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Reinsurance; Distortion (music); Regular polygon; Convex optimization; Noise (video); Risk premium","score_opus":0.07792061275035057,"score_gpt":0.3047626779010117,"score_spread":0.22684206515066113,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7116295801","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":[0.65179116,0.0006651081,0.34513795,0.00016154912,0.00018694956,0.0002051964,0.000024019742,0.000022290395,0.00180576],"genre_scores_gemma":[0.94918513,0.0107868705,0.039092652,0.00007475619,0.000030213881,0.00001956254,0.0000021943406,0.000012262218,0.0007963635],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99848264,0.00006662876,0.0007182041,0.00039508948,0.00015660713,0.00018082073],"domain_scores_gemma":[0.9982971,0.0006645263,0.00043969974,0.000389939,0.00013384725,0.00007488941],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018363667,0.00017031125,0.00036643227,0.00014525266,0.00025784184,0.00034839258,0.00021641185,0.00010506242,0.000008340472],"category_scores_gemma":[0.0006076297,0.00014049302,0.000049379185,0.00016750247,0.00015813523,0.00032050724,0.00007696148,0.00010860795,0.000012193184],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016150082,0.00016204375,0.18302453,0.00006182227,0.00012964892,0.000002008472,0.0025589487,0.6095346,0.00008837956,0.0711225,0.0008907101,0.13226329],"study_design_scores_gemma":[0.00095451326,0.000067770176,0.17362052,0.00008798069,0.000051865678,0.000017904087,0.00079959753,0.6252285,0.0008821807,0.18610542,0.011726179,0.00045755255],"about_ca_topic_score_codex":0.00001604887,"about_ca_topic_score_gemma":0.000024737023,"teacher_disagreement_score":0.3060453,"about_ca_system_score_codex":0.00003472419,"about_ca_system_score_gemma":0.000060814244,"threshold_uncertainty_score":0.57291394},"labels":[],"label_agreement":null},{"id":"W7117467720","doi":"10.1016/j.jeconom.2025.106166","title":"Exogenous consideration and extended random utility","year":2025,"lang":"en","type":"article","venue":"Journal of Econometrics","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Western University","funders":"Social Sciences and Humanities Research Council of Canada; Western University; Rice University","keywords":"Random variable; Stochastic process; Utility theory; Estimation","score_opus":0.24081012066253546,"score_gpt":0.36277255812752285,"score_spread":0.12196243746498739,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7117467720","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":[0.77670646,0.0047541675,0.1945327,0.0011282983,0.0013417293,0.00014267907,0.000006452727,0.0000065795048,0.021380961],"genre_scores_gemma":[0.9942398,0.0020065985,0.0028241242,0.00020555792,0.00005232765,4.1899935e-7,4.7139872e-7,0.0000021073606,0.00066860864],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9985086,0.000104045794,0.00091105717,0.0001266659,0.0002632329,0.00008642789],"domain_scores_gemma":[0.996881,0.0016998413,0.00060519116,0.00016107241,0.00057919824,0.00007367961],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003912571,0.00006226098,0.00029237935,0.0015776255,0.00010265753,0.00027867922,0.00016027514,0.00005569536,0.00017210751],"category_scores_gemma":[0.0060279747,0.000045639605,0.00008564722,0.0014391597,0.00004571443,0.00043945282,0.000031152635,0.00010116477,0.000007721012],"study_design_candidate":"design_other","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":[0.00034867701,0.00018708104,0.229854,0.000005787332,0.000086566404,0.000024195568,0.0004760287,0.0027098346,0.00003173298,0.005267978,0.023988985,0.7370191],"study_design_scores_gemma":[0.006900133,0.00042574434,0.46058288,0.000026252546,0.0001275175,0.00024384825,0.0014722758,0.04515323,0.0007320761,0.31636974,0.16767345,0.00029286547],"about_ca_topic_score_codex":0.000004823783,"about_ca_topic_score_gemma":0.000007691205,"teacher_disagreement_score":0.7367263,"about_ca_system_score_codex":0.000026978398,"about_ca_system_score_gemma":0.00015546019,"threshold_uncertainty_score":0.7216484},"labels":[],"label_agreement":null},{"id":"W7117489396","doi":"10.2139/ssrn.5983154","title":"Randomized Promises in a Moral Hazard Model&amp;nbsp;","year":2025,"lang":"","type":"preprint","venue":"SSRN Electronic Journal","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"York University","funders":"","keywords":"Constraint (computer-aided design); Moral hazard; Rationality; Ex-ante; Principal (computer security); Expected utility hypothesis; Limited liability; Participation constraint","score_opus":0.062321320068435465,"score_gpt":0.3651608731386371,"score_spread":0.3028395530702016,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7117489396","genre_codex":"methods","genre_gemma":"review","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":[0.10285907,0.035568025,0.8197131,0.0034481394,0.003935335,0.004110557,0.00007140326,0.000118145625,0.030176206],"genre_scores_gemma":[0.46579787,0.47013903,0.0063024173,0.00021365652,0.00049024384,0.00020989627,0.000044262786,0.00007084996,0.056731764],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9814761,0.0028007166,0.0051227626,0.0016608688,0.0028399196,0.006099609],"domain_scores_gemma":[0.99341756,0.0012601655,0.0023202086,0.0014311549,0.0011543073,0.00041657916],"candidate_categories":["metaresearch","metaepi_narrow","scholarly_communication","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.03880562,0.0012318972,0.003653296,0.0029259135,0.0006283752,0.0015069065,0.00295066,0.0011536416,0.0002587073],"category_scores_gemma":[0.00332296,0.0009955234,0.0019295663,0.002145348,0.00051798567,0.00096913293,0.0007652305,0.012249341,0.00016085553],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.038091242,0.00026734115,0.0009395492,0.000024728386,0.00057397207,0.0000103325265,0.0013103787,0.81212187,0.000005628877,0.080651894,0.00065902516,0.06534405],"study_design_scores_gemma":[0.0659944,0.00005872454,0.00001802688,0.0002347468,0.00023304457,0.00014389689,0.00041974836,0.35039982,0.000008555738,0.58098125,0.0009248042,0.00058296416],"about_ca_topic_score_codex":0.00024659908,"about_ca_topic_score_gemma":0.0047887615,"teacher_disagreement_score":0.8134107,"about_ca_system_score_codex":0.0032277412,"about_ca_system_score_gemma":0.040632877,"threshold_uncertainty_score":0.9995296},"labels":[],"label_agreement":null},{"id":"W7117851272","doi":"","title":"Lambda Expected Shortfall","year":2025,"lang":"","type":"article","venue":"ArXiv.org","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Generalization; Expected shortfall; Measure (data warehouse); Lambda; Relation (database); Risk measure; Dual (grammatical number)","score_opus":0.0875682278821364,"score_gpt":0.380504471667738,"score_spread":0.29293624378560157,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7117851272","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":[0.89012444,0.0036955313,0.0130315935,0.0024745776,0.004566803,0.00049040664,0.000018531691,0.00014757559,0.08545053],"genre_scores_gemma":[0.917768,0.0051479572,0.0006016163,0.001229337,0.00028051427,0.000026728154,0.000022470416,0.000026872898,0.0748965],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99485767,0.0003849577,0.0016150096,0.0012697595,0.0011261718,0.0007464333],"domain_scores_gemma":[0.996215,0.0007546426,0.00042920918,0.0016436453,0.0006829506,0.000274556],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.001430256,0.00045677435,0.0006697492,0.00078603823,0.00061683706,0.0004781486,0.0013873046,0.0004128591,0.0033371046],"category_scores_gemma":[0.0018743993,0.0004003567,0.00034466773,0.0041618887,0.0002950398,0.0007714719,0.00042197228,0.00043765816,0.0033825238],"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":[0.000084882675,0.00018762256,0.91282237,0.0000062575764,0.00007557083,0.000035273,0.0007177984,0.00082193303,0.0002888508,0.0013943632,0.046433825,0.037131265],"study_design_scores_gemma":[0.000790519,0.00011064071,0.6624865,0.000095875774,0.000115038165,0.0000049347464,0.0012152793,0.0058381055,0.0018777462,0.002073948,0.32489493,0.00049647625],"about_ca_topic_score_codex":0.00017496514,"about_ca_topic_score_gemma":0.00012433472,"teacher_disagreement_score":0.2784611,"about_ca_system_score_codex":0.00009826694,"about_ca_system_score_gemma":0.00052700046,"threshold_uncertainty_score":0.99984485},"labels":[],"label_agreement":null},{"id":"W7118595361","doi":"10.70764/gdpu-sft.2025.1(2)-07","title":"A Hybrid NAKA-FA-PSO Algorithm with Nakagami Distribution for Multi-Objective Portfolio Optimization","year":2025,"lang":"","type":"article","venue":"Start-up and Financial Technology","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Particle swarm optimization; Portfolio; Portfolio optimization; Cardinality (data modeling); Multi-objective optimization; Optimization problem; Firefly algorithm; Nakagami distribution","score_opus":0.019581559445201128,"score_gpt":0.3063777615289546,"score_spread":0.28679620208375345,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7118595361","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":[0.014092183,0.0025263054,0.9755834,0.0014495532,0.0018989046,0.0021494858,0.0017261897,0.00024028053,0.00033374826],"genre_scores_gemma":[0.66029024,0.016502118,0.30771756,0.0009693009,0.000493611,0.0010844518,0.0016887205,0.0001332387,0.011120759],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99535626,0.00011997324,0.0013389188,0.0016602523,0.00055650924,0.0009681007],"domain_scores_gemma":[0.99577224,0.00026519582,0.0007720756,0.00081529235,0.0022099488,0.00016524064],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0010815038,0.0006506926,0.0010260941,0.0014212789,0.001303187,0.00030902078,0.00062678073,0.0009132642,0.000063625426],"category_scores_gemma":[0.002527726,0.0005797179,0.00019596901,0.0035727152,0.0011158407,0.00065172854,0.00030434688,0.00055069255,0.000014003702],"study_design_candidate":"design_other","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":[0.0010954352,0.00043430188,0.0028484524,0.000038482558,0.00011490502,0.00004815131,0.00036050417,0.0059853666,0.00002329036,0.026665248,0.0070406464,0.9553452],"study_design_scores_gemma":[0.009584514,0.0028017466,0.003733049,0.0003311083,0.0005544325,0.00011562458,0.002077062,0.7249238,0.0032887054,0.04451333,0.20657417,0.0015025005],"about_ca_topic_score_codex":0.00008753588,"about_ca_topic_score_gemma":0.00008757636,"teacher_disagreement_score":0.9538427,"about_ca_system_score_codex":0.00020313564,"about_ca_system_score_gemma":0.0011298272,"threshold_uncertainty_score":0.99999696},"labels":[],"label_agreement":null},{"id":"W7125360105","doi":"","title":"Robust Fine-Tuning from Non-Robust Pretrained Models: Mitigating Suboptimal Transfer With Epsilon-Scheduling","year":2025,"lang":"en","type":"article","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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; Consortium de Recherche et d’innovation en Aérospatiale au Québec","keywords":"Noise (video); Key (lock); Robustness (evolution); Transfer (computing); Feature (linguistics)","score_opus":0.04455545748887656,"score_gpt":0.26269691039877996,"score_spread":0.2181414529099034,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7125360105","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":[0.20901276,0.00026064573,0.730069,0.0032527167,0.00011168746,0.00027753264,0.00003357675,0.00013130756,0.056850813],"genre_scores_gemma":[0.65430725,0.00012593536,0.3397206,0.00011197217,0.000015713826,0.000028762492,0.00014892092,0.000028472034,0.0055123977],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99438876,0.0021856013,0.00095285976,0.0010056057,0.0010007917,0.00046641135],"domain_scores_gemma":[0.991555,0.0036104566,0.00030649404,0.001657585,0.0026613276,0.00020916607],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0071023214,0.00033842688,0.00046115616,0.00041760673,0.0007521129,0.00090962485,0.0013814701,0.00020512237,0.0003385664],"category_scores_gemma":[0.0020913705,0.00029321457,0.00018148792,0.0019211977,0.0002646623,0.00083068036,0.00022760543,0.00043909278,0.00003034016],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001241075,0.000411972,0.015191578,0.000021859767,0.00014132766,0.000013742934,0.018413346,0.82213026,0.0033916817,0.043256868,0.00092789513,0.09597536],"study_design_scores_gemma":[0.0010207033,0.0000010957244,0.00241429,0.0007246275,0.000050749826,0.0000036034373,0.0008340372,0.9775031,0.011608391,0.004652734,0.00084287213,0.00034375227],"about_ca_topic_score_codex":0.00095863256,"about_ca_topic_score_gemma":0.0020072763,"teacher_disagreement_score":0.44529447,"about_ca_system_score_codex":0.00006976401,"about_ca_system_score_gemma":0.00035255114,"threshold_uncertainty_score":0.999952},"labels":[],"label_agreement":null},{"id":"W7125917433","doi":"10.1145/3768292.3793404","title":"10.1145/3768292.3793404","year":2000,"lang":"en","type":"article","venue":"Time to knit","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Session (web analytics); Robust optimization; Robustness (evolution); Work (physics)","score_opus":0.028147181656655487,"score_gpt":0.2802145995348411,"score_spread":0.25206741787818565,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7125917433","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0023044087,0.000043064756,0.000044483735,0.0005270198,0.00000807513,0.00013650474,0.000011776739,0.00008396795,0.9968407],"genre_scores_gemma":[0.000832461,0.0000034612156,0.0006352045,0.00014331663,0.00012399962,0.000007743659,0.000009086486,0.0000132366,0.9982315],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9982178,0.0000823866,0.00037815474,0.0003445701,0.0007396631,0.00023738903],"domain_scores_gemma":[0.9988758,0.00018602854,0.000062082494,0.00054777355,0.00014145263,0.00018687437],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0007857935,0.00011597656,0.00018467645,0.00018085864,0.000133081,0.00019864891,0.00050300884,0.00006506251,0.9967991],"category_scores_gemma":[0.00031050012,0.00009047582,0.00007668941,0.00084838073,0.000031588865,0.00024941194,0.00004228746,0.00006316368,0.9952863],"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":[0.000027509952,0.000015924019,0.000002677023,1.072687e-7,0.0000022736647,0.0000025355264,0.000024292842,0.0029843405,0.0000049903047,0.0000028055367,0.34794557,0.64898694],"study_design_scores_gemma":[0.00013092115,0.000072428586,0.00023311915,0.0000028348597,0.000005355649,0.0000059612335,0.000002181812,0.0029858088,0.000036080335,0.00019887353,0.99619746,0.00012896594],"about_ca_topic_score_codex":0.00001970388,"about_ca_topic_score_gemma":2.7398795e-7,"teacher_disagreement_score":0.648858,"about_ca_system_score_codex":0.000015814168,"about_ca_system_score_gemma":0.00003641377,"threshold_uncertainty_score":0.36894968},"labels":[],"label_agreement":null},{"id":"W7126407628","doi":"10.21428/594757db.fff2144f","title":"Deep Hedging with Market Impact","year":2024,"lang":"en","type":"article","venue":"","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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; Concordia University","funders":"","keywords":"Portfolio; Market liquidity; Reinforcement learning; Factoring; Market impact; Replicating portfolio; Investment (military); Forward contract","score_opus":0.0398853827878986,"score_gpt":0.38940357802729275,"score_spread":0.34951819523939415,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7126407628","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":[0.028173655,0.00044659135,0.47574005,0.0004455879,0.00025493736,0.00007549699,0.0000016501606,0.00014206885,0.49471998],"genre_scores_gemma":[0.9620834,0.00009050266,0.004720277,0.00008931991,0.00006456358,0.0000020990826,0.0000015781385,0.000008158117,0.03294012],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989271,0.000037071077,0.00017267719,0.0002287035,0.0005040529,0.00013036089],"domain_scores_gemma":[0.9992932,0.0003123343,0.000023584673,0.0002297463,0.00007078354,0.0000703618],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0008539663,0.00006925288,0.00008800408,0.00019730625,0.000052082345,0.00058368384,0.00016436966,0.000019499506,0.008381972],"category_scores_gemma":[0.00012191941,0.000032482334,0.000054239736,0.000859498,0.000022603903,0.00038685478,0.000022318907,0.000050955463,0.0006269243],"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":[0.00006696237,0.000020189902,0.057685234,0.000002774244,0.000045965397,0.00014275224,0.000832421,0.038542926,0.000024240606,0.0036249012,0.30828384,0.5907278],"study_design_scores_gemma":[0.0000901682,0.000072239825,0.013212515,0.000013439835,0.000009494113,0.00007370658,0.0003526286,0.903055,0.00006944408,0.007026803,0.07588962,0.0001349295],"about_ca_topic_score_codex":0.000029981627,"about_ca_topic_score_gemma":0.000032525368,"teacher_disagreement_score":0.9339097,"about_ca_system_score_codex":0.000015730468,"about_ca_system_score_gemma":0.000058091573,"threshold_uncertainty_score":0.9925245},"labels":[],"label_agreement":null},{"id":"W7132860179","doi":"","title":"Risk Parity Return Trade-off in Relaxed Risk Parity Portfolio Optimization","year":2020,"lang":"","type":"dissertation","venue":"TSpace","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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 Toronto","keywords":"Parity (physics); Portfolio; Portfolio optimization; Interest rate parity; Rate of return on a portfolio; Robustness (evolution); Risk–return spectrum; Asset allocation","score_opus":0.03444972855854539,"score_gpt":0.35560464813178727,"score_spread":0.3211549195732419,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7132860179","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":[0.68638915,0.016684178,0.19003682,0.0052619292,0.013749328,0.008940963,0.0010147042,0.0010114469,0.07691148],"genre_scores_gemma":[0.75030434,0.21236256,0.021924311,0.00028603882,0.00075475493,0.00011174681,0.003974438,0.0002993365,0.009982478],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9821577,0.003851494,0.0046261107,0.0037281555,0.004100529,0.001536019],"domain_scores_gemma":[0.98650575,0.0019348296,0.007219509,0.0023626422,0.00086709054,0.0011101638],"candidate_categories":["metaresearch","metaepi_narrow","sts","scholarly_communication","research_integrity","insufficient_payload"],"consensus_categories":["metaepi_narrow","research_integrity"],"category_scores_codex":[0.0071723657,0.0017558126,0.0026956233,0.0016654929,0.0013889892,0.0013139835,0.0021500462,0.0027699461,0.0039508813],"category_scores_gemma":[0.013613598,0.0017537813,0.0010106905,0.007640519,0.0003388702,0.0016288233,0.00025943192,0.00455761,0.00055500225],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.002338149,0.00084324303,0.23210976,0.00008483659,0.0002487176,0.00020697694,0.047794018,0.6211825,0.00002661742,0.00037691992,0.017378297,0.07740994],"study_design_scores_gemma":[0.0028550804,0.00049223495,0.1248558,0.0002993464,0.00089682016,0.000018828601,0.018883137,0.817523,0.00039048196,0.0030212367,0.028506542,0.0022575164],"about_ca_topic_score_codex":0.0048282193,"about_ca_topic_score_gemma":0.0028710952,"teacher_disagreement_score":0.19634044,"about_ca_system_score_codex":0.00047171026,"about_ca_system_score_gemma":0.0014902828,"threshold_uncertainty_score":0.99991107},"labels":[],"label_agreement":null},{"id":"W7132946526","doi":"","title":"A Look at Model Uncertainty in the Evaluation of Commodity Contingent Claims: A Practitioner&apos;s Guide","year":2013,"lang":"en","type":"dissertation","venue":"TSpace","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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; University of Toronto","keywords":"Flexibility (engineering); Measure (data warehouse); Commodity; Characterization (materials science); Key (lock); Financial market; Model risk; Uncertainty analysis","score_opus":0.22093798230498168,"score_gpt":0.5043900127806263,"score_spread":0.28345203047564466,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7132946526","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":[0.9168382,0.0005853986,0.0011670074,0.0008816021,0.00046186792,0.0017523189,0.000043405467,0.000022492346,0.07824766],"genre_scores_gemma":[0.95423526,0.0006766825,0.00096285687,0.00022212701,0.0000971601,0.00034802448,0.0018224772,0.000029984812,0.041605435],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9934128,0.0011077472,0.0011180467,0.00052099937,0.003596894,0.00024350868],"domain_scores_gemma":[0.9941385,0.0008904124,0.0016560947,0.00095874415,0.0022969854,0.0000592858],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0097791,0.00026110653,0.00047959594,0.0003652522,0.00018343641,0.00016598447,0.0007469531,0.0003159509,0.00078958535],"category_scores_gemma":[0.0028310795,0.00017402665,0.00017543168,0.00070489093,0.00006403214,0.00030912942,0.000050920113,0.00031653757,0.00013848548],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020124968,0.0002193003,0.0006831928,0.000022271268,0.00003593461,0.0000017259343,0.038450293,0.7666971,0.00036098872,0.0014088281,0.17516266,0.016756434],"study_design_scores_gemma":[0.0007836685,0.000051310268,0.004729585,0.00009723937,0.00017318419,0.0000034721206,0.020021869,0.94854563,0.000317249,0.014670101,0.010329791,0.00027691797],"about_ca_topic_score_codex":0.002084398,"about_ca_topic_score_gemma":0.009055301,"teacher_disagreement_score":0.1818485,"about_ca_system_score_codex":0.00028114067,"about_ca_system_score_gemma":0.0006351581,"threshold_uncertainty_score":0.8645406},"labels":[],"label_agreement":null},{"id":"W7134188215","doi":"10.1109/bigdata66926.2025.11402167","title":"ABL: An LLM-Based Agentic Framework for Applying Black-Litterman Portfolio Optimization","year":2025,"lang":"","type":"article","venue":"","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Portfolio; Portfolio optimization; Context (archaeology); Efficient frontier; Process (computing)","score_opus":0.0665383814378693,"score_gpt":0.4059867359715864,"score_spread":0.33944835453371713,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7134188215","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":[0.0040338663,0.0003319785,0.97158974,0.0017015039,0.0028855808,0.003278752,0.000057390662,0.00022947654,0.015891733],"genre_scores_gemma":[0.5055313,0.0009644817,0.46925727,0.0062851547,0.00043781425,0.00039419622,0.00035726643,0.00009143527,0.016681092],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99267477,0.00043056178,0.0025046517,0.0018643903,0.0014703986,0.00105523],"domain_scores_gemma":[0.99332774,0.0017982194,0.0009999181,0.002089915,0.0013119585,0.0004722659],"candidate_categories":["metaepi_narrow","scholarly_communication","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0034129773,0.00071020913,0.00092846167,0.0016862948,0.0011723596,0.0022249345,0.0015650949,0.0008191694,0.004815595],"category_scores_gemma":[0.0019369561,0.00065669214,0.0005532419,0.0040621767,0.00038197753,0.0015439355,0.00017869327,0.00041079218,0.00019718103],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002608469,0.0005908912,0.0038608946,0.000059164602,0.00007316904,0.0000072159137,0.00035246846,0.88797754,0.00001707559,0.027319323,0.012449083,0.06703232],"study_design_scores_gemma":[0.0013073197,0.00029900504,0.00081135763,0.00022035625,0.00027483812,0.0000014806245,0.0008960807,0.9289044,0.0006227092,0.036999714,0.02898403,0.00067870994],"about_ca_topic_score_codex":0.000053640488,"about_ca_topic_score_gemma":0.000019323035,"teacher_disagreement_score":0.50233245,"about_ca_system_score_codex":0.00015877647,"about_ca_system_score_gemma":0.00096098357,"threshold_uncertainty_score":0.99958843},"labels":[],"label_agreement":null},{"id":"W7142732056","doi":"10.23952/jano.8.2026.1.04","title":"Primal-dual resampling for solution validation in convex stochastic programming","year":2025,"lang":"","type":"article","venue":"Journal of Applied and Numerical Optimization","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Resampling; Convex optimization; Regular polygon; Measure (data warehouse); Linear programming","score_opus":0.03453323664915676,"score_gpt":0.3424886123195521,"score_spread":0.3079553756703953,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7142732056","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":[0.004692113,0.0005972502,0.99121124,0.0011340277,0.00083832693,0.0010283706,0.000007010401,0.0000130707185,0.00047857242],"genre_scores_gemma":[0.7762791,0.00085329864,0.22235061,0.0001306191,0.00022009488,0.000029485916,0.000028720511,0.000021137836,0.000086959204],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9958052,0.00013107508,0.0023870824,0.0004906124,0.00077639555,0.00040964416],"domain_scores_gemma":[0.9957855,0.0011092339,0.0017828905,0.00019453626,0.00095920276,0.0001686538],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0032280798,0.00029392156,0.0007903358,0.0011111922,0.0003414716,0.0005259787,0.0002446655,0.00031606507,0.00003559594],"category_scores_gemma":[0.0012564696,0.0002599325,0.00016617839,0.0017009634,0.00013315711,0.0007139316,0.00008087794,0.00036717023,0.0000018480434],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014314641,0.000197451,0.0001997086,0.000029560368,0.000037201917,0.000001385991,0.0005614962,0.84049124,0.00015599128,0.0025551987,0.00018648953,0.15415283],"study_design_scores_gemma":[0.0029518532,0.0003973263,0.00037025413,0.00021611106,0.00018768746,0.000012145282,0.00071664614,0.9855794,0.00033013764,0.006660446,0.0023127077,0.00026529355],"about_ca_topic_score_codex":0.00000849278,"about_ca_topic_score_gemma":7.8826736e-7,"teacher_disagreement_score":0.77158695,"about_ca_system_score_codex":0.000183384,"about_ca_system_score_gemma":0.00044158517,"threshold_uncertainty_score":0.9999853},"labels":[],"label_agreement":null},{"id":"W7154589382","doi":"10.66573/001c.127632","title":"Reciprocal Reinsurance Treaties Under an Optimal and Fair Joint Survival Probability","year":2017,"lang":"en","type":"article","venue":"Variance","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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; Concordia University","keywords":"Reinsurance; Reciprocal; Joint (building); Point (geometry); Focus (optics); Value (mathematics)","score_opus":0.20618043695551266,"score_gpt":0.3869639175119542,"score_spread":0.18078348055644153,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7154589382","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":[0.87771195,0.00014175907,0.10427899,0.0017283717,0.00067539385,0.0002846944,0.000024563136,0.00006492903,0.015089358],"genre_scores_gemma":[0.9783625,0.00018605121,0.018623766,0.000058582867,0.00014317196,0.000014996305,0.0000033175481,0.000010444963,0.002597185],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9979349,0.00019441407,0.00042292525,0.00062434754,0.0005969417,0.00022648807],"domain_scores_gemma":[0.99779254,0.00017295388,0.00034523307,0.001296027,0.0002567592,0.0001364764],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020224568,0.00014907701,0.00028990288,0.00005560025,0.0006864209,0.00080045,0.00055850606,0.00009904554,0.000111657195],"category_scores_gemma":[0.0014858536,0.00010999898,0.000048807757,0.0001274304,0.00032272763,0.001332183,0.00012480699,0.00011269679,0.000048354224],"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":[0.0012197281,0.00052971754,0.38691118,0.000025486306,0.00006334317,0.000044059405,0.0039030572,0.050085157,0.00070301973,0.10506052,0.0030317164,0.448423],"study_design_scores_gemma":[0.0005601108,0.00019239256,0.88457966,0.000016991637,0.000009449706,0.0000114140375,0.00020556603,0.03179184,0.00035847333,0.076290585,0.005711982,0.00027153263],"about_ca_topic_score_codex":0.00018126534,"about_ca_topic_score_gemma":0.00017590477,"teacher_disagreement_score":0.49766847,"about_ca_system_score_codex":0.000022424996,"about_ca_system_score_gemma":0.00008089049,"threshold_uncertainty_score":0.77187574},"labels":[],"label_agreement":null},{"id":"W7154618392","doi":"10.66573/001c.141992","title":"Risk Aggregation: A General Approach via the Class of Generalized Gamma Convolutions","year":2021,"lang":"en","type":"article","venue":"Variance","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"York University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Erlang (programming language); Simple (philosophy); Class (philosophy); Probability distribution; Erlang distribution; Distribution (mathematics); Random variable","score_opus":0.051998397643609376,"score_gpt":0.31785703788566094,"score_spread":0.2658586402420516,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7154618392","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":[0.05109622,0.0013444434,0.93044436,0.0012888596,0.00060896546,0.00019091049,0.000054981145,0.00002932108,0.014941922],"genre_scores_gemma":[0.87533796,0.001611546,0.104877524,0.00043751975,0.00043002033,0.00004677464,0.00005081899,0.000016112095,0.017191699],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977198,0.0005257025,0.00054292206,0.00035509517,0.00068467524,0.00017180409],"domain_scores_gemma":[0.99777204,0.00031972918,0.0004249641,0.0008081299,0.00061715627,0.00005795338],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012411058,0.000103091326,0.00021783187,0.0000680073,0.00026764287,0.00011517744,0.00042585688,0.00007904428,0.00030877377],"category_scores_gemma":[0.00089807564,0.00006687189,0.00012701386,0.0013805405,0.00012994776,0.00019644092,0.00007478298,0.00012208064,0.00007134264],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009526606,0.00036997162,0.045445085,0.000008056322,0.00016552056,0.000020448408,0.0024468275,0.4967021,0.0031856156,0.28948647,0.06274497,0.09932967],"study_design_scores_gemma":[0.00081720075,0.000024943853,0.03335126,0.000007519571,0.00005803435,0.000055432458,0.0001926247,0.7639914,0.002442344,0.050700825,0.14815041,0.00020795953],"about_ca_topic_score_codex":0.00012297151,"about_ca_topic_score_gemma":0.000034435147,"teacher_disagreement_score":0.8255668,"about_ca_system_score_codex":0.000017926253,"about_ca_system_score_gemma":0.00017841313,"threshold_uncertainty_score":0.33808562},"labels":[],"label_agreement":null},{"id":"W7154622529","doi":"10.66573/001c.115975","title":"Analysis of Bivariate Excess Losses","year":2017,"lang":"en","type":"article","venue":"Variance","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Western University","funders":"","keywords":"Bivariate analysis; Univariate; Joint probability distribution; Table (database); Reinsurance; Joint (building)","score_opus":0.11927186185633346,"score_gpt":0.42119833910744536,"score_spread":0.3019264772511119,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7154622529","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":[0.43349195,0.00024134769,0.47627708,0.0013245764,0.0011954234,0.00015213061,0.000091967246,0.000039172257,0.087186344],"genre_scores_gemma":[0.99333644,0.00020895479,0.002786867,0.000039942606,0.000046917874,0.0000021658782,0.0000032048665,0.0000036456636,0.0035718752],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9986092,0.000058707843,0.0003929594,0.00027103868,0.00055247935,0.00011565828],"domain_scores_gemma":[0.99738497,0.00023966562,0.0007017306,0.0013316686,0.0002941065,0.000047876663],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011791595,0.00006794576,0.0002925691,0.0003048386,0.00024363628,0.00032883036,0.0010303973,0.00004758423,0.00050219573],"category_scores_gemma":[0.0018604838,0.00004990746,0.00012163393,0.0009086236,0.00009433826,0.0004886583,0.00009318769,0.000037927963,0.00008688671],"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":[0.000041284802,0.00007759406,0.84872365,0.0000023056855,0.0003933453,0.000011641117,0.0005310736,0.057827495,0.00030831515,0.025255922,0.0022363828,0.06459096],"study_design_scores_gemma":[0.0001429495,0.000013049982,0.914212,0.0000047726066,0.00018274458,4.5689202e-7,0.00002303531,0.061356463,0.00036976964,0.011168485,0.0124344,0.000091864014],"about_ca_topic_score_codex":0.0003297105,"about_ca_topic_score_gemma":0.000087320754,"teacher_disagreement_score":0.5598445,"about_ca_system_score_codex":0.0000049530067,"about_ca_system_score_gemma":0.000039329072,"threshold_uncertainty_score":0.5498691},"labels":[],"label_agreement":null},{"id":"W7160315630","doi":"10.1109/aixb65684.2025.00015","title":"AI-FINALYST: Multi-Agentic Debating Framework with Confidence-Weighting for Investment Decisions","year":2025,"lang":"","type":"article","venue":"","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"","keywords":"Investment (military); Investment decisions; Context (archaeology); Government (linguistics); Work (physics)","score_opus":0.10468980301671502,"score_gpt":0.42138974298586795,"score_spread":0.3166999399691529,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7160315630","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":[0.008338558,0.0014974592,0.974329,0.004238681,0.0015269028,0.0019949935,0.000023549672,0.000097783886,0.007953097],"genre_scores_gemma":[0.42518657,0.0010928892,0.5376814,0.0080786375,0.00012861664,0.00019331959,0.000017066843,0.000039641425,0.027581844],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99255794,0.0003783484,0.0026181797,0.0017171889,0.0017022215,0.0010261483],"domain_scores_gemma":[0.98433185,0.010616296,0.0010117126,0.0015551376,0.0020542499,0.00043072854],"candidate_categories":["metaresearch","metaepi_narrow","sts","scholarly_communication","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0039129504,0.0006511569,0.0010112051,0.0011907239,0.0020098218,0.001916648,0.0013660432,0.00049662584,0.0011279328],"category_scores_gemma":[0.014070545,0.0004578149,0.00043911455,0.004572059,0.00030412088,0.0009220428,0.0003614679,0.0005736543,0.00021336987],"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":[0.00068119366,0.0009705967,0.04829633,0.0000687856,0.00071701006,0.00004494369,0.004958747,0.08153415,0.00015949947,0.64508456,0.028408911,0.1890753],"study_design_scores_gemma":[0.0025510401,0.00042740736,0.0034004855,0.001999898,0.0006148382,0.000012874144,0.005352931,0.8081945,0.0014963722,0.12734103,0.047751218,0.000857397],"about_ca_topic_score_codex":0.00015094882,"about_ca_topic_score_gemma":0.00025897278,"teacher_disagreement_score":0.7266604,"about_ca_system_score_codex":0.00015492828,"about_ca_system_score_gemma":0.0013261446,"threshold_uncertainty_score":0.99978733},"labels":[],"label_agreement":null},{"id":"W854094664","doi":"10.1016/j.insmatheco.2015.06.007","title":"Expected utility and catastrophic consumption risk","year":2015,"lang":"en","type":"article","venue":"Insurance Mathematics and Economics","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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":"Simon Fraser University","funders":"","keywords":"Consumption (sociology); Risk analysis (engineering); Economics; Medicine; Sociology","score_opus":0.13310145171491722,"score_gpt":0.326898201326482,"score_spread":0.1937967496115648,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W854094664","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":[0.99095577,0.00036083625,0.007430641,0.00006551691,0.00014365128,0.00010737881,0.00007589649,0.000018660754,0.00084166176],"genre_scores_gemma":[0.9846137,0.0029164068,0.012335005,0.000021474949,0.00003094303,0.0000050962635,0.0000053755725,0.0000070714664,0.000064915184],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990338,0.000034089986,0.00044893922,0.00024767176,0.000117047726,0.000118458534],"domain_scores_gemma":[0.9989307,0.00022391653,0.00030105273,0.00030073538,0.00010526646,0.00013830722],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012139814,0.000099845434,0.00022838297,0.000077541394,0.000096962685,0.00021660149,0.000116770396,0.000057362624,0.00001917431],"category_scores_gemma":[0.00056485995,0.000081025784,0.000023168526,0.00007436503,0.00011331115,0.00025843395,0.000057019533,0.00006723529,0.000058454178],"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":[0.000041176925,0.00007540339,0.9072516,0.000012964331,0.000020305066,0.0000015657105,0.0037562842,0.0018296512,0.000006500577,0.0053257653,0.00090707105,0.0807717],"study_design_scores_gemma":[0.0012573703,0.00007756084,0.355054,0.000017965043,0.000023692115,0.00006274543,0.0016278479,0.38324454,0.00007412846,0.25157285,0.0066331527,0.0003541645],"about_ca_topic_score_codex":0.000029577528,"about_ca_topic_score_gemma":0.000038680337,"teacher_disagreement_score":0.55219764,"about_ca_system_score_codex":0.000012706959,"about_ca_system_score_gemma":0.000026183186,"threshold_uncertainty_score":0.33041358},"labels":[],"label_agreement":null}]}