{"meta":{"query_hash":"c3915d28c111","filters":{"venue":"Risks"},"cohort_total":140,"direct_labels_cover":0,"predictions_cover":140,"exported":140,"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/c3915d28c111","api":"https://metacan.xera.ac/api/v1/cohort?venue=Risks"},"results":[{"id":"W2036080481","doi":"10.3390/risks2040467","title":"Special Issue on Risk Management Techniques for Catastrophic and Heavy-Tailed Risks","year":2014,"lang":"en","type":"article","venue":"Risks","topic":"Infrastructure Resilience and Vulnerability Analysis","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Risk management; Quality (philosophy); Political science; Engineering; Risk analysis (engineering); Business; Finance","score_opus":0.01180566579916412,"score_gpt":0.26642094563049756,"score_spread":0.2546152798313334,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2036080481","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6225126,0.0002058063,0.33397427,0.0003100491,0.0011572574,0.0018387074,0.0002251951,0.0010205032,0.03875567],"genre_scores_gemma":[0.9877206,0.00021446656,0.008577659,0.000049110608,0.0032350775,0.000079375575,0.000015487178,0.000027187989,0.0000810516],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99912214,0.000054028034,0.00019043796,0.00024435399,0.00013136736,0.00025769018],"domain_scores_gemma":[0.9994752,0.00008823867,0.000037720572,0.0003065291,0.000022939721,0.00006939871],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003871505,0.00017260863,0.00022435596,0.00012577028,0.0001474891,0.00004572448,0.00012586283,0.00009385281,0.00009380093],"category_scores_gemma":[0.00004446151,0.0001539664,0.00008129376,0.000112581445,0.000050787497,0.00006226362,0.000025497318,0.00019046139,0.00003265],"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":[0.00023942646,0.00003235121,0.0045935055,0.00018084112,0.00015521779,0.000002430304,0.00015812884,0.0414632,0.00007922548,0.0010685155,0.015262684,0.9367645],"study_design_scores_gemma":[0.0011206622,0.0006798977,0.05503345,0.000095419455,0.00044161634,0.0000045523784,0.00020305664,0.068350375,0.019786077,0.011162224,0.84226674,0.00085590756],"about_ca_topic_score_codex":0.000058192974,"about_ca_topic_score_gemma":0.00003195337,"teacher_disagreement_score":0.93590856,"about_ca_system_score_codex":0.000053884294,"about_ca_system_score_gemma":0.0000029102548,"threshold_uncertainty_score":0.62785673},"labels":[],"label_agreement":null},{"id":"W2061260390","doi":"10.3390/risks2010025","title":"An Academic Response to Basel 3.5","year":2014,"lang":"en","type":"article","venue":"Risks","topic":"Insurance and Financial Risk Management","field":"Economics, Econometrics and Finance","cited_by":256,"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":"Ministero dell’Istruzione, dell’Università e della Ricerca; Swiss Finance Institute","keywords":"Basel II; Basel III; Operational risk; Context (archaeology); Actuarial science; Risk analysis (engineering); Risk-weighted asset; Minor (academic); Strengths and weaknesses; Risk management; Economics; Business; Capital requirement; Political science; Finance; Microeconomics; Psychology; Geography; Law","score_opus":0.05474897456735546,"score_gpt":0.2895317101856007,"score_spread":0.23478273561824525,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2061260390","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.94376975,0.0002115273,0.033931114,0.0013735298,0.00041580657,0.00019742473,0.00004876857,0.00007225555,0.01997983],"genre_scores_gemma":[0.9948883,0.00009039301,0.00090964924,0.0026647723,0.00022963044,0.000030429324,0.0000044402063,0.000021061014,0.0011613476],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99896663,0.000033533077,0.00033647037,0.00034335023,0.00003339975,0.00028659648],"domain_scores_gemma":[0.99928194,0.000042394367,0.000106334635,0.00043695918,0.000015502354,0.00011685682],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0015350267,0.000107097796,0.00021608801,0.0002052923,0.00010043679,0.000034436038,0.00029367564,0.00009671265,0.00014414168],"category_scores_gemma":[0.00025038375,0.00012744556,0.000055121916,0.00021954805,0.000017807868,0.0001677924,0.000041686777,0.00016381481,0.0049924944],"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":[0.0008053622,0.00015935325,0.21209824,0.000026050357,0.000026632753,0.000007896634,0.0016977725,0.0016007631,0.0004917522,0.6708752,0.020767072,0.09144394],"study_design_scores_gemma":[0.00024387946,0.0001584111,0.39470193,0.000006261348,0.0000018052787,3.621826e-7,0.000020725192,0.0015312928,0.00009399728,0.014774686,0.58827907,0.00018755451],"about_ca_topic_score_codex":0.0002484248,"about_ca_topic_score_gemma":0.000014887929,"teacher_disagreement_score":0.6561005,"about_ca_system_score_codex":0.000051918083,"about_ca_system_score_gemma":0.0000068365152,"threshold_uncertainty_score":0.99578226},"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":"W2178609167","doi":"10.3390/risks5040055","title":"Optimal Form of Retention for Securitized Loans under Moral Hazard","year":2017,"lang":"en","type":"article","venue":"Risks","topic":"Banking stability, regulation, efficiency","field":"Economics, Econometrics and Finance","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"","keywords":"Securitization; Moral hazard; Asset (computer security); Principal (computer security); Actuarial science; Business; Economics; Microeconomics; Computer science; Finance; Computer security; Incentive","score_opus":0.09395051479507263,"score_gpt":0.301348544086753,"score_spread":0.20739802929168036,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2178609167","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9557289,0.0003151213,0.038189705,0.00026462117,0.0003759089,0.000293663,0.00021261453,0.000027522066,0.00459194],"genre_scores_gemma":[0.9956415,0.00003633824,0.0040282146,0.000010063484,0.00006168179,0.000021353979,0.000019045085,0.000015725213,0.00016603609],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.999172,0.000004333212,0.0003873898,0.0002242914,0.000030441402,0.0001815751],"domain_scores_gemma":[0.9990388,0.000028491597,0.0003615019,0.00048983283,0.0000539232,0.000027423695],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004648591,0.000084136394,0.00024228728,0.000075448545,0.00020044715,0.00006799755,0.000231646,0.00009199031,0.00006886851],"category_scores_gemma":[0.00011563856,0.00010032252,0.00014142961,0.00003873959,0.000115253395,0.00022600968,0.000035506,0.00005366629,0.000019134954],"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":[0.000050493567,0.00013398938,0.260133,0.00013772068,0.00004891947,2.3341686e-7,0.00056717586,0.0016503863,0.000055999117,0.73430055,0.00050494797,0.0024166019],"study_design_scores_gemma":[0.0008356615,0.000066154375,0.62590367,0.000011324478,0.0000086465,6.43304e-7,0.000021697917,0.03359368,0.00029223872,0.33825296,0.00087430875,0.00013901753],"about_ca_topic_score_codex":0.00014186629,"about_ca_topic_score_gemma":0.00006219141,"teacher_disagreement_score":0.39604756,"about_ca_system_score_codex":0.000062685176,"about_ca_system_score_gemma":0.000013489035,"threshold_uncertainty_score":0.40910333},"labels":[],"label_agreement":null},{"id":"W2254900144","doi":"10.3390/risks4030030","title":"On the Capital Allocation Problem for a New Coherent Risk Measure in Collective Risk Theory","year":2016,"lang":"en","type":"article","venue":"Risks","topic":"Stochastic processes and financial applications","field":"Economics, Econometrics and Finance","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":"Royal Bank of Canada; Université de Montréal","funders":"","keywords":"Capital allocation line; Measure (data warehouse); Risk measure; Dynamic risk measure; Context (archaeology); Coherent risk measure; Capital (architecture); Aggregate (composite); Space (punctuation); Mathematical economics; Spectral risk measure; Simple (philosophy); Computer science; Economics; Risk management; Econometrics; Microeconomics; Expected shortfall; Financial economics; Finance; Data mining","score_opus":0.043187004207035515,"score_gpt":0.24065309312111938,"score_spread":0.19746608891408385,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2254900144","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":[0.052669454,0.0006665858,0.93933624,0.0016785352,0.000091402,0.0012084701,0.00047529014,0.000023531877,0.0038504757],"genre_scores_gemma":[0.997161,0.00013741193,0.00083514367,0.000102671176,0.000103113955,0.0008372464,0.0000034496804,0.00001807239,0.0008019248],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9991571,0.000013497806,0.00029924704,0.00030461128,0.000031689247,0.0001938883],"domain_scores_gemma":[0.9985557,0.00079433835,0.00032684172,0.00023371937,0.00004624393,0.00004312269],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006906576,0.00011012677,0.00017697312,0.00008203705,0.0001771882,0.00002551574,0.00017997352,0.00007923156,0.00006052133],"category_scores_gemma":[0.0011455993,0.0000737661,0.000072728726,0.00022136033,0.00004095636,0.00005725088,0.00001957441,0.00011504124,0.00022849701],"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":[0.000081496575,0.00006714493,0.004014448,0.0000035181483,0.000023229375,5.2745808e-8,0.0007270701,0.00003341904,0.000004982028,0.97765446,0.0010365781,0.016353628],"study_design_scores_gemma":[0.0007347447,0.000112227826,0.033102967,0.00002430352,0.000009311393,2.0484961e-7,0.000068468784,0.00025333135,0.000048012636,0.96334237,0.0021808504,0.00012319864],"about_ca_topic_score_codex":0.00090786454,"about_ca_topic_score_gemma":0.00039772864,"teacher_disagreement_score":0.9444915,"about_ca_system_score_codex":0.0001797088,"about_ca_system_score_gemma":0.000087932975,"threshold_uncertainty_score":0.30080944},"labels":[],"label_agreement":null},{"id":"W2256208696","doi":"10.3390/risks4010002","title":"Ruin Analysis of a Discrete-Time Dependent Sparre Andersen Model with External Financial Activities and Randomized Dividends","year":2016,"lang":"en","type":"article","venue":"Risks","topic":"Probability and Risk Models","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":"Dividend; Discrete time and continuous time; Economics; Econometrics; Mathematics; Capital (architecture); Ruin theory; Mathematical economics; Risk model; Actuarial science; Applied mathematics; Statistics; Finance","score_opus":0.08642320778773098,"score_gpt":0.3607246153516478,"score_spread":0.2743014075639168,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2256208696","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.63978475,0.00014090435,0.35912567,0.00029884579,0.00002047049,0.00016025329,0.00007377047,0.000013324369,0.0003820111],"genre_scores_gemma":[0.99600357,0.00010585341,0.0016738474,0.000027234015,0.000024042027,0.000022282693,0.0000011546028,0.00000715178,0.002134893],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9971881,0.00039539227,0.000577935,0.00051099015,0.0010817465,0.00024582705],"domain_scores_gemma":[0.9968344,0.0020485204,0.00035335217,0.0005220997,0.00012190928,0.000119715165],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0030572193,0.00018031255,0.0010451877,0.00039713396,0.00011676208,0.00009790133,0.00036594534,0.000100262856,0.00018637738],"category_scores_gemma":[0.0010715427,0.00008154459,0.00029981352,0.00044255998,0.0005668689,0.0004950464,0.00016572145,0.00009171621,0.000008758732],"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":[0.21339749,0.00092250895,0.25997153,0.00006298296,0.0046511195,0.00007442319,0.015337972,0.16235307,0.014795556,0.029516185,0.0010609231,0.29785624],"study_design_scores_gemma":[0.056621887,0.00017766202,0.02235468,0.0001281159,0.0019891844,0.000009939478,0.00026405833,0.64366686,0.003053383,0.27113923,0.00003352777,0.0005614318],"about_ca_topic_score_codex":0.00018522143,"about_ca_topic_score_gemma":0.0006428527,"teacher_disagreement_score":0.48131382,"about_ca_system_score_codex":0.00002766738,"about_ca_system_score_gemma":0.0001219767,"threshold_uncertainty_score":0.3325292},"labels":[],"label_agreement":null},{"id":"W2288210737","doi":"10.3390/risks4020012","title":"Macro vs. Micro Methods in Non-Life Claims Reserving (an Econometric Perspective)","year":2016,"lang":"en","type":"article","venue":"Risks","topic":"Probability and Risk Models","field":"Decision Sciences","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Macro; Poisson distribution; Econometrics; Econometric model; Count data; Perspective (graphical); Poisson regression; Gaussian; Payment; Computer science; Statistics; Economics; Mathematics; Artificial intelligence; Finance","score_opus":0.334250066049376,"score_gpt":0.5312856378652947,"score_spread":0.1970355718159187,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2288210737","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8676527,0.0005605994,0.11784816,0.003994415,0.0004340239,0.00030030136,0.000020487225,0.000048033984,0.009141272],"genre_scores_gemma":[0.96002096,0.00015703304,0.037691288,0.00031087577,0.00012441153,0.00002003163,4.6618507e-7,0.000017412542,0.0016575353],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9961991,0.0010043205,0.0007903203,0.0009817542,0.00053061347,0.00049392483],"domain_scores_gemma":[0.9946609,0.0032016404,0.00022931628,0.0013030817,0.00030762062,0.00029742625],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0108237,0.00019071283,0.0004747242,0.0010222392,0.00017155251,0.0002020179,0.0014711503,0.00019776094,0.0009988197],"category_scores_gemma":[0.0070499224,0.000112504975,0.00016933035,0.0017877674,0.0001919915,0.0009128862,0.00039118063,0.00025923207,0.00060981273],"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":[0.0005324776,0.00075077976,0.27217957,0.000020995465,0.00006652464,0.00003451114,0.009030508,0.0015757638,0.013143231,0.021728616,0.006990923,0.6739461],"study_design_scores_gemma":[0.0011305434,0.00015777958,0.26637745,0.000032362936,0.000009700533,0.000006389741,0.0016981445,0.0077554863,0.0054391394,0.70901316,0.007964641,0.00041521038],"about_ca_topic_score_codex":0.0015435591,"about_ca_topic_score_gemma":0.0008057409,"teacher_disagreement_score":0.6872845,"about_ca_system_score_codex":0.00030292376,"about_ca_system_score_gemma":0.00018339562,"threshold_uncertainty_score":0.9999144},"labels":[],"label_agreement":null},{"id":"W2296619938","doi":"10.3390/risks4010006","title":"Analysis of Insurance Claim Settlement Process with Markovian Arrival Processes","year":2016,"lang":"en","type":"article","venue":"Risks","topic":"Probability and Risk Models","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":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Markov process; Joint probability distribution; Markovian arrival process; Flexibility (engineering); Poisson process; Process (computing); Settlement (finance); Poisson distribution; Compound Poisson process; Actuarial science; Econometrics; Computer science; Operations research; Business; Economics; Mathematics; Statistics; Finance","score_opus":0.13084992081439065,"score_gpt":0.40773810976622876,"score_spread":0.2768881889518381,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2296619938","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.970998,0.00013651539,0.02581972,0.00094687025,0.0000401602,0.00020149794,0.0002646863,0.000030458317,0.001562073],"genre_scores_gemma":[0.99889135,0.000054008196,0.0005203111,0.000058040492,0.00002167786,0.00002587327,0.0000029373177,0.00000689558,0.0004189025],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99717915,0.00012063124,0.00058063987,0.0005413384,0.0013278361,0.00025037618],"domain_scores_gemma":[0.99736345,0.0006749349,0.00037423568,0.0006950724,0.0007864395,0.000105862],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014990668,0.00014271634,0.00044611617,0.00034888636,0.00009823381,0.0000520984,0.0007250076,0.00006488619,0.00035253118],"category_scores_gemma":[0.0010570564,0.000063613166,0.00011157393,0.0026539578,0.00025892933,0.00037938345,0.00006292389,0.00006788879,0.000036726517],"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":[0.00018985584,0.00013650388,0.9480394,0.00003716227,0.00024296263,0.0000026287687,0.00083419995,0.0020847602,0.00011721003,0.00023920447,0.00014707977,0.047928996],"study_design_scores_gemma":[0.0010532136,0.00030680367,0.93514675,0.00014358906,0.00040054193,0.0000032180483,0.0005770275,0.0015145568,0.010571991,0.046995852,0.002868648,0.00041779113],"about_ca_topic_score_codex":0.000046034278,"about_ca_topic_score_gemma":0.0006867979,"teacher_disagreement_score":0.047511205,"about_ca_system_score_codex":0.000027995427,"about_ca_system_score_gemma":0.00022822419,"threshold_uncertainty_score":0.3859969},"labels":[],"label_agreement":null},{"id":"W2299773574","doi":"10.3390/risks4010008","title":"Optimal Insurance for a Minimal Expected Retention: The Case of an Ambiguity-Seeking Insurer","year":2016,"lang":"en","type":"article","venue":"Risks","topic":"Decision-Making and Behavioral Economics","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 de Montréal","funders":"Georgia State University","keywords":"Expected utility hypothesis; Deductible; Ambiguity; Constraint (computer-aided design); Indemnity; Actuarial science; Distortion (music); Schedule; Equivalence (formal languages); Insurance policy; Mathematics; Arrow; Economics; Mathematical economics; Computer science; Discrete mathematics","score_opus":0.2717739886010315,"score_gpt":0.44497642256905096,"score_spread":0.17320243396801943,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2299773574","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99126756,0.000053978136,0.0072815716,0.00029114587,0.00046484603,0.00022100346,0.00016989728,0.000029999705,0.00021998354],"genre_scores_gemma":[0.9950033,0.000004762157,0.004519112,0.00004406018,0.00012880276,0.000028188284,0.0000013635871,0.000013919032,0.00025652372],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9980937,0.00016286469,0.0007184558,0.00046150672,0.00033181236,0.00023166352],"domain_scores_gemma":[0.9967897,0.0014627795,0.00043071294,0.0008343017,0.00039744086,0.00008507183],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023291882,0.00013139832,0.00028524984,0.00013330577,0.0002442765,0.00015785251,0.0006683897,0.00008436561,0.00016125473],"category_scores_gemma":[0.0018753293,0.00006527018,0.00019659051,0.00029932577,0.00017988245,0.00041305993,0.00011243121,0.000083147155,0.00005403582],"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":[0.00027505792,0.00012529649,0.00984634,0.0000020396974,0.000011743604,0.00010588436,0.00061184145,0.00008456455,0.0027258932,0.00024842788,0.0014268486,0.98453605],"study_design_scores_gemma":[0.0117759295,0.0035472931,0.6989269,0.00050184067,0.0002433216,0.004905343,0.013527962,0.026194569,0.018195288,0.15710957,0.06274009,0.0023318916],"about_ca_topic_score_codex":0.00013245946,"about_ca_topic_score_gemma":0.00021173777,"teacher_disagreement_score":0.9822042,"about_ca_system_score_codex":0.000030284767,"about_ca_system_score_gemma":0.0000528699,"threshold_uncertainty_score":0.26616406},"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":"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":"W2525115141","doi":"10.3390/risks4040035","title":"A Note on the Impact of Parameter Uncertainty on Barrier Derivatives","year":2016,"lang":"en","type":"article","venue":"Risks","topic":"Stochastic processes and financial applications","field":"Economics, Econometrics and Finance","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":"Randomness; Barrier option; Context (archaeology); Estimation; Extension (predicate logic); Covariance matrix; Econometrics; Distribution (mathematics); Mathematics; Computer science; Mathematical optimization; Economics; Statistics","score_opus":0.06479592631632419,"score_gpt":0.3018199547440658,"score_spread":0.23702402842774162,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2525115141","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6585517,0.00012444056,0.32821417,0.0016911413,0.00007677478,0.00027949372,0.0006114548,0.000019359379,0.010431444],"genre_scores_gemma":[0.99942297,0.000030498808,0.00017626841,0.00015149669,0.000049913782,0.00005675873,0.0000011526772,0.00000939997,0.00010155758],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9994214,0.00000348148,0.00022303282,0.00018640343,0.00002321514,0.0001424639],"domain_scores_gemma":[0.9990145,0.00044468918,0.00018640175,0.000296305,0.000023949051,0.00003412996],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001397341,0.0000900297,0.00017154026,0.00005558714,0.00006634458,0.00001066728,0.00016469299,0.000048570135,0.00022320786],"category_scores_gemma":[0.00053725403,0.000047991707,0.000119068995,0.00013256865,0.00007889697,0.000034303444,0.000019828489,0.000063020125,0.00037424878],"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":[0.00004819222,0.00006690598,0.0038189671,0.0000025231918,0.00002775725,1.6682387e-7,0.00023591524,0.000085441396,0.00015828911,0.98645985,0.00035353456,0.008742477],"study_design_scores_gemma":[0.00032434348,0.00032260173,0.1532203,0.000033561,0.0000029744492,3.9030192e-7,0.000013054048,0.00058337726,0.0008475177,0.8418689,0.002632947,0.00015003455],"about_ca_topic_score_codex":0.0002880821,"about_ca_topic_score_gemma":0.0000042472084,"teacher_disagreement_score":0.34087124,"about_ca_system_score_codex":0.00005502877,"about_ca_system_score_gemma":0.000019613033,"threshold_uncertainty_score":0.4810336},"labels":[],"label_agreement":null},{"id":"W2555420981","doi":"10.3390/risks4040041","title":"Incorporation of Stochastic Policyholder Behavior in Analytical Pricing of GMABs and GMDBs","year":2016,"lang":"en","type":"article","venue":"Risks","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social 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 Waterloo; Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Surrender; Life insurance; Maturity (psychological); Economics; Actuarial science; Stochastic modelling; Variable (mathematics); Cox–Ingersoll–Ross model; Financial market; Econometrics; Interest rate; Finance; Mathematics","score_opus":0.06479999742872179,"score_gpt":0.3729896586299695,"score_spread":0.30818966120124774,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2555420981","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99516445,0.00004335435,0.0019001295,0.00020287359,0.00006387882,0.00029832948,0.000008824708,0.000012737866,0.0023054413],"genre_scores_gemma":[0.99968046,0.00004290103,0.00012767482,0.000014528307,0.000041560237,0.000011977356,6.530538e-7,0.000005464963,0.00007479936],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99898946,0.00010106605,0.00028531835,0.0001504304,0.00030107034,0.00017267198],"domain_scores_gemma":[0.9994976,0.00008654316,0.00016205912,0.00013635353,0.000069813934,0.00004765288],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00066580327,0.00006698924,0.00017361289,0.00022933635,0.000053362826,0.000009613583,0.000103231876,0.00006408783,0.000020609938],"category_scores_gemma":[0.00013340727,0.00005284621,0.000040902865,0.00036137353,0.00035184345,0.00013110053,0.00004086245,0.000048699458,0.000002868212],"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":[0.000016822212,0.00011635134,0.93955404,0.000020655161,0.000013393675,0.0000024640176,0.0017531345,0.000022163986,0.000385521,0.0376392,0.000037175283,0.020439079],"study_design_scores_gemma":[0.000327836,0.000037535465,0.9959861,0.00004690676,0.00003856805,1.5586062e-7,0.00050680037,0.000118404416,0.0000949412,0.00271711,0.000048924452,0.00007671164],"about_ca_topic_score_codex":0.0067777396,"about_ca_topic_score_gemma":0.0034668576,"teacher_disagreement_score":0.05643207,"about_ca_system_score_codex":0.000043188822,"about_ca_system_score_gemma":0.00004460408,"threshold_uncertainty_score":0.9998362},"labels":[],"label_agreement":null},{"id":"W2560512335","doi":"10.3390/risks4040046","title":"Deflation Risk and Implications for Life Insurers","year":2016,"lang":"en","type":"article","venue":"Risks","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social 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":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada; HEC Montréal; Society of Actuaries","keywords":"Deflation; Economics; Inflation (cosmology); Life insurance; Interest rate; Econometrics; Investment (military); Variance (accounting); Real interest rate; Monetary economics; Actuarial science; Financial economics; Monetary policy","score_opus":0.06686069432047792,"score_gpt":0.36740675908466447,"score_spread":0.30054606476418655,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2560512335","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9449462,0.0004175129,0.03087309,0.006649768,0.00035116408,0.0010747418,0.00015405413,0.00017153217,0.015361928],"genre_scores_gemma":[0.99684596,0.0016371864,0.0008278247,0.00013674963,0.0001802317,0.00011299479,0.0000030551296,0.0000091485745,0.00024684475],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9991884,0.000101866695,0.0001491739,0.00020727322,0.00013252678,0.00022072699],"domain_scores_gemma":[0.99929607,0.00021015063,0.000114025526,0.00018396153,0.00009115138,0.00010461205],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00075025234,0.00007041734,0.00008919401,0.000077660785,0.0006017824,0.000047662237,0.0001228144,0.00006590232,0.000020036747],"category_scores_gemma":[0.0003910623,0.000055059092,0.000059243623,0.00016978246,0.00020064348,0.00017468416,0.000023352524,0.00003742881,0.000020092693],"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":[0.000007123374,0.000018676355,0.8147492,0.000005006613,0.00002397297,4.759952e-8,0.0006957712,0.0000024878493,0.000031724154,0.07697135,0.0025553461,0.104939334],"study_design_scores_gemma":[0.00027337257,0.000015468166,0.8625513,0.000005856594,0.00003290063,4.2488644e-8,0.00022138612,0.0000068564264,0.000009417267,0.02983311,0.106960595,0.00008975214],"about_ca_topic_score_codex":0.0015983602,"about_ca_topic_score_gemma":0.0022506167,"teacher_disagreement_score":0.104849584,"about_ca_system_score_codex":0.000038025093,"about_ca_system_score_gemma":0.00004328864,"threshold_uncertainty_score":0.46284848},"labels":[],"label_agreement":null},{"id":"W2580186021","doi":"10.3390/risks5010008","title":"n-Dimensional Laplace Transforms of Occupation Times for Spectrally Negative Lévy Processes","year":2017,"lang":"en","type":"article","venue":"Risks","topic":"Probability and Risk Models","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":"Concordia University","funders":"","keywords":"Laplace transform; Mathematics; Mathematical analysis; Physics; Mathematical physics","score_opus":0.24557269250471775,"score_gpt":0.4611284203797574,"score_spread":0.21555572787503963,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2580186021","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9421214,0.00016946916,0.047263097,0.0019450133,0.00025296025,0.0008135062,0.0002011096,0.000034035456,0.00719942],"genre_scores_gemma":[0.99139684,0.000023572054,0.0071045575,0.000047206333,0.00005464735,0.00002391108,0.0000046833247,0.000006460093,0.0013381029],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99834096,0.000042088253,0.0004314373,0.00034620546,0.00065524376,0.00018404398],"domain_scores_gemma":[0.99726886,0.0010907406,0.00038867776,0.0005378314,0.00066024147,0.00005367228],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014564604,0.00011163892,0.00026769575,0.00007646478,0.00043670068,0.00014099893,0.0006860092,0.0000904527,0.00012545827],"category_scores_gemma":[0.004321536,0.000069890746,0.000121926365,0.00011135086,0.00024496458,0.00069161627,0.00004320622,0.00008104864,0.000041993215],"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":[0.010665374,0.002912609,0.15073025,0.0010588743,0.0005301357,0.000015357393,0.03191455,0.0351915,0.011867544,0.12348479,0.070852436,0.5607766],"study_design_scores_gemma":[0.0011417461,0.0003152128,0.05279648,0.000056219505,0.000030023264,0.00000280217,0.00017641508,0.01082715,0.0832036,0.8483773,0.0028543829,0.0002187099],"about_ca_topic_score_codex":0.00016093846,"about_ca_topic_score_gemma":0.0004635864,"teacher_disagreement_score":0.7248925,"about_ca_system_score_codex":0.000018865168,"about_ca_system_score_gemma":0.0002609881,"threshold_uncertainty_score":0.51735944},"labels":[],"label_agreement":null},{"id":"W2581196496","doi":"10.3390/risks5010006","title":"Optimal Investment and Liability Ratio Policies in a Multidimensional Regime Switching Model","year":2017,"lang":"en","type":"article","venue":"Risks","topic":"Insurance and Financial Risk Management","field":"Economics, Econometrics and Finance","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; University of Alberta","keywords":"Economics; Capital (architecture); Investment (military); Expected utility hypothesis; Risk aversion (psychology); Jump; Liability; Limited liability; Logarithm; Jump diffusion; Econometrics; Monetary economics; Microeconomics; Financial economics; Finance; Mathematics","score_opus":0.0733361385777352,"score_gpt":0.2864285326888444,"score_spread":0.2130923941111092,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2581196496","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98118937,0.00044437277,0.0041668876,0.0007573647,0.00011558655,0.00021748152,0.000025419766,0.00001345984,0.013070054],"genre_scores_gemma":[0.9950292,0.000297087,0.0038446763,0.00034536037,0.000046720193,0.00002921325,0.0000023448256,0.000010201232,0.00039520374],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.99907726,0.00000707954,0.00033828392,0.00031719403,0.00003327734,0.00022689198],"domain_scores_gemma":[0.99926364,0.000015696853,0.00023438732,0.00042618948,0.000013200257,0.000046898498],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00052029477,0.00011447165,0.00025314235,0.00010412392,0.0002926725,0.00009750165,0.00015300153,0.00006527976,0.000009143049],"category_scores_gemma":[0.00014218244,0.00012710788,0.000045329398,0.00003339122,0.00006693606,0.0003192422,0.00016239149,0.00013248394,0.000051627947],"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":[0.000029325898,0.00009646828,0.35973826,0.000021398879,0.000013027458,0.0000051184225,0.0014399266,0.01049895,0.000029064353,0.6263255,0.00019919488,0.0016037645],"study_design_scores_gemma":[0.00058623945,0.000025490272,0.718446,0.000017025444,0.000002340208,5.3760294e-7,0.000039012528,0.23634084,0.000041587344,0.041215345,0.003108369,0.00017722379],"about_ca_topic_score_codex":0.0033924826,"about_ca_topic_score_gemma":0.00024420783,"teacher_disagreement_score":0.5851101,"about_ca_system_score_codex":0.000074547956,"about_ca_system_score_gemma":0.00001603005,"threshold_uncertainty_score":0.5183309},"labels":[],"label_agreement":null},{"id":"W2587736478","doi":"10.3390/risks5010011","title":"Optimal Reinsurance Policies under the VaR Risk Measure When the Interests of Both the Cedent and the Reinsurer Are Taken into Account","year":2017,"lang":"en","type":"article","venue":"Risks","topic":"Insurance and Financial Risk Management","field":"Economics, Econometrics and Finance","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":"Western University","funders":"","keywords":"Reinsurance; Pareto optimal; Pareto principle; Risk measure; Point (geometry); Actuarial science; Database transaction; Measure (data warehouse); Economics; Transaction cost; Value (mathematics); Microeconomics; Computer science; Financial economics; Mathematical optimization; Multi-objective optimization; Mathematics; Operations management; Statistics","score_opus":0.05126354890339462,"score_gpt":0.2647595228523593,"score_spread":0.2134959739489647,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2587736478","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9423708,0.016648257,0.0020444223,0.025769165,0.0009303791,0.0010403112,0.00014982275,0.000023641196,0.011023219],"genre_scores_gemma":[0.98929423,0.008887013,0.000049448092,0.0007630507,0.00031262683,0.00008404596,0.0000011377061,0.000026648222,0.00058178726],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.99849045,0.00011434955,0.0005649362,0.00034771193,0.00014583106,0.0003367311],"domain_scores_gemma":[0.9966064,0.00023475649,0.0013145673,0.0017344964,0.00007819148,0.00003156334],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0028235656,0.00023561345,0.0004302858,0.000050166393,0.0018385694,0.00045241136,0.0016030343,0.00010029688,0.00002770457],"category_scores_gemma":[0.000414162,0.000109230285,0.00020463631,0.000094519804,0.0010786711,0.00023917538,0.00044060012,0.0004728547,0.00006108651],"study_design_candidate":"observational","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":[0.00047111433,0.00009969127,0.36911318,0.00007169986,0.0005646851,0.000003241824,0.020043986,0.002874031,0.0000044275776,0.5830785,0.0115736155,0.0121018095],"study_design_scores_gemma":[0.00087466114,0.000033741882,0.8934649,0.00005008753,0.000046072102,0.0000022444601,0.0015111301,0.000971764,0.000017232773,0.048295334,0.054551937,0.0001808914],"about_ca_topic_score_codex":0.019867921,"about_ca_topic_score_gemma":0.002724966,"teacher_disagreement_score":0.5347832,"about_ca_system_score_codex":0.00005660937,"about_ca_system_score_gemma":0.00002118645,"threshold_uncertainty_score":0.9994609},"labels":[],"label_agreement":null},{"id":"W2607441201","doi":"10.3390/risks5020027","title":"Risk Management under Omega Measure","year":2017,"lang":"en","type":"article","venue":"Risks","topic":"Financial Risk and Volatility Modeling","field":"Economics, Econometrics and Finance","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":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Sharpe ratio; Measure (data warehouse); Omega; Portfolio; Portfolio optimization; Project portfolio management; Econometrics; Mathematics; Set (abstract data type); Risk measure; Economics; Actuarial science; Financial economics; Computer science; Physics; Data mining","score_opus":0.11363292324072134,"score_gpt":0.28622197584567344,"score_spread":0.1725890526049521,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2607441201","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.57590085,0.002811795,0.11530385,0.00053835864,0.0010559533,0.00029702147,0.00009964397,0.00008296028,0.30390957],"genre_scores_gemma":[0.9956137,0.0011049599,0.0013426355,0.00007715443,0.00013699022,0.00001382683,0.0000025632069,0.000019619927,0.0016885682],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9989989,0.000008714377,0.00032774746,0.00036157927,0.000039428433,0.0002636416],"domain_scores_gemma":[0.99867654,0.000013382122,0.0003562335,0.00086837134,0.000021347327,0.00006411737],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00050888554,0.0001235711,0.00024405695,0.000089098445,0.0006528967,0.00017781793,0.0004041864,0.00009643239,0.00006907052],"category_scores_gemma":[0.000107323416,0.0001415342,0.000118255004,0.000041660827,0.000052252584,0.00022729817,0.00012546714,0.00018506395,0.0009113065],"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":[0.000019793484,0.000057110912,0.53968143,0.00001879072,0.00006927484,0.0000082012475,0.00015255425,0.00047531998,4.2203246e-7,0.4199054,0.00097293145,0.03863874],"study_design_scores_gemma":[0.00050930295,0.000017964325,0.69442225,0.000015458223,0.000013000829,7.3724505e-7,0.000031978998,0.008388869,0.000007940243,0.2624528,0.033912227,0.00022747791],"about_ca_topic_score_codex":0.001489237,"about_ca_topic_score_gemma":0.00010449081,"teacher_disagreement_score":0.41971284,"about_ca_system_score_codex":0.000058482394,"about_ca_system_score_gemma":0.000006270468,"threshold_uncertainty_score":0.9998666},"labels":[],"label_agreement":null},{"id":"W2756371030","doi":"10.3390/risks5030048","title":"A Cointegrated Regime-Switching Model Approach with Jumps Applied to Natural Gas Futures Prices","year":2017,"lang":"en","type":"article","venue":"Risks","topic":"Market Dynamics and Volatility","field":"Economics, Econometrics and Finance","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Futures contract; Econometrics; Cointegration; Economics; Volatility (finance); Markov chain; Jump; Financial economics; Mathematics; Statistics; Physics","score_opus":0.05130236158355294,"score_gpt":0.2615231725474575,"score_spread":0.21022081096390455,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2756371030","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7694062,0.00022956303,0.08186264,0.00039060862,0.00018544847,0.00044059326,0.0000835068,0.00006373761,0.14733768],"genre_scores_gemma":[0.98386556,0.000031011423,0.014920825,0.00014850026,0.00009921727,0.00004254951,0.000022656095,0.000029680195,0.0008399729],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99869657,0.0000066094685,0.0003344039,0.00057042023,0.000056940127,0.0003350425],"domain_scores_gemma":[0.9985066,0.000021300066,0.0004213246,0.00088876847,0.0000395183,0.00012248199],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005405206,0.00021659094,0.00041308947,0.00012703695,0.00053318625,0.00036104795,0.00054733444,0.00010707802,0.000028250328],"category_scores_gemma":[0.00007637071,0.00018740143,0.000075879885,0.00009607769,0.00004435639,0.00019755404,0.00012400969,0.00033411497,0.000030624728],"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":[0.0015300208,0.00061912346,0.27120167,0.00030990198,0.00051608664,0.000014957543,0.00580156,0.009194943,0.00021633551,0.67209125,0.004843346,0.0336608],"study_design_scores_gemma":[0.0005118228,0.000030084924,0.052741356,0.0000140163265,0.000008588441,0.0000026111918,0.00011331466,0.9267061,0.00001355826,0.017772032,0.001745146,0.0003413751],"about_ca_topic_score_codex":0.0010871158,"about_ca_topic_score_gemma":0.00039925342,"teacher_disagreement_score":0.91751117,"about_ca_system_score_codex":0.00006782495,"about_ca_system_score_gemma":0.000025867586,"threshold_uncertainty_score":0.76420087},"labels":[],"label_agreement":null},{"id":"W2758018149","doi":"10.3390/risks5040054","title":"Exposure as Duration and Distance in Telematics Motor Insurance Using Generalized Additive Models","year":2017,"lang":"en","type":"article","venue":"Risks","topic":"Insurance and Financial Risk Management","field":"Economics, Econometrics and Finance","cited_by":68,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Institució Catalana de Recerca i Estudis Avançats; Ministerio de Economía y Competitividad","keywords":"Telematics; Duration (music); Automobile insurance; Actuarial science; Carry (investment); Econometrics; Insurance policy; Accident (philosophy); Insurance premium; Computer science; Business; Economics; Telecommunications; Finance","score_opus":0.0790884478997179,"score_gpt":0.2806393256655249,"score_spread":0.201550877765807,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2758018149","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.975713,0.0014172895,0.013234663,0.000114203984,0.00024298963,0.00034383064,0.000253957,0.000014633665,0.008665436],"genre_scores_gemma":[0.99490595,0.0021582593,0.002364652,0.000087154396,0.000092969225,0.000040176208,0.000008146559,0.000018100805,0.00032461743],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.99886274,0.000012107696,0.0005323085,0.00031939504,0.0000434653,0.00022997143],"domain_scores_gemma":[0.9989478,0.00001642297,0.00056314905,0.00040451527,0.000029460109,0.000038680533],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033976292,0.00014069203,0.000334439,0.000106039086,0.0003070716,0.00016807111,0.00019551878,0.00008978976,0.000021246553],"category_scores_gemma":[0.00014790123,0.00016426951,0.00004810727,0.00006529968,0.00007294221,0.0006917946,0.00006643231,0.000117630436,0.000040995794],"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":[0.00010751568,0.000101661564,0.41144055,0.00008448629,0.000034495315,0.000031314234,0.0014955422,0.0017786361,0.00005900449,0.57204205,0.000100623016,0.012724137],"study_design_scores_gemma":[0.0012155842,0.000053397922,0.7662853,0.00007748299,0.0000047673657,0.0000019246315,0.0000732015,0.06415594,0.00007559732,0.1643737,0.0033475205,0.00033559892],"about_ca_topic_score_codex":0.0014877801,"about_ca_topic_score_gemma":0.00028002408,"teacher_disagreement_score":0.40766835,"about_ca_system_score_codex":0.00007973399,"about_ca_system_score_gemma":0.00001234549,"threshold_uncertainty_score":0.6698717},"labels":[],"label_agreement":null},{"id":"W2766994191","doi":"10.3390/risks5040056","title":"Optional Defaultable Markets","year":2017,"lang":"en","type":"article","venue":"Risks","topic":"Stochastic processes and financial applications","field":"Economics, Econometrics and Finance","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 Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Credit default swap; Probability of default; Arbitrage; Swap (finance); Financial market; Mathematical economics; Mathematical finance; Bond; Econometrics; Economics; Financial economics; Computer science; Actuarial science; Credit risk; Finance","score_opus":0.08974303752797337,"score_gpt":0.29480862360219423,"score_spread":0.20506558607422087,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2766994191","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":[0.03967725,0.0013000119,0.6713832,0.0015131137,0.00043810712,0.000170746,0.00025500153,0.00004728762,0.2852153],"genre_scores_gemma":[0.99432766,0.000069608344,0.0038511532,0.00009095649,0.0001817377,0.000054699733,0.000007855185,0.000009650193,0.0014066736],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.999445,5.133662e-7,0.00018124304,0.00020319017,0.000018397817,0.00015162397],"domain_scores_gemma":[0.999314,0.000016831633,0.0002113047,0.0003896298,0.000022577986,0.000045643577],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00015323797,0.00006211129,0.00012805962,0.00003545124,0.0004700206,0.0001115865,0.0003006491,0.00005311481,0.00026984263],"category_scores_gemma":[0.00017947488,0.00007303794,0.000045960165,0.000033072283,0.000049980277,0.0001489426,0.00006582554,0.00006715341,0.0017900214],"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":[0.000004376261,0.000025476487,0.010304277,0.0000044548765,0.0000064307455,5.9123175e-7,0.000015476591,0.0000044315425,0.00000486871,0.98571503,0.0007565143,0.0031580445],"study_design_scores_gemma":[0.0001808207,0.0000070777787,0.37743002,0.0000039699066,0.0000017903704,0.0000018813353,0.000003615613,0.00086495414,0.000022086517,0.54689497,0.07448596,0.000102892394],"about_ca_topic_score_codex":0.0002576391,"about_ca_topic_score_gemma":0.000011223822,"teacher_disagreement_score":0.9546504,"about_ca_system_score_codex":0.000022196396,"about_ca_system_score_gemma":0.000013051346,"threshold_uncertainty_score":0.9989872},"labels":[],"label_agreement":null},{"id":"W2786627586","doi":"10.3390/risks6010008","title":"Health Care Workers’ Risk Perceptions and Willingness to Report for Work during an Influenza Pandemic","year":2018,"lang":"en","type":"article","venue":"Risks","topic":"Disaster Response and Management","field":"Health Professions","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"McGill University; Jewish General Hospital; HEC Montréal","funders":"Canadian Institutes of Health Research","keywords":"Risk perception; Pandemic; Perception; Multinomial logistic regression; Work (physics); Health care; Psychology; Influenza pandemic; Environmental health; Public health; Logistic regression; Medicine; Actuarial science; Nursing; Business; Coronavirus disease 2019 (COVID-19); Political science; Disease; Statistics; Infectious disease (medical specialty)","score_opus":0.13217843215369932,"score_gpt":0.5005343653726936,"score_spread":0.3683559332189943,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2786627586","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9916989,0.00017150001,0.004650129,0.00037886118,0.00044230832,0.0015814783,0.000036384947,0.00015855844,0.0008818886],"genre_scores_gemma":[0.9925212,0.00005494548,0.0022146546,0.0019003866,0.0005906913,0.0004455486,0.000032239754,0.000036835714,0.0022034913],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99823964,0.00027852837,0.00044003542,0.0003980029,0.00013107674,0.0005127189],"domain_scores_gemma":[0.99875516,0.000102032114,0.00020883996,0.0004900707,0.00013993369,0.0003039772],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.00069051137,0.00014053992,0.00023251558,0.00009969483,0.0015254357,0.00002264617,0.00012136279,0.000102012986,0.000070969385],"category_scores_gemma":[0.00014357083,0.00012797232,0.00004111001,0.00019165513,0.000047107595,0.00011255782,0.00015144728,0.00023357803,0.00011447238],"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":[0.00042304734,0.000023326988,0.89842796,0.00017124607,0.000024340365,0.0000036067336,0.08139555,0.000013721853,0.000012254558,0.000028241333,0.0042629796,0.015213701],"study_design_scores_gemma":[0.00064910227,0.00018661558,0.818397,0.00028072603,0.00003084486,0.0000024114888,0.045315243,0.000005660473,0.0000011503139,0.000084360436,0.13489896,0.00014790548],"about_ca_topic_score_codex":0.0010886734,"about_ca_topic_score_gemma":0.0023492845,"teacher_disagreement_score":0.13063599,"about_ca_system_score_codex":0.0001664317,"about_ca_system_score_gemma":0.00014526969,"threshold_uncertainty_score":0.99977446},"labels":[],"label_agreement":null},{"id":"W2789997224","doi":"10.3390/risks6010014","title":"A Risk-Based Approach for Asset Allocation with A Defaultable Share","year":2018,"lang":"en","type":"article","venue":"Risks","topic":"Stochastic processes and financial applications","field":"Economics, Econometrics and Finance","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"Australian Research Council; Natural Sciences and Engineering Research Council of Canada","keywords":"Risk measure; Measure (data warehouse); Mathematical optimization; Computer science; Stochastic differential equation; Markov process; Dynamic risk measure; Portfolio; Time consistency; Asset allocation; Economics; Mathematics; Actuarial science; Finance; Applied mathematics","score_opus":0.061324712589276215,"score_gpt":0.267013266438597,"score_spread":0.2056885538493208,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2789997224","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":[0.004492617,0.00023293063,0.9878519,0.00013322121,0.000037422258,0.00048743127,0.0007451649,0.000049238242,0.005970035],"genre_scores_gemma":[0.9030298,0.000006604137,0.09556706,0.00015442772,0.00019477571,0.0007364319,0.00017497489,0.000022536904,0.000113416005],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991962,0.0000015834354,0.00022505391,0.00034336725,0.000024724486,0.00020909008],"domain_scores_gemma":[0.9992804,0.000042226315,0.0002541759,0.000259558,0.00011382248,0.000049798422],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002042564,0.000101569545,0.00017942817,0.00007714626,0.00023234646,0.000048524143,0.00016053201,0.000074108364,0.000039284532],"category_scores_gemma":[0.00012950951,0.000101150225,0.000044978395,0.00028243527,0.000053952706,0.00008261011,0.000013542127,0.00006841723,0.00015529021],"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":[0.00016236691,0.00035283275,0.01915742,0.00010273404,0.000059202568,1.839527e-7,0.00029679775,0.000977112,0.000014299115,0.97061175,0.0025351197,0.0057302015],"study_design_scores_gemma":[0.003473344,0.0010814595,0.050957292,0.000040071463,0.0000748317,0.0000043195114,0.00014456897,0.5091991,0.0005744914,0.30116057,0.13231498,0.000974969],"about_ca_topic_score_codex":0.000420089,"about_ca_topic_score_gemma":0.000056182555,"teacher_disagreement_score":0.89853716,"about_ca_system_score_codex":0.000040755913,"about_ca_system_score_gemma":0.00004124581,"threshold_uncertainty_score":0.41247866},"labels":[],"label_agreement":null},{"id":"W2796329978","doi":"10.3390/risks6020031","title":"An Optimal Investment Strategy for Insurers in Incomplete Markets","year":2018,"lang":"en","type":"article","venue":"Risks","topic":"Stochastic processes and financial applications","field":"Economics, Econometrics and Finance","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 Calgary","funders":"University of Calgary","keywords":"Hamilton–Jacobi–Bellman equation; Incomplete markets; Uniqueness; Exponential utility; Bellman equation; Monte Carlo method; Mathematics; Mathematical economics; Stochastic volatility; Stochastic differential equation; Expected utility hypothesis; Economics; Volatility (finance); Mathematical optimization; Applied mathematics; Econometrics; Microeconomics; Mathematical analysis","score_opus":0.10561084746904019,"score_gpt":0.31440593438146314,"score_spread":0.20879508691242293,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2796329978","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":[0.30252287,0.0005147772,0.6784446,0.00024188388,0.00018266441,0.00055054174,0.00032352953,0.00003782502,0.01718127],"genre_scores_gemma":[0.9901403,0.000016141053,0.009065202,0.00031557583,0.0001841384,0.00020655247,0.00002358838,0.000014543758,0.00003396209],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9991674,0.000002206457,0.00030984983,0.00028870694,0.000016660082,0.00021516468],"domain_scores_gemma":[0.99954367,0.00002603139,0.0001224666,0.00021335745,0.000031530573,0.000062971594],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024552605,0.00008844672,0.00017760474,0.0001132336,0.00008870542,0.000035042256,0.00017973248,0.00006794807,0.000064729415],"category_scores_gemma":[0.000044196306,0.00010505422,0.00003477217,0.00018718417,0.00006460364,0.00011128346,0.00002092375,0.000059964976,0.00012961203],"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":[0.000053422656,0.00011755264,0.0036624577,0.000012758607,0.000008316727,4.0608774e-7,0.00022671136,0.00010578822,0.000017067976,0.9894828,0.00024077987,0.0060719475],"study_design_scores_gemma":[0.0009794151,0.00047184998,0.26866803,0.00001070407,0.0000041404132,0.0000015622857,0.000096864744,0.032127216,0.00009336276,0.6552711,0.041954923,0.000320789],"about_ca_topic_score_codex":0.00031309258,"about_ca_topic_score_gemma":0.00010610035,"teacher_disagreement_score":0.6876174,"about_ca_system_score_codex":0.000053135856,"about_ca_system_score_gemma":0.000027045746,"threshold_uncertainty_score":0.42839867},"labels":[],"label_agreement":null},{"id":"W2804584838","doi":"10.3390/risks6020059","title":"On the Moments and the Distribution of Aggregate Discounted Claims in a Markovian Environment","year":2018,"lang":"en","type":"article","venue":"Risks","topic":"Probability and Risk Models","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":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Markov process; Laplace transform; Applied mathematics; Distribution (mathematics); Mathematics; Markov chain; Aggregate (composite); Order (exchange); State space; Discounting; State (computer science); Matrix (chemical analysis); Statistical physics; Mathematical optimization; Mathematical analysis; Algorithm; Statistics; Economics; Physics; Finance","score_opus":0.10344905325157423,"score_gpt":0.36077510709690347,"score_spread":0.25732605384532925,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2804584838","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99206734,0.000079795995,0.002463556,0.004220037,0.000060197977,0.0002877749,0.00003736063,0.000003145268,0.0007807676],"genre_scores_gemma":[0.9995091,0.00015130264,0.000013112374,0.00010865986,0.000016509539,0.000015141679,0.000002261534,0.0000021501291,0.00018174908],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99846476,0.00036109236,0.00029906057,0.00021564568,0.00053213804,0.00012730734],"domain_scores_gemma":[0.99846405,0.00087430567,0.00013492748,0.00047553197,0.000024416677,0.0000267845],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003174505,0.00006779741,0.00013589824,0.00002810359,0.0001421695,0.000046653142,0.0003356472,0.000040016603,0.00010861226],"category_scores_gemma":[0.0003275879,0.000027213875,0.000042768028,0.00016675619,0.00089267554,0.000073978226,0.00012607191,0.0001063704,0.000062255145],"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":[0.004430799,0.0010466296,0.0676428,0.000018071045,0.00011149976,0.0000070765655,0.017162317,0.0020866243,0.0004997305,0.40931603,0.00797759,0.48970082],"study_design_scores_gemma":[0.0009975515,0.00011890567,0.12469604,0.000034241402,0.0000093264525,0.000001178105,0.00041746648,0.027158916,0.00075685844,0.84124964,0.004479591,0.00008030563],"about_ca_topic_score_codex":0.00019460432,"about_ca_topic_score_gemma":0.000095132134,"teacher_disagreement_score":0.4896205,"about_ca_system_score_codex":0.000033616707,"about_ca_system_score_gemma":0.000011089687,"threshold_uncertainty_score":0.32891002},"labels":[],"label_agreement":null},{"id":"W2885443624","doi":"10.3390/risks6030079","title":"On a Multiplicative Multivariate Gamma Distribution with Applications in Insurance","year":2018,"lang":"en","type":"article","venue":"Risks","topic":"Statistical Distribution Estimation and Applications","field":"Mathematics","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":"York University","funders":"","keywords":"Multiplicative function; Copula (linguistics); Univariate; Multivariate statistics; Mathematics; Marginal distribution; Probability density function; Random variable; Applied mathematics; Econometrics; Statistical physics; Distribution (mathematics); Gamma distribution; Heavy-tailed distribution; Multivariate normal distribution; Statistics; Mathematical analysis; Physics","score_opus":0.09239675383624114,"score_gpt":0.40677813126442086,"score_spread":0.31438137742817973,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2885443624","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":[0.11373666,0.0000029902083,0.88159084,0.00025329518,0.000011504466,0.0007346233,0.0009811681,0.000121267156,0.0025676186],"genre_scores_gemma":[0.9863489,0.0000018379652,0.01236299,0.00006017533,0.000037810405,0.0007906697,0.0003016807,0.000014335558,0.00008156817],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.9990179,0.000049590366,0.00025870386,0.00029761856,0.0001734797,0.00020273631],"domain_scores_gemma":[0.9985813,0.0006561231,0.00012963393,0.00035707463,0.00019032456,0.00008552819],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012254444,0.00014125719,0.00015523417,0.00004341034,0.00017792622,0.00002285453,0.00013453282,0.00006486209,0.00010276813],"category_scores_gemma":[0.00045109776,0.00011325703,0.00002636318,0.0004958422,0.0002001922,0.000057645437,0.000019253594,0.00014881122,0.00028977756],"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":[0.000048051377,0.00035943685,0.0018911113,0.0000108552285,0.000008450709,4.998189e-7,0.00014415906,0.000015281634,0.00010178395,0.99397796,0.00068191614,0.0027604823],"study_design_scores_gemma":[0.001621991,0.00017162296,0.642437,0.0000876886,0.000022296714,0.0000033594793,0.00013537322,0.010886154,0.002293363,0.33801994,0.003975947,0.00034531255],"about_ca_topic_score_codex":0.00006222386,"about_ca_topic_score_gemma":0.00006178939,"teacher_disagreement_score":0.8726123,"about_ca_system_score_codex":0.00010881863,"about_ca_system_score_gemma":0.000029162178,"threshold_uncertainty_score":0.46184877},"labels":[],"label_agreement":null},{"id":"W2886792289","doi":"10.3390/risks6030078","title":"On Fund Mapping Regressions Applied to Segregated Funds Hedging Under Regime-Switching Dynamics","year":2018,"lang":"en","type":"article","venue":"Risks","topic":"Market Dynamics and Volatility","field":"Economics, Econometrics and Finance","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":"Concordia University; Actua; Université Laval","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada; Autorité des Marchés Financiers","keywords":"Imperfect; Econometrics; Regression; Dynamics (music); Economics; Capital (architecture); Business; Statistics; Mathematics","score_opus":0.09973805446526131,"score_gpt":0.2961118460624723,"score_spread":0.19637379159721102,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2886792289","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.68758285,0.00006850841,0.14796746,0.00082133594,0.00058972463,0.00029642423,0.00008712517,0.00011545001,0.16247113],"genre_scores_gemma":[0.99514246,0.000017919912,0.0020002392,0.0008628881,0.00020094585,0.000021491764,0.000031772186,0.00004940688,0.0016728808],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981269,0.0000224811,0.000579908,0.00069255114,0.00006893089,0.0005092309],"domain_scores_gemma":[0.9985632,0.00013688538,0.00030152051,0.0007427926,0.00004673429,0.00020890021],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008796052,0.0002497885,0.0004185131,0.0003849656,0.00044208494,0.00014255554,0.0003198324,0.00018080312,0.00042442128],"category_scores_gemma":[0.00013424695,0.00027303188,0.00010443261,0.00054925465,0.00005196589,0.00010063559,0.00017088761,0.00034275072,0.00046991676],"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":[0.00017649776,0.00014369625,0.0832015,0.00005848304,0.00013962718,0.0000055858163,0.0011182715,0.00078127574,0.00012665582,0.89241844,0.0020581053,0.01977187],"study_design_scores_gemma":[0.000705715,0.0001161666,0.08915432,0.00012941947,0.000009366061,0.0000030990975,0.00032092005,0.7174281,0.000025413732,0.17922065,0.012173588,0.0007132704],"about_ca_topic_score_codex":0.0004959413,"about_ca_topic_score_gemma":0.00032885946,"teacher_disagreement_score":0.7166468,"about_ca_system_score_codex":0.00042060815,"about_ca_system_score_gemma":0.000028294067,"threshold_uncertainty_score":0.99997216},"labels":[],"label_agreement":null},{"id":"W2888590466","doi":"10.3390/risks6030085","title":"A VaR-Type Risk Measure Derived from Cumulative Parisian Ruin for the Classical Risk Model","year":2018,"lang":"en","type":"article","venue":"Risks","topic":"Probability and Risk Models","field":"Decision Sciences","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Université du Québec à Montréal","keywords":"Measure (data warehouse); Risk measure; Ruin theory; Mathematics; Type (biology); Risk model; Cumulative risk; Statistics; Econometrics; Computer science; Economics; Medicine; Financial economics; Data mining","score_opus":0.31341131513279386,"score_gpt":0.4314612828122154,"score_spread":0.11804996767942155,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2888590466","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":[0.38633767,0.00043161487,0.60966647,0.001102023,0.00049370463,0.00064220314,0.00037795832,0.000070995746,0.0008773497],"genre_scores_gemma":[0.9823181,0.00015461461,0.016059313,0.00024180583,0.00052724325,0.00005320146,0.0000074104937,0.000025833822,0.000612483],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9957026,0.00080275774,0.00074340455,0.0009576516,0.0012888866,0.0005046835],"domain_scores_gemma":[0.9908218,0.005965999,0.00052269344,0.0015292366,0.00093911274,0.00022117085],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0047083343,0.00028579312,0.00047847442,0.00008837016,0.0012433389,0.00029650587,0.0013741825,0.00032265522,0.00021170767],"category_scores_gemma":[0.009685002,0.0001535988,0.0003459626,0.00053417514,0.00072595675,0.0003339817,0.00025912994,0.00059010246,0.0005890626],"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":[0.0044869934,0.0008274356,0.07600108,0.000007318984,0.0008849017,0.000005096499,0.043265995,0.1764669,0.0008494409,0.011252091,0.070451304,0.61550146],"study_design_scores_gemma":[0.000490971,0.00012204235,0.014312441,0.000007004153,0.000106887994,3.9988365e-7,0.00033333967,0.5998139,0.0003842383,0.37814873,0.006119371,0.00016067187],"about_ca_topic_score_codex":0.0015118403,"about_ca_topic_score_gemma":0.0027202247,"teacher_disagreement_score":0.61534077,"about_ca_system_score_codex":0.0000695346,"about_ca_system_score_gemma":0.0002444139,"threshold_uncertainty_score":0.99865687},"labels":[],"label_agreement":null},{"id":"W2890015635","doi":"10.3390/risks6040106","title":"Effects of the Age Process on Aggregate Discounted Claims","year":2018,"lang":"en","type":"article","venue":"Risks","topic":"Insurance and Financial Risk Management","field":"Economics, Econometrics and Finance","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","funders":"Université Laval","keywords":"Aggregate (composite); Portfolio; Econometrics; Economics; Joint probability distribution; Function (biology); Moment (physics); Distribution (mathematics); Actuarial science; Mathematics; Statistics; Financial economics","score_opus":0.025562466748984845,"score_gpt":0.2564664393706074,"score_spread":0.23090397262162257,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2890015635","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9598282,0.0003096666,0.00038132508,0.00017006126,0.00075452775,0.0003318549,0.000025884134,0.000018136232,0.038180374],"genre_scores_gemma":[0.9982543,0.00013907954,0.00001946304,0.00036958107,0.00016827136,0.00002604293,0.0000013979516,0.0000142845975,0.0010075641],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.99926573,0.000009126024,0.00026825457,0.00021840584,0.000044864708,0.00019364145],"domain_scores_gemma":[0.99937564,0.00002394034,0.00025426,0.00030370755,0.00002505342,0.000017395512],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017347638,0.000101907244,0.00021340509,0.00006975,0.000117303025,0.000020761423,0.00028007745,0.000056099823,0.000033387656],"category_scores_gemma":[0.000069028145,0.00007972168,0.00008957802,0.00025833366,0.000127031,0.00007209903,0.00004558347,0.00010266891,0.00044083936],"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":[0.0002535111,0.00073716365,0.18938805,0.0006849584,0.00018260002,0.000029050574,0.0045295455,0.00012368172,0.00022845525,0.7667914,0.007287311,0.02976429],"study_design_scores_gemma":[0.0009008259,0.00029308998,0.8501891,0.00017846002,0.0000147245,4.3856684e-7,0.00003543802,0.00035648176,0.013632047,0.08363877,0.050478444,0.0002821921],"about_ca_topic_score_codex":0.00018209244,"about_ca_topic_score_gemma":0.0000416262,"teacher_disagreement_score":0.6831526,"about_ca_system_score_codex":0.000030569023,"about_ca_system_score_gemma":0.0000074344694,"threshold_uncertainty_score":0.5666245},"labels":[],"label_agreement":null},{"id":"W2891933565","doi":"10.3390/risks6030100","title":"A User-Friendly Algorithm for Detecting the Influence of Background Risks on a Model","year":2018,"lang":"en","type":"article","venue":"Risks","topic":"Leadership, Behavior, and Decision-Making Studies","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":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Probabilistic logic; Point (geometry); Statistical model; Carry (investment); Natural (archaeology); Order (exchange); Algorithm; Data mining; Machine learning; Artificial intelligence; Finance; Mathematics","score_opus":0.3284631170235385,"score_gpt":0.47641116192799043,"score_spread":0.14794804490445196,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2891933565","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.85114753,0.0000846693,0.14693774,0.00022719623,0.00030594066,0.00038594028,0.000048608017,0.000041225023,0.0008211231],"genre_scores_gemma":[0.97956514,0.0000090248295,0.019218639,0.00027979768,0.00021827559,0.00005507064,3.9149745e-7,0.000021621498,0.00063204154],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9965417,0.00015464392,0.0007701207,0.00064125546,0.0014453115,0.0004469976],"domain_scores_gemma":[0.99209195,0.005236135,0.00048708075,0.0010784856,0.0010247257,0.000081648875],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003246928,0.00023308481,0.00041494262,0.00021624382,0.0007952053,0.00022449197,0.001174509,0.00013308851,0.000027490349],"category_scores_gemma":[0.004272866,0.00013267745,0.00024187844,0.0006986608,0.0005608662,0.00028332561,0.00022991865,0.00024471362,0.00017342524],"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":[0.00030097758,0.00023472554,0.006481476,0.000009683278,0.00008044171,0.000006612585,0.0114602335,0.011094887,0.00051143556,0.0020724402,0.010676317,0.95707077],"study_design_scores_gemma":[0.0047654724,0.004871159,0.17358243,0.00058756355,0.0004131514,0.000045825353,0.06598566,0.5433684,0.011401194,0.15708919,0.035957117,0.0019328283],"about_ca_topic_score_codex":0.0002295676,"about_ca_topic_score_gemma":0.00017120858,"teacher_disagreement_score":0.95513797,"about_ca_system_score_codex":0.000047988164,"about_ca_system_score_gemma":0.000096843105,"threshold_uncertainty_score":0.61161566},"labels":[],"label_agreement":null},{"id":"W2897293744","doi":"10.3390/risks6040115","title":"A Maximal Tail Dependence-Based Clustering Procedure for Financial Time Series and Its Applications in Portfolio Selection","year":2018,"lang":"en","type":"article","venue":"Risks","topic":"Complex Systems and Time Series Analysis","field":"Economics, Econometrics and Finance","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"National Natural Science Foundation of China","keywords":"Cluster analysis; Portfolio; Series (stratigraphy); Selection (genetic algorithm); Variance (accounting); Computer science; Econometrics; Mathematics; Matrix (chemical analysis); Covariance matrix; Statistics; Algorithm; Finance; Economics; Artificial intelligence; Accounting; Materials science","score_opus":0.034124031007297675,"score_gpt":0.2468391286666622,"score_spread":0.21271509765936453,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2897293744","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.92871076,0.0014157132,0.061589234,0.0005400727,0.00013434605,0.0015487812,0.0003173047,0.00009870157,0.0056450972],"genre_scores_gemma":[0.99672836,0.000019161042,0.0011838551,0.00003444524,0.00019105854,0.0002269553,0.000018446934,0.0000142709305,0.0015834684],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991646,0.0000046620953,0.00032482017,0.00029724775,0.000021070236,0.00018761306],"domain_scores_gemma":[0.9996246,0.000017918512,0.00016520417,0.000100099285,0.00005277178,0.00003941136],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018872037,0.00010077898,0.00024355309,0.00018117613,0.00017303074,0.00005489141,0.00007437321,0.00007726626,0.00025998268],"category_scores_gemma":[0.000057669833,0.000116209114,0.00005570715,0.00031603547,0.00002579994,0.00015975093,0.000026207219,0.000055398745,0.00007099502],"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":[0.0013467969,0.00069590117,0.6544396,0.0015117043,0.00033060854,0.000007005347,0.002181135,0.0032170312,0.0016926137,0.310253,0.0062750527,0.018049551],"study_design_scores_gemma":[0.0018528117,0.0008013256,0.21288952,0.000077633,0.00004761521,0.000023621107,0.0001661384,0.5289621,0.0009908515,0.02141788,0.2318338,0.0009366989],"about_ca_topic_score_codex":0.00034346106,"about_ca_topic_score_gemma":0.0013090839,"teacher_disagreement_score":0.5257451,"about_ca_system_score_codex":0.000048918653,"about_ca_system_score_gemma":0.00003359807,"threshold_uncertainty_score":0.473887},"labels":[],"label_agreement":null},{"id":"W2899388332","doi":"10.3390/risks6040124","title":"Alpha Beta Risk and Stock Returns—A Decomposition Analysis of Idiosyncratic Volatility with Conditional Models","year":2018,"lang":"en","type":"article","venue":"Risks","topic":"Financial Markets and Investment Strategies","field":"Economics, Econometrics and Finance","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 Manitoba; University of Northern British Columbia","funders":"","keywords":"Predictability; Econometrics; Stock (firearms); Conditional variance; BETA (programming language); Economics; Volatility (finance); Systematic risk; Covariance; Realized variance; Variance decomposition of forecast errors; Mathematics; Statistics; Autoregressive conditional heteroskedasticity; Computer science","score_opus":0.050462595865455706,"score_gpt":0.2682142807804922,"score_spread":0.2177516849150365,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2899388332","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9637906,0.00051458395,0.025670247,0.000063870284,0.000039748295,0.00013042633,0.00064520416,0.000013798375,0.009131498],"genre_scores_gemma":[0.9981034,0.00016380228,0.0015027589,0.00005495385,0.00004037743,0.000009473643,0.000078667006,0.000007676778,0.000038883984],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9991362,0.00002285888,0.00038308877,0.00027918033,0.000042558797,0.00013608488],"domain_scores_gemma":[0.9992614,0.00004836521,0.00036617636,0.00021292234,0.00006320537,0.000047944915],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030700894,0.00011168001,0.00038412717,0.00022793896,0.00012956256,0.000038417827,0.00007410531,0.00006567526,0.00021330641],"category_scores_gemma":[0.000018453722,0.00010601432,0.000075647644,0.0003466523,0.00020566613,0.00032918275,0.000021705851,0.000077942306,0.000008282527],"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":[0.00012077072,0.00009742857,0.61097556,0.000029070588,0.0007348266,8.7938287e-7,0.0004118777,0.00053485157,0.000016128499,0.38633642,0.00020481128,0.0005373337],"study_design_scores_gemma":[0.00025171365,0.00022352359,0.6782664,0.000008254691,0.00016594834,5.8032595e-7,0.000021787471,0.1678443,0.00006457085,0.15289219,0.00014404222,0.00011670493],"about_ca_topic_score_codex":0.00072726473,"about_ca_topic_score_gemma":0.00027561188,"teacher_disagreement_score":0.23344424,"about_ca_system_score_codex":0.000027183061,"about_ca_system_score_gemma":0.000015973956,"threshold_uncertainty_score":0.43231383},"labels":[],"label_agreement":null},{"id":"W2902606947","doi":"10.3390/risks6040138","title":"On the Failure to Reach the Optimal Government Debt Ceiling","year":2018,"lang":"en","type":"article","venue":"Risks","topic":"Fiscal Policies and Political Economy","field":"Economics, Econometrics and Finance","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":"Social Sciences and Humanities Research Council of Canada; Université de Lausanne","keywords":"Ceiling (cloud); Debt; Government debt; Internal debt; Monetary economics; Debt-to-GDP ratio; Economics; Debt ratio; Business; Finance; Engineering","score_opus":0.05139238170106793,"score_gpt":0.2560069676006194,"score_spread":0.2046145858995515,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2902606947","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5908352,0.000091529386,0.0011220542,0.05876109,0.00036648262,0.0002733144,0.00015761238,0.000024259325,0.34836844],"genre_scores_gemma":[0.98614687,0.000007928852,0.00020804732,0.011163675,0.0005663789,0.000028608567,0.0000010387564,0.000015202661,0.0018622592],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99911875,0.000013534687,0.00026367576,0.0002204942,0.000042127245,0.00034142518],"domain_scores_gemma":[0.9991761,0.00020848794,0.00007598067,0.00043578495,0.000010749827,0.00009290672],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0003809258,0.00010922299,0.00015316464,0.000018394468,0.00025315082,0.00009133762,0.00033718473,0.00005613791,0.0009777539],"category_scores_gemma":[0.00017722738,0.00006889246,0.000086767,0.00009160984,0.00010977218,0.000039789556,0.00009456339,0.00016799221,0.0044406243],"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":[0.0000090572075,0.000017930503,0.00071288564,0.0000016381579,0.000016463784,2.6460617e-7,0.0003670679,0.00008797069,0.0000025775237,0.9492425,0.049059063,0.00048262946],"study_design_scores_gemma":[0.00013619942,0.00019907323,0.008980644,0.000008622816,0.000004066506,0.0000015099397,0.0002796729,0.0024253668,0.00026332974,0.05685349,0.9306683,0.000179748],"about_ca_topic_score_codex":0.0006292313,"about_ca_topic_score_gemma":0.00007388474,"teacher_disagreement_score":0.89238894,"about_ca_system_score_codex":0.000105076804,"about_ca_system_score_gemma":0.000006418233,"threshold_uncertainty_score":0.9999355},"labels":[],"label_agreement":null},{"id":"W2912160920","doi":"10.3390/risks7010014","title":"Changes of Relation in Multi-Population Mortality Dependence: An Application of Threshold VECM","year":2019,"lang":"en","type":"article","venue":"Risks","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social 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":"Error correction model; Econometrics; Longevity; Population; Statistics; Variable (mathematics); Mathematics; Multivariate statistics; Forcing (mathematics); Mortality rate; Contrast (vision); Economics; Demography; Cointegration; Medicine; Computer science; Internal medicine; Gerontology","score_opus":0.08248672557423722,"score_gpt":0.3922807216623483,"score_spread":0.3097939960881111,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2912160920","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9950856,0.000069120775,0.00075633434,0.000046271067,0.00015087737,0.00082884205,0.000009577992,0.000032083863,0.0030212991],"genre_scores_gemma":[0.9993461,0.00012224387,0.0003712922,0.000012808713,0.000035379227,0.00003331739,0.0000344349,0.000007723133,0.0000367363],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99863744,0.00015818622,0.00031660375,0.00024201206,0.00048061,0.00016516686],"domain_scores_gemma":[0.9991656,0.00002807561,0.000324287,0.00035590463,0.00009182867,0.00003433319],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013690074,0.00008063949,0.00018314944,0.00017032785,0.00006290698,0.000011148049,0.0002067224,0.00012662383,0.000027790727],"category_scores_gemma":[0.00003513266,0.00008696543,0.000046320783,0.00045998048,0.00008794477,0.0002872098,0.00002787386,0.000095870826,0.000009167176],"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":[0.000009445202,0.000117777694,0.9759192,0.000041595118,0.000009381162,1.7974543e-7,0.002090867,0.0009620765,0.00041074608,0.01596778,0.0000021101553,0.0044688676],"study_design_scores_gemma":[0.00023960264,0.000027988059,0.9908971,0.000019949479,0.000015970205,2.4880398e-8,0.00088901294,0.005098078,0.00020701303,0.0024530971,0.00006426314,0.000087887725],"about_ca_topic_score_codex":0.0353206,"about_ca_topic_score_gemma":0.08169309,"teacher_disagreement_score":0.046372496,"about_ca_system_score_codex":0.00006126002,"about_ca_system_score_gemma":0.000020445315,"threshold_uncertainty_score":0.9711033},"labels":[],"label_agreement":null},{"id":"W2935932094","doi":"10.3390/risks7020040","title":"Recent Regulation in Credit Risk Management: A Statistical Framework","year":2019,"lang":"en","type":"article","venue":"Risks","topic":"Credit Risk and Financial Regulations","field":"Economics, Econometrics and Finance","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 Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Credit risk; Volatility (finance); Loan; Risk management; Business; Actuarial science; European union; Credit valuation adjustment; Economics; Stress test; Credit reference; Finance; International economics","score_opus":0.03415914382899445,"score_gpt":0.2661351976686107,"score_spread":0.23197605383961625,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2935932094","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7023595,0.0022590323,0.16081046,0.00042611585,0.0019407672,0.0008201182,0.0005480041,0.00008723136,0.13074875],"genre_scores_gemma":[0.9844623,0.0033029176,0.010916834,0.000023302382,0.00018093038,0.00002829828,0.000062831634,0.000020948537,0.0010016316],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.99884,0.000017075172,0.00047302197,0.00036236897,0.000052515756,0.0002550548],"domain_scores_gemma":[0.999267,0.00009860772,0.00020279156,0.00035895788,0.000016993883,0.000055651275],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00038438188,0.00011054204,0.00024867177,0.00023762618,0.00006131543,0.00004311715,0.0001264874,0.00013575298,0.002086],"category_scores_gemma":[0.00016207855,0.00013485791,0.000048850743,0.0003881371,0.000028956538,0.00012515007,0.000046831738,0.00023317151,0.0026942254],"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":[0.000016786838,0.000043923246,0.36188436,0.000008116776,0.000009154561,0.000002113316,0.00011157819,0.00054843974,1.869325e-7,0.59369385,0.00087846577,0.04280304],"study_design_scores_gemma":[0.00025455956,0.000019417706,0.5948965,0.000013805628,0.0000032907826,3.1198618e-7,0.000020135376,0.0040422278,0.0000011438342,0.24494188,0.15570208,0.00010464412],"about_ca_topic_score_codex":0.00025734876,"about_ca_topic_score_gemma":0.000050780905,"teacher_disagreement_score":0.34875196,"about_ca_system_score_codex":0.00012103043,"about_ca_system_score_gemma":0.000008544813,"threshold_uncertainty_score":0.9988262},"labels":[],"label_agreement":null},{"id":"W2936364519","doi":"10.3390/risks7020042","title":"Defining Geographical Rating Territories in Auto Insurance Regulation by Spatially Constrained Clustering","year":2019,"lang":"en","type":"article","venue":"Risks","topic":"Data-Driven Disease Surveillance","field":"Medicine","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 Guelph; Toronto Metropolitan University","funders":"","keywords":"Cluster analysis; Statistic; Computer science; Econometrics; Entropy (arrow of time); Homogeneity (statistics); Actuarial science; Data mining; Statistics; Business; Economics; Mathematics; Machine learning","score_opus":0.01185182908226527,"score_gpt":0.27967241732990006,"score_spread":0.26782058824763477,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2936364519","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99591124,0.00028634624,0.0007436668,0.00023071944,0.0002576517,0.00033967127,0.00016743269,0.00014086593,0.0019223961],"genre_scores_gemma":[0.9971864,0.000014850893,0.0021084605,0.00013345266,0.00007798377,0.00001544031,0.00038510942,0.000020649717,0.000057659297],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9987999,0.00007042045,0.00033449792,0.00029808327,0.00025219165,0.00024490152],"domain_scores_gemma":[0.99934417,0.0001155988,0.000111128975,0.000288733,0.00005158523,0.000088811285],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002840759,0.00013486427,0.00029325805,0.00009584754,0.000041007486,0.00003088169,0.00007786161,0.00009123303,0.00008968665],"category_scores_gemma":[0.00024168972,0.00013581138,0.00005279575,0.00021932334,0.00007096726,0.00014323654,0.000051772146,0.00020852173,0.000040465253],"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":[0.00012860869,0.000037466536,0.98200256,0.00008585889,0.000016458956,0.000012280166,0.00013284577,0.00026791956,0.0055551846,0.00013519217,0.00013036792,0.0114952875],"study_design_scores_gemma":[0.0017202623,0.00007945637,0.979716,0.00032026766,0.000009481533,0.000011922723,0.0000532644,0.015963921,0.00035126225,0.000052170722,0.0015638,0.00015816775],"about_ca_topic_score_codex":0.00067875744,"about_ca_topic_score_gemma":0.00041856573,"teacher_disagreement_score":0.015696002,"about_ca_system_score_codex":0.00005080669,"about_ca_system_score_gemma":0.0000734863,"threshold_uncertainty_score":0.55382276},"labels":[],"label_agreement":null},{"id":"W2941766477","doi":"10.3390/risks7020047","title":"Contingent Convertible Debt: The Impact on Equity Holders","year":2019,"lang":"en","type":"article","venue":"Risks","topic":"Credit Risk and Financial Regulations","field":"Economics, Econometrics and Finance","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Group for Research in Decision Analysis; HEC Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; HEC Montréal","keywords":"Convertible bond; Coco; Equity value; Business; Debt; Debt-to-equity ratio; Equity (law); Capital structure; Monetary economics; Cost of capital; Recourse debt; Financial system; Finance; Internal debt; Economics; Debt levels and flows; Profit (economics); Microeconomics","score_opus":0.09597248910661259,"score_gpt":0.32411023467798217,"score_spread":0.22813774557136957,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2941766477","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.92105097,0.00055783393,0.0004842557,0.00045668692,0.00058900146,0.0002663928,0.00009353953,0.000030370653,0.076470956],"genre_scores_gemma":[0.99795485,0.000116157455,0.000020065243,0.000095649986,0.00014392385,0.000011412506,0.00001068564,0.00001508553,0.0016321796],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9991624,0.000008952716,0.00027599005,0.00023916169,0.00004161956,0.00027187474],"domain_scores_gemma":[0.99926823,0.00007930396,0.00017341784,0.00040180126,0.000018866083,0.000058398997],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003773672,0.000117870586,0.00022976709,0.00007820776,0.00013063895,0.00007068143,0.00021489487,0.000074360534,0.00087956653],"category_scores_gemma":[0.00006841468,0.000088227804,0.00017551777,0.00016002283,0.00004404814,0.00008584213,0.00006380896,0.0001558971,0.0036638582],"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":[0.000042499658,0.000094176794,0.7014317,0.000008237134,0.000050054456,0.0000012193758,0.00052895787,0.0017750916,0.00003189762,0.2792913,0.0086976085,0.008047282],"study_design_scores_gemma":[0.00044610514,0.00011854265,0.94155896,0.000008712393,0.000005140076,0.0000010312394,0.00006178079,0.005010522,0.000106399486,0.014925596,0.037591428,0.00016576509],"about_ca_topic_score_codex":0.0014998831,"about_ca_topic_score_gemma":0.000029995163,"teacher_disagreement_score":0.2643657,"about_ca_system_score_codex":0.00010220877,"about_ca_system_score_gemma":0.000023103472,"threshold_uncertainty_score":0.9971119},"labels":[],"label_agreement":null},{"id":"W2943460946","doi":"10.3390/risks7020050","title":"Practice Oriented and Monte Carlo Based Estimation of the Value-at-Risk for Operational Risk Measurement","year":2019,"lang":"en","type":"article","venue":"Risks","topic":"Financial Risk and Volatility Modeling","field":"Economics, Econometrics and Finance","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":"Monte Carlo method; Quantile; Computer science; Sampling (signal processing); Monte Carlo integration; Importance sampling; Statistics; Econometrics; Hybrid Monte Carlo; Mathematics; Markov chain Monte Carlo","score_opus":0.05299582202422654,"score_gpt":0.26140315544646964,"score_spread":0.2084073334222431,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2943460946","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.90718055,0.0010069447,0.08971567,0.0003009711,0.00028392626,0.00061356626,0.0003956115,0.000009632842,0.00049313804],"genre_scores_gemma":[0.99065256,0.00010670252,0.009013675,0.00008166089,0.00002765428,0.000029253399,0.00000598311,0.000011353318,0.00007112763],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990944,0.000041342504,0.00040489685,0.00024912914,0.00008667286,0.000123518],"domain_scores_gemma":[0.9989157,0.00016831914,0.00047700276,0.00025913425,0.00015219615,0.000027665123],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015312139,0.00009029094,0.00019521281,0.000050006263,0.00018941634,0.000021567175,0.00008465884,0.00006781625,0.000020262212],"category_scores_gemma":[0.0017392951,0.000081226346,0.0000828821,0.00009412993,0.00002526442,0.00017315724,0.000035071593,0.000108701264,0.00001819039],"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":[0.00028764247,0.00013910125,0.557057,0.00007858439,0.000074255084,8.7497526e-8,0.0008500109,0.4014269,0.000038474158,0.03508942,0.00030324317,0.004655259],"study_design_scores_gemma":[0.00080305024,0.00006589535,0.11754636,0.000017378152,0.000028547232,2.957735e-7,0.00002820028,0.8721002,0.00017480864,0.002295715,0.00684788,0.00009171608],"about_ca_topic_score_codex":0.0025101874,"about_ca_topic_score_gemma":0.00009978635,"teacher_disagreement_score":0.47067323,"about_ca_system_score_codex":0.00011751797,"about_ca_system_score_gemma":0.000046889876,"threshold_uncertainty_score":0.37946692},"labels":[],"label_agreement":null},{"id":"W2944822875","doi":"10.3390/risks7020057","title":"The Determinants of Market-Implied Recovery Rates","year":2019,"lang":"en","type":"article","venue":"Risks","topic":"Credit Risk and Financial Regulations","field":"Economics, Econometrics and Finance","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":"HEC Montréal","funders":"","keywords":"Issuer; Econometrics; Recovery rate; Economics; Tobit model; Credit risk; Interest rate; Variable (mathematics); Sample (material); Contrast (vision); Financial economics; Monetary economics; Actuarial science; Mathematics; Finance; Computer science","score_opus":0.041580403618877076,"score_gpt":0.26690683630999457,"score_spread":0.2253264326911175,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2944822875","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9488217,0.0011163224,0.00012392357,0.00007733598,0.0007184733,0.00015782911,0.00008441838,0.000010843422,0.048889115],"genre_scores_gemma":[0.99293464,0.0007039932,0.00010093383,0.000009155521,0.00007034922,0.000009226952,0.0000028161887,0.000011383995,0.006157489],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99923325,0.0000075078274,0.00039670945,0.0001645072,0.000024549498,0.00017348614],"domain_scores_gemma":[0.9991761,0.00016724771,0.00027667228,0.00033221775,0.00002092496,0.000026847789],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003736516,0.00007377224,0.00021770809,0.00006218549,0.00009923943,0.000030386796,0.00016947434,0.00006402397,0.00032709222],"category_scores_gemma":[0.00012038603,0.00006365774,0.00009515168,0.00012939937,0.00004543284,0.000084877225,0.000039809456,0.00006860332,0.00053804653],"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":[0.00003327763,0.000019085934,0.93895245,0.0000092192995,0.000011525679,3.4575805e-7,0.00005512426,0.000023897644,0.000013760535,0.035684846,0.0026302454,0.022566212],"study_design_scores_gemma":[0.00019087351,0.000039971845,0.8899709,0.000007559605,0.0000023111045,7.9553536e-7,0.000028442295,0.0013892298,0.00016192232,0.023713486,0.08440472,0.00008977027],"about_ca_topic_score_codex":0.00019346656,"about_ca_topic_score_gemma":0.00008303421,"teacher_disagreement_score":0.08177447,"about_ca_system_score_codex":0.00002424268,"about_ca_system_score_gemma":0.00001741397,"threshold_uncertainty_score":0.6915679},"labels":[],"label_agreement":null},{"id":"W2949409534","doi":"10.3390/risks7030073","title":"De Finetti’s Control Problem with Parisian Ruin for Spectrally Negative Lévy Processes","year":2019,"lang":"en","type":"preprint","venue":"Risks","topic":"Probability and Risk Models","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":"Université du Québec à Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Optimal control; Measure (data warehouse); Lévy process; Control (management); Zero (linguistics); Exponential function; Mathematics; Stochastic control; First-hitting-time model; Mathematical economics; Applied mathematics; Mathematical optimization; Computer science; Mathematical analysis; Statistics","score_opus":0.15006831416366845,"score_gpt":0.401800643291381,"score_spread":0.25173232912771254,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2949409534","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":[0.06545629,0.0014550368,0.90482605,0.0066551026,0.0004644149,0.007857193,0.0009588322,0.00023348488,0.012093601],"genre_scores_gemma":[0.9493922,0.00010329889,0.045454875,0.0005208216,0.00027934855,0.00075043284,0.000028252794,0.000051388193,0.0034193918],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9952496,0.00032032054,0.000956427,0.0015248216,0.0012196042,0.00072921533],"domain_scores_gemma":[0.99325794,0.0030428667,0.0009231884,0.0013754482,0.0012103284,0.0001902145],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.003404263,0.0005116175,0.0010738338,0.00022556742,0.00021403162,0.0007959572,0.0018130692,0.00055136945,0.0001246247],"category_scores_gemma":[0.0030037207,0.0003202588,0.00028679802,0.0003911397,0.00025357536,0.00028051218,0.00031622316,0.0009089323,0.00013640107],"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":[0.004383539,0.0010812175,0.066640936,0.0023455052,0.0008935342,0.000038808324,0.01375935,0.7971424,0.000089694026,0.011130909,0.05004997,0.052444115],"study_design_scores_gemma":[0.0024551165,0.0006506171,0.007581504,0.00043246264,0.00019215223,0.000013565672,0.00034349845,0.03757796,0.0009231631,0.9334835,0.015483333,0.0008631656],"about_ca_topic_score_codex":0.0006030343,"about_ca_topic_score_gemma":0.0013942056,"teacher_disagreement_score":0.92235255,"about_ca_system_score_codex":0.00017246637,"about_ca_system_score_gemma":0.0025763814,"threshold_uncertainty_score":0.99992496},"labels":[],"label_agreement":null},{"id":"W2956961633","doi":"10.3390/risks7030079","title":"Individual Loss Reserving Using a Gradient Boosting-Based Approach","year":2019,"lang":"en","type":"article","venue":"Risks","topic":"Probability and Risk Models","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":"Université du Québec à Montréal","funders":"","keywords":"Boosting (machine learning); Gradient boosting; Portfolio; Computer science; Payment; Aggregate (composite); Property (philosophy); Econometrics; Contrast (vision); Artificial intelligence; Machine learning; Mathematics; Economics; Finance","score_opus":0.40776575384962654,"score_gpt":0.4322815124342119,"score_spread":0.02451575858458538,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2956961633","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9561406,0.00014829045,0.036957677,0.00016959356,0.00030880416,0.0003516083,0.000021509732,0.00005995015,0.0058420105],"genre_scores_gemma":[0.98640096,0.0000013618562,0.01301855,0.00021439347,0.000071100665,0.0000065809427,0.0000058765,0.000013657371,0.00026748978],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99640435,0.00034994853,0.00055220176,0.0007076154,0.0015522684,0.0004336375],"domain_scores_gemma":[0.99756354,0.00080134725,0.00023827954,0.0010560397,0.00019299722,0.00014781803],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0050797323,0.00016475083,0.00031596964,0.00023976866,0.0002474841,0.00030201176,0.001155737,0.00013949355,0.00018158997],"category_scores_gemma":[0.00123735,0.00011726178,0.00016180059,0.0007201769,0.00012248909,0.00032376655,0.00034937364,0.00029761586,0.00033716517],"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":[0.00014172036,0.00044431046,0.5277033,0.000058568177,0.00003933917,0.000016355178,0.0030309383,0.44180357,0.00050541514,0.005192817,0.0009958546,0.020067817],"study_design_scores_gemma":[0.0009790857,0.00012774834,0.028673995,0.000059301667,0.00002982601,0.000016582886,0.00053004065,0.8964288,0.0006807678,0.06850208,0.0035665387,0.00040518187],"about_ca_topic_score_codex":0.0003067265,"about_ca_topic_score_gemma":0.000029253984,"teacher_disagreement_score":0.49902928,"about_ca_system_score_codex":0.00006965444,"about_ca_system_score_gemma":0.00016962955,"threshold_uncertainty_score":0.47817963},"labels":[],"label_agreement":null},{"id":"W2964481120","doi":"10.3390/risks7030085","title":"Potential Densities for Taxed Spectrally Negative Lévy Risk Processes","year":2019,"lang":"en","type":"article","venue":"Risks","topic":"Probability and Risk Models","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":"Program for New Century Excellent Talents in University; Concordia University; National Natural Science Foundation of China","keywords":"Laplace transform; Lévy process; Scale (ratio); Process (computing); Econometrics; Economics; Mathematics; Applied mathematics; Computer science; Physics; Mathematical analysis; Quantum mechanics","score_opus":0.1173618940914306,"score_gpt":0.38633871086134725,"score_spread":0.26897681676991664,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2964481120","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.942277,0.0002606755,0.052986335,0.0005408022,0.0005502036,0.0008073046,0.0001502603,0.00007432291,0.002353061],"genre_scores_gemma":[0.98861915,0.000093124596,0.0063503436,0.00015033766,0.00017443225,0.000032672655,0.0000041176017,0.000013873698,0.0045619574],"study_design_codex":"observational","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9975664,0.00016998102,0.0004787029,0.0006097566,0.00081413245,0.00036105097],"domain_scores_gemma":[0.99663216,0.001708645,0.00031325937,0.0005546805,0.0007083763,0.00008286925],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017474993,0.00017257695,0.0003525852,0.00013041595,0.0002603305,0.00027846725,0.0006302766,0.00012459495,0.00033621496],"category_scores_gemma":[0.0038403417,0.00011694264,0.00018730084,0.00039000768,0.00015191174,0.0004902916,0.00010176653,0.00018753078,0.00075527484],"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":[0.0072951056,0.0021604626,0.31336653,0.00088650524,0.00096486986,0.000070373666,0.052148007,0.17155984,0.010385609,0.054244168,0.11015078,0.27676776],"study_design_scores_gemma":[0.0012969625,0.00038931644,0.03089191,0.000029752244,0.000059417343,0.0000103239345,0.0019907404,0.022839088,0.012123637,0.9211952,0.00875586,0.00041777475],"about_ca_topic_score_codex":0.00028870758,"about_ca_topic_score_gemma":0.0003829787,"teacher_disagreement_score":0.86695105,"about_ca_system_score_codex":0.00003747158,"about_ca_system_score_gemma":0.0002605314,"threshold_uncertainty_score":0.9707782},"labels":[],"label_agreement":null},{"id":"W2971493747","doi":"10.3390/risks7030096","title":"Special Issue “Risk, Ruin and Survival: Decision Making in Insurance and Finance”","year":2019,"lang":"en","type":"article","venue":"Risks","topic":"Insurance and Financial Risk Management","field":"Economics, Econometrics and Finance","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":"Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Actuarial science; Point (geometry); Ruin theory; Life insurance; Economics; History; Risk model; Mathematics","score_opus":0.024989714170066235,"score_gpt":0.2550626347050473,"score_spread":0.23007292053498107,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2971493747","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9537503,0.0030272822,0.0003441886,0.000066449946,0.0013047478,0.0003003315,0.00006675866,0.000014753529,0.041125204],"genre_scores_gemma":[0.98550224,0.012341494,0.0007386553,0.00010349861,0.0007550687,0.000012724741,0.000001955937,0.000020969219,0.0005233993],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9986746,0.000017206456,0.0004635605,0.0004917793,0.000049751743,0.00030314614],"domain_scores_gemma":[0.99936146,0.00009217463,0.00024080789,0.00026841369,0.000013582917,0.000023551398],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00068017526,0.00016262925,0.00042701236,0.00022191377,0.00008417097,0.000068726455,0.00013504518,0.0001125254,0.00013733648],"category_scores_gemma":[0.00009869678,0.00018860544,0.000042693828,0.0002599656,0.000044785585,0.0002534914,0.000114066504,0.00022739674,0.0005035146],"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":[0.000052445786,0.000024996463,0.7866858,0.000018922436,0.0000052039427,0.000006208358,0.00034161998,0.00012071829,4.3615415e-7,0.028545268,0.00060483516,0.18359356],"study_design_scores_gemma":[0.00069774786,0.000042639742,0.77223337,0.00005540914,0.000001454723,8.518871e-7,0.00004624672,0.000671553,0.0000024447868,0.025667207,0.20038348,0.00019760856],"about_ca_topic_score_codex":0.00078292895,"about_ca_topic_score_gemma":0.0005188286,"teacher_disagreement_score":0.19977865,"about_ca_system_score_codex":0.00004676611,"about_ca_system_score_gemma":0.0000065176846,"threshold_uncertainty_score":0.7691107},"labels":[],"label_agreement":null},{"id":"W2983513692","doi":"10.3390/risks7040110","title":"Quantitative and Comparative Analyses of Limit Order Books with General Compound Hawkes Processes","year":2019,"lang":"en","type":"article","venue":"Risks","topic":"Point processes and geometric inequalities","field":"Mathematics","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Limit (mathematics); Calibration; Order (exchange); Particle swarm optimization; Computer science; Maximum likelihood; Econometrics; Applied mathematics; Mathematical optimization; Mathematics; Algorithm; Statistics; Economics","score_opus":0.38840119446100557,"score_gpt":0.45041140396002954,"score_spread":0.06201020949902397,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2983513692","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9835801,0.0014393347,0.0030040387,0.000038939783,0.000022448605,0.00022291148,0.000034379194,0.00002961392,0.011628227],"genre_scores_gemma":[0.97644806,0.0000881559,0.02140848,0.000031226627,0.000019187597,0.000010945078,0.0000071874206,0.000011540916,0.0019752008],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9990709,0.00004571449,0.00027356463,0.00020065381,0.00023347374,0.0001757358],"domain_scores_gemma":[0.9981105,0.000897695,0.00025419085,0.00015816468,0.0005386515,0.00004082159],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015040344,0.00016587578,0.00048271925,0.00016151038,0.000057300025,0.000041842308,0.000100444566,0.00004459208,0.00006415795],"category_scores_gemma":[0.00020522601,0.000112290996,0.000027224085,0.0004934716,0.0001380879,0.00017607464,0.000040074716,0.000094803436,0.000008882048],"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":[0.0016845901,0.0011468695,0.070645966,0.011919182,0.002585135,0.000024051838,0.04126304,0.0011645519,0.0044584833,0.8608933,0.0034139582,0.000800887],"study_design_scores_gemma":[0.01380584,0.015151658,0.03593056,0.0033025932,0.002514152,0.000208871,0.13378055,0.016785204,0.32311428,0.43938693,0.011274994,0.0047443626],"about_ca_topic_score_codex":0.0003176849,"about_ca_topic_score_gemma":0.0001604735,"teacher_disagreement_score":0.42150635,"about_ca_system_score_codex":0.000009989394,"about_ca_system_score_gemma":0.0001012774,"threshold_uncertainty_score":0.45790938},"labels":[],"label_agreement":null},{"id":"W2994842297","doi":"10.3390/risks7040120","title":"The Løkka–Zervos Alternative for a Cramér–Lundberg Process with Exponential Jumps","year":2019,"lang":"en","type":"article","venue":"Risks","topic":"Probability and Risk Models","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":"Université du Québec à Montréal","funders":"","keywords":"Dividend; Capital (architecture); Risk process; Exponential function; Meaning (existential); Brownian motion; Zero (linguistics); Mathematical economics; Economics; Process (computing); Actuarial science; Mathematics; Applied mathematics; Computer science; Finance; Mathematical analysis; Statistics; Psychology; Philosophy; History","score_opus":0.19837414055614264,"score_gpt":0.45078477785426213,"score_spread":0.2524106372981195,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2994842297","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9452704,0.00020198581,0.0471455,0.0013323096,0.0006213724,0.0012944217,0.00003293461,0.000046638397,0.004054387],"genre_scores_gemma":[0.99395746,0.000024257914,0.0007102662,0.00013179824,0.00015550114,0.00015249952,0.0000024138428,0.0000152223965,0.0048506055],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99734277,0.0001440711,0.00043203947,0.0005876205,0.0011168964,0.00037659737],"domain_scores_gemma":[0.99653655,0.0017914837,0.00027131807,0.0007768927,0.0005256104,0.00009813502],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002266652,0.00016258599,0.00026117315,0.00007180257,0.00041616967,0.0003779404,0.0011328151,0.00008033403,0.00011201422],"category_scores_gemma":[0.0007561511,0.00007606673,0.00013910064,0.0003232748,0.00020091502,0.0004008829,0.00010729334,0.00017942538,0.00042655945],"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":[0.011515578,0.0016134869,0.1332577,0.00024205948,0.000896843,0.00004719946,0.03902697,0.09305295,0.0014917811,0.1496069,0.031787243,0.5374613],"study_design_scores_gemma":[0.0025578577,0.0007504907,0.008378795,0.00006302972,0.000049399976,0.000015762938,0.0026128646,0.09100294,0.0053932504,0.8191761,0.069484934,0.0005146212],"about_ca_topic_score_codex":0.00017340662,"about_ca_topic_score_gemma":0.00037851752,"teacher_disagreement_score":0.66956913,"about_ca_system_score_codex":0.00003280124,"about_ca_system_score_gemma":0.00015201978,"threshold_uncertainty_score":0.5482701},"labels":[],"label_agreement":null},{"id":"W2996007092","doi":"10.3390/risks7040121","title":"On the Padé and Laguerre–Tricomi–Weeks Moments Based Approximations of the Scale Function W and of the Optimal Dividends Barrier for Spectrally Negative Lévy Risk Processes","year":2019,"lang":"en","type":"article","venue":"Risks","topic":"Probability and Risk Models","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":"Western University","funders":"","keywords":"Laguerre polynomials; Dividend; Mathematics; Scale (ratio); Brownian motion; Function (biology); Applied mathematics; Distribution (mathematics); Mathematical analysis; Statistics; Physics; Economics; Quantum mechanics; Finance","score_opus":0.07506076413003697,"score_gpt":0.3266957580190006,"score_spread":0.2516349938889636,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2996007092","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98530084,0.0000950638,0.011468082,0.0011540476,0.0001339679,0.0010907644,0.00019468633,0.0000068216505,0.00055570295],"genre_scores_gemma":[0.9989179,0.000019408279,0.0005099986,0.00010354712,0.000015753965,0.00004720934,7.4579606e-7,0.0000062205017,0.00037924846],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9982602,0.00029832087,0.00036540703,0.00030736288,0.000625579,0.0001431287],"domain_scores_gemma":[0.995359,0.0033669563,0.0004151395,0.00056569953,0.00026005908,0.000033144366],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018267821,0.00011631937,0.00020854558,0.00006229084,0.0003548576,0.000075686716,0.00046432868,0.00007122585,0.000047882142],"category_scores_gemma":[0.00271042,0.00004666796,0.000106300875,0.00049027684,0.0003499446,0.00019058859,0.00013210987,0.00016570128,0.0000028154943],"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":[0.001422352,0.00085036765,0.884229,0.00030479193,0.00021589962,9.328605e-8,0.010876338,0.053513292,0.0027830952,0.020228643,0.003319411,0.022256706],"study_design_scores_gemma":[0.0013761556,0.00040544197,0.5233378,0.00009971532,0.00012054547,7.773856e-7,0.0014122421,0.08448993,0.016193561,0.37214088,0.00026418603,0.00015879392],"about_ca_topic_score_codex":0.00009426438,"about_ca_topic_score_gemma":0.00011058168,"teacher_disagreement_score":0.36089122,"about_ca_system_score_codex":0.000015939384,"about_ca_system_score_gemma":0.00011366577,"threshold_uncertainty_score":0.32448214},"labels":[],"label_agreement":null},{"id":"W2999910338","doi":"10.3390/risks8010006","title":"Markov Chain Monte Carlo Methods for Estimating Systemic Risk Allocations","year":2020,"lang":"en","type":"article","venue":"Risks","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":16,"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":"Markov chain Monte Carlo; Estimator; Monte Carlo method; Markov chain; Conditional probability distribution; Econometrics; Expected shortfall; Hybrid Monte Carlo; Mathematics; Computer science; Statistics; Economics; Finance; Risk management","score_opus":0.24751501589596003,"score_gpt":0.48976356885886757,"score_spread":0.24224855296290754,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2999910338","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":[0.0033469086,0.0001717401,0.9944104,0.00038940596,0.00018914996,0.000555484,0.00012603162,0.00013163048,0.0006792696],"genre_scores_gemma":[0.07171949,0.00001372951,0.92766505,0.00013762146,0.00019156197,0.00017216867,0.0000020965238,0.00003040923,0.000067898145],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99853337,0.00045168598,0.00040671974,0.00026696842,0.00010650683,0.00023477817],"domain_scores_gemma":[0.99358404,0.0056777797,0.00021459656,0.0002472843,0.00012455322,0.00015177239],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0012649031,0.00014643109,0.00033682285,0.000026042473,0.00015480972,0.000039793187,0.00018320786,0.00008004539,0.000048472328],"category_scores_gemma":[0.021712014,0.0001268976,0.0000922578,0.00013970386,0.00003511692,0.000035918914,0.000053142823,0.00019146333,0.000013408454],"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":[0.000039912975,0.000054499866,0.0006127278,0.0009986119,0.00012886505,0.0000024238552,0.002965178,0.0005526736,0.0010329824,0.15076046,0.004368683,0.838483],"study_design_scores_gemma":[0.00027538175,0.00008714905,0.0001599289,0.00008964193,0.00013905064,0.000003943102,0.00026935962,0.8770999,0.00028755917,0.12094214,0.00047423202,0.00017175915],"about_ca_topic_score_codex":0.00010965054,"about_ca_topic_score_gemma":0.000003413109,"teacher_disagreement_score":0.87654716,"about_ca_system_score_codex":0.000031419087,"about_ca_system_score_gemma":0.00003997513,"threshold_uncertainty_score":0.9865285},"labels":[],"label_agreement":null},{"id":"W3005018724","doi":"10.3390/risks8010015","title":"Portfolio Optimization under Correlation Constraint","year":2020,"lang":"en","type":"article","venue":"Risks","topic":"Stochastic processes and financial applications","field":"Economics, Econometrics and Finance","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Portfolio optimization; Portfolio; Precommitment; Constraint (computer-aided design); Mathematical optimization; Post-modern portfolio theory; Economics; Computer science; Econometrics; Mathematical economics; Replicating portfolio; Mathematics; Microeconomics; Financial economics","score_opus":0.07884797801892703,"score_gpt":0.25355546928464706,"score_spread":0.17470749126572005,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3005018724","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":[0.0008845452,0.00036601967,0.9728373,0.0018364663,0.0000979786,0.00013542401,0.000063579246,0.00005720438,0.02372145],"genre_scores_gemma":[0.98954886,0.000047045174,0.009111901,0.0010317423,0.00013299329,0.000023937031,0.00004143835,0.0000121456,0.000049917646],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99937755,0.0000010514373,0.0002773919,0.00021383043,0.000019528208,0.00011061958],"domain_scores_gemma":[0.9996224,0.000016447355,0.00016884154,0.00009658754,0.000025689136,0.00007003592],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000061128514,0.0000686914,0.0001356356,0.000038054957,0.000074050375,0.000027822602,0.000082573606,0.00006456976,0.0004278464],"category_scores_gemma":[0.00007581963,0.00008439309,0.000040087238,0.0002277073,0.000031659947,0.0000860645,0.000019561154,0.00008013729,0.00062861625],"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":[0.0000039253564,0.000015646336,0.0012167215,0.0000046766554,0.000006683163,3.5774124e-7,0.000094951676,0.048451655,0.0000025585475,0.94895625,0.00030822423,0.00093832775],"study_design_scores_gemma":[0.00060790597,0.00007520684,0.016129907,0.000007499891,0.000012637821,0.000004280151,0.00013806846,0.59400845,0.000020383293,0.37419304,0.014460892,0.00034173488],"about_ca_topic_score_codex":0.00006015496,"about_ca_topic_score_gemma":0.0000010314884,"teacher_disagreement_score":0.9886643,"about_ca_system_score_codex":0.000024365525,"about_ca_system_score_gemma":0.000017909439,"threshold_uncertainty_score":0.80798},"labels":[],"label_agreement":null},{"id":"W3009519538","doi":"10.3390/risks8020040","title":"Deep Arbitrage-Free Learning in a Generalized HJM Framework via Arbitrage-Regularization","year":2020,"lang":"en","type":"article","venue":"Risks","topic":"Statistical Methods and Inference","field":"Mathematics","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":"Concordia University","funders":"ETH Zürich Foundation; Natural Sciences and Engineering Research Council of Canada; Eidgenössische Technische Hochschule Zürich","keywords":"Heath–Jarrow–Morton framework; Regularization (linguistics); Affine transformation; Limit (mathematics); Model selection; Limit point; Selection (genetic algorithm); Probably approximately correct learning; Factor (programming language)","score_opus":0.10726789910313585,"score_gpt":0.3706290737208599,"score_spread":0.26336117461772407,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3009519538","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":[0.027906537,0.00008962172,0.96757084,0.0010398761,0.000084671265,0.0002173479,0.000004819914,0.00015367015,0.0029326328],"genre_scores_gemma":[0.27999657,0.000023559325,0.71889114,0.0008057827,0.00016201285,0.000024383262,0.000006292043,0.000035631972,0.00005464045],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9983746,0.00034232906,0.00040745965,0.00031883994,0.00025553824,0.00030122694],"domain_scores_gemma":[0.9983035,0.001062222,0.00013309445,0.0002967691,0.00005160223,0.00015285175],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.00037434636,0.00018076418,0.0003540355,0.00005432429,0.00008344108,0.000053317544,0.00026094037,0.0001966778,0.0005981134],"category_scores_gemma":[0.0096045,0.00017001618,0.00007049344,0.00038424702,0.00004997364,0.00006073916,0.00009788643,0.00093128526,0.000050965325],"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":[0.00007333688,0.00011353418,0.0035108721,0.00018649522,0.000039424547,0.000063726206,0.0025833985,0.00093124097,0.0019686713,0.95392877,0.00039192077,0.036208596],"study_design_scores_gemma":[0.00050850597,0.00008068086,0.0026769135,0.00006162147,0.000029095336,0.0000023058349,0.000055217017,0.1758052,0.0007862187,0.81950885,0.00028206632,0.00020333104],"about_ca_topic_score_codex":0.00007083978,"about_ca_topic_score_gemma":0.000013678816,"teacher_disagreement_score":0.25209004,"about_ca_system_score_codex":0.000030780015,"about_ca_system_score_gemma":0.000037296875,"threshold_uncertainty_score":0.99873805},"labels":[],"label_agreement":null},{"id":"W3011783965","doi":"10.3390/risks8010025","title":"Importance Sampling in the Presence of PD-LGD Correlation","year":2020,"lang":"en","type":"article","venue":"Risks","topic":"Credit Risk and Financial Regulations","field":"Economics, Econometrics and Finance","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":"Western University; Wilfrid Laurier University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Default; Loss given default; Portfolio; Correlation; Econometrics; Sampling (signal processing); Importance sampling; Statistics; Mathematics; Computer science; Economics; Monte Carlo method; Financial economics","score_opus":0.14564561611426338,"score_gpt":0.2894284327285236,"score_spread":0.1437828166142602,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3011783965","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9590801,0.001014625,0.03138079,0.001333668,0.00015029013,0.00018391022,0.000067586625,0.000012389229,0.006776628],"genre_scores_gemma":[0.99904275,0.00013645952,0.00059928,0.000061921004,0.000109065535,0.000009025151,0.000010540169,0.000005482714,0.000025456498],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99933475,0.000008041387,0.00036821185,0.00015487836,0.000031001746,0.0001031033],"domain_scores_gemma":[0.99950826,0.000093113464,0.00020686862,0.0001565714,0.00001289861,0.00002228225],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025373613,0.000053323736,0.00014685618,0.00004474771,0.000042332962,0.000014327281,0.0001592239,0.000041244337,0.0000524796],"category_scores_gemma":[0.00034189125,0.000051075676,0.00004777653,0.00031461162,0.000031453055,0.000099774516,0.000018692954,0.00011357878,0.000058674832],"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":[0.000011268442,0.000020527188,0.86638606,0.000008557568,0.000002720941,8.0971233e-7,0.0017389196,0.003967758,0.000010641867,0.12514766,0.00044871785,0.0022563862],"study_design_scores_gemma":[0.000136322,0.000026578728,0.9533393,0.000006026738,0.000001702765,3.9725194e-7,0.0000889217,0.018704008,0.000009569849,0.01535794,0.012266443,0.00006279455],"about_ca_topic_score_codex":0.00026296836,"about_ca_topic_score_gemma":0.00006586255,"teacher_disagreement_score":0.10978972,"about_ca_system_score_codex":0.000014336682,"about_ca_system_score_gemma":0.00001087006,"threshold_uncertainty_score":0.20828056},"labels":[],"label_agreement":null},{"id":"W3011793132","doi":"10.3390/risks8010028","title":"General Compound Hawkes Processes in Limit Order Books","year":2020,"lang":"en","type":"article","venue":"Risks","topic":"Point processes and geometric inequalities","field":"Mathematics","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 Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Volatility (finance); Limit (mathematics); Order (exchange); Mathematical economics; Econometrics; Central limit theorem; Order book; Computer science; Mathematics; Economics; Statistics; Mathematical analysis","score_opus":0.21381996563073713,"score_gpt":0.36562227625102517,"score_spread":0.15180231062028804,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3011793132","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.964489,0.0015928294,0.0047338605,0.002584718,0.00010279814,0.00027884386,0.000030178406,0.00020370266,0.025984116],"genre_scores_gemma":[0.9898611,0.00014236725,0.0069043655,0.0010658139,0.00028223093,0.000026026937,0.000007858351,0.000028285134,0.0016819675],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9989748,0.0000299727,0.00030701968,0.00021088588,0.00021083008,0.00026649956],"domain_scores_gemma":[0.99924964,0.00030645702,0.000090926864,0.00012936555,0.00014439129,0.0000792356],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015273102,0.00014535348,0.00026685742,0.00009802735,0.000054793876,0.000066059954,0.00018637216,0.000070537564,0.00015374065],"category_scores_gemma":[0.0015279113,0.00012503918,0.000034103774,0.00069555314,0.000034464963,0.00013304793,0.00006886807,0.00016974048,0.00005252128],"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":[0.00079974683,0.0023040958,0.07556262,0.02764216,0.0005922835,0.0006967401,0.09272889,0.0011998926,0.0010989646,0.5908359,0.16951814,0.03702055],"study_design_scores_gemma":[0.0052180523,0.0009608649,0.004431271,0.00054368074,0.00019526607,0.00006595477,0.0073844027,0.008124791,0.017127473,0.61048925,0.34299678,0.0024621917],"about_ca_topic_score_codex":0.00016041416,"about_ca_topic_score_gemma":0.00012726586,"teacher_disagreement_score":0.17347865,"about_ca_system_score_codex":0.000019468385,"about_ca_system_score_gemma":0.00011488647,"threshold_uncertainty_score":0.50989497},"labels":[],"label_agreement":null},{"id":"W3025016049","doi":"10.3390/risks8020047","title":"Implementing the Rearrangement Algorithm: An Example from Computational Risk Management","year":2020,"lang":"en","type":"article","venue":"Risks","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Risk management; Computer science; Quantile; Value (mathematics); Value at risk; Computational complexity theory; Algorithm; Mathematical optimization; Mathematics; Machine learning; Econometrics; Finance","score_opus":0.3102157643071423,"score_gpt":0.4246778745782219,"score_spread":0.11446211027107961,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3025016049","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":[0.021749986,0.000031377072,0.97398007,0.00029186747,0.00006132947,0.00027493658,0.00015667148,0.00006795767,0.0033857804],"genre_scores_gemma":[0.081872575,0.000023591972,0.91733325,0.00047816447,0.00018724198,0.000029886261,0.00003937172,0.000015250823,0.000020697476],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9987354,0.00021483062,0.00027187873,0.00025799006,0.0002854909,0.00023439537],"domain_scores_gemma":[0.9987002,0.0008210325,0.00012439686,0.0002173192,0.000040905783,0.00009616928],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00059255835,0.00010851678,0.00014406526,0.00001259547,0.00023395695,0.00006715147,0.00021165996,0.000022462156,0.001044826],"category_scores_gemma":[0.00010952159,0.00007632968,0.00003984122,0.0000923722,0.000032809345,0.000039860708,0.00016107348,0.00014030025,0.000046954832],"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":[0.000012102393,0.00006048523,0.0007457202,0.000020015286,0.0001287039,0.000008026154,0.0023582752,0.00006460503,0.000012357437,0.20072651,0.0044953604,0.7913678],"study_design_scores_gemma":[0.000561805,0.0000799521,0.011257418,0.000012829618,0.00013467453,3.6195613e-7,0.0018808455,0.14243978,0.000048568454,0.82832587,0.015100628,0.00015725761],"about_ca_topic_score_codex":0.0015673314,"about_ca_topic_score_gemma":0.000028440758,"teacher_disagreement_score":0.7912106,"about_ca_system_score_codex":0.000015657473,"about_ca_system_score_gemma":0.000007779037,"threshold_uncertainty_score":0.99986833},"labels":[],"label_agreement":null},{"id":"W3035336702","doi":"10.3390/risks8020065","title":"A New Approach to Risk Attribution and Its Application in Credit Risk Analysis","year":2020,"lang":"en","type":"article","venue":"Risks","topic":"Credit Risk and Financial Regulations","field":"Economics, Econometrics and Finance","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 Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Context (archaeology); Risk analysis (engineering); Credit risk; Affect (linguistics); Computer science; Decomposition; Actuarial science; Euler's formula; Econometrics; Economics; Business; Mathematics; Psychology; Geography","score_opus":0.06733987187044789,"score_gpt":0.26196791383662116,"score_spread":0.19462804196617328,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3035336702","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":[0.4584954,0.0007666355,0.5379694,0.00043980888,0.000037087248,0.00028729677,0.00041863817,0.000028626122,0.0015570835],"genre_scores_gemma":[0.9961236,0.00063882174,0.0026937358,0.00003339392,0.00027190233,0.000045517805,0.00008702019,0.000011042903,0.0000949425],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9988565,0.000018335093,0.0004101989,0.0004889677,0.000039313818,0.00018668585],"domain_scores_gemma":[0.9992991,0.000032559503,0.00025083424,0.00019814384,0.00002230585,0.00019702793],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034177784,0.000112292415,0.00033926018,0.00029840088,0.00010333012,0.000041348547,0.000121730234,0.00010824897,0.000042037176],"category_scores_gemma":[0.0002784017,0.00013760923,0.000095675605,0.0016152448,0.000010351777,0.00012213369,0.00005204295,0.00018032754,0.0003692117],"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":[0.00002437166,0.000040093826,0.919969,0.0000063396583,0.00006336966,2.4447152e-7,0.00087155984,0.010005722,0.000005557422,0.04960433,0.0008886351,0.01852074],"study_design_scores_gemma":[0.00028351016,0.000022795952,0.8341267,0.0000011193868,0.000060464372,1.6076233e-7,0.000024788333,0.129318,0.000010555468,0.0030643013,0.032947008,0.00014058242],"about_ca_topic_score_codex":0.003681142,"about_ca_topic_score_gemma":0.00028851806,"teacher_disagreement_score":0.53762823,"about_ca_system_score_codex":0.000063213294,"about_ca_system_score_gemma":0.000016101225,"threshold_uncertainty_score":0.5611541},"labels":[],"label_agreement":null},{"id":"W3046535124","doi":"10.3390/risks8030080","title":"The Impact of Model Uncertainty on Index-Based Longevity Hedging and Measurement of Longevity Basis Risk","year":2020,"lang":"en","type":"article","venue":"Risks","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social 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":"Hedge; Longevity risk; Bootstrapping (finance); Econometrics; Bivariate analysis; Index (typography); Basis risk; Portfolio; Longevity; Selection (genetic algorithm); Actuarial science; Computer science; Statistics; Mathematics; Economics; Artificial intelligence","score_opus":0.10681548433332545,"score_gpt":0.36082785378888416,"score_spread":0.2540123694555587,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3046535124","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9918798,0.00047452707,0.0031865074,0.00067339564,0.00006717522,0.00060718367,0.000079407335,0.00004331832,0.0029886474],"genre_scores_gemma":[0.99900043,0.00072297035,0.00010813317,0.000078389116,0.000055748704,0.000015780668,0.0000013091687,0.000012641443,0.0000046138625],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99720997,0.0005975491,0.0003779654,0.00032749944,0.0011272463,0.00035976397],"domain_scores_gemma":[0.9985404,0.00021251853,0.00042993645,0.0003787289,0.00026600767,0.00017242803],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0033204423,0.00018825772,0.0003398211,0.00007474272,0.00052701106,0.000050700677,0.000372836,0.00008758904,0.000016269587],"category_scores_gemma":[0.00063819654,0.0001355086,0.00028784448,0.00045710412,0.0005841733,0.00009435956,0.00007238514,0.0002506782,0.0000023008415],"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":[0.0002145615,0.00014331419,0.84500235,0.00006215159,0.00017984115,0.0000012736656,0.0022075775,0.12519665,0.000047494817,0.0007664042,0.00037813533,0.025800232],"study_design_scores_gemma":[0.0006486339,0.00020072615,0.8986079,0.00005895122,0.00011304327,3.0530394e-8,0.0007553525,0.097467706,0.0003377526,0.0014128191,0.00018654707,0.00021050803],"about_ca_topic_score_codex":0.023781443,"about_ca_topic_score_gemma":0.0051288605,"teacher_disagreement_score":0.053605564,"about_ca_system_score_codex":0.00017761625,"about_ca_system_score_gemma":0.0002189022,"threshold_uncertainty_score":0.9827193},"labels":[],"label_agreement":null},{"id":"W3081850492","doi":"10.3390/risks8030091","title":"A Longitudinal Analysis of the Impact of Distance Driven on the Probability of Car Accidents","year":2020,"lang":"en","type":"article","venue":"Risks","topic":"Insurance and Financial Risk Management","field":"Economics, Econometrics and Finance","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Université du Québec à Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Poisson distribution; Generalized additive model; Econometrics; Generalized linear model; Residual; Count data; Poisson regression; Scale (ratio); Telematics; Mathematics; Statistics; Geography; Computer science; Cartography; Telecommunications; Algorithm; Population","score_opus":0.09341719309052182,"score_gpt":0.2857348035299581,"score_spread":0.1923176104394363,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3081850492","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99569017,0.00016420086,0.00091034296,0.00026742535,0.00004064342,0.00025357105,0.000344179,0.0000032351654,0.0023262068],"genre_scores_gemma":[0.99981636,0.00008246243,0.000023162567,0.000028812237,0.00001586567,0.0000086317505,0.0000025062216,0.000005286117,0.00001693081],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9990944,0.000022281687,0.000498743,0.00019996299,0.000064228036,0.00012034815],"domain_scores_gemma":[0.99879456,0.00004980296,0.0006325692,0.0004551142,0.000045992165,0.000021951912],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028694782,0.000089516594,0.00044466747,0.00006722585,0.00004226355,0.0000070547167,0.00038836093,0.000035373298,0.00008889637],"category_scores_gemma":[0.00019280521,0.0000570472,0.00050037686,0.0009251485,0.00009625095,0.000047162746,0.00007820182,0.00009331412,0.000009351717],"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":[0.000046670677,0.000076849145,0.9569003,0.000022386357,0.0004107234,2.0568149e-7,0.0005741918,0.0077118883,0.000011455564,0.0338357,0.00012495465,0.0002846777],"study_design_scores_gemma":[0.00012717627,0.00009028058,0.9918512,0.000011017204,0.000071118106,1.6003156e-8,0.000027425689,0.003798137,0.00017529655,0.0036502273,0.00014203646,0.000056077028],"about_ca_topic_score_codex":0.0017693312,"about_ca_topic_score_gemma":0.0001244192,"teacher_disagreement_score":0.034950897,"about_ca_system_score_codex":0.00005187797,"about_ca_system_score_gemma":0.000015676249,"threshold_uncertainty_score":0.26747113},"labels":[],"label_agreement":null},{"id":"W3084901423","doi":"10.3390/risks8030098","title":"Multivariate General Compound Point Processes in Limit Order Books","year":2020,"lang":"en","type":"article","venue":"Risks","topic":"Point processes and geometric inequalities","field":"Mathematics","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":"HEC Montréal; University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Multivariate statistics; Limit (mathematics); Point process; Focus (optics); Generalization; Order (exchange); Computer science; Point (geometry); Process (computing); Multivariate analysis; Mathematics; Econometrics; Statistics; Mathematical analysis; Economics; Physics; Geometry; Programming language","score_opus":0.20563315830612702,"score_gpt":0.37090388213300335,"score_spread":0.16527072382687633,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3084901423","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9559596,0.0009671442,0.018276857,0.0037183673,0.00016511991,0.0005220229,0.00005346164,0.0002975822,0.020039815],"genre_scores_gemma":[0.9839831,0.00007378764,0.013494464,0.0010725872,0.0002519598,0.00003165665,0.000009263018,0.000034349207,0.0010488549],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99875593,0.000047139823,0.00039144885,0.0002620912,0.00022542923,0.00031798586],"domain_scores_gemma":[0.9990708,0.00037021935,0.00012541022,0.00015653002,0.00017688602,0.00010015592],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022612771,0.00017856066,0.0003180566,0.00011270999,0.00006069352,0.00006793901,0.00019994409,0.00008373346,0.00017202132],"category_scores_gemma":[0.0021611266,0.00015274814,0.000042787262,0.0007158495,0.00003221059,0.0001616421,0.00010038086,0.00021702416,0.00006273689],"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":[0.0016561832,0.0038884785,0.03406629,0.02776391,0.00096102717,0.0010986591,0.16354185,0.0026435356,0.0036195468,0.65500605,0.07368812,0.03206637],"study_design_scores_gemma":[0.0113028325,0.0012815677,0.00755559,0.0008244771,0.00027884828,0.00008946085,0.008481239,0.035293348,0.023980293,0.7675975,0.13978353,0.0035312944],"about_ca_topic_score_codex":0.0004059799,"about_ca_topic_score_gemma":0.00010785131,"teacher_disagreement_score":0.1550606,"about_ca_system_score_codex":0.000030320089,"about_ca_system_score_gemma":0.00012041961,"threshold_uncertainty_score":0.6228888},"labels":[],"label_agreement":null},{"id":"W3092314949","doi":"10.3390/risks8040103","title":"Grouped Normal Variance Mixtures","year":2020,"lang":"en","type":"article","venue":"Risks","topic":"Financial Risk and Volatility Modeling","field":"Economics, Econometrics and Finance","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":"Copula (linguistics); Mathematics; Multivariate normal distribution; Multivariate statistics; Variance (accounting); Monte Carlo method; Statistics; Mixing (physics); Multivariate t-distribution; Applied mathematics; Econometrics","score_opus":0.1129438916127381,"score_gpt":0.2579721307069157,"score_spread":0.1450282390941776,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3092314949","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6117809,0.0065634497,0.3443817,0.0036085483,0.0006254477,0.00024170977,0.00019364219,0.00018146004,0.032423183],"genre_scores_gemma":[0.9957354,0.00020631163,0.002414545,0.0011527517,0.00035197748,0.000007033755,0.000008379449,0.000016084345,0.00010753826],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990747,0.00000748465,0.00035109723,0.0003137435,0.000026584908,0.00022641357],"domain_scores_gemma":[0.9995738,0.000021647438,0.0001173694,0.00017169474,0.000015681422,0.00009978286],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000176646,0.00010533638,0.00024448874,0.000037127716,0.000093725095,0.000041609484,0.00018263441,0.00009009697,0.000290738],"category_scores_gemma":[0.00017127125,0.00012589875,0.00008989179,0.00016640175,0.000023182407,0.00016952204,0.00004912113,0.00018046281,0.0010303316],"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":[0.00014134993,0.00011608954,0.24837448,0.00009532872,0.000046606674,0.000019277943,0.00313937,0.0021113104,0.00025013604,0.72285926,0.0056950673,0.017151706],"study_design_scores_gemma":[0.0016670007,0.00026403167,0.18092589,0.000022480242,0.000016201791,0.0000033808237,0.00007234713,0.3453835,0.0007782809,0.15347308,0.31636822,0.0010255683],"about_ca_topic_score_codex":0.00046244013,"about_ca_topic_score_gemma":0.000011259828,"teacher_disagreement_score":0.5693862,"about_ca_system_score_codex":0.000018900402,"about_ca_system_score_gemma":0.0000113597125,"threshold_uncertainty_score":0.99974746},"labels":[],"label_agreement":null},{"id":"W3094363278","doi":"10.3390/risks8040111","title":"Application of a Vine Copula for Multi-Line Insurance Reserving","year":2020,"lang":"en","type":"article","venue":"Risks","topic":"Probability and Risk Models","field":"Decision Sciences","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Simon Fraser University; Society of Actuaries","keywords":"Copula (linguistics); Vine copula; Joint probability distribution; Portfolio; Econometrics; Marginal distribution; Computer science; Line of business; Actuarial science; Economics; Mathematics; Statistics; Business model; Finance; Random variable","score_opus":0.5559459479945559,"score_gpt":0.49987450683144924,"score_spread":0.05607144116310664,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3094363278","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":[0.31566653,0.0002235106,0.6814731,0.0020511816,0.000039021597,0.00036957307,0.00007042369,0.000024737785,0.00008193808],"genre_scores_gemma":[0.9697512,0.00001925862,0.029813124,0.00023773828,0.0000630673,0.000038953975,0.000005158801,0.0000057987486,0.00006571014],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99862176,0.00006561693,0.00047661096,0.0003300599,0.0003870945,0.00011887212],"domain_scores_gemma":[0.998563,0.00043850884,0.00021075114,0.0004046709,0.00030697352,0.00007612084],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012022167,0.0000676466,0.00021894468,0.000038682574,0.00007137504,0.000023733903,0.00053820596,0.00006047557,0.00001613019],"category_scores_gemma":[0.0031073203,0.000049008915,0.00008633838,0.0004112661,0.00005682234,0.00013756988,0.00010196637,0.00007561282,0.00004222566],"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":[0.0010612466,0.000598991,0.23571892,0.00026284056,0.00004186479,0.0000015538251,0.005250671,0.04904301,0.042906664,0.014328965,0.004594486,0.64619076],"study_design_scores_gemma":[0.0007164288,0.0001038527,0.032471303,0.000010558311,0.000005600179,3.6588872e-7,0.000089463705,0.9201493,0.00708285,0.03004354,0.009233631,0.000093058414],"about_ca_topic_score_codex":0.00013651833,"about_ca_topic_score_gemma":0.00008256576,"teacher_disagreement_score":0.8711063,"about_ca_system_score_codex":0.00000994057,"about_ca_system_score_gemma":0.000028810993,"threshold_uncertainty_score":0.3719977},"labels":[],"label_agreement":null},{"id":"W3096852147","doi":"10.3390/risks8040115","title":"First Quarter Chronicle of COVID-19: An Attempt to Measure Governments’ Responses","year":2020,"lang":"en","type":"article","venue":"Risks","topic":"COVID-19 epidemiological studies","field":"Mathematics","cited_by":6,"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":"Economic and Social Research Council; Engineering and Physical Sciences Research Council; Ministerio de Ciencia e Innovación; Austrian Science Fund","keywords":"Quarter (Canadian coin); Coronavirus disease 2019 (COVID-19); Pandemic; Case fatality rate; Government (linguistics); Order (exchange); China; Econometrics; 2019-20 coronavirus outbreak; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Measure (data warehouse); Relation (database); Outbreak; Geography; Political science; Computer science; Economics; Demography; Sociology; Virology; Data mining; Medicine; Law","score_opus":0.4591342997129438,"score_gpt":0.4614185590668246,"score_spread":0.0022842593538808242,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3096852147","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8577827,0.00056128146,0.024265857,0.1145766,0.00010288706,0.0010179074,0.00028101937,0.00036033755,0.0010514063],"genre_scores_gemma":[0.98347706,0.000024000687,0.002708193,0.0135513395,0.00011737105,0.000039949777,0.0000011292769,0.000019449155,0.00006153251],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99814177,0.00032788314,0.00043845625,0.00040215143,0.00039830906,0.0002914173],"domain_scores_gemma":[0.9961479,0.002664586,0.0001716904,0.00040979264,0.000049923558,0.0005561636],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.000881764,0.00018341123,0.0004749022,0.000021981961,0.00015036041,0.000012900042,0.00032382188,0.00009519216,0.00064524607],"category_scores_gemma":[0.027090484,0.0001446481,0.0001168085,0.00019803153,0.000079920756,0.00006397708,0.00020780717,0.00014436709,0.000109858745],"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":[0.002874435,0.0010241333,0.292641,0.0020359308,0.00055598724,0.00009187991,0.027400121,0.0015263669,0.004178023,0.014783675,0.6492141,0.0036743744],"study_design_scores_gemma":[0.004370095,0.005007109,0.29264626,0.00023062978,0.0003367525,0.0000075845574,0.0058360016,0.0031243071,0.0033038326,0.040893164,0.6426704,0.0015738882],"about_ca_topic_score_codex":0.00032906915,"about_ca_topic_score_gemma":0.00035414036,"teacher_disagreement_score":0.12569433,"about_ca_system_score_codex":0.00029762194,"about_ca_system_score_gemma":0.00010372588,"threshold_uncertainty_score":0.98110473},"labels":[],"label_agreement":null},{"id":"W3115018051","doi":"10.3390/risks9010007","title":"Mining Actuarial Risk Predictors in Accident Descriptions Using Recurrent Neural Networks","year":2020,"lang":"en","type":"article","venue":"Risks","topic":"Topic Modeling","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Accident (philosophy); Computer science; Artificial neural network; Poisson regression; Regression; Artificial intelligence; Machine learning; Task (project management); Representation (politics); Profit (economics); Data mining; Econometrics; Statistics; Mathematics; Engineering","score_opus":0.12687238660100658,"score_gpt":0.3084885536545194,"score_spread":0.1816161670535128,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3115018051","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":[0.49769565,0.00009691784,0.50099254,0.00020354612,0.0008242364,0.00008715399,6.5704876e-7,0.00007581411,0.00002350673],"genre_scores_gemma":[0.9682612,0.000025831283,0.031001775,0.00015860242,0.00053401873,0.0000061971873,0.0000015145921,0.00000904546,0.0000018143672],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998654,0.00012961867,0.0003062721,0.00039682363,0.00020146673,0.00031183567],"domain_scores_gemma":[0.9993807,0.000065487475,0.00011486853,0.00026924137,0.000024324709,0.00014537855],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020706687,0.00012725685,0.00015623438,0.00006707521,0.000115141105,0.00015084016,0.0005410641,0.00007395785,0.000010440916],"category_scores_gemma":[0.00012972081,0.00012855297,0.00006284655,0.00033527467,0.000015293599,0.00038522162,0.00031813385,0.00034625555,0.0000041076264],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012115685,0.000026086762,0.1774241,0.000003883724,0.0000125801125,0.00002083003,0.0042069177,0.7391872,0.000049581893,0.00031263335,0.00017801783,0.078566074],"study_design_scores_gemma":[0.0002895835,0.00003478444,0.018169127,0.000019771911,0.000010508495,0.0000032670891,0.0000636601,0.9810802,0.000019738103,0.00007061092,0.00011749853,0.00012128037],"about_ca_topic_score_codex":0.0003476195,"about_ca_topic_score_gemma":0.00008316592,"teacher_disagreement_score":0.47056556,"about_ca_system_score_codex":0.00008169627,"about_ca_system_score_gemma":0.0000442271,"threshold_uncertainty_score":0.5242238},"labels":[],"label_agreement":null},{"id":"W3115683849","doi":"10.3390/risks9010004","title":"Machine Learning in P&C Insurance: A Review for Pricing and Reserving","year":2020,"lang":"en","type":"review","venue":"Risks","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":46,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada; Université Laval","keywords":"Machine learning; Artificial intelligence; Computer science; Artificial neural network; Field (mathematics); State (computer science); Work (physics); Data science; Algorithm; Engineering; Mathematics","score_opus":0.17190130679763913,"score_gpt":0.44687395260805907,"score_spread":0.2749726458104199,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3115683849","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":[0.000015606327,0.9897777,0.00008315618,0.00043262503,0.00024726588,0.0034639705,0.00004789808,0.00013085135,0.005800938],"genre_scores_gemma":[0.00011174023,0.9977074,0.0006599282,0.000228094,0.0002995038,0.00047344342,0.00006197012,0.00006361279,0.0003942996],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9962364,0.0010815269,0.0008842052,0.0007069646,0.00050643075,0.0005845151],"domain_scores_gemma":[0.99833035,0.00048524936,0.00062192534,0.0003363337,0.00007974702,0.000146397],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0037315185,0.00040257876,0.0019082478,0.00028118494,0.0005077842,0.00012946369,0.0005795821,0.00024593534,0.000034433433],"category_scores_gemma":[0.0015697817,0.0003718395,0.00043862552,0.001389213,0.00014394827,0.00016688369,0.00024214273,0.0008671228,0.000027293356],"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":[0.000002887121,0.000017175169,0.0038120975,0.06178062,0.000063965716,0.000010799469,0.0006762939,0.0000014874216,8.893543e-9,0.0009170761,0.0005709643,0.9321466],"study_design_scores_gemma":[0.000150283,0.000019233405,0.00046380432,0.042912856,0.00028738528,0.0000013215665,0.000068897905,0.000012535251,4.6951008e-9,0.00021347599,0.9555025,0.00036771767],"about_ca_topic_score_codex":0.0035938933,"about_ca_topic_score_gemma":0.0045093326,"teacher_disagreement_score":0.9549315,"about_ca_system_score_codex":0.000181322,"about_ca_system_score_gemma":0.00021301734,"threshold_uncertainty_score":0.99987334},"labels":[],"label_agreement":null},{"id":"W3120513924","doi":"10.3390/risks9010019","title":"An Expectation-Maximization Algorithm for the Exponential-Generalized Inverse Gaussian Regression Model with Varying Dispersion and Shape for Modelling the Aggregate Claim Amount","year":2021,"lang":"en","type":"article","venue":"Risks","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","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":"Simon Fraser University","funders":"","keywords":"Expectation–maximization algorithm; Inverse Gaussian distribution; Exponential family; Exponential function; Gaussian; Inverse; Dispersion (optics); Maximization; Mathematics; Applied mathematics; Mixture model; Generalized linear model; Algorithm; Aggregate (composite); Flexibility (engineering); Mathematical optimization; Distribution (mathematics); Maximum likelihood; Statistics; Mathematical analysis","score_opus":0.12189029564465068,"score_gpt":0.37926580771480695,"score_spread":0.2573755120701563,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3120513924","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":[0.0120668225,0.00012971072,0.9865503,0.00041099495,0.00007038511,0.00064680254,0.00006807515,0.000033540793,0.000023395542],"genre_scores_gemma":[0.067548364,0.0002442488,0.9317022,0.00008531251,0.00009204274,0.00020098576,0.000048655274,0.000033612272,0.000044602486],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998936,0.00013250358,0.00023451554,0.0003062322,0.00018661117,0.00020414958],"domain_scores_gemma":[0.99818337,0.0011020014,0.00016598374,0.00029711644,0.0001828759,0.00006866029],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031388618,0.0001614447,0.0001959612,0.00002877152,0.00063601084,0.00013619808,0.000118915246,0.00007473883,0.000020977568],"category_scores_gemma":[0.00019501561,0.0000834323,0.000056528734,0.00010829406,0.00007387344,0.00015712013,0.000030273752,0.00010108138,2.9198068e-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":[0.00060320704,0.00025434513,0.000060339913,0.00028550928,0.00016979178,0.0000095671685,0.008759717,0.09274923,0.0046494734,0.2141285,0.00049910945,0.67783123],"study_design_scores_gemma":[0.0006798688,0.000051629668,0.000006445988,0.000121462355,0.0001314376,0.0000042670426,0.0005112188,0.8003177,0.0018705666,0.19616643,0.000018409546,0.000120575794],"about_ca_topic_score_codex":0.00004181282,"about_ca_topic_score_gemma":0.000013702626,"teacher_disagreement_score":0.70756847,"about_ca_system_score_codex":0.000025960115,"about_ca_system_score_gemma":0.000054866556,"threshold_uncertainty_score":0.48917454},"labels":[],"label_agreement":null},{"id":"W3121381330","doi":"10.3390/risks4040037","title":"A Note on Realistic Dividends in Actuarial Surplus Models","year":2016,"lang":"en","type":"article","venue":"Risks","topic":"Probability and Risk Models","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":"Université de Montréal","funders":"Australian Research Council; University of New South Wales","keywords":"Dividend; Economics; Value (mathematics); Dividend policy; Cash; Present value; Actuarial science; Financial economics; Mathematical economics; Mathematics; Finance; Statistics","score_opus":0.3810247044451999,"score_gpt":0.45674422865341546,"score_spread":0.07571952420821554,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3121381330","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.74576503,0.00007375863,0.22236238,0.0065286583,0.0013002148,0.00039844672,0.00012645614,0.000106382264,0.023338674],"genre_scores_gemma":[0.99786294,0.000044499993,0.00034321457,0.00018639059,0.0001597224,0.000015462514,0.0000010273825,0.000009832631,0.0013769446],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99703443,0.0003370065,0.00057521387,0.0006077522,0.0010868786,0.00035869333],"domain_scores_gemma":[0.9956532,0.0030769457,0.00013276415,0.0008987089,0.00010712833,0.00013123557],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003278927,0.00015007699,0.00031134798,0.00025769518,0.000093029135,0.000100173456,0.0007406292,0.00016150947,0.00021580665],"category_scores_gemma":[0.003974308,0.00007702401,0.00011958177,0.00039384977,0.00013148863,0.00043232582,0.00015329882,0.00016390721,0.00075612095],"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":[0.0015827429,0.00042942725,0.0060071955,0.0000064927804,0.000017005552,0.000079010555,0.0031934173,0.037173167,0.0006203545,0.118105605,0.010882663,0.82190293],"study_design_scores_gemma":[0.00092274393,0.00009735492,0.013982637,0.000049220303,0.000005257222,0.0000024672268,0.000017381233,0.026216371,0.00022640795,0.9559124,0.0023930795,0.00017464698],"about_ca_topic_score_codex":0.0010921505,"about_ca_topic_score_gemma":0.0015654129,"teacher_disagreement_score":0.8378068,"about_ca_system_score_codex":0.00012918997,"about_ca_system_score_gemma":0.000119671364,"threshold_uncertainty_score":0.9718658},"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":"W3122893468","doi":"10.3390/risks3040543","title":"Production Flexibility and Hedging","year":2015,"lang":"en","type":"article","venue":"Risks","topic":"Risk Management in Financial Firms","field":"Business, Management and Accounting","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":"Futures contract; Flexibility (engineering); Hedge; Incentive; Production (economics); Economics; Microeconomics; Context (archaeology); Position (finance); Financial economics; Finance","score_opus":0.13156049328618633,"score_gpt":0.3015883180483708,"score_spread":0.17002782476218445,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3122893468","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.95958716,0.00015831305,0.00043048797,0.001230826,0.0009956874,0.00026079325,2.9513174e-7,0.00022156423,0.03711485],"genre_scores_gemma":[0.9970959,0.000012391733,0.0002977637,0.00038945384,0.0015194233,0.000011301861,0.0000056980243,0.000013952269,0.0006541348],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.9992469,0.000006021515,0.00013526104,0.00027286165,0.00016884586,0.00017011513],"domain_scores_gemma":[0.999574,0.000006193851,0.00008517986,0.00023115257,0.00009165391,0.000011786123],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000624196,0.00009767279,0.00010373024,0.000105225314,0.000103608596,0.00016419795,0.00009801742,0.000034002092,0.00001787666],"category_scores_gemma":[0.00035519662,0.00009109533,0.000020850732,0.0002652741,0.0000466702,0.00093669427,0.00017305672,0.00008023591,0.0003225457],"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":[0.00009541925,0.00013566086,0.6481364,0.00027874392,0.00002401337,0.000013216184,0.00022424644,0.0005787195,0.0001552921,0.056542058,0.07831498,0.21550125],"study_design_scores_gemma":[0.0005753762,0.000014908721,0.30141544,0.00004108558,0.00006525374,0.000002316158,0.00040207524,0.0044274293,0.0000963871,0.08224561,0.61031246,0.00040164808],"about_ca_topic_score_codex":0.0007787129,"about_ca_topic_score_gemma":0.000051432253,"teacher_disagreement_score":0.5319975,"about_ca_system_score_codex":0.000027139356,"about_ca_system_score_gemma":0.0000075792746,"threshold_uncertainty_score":0.414578},"labels":[],"label_agreement":null},{"id":"W3123074391","doi":"10.3390/risks5040058","title":"Optimal Claiming Strategies in Bonus Malus Systems and Implied Markov Chains","year":2017,"lang":"en","type":"article","venue":"Risks","topic":"Probability and Risk Models","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":"Natural Sciences and Engineering Research Council of Canada","keywords":"Markov chain; Econometrics; Mathematical economics; Economics; Computer science; Mathematics; Machine learning","score_opus":0.24970499967709567,"score_gpt":0.44244402254851173,"score_spread":0.19273902287141606,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3123074391","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98116136,0.0006183133,0.005061864,0.00043522168,0.0003649056,0.00024399135,0.000015239354,0.00002650019,0.012072621],"genre_scores_gemma":[0.99820596,0.00008007366,0.0008997918,0.00001802336,0.00007701549,0.00001542871,7.057984e-7,0.0000074606533,0.000695519],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99804026,0.0001448232,0.00047078865,0.00049544167,0.00052108953,0.00032759062],"domain_scores_gemma":[0.99822944,0.00035958178,0.00025547715,0.00096805685,0.000077748,0.000109680164],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0029787228,0.00013977975,0.00033057504,0.00014055819,0.00053302135,0.0015509524,0.00077184907,0.00014355384,0.000023362953],"category_scores_gemma":[0.0006615535,0.0000993734,0.000053356147,0.00008524681,0.0002546323,0.00071619067,0.00031501838,0.00021591684,0.000041242285],"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":[0.0006990764,0.00036257415,0.33315894,0.00018196696,0.00008502843,0.00041446064,0.014496506,0.08969016,0.0019385921,0.28044096,0.0026000738,0.2759317],"study_design_scores_gemma":[0.0016820419,0.00015181853,0.6193621,0.00012967862,0.000020482492,0.000074381416,0.010173093,0.27922633,0.00020576912,0.08545823,0.0028970365,0.0006190506],"about_ca_topic_score_codex":0.0018586228,"about_ca_topic_score_gemma":0.00074265635,"teacher_disagreement_score":0.28620318,"about_ca_system_score_codex":0.00003571592,"about_ca_system_score_gemma":0.000100071244,"threshold_uncertainty_score":0.99948555},"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":"W3124621708","doi":"10.3390/risks4030029","title":"Optimal Insurance with Heterogeneous Beliefs and Disagreement about Zero-Probability Events","year":2016,"lang":"en","type":"article","venue":"Risks","topic":"Decision-Making and Behavioral Economics","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; HEC Montréal; Social Sciences and Humanities Research Council of Canada; Università Bocconi; Université de Montréal; Imperial College London; Georgia State University; Society of Actuaries","keywords":"Indemnity; Arrow; Actuarial science; Deductible; Mathematical economics; Probabilistic logic; Expected utility hypothesis; Monotone polygon; Incentive compatibility; Mathematics; Schedule; Econometrics; Economics; Statistics; Computer science; Microeconomics; Incentive","score_opus":0.1468276934601342,"score_gpt":0.38609035373552975,"score_spread":0.23926266027539556,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3124621708","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9946604,0.00013866929,0.0041430327,0.00031244828,0.00021607228,0.00019310565,0.000080841324,0.000032206328,0.00022323617],"genre_scores_gemma":[0.99683654,0.000050873423,0.0025817186,0.000071682916,0.000040312712,0.000015096257,7.110408e-7,0.000012111322,0.0003909702],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9977932,0.00009123307,0.00051046925,0.000685797,0.00063319626,0.00028612],"domain_scores_gemma":[0.9982505,0.0004714557,0.00021761935,0.0007540292,0.00013398132,0.00017241492],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010996322,0.00017806023,0.00029534183,0.00007512127,0.00015742161,0.0001341877,0.00047224874,0.000074422605,0.00019586257],"category_scores_gemma":[0.00035079257,0.00008977769,0.00007426929,0.00014941783,0.00018523075,0.00026701041,0.00019784577,0.0000768107,0.00034941317],"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":[0.00008838949,0.00008059662,0.3657962,0.0000011851324,0.00000759098,0.000008983994,0.00008765286,0.00030476277,0.00008622223,0.000030067025,0.00019675096,0.63331157],"study_design_scores_gemma":[0.0014747927,0.00053389254,0.9273017,0.0001284419,0.000017912462,0.000065728374,0.00004066467,0.00015834779,0.00084814895,0.058457308,0.010526768,0.00044626827],"about_ca_topic_score_codex":0.000054430016,"about_ca_topic_score_gemma":0.00008627358,"teacher_disagreement_score":0.6328653,"about_ca_system_score_codex":0.00006501313,"about_ca_system_score_gemma":0.000037082653,"threshold_uncertainty_score":0.44911158},"labels":[],"label_agreement":null},{"id":"W3126503003","doi":"10.3390/risks9040058","title":"Synthetic Dataset Generation of Driver Telematics","year":2021,"lang":"en","type":"preprint","venue":"Risks","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal","funders":"Society of Actuaries","keywords":"Telematics; Computer science; Machine learning; Data mining; Artificial intelligence; Automatic summarization; Artificial neural network; Set (abstract data type); Feature (linguistics)","score_opus":0.06514654319048209,"score_gpt":0.28686386425032556,"score_spread":0.2217173210598435,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3126503003","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":[0.099109136,0.0008093714,0.8892401,0.00004706527,0.0016259472,0.00054685044,0.001879434,0.0033470215,0.0033950647],"genre_scores_gemma":[0.98633724,0.0011257572,0.009236091,0.000016183425,0.00007498856,0.00003097278,0.0031460284,0.000019929175,0.000012815078],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994358,0.000018219169,0.00021533473,0.00013611591,0.0001189629,0.0000755839],"domain_scores_gemma":[0.99945575,0.000008894165,0.00004688736,0.00044343728,0.000022007995,0.000023012635],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008693861,0.000112641486,0.00016969495,0.00007737891,0.000011919167,0.000029323868,0.00013562618,0.00013079889,0.000055705263],"category_scores_gemma":[0.000012280748,0.00012255298,0.000046948982,0.000040118957,0.00001551186,0.000035775483,0.00016284657,0.00019289601,0.0000068969043],"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":[0.0000011024428,0.000072631985,0.00005771284,0.0017440108,0.00028230777,0.000014045916,0.0003384627,0.19340001,0.0053508687,0.00065979816,0.7606865,0.037392527],"study_design_scores_gemma":[0.00012851572,0.000013213447,0.0007071179,0.00033102577,0.00020419873,0.0000029745615,0.00006757257,0.9448077,0.025525669,0.00009188242,0.027778443,0.0003417065],"about_ca_topic_score_codex":0.000019374067,"about_ca_topic_score_gemma":0.000013182231,"teacher_disagreement_score":0.8872281,"about_ca_system_score_codex":0.000031956708,"about_ca_system_score_gemma":0.0000110469455,"threshold_uncertainty_score":0.49975654},"labels":[],"label_agreement":null},{"id":"W3128789444","doi":"10.3390/risks9020035","title":"Mortality Forecasting with an Age-Coherent Sparse VAR Model","year":2021,"lang":"en","type":"article","venue":"Risks","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Autoregressive model; Vector autoregression; Econometrics; Context (archaeology); Term (time); Dimension (graph theory); Population; Computer science; Mortality rate; Statistics; Mathematics; Demography; Geography","score_opus":0.19859721979741646,"score_gpt":0.3745388889291356,"score_spread":0.17594166913171916,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3128789444","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9176223,0.00010574812,0.0039727883,0.00014889696,0.00024007703,0.0003858744,0.000021385631,0.00017257688,0.07733033],"genre_scores_gemma":[0.99288756,0.00011110377,0.0054169204,0.00023003027,0.0002387141,0.000052025072,0.000024860425,0.000026845564,0.001011964],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9975346,0.00030063745,0.00028018476,0.00052230444,0.0007918406,0.0005704643],"domain_scores_gemma":[0.9988442,0.000038180486,0.00014690791,0.00055983494,0.00019313206,0.00021776589],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010118815,0.00019296895,0.00025659014,0.000067162175,0.000636904,0.00020594629,0.00032258235,0.00009883473,0.00015930069],"category_scores_gemma":[0.00007299541,0.00018051217,0.00011674942,0.0005037434,0.00028524903,0.0003539895,0.000089109686,0.00021385119,0.000023515247],"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":[0.00006862061,0.00095425907,0.85477597,0.00011511486,0.00039604047,0.0010200143,0.016116075,0.023216559,0.000052458352,0.069166616,0.0014339297,0.032684345],"study_design_scores_gemma":[0.001982037,0.00027591232,0.7997653,0.00019314069,0.00063567964,0.000010022299,0.01645701,0.10744821,0.0004089365,0.04921601,0.0217542,0.001853562],"about_ca_topic_score_codex":0.006820714,"about_ca_topic_score_gemma":0.047881156,"teacher_disagreement_score":0.08423166,"about_ca_system_score_codex":0.00009084054,"about_ca_system_score_gemma":0.00020266438,"threshold_uncertainty_score":0.99979293},"labels":[],"label_agreement":null},{"id":"W3152245405","doi":"10.3390/risks9040068","title":"Matrix-Tilted Archimedean Copulas","year":2021,"lang":"en","type":"article","venue":"Risks","topic":"Financial Risk and Volatility Modeling","field":"Economics, Econometrics and Finance","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":"Copula (linguistics); Cholesky decomposition; Mathematics; Pure mathematics; Matrix (chemical analysis); Extension (predicate logic); Econometrics; Computer science; Physics; Eigenvalues and eigenvectors","score_opus":0.12287764485949941,"score_gpt":0.3085838189170331,"score_spread":0.1857061740575337,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3152245405","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.941948,0.0052030166,0.018368712,0.0005204105,0.00057876244,0.000098719924,0.00013344472,0.00008232931,0.033066615],"genre_scores_gemma":[0.99343735,0.00035475704,0.0032858746,0.00014684488,0.00015948222,0.000006652381,0.00003453553,0.000021428923,0.0025530558],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9988787,0.000015316842,0.00043732286,0.00036219347,0.000032958105,0.00027352024],"domain_scores_gemma":[0.99936056,0.00003854523,0.00011967032,0.00036509958,0.00003708458,0.000079062884],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00028530217,0.000111664376,0.0002923808,0.00008661227,0.00011500147,0.000055130873,0.00013257889,0.000093684226,0.00058123417],"category_scores_gemma":[0.00022837515,0.000140805,0.00012699494,0.00022531791,0.000027485503,0.00009590274,0.000068073394,0.00018444133,0.001275489],"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":[0.000041753232,0.00023115952,0.38936877,0.00006574776,0.00006792682,0.00006383234,0.0009714711,0.00083171506,0.00022415571,0.58965033,0.003772475,0.0147106955],"study_design_scores_gemma":[0.0012944377,0.000073205374,0.24549673,0.000048481048,0.000017528724,0.000020250736,0.00015210584,0.08985301,0.0017580844,0.36090392,0.29957578,0.00080645696],"about_ca_topic_score_codex":0.00059609517,"about_ca_topic_score_gemma":0.0000931142,"teacher_disagreement_score":0.2958033,"about_ca_system_score_codex":0.000046807305,"about_ca_system_score_gemma":0.000036337162,"threshold_uncertainty_score":0.9995021},"labels":[],"label_agreement":null},{"id":"W3173285506","doi":"10.3390/risks9070126","title":"Improving Explainability of Major Risk Factors in Artificial Neural Networks for Auto Insurance Rate Regulation","year":2021,"lang":"en","type":"article","venue":"Risks","topic":"Machine Learning in Healthcare","field":"Computer Science","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":"Toronto Metropolitan University","funders":"","keywords":"Transparency (behavior); Artificial neural network; Variable (mathematics); Computer science; Work (physics); Actuarial science; Insurance industry; Econometrics; Machine learning; Risk analysis (engineering); Artificial intelligence; Economics; Business; Engineering; Mathematics","score_opus":0.03912109199705017,"score_gpt":0.316868814324062,"score_spread":0.27774772232701184,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3173285506","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.655058,0.00007762293,0.34397194,0.0002537432,0.00035441061,0.00021915844,0.000007973615,0.000050414226,0.000006690771],"genre_scores_gemma":[0.9917035,0.0000032986184,0.008124039,0.000026492771,0.00008083326,0.000027125016,0.000011278662,0.000009957499,0.00001345208],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982077,0.0004983033,0.00043426477,0.00042979248,0.00014054835,0.00028941742],"domain_scores_gemma":[0.99839085,0.000564345,0.000292494,0.00051351317,0.00018251945,0.000056302368],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00088426983,0.00011839068,0.00022004018,0.000067345936,0.00013025258,0.000053763357,0.00028518884,0.000101733494,0.0000060191283],"category_scores_gemma":[0.0011688668,0.00011844557,0.00007789581,0.00043532706,0.000029455578,0.00025444338,0.00012514605,0.00031205523,5.2054315e-7],"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":[0.000028312672,0.00003828219,0.71828854,0.000099744124,0.0000037503276,0.0000030789024,0.00079050486,0.17301495,0.00043795578,0.0019019453,0.000007866835,0.105385035],"study_design_scores_gemma":[0.00010628258,0.000026088876,0.46359912,0.000008521983,0.0000015621722,5.196145e-7,0.00002192931,0.5337253,0.0009571225,0.0014803562,0.000012603473,0.000060590544],"about_ca_topic_score_codex":0.0024928781,"about_ca_topic_score_gemma":0.0007330936,"teacher_disagreement_score":0.36071035,"about_ca_system_score_codex":0.00008071741,"about_ca_system_score_gemma":0.00009438303,"threshold_uncertainty_score":0.483007},"labels":[],"label_agreement":null},{"id":"W3181853338","doi":"10.3390/risks10070141","title":"Reverse Sensitivity Analysis for Risk Modelling","year":2022,"lang":"en","type":"article","venue":"Risks","topic":"Probabilistic and Robust Engineering Design","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 Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Connaught Fund","keywords":"Sensitivity (control systems); Monte Carlo method; Measure (data warehouse); Random variable; Distortion (music); Set (abstract data type); Probability distribution; Mathematics; Variance (accounting); Computer science; Baseline (sea); Mathematical optimization; Statistics; Engineering; Data mining","score_opus":0.283513783390662,"score_gpt":0.37956050230346655,"score_spread":0.09604671891280453,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3181853338","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":[0.04729387,0.000064943386,0.9516891,0.000095760566,0.0002177758,0.00015168524,0.00019365785,0.000049917344,0.00024327356],"genre_scores_gemma":[0.96862125,0.0000070143037,0.030172285,0.000049556813,0.00005155417,0.00003332865,0.000007752553,0.000007898433,0.001049384],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99819964,0.0002948487,0.0002908099,0.0003836843,0.0006360267,0.00019500962],"domain_scores_gemma":[0.99710023,0.0020852943,0.00014243754,0.00048757385,0.000116070434,0.00006837609],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006041952,0.00008685664,0.00025615894,0.0002780379,0.00046484105,0.000052954325,0.00024094623,0.000029893305,0.00022645487],"category_scores_gemma":[0.0014075403,0.000071247974,0.0002798165,0.001224139,0.000025426762,0.00006703187,0.00011678672,0.00017138528,0.00002862494],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001959086,0.000019913929,0.0025487426,9.635864e-7,0.00007731952,0.000004412641,0.00014051798,0.9912248,0.000011883539,0.0008745299,0.0029851703,0.0020921503],"study_design_scores_gemma":[0.00012319861,0.000025363166,0.0010714072,5.047909e-7,0.00019877049,0.0000019541121,0.00019024186,0.96873695,0.000013886257,0.015489287,0.0140464185,0.00010202118],"about_ca_topic_score_codex":0.00025458497,"about_ca_topic_score_gemma":0.000014174724,"teacher_disagreement_score":0.92151684,"about_ca_system_score_codex":0.000059384787,"about_ca_system_score_gemma":0.000036859965,"threshold_uncertainty_score":0.35752288},"labels":[],"label_agreement":null},{"id":"W3183322001","doi":"10.3390/risks11080140","title":"Deep Equal Risk Pricing of Financial Derivatives with Non-Translation Invariant Risk Measures","year":2023,"lang":"en","type":"article","venue":"Risks","topic":"Stochastic processes and financial applications","field":"Economics, Econometrics and Finance","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":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada","keywords":"Invariant (physics); Computer science; Valuation (finance); Artificial neural network; Risk management; Econometrics; Artificial intelligence; Mathematical optimization; Finance; Mathematics; Economics","score_opus":0.061904777666974824,"score_gpt":0.2562005581288131,"score_spread":0.19429578046183826,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3183322001","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":[0.15644006,0.00049935695,0.84088874,0.00005754457,0.000069976246,0.00025045243,0.00018325637,0.000050161965,0.0015604245],"genre_scores_gemma":[0.9920685,0.0005521157,0.0071157175,0.000019406778,0.0001055279,0.00008097867,0.00002124333,0.000022485481,0.0000139884],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.9988706,0.000007920173,0.00048053075,0.00033041526,0.00006420694,0.00024632778],"domain_scores_gemma":[0.998911,0.00014973116,0.0005844271,0.00023791574,0.00006680558,0.00005008769],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004872694,0.00013882096,0.0003260858,0.00021273442,0.00022216071,0.000024364053,0.00017394034,0.00009844162,0.000014653822],"category_scores_gemma":[0.00038130712,0.00013884767,0.00006943682,0.00089356344,0.00007627398,0.00013243775,0.000031791245,0.00018337021,0.00015026754],"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":[0.0002096563,0.00021835572,0.23123844,0.00012218018,0.00014526676,0.0000049887103,0.00796904,0.014945162,0.00023834228,0.6394217,0.00012821193,0.10535862],"study_design_scores_gemma":[0.0008531911,0.00017885088,0.7040973,0.000036265057,0.000036991118,0.0000012695502,0.00013426656,0.03561561,0.0003665601,0.2570323,0.0013283177,0.0003191028],"about_ca_topic_score_codex":0.0015605573,"about_ca_topic_score_gemma":0.00026233753,"teacher_disagreement_score":0.83562845,"about_ca_system_score_codex":0.000026678908,"about_ca_system_score_gemma":0.0000509164,"threshold_uncertainty_score":0.56620437},"labels":[],"label_agreement":null},{"id":"W3183927403","doi":"10.3390/risks9080141","title":"Mean-Reverting 4/2 Principal Components Model. Financial Applications","year":2021,"lang":"en","type":"article","venue":"Risks","topic":"Stochastic processes and financial applications","field":"Economics, Econometrics and Finance","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":"Western University","funders":"","keywords":"Stochastic volatility; Value at risk; Econometrics; SABR volatility model; Bivariate analysis; Volatility (finance); Principal component analysis; Mathematics; Multivariate statistics; Mean reversion; Applied mathematics; Economics; Statistics; Risk management; Finance","score_opus":0.09350443412597176,"score_gpt":0.2801310647629399,"score_spread":0.18662663063696816,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3183927403","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":[0.020463668,0.0015220101,0.9625667,0.00036588838,0.000106236985,0.00022760461,0.00022698454,0.00006580012,0.01445513],"genre_scores_gemma":[0.9823506,0.00008024324,0.016055925,0.000401303,0.00022378995,0.0003383597,0.00005706391,0.000022469107,0.00047023248],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99876255,0.000002335322,0.00048338214,0.0004368197,0.000043373573,0.00027153254],"domain_scores_gemma":[0.99921125,0.000030162922,0.00021308583,0.00037570822,0.00008133151,0.00008844223],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014904697,0.00012490308,0.0002629113,0.00006233879,0.00025885395,0.00004685894,0.00021743012,0.00010091406,0.00006558789],"category_scores_gemma":[0.00011581855,0.00016159809,0.000100067984,0.0003574701,0.00003754306,0.00009440729,0.00010935606,0.0001604466,0.00075061055],"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":[0.0000030204799,0.00010590755,0.0011315644,0.000017859096,0.000008610966,0.0000013765876,0.000121767756,0.00070440356,0.00011230074,0.99421793,0.000122039484,0.003453214],"study_design_scores_gemma":[0.00045600004,0.000012293772,0.019273475,0.000015522033,0.000012857641,0.000007874219,0.000024444256,0.05002816,0.00030461114,0.84400207,0.08548333,0.00037934267],"about_ca_topic_score_codex":0.00010991414,"about_ca_topic_score_gemma":0.000033189765,"teacher_disagreement_score":0.96188694,"about_ca_system_score_codex":0.00006403009,"about_ca_system_score_gemma":0.000076290984,"threshold_uncertainty_score":0.964783},"labels":[],"label_agreement":null},{"id":"W3194889306","doi":"10.3390/risks9080147","title":"Transformations of Telegraph Processes and Their Financial Applications","year":2021,"lang":"en","type":"article","venue":"Risks","topic":"Stochastic processes and financial applications","field":"Economics, Econometrics and Finance","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Telegrapher's equations; Mathematics; Diffusion process; Partial differential equation; Stochastic process; Novelty; Brownian motion; Limiting; Stochastic differential equation; Mathematical economics; Applied mathematics; Mathematical analysis; Computer science; Telecommunications; Statistics","score_opus":0.03404264702752661,"score_gpt":0.237212638871925,"score_spread":0.2031699918443984,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3194889306","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":[0.008730235,0.00823852,0.97386473,0.00038694034,0.000029131552,0.00020076199,0.0005790821,0.000024400713,0.00794619],"genre_scores_gemma":[0.99544007,0.00081124733,0.0032657282,0.00008739129,0.000055058194,0.00025059856,0.000033242894,0.000008769585,0.000047889927],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9993329,0.0000012844928,0.0003401523,0.00019367585,0.000015822583,0.00011613852],"domain_scores_gemma":[0.99949366,0.00005013798,0.0001391533,0.0001690126,0.00010748747,0.000040529725],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007366878,0.000080032296,0.00020254777,0.00007474259,0.00012540768,0.000019239715,0.00009238627,0.00006113146,0.000031138323],"category_scores_gemma":[0.000101089245,0.000084974876,0.00004526439,0.00057142676,0.00006423711,0.000099582176,0.000019296835,0.00006963739,0.000026926265],"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":[0.0000020311923,0.00008225074,0.001123867,0.00011885917,0.000009272852,1.2448322e-7,0.00044820854,0.0000042577494,0.00005117122,0.9885364,0.000054983295,0.009568571],"study_design_scores_gemma":[0.000245897,0.000021575433,0.014472401,0.0000159492,0.000007592015,0.000008128667,0.0001397155,0.00014465989,0.0015358487,0.92066133,0.062584326,0.00016257615],"about_ca_topic_score_codex":0.000047767833,"about_ca_topic_score_gemma":0.000043441556,"teacher_disagreement_score":0.98670983,"about_ca_system_score_codex":0.000007960901,"about_ca_system_score_gemma":0.00008455915,"threshold_uncertainty_score":0.34651747},"labels":[],"label_agreement":null},{"id":"W3194943772","doi":"10.3390/risks9090151","title":"Coherent Mortality Forecasting for Less Developed Countries","year":2021,"lang":"en","type":"article","venue":"Risks","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social 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":"Concordia University; University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada; Society of Actuaries","keywords":"Developing country; Convergence (economics); Developed country; China; Development economics; Term (time); Mortality rate; Population; Population projection; Socioeconomic status; Projections of population growth; Economics; Geography; Econometrics; Economic growth; Demography; Population growth","score_opus":0.29029817206745506,"score_gpt":0.4170142000064221,"score_spread":0.12671602793896702,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3194943772","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9483143,0.0005459737,0.005832706,0.000871684,0.0013156013,0.00087621185,0.000089096204,0.00016972148,0.0419847],"genre_scores_gemma":[0.9952077,0.00034109977,0.0022121554,0.0003358726,0.00035898515,0.00015799425,0.000033881126,0.000019843033,0.0013324375],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.99800694,0.00019210995,0.00033964516,0.0003775898,0.0005527715,0.00053095585],"domain_scores_gemma":[0.9988211,0.00019169631,0.00016051631,0.0002830768,0.00043768366,0.00010595796],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014239358,0.0001534921,0.00025330935,0.000053857773,0.0009211649,0.00020434317,0.0002641408,0.00010404171,0.00014325188],"category_scores_gemma":[0.00034198363,0.00016115297,0.00015449019,0.0003752043,0.00023851941,0.00018233547,0.000087455315,0.00011144861,0.000021697288],"study_design_candidate":"observational","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":[0.000026585094,0.00014439317,0.84993327,0.00019722125,0.00026669525,0.000050955667,0.0070539895,0.00008023935,0.0000107828055,0.10122982,0.008628979,0.032377094],"study_design_scores_gemma":[0.0007818269,0.000026661495,0.44975075,0.00007533739,0.0001599297,0.0000013366466,0.012790139,0.00039733204,0.0005085102,0.012546252,0.5224183,0.0005436242],"about_ca_topic_score_codex":0.003532833,"about_ca_topic_score_gemma":0.04190336,"teacher_disagreement_score":0.51378936,"about_ca_system_score_codex":0.000120135446,"about_ca_system_score_gemma":0.00031882233,"threshold_uncertainty_score":0.9755794},"labels":[],"label_agreement":null},{"id":"W3208048790","doi":"10.3390/risks9110191","title":"Crop Insurance Policies in India: An Empirical Analysis of Pradhan Mantri Fasal Bima Yojana","year":2021,"lang":"en","type":"article","venue":"Risks","topic":"Agricultural risk and resilience","field":"Agricultural and Biological 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 Toronto","funders":"Indian Council of Social Science Research","keywords":"Crop insurance; Beneficiary; Subsidy; Agriculture; Government (linguistics); Business; Agricultural economics; Economics; Agricultural science; Geography; Finance","score_opus":0.04525137562235173,"score_gpt":0.3246530985653472,"score_spread":0.2794017229429955,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3208048790","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9982859,0.0004707272,0.000001913175,0.0005789511,0.000043928445,0.00007506633,0.00008661471,0.000025662046,0.00043125584],"genre_scores_gemma":[0.999067,0.00039799794,0.0000704213,0.00014386231,0.000088999535,0.000004837788,0.00008308296,5.753846e-7,0.00014320023],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9986507,0.00015562982,0.00030360342,0.00033196664,0.00026220988,0.00029587062],"domain_scores_gemma":[0.9994695,0.00012377133,0.00011626459,0.00008380095,0.000097746946,0.00010890233],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013743568,0.00013442493,0.00035230545,0.0000464813,0.00010799373,0.00004013397,0.0002615249,0.0001332101,0.00014819446],"category_scores_gemma":[0.00007163195,0.000045002325,0.00015784628,0.0028365958,0.00010417501,0.00014504968,0.00008135147,0.00016125268,0.000009623697],"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":[0.000013145216,0.0001644138,0.9484393,0.0000030809633,0.000043043026,0.00002839469,0.0003276586,0.0001652326,0.035588987,0.0000615273,0.00006403878,0.015101199],"study_design_scores_gemma":[0.00008224983,0.00009815893,0.99234164,0.000010031297,0.00005124543,0.0000047101307,0.0009903307,0.0001465181,0.0054607745,0.0000703967,0.0006016304,0.00014233946],"about_ca_topic_score_codex":0.0022405325,"about_ca_topic_score_gemma":0.006960731,"teacher_disagreement_score":0.043902338,"about_ca_system_score_codex":0.000025193367,"about_ca_system_score_gemma":0.00001589585,"threshold_uncertainty_score":0.3884251},"labels":[],"label_agreement":null},{"id":"W4214813713","doi":"10.3390/risks10030057","title":"The Effect of COVID-19 on the Relationship between Idiosyncratic Volatility and Expected Stock Returns","year":2022,"lang":"en","type":"article","venue":"Risks","topic":"Financial Markets and Investment Strategies","field":"Economics, Econometrics and Finance","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Northern British Columbia","funders":"","keywords":"Volatility (finance); Coronavirus disease 2019 (COVID-19); Economics; Stock (firearms); Portfolio; Econometrics; Pandemic; Financial economics; 2019-20 coronavirus outbreak; Skewness; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Predictability; Stock market; Monetary economics; Internal medicine; Statistics","score_opus":0.11856301887061062,"score_gpt":0.2964787410656053,"score_spread":0.17791572219499469,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4214813713","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99031425,0.0011460603,0.000044216828,0.0022582184,0.000112091264,0.00039356752,0.00013822239,0.000016521904,0.0055768793],"genre_scores_gemma":[0.9994911,0.000029720572,0.000006179701,0.000107266314,0.00003362637,0.00008851318,0.00000861008,0.000007990184,0.00022698524],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99908173,0.00021296529,0.00033214438,0.0001873462,0.000051486873,0.00013435552],"domain_scores_gemma":[0.99634236,0.003031599,0.0002629678,0.0003150066,0.000005945633,0.000042136297],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001612455,0.000093341034,0.000208863,0.000052025676,0.00084629416,0.000043146563,0.00018168053,0.00003577602,0.0001326416],"category_scores_gemma":[0.0020527202,0.00006191189,0.000060899325,0.00019754912,0.00012320423,0.000053974753,0.00007258875,0.00024561532,0.000007090576],"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":[0.000043245112,0.000006196728,0.7700739,0.000018059804,0.000014998308,2.8160326e-7,0.00034532364,0.000015888952,7.9357113e-7,0.2278653,0.0015074417,0.00010858394],"study_design_scores_gemma":[0.0002605725,0.00042455448,0.895397,0.0000034838204,0.000008053141,4.4256336e-7,0.00016241363,0.00055755407,0.000012476229,0.094502255,0.008592664,0.00007852918],"about_ca_topic_score_codex":0.0003091871,"about_ca_topic_score_gemma":0.0000227803,"teacher_disagreement_score":0.13336304,"about_ca_system_score_codex":0.00008209352,"about_ca_system_score_gemma":0.000026057207,"threshold_uncertainty_score":0.6509096},"labels":[],"label_agreement":null},{"id":"W4214939547","doi":"10.3390/risks10030054","title":"Approximation of Zero-Inflated Poisson Credibility Premium via Variational Bayes Approach","year":2022,"lang":"en","type":"article","venue":"Risks","topic":"Probability and Risk Models","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":"Simon Fraser University","funders":"","keywords":"Credibility; Credibility theory; Poisson distribution; Bayes' theorem; Econometrics; Bayesian probability; Computation; Computer science; Zero-inflated model; Inflation (cosmology); Approximate Bayesian computation; Zero (linguistics); Posterior probability; Actuarial science; Economics; Poisson regression; Mathematics; Statistics; Algorithm; Artificial intelligence","score_opus":0.18140522408954018,"score_gpt":0.3799415699067308,"score_spread":0.1985363458171906,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4214939547","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":[0.42686817,0.00010275984,0.5661313,0.00034444334,0.0002873704,0.0005297273,0.00012396419,0.000069061454,0.005543235],"genre_scores_gemma":[0.9847208,0.0000025880775,0.014715571,0.00004367407,0.00004102423,0.00006530273,0.000065730026,0.000008161348,0.00033709346],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99558926,0.0007715442,0.00087928004,0.0006147881,0.0019262275,0.00021891207],"domain_scores_gemma":[0.9975979,0.0007050359,0.00047220354,0.00079803023,0.00034815032,0.000078717756],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0070138434,0.00013199859,0.00031428784,0.00018742247,0.00036653856,0.000056484878,0.00076370273,0.00009528377,0.0008034074],"category_scores_gemma":[0.0013716373,0.00010540996,0.00014492251,0.00089050503,0.000115336334,0.000348279,0.0003699743,0.00030153952,0.000027206383],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00081306486,0.0027912,0.07975324,0.00009693406,0.00012898407,0.0000023540852,0.010030428,0.76584524,0.006552098,0.06388832,0.00678151,0.06331662],"study_design_scores_gemma":[0.00031001057,0.00007342232,0.043004025,0.000001752044,0.000013400853,0.000005015532,0.0001543602,0.5798662,0.00066512625,0.37511757,0.00067490054,0.000114241346],"about_ca_topic_score_codex":0.00027227582,"about_ca_topic_score_gemma":0.000005735286,"teacher_disagreement_score":0.5578527,"about_ca_system_score_codex":0.00012059961,"about_ca_system_score_gemma":0.00015006849,"threshold_uncertainty_score":0.8796748},"labels":[],"label_agreement":null},{"id":"W4224303238","doi":"10.3390/risks10040078","title":"Unit-Linked Tontine: Utility-Based Design, Pricing and Performance","year":2022,"lang":"en","type":"article","venue":"Risks","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval; Actua","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Unit (ring theory); Attractiveness; Economics; Stochastic game; Transferable utility; Payment; Unit of account; Microeconomics; Unit price; Actuarial science; Computer science; Finance; Game theory","score_opus":0.09384686988635593,"score_gpt":0.33582738915993054,"score_spread":0.2419805192735746,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4224303238","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9821114,0.00027592268,0.0020597808,0.0005196869,0.000381806,0.0006280914,0.000009108422,0.00016490513,0.0138493255],"genre_scores_gemma":[0.9976056,0.00015656752,0.0011833205,0.00040585126,0.00008360827,0.000088033456,0.0000061788664,0.000013884871,0.00045693625],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99792975,0.0005524242,0.00021862714,0.0003045741,0.0005915283,0.00040307088],"domain_scores_gemma":[0.9993422,0.0001248172,0.00011571902,0.00026543782,0.00006190726,0.00008989455],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0025188676,0.00012586848,0.00016339238,0.00015193432,0.0017779628,0.0000774518,0.00031609685,0.000043040007,0.00028442094],"category_scores_gemma":[0.000060244904,0.00013612148,0.000059720303,0.00066520454,0.0002402752,0.0001422587,0.00013237145,0.00026501017,0.000009659819],"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":[0.000075339645,0.00012349503,0.95634115,0.000049948674,0.00003162704,0.000012378541,0.0039454126,0.0011752361,0.000020243553,0.0020326257,0.0011885555,0.035003997],"study_design_scores_gemma":[0.00060451624,0.000141767,0.8541051,0.00001424221,0.00004801794,6.0958297e-7,0.0029558588,0.012681222,0.000069711845,0.000592589,0.12847704,0.0003092933],"about_ca_topic_score_codex":0.0025994515,"about_ca_topic_score_gemma":0.00034092471,"teacher_disagreement_score":0.12728849,"about_ca_system_score_codex":0.00006695108,"about_ca_system_score_gemma":0.00011613847,"threshold_uncertainty_score":0.9995216},"labels":[],"label_agreement":null},{"id":"W4280625223","doi":"10.3390/risks10050101","title":"Portfolio Optimization for Extreme Risks with Maximum Diversification: An Empirical Analysis","year":2022,"lang":"en","type":"article","venue":"Risks","topic":"Financial Risk and Volatility Modeling","field":"Economics, Econometrics and Finance","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":"Diversification (marketing strategy); Portfolio; Portfolio optimization; Sharpe ratio; Rate of return on a portfolio; Econometrics; Modern portfolio theory; Downside risk; Stock (firearms); Economics; Post-modern portfolio theory; Actuarial science; Portfolio insurance; Financial economics; Replicating portfolio; Business; Geography","score_opus":0.2367734353951367,"score_gpt":0.3219727718150009,"score_spread":0.08519933641986419,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4280625223","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":[0.33947492,0.00033060176,0.6579813,0.000225641,0.00010408162,0.00026179667,0.00041687954,0.00004847624,0.0011563335],"genre_scores_gemma":[0.98194647,0.000058702255,0.017057203,0.00012448305,0.000074687414,0.000111399546,0.00039865985,0.000020766323,0.00020760957],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987632,0.00002345397,0.00041151923,0.0005019426,0.00006903526,0.00023086456],"domain_scores_gemma":[0.9991435,0.00002668665,0.0002943076,0.0003962058,0.000061014536,0.00007828244],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00056759786,0.000123828,0.0003239576,0.00036368083,0.0005258661,0.00005356029,0.00021895758,0.00006020015,0.0010946515],"category_scores_gemma":[0.000038028196,0.00014432395,0.00015744944,0.0008327387,0.000027899987,0.00022638452,0.000059763395,0.00014420807,0.000013396904],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000105204635,0.00013508173,0.43468675,0.0000052661144,0.00011140064,0.0000012704135,0.00040444807,0.5586774,4.0130266e-7,0.0037323367,0.00029112076,0.0018493234],"study_design_scores_gemma":[0.00046817696,0.00018196317,0.094326705,8.8854233e-7,0.00011209165,9.3580877e-7,0.00020943744,0.88826644,0.0000022411814,0.0054239337,0.010785715,0.00022147306],"about_ca_topic_score_codex":0.00086847116,"about_ca_topic_score_gemma":0.00006148712,"teacher_disagreement_score":0.64247155,"about_ca_system_score_codex":0.00013592497,"about_ca_system_score_gemma":0.000031716983,"threshold_uncertainty_score":0.9998185},"labels":[],"label_agreement":null},{"id":"W4282569808","doi":"10.3390/risks10060124","title":"Meta-Learning Approaches for Recovery Rate Prediction","year":2022,"lang":"en","type":"article","venue":"Risks","topic":"Credit Risk and Financial Regulations","field":"Economics, Econometrics and Finance","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":"HEC Montréal","funders":"Belgian Federal Science Policy Office","keywords":"Exploit; Computer science; Macro; Set (abstract data type); Machine learning; Predictive power; Artificial intelligence; Big data; Predictive modelling; Econometrics; Data mining; Economics; Computer security","score_opus":0.2883052977490098,"score_gpt":0.2590919819957141,"score_spread":0.02921331575329572,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4282569808","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6224236,0.0106408885,0.3166749,0.0017973544,0.0037004498,0.0016391948,0.0045109084,0.00035186432,0.03826087],"genre_scores_gemma":[0.99247605,0.0001217185,0.000926214,0.00002606943,0.00025198254,0.00063335785,0.00019388668,0.000024198172,0.0053465054],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99917835,0.000024455758,0.00032241005,0.0002745535,0.000026459094,0.00017376816],"domain_scores_gemma":[0.9994997,0.000082889455,0.00021847995,0.00015484796,0.0000115196,0.000032609973],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00085444987,0.000086442764,0.0002593939,0.00014199155,0.0005232612,0.000035412155,0.000102848564,0.000041135467,0.00049419416],"category_scores_gemma":[0.0001149822,0.00010350954,0.00024716344,0.00018588056,0.000017081666,0.00012119456,0.000055169396,0.00017132773,0.000044354914],"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":[0.00016464671,0.00024345562,0.07838217,0.000037038364,0.001171794,0.0000017420722,0.0011838737,0.34447983,0.000014081313,0.5310151,0.014166291,0.029139964],"study_design_scores_gemma":[0.000414653,0.00020456218,0.10061736,7.8284296e-7,0.00013853365,0.0000021437627,0.00016322584,0.058391538,0.000013697326,0.065595195,0.77426887,0.0001894334],"about_ca_topic_score_codex":0.00011491246,"about_ca_topic_score_gemma":0.000007750133,"teacher_disagreement_score":0.76010257,"about_ca_system_score_codex":0.000088009074,"about_ca_system_score_gemma":0.000015549966,"threshold_uncertainty_score":0.54110795},"labels":[],"label_agreement":null},{"id":"W4288717471","doi":"10.3390/risks10080152","title":"Multiple Bonus–Malus Scale Models for Insureds of Different Sizes","year":2022,"lang":"en","type":"article","venue":"Risks","topic":"Insurance and Financial Risk Management","field":"Economics, Econometrics and Finance","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Université du Québec à Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Credibility; Credibility theory; A priori and a posteriori; Scale (ratio); Computer science; Quality (philosophy); Product (mathematics); Actuarial science; Econometrics; Class (philosophy); Mathematics; Business; Artificial intelligence","score_opus":0.06246705572382515,"score_gpt":0.24411395308380754,"score_spread":0.18164689735998238,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4288717471","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96040064,0.001876821,0.024882307,0.00017425197,0.00072152086,0.00064576964,0.0013229701,0.000036683607,0.009939057],"genre_scores_gemma":[0.9978917,0.0002165681,0.00050526316,0.00010386403,0.00006691692,0.0003138835,0.000034594173,0.000024114514,0.00084307557],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99890894,0.000011094848,0.00047916512,0.0002896634,0.000055756645,0.0002553926],"domain_scores_gemma":[0.99931484,0.00005104637,0.00028642,0.000289737,0.000023731141,0.000034216893],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033226004,0.00012072117,0.00038438392,0.00015496978,0.00020960899,0.000014895401,0.00024782564,0.00004156574,0.00009313066],"category_scores_gemma":[0.00003387892,0.00014034008,0.00018635728,0.00015174696,0.000030148485,0.00009684859,0.00015143254,0.00011074938,0.000015616593],"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":[0.00041908174,0.0010868732,0.3230691,0.00020761443,0.00015184903,0.0000034089117,0.0027262415,0.03385874,0.00008750123,0.60879123,0.005479726,0.02411864],"study_design_scores_gemma":[0.004411021,0.00080848316,0.28109837,0.000018460727,0.000035243283,0.0000014574589,0.0006109881,0.14420757,0.0008603138,0.4049195,0.1622078,0.0008207781],"about_ca_topic_score_codex":0.00051141053,"about_ca_topic_score_gemma":0.00006083261,"teacher_disagreement_score":0.20387171,"about_ca_system_score_codex":0.00008920726,"about_ca_system_score_gemma":0.000009507593,"threshold_uncertainty_score":0.57229024},"labels":[],"label_agreement":null},{"id":"W4295973523","doi":"10.3390/risks10080160","title":"Optimal Liquidation, Acquisition and Market Making Problems in HFT under Hawkes Models for LOB","year":2022,"lang":"en","type":"article","venue":"Risks","topic":"Point processes and geometric inequalities","field":"Mathematics","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 Calgary","funders":"","keywords":"Hamilton–Jacobi–Bellman equation; Process (computing); Bellman equation; Computer science; Mathematical optimization; Asset (computer security); Relation (database); Value (mathematics); Function (biology); Optimal control; Implementation; Mathematics; Data mining","score_opus":0.13985375754528923,"score_gpt":0.3638818690868753,"score_spread":0.22402811154158608,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4295973523","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5548607,0.00083867897,0.43905127,0.00055820803,0.000104319326,0.00071280857,0.00012931817,0.000077679215,0.0036670081],"genre_scores_gemma":[0.9886376,0.00003510391,0.0099407695,0.00013423209,0.000042616164,0.00023378606,0.00002032227,0.000017737782,0.0009377773],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99918646,0.000050031802,0.00024242383,0.00016977252,0.00016309695,0.00018818969],"domain_scores_gemma":[0.9993961,0.00032277568,0.00010270907,0.000104654835,0.000054973018,0.000018791534],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00076321117,0.000088411034,0.00014548247,0.00018632303,0.00015783249,0.00004580558,0.00008011069,0.000036702855,0.00019752399],"category_scores_gemma":[0.00008802392,0.00008889984,0.000029912131,0.00024051558,0.000015672178,0.00019791682,0.000098276185,0.00009184742,5.4852046e-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":[0.00040034376,0.0005006827,0.0011003249,0.0025061036,0.00010759352,0.0000060408242,0.010998699,0.20882852,0.000115841205,0.756689,0.014158897,0.0045879157],"study_design_scores_gemma":[0.0007412202,0.00017235069,0.00035492697,0.000060518145,0.000025907302,0.000013519053,0.0033662526,0.14316772,0.000085761625,0.8506599,0.0011439279,0.00020798726],"about_ca_topic_score_codex":0.000034476827,"about_ca_topic_score_gemma":0.000012903567,"teacher_disagreement_score":0.43377694,"about_ca_system_score_codex":0.00006866215,"about_ca_system_score_gemma":0.00003047696,"threshold_uncertainty_score":0.36252302},"labels":[],"label_agreement":null},{"id":"W4303980577","doi":"10.3390/risks10100194","title":"Exploring Industry-Level Fairness of Auto Insurance Premiums by Statistical Modeling of Automobile Rate and Classification Data","year":2022,"lang":"en","type":"article","venue":"Risks","topic":"Insurance and Financial Risk Management","field":"Economics, Econometrics and Finance","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":"Toronto Metropolitan University","funders":"","keywords":"Actuarial science; Insurance industry; Econometrics; Risk premium; Statistical model; Insurance premium; Business; Economics; Statistics; Mathematics","score_opus":0.3971100853978381,"score_gpt":0.309236368002987,"score_spread":0.08787371739485111,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4303980577","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.91539925,0.0007794352,0.07551132,0.00007145796,0.00023314486,0.00024257322,0.0063686757,0.000019376323,0.0013747523],"genre_scores_gemma":[0.99824154,0.00062706583,0.0007274474,0.000019562824,0.000021741263,0.000093809984,0.0001696644,0.000016166796,0.000083013016],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99874675,0.000028938657,0.0006018217,0.00038303912,0.00006634469,0.00017312911],"domain_scores_gemma":[0.9990584,0.00004453434,0.00035218586,0.000483235,0.000028003878,0.000033624285],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008650525,0.00009983822,0.0003114396,0.00011118499,0.0001138524,0.000015532654,0.00033812367,0.000055580702,0.000058973157],"category_scores_gemma":[0.00009428047,0.00012666768,0.000021843935,0.0002303051,0.000050898292,0.0003478199,0.0003120549,0.0002668954,0.000006437208],"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":[0.00023003,0.0007424326,0.4209569,0.0005227562,0.0001573204,0.000006408469,0.0028018916,0.038440295,0.0006429641,0.44601032,0.003105874,0.08638279],"study_design_scores_gemma":[0.00063394743,0.00009331729,0.3950576,0.000023393415,0.000011154822,7.3205257e-7,0.00056328287,0.58824456,0.00010135468,0.006702889,0.008312369,0.00025543824],"about_ca_topic_score_codex":0.0010370567,"about_ca_topic_score_gemma":0.000008900878,"teacher_disagreement_score":0.5498042,"about_ca_system_score_codex":0.000051445386,"about_ca_system_score_gemma":0.00002254479,"threshold_uncertainty_score":0.5165358},"labels":[],"label_agreement":null},{"id":"W4307189192","doi":"10.3390/risks10110202","title":"Bivariate Copulas Based on Counter-Monotonic Shock Method","year":2022,"lang":"en","type":"article","venue":"Risks","topic":"Statistical Distribution Estimation and Applications","field":"Mathematics","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Trois-Rivières","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Copula (linguistics); Bivariate analysis; Monotonic function; Estimator; Mathematics; Convexity; Econometrics; Applied mathematics; Tail dependence; Statistical physics; Quantile; Statistics; Mathematical analysis; Multivariate statistics; Economics; Physics","score_opus":0.17089438186288283,"score_gpt":0.46112198385997977,"score_spread":0.29022760199709696,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4307189192","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":[0.0011732981,0.0000040515984,0.9860784,0.0013121635,0.000083620165,0.00028153922,0.0009710679,0.00014364623,0.009952239],"genre_scores_gemma":[0.91117847,8.1901726e-7,0.08625926,0.0013440058,0.000026091397,0.0004153845,0.00020409003,0.000021177733,0.00055066845],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989818,0.00015907634,0.00022138911,0.00019552746,0.00028901186,0.00015322737],"domain_scores_gemma":[0.99855196,0.0009415107,0.00009352547,0.0003128388,0.000042629796,0.00005751487],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00036064282,0.000095897725,0.00013450328,0.000050518374,0.00030783625,0.000024563498,0.00015211388,0.000027309881,0.00502659],"category_scores_gemma":[0.00036724345,0.000095562216,0.00005828274,0.00022184468,0.000020545367,0.000017719081,0.000043462805,0.0001916387,0.00020374455],"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":[0.000021280286,0.0002589827,0.00003494637,0.000012556135,0.000009673205,0.0000025154236,0.00003387418,0.003063594,0.00005843445,0.9576798,0.036976274,0.0018480583],"study_design_scores_gemma":[0.00071066746,0.0001015403,0.00225711,0.000007838571,0.000049334507,0.00000449431,0.000064377244,0.73869747,0.000261698,0.19634743,0.061305143,0.00019287705],"about_ca_topic_score_codex":0.00002893043,"about_ca_topic_score_gemma":0.00000201971,"teacher_disagreement_score":0.9100052,"about_ca_system_score_codex":0.00014921243,"about_ca_system_score_gemma":0.00005304113,"threshold_uncertainty_score":0.9958829},"labels":[],"label_agreement":null},{"id":"W4309849231","doi":"10.3390/risks10120224","title":"A Generalized Linear Mixed Model for Data Breaches and Its Application in Cyber Insurance","year":2022,"lang":"en","type":"article","venue":"Risks","topic":"Information and Cyber Security","field":"Computer Science","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":"Simon Fraser University","funders":"","keywords":"Data breach; Computer science; Computer security; Analytics; Risk management; Actuarial science; Data science; Business; Finance","score_opus":0.09414441132079525,"score_gpt":0.318691204330746,"score_spread":0.22454679300995073,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4309849231","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":[0.38643113,0.00020877946,0.6119229,0.0006236766,0.0000679765,0.00038631892,0.00012754867,0.00005804559,0.0001736082],"genre_scores_gemma":[0.98210233,0.000020235855,0.017113008,0.0004323515,0.000013776306,0.00016446858,0.00009075208,0.0000033515955,0.00005972693],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994134,0.000029349585,0.0001441009,0.00019301478,0.00011452249,0.00010561797],"domain_scores_gemma":[0.9994798,0.000020247882,0.00005677494,0.00039308518,0.000024086296,0.000026042044],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035770395,0.00005299009,0.0000735093,0.00004571341,0.000117859134,0.000028400922,0.00056009635,0.00002003268,0.000001669478],"category_scores_gemma":[0.0000117383315,0.000055002933,0.000011792966,0.0001467578,0.000006356134,0.0004422183,0.0004468712,0.00008106336,0.0000037242087],"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":[0.00013098094,0.00041334968,0.00479969,0.00012216905,0.000034361037,0.0000022092959,0.030067692,0.071196824,0.0010377055,0.5562087,0.0068806005,0.32910568],"study_design_scores_gemma":[0.00048158606,0.000007013754,0.0018052559,0.0000010247828,0.0000010887593,0.0000026978366,0.000019710005,0.9893747,0.00012070495,0.0016413807,0.006472794,0.0000720243],"about_ca_topic_score_codex":0.00010648375,"about_ca_topic_score_gemma":0.0000742486,"teacher_disagreement_score":0.9181779,"about_ca_system_score_codex":0.000019266094,"about_ca_system_score_gemma":0.000032796634,"threshold_uncertainty_score":0.22429545},"labels":[],"label_agreement":null},{"id":"W4310396511","doi":"10.3390/risks10120228","title":"Spectral Expansions for Credit Risk Modelling with Occupation Times","year":2022,"lang":"en","type":"article","venue":"Risks","topic":"Credit Risk and Financial Regulations","field":"Economics, Econometrics and Finance","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":"Wilfrid Laurier University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Credit risk; Econometrics; Credit default swap; Default; Economics; Geometric Brownian motion; Volatility (finance); Mathematics; Actuarial science; Diffusion process; Finance","score_opus":0.06478554967609276,"score_gpt":0.25738760693462726,"score_spread":0.1926020572585345,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4310396511","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":[0.49261138,0.00043898058,0.5015105,0.00015611779,0.00035024172,0.00031754567,0.0013018196,0.000051114235,0.0032622786],"genre_scores_gemma":[0.9872095,0.00009558788,0.011021008,0.000014338449,0.0003110333,0.00021139297,0.00013882082,0.000026050133,0.00097227294],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991324,0.000009725795,0.0002936179,0.00029303128,0.000048605354,0.00022265293],"domain_scores_gemma":[0.99938536,0.00006927357,0.00024363802,0.00022644026,0.000025524127,0.00004976949],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029015003,0.000101043755,0.00019675963,0.00015879529,0.00068626157,0.000033855198,0.00012318509,0.000037904516,0.0004050949],"category_scores_gemma":[0.000027759605,0.00011407796,0.00010032384,0.00022256667,0.000026797969,0.00012205509,0.00003482502,0.00016406723,0.00004563489],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012917108,0.00012655085,0.12663776,0.0000068099266,0.000038842383,0.0000015277126,0.00081658637,0.52012837,0.0000027398094,0.34362897,0.0061870604,0.0022956338],"study_design_scores_gemma":[0.0010481554,0.00038345053,0.105813876,0.0000049929954,0.000032812946,0.0000044507997,0.00026989254,0.48216012,0.000031134125,0.1076698,0.30219084,0.000390499],"about_ca_topic_score_codex":0.00069434,"about_ca_topic_score_gemma":0.00005038461,"teacher_disagreement_score":0.4945981,"about_ca_system_score_codex":0.0000977599,"about_ca_system_score_gemma":0.000033436954,"threshold_uncertainty_score":0.52782387},"labels":[],"label_agreement":null},{"id":"W4315786905","doi":"10.3390/risks11010020","title":"Adversarial Artificial Intelligence in Insurance: From an Example to Some Potential Remedies","year":2023,"lang":"en","type":"article","venue":"Risks","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","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":"McGill University","funders":"","keywords":"Underwriting; Adversarial system; Intermediary; Actuarial science; Business; Robustness (evolution); Taxonomy (biology); Financial services; Computer science; Artificial intelligence; Finance","score_opus":0.08929388574500674,"score_gpt":0.3478451552789508,"score_spread":0.2585512695339441,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4315786905","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":[0.4622798,0.000017799628,0.534055,0.0007656755,0.0022580943,0.00016733064,0.000007777505,0.00037944093,0.00006911854],"genre_scores_gemma":[0.9718832,0.00001601773,0.026732598,0.00021018018,0.001066149,0.000021385475,0.00002124318,0.000019901014,0.000029333945],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977066,0.00021358482,0.00038858075,0.0007214373,0.0004754418,0.0004943643],"domain_scores_gemma":[0.99883187,0.00020228692,0.000088644614,0.000672495,0.000049331204,0.0001553517],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006746476,0.00019513034,0.00025902627,0.00034624102,0.00018099113,0.00016572604,0.0012381511,0.00011973142,0.00004020322],"category_scores_gemma":[0.00031625642,0.00019998655,0.00005926845,0.0011394663,0.000056287205,0.0009155653,0.0006771316,0.00039804907,0.00055262935],"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":[0.0001307836,0.00009256466,0.005554486,0.0000079506235,0.000021468786,0.0002694761,0.010123647,0.6080254,0.002780591,0.03519889,0.00032495032,0.33746982],"study_design_scores_gemma":[0.00028780402,0.00015646752,0.1075733,0.00004673619,0.0000073702818,0.0000026367536,0.0007465248,0.7545268,0.0018173861,0.13348444,0.0008054505,0.00054509996],"about_ca_topic_score_codex":0.007460867,"about_ca_topic_score_gemma":0.00065388624,"teacher_disagreement_score":0.5096034,"about_ca_system_score_codex":0.000073888405,"about_ca_system_score_gemma":0.00007735664,"threshold_uncertainty_score":0.99914855},"labels":[],"label_agreement":null},{"id":"W4318480457","doi":"10.3390/risks11020030","title":"The SEV-SV Model—Applications in Portfolio Optimization","year":2023,"lang":"en","type":"article","venue":"Risks","topic":"Stochastic processes and financial applications","field":"Economics, Econometrics and Finance","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","funders":"","keywords":"Portfolio; Volatility (finance); Stock (firearms); Mean reversion; Economics; Econometrics; Stochastic volatility; Bellman equation; Stochastic modelling; Expected utility hypothesis; Stochastic process; Financial economics; Mathematical economics; Mathematics; Finance; Engineering","score_opus":0.06381840773620871,"score_gpt":0.27890036734730844,"score_spread":0.21508195961109972,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4318480457","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":[0.0023136388,0.0005488903,0.9814638,0.0007612168,0.00005611942,0.00030311765,0.000078029196,0.00008065493,0.014394533],"genre_scores_gemma":[0.9927502,0.0011150371,0.0036092682,0.000106999796,0.000093617906,0.00096800487,0.00008283885,0.000026289632,0.0012477011],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992407,0.0000012571346,0.00031972842,0.0002166225,0.000023901575,0.0001978151],"domain_scores_gemma":[0.99949425,0.00005832433,0.00012757025,0.0002635487,0.000023328359,0.00003295424],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023734955,0.000070362825,0.000116424846,0.00012273375,0.00023711678,0.000043920965,0.00020360018,0.00005783289,0.000016325894],"category_scores_gemma":[0.000060379738,0.000067100336,0.000038550246,0.00087981176,0.000033036584,0.0000694198,0.000043605807,0.000087514505,0.00043322335],"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":[0.0000015070488,0.000016617592,0.0016540127,0.0000029860805,0.000002893892,1.5519893e-7,0.00007030246,0.14177863,6.5449996e-7,0.8545214,0.00042141048,0.0015294417],"study_design_scores_gemma":[0.000089999485,0.0000041042194,0.0070939614,0.0000019036048,0.0000014264922,2.6473404e-7,0.000042453248,0.5578877,0.0000024525111,0.4231567,0.011639093,0.00007998261],"about_ca_topic_score_codex":0.00015245899,"about_ca_topic_score_gemma":0.000036639125,"teacher_disagreement_score":0.9904366,"about_ca_system_score_codex":0.00003891769,"about_ca_system_score_gemma":0.000022867524,"threshold_uncertainty_score":0.5568354},"labels":[],"label_agreement":null},{"id":"W4378221222","doi":"10.3390/risks11060099","title":"Estimating Territory Risk Relativity Using Generalized Linear Mixed Models and Fuzzy C-Means Clustering","year":2023,"lang":"en","type":"article","venue":"Risks","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","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 Guelph; Toronto Metropolitan University","funders":"","keywords":"Cluster analysis; Fuzzy logic; Computer science; Set (abstract data type); Generalized linear model; Econometrics; Estimation; Fuzzy clustering; Data mining; Actuarial science; Mathematics; Economics; Artificial intelligence; Machine learning","score_opus":0.11065269454241132,"score_gpt":0.34690367362580676,"score_spread":0.23625097908339543,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4378221222","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":[0.21716365,0.00011245685,0.78135407,0.00007807032,0.0005292869,0.0001209928,0.000006674434,0.00030973597,0.00032505384],"genre_scores_gemma":[0.21178806,0.000039562172,0.78783697,0.000044467168,0.0001932922,0.0000067388396,0.0000017718872,0.00002016218,0.000068953406],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982482,0.00037803422,0.0002746497,0.00050759484,0.00021285722,0.00037866086],"domain_scores_gemma":[0.99901164,0.0001774238,0.00014760034,0.0004918392,0.00003537106,0.00013611889],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012097378,0.00019369026,0.00026978503,0.00014496852,0.00036306237,0.00013077926,0.00032824618,0.00012563932,0.0000011633896],"category_scores_gemma":[0.00007885585,0.00018264525,0.00007648418,0.00032722062,0.00004763597,0.0005872625,0.00047655054,0.0003020216,0.000007823378],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001245118,0.000031560245,0.0013623815,0.00008358478,0.000059181402,0.00003791211,0.0036154778,0.6698868,0.002926589,0.010453315,0.00027335776,0.31125736],"study_design_scores_gemma":[0.00030325633,0.00001670079,0.0010846816,0.00004843974,0.000017733384,0.000013170125,0.000007964271,0.92469877,0.00019326145,0.0733669,0.000043503573,0.00020561574],"about_ca_topic_score_codex":0.00052152603,"about_ca_topic_score_gemma":0.000029151142,"teacher_disagreement_score":0.31105176,"about_ca_system_score_codex":0.00003839743,"about_ca_system_score_gemma":0.00004131025,"threshold_uncertainty_score":0.7448057},"labels":[],"label_agreement":null},{"id":"W4382775723","doi":"10.3390/risks11070119","title":"Using US Stock Sectors to Diversify, Hedge, and Provide Safe Havens for NFT Coins","year":2023,"lang":"en","type":"article","venue":"Risks","topic":"Market Dynamics and Volatility","field":"Economics, Econometrics and Finance","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":"York University","funders":"York University","keywords":"Diversification (marketing strategy); Sharpe ratio; Portfolio; Financial economics; Hedge fund; Stock (firearms); Economics; Stock market; Autoregressive conditional heteroskedasticity; Business; Finance; Geography; Marketing","score_opus":0.18176846201629598,"score_gpt":0.32501777270190785,"score_spread":0.14324931068561186,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4382775723","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9873772,0.000101532656,0.0078528365,0.00017248138,0.00032984247,0.00051339343,0.00081664376,0.00004225878,0.0027937775],"genre_scores_gemma":[0.9972704,0.000034303303,0.001283241,0.00011275072,0.00007188115,0.000019059109,0.00002212413,0.000020658448,0.0011655833],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990862,0.0000083954565,0.00026278774,0.00034624132,0.000023835759,0.00027251992],"domain_scores_gemma":[0.99946,0.00008874161,0.000106319705,0.00021140483,0.000024068691,0.00010950618],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044561832,0.000109458764,0.00024629725,0.00017431297,0.00020269693,0.000058303125,0.00010152979,0.00007544434,0.00008406078],"category_scores_gemma":[0.00016066416,0.00013019488,0.000073145224,0.00021437573,0.000026821535,0.000081398684,0.00011682709,0.000073248775,0.00004084839],"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":[0.00003994493,0.000025029352,0.99130946,0.00007021341,0.000035433965,0.0000021146166,0.0003437548,0.0003098623,0.000018264443,0.004004209,0.0022193627,0.0016223225],"study_design_scores_gemma":[0.00043940963,0.00007679288,0.44240195,0.000011789927,0.00000919639,0.0000010023339,0.00005053426,0.48512155,0.000009420118,0.008057331,0.063589826,0.00023121516],"about_ca_topic_score_codex":0.0011544063,"about_ca_topic_score_gemma":0.0004804799,"teacher_disagreement_score":0.5489076,"about_ca_system_score_codex":0.00008178313,"about_ca_system_score_gemma":0.000016777327,"threshold_uncertainty_score":0.5309193},"labels":[],"label_agreement":null},{"id":"W4383908736","doi":"10.3390/risks11070126","title":"Cox-Based and Elliptical Telegraph Processes and Their Applications","year":2023,"lang":"en","type":"article","venue":"Risks","topic":"Diffusion and Search Dynamics","field":"Biochemistry, Genetics and Molecular Biology","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 Calgary","funders":"","keywords":"Ellipse; Telegrapher's equations; Stochastic process; Process (computing); Mathematics; Position (finance); Line (geometry); Probability density function; Statistical physics; Mathematical analysis; Calculus (dental); Physics; Geometry; Computer science; Telecommunications; Statistics; Transmission line","score_opus":0.02410568842875886,"score_gpt":0.30513373507321184,"score_spread":0.28102804664445297,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4383908736","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.988819,0.00047457853,0.009324155,0.00034825192,0.000011357294,0.00016473631,0.000038291808,0.000031757416,0.0007878878],"genre_scores_gemma":[0.99823385,0.00084830413,0.00023535083,0.00017722734,0.000036307498,0.000043708194,0.00010205588,0.000008301375,0.00031486526],"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","domain_scores_codex":[0.99963254,0.000011681579,0.00005205781,0.0001572214,0.00003898241,0.00010754692],"domain_scores_gemma":[0.9997545,0.000022485496,0.000011113002,0.00010962816,0.00003427408,0.000068009576],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006671101,0.000057867288,0.000050826427,0.000029279545,0.00006625735,0.000022212125,0.000048840295,0.000056166627,0.000003644836],"category_scores_gemma":[0.000039259314,0.00004489755,0.000013377978,0.00011453378,0.00009337834,9.922712e-7,0.000044991324,0.000040407736,0.000006722328],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001890946,0.00042090603,0.055792864,0.00066506787,0.00012330945,0.0000094845855,0.00031917338,0.00007329948,0.6906298,0.006909576,0.008272638,0.2365948],"study_design_scores_gemma":[0.002763687,0.00079811097,0.07588297,0.000058435555,0.000049520902,0.000034731245,0.0010172692,0.02957251,0.106062315,0.0075316816,0.7751944,0.0010343727],"about_ca_topic_score_codex":0.0000045699917,"about_ca_topic_score_gemma":0.00002161476,"teacher_disagreement_score":0.76692176,"about_ca_system_score_codex":0.0000011043429,"about_ca_system_score_gemma":0.0000359602,"threshold_uncertainty_score":0.1830869},"labels":[],"label_agreement":null},{"id":"W4385520574","doi":"10.3390/risks11080141","title":"Pricing of Pseudo-Swaps Based on Pseudo-Statistics","year":2023,"lang":"en","type":"article","venue":"Risks","topic":"Financial Risk and Volatility Modeling","field":"Economics, Econometrics and Finance","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":"Natural Sciences and Engineering Research Council of Canada","keywords":"Stochastic volatility; Variance swap; Econometrics; Covariance; Volatility (finance); Covariance and correlation; Mathematics; Economics; Statistics; Forward volatility; Random variable","score_opus":0.0957719064206476,"score_gpt":0.28913672107937155,"score_spread":0.19336481465872396,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385520574","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8244141,0.00013704001,0.1581818,0.00020956472,0.0005780095,0.0002310158,0.0007200401,0.00013501584,0.015393426],"genre_scores_gemma":[0.992455,0.00020453059,0.0067680837,0.00011529598,0.00007670225,0.000009905848,0.000039945073,0.000027533779,0.00030300507],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99877626,0.000012888403,0.00055897207,0.00030983333,0.000061186984,0.00028084463],"domain_scores_gemma":[0.9990877,0.00023031584,0.00024587265,0.00035035703,0.000030200556,0.000055587316],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006270518,0.00012762891,0.00034141494,0.00031125563,0.00009519,0.000020828882,0.00016320692,0.0001021881,0.0001289111],"category_scores_gemma":[0.0004995743,0.00015052424,0.00009230916,0.0004612501,0.000037419926,0.000060892347,0.00003407286,0.00016478202,0.00075857923],"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":[0.00015474384,0.00034907137,0.36944368,0.00036823403,0.000057212696,0.000027051587,0.0014090234,0.0747302,0.0001763684,0.50492334,0.010209628,0.03815143],"study_design_scores_gemma":[0.00039031258,0.00010033115,0.08242639,0.00003491833,0.0000053658127,2.3855844e-7,0.000026847365,0.8752434,0.00019216798,0.036500957,0.004887694,0.00019133891],"about_ca_topic_score_codex":0.00057787617,"about_ca_topic_score_gemma":0.00002042775,"teacher_disagreement_score":0.8005132,"about_ca_system_score_codex":0.000048999333,"about_ca_system_score_gemma":0.000030098627,"threshold_uncertainty_score":0.9750254},"labels":[],"label_agreement":null},{"id":"W4385805298","doi":"10.3390/risks11080150","title":"Overview of Some Recent Results of Energy Market Modeling and Clean Energy Vision in Canada","year":2023,"lang":"en","type":"article","venue":"Risks","topic":"Stochastic processes and financial applications","field":"Economics, Econometrics and Finance","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 Calgary","funders":"","keywords":"Mean reversion; Futures contract; Volatility (finance); Financial economics; West Texas Intermediate; Economics; Valuation of options; Black–Scholes model; Energy market; Renewable energy; Econometrics; Engineering","score_opus":0.069788278445599,"score_gpt":0.26514204843571043,"score_spread":0.19535376999011145,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385805298","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7135272,0.041163202,0.21721528,0.0028872613,0.00071298174,0.0003623116,0.0029549997,0.000060044007,0.021116694],"genre_scores_gemma":[0.9840027,0.01572622,0.000114193856,0.000056846326,0.000019881258,0.000012036194,0.00001825839,0.000008178398,0.000041688574],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9991399,0.0000029554358,0.0005043342,0.00019294818,0.000031494135,0.00012835099],"domain_scores_gemma":[0.99953204,0.000050503477,0.00019995305,0.00015877314,0.00002576194,0.000032953914],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019420366,0.000062360596,0.00023945769,0.00015189553,0.00002227074,0.0000038367066,0.000092262155,0.000038795955,0.000010870367],"category_scores_gemma":[0.00006918617,0.00007238667,0.000020779702,0.00047355206,0.00001331414,0.000046535664,0.000057124973,0.000035479134,0.000001202398],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006888533,0.000041441806,0.0017502669,0.00008477699,0.000012526202,0.0000016302168,0.00007700144,0.0035797572,0.000013861338,0.8915437,0.0010381849,0.10178798],"study_design_scores_gemma":[0.0009367654,0.000059715625,0.04065174,0.00011774605,0.000005162428,8.793119e-7,0.00011565618,0.45660546,0.00022450282,0.47467306,0.026369752,0.00023953721],"about_ca_topic_score_codex":0.8135016,"about_ca_topic_score_gemma":0.4594692,"teacher_disagreement_score":0.45302573,"about_ca_system_score_codex":0.00006577021,"about_ca_system_score_gemma":0.00009164246,"threshold_uncertainty_score":0.55039406},"labels":[],"label_agreement":null},{"id":"W4386608012","doi":"10.3390/risks11090162","title":"Pricing of Averaged Variance, Volatility, Covariance and Correlation Swaps with Semi-Markov Volatilities","year":2023,"lang":"en","type":"article","venue":"Risks","topic":"Stochastic processes and financial applications","field":"Economics, Econometrics and Finance","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":"Variance swap; Covariance; Volatility (finance); Econometrics; Stochastic volatility; Covariance and correlation; Markov chain; Implied volatility; Futures contract; Forward volatility; Swap (finance); Economics; Financial economics; Mathematics; Finance; Statistics; Random variable","score_opus":0.035733098181125834,"score_gpt":0.24006255319187897,"score_spread":0.20432945501075314,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386608012","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":[0.15860933,0.00049736287,0.8348253,0.0001057051,0.00009883801,0.00024160996,0.00013502328,0.00006577919,0.0054210844],"genre_scores_gemma":[0.996234,0.00015387646,0.0030291292,0.000029959187,0.000045050412,0.000045601657,0.000024119696,0.000015212861,0.0004230406],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.99911875,0.0000037531074,0.0003733416,0.00028734445,0.00004178085,0.00017503872],"domain_scores_gemma":[0.99923205,0.00018526171,0.00028031415,0.00021819091,0.000043863223,0.000040339644],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031547464,0.00010549371,0.00026791941,0.00012906079,0.00013555924,0.000026168342,0.0000956627,0.00007531975,0.000034782046],"category_scores_gemma":[0.00018703306,0.00011259455,0.000028141183,0.0005908138,0.00007805368,0.00015465799,0.000044129767,0.00011525757,0.00004760789],"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":[0.00009000251,0.000054972257,0.14939207,0.00019512895,0.000050723538,0.000001826561,0.0017282402,0.0009742712,0.000035430374,0.84143406,0.00020143198,0.0058418238],"study_design_scores_gemma":[0.00058573845,0.00008720023,0.55035216,0.000058619436,0.000013787281,0.0000037152395,0.00012769358,0.19826514,0.000033175304,0.2463776,0.003860218,0.00023497951],"about_ca_topic_score_codex":0.0004298979,"about_ca_topic_score_gemma":0.000029479776,"teacher_disagreement_score":0.83762467,"about_ca_system_score_codex":0.00002526721,"about_ca_system_score_gemma":0.0000261926,"threshold_uncertainty_score":0.45914724},"labels":[],"label_agreement":null},{"id":"W4386848513","doi":"10.3390/risks11090164","title":"Machine Learning in Forecasting Motor Insurance Claims","year":2023,"lang":"en","type":"article","venue":"Risks","topic":"Insurance and Financial Risk Management","field":"Economics, Econometrics and Finance","cited_by":22,"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":"Actuarial science; Random forest; Quarter (Canadian coin); Econometrics; Computer science; Economics; Business; Artificial intelligence","score_opus":0.09004306024869731,"score_gpt":0.2568708583974556,"score_spread":0.1668277981487583,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386848513","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9533656,0.0012595283,0.0005573612,0.0002491501,0.00047299583,0.00022487987,0.00007938243,0.0001668323,0.04362423],"genre_scores_gemma":[0.99540526,0.0013871422,0.00015499251,0.00009957271,0.00010957933,0.000047349156,0.000022283528,0.000027102085,0.0027467061],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99878246,0.000012419306,0.00044838054,0.00032436202,0.000038330836,0.00039403088],"domain_scores_gemma":[0.99955577,0.000042768483,0.00017764432,0.00017698183,0.000011564543,0.000035259192],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00068996154,0.00012341424,0.0002803756,0.00037769508,0.00012505898,0.000039611677,0.00017169831,0.00007535218,0.000071001836],"category_scores_gemma":[0.00014957663,0.0001496295,0.00007804098,0.00075098505,0.00002220374,0.0001657564,0.00007908725,0.00027471432,0.001901392],"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":[0.000017492006,0.000024843388,0.93102574,0.00002993058,0.000008603443,0.00003700674,0.0003655362,0.0027321698,0.000005731174,0.03737754,0.00022547528,0.02814992],"study_design_scores_gemma":[0.0005067293,0.000048732534,0.8443452,0.000027398728,9.4012955e-7,8.9026133e-7,0.000047794514,0.055899795,0.0000100411025,0.021178544,0.07771499,0.00021892198],"about_ca_topic_score_codex":0.0010573968,"about_ca_topic_score_gemma":0.0001518004,"teacher_disagreement_score":0.086680524,"about_ca_system_score_codex":0.00006680414,"about_ca_system_score_gemma":0.0000066697103,"threshold_uncertainty_score":0.99887574},"labels":[],"label_agreement":null},{"id":"W4387454750","doi":"10.3390/risks11100175","title":"Microinsurance and Economic Growth in Africa","year":2023,"lang":"en","type":"article","venue":"Risks","topic":"Agricultural risk and resilience","field":"Agricultural and Biological 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":"York University","funders":"","keywords":"Microinsurance; Economics; Nexus (standard); Development economics; Poverty; Economic growth","score_opus":0.033912959262669296,"score_gpt":0.2359882357856747,"score_spread":0.20207527652300541,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387454750","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9973218,0.00029842707,6.661104e-8,0.0009307856,0.000051489227,0.000068406785,0.000012977279,0.00005544577,0.0012605976],"genre_scores_gemma":[0.99828386,0.0010744244,0.000010011157,0.000021528025,0.000069609174,0.0000064423143,0.0000065606205,2.7147854e-7,0.0005272872],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9994676,0.000020150599,0.00008987158,0.00017715125,0.00004409105,0.0002011362],"domain_scores_gemma":[0.99982375,0.00008483623,0.00002309452,0.000015993443,0.000006666019,0.00004567684],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009268487,0.000065010914,0.000082826606,0.000008474144,0.000076612014,0.000023363602,0.000101763275,0.00004373504,0.00003382088],"category_scores_gemma":[0.000012616238,0.000019794643,0.000022668637,0.00022023653,0.000036672725,0.000066932786,0.000047271704,0.000061333834,0.0002451215],"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":[0.000016562088,0.00002314937,0.84098744,0.000007040672,0.0000037073305,0.000020936053,0.00029827794,0.000043760236,0.0886931,0.00054660393,0.0067227636,0.062636636],"study_design_scores_gemma":[0.000050598723,0.0000265529,0.99266684,0.000006912595,8.550446e-7,0.0000033505423,0.00016890223,0.000079081765,0.000993814,0.00051628955,0.005405906,0.000080904116],"about_ca_topic_score_codex":0.0011794578,"about_ca_topic_score_gemma":0.00085456256,"teacher_disagreement_score":0.15167937,"about_ca_system_score_codex":0.0000123026,"about_ca_system_score_gemma":0.000002028828,"threshold_uncertainty_score":0.3150623},"labels":[],"label_agreement":null},{"id":"W4387975017","doi":"10.3390/risks11110187","title":"Rank-Based Multivariate Sarmanov for Modeling Dependence between Loss Reserves","year":2023,"lang":"en","type":"article","venue":"Risks","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social 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":true,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada; Alliance de recherche numérique du Canada","keywords":"Solvency; Multivariate statistics; Econometrics; Line of business; Flexibility (engineering); Diversification (marketing strategy); Inference; Rank (graph theory); Computer science; Operational risk; Capital (architecture); Estimation; Actuarial science; Economics; Risk management; Statistics; Business; Mathematics; Finance; Market liquidity; Business model; Artificial intelligence","score_opus":0.19774733076733125,"score_gpt":0.42998656699577337,"score_spread":0.23223923622844211,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387975017","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.95470357,0.00010474098,0.037336838,0.0015912703,0.00061145035,0.0012626546,0.00012311956,0.0006200674,0.0036462748],"genre_scores_gemma":[0.996876,0.00012428613,0.0017587385,0.000084258674,0.00046002155,0.0001479683,0.000044507364,0.00003244155,0.00047178342],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99749464,0.00026847227,0.00035437546,0.00046027268,0.00070006243,0.0007221501],"domain_scores_gemma":[0.9987921,0.00038911478,0.00012284121,0.000379362,0.00017937062,0.00013717571],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028419741,0.00017514282,0.00026001927,0.00025683094,0.0009369017,0.00013980774,0.0006292939,0.00014598362,0.000033750723],"category_scores_gemma":[0.00033752492,0.00018052873,0.00021368865,0.0008665227,0.00018021201,0.00025802973,0.000096903,0.00017552533,0.00013508204],"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":[0.00016293718,0.00014382537,0.8654914,0.00028084603,0.00028411305,0.000048565078,0.0069315163,0.08129658,0.000098615856,0.019181546,0.003809261,0.022270827],"study_design_scores_gemma":[0.004212087,0.00013801514,0.48612952,0.00023308232,0.0003321362,1.9002185e-7,0.0043215957,0.41454342,0.0005708914,0.050797265,0.037232786,0.0014890252],"about_ca_topic_score_codex":0.012753294,"about_ca_topic_score_gemma":0.0032161768,"teacher_disagreement_score":0.37936184,"about_ca_system_score_codex":0.00007222519,"about_ca_system_score_gemma":0.00011023979,"threshold_uncertainty_score":0.99382085},"labels":[],"label_agreement":null},{"id":"W4388731251","doi":"10.3390/risks11110200","title":"Macroeconomic Risks and Monetary Policy in Central European Countries: Parallels in the Czech Republic, Hungary, and Poland","year":2023,"lang":"en","type":"article","venue":"Risks","topic":"Monetary Policy and Economic Impact","field":"Economics, Econometrics and Finance","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":"Wilfrid Laurier University; Balsillie School of International Affairs","funders":"","keywords":"Monetary policy; Economics; Parallels; Inflation targeting; Czech; Exchange-rate flexibility; Inflation (cosmology); Exchange rate; Monetary economics; Interest rate; Macroeconomics; Flexibility (engineering); Monetary hegemony; Exchange-rate regime","score_opus":0.10481482646560347,"score_gpt":0.280566760673553,"score_spread":0.17575193420794955,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388731251","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9806514,0.0019227013,0.0000101332935,0.004606108,0.00010849075,0.00030931094,0.00036526698,0.000038916824,0.011987679],"genre_scores_gemma":[0.9906073,0.0074864533,0.000029894434,0.0012959844,0.00020887019,0.00001493773,0.000048314552,0.000031143623,0.00027709833],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99794024,0.000099528406,0.00070967694,0.0005099466,0.000025697313,0.0007148824],"domain_scores_gemma":[0.9991078,0.00016055029,0.00020693545,0.0003905307,0.0000024669723,0.00013174118],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016481556,0.00022547133,0.00043641767,0.00063916604,0.00012549142,0.0002037649,0.00028342273,0.000103888975,0.00010412424],"category_scores_gemma":[0.000094557945,0.00022324013,0.00005824444,0.0002809809,0.00012755352,0.00037100754,0.00011999806,0.00031854317,0.00034858612],"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":[0.000041042204,0.000027533752,0.9819338,0.000031576208,0.000032845703,0.000039708528,0.0034796032,0.001258229,0.000001095625,0.008125379,0.0023345544,0.0026946717],"study_design_scores_gemma":[0.0009978748,0.000046063957,0.94741666,0.000011793506,0.000003415315,0.000025416926,0.00024573217,0.012886153,0.000001399468,0.009218457,0.028899465,0.00024755823],"about_ca_topic_score_codex":0.028038537,"about_ca_topic_score_gemma":0.0012002188,"teacher_disagreement_score":0.03451709,"about_ca_system_score_codex":0.00009578167,"about_ca_system_score_gemma":0.000019962737,"threshold_uncertainty_score":0.97843385},"labels":[],"label_agreement":null},{"id":"W4389047219","doi":"10.3390/risks11120206","title":"On Risk Management of Mortality and Longevity Capital Requirement: A Predictive Simulation Approach","year":2023,"lang":"en","type":"article","venue":"Risks","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social 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":"TD Bank Group","funders":"","keywords":"Longevity risk; Solvency; Actuarial science; Risk management; Risk analysis (engineering); Context (archaeology); Economic capital; Capital requirement; Insolvency; Capital (architecture); Economics; Life insurance; Business; Computer science; Econometrics; Finance; Pension; Microeconomics; Geography","score_opus":0.0954989682927925,"score_gpt":0.38197958889800143,"score_spread":0.28648062060520896,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389047219","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9389075,0.0001040048,0.001948352,0.000031535954,0.00022990961,0.0010607137,0.00008664687,0.00018683568,0.057444498],"genre_scores_gemma":[0.9981031,0.0012546463,0.00022753034,0.000017522094,0.00007884335,0.00006963944,0.000025662885,0.00001444854,0.0002086447],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99760574,0.0003601947,0.0003223847,0.00045185952,0.0008977348,0.00036211393],"domain_scores_gemma":[0.99906987,0.00012653762,0.0002536651,0.00037450867,0.00007907507,0.00009634618],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002072119,0.0001609668,0.00022547843,0.00019203548,0.00043019577,0.00004942768,0.0002114866,0.00009138136,0.000024699595],"category_scores_gemma":[0.00007481259,0.00016162693,0.0001071722,0.00072105543,0.00037852576,0.00020982424,0.00013413979,0.00014477977,0.000026085861],"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":[0.0001110309,0.00047357834,0.81633896,0.00033746022,0.0007386392,0.000022670243,0.013645368,0.04168006,0.0000018461452,0.10502321,0.0005603517,0.02106681],"study_design_scores_gemma":[0.00043070235,0.000080267804,0.96645695,0.00003735086,0.00017773086,3.6854686e-8,0.00436689,0.011702237,0.0000058538576,0.016103934,0.00046760304,0.00017042107],"about_ca_topic_score_codex":0.0041922987,"about_ca_topic_score_gemma":0.00045087465,"teacher_disagreement_score":0.150118,"about_ca_system_score_codex":0.000091248876,"about_ca_system_score_gemma":0.000016660577,"threshold_uncertainty_score":0.65909547},"labels":[],"label_agreement":null},{"id":"W4389432236","doi":"10.3390/risks11120213","title":"The Applications of Generalized Poisson Regression Models to Insurance Claim Data","year":2023,"lang":"en","type":"article","venue":"Risks","topic":"Probability and Risk Models","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":"Western University; University of Prince Edward Island","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Count data; Poisson regression; Negative binomial distribution; Poisson distribution; Zero-inflated model; Covariate; Generalized linear model; Econometrics; Computer science; Zero (linguistics); Mathematics; Statistics; Medicine; Population","score_opus":0.492465044895535,"score_gpt":0.4916763516318289,"score_spread":0.0007886932637061195,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389432236","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7046271,0.0015258914,0.2743595,0.0128769,0.00044520138,0.0014648633,0.00076677476,0.00021615071,0.0037176595],"genre_scores_gemma":[0.9946181,0.00062474416,0.0024644872,0.00011220896,0.000061893246,0.000083081075,0.000026860003,0.000008637023,0.0019999552],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9975814,0.00021945372,0.0005328593,0.00051542284,0.00092822645,0.00022260357],"domain_scores_gemma":[0.9955855,0.0010724268,0.00018275254,0.0028316802,0.0002293399,0.00009830162],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0039895233,0.000092124545,0.00020259974,0.00011729338,0.00040026262,0.000095280986,0.0025044635,0.00007250918,0.000015905389],"category_scores_gemma":[0.0007263311,0.000047315058,0.00006035007,0.0015147531,0.00011152628,0.0002941855,0.0008116333,0.00011552508,0.00037347185],"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":[0.00015594582,0.00009961281,0.003493156,0.000010947565,0.00002198895,0.0000014652043,0.0017717861,0.07706041,0.0016205614,0.0586318,0.11684432,0.740288],"study_design_scores_gemma":[0.00019651132,0.000018178349,0.006651602,0.000018326386,0.000005338014,8.175423e-7,0.00021189028,0.20972042,0.00071615237,0.6823893,0.09996484,0.00010662519],"about_ca_topic_score_codex":0.0003026308,"about_ca_topic_score_gemma":0.00024586136,"teacher_disagreement_score":0.7401814,"about_ca_system_score_codex":0.000014171341,"about_ca_system_score_gemma":0.000066228065,"threshold_uncertainty_score":0.48003495},"labels":[],"label_agreement":null},{"id":"W4389675132","doi":"10.3390/risks11120217","title":"Option Pricing and Portfolio Optimization under a Multi-Asset Jump-Diffusion Model with Systemic Risk","year":2023,"lang":"en","type":"article","venue":"Risks","topic":"Stochastic processes and financial applications","field":"Economics, Econometrics and Finance","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":"Wilfrid Laurier University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Portfolio; Econometrics; Jump diffusion; Asset (computer security); Computer science; Mathematics; Economics; Jump; Financial economics","score_opus":0.057412412069625995,"score_gpt":0.2592535911922164,"score_spread":0.20184117912259042,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389675132","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":[0.15688103,0.00037248145,0.8419438,0.00007160426,0.00004166569,0.0002588247,0.00009888983,0.00009607272,0.00023562067],"genre_scores_gemma":[0.98141736,0.00106753,0.017075777,0.000036710426,0.00003866666,0.00010480893,0.00005688882,0.000026761372,0.00017549706],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99912274,0.0000028157335,0.00030365633,0.000345351,0.000035818186,0.00018963344],"domain_scores_gemma":[0.9993969,0.000026215144,0.0002923552,0.0001878121,0.00003980058,0.00005687696],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021217517,0.00011677247,0.0002057171,0.00020610515,0.00021338246,0.000050433307,0.00007953365,0.00009205618,0.000005158842],"category_scores_gemma":[0.000038301703,0.000117732794,0.000028337376,0.00048118082,0.000026825443,0.00013702273,0.000046526042,0.00010470693,0.00008880302],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001671301,0.000048725193,0.0139298355,0.000050083036,0.000019616227,0.0000010707678,0.00027535117,0.7618402,0.000024566356,0.22280398,0.0000493973,0.0009404346],"study_design_scores_gemma":[0.0004914742,0.000027781769,0.029260322,0.00003257838,0.000014187161,0.0000068354993,0.000093025716,0.9379055,0.0000035512817,0.031959716,0.000045216955,0.00015984857],"about_ca_topic_score_codex":0.0004099109,"about_ca_topic_score_gemma":0.000022671651,"teacher_disagreement_score":0.824868,"about_ca_system_score_codex":0.000053850745,"about_ca_system_score_gemma":0.000020658958,"threshold_uncertainty_score":0.4801004},"labels":[],"label_agreement":null},{"id":"W4390196590","doi":"10.3390/risks12010002","title":"Board Response to Transnational Regulation on Corporate Governance: A Case Study on EU Banking Regulation","year":2023,"lang":"en","type":"article","venue":"Risks","topic":"Corporate Finance and Governance","field":"Business, Management and Accounting","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"","keywords":"Corporate governance; Distrust; Accounting; Accountability; Business; European union; Resistance (ecology); Compliance (psychology); Financial crisis; Political science; International trade; Economics; Finance; Law","score_opus":0.08540035715919506,"score_gpt":0.29228339764563005,"score_spread":0.206883040486435,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390196590","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9918734,0.0000028002305,0.0002945362,0.005335295,0.00041749811,0.000670721,0.000021809301,0.00022191921,0.0011620382],"genre_scores_gemma":[0.99694216,0.0000013677076,0.000027176799,0.0010923706,0.00052039913,0.00007221539,0.000028848473,0.000037451548,0.0012779818],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9983262,0.000041628606,0.00030719614,0.00045603627,0.00061366183,0.00025529056],"domain_scores_gemma":[0.9989149,0.00011632393,0.00045569448,0.00034193113,0.00015491743,0.000016222464],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00084334094,0.00020472966,0.00018327433,0.0002572974,0.00029342354,0.00016378828,0.00013909845,0.00006689437,0.0001245218],"category_scores_gemma":[0.00012891894,0.0002063811,0.00006448153,0.0014806548,0.000014166598,0.0005721397,0.000039935985,0.0001392782,0.000859708],"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":[0.01757704,0.0015807784,0.31353536,0.00019030453,0.00014667804,0.0068391426,0.0023957263,0.22570963,0.007989257,0.26952097,0.106228,0.048287123],"study_design_scores_gemma":[0.0011418237,0.00013082633,0.96914095,0.00006206482,0.000024714815,0.0000075056882,0.00018262854,0.006313405,0.00007035522,0.0029815088,0.019684663,0.0002595297],"about_ca_topic_score_codex":0.00037953968,"about_ca_topic_score_gemma":0.00037853207,"teacher_disagreement_score":0.6556056,"about_ca_system_score_codex":0.00011355916,"about_ca_system_score_gemma":0.000032998807,"threshold_uncertainty_score":0.9999182},"labels":[],"label_agreement":null},{"id":"W4390509555","doi":"10.3390/risks12010008","title":"Centrality-Based Equal Risk Contribution Portfolio","year":2024,"lang":"en","type":"article","venue":"Risks","topic":"Complex Systems and Time Series Analysis","field":"Economics, Econometrics and Finance","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":"Betweenness centrality; Centrality; Portfolio; Minimum spanning tree; Portfolio optimization; Network theory; Modern portfolio theory; Spanning tree; Econometrics; Computer science; Mathematics; Financial economics; Economics; Statistics; Combinatorics; Algorithm","score_opus":0.05594724112682618,"score_gpt":0.2694344534723488,"score_spread":0.2134872123455226,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390509555","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5569038,0.04805251,0.3152222,0.0027501662,0.0038642285,0.0007895882,0.005057386,0.00084158336,0.06651851],"genre_scores_gemma":[0.99797153,0.00015580846,0.00011556115,0.000061459265,0.00022823049,0.0000127426,0.00009253978,0.000017613513,0.0013444934],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99878716,0.000023076214,0.0005109434,0.000359203,0.00004062301,0.00027901545],"domain_scores_gemma":[0.9993869,0.00005427949,0.00016467861,0.00028302358,0.00002768774,0.00008341252],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00059962034,0.00012341833,0.00032420302,0.00019584739,0.0001158063,0.00018346848,0.00011073509,0.000079822756,0.0032657748],"category_scores_gemma":[0.000067513356,0.0001309361,0.000270016,0.0003886337,0.00002844843,0.00012054138,0.000023911964,0.00015253408,0.0020464642],"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":[0.000016250582,0.000055096465,0.08272434,0.000055696786,0.00026236617,0.00002408762,0.000088978566,0.0007742128,0.000009222424,0.9035662,0.007763913,0.004659647],"study_design_scores_gemma":[0.00030316116,0.00004355827,0.028201072,0.000023344553,0.000042271364,0.0000022955144,0.000022945764,0.080142885,0.00005116231,0.038368117,0.85254556,0.00025360507],"about_ca_topic_score_codex":0.0037810977,"about_ca_topic_score_gemma":0.00008464797,"teacher_disagreement_score":0.8651981,"about_ca_system_score_codex":0.00010915715,"about_ca_system_score_gemma":0.000022370506,"threshold_uncertainty_score":0.99873054},"labels":[],"label_agreement":null},{"id":"W4390535457","doi":"10.3390/risks12010010","title":"Credibility Distribution Estimation with Weighted or Grouped Observations","year":2024,"lang":"en","type":"article","venue":"Risks","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social 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":"University of Piraeus Research Centre","keywords":"Credibility; Estimator; Credibility theory; Econometrics; Estimation; Distribution (mathematics); Actuarial science; Projection (relational algebra); Mathematics; Economics; Computer science; Statistics; Algorithm","score_opus":0.098588550216306,"score_gpt":0.37496435487370405,"score_spread":0.2763758046573981,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390535457","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.89512694,0.00021711359,0.09192972,0.002401928,0.0007861371,0.0009262924,0.00017682668,0.00080923963,0.0076258024],"genre_scores_gemma":[0.996113,0.00012160668,0.0025233165,0.000050237784,0.00017980357,0.0000815044,0.0002350374,0.000011311218,0.00068418146],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.9985184,0.00016862432,0.00022031573,0.00032182352,0.00049977127,0.00027106362],"domain_scores_gemma":[0.99935955,0.00012710913,0.00005730199,0.00025357745,0.00012479455,0.00007766252],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008846224,0.000115578565,0.00012152853,0.0000671149,0.0005495628,0.00024186567,0.0001664334,0.00008150444,0.00016386679],"category_scores_gemma":[0.00010396494,0.00008796682,0.00006448213,0.0010999285,0.00024423786,0.0004899093,0.000026327216,0.00014534309,0.000067064444],"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":[0.00014274614,0.0004476915,0.38586476,0.00033980564,0.00030143274,0.00006517739,0.00895869,0.00047389776,0.000023690438,0.45454252,0.020912888,0.12792669],"study_design_scores_gemma":[0.00023491432,0.00007501237,0.86981475,0.00008851686,0.00012799827,8.030878e-7,0.000918919,0.03850015,0.00002089344,0.0139956605,0.07596941,0.00025294465],"about_ca_topic_score_codex":0.00497315,"about_ca_topic_score_gemma":0.007008559,"teacher_disagreement_score":0.48395002,"about_ca_system_score_codex":0.00019097036,"about_ca_system_score_gemma":0.0001528583,"threshold_uncertainty_score":0.7517948},"labels":[],"label_agreement":null},{"id":"W4391138556","doi":"10.3390/risks12020018","title":"Stochastic Modeling of Wind Derivatives with Application to the Alberta Energy Market","year":2024,"lang":"en","type":"article","venue":"Risks","topic":"Wind Energy Research and Development","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Wind power; Environmental science; Meteorology; Econometrics; Economics; Engineering; Geography; Electrical engineering","score_opus":0.0149265328524117,"score_gpt":0.24499841524279167,"score_spread":0.23007188239037998,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391138556","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":[0.034027226,0.0004447839,0.95485985,0.0001892242,0.00004035314,0.00007804273,0.000002941194,0.000055767046,0.010301804],"genre_scores_gemma":[0.9986881,0.000022634651,0.0007744972,0.0000152212915,0.000048776834,0.000046757956,0.000004260883,0.000016941793,0.00038278164],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995549,0.000008439713,0.000082351675,0.000095942596,0.00012622579,0.0001321405],"domain_scores_gemma":[0.9997396,0.00006310129,0.000004143064,0.00012593044,0.000019075964,0.00004817287],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006974557,0.00006668111,0.00006220085,0.000054049317,0.000025930854,0.000021115055,0.00009249273,0.00002025209,0.000025945817],"category_scores_gemma":[0.000010138678,0.000041821793,0.000013504603,0.00019898819,0.000010493786,0.00003805403,0.00002223649,0.000056809535,0.000012442441],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010769174,0.0000030360115,0.000010551829,0.000022324391,0.000042258078,5.6460925e-7,0.000356652,0.9844982,0.0002832257,0.001358676,0.00082684093,0.01258687],"study_design_scores_gemma":[0.000038881248,0.00001648048,0.00021941705,0.000049135044,0.0000043155715,0.000001818544,0.000058618905,0.99202424,0.0006788183,0.00024861618,0.00659341,0.000066235385],"about_ca_topic_score_codex":0.00045049557,"about_ca_topic_score_gemma":0.00032453667,"teacher_disagreement_score":0.9646609,"about_ca_system_score_codex":0.000024121637,"about_ca_system_score_gemma":0.000026155894,"threshold_uncertainty_score":0.17054433},"labels":[],"label_agreement":null},{"id":"W4391431695","doi":"10.3390/risks12020027","title":"LSTM-Based Coherent Mortality Forecasting for Developing Countries","year":2024,"lang":"en","type":"article","venue":"Risks","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social 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":"Concordia University","funders":"Xi’an Jiaotong-Liverpool University; Concordia University; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Life expectancy; Developing country; Benchmark (surveying); Computer science; Term (time); Econometrics; Economics; Demography; Geography; Economic growth; Population; Sociology","score_opus":0.20391683315919754,"score_gpt":0.4229996332222281,"score_spread":0.21908280006303057,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391431695","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8528412,0.0025411185,0.08955504,0.0042932616,0.004830056,0.0029540907,0.00020554446,0.0012040595,0.041575637],"genre_scores_gemma":[0.99529415,0.0002154368,0.0028993767,0.00046461218,0.00048456228,0.0002448744,0.000023938419,0.000028164264,0.00034488874],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99803716,0.00012075396,0.0003355067,0.00040525012,0.0005569971,0.0005443475],"domain_scores_gemma":[0.99912655,0.0003309149,0.00009037344,0.00021651978,0.00015746243,0.000078157966],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018837734,0.00017490078,0.0002115363,0.0001399066,0.0007674491,0.00039539812,0.00027343424,0.000103201746,0.0000758987],"category_scores_gemma":[0.00015890476,0.00017079688,0.00018808318,0.0004423315,0.0002688912,0.00021461245,0.00004190781,0.00012676322,0.000039363676],"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":[0.000044239132,0.00008733365,0.30785033,0.0014035944,0.00044364194,0.00006203969,0.0072551747,0.00045624402,0.000008762999,0.6160512,0.016618038,0.049719393],"study_design_scores_gemma":[0.0003769163,0.00004350877,0.0653453,0.0003265173,0.00016720431,4.197009e-7,0.0013455,0.00936815,0.00020948146,0.016339302,0.9059438,0.00053390965],"about_ca_topic_score_codex":0.0033086974,"about_ca_topic_score_gemma":0.007308529,"teacher_disagreement_score":0.88932574,"about_ca_system_score_codex":0.00023224615,"about_ca_system_score_gemma":0.00037539034,"threshold_uncertainty_score":0.69648945},"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":"W4391821745","doi":"10.3390/risks12020037","title":"An Objective Measure of Distributional Estimability as Applied to the Phase-Type Aging Model","year":2024,"lang":"en","type":"article","venue":"Risks","topic":"Statistical Distribution Estimation and Applications","field":"Mathematics","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":"Natural Sciences and Engineering Research Council of Canada","keywords":"Sensitivity (control systems); Context (archaeology); Computer science; Measure (data warehouse); Parametric statistics; Class (philosophy); Inference; Parametric model; Function (biology); Type (biology); Statistical inference; Mathematics; Econometrics; Statistics; Data mining; Artificial intelligence; Engineering","score_opus":0.1613125023371852,"score_gpt":0.48484756683779257,"score_spread":0.32353506450060737,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391821745","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":[0.03417393,0.000020908692,0.96058196,0.000888968,0.000043612203,0.00039639385,0.0013399398,0.00013606463,0.0024182077],"genre_scores_gemma":[0.9891071,8.050444e-7,0.010419478,0.000079511934,0.000037352005,0.00009306772,0.00020989483,0.00001060164,0.000042190164],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99908084,0.000037532154,0.00024067696,0.00023009152,0.00027730467,0.00013355736],"domain_scores_gemma":[0.99890876,0.0004362835,0.000040621988,0.0003132998,0.00020401143,0.000096996366],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000358726,0.00010093871,0.0001308737,0.000027536493,0.00013874802,0.00004427104,0.00015775237,0.000045499204,0.0001790574],"category_scores_gemma":[0.0007145054,0.00007458868,0.00004551323,0.00035661203,0.00008534181,0.000048564503,0.000026158421,0.00014471103,0.00012822969],"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":[0.000020896685,0.00020594789,0.000011609414,0.000032268737,0.000020397696,3.1620175e-7,0.00030286642,0.0026516577,0.0014699585,0.9847297,0.0029526048,0.007601792],"study_design_scores_gemma":[0.00018040577,0.000043741016,0.0008496698,0.000026496855,0.0000677315,0.000002638033,0.00010834959,0.4622042,0.0036594581,0.5321153,0.0006302143,0.00011182288],"about_ca_topic_score_codex":0.00002111568,"about_ca_topic_score_gemma":0.000005570742,"teacher_disagreement_score":0.95493317,"about_ca_system_score_codex":0.00008987478,"about_ca_system_score_gemma":0.00014871886,"threshold_uncertainty_score":0.3041638},"labels":[],"label_agreement":null},{"id":"W4391924922","doi":"10.3390/risks12020039","title":"In Memory of Peter Carr (1958–2022)","year":2024,"lang":"en","type":"article","venue":"Risks","topic":"Climate Change and Health Impacts","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Wilfrid Laurier University","funders":"","keywords":"Carr; Biology","score_opus":0.08648208131599713,"score_gpt":0.36783340696764943,"score_spread":0.28135132565165233,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391924922","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97204095,0.00060756935,0.0000043505424,0.0007954107,0.00027185117,0.00008559393,0.000012699123,0.000016983227,0.026164599],"genre_scores_gemma":[0.99841565,0.00023773385,0.000048510705,0.00041227267,0.000056277084,0.0000055888686,0.0000019400286,0.000007340088,0.0008146809],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.9994611,0.000015298832,0.00011485418,0.00012014474,0.00011664316,0.00017195792],"domain_scores_gemma":[0.99978536,0.00003347415,0.000014438006,0.000106347965,0.000001011481,0.000059384824],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00016761545,0.00004598046,0.00007684482,0.00003497572,0.000013845349,0.00000697593,0.00006296723,0.00003525151,0.004840667],"category_scores_gemma":[0.000012520192,0.00004107651,0.000023123055,0.00014884697,0.000036035977,0.000096565986,0.00005732524,0.000089832654,0.000919272],"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":[0.000087293854,0.00030540308,0.26737085,0.0011119291,0.00002047213,0.0006788882,0.03523112,0.0005761652,0.05944253,0.00036122347,0.36937788,0.26543623],"study_design_scores_gemma":[0.00055324356,0.00023049256,0.8295661,0.00032828172,0.00002345488,0.000031452146,0.0005981643,0.002793301,0.012122694,0.0036250078,0.14976215,0.00036561867],"about_ca_topic_score_codex":0.0015949073,"about_ca_topic_score_gemma":0.0006610119,"teacher_disagreement_score":0.56219524,"about_ca_system_score_codex":0.000083035,"about_ca_system_score_gemma":0.000006733838,"threshold_uncertainty_score":0.9998586},"labels":[],"label_agreement":null},{"id":"W4391925150","doi":"10.3390/risks12020040","title":"Analyzing Size of Loss Frequency Distribution Patterns: Uncovering the Impact of the COVID-19 Pandemic","year":2024,"lang":"en","type":"article","venue":"Risks","topic":"Forecasting Techniques and Applications","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":"Coronavirus disease 2019 (COVID-19); Pandemic; 2019-20 coronavirus outbreak; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Distribution (mathematics); Virology; Medicine; Mathematics; Infectious disease (medical specialty); Outbreak; Mathematical analysis; Internal medicine","score_opus":0.2078106295736651,"score_gpt":0.4723584579279146,"score_spread":0.2645478283542495,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391925150","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9110487,0.00022612,0.08728657,0.00054242346,0.00005897414,0.00015682018,0.0004697563,0.0000517113,0.00015891122],"genre_scores_gemma":[0.9996979,0.00005274011,0.00013367004,0.000021822889,0.0000321909,0.00001245449,0.0000042486995,0.0000050764243,0.000039855946],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9988365,0.00009911696,0.0003968893,0.00019047165,0.00035980967,0.000117208714],"domain_scores_gemma":[0.9973186,0.001778013,0.00020821298,0.0005684809,0.00008579662,0.000040852887],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017329581,0.00007605956,0.00013226196,0.00003277693,0.00013005844,0.00007353419,0.00065245247,0.000046526493,0.00012322038],"category_scores_gemma":[0.0025080275,0.00003323346,0.00024202195,0.00084645534,0.000139414,0.000078182995,0.00013344736,0.0001513361,0.0000067360397],"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":[0.000004940472,0.000018177609,0.9744227,0.000019470986,0.000027759486,0.0000010190619,0.0003455202,0.0026357814,0.0024415217,0.0036153777,0.003529351,0.0129383765],"study_design_scores_gemma":[0.00015795688,0.00010222191,0.5824899,0.00019847808,0.00006666427,0.00005162415,0.00028456113,0.015415217,0.0028625098,0.39412433,0.0040442464,0.00020229118],"about_ca_topic_score_codex":0.0041190926,"about_ca_topic_score_gemma":0.000094099814,"teacher_disagreement_score":0.3919328,"about_ca_system_score_codex":0.00012542233,"about_ca_system_score_gemma":0.00014944824,"threshold_uncertainty_score":0.6226863},"labels":[],"label_agreement":null},{"id":"W4392916835","doi":"10.3390/risks12030055","title":"Capital Structure Models and Contingent Convertible Securities","year":2024,"lang":"en","type":"article","venue":"Risks","topic":"Credit Risk and Financial Regulations","field":"Economics, Econometrics and Finance","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":"Wilfrid Laurier University","funders":"Natural Sciences and Engineering Research Council of Canada; Wilfrid Laurier University","keywords":"Convertible; Convertible bond; Convertible arbitrage; Business; Financial system; Monetary economics; Financial economics; Economics; Finance; Capital asset pricing model; Bond; Engineering; Structural engineering","score_opus":0.05531117096701912,"score_gpt":0.2508579413275857,"score_spread":0.19554677036056659,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392916835","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.94144815,0.045413706,0.0041222787,0.00024702778,0.0007182769,0.00010454894,0.00057191256,0.000073629046,0.007300452],"genre_scores_gemma":[0.9974015,0.0012034308,0.00018038093,0.000018005692,0.00021767832,0.0000067025603,0.000019586463,0.000015373813,0.00093735784],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9993662,0.0000028599322,0.00021995162,0.00023466656,0.000021881724,0.00015441903],"domain_scores_gemma":[0.9997632,0.000025008596,0.000039112972,0.000112141985,0.000012285838,0.00004823156],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000079653815,0.00008959419,0.00016774973,0.00011104204,0.00008452258,0.00013962918,0.000054507178,0.00007798368,0.0002184608],"category_scores_gemma":[0.000015838677,0.000097319986,0.000050576033,0.00009322103,0.00005579905,0.00022002083,0.000032352367,0.00011180183,0.00006789507],"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":[0.0000027415895,0.00000674167,0.0102856895,0.00003997476,0.000023155068,0.000004788149,0.0019288176,0.00015518913,0.000015946665,0.9814586,0.0013207457,0.0047576088],"study_design_scores_gemma":[0.00028211906,0.00004522389,0.11561967,0.00004631438,0.000014288355,0.000013966936,0.00023212373,0.08910354,0.000083884144,0.6631036,0.1311198,0.00033548378],"about_ca_topic_score_codex":0.00047899704,"about_ca_topic_score_gemma":0.000081235885,"teacher_disagreement_score":0.31835502,"about_ca_system_score_codex":0.000027509008,"about_ca_system_score_gemma":0.0000139158055,"threshold_uncertainty_score":0.39685938},"labels":[],"label_agreement":null},{"id":"W4393164483","doi":"10.3390/risks12040058","title":"The Impact of Village Savings and Loan Associations as a Financial and Climate Resilience Strategy for Mitigating Food Insecurity in Northern Ghana","year":2024,"lang":"en","type":"article","venue":"Risks","topic":"Microfinance and Financial Inclusion","field":"Economics, Econometrics and Finance","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":"Western University","funders":"","keywords":"Food insecurity; Resilience (materials science); Loan; Food security; Business; Psychological resilience; Food prices; Economic growth; Finance; Natural resource economics; Geography; Economics; Psychology; Agriculture","score_opus":0.03743113980110998,"score_gpt":0.30080928996779865,"score_spread":0.2633781501666887,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393164483","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9877403,0.010348075,0.0001272584,0.00011475187,0.00006867732,0.0002518459,0.00057649583,0.000011400973,0.00076121755],"genre_scores_gemma":[0.99765825,0.0021671746,0.00005355795,0.000015765174,0.00004688066,0.000020361944,0.000007813865,0.000011971376,0.000018197305],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9990183,0.000011958666,0.00042263942,0.00026307255,0.000024867455,0.00025915433],"domain_scores_gemma":[0.9994247,0.0002047491,0.00019706399,0.00011354599,0.000027910835,0.00003205345],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006944083,0.0001065658,0.00024050301,0.00009621916,0.00026105717,0.00009947728,0.00009190144,0.00010348183,0.00000312735],"category_scores_gemma":[0.00041846622,0.00009502374,0.00008210878,0.00023019146,0.00007761624,0.00016611115,0.00007771797,0.0001385073,0.0000052245873],"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":[0.000053797092,0.0000609311,0.75763816,0.00017098094,0.000029175299,0.0000045744573,0.004035921,0.000070137445,0.00052872987,0.2133656,0.00016495479,0.023877075],"study_design_scores_gemma":[0.0003813952,0.00043544962,0.88166684,0.00012483467,0.000005957109,0.0000028363165,0.00017838272,0.002766606,0.00022278514,0.113469295,0.00055069267,0.00019492614],"about_ca_topic_score_codex":0.0029297976,"about_ca_topic_score_gemma":0.011346449,"teacher_disagreement_score":0.124028705,"about_ca_system_score_codex":0.00009082438,"about_ca_system_score_gemma":0.000082163584,"threshold_uncertainty_score":0.6331585},"labels":[],"label_agreement":null},{"id":"W4394962853","doi":"10.3390/risks12040070","title":"Determining Safe Withdrawal Rates for Post-Retirement via a Ruin-Theory Approach","year":2024,"lang":"en","type":"article","venue":"Risks","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social 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":"Western University","funders":"","keywords":"Economics; Ruin theory; Actuarial science; Econometrics; Risk model","score_opus":0.04918025630331083,"score_gpt":0.39126112177867334,"score_spread":0.3420808654753625,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394962853","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6880495,0.0039118347,0.09500686,0.0016703514,0.004408183,0.0049318518,0.00014694539,0.0012187152,0.20065574],"genre_scores_gemma":[0.99270284,0.00011500284,0.0040036505,0.00029281527,0.00054688466,0.00031881797,0.000026355523,0.000040607643,0.0019530368],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9977471,0.00027321198,0.0003276181,0.00052331743,0.0005258869,0.00060284557],"domain_scores_gemma":[0.999148,0.00023803262,0.000085325366,0.00028627034,0.00011461234,0.00012777564],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023454796,0.00021728489,0.00023764219,0.00020316432,0.00066707644,0.00033346348,0.0003841799,0.00011791447,0.0001266916],"category_scores_gemma":[0.000104398365,0.00020188437,0.00023995579,0.0004214689,0.00033233457,0.00025127843,0.00007986225,0.0001784328,0.00008424515],"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":[0.00023735662,0.0004621198,0.053472925,0.0009375761,0.0008636917,0.00008535405,0.04914995,0.00020069804,0.00046122252,0.41413435,0.008871348,0.47112343],"study_design_scores_gemma":[0.0027782454,0.0011223021,0.13968968,0.0006894051,0.0013112149,0.000012110174,0.046396133,0.023901729,0.0012959928,0.13490252,0.64449114,0.0034095005],"about_ca_topic_score_codex":0.00091357226,"about_ca_topic_score_gemma":0.000586827,"teacher_disagreement_score":0.6356198,"about_ca_system_score_codex":0.00012948184,"about_ca_system_score_gemma":0.00012345047,"threshold_uncertainty_score":0.82326055},"labels":[],"label_agreement":null},{"id":"W4398202932","doi":"10.3390/risks12060085","title":"Use of Prediction Bias in Active Learning and Its Application to Large Variable Annuity Portfolios","year":2024,"lang":"en","type":"article","venue":"Risks","topic":"Machine Learning and Algorithms","field":"Computer Science","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":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Metric (unit); Machine learning; Ambiguity; Portfolio; Econometrics; Sampling (signal processing); Artificial intelligence; Data mining; Mathematics; Engineering; Finance","score_opus":0.03808635608426609,"score_gpt":0.31956280269576604,"score_spread":0.28147644661149995,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4398202932","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":[0.3741718,0.00018331718,0.6245048,0.00016157138,0.00013086929,0.00019881086,0.000018807605,0.00017803183,0.00045197256],"genre_scores_gemma":[0.99594516,0.000031995438,0.0036149076,0.000022382803,0.00003911997,0.000016230873,0.0000054824113,0.0000050968015,0.0003196352],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99934417,0.00006592018,0.000116702366,0.00024187913,0.000111960646,0.00011937842],"domain_scores_gemma":[0.99970263,0.00008477958,0.000035555877,0.00010090382,0.00003311841,0.000043019387],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036230544,0.000057835576,0.00008262265,0.00013955527,0.000043078424,0.00006428454,0.00008726386,0.000040601357,0.000005773315],"category_scores_gemma":[0.00011790209,0.00005478752,0.000013058595,0.000498339,0.0000041155013,0.00032044094,0.0001015125,0.00020996884,0.0000116098445],"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":[0.000028914805,0.0001968421,0.11080572,0.00020621621,0.00004966717,0.000020992125,0.0070917606,0.033443745,0.007924963,0.059448015,0.000622603,0.78016055],"study_design_scores_gemma":[0.00007316051,0.00005694019,0.0573687,0.00005075142,0.000003556043,0.0000038225803,0.00001875849,0.919193,0.0007733144,0.000331415,0.02206798,0.000058564463],"about_ca_topic_score_codex":0.00032840855,"about_ca_topic_score_gemma":0.000008425814,"teacher_disagreement_score":0.8857493,"about_ca_system_score_codex":0.000020443944,"about_ca_system_score_gemma":0.00002591304,"threshold_uncertainty_score":0.22341703},"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":"W4399728247","doi":"10.3390/risks12060097","title":"Dependence Modelling for Heavy-Tailed Multi-Peril Insurance Losses","year":2024,"lang":"en","type":"article","venue":"Risks","topic":"Hydrology and Drought Analysis","field":"Environmental Science","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":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Copula (linguistics); Econometrics; Pairwise comparison; Component (thermodynamics); Statistics; Aggregate (composite); Actuarial science; Computer science; Environmental science; Mathematics; Economics; Materials science","score_opus":0.05400544254076874,"score_gpt":0.3101726964354463,"score_spread":0.2561672538946776,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399728247","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6076733,0.0009925277,0.38892928,0.00021946598,0.00015775459,0.00013788858,0.0000189686,0.00010129788,0.0017695186],"genre_scores_gemma":[0.9908925,0.00013371227,0.007460987,0.00012147516,0.00005226093,0.000043346325,0.000005271419,0.000012715142,0.0012777238],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991277,0.000025765536,0.00014333076,0.0003431152,0.00012190897,0.00023822028],"domain_scores_gemma":[0.9996439,0.00009577659,0.000021905165,0.00017532127,0.000004648418,0.000058417332],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00021073659,0.00010558899,0.0001276112,0.00002696095,0.00015492272,0.000038285198,0.00016572766,0.00008822876,0.00088396814],"category_scores_gemma":[0.000022518694,0.00009007383,0.00010058111,0.00019789806,0.00010361276,0.00021415594,0.00004934128,0.000122567,0.0009220151],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007621538,0.00009972034,0.1329162,0.000048257174,0.00007985868,0.00004617681,0.00088970846,0.84703183,0.0014027514,0.0003595374,0.0012480258,0.015801739],"study_design_scores_gemma":[0.00015691132,0.000027120139,0.002542034,0.000015724976,0.00003551406,0.000005460014,0.000020175468,0.98364395,0.001786968,0.0019235695,0.0096866,0.00015597291],"about_ca_topic_score_codex":0.00047049287,"about_ca_topic_score_gemma":0.0003573997,"teacher_disagreement_score":0.3832192,"about_ca_system_score_codex":0.0000458295,"about_ca_system_score_gemma":0.000009530031,"threshold_uncertainty_score":0.9998559},"labels":[],"label_agreement":null},{"id":"W4400195546","doi":"10.3390/risks12070108","title":"Unified Spatial Clustering of Territory Risk to Uncover Impact of COVID-19 Pandemic on Major Coverages of Auto Insurance","year":2024,"lang":"en","type":"article","venue":"Risks","topic":"Data-Driven Disease Surveillance","field":"Medicine","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":"Toronto Metropolitan University","funders":"","keywords":"Cluster analysis; Computer science; Data mining; Data science; Credibility; Machine learning","score_opus":0.04616120389319194,"score_gpt":0.37652674146910914,"score_spread":0.3303655375759172,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400195546","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9899025,0.0004995818,0.0030776055,0.000055922395,0.0002831469,0.00041665218,0.0053155953,0.00009400901,0.0003550061],"genre_scores_gemma":[0.99916303,0.00020526513,0.00013865065,0.00010686749,0.00011723972,0.000009729943,0.00008693158,0.000030970714,0.00014133405],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9985303,0.00012659913,0.00046217113,0.00030426736,0.0003792482,0.00019737649],"domain_scores_gemma":[0.9986112,0.00033376258,0.00019745661,0.0005423412,0.00007205186,0.00024315124],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037712793,0.00018022732,0.0005477501,0.00021739642,0.000020434578,0.000007629459,0.00015658293,0.000089641435,0.00015193476],"category_scores_gemma":[0.00061438483,0.00014809742,0.00021861134,0.00024032533,0.00008925329,0.000064914944,0.00008808008,0.00020640399,0.000022903498],"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":[0.0020370537,0.00014939258,0.95896024,0.0012765296,0.00033810255,0.000053367563,0.00053069403,0.010143385,0.014977879,0.000008065395,0.0028698477,0.008655469],"study_design_scores_gemma":[0.001516612,0.0007960437,0.99034655,0.00062510034,0.00010408978,0.000009818731,0.000026376776,0.0020185038,0.0017177882,0.000046688758,0.0026462227,0.00014622736],"about_ca_topic_score_codex":0.01204385,"about_ca_topic_score_gemma":0.00081341946,"teacher_disagreement_score":0.031386312,"about_ca_system_score_codex":0.00027899758,"about_ca_system_score_gemma":0.0005399036,"threshold_uncertainty_score":0.994535},"labels":[],"label_agreement":null},{"id":"W4403102384","doi":"10.3390/risks12100157","title":"Advantages of Accounting for Stochasticity in the Premium Process","year":2024,"lang":"en","type":"article","venue":"Risks","topic":"Accounting Theory and Financial Reporting","field":"Business, Management and Accounting","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":"Western University","funders":"Western University","keywords":"Process (computing); Accounting; Econometrics; Economics; Computer science","score_opus":0.03752684915440863,"score_gpt":0.32939728774709776,"score_spread":0.29187043859268913,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403102384","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9520979,0.00032505803,0.035314437,0.00028911463,0.00045005078,0.0005504105,0.0000046921923,0.00011379328,0.0108545255],"genre_scores_gemma":[0.9984784,0.00000201048,0.00009506887,0.00024423777,0.0010578554,0.000066621215,0.000006655721,0.000020371728,0.000028737864],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99899316,0.0000060757957,0.0003637543,0.00021414201,0.00018717791,0.00023567645],"domain_scores_gemma":[0.999269,0.00025091472,0.00023210575,0.00013766726,0.00010762728,0.0000026523983],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018229176,0.00011610399,0.00016339784,0.0001488753,0.00012182195,0.00019664463,0.00025784195,0.00005723982,0.000015366533],"category_scores_gemma":[0.00087289436,0.00008269208,0.000079505815,0.0004786268,0.000044437984,0.0007630085,0.000053692096,0.00016469143,0.000013360802],"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":[0.00024353432,0.00026839468,0.18502606,0.008003033,0.000060950613,0.00006661895,0.0027856906,0.0036083201,0.0018230823,0.6906851,0.0016280551,0.10580117],"study_design_scores_gemma":[0.001662562,0.00009162963,0.2070719,0.003623344,0.00062217383,0.000035755205,0.009426918,0.22490992,0.0025463712,0.38878027,0.15950605,0.0017231149],"about_ca_topic_score_codex":0.00014784907,"about_ca_topic_score_gemma":0.00003219353,"teacher_disagreement_score":0.30190483,"about_ca_system_score_codex":0.000010761697,"about_ca_system_score_gemma":0.000029688515,"threshold_uncertainty_score":0.3372085},"labels":[],"label_agreement":null},{"id":"W4403946708","doi":"10.3390/risks12110173","title":"Spread Option Pricing Under Finite Liquidity Framework","year":2024,"lang":"en","type":"article","venue":"Risks","topic":"Stochastic processes and financial applications","field":"Economics, Econometrics and Finance","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":"Systems, Applications & Products in Data Processing (Canada); McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Market liquidity; Business; Valuation of options; Economics; Financial economics; Finance","score_opus":0.08289140657884511,"score_gpt":0.30102566087796945,"score_spread":0.21813425429912434,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403946708","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":[0.012792283,0.0050083995,0.9750952,0.00092128484,0.00054949685,0.00012436837,0.00008096955,0.0001328248,0.0052951653],"genre_scores_gemma":[0.9931196,0.00027409778,0.005675901,0.00021242893,0.00039475685,0.00005864595,0.0000124240705,0.000020407519,0.0002317179],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99919635,0.0000015122706,0.00027740007,0.0003181818,0.000025036828,0.0001815018],"domain_scores_gemma":[0.99950904,0.00014268815,0.00007249239,0.00021235312,0.000017008388,0.000046432036],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00017641959,0.000094917246,0.0001569699,0.00010816833,0.00010483239,0.00010364858,0.000124028,0.00012339163,0.00012268471],"category_scores_gemma":[0.00012859554,0.000106750485,0.000071735056,0.0003548241,0.000028314196,0.00012440517,0.000040012845,0.0002070798,0.0021093972],"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":[0.0000025298143,0.000019750176,0.00017259404,0.000029023999,0.0000119496035,0.0000010538245,0.00015494571,0.00050630374,0.000007262285,0.995406,0.00014097813,0.003547642],"study_design_scores_gemma":[0.000044475444,0.000025078774,0.00530446,0.00005562095,0.000006558192,0.0000016850954,0.00001986401,0.01860947,0.00003630549,0.9439335,0.031815115,0.00014787551],"about_ca_topic_score_codex":0.00020659632,"about_ca_topic_score_gemma":0.0000050482668,"teacher_disagreement_score":0.9803273,"about_ca_system_score_codex":0.000064424705,"about_ca_system_score_gemma":0.00002090105,"threshold_uncertainty_score":0.9986676},"labels":[],"label_agreement":null},{"id":"W4404584092","doi":"10.3390/risks12120183","title":"Dynamic Programming for Designing and Valuing Two-Dimensional Financial Derivatives","year":2024,"lang":"en","type":"article","venue":"Risks","topic":"Stochastic processes and financial applications","field":"Economics, Econometrics and Finance","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":"Canadian Statistical Sciences Institute","keywords":"Dynamic programming; Computer science; Economics; Algorithm","score_opus":0.050192040669852826,"score_gpt":0.29811647583451556,"score_spread":0.24792443516466273,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404584092","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":[0.046327364,0.008091018,0.94437516,0.00029171183,0.00020046587,0.00033581597,0.00007928086,0.000076862714,0.00022234423],"genre_scores_gemma":[0.91570586,0.000022099719,0.08372816,0.00006358121,0.000106779386,0.000265302,0.000015024211,0.000021530232,0.00007164716],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9991792,0.0000013906795,0.00025229872,0.00034045454,0.000020163827,0.00020648338],"domain_scores_gemma":[0.99964863,0.00013540582,0.00006824712,0.000082565086,0.000023162811,0.00004196909],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023881569,0.00010141915,0.00017135366,0.000104793675,0.00019383404,0.000102440295,0.00006946374,0.000052974363,0.000008258479],"category_scores_gemma":[0.00015880188,0.000113573624,0.000055069384,0.0001915783,0.000046771616,0.00012003751,0.000035514215,0.00009128582,0.000034571585],"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":[0.00000891408,0.000020814736,0.0004539218,0.00009434012,0.000017601265,0.000001447773,0.0004415436,0.000116544456,0.00013074811,0.9344806,0.00006042221,0.06417311],"study_design_scores_gemma":[0.00033441136,0.00007707853,0.006352499,0.000088928966,0.0000146479,0.000008549592,0.000033106975,0.09785886,0.000088766355,0.88180274,0.013069394,0.0002710249],"about_ca_topic_score_codex":0.000048659535,"about_ca_topic_score_gemma":0.000008295657,"teacher_disagreement_score":0.8693785,"about_ca_system_score_codex":0.00003619978,"about_ca_system_score_gemma":0.000035408466,"threshold_uncertainty_score":0.46313977},"labels":[],"label_agreement":null},{"id":"W4404931680","doi":"10.3390/risks12120192","title":"Nonparametric Testing for Information Asymmetry in the Mortgage Servicing Market","year":2024,"lang":"en","type":"article","venue":"Risks","topic":"Housing Market and Economics","field":"Economics, Econometrics and Finance","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":"Information asymmetry; Nonparametric statistics; Asymmetry; Mortgage underwriting; Business; Commercial mortgage-backed security; Econometrics; Economics; Actuarial science; Mortgage insurance; Finance; Physics","score_opus":0.07103160569246285,"score_gpt":0.26894788983913576,"score_spread":0.1979162841466729,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404931680","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":[0.41321427,0.0010534577,0.0110878395,0.00064287847,0.0011011832,0.0005850176,0.00011780496,0.00011767749,0.5720799],"genre_scores_gemma":[0.9969751,0.000085408996,0.0022031711,0.00041628187,0.00014130746,0.00004745964,0.000017628114,0.000016551403,0.00009707934],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99904156,0.000013053676,0.0004989108,0.00017761538,0.000021692313,0.00024718378],"domain_scores_gemma":[0.99900144,0.0006406025,0.00011811998,0.0002009191,0.000014306891,0.000024627341],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020895612,0.000103265775,0.00018563888,0.0005292782,0.00009027344,0.0003704617,0.00020576808,0.000081714796,0.000052304156],"category_scores_gemma":[0.00051075,0.00009868546,0.000073701296,0.0008420185,0.000013086341,0.00058074755,0.00003137814,0.00018509445,0.00026852923],"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":[0.000054631626,0.00009022395,0.37750456,0.0010724685,0.000088479064,0.000012205162,0.0044632726,0.0027482426,0.0000038712355,0.111410856,0.020266576,0.4822846],"study_design_scores_gemma":[0.00043967486,0.00006893319,0.12557858,0.00007940176,0.000012207649,0.00001113859,0.00046098756,0.6571008,0.000008703921,0.032484613,0.18340193,0.00035300647],"about_ca_topic_score_codex":0.00045664638,"about_ca_topic_score_gemma":0.000038533566,"teacher_disagreement_score":0.65435255,"about_ca_system_score_codex":0.00010569228,"about_ca_system_score_gemma":0.000019548683,"threshold_uncertainty_score":0.4024276},"labels":[],"label_agreement":null},{"id":"W4405615149","doi":"10.3390/risks12120206","title":"riskAIchain: AI-Driven IT Infrastructure—Blockchain-Backed Approach for Enhanced Risk Management","year":2024,"lang":"en","type":"article","venue":"Risks","topic":"Blockchain Technology Applications and Security","field":"Computer Science","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":"Sault College","funders":"Emporia State University","keywords":"Blockchain; Risk management; Computer science; Business; Computer security","score_opus":0.015316512173306644,"score_gpt":0.2797889992973312,"score_spread":0.26447248712402455,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405615149","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":[0.009058455,0.0005919925,0.9823407,0.0018480739,0.00031432012,0.0010747123,0.000043974716,0.0010679103,0.0036598416],"genre_scores_gemma":[0.76066,0.00018591498,0.23750098,0.0003678523,0.000109311324,0.000740845,0.000015832802,0.000023858638,0.00039539533],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979515,0.000057168676,0.00033818852,0.0009365688,0.00023216005,0.00048440372],"domain_scores_gemma":[0.9984549,0.00011440688,0.000101379286,0.0011485532,0.000081102684,0.0000996096],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00041555913,0.0002700215,0.0002573873,0.00028164856,0.00034392742,0.00019226914,0.0013519218,0.0002632852,0.00001779852],"category_scores_gemma":[0.000022716742,0.00024679556,0.00017130516,0.0008150603,0.000119845536,0.000086960325,0.00042083208,0.0005086965,0.00005909296],"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":[0.000011795452,0.000118550706,0.00010289724,0.0001886384,0.00024664775,0.000011638444,0.0012701218,0.005617124,0.00021474912,0.6471881,0.01999327,0.32503644],"study_design_scores_gemma":[0.00043846972,0.0000778581,0.00035213114,0.000026421723,0.000063619824,0.000012458775,0.00014217327,0.8019007,0.0027402493,0.12940864,0.06446141,0.00037589468],"about_ca_topic_score_codex":0.000024415873,"about_ca_topic_score_gemma":0.000006874527,"teacher_disagreement_score":0.79628354,"about_ca_system_score_codex":0.00008835081,"about_ca_system_score_gemma":0.000042938416,"threshold_uncertainty_score":0.99999845},"labels":[],"label_agreement":null},{"id":"W4407870583","doi":"10.3390/risks13030040","title":"Deep Reinforcement Learning in Non-Markov Market-Making","year":2025,"lang":"en","type":"article","venue":"Risks","topic":"Reinforcement Learning in Robotics","field":"Computer Science","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 Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Reinforcement learning; Artificial intelligence; Markov chain; Reinforcement; Computer science; Economics; Machine learning; Psychology; Social psychology","score_opus":0.016540782736329213,"score_gpt":0.2945006458921195,"score_spread":0.2779598631557903,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407870583","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":[0.0008516671,0.00006819561,0.84061617,0.00028732867,0.00039004264,0.0001909487,1.48897685e-8,0.00014696184,0.15744866],"genre_scores_gemma":[0.9724175,0.00004521057,0.017787844,0.0004578778,0.000029230137,0.000023263356,0.0000015275001,0.0000101504165,0.009227424],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983227,0.00010114019,0.00041556536,0.0003745089,0.0003179505,0.00046811096],"domain_scores_gemma":[0.9989645,0.00024195759,0.00014283373,0.000552358,0.000052587213,0.000045770426],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00069929584,0.00017725627,0.0002072018,0.00036207348,0.00016812333,0.0002072263,0.00089182757,0.00009555964,0.00008601549],"category_scores_gemma":[0.00030019056,0.00018509325,0.00006795049,0.0007939763,0.000031189105,0.0003393969,0.00060434756,0.00049911905,0.000075503856],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007835582,0.000008691246,0.0139854895,0.000030361989,0.000015919511,0.000017571492,0.00038239887,0.9497623,0.000024498044,0.0040308055,0.0006361229,0.031098012],"study_design_scores_gemma":[0.00037388224,0.000046202018,0.020388126,0.00017805559,0.000005418413,0.0000015207892,0.00006094736,0.9712558,0.00007499608,0.00020329768,0.0072329137,0.00017888339],"about_ca_topic_score_codex":0.000060209375,"about_ca_topic_score_gemma":0.000012814198,"teacher_disagreement_score":0.9715658,"about_ca_system_score_codex":0.00019348499,"about_ca_system_score_gemma":0.00008426786,"threshold_uncertainty_score":0.7547884},"labels":[],"label_agreement":null},{"id":"W4407971656","doi":"10.3390/risks13030044","title":"Copula-Based Risk Aggregation and the Significance of Reinsurance","year":2025,"lang":"en","type":"article","venue":"Risks","topic":"Insurance and Financial Risk Management","field":"Economics, Econometrics and Finance","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":"Copula (linguistics); Reinsurance; Econometrics; Actuarial science; Economics","score_opus":0.023088418811607014,"score_gpt":0.23825453524307835,"score_spread":0.21516611643147135,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407971656","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.85075,0.014702835,0.07584755,0.0018296557,0.00071256404,0.001111135,0.0002124389,0.000045752327,0.054788016],"genre_scores_gemma":[0.99706703,0.0017139631,0.00036480045,0.0002634439,0.000025496664,0.000056640372,0.0000030390372,0.0000062680215,0.000499313],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.9991778,0.000029482764,0.00040974494,0.00022102406,0.00002996538,0.00013195742],"domain_scores_gemma":[0.9991622,0.000116445364,0.0003761217,0.00029984568,0.000031808682,0.000013589577],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00076980866,0.0000882765,0.00028127767,0.00010482231,0.00013307662,0.000024497525,0.0001514366,0.00005163789,0.0000161889],"category_scores_gemma":[0.00021956363,0.00007686242,0.00007372515,0.00029730384,0.00018674793,0.00006617411,0.000026489466,0.000111896385,0.000029367116],"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":[0.00016201516,0.000034605462,0.26981345,0.00007147648,0.000033796743,6.14132e-7,0.00015139059,0.00089748175,0.0000028443733,0.6912555,0.00057931466,0.036997512],"study_design_scores_gemma":[0.0032650153,0.0000417586,0.7408846,0.00007528528,0.000026117046,1.2160673e-7,0.000050992825,0.015590997,0.00072324404,0.19053368,0.048611265,0.00019694025],"about_ca_topic_score_codex":0.0017433566,"about_ca_topic_score_gemma":0.00006239883,"teacher_disagreement_score":0.5007218,"about_ca_system_score_codex":0.000025944157,"about_ca_system_score_gemma":0.000014969152,"threshold_uncertainty_score":0.31343585},"labels":[],"label_agreement":null},{"id":"W4408537810","doi":"10.3390/risks13030053","title":"Modeling Financial Bubbles with Optional Semimartingales in Nonstandard Probability Spaces","year":2025,"lang":"en","type":"article","venue":"Risks","topic":"Complex Systems and Time Series Analysis","field":"Economics, Econometrics and Finance","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":"Econometrics; Business; Mathematics; Economics","score_opus":0.05059254018917566,"score_gpt":0.25330712210324335,"score_spread":0.2027145819140677,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408537810","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9519934,0.0014642508,0.026255641,0.0006977371,0.00008272941,0.0001787941,0.000090087866,0.000025051431,0.019212289],"genre_scores_gemma":[0.99695736,0.000034782275,0.0022749717,0.000034387023,0.000039848746,0.000028309436,0.000007806622,0.000006261885,0.0006162787],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989636,0.000013123251,0.00045797598,0.00033042303,0.000036861624,0.00019801791],"domain_scores_gemma":[0.99958867,0.000027327545,0.000105131185,0.00020741443,0.00004324256,0.000028202663],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00049428205,0.00011041511,0.0003533537,0.00020734419,0.00011007368,0.00007774154,0.000110657864,0.00005873435,0.00018455659],"category_scores_gemma":[0.00009679253,0.00010900614,0.00007959171,0.00040820756,0.00004409999,0.00011794814,0.000048198763,0.000113738446,0.000028277369],"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":[0.000079851845,0.000083606974,0.5185991,0.0000685052,0.00005084194,0.0000030521305,0.0002013746,0.11130009,0.0000045945208,0.36803216,0.00025611656,0.0013207077],"study_design_scores_gemma":[0.0015968787,0.00010941953,0.12198357,0.00033401957,0.000028857105,0.0000034670406,0.0003995343,0.65484035,0.00003171012,0.18872242,0.03128085,0.00066893816],"about_ca_topic_score_codex":0.0033760848,"about_ca_topic_score_gemma":0.0037131482,"teacher_disagreement_score":0.54354024,"about_ca_system_score_codex":0.00011713744,"about_ca_system_score_gemma":0.000067628105,"threshold_uncertainty_score":0.5103653},"labels":[],"label_agreement":null},{"id":"W4408830389","doi":"10.3390/risks13040061","title":"An Optional Semimartingales Approach to Risk Theory","year":2025,"lang":"en","type":"article","venue":"Risks","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social 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":"Mathematics; Econometrics; Computer science; Applied mathematics","score_opus":0.033487629883784294,"score_gpt":0.3801935313910686,"score_spread":0.3467059015072843,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408830389","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":[0.4777062,0.00021907542,0.019317698,0.00032689367,0.00044999333,0.0005282251,0.000030020348,0.00020016429,0.5012217],"genre_scores_gemma":[0.99121535,0.00013010218,0.0050465874,0.00056121446,0.00025084842,0.00008377,0.000010964646,0.0000098915425,0.0026912668],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.99787456,0.0007066353,0.00022252485,0.00038180288,0.00043491294,0.00037958453],"domain_scores_gemma":[0.9991961,0.0001270743,0.00008552357,0.00036665378,0.00009618055,0.00012844731],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003129133,0.00012561714,0.00015346699,0.00020592433,0.00086415,0.00014503681,0.00048362615,0.000091160575,0.00009153932],"category_scores_gemma":[0.00023405973,0.00012471149,0.00010569696,0.0006538043,0.00024801187,0.0001761803,0.000075091666,0.0001858412,0.00009199101],"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":[0.00002981997,0.00026963663,0.306216,0.000016871289,0.00007270068,0.0000012918185,0.004447731,0.0010902572,0.000016772075,0.64991385,0.00657766,0.031347413],"study_design_scores_gemma":[0.0002380562,0.000029403891,0.8204814,0.000022148624,0.000078147226,1.308129e-7,0.0074856207,0.00040085247,0.00003891273,0.0735502,0.097392,0.0002831367],"about_ca_topic_score_codex":0.0027107233,"about_ca_topic_score_gemma":0.0007762179,"teacher_disagreement_score":0.5763636,"about_ca_system_score_codex":0.00007907607,"about_ca_system_score_gemma":0.00008688355,"threshold_uncertainty_score":0.66464305},"labels":[],"label_agreement":null},{"id":"W4410344298","doi":"10.3390/risks13050093","title":"How Do Asymmetric Oil Prices and Economic Policy Uncertainty Shapes Stock Returns Across Oil Importing and Exporting Countries? Evidence from Instrumental Variable Quantile Regression Approach","year":2025,"lang":"en","type":"article","venue":"Risks","topic":"Market Dynamics and Volatility","field":"Economics, Econometrics and Finance","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":"Quantile regression; Instrumental variable; Economics; Stock (firearms); Econometrics; Quantile; Variable (mathematics); Oil price; Financial economics; Monetary economics; Mathematics","score_opus":0.04216917742197183,"score_gpt":0.30213007523348084,"score_spread":0.259960897811509,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410344298","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9825971,0.008398928,0.000767623,0.00042391213,0.00021228408,0.000111528105,0.0005909729,0.000044225642,0.006853393],"genre_scores_gemma":[0.9920201,0.0045029917,0.0023736593,0.000086688604,0.00013304055,0.000032203352,0.000037379174,0.000021548756,0.00079244084],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975847,0.00003790649,0.00089394394,0.0009208001,0.00006392062,0.0004987081],"domain_scores_gemma":[0.9979682,0.00038625867,0.001110312,0.0003714497,0.000034841352,0.00012891857],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0016841261,0.0002944713,0.00064418913,0.00028934632,0.0004607988,0.00083414285,0.00026819608,0.0002292046,0.000040416202],"category_scores_gemma":[0.00086209085,0.00029943578,0.00007835794,0.0004139381,0.00013352417,0.00072883785,0.0004209544,0.00030698077,0.0000022781524],"study_design_candidate":"observational","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":[0.00006937689,0.000034670647,0.95322204,0.00025565719,0.00006801936,0.0000012765356,0.00060625706,0.000044338773,0.000019843046,0.010500714,0.00006378064,0.035114046],"study_design_scores_gemma":[0.0012428564,0.00006481544,0.35175657,0.00048825683,0.000034091012,0.0000058586875,0.0015829798,0.6259485,0.00003847366,0.011925081,0.006213426,0.00069911586],"about_ca_topic_score_codex":0.013656583,"about_ca_topic_score_gemma":0.00033964892,"teacher_disagreement_score":0.62590414,"about_ca_system_score_codex":0.0003261104,"about_ca_system_score_gemma":0.000113016984,"threshold_uncertainty_score":0.99994576},"labels":[],"label_agreement":null},{"id":"W4410599930","doi":"10.3390/risks13060100","title":"Modeling Age-to-Age Development Factors in Auto Insurance Through Principal Component Analysis and Temporal Clustering","year":2025,"lang":"en","type":"article","venue":"Risks","topic":"Probability and Risk Models","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":"Principal component analysis; Cluster analysis; Component (thermodynamics); Computer science; Econometrics; Artificial intelligence; Mathematics","score_opus":0.21988507479710853,"score_gpt":0.41443608444214003,"score_spread":0.1945510096450315,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410599930","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.67553204,0.000083535786,0.32363403,0.00012234457,0.00009197345,0.00018208609,0.0000052644928,0.000027868107,0.00032081688],"genre_scores_gemma":[0.9816173,0.000019430086,0.017992007,0.0001167705,0.0000077625455,0.000017305749,0.0000055979895,0.0000051956063,0.00021864066],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99738854,0.00016610224,0.0008399649,0.000671381,0.0006227549,0.0003112277],"domain_scores_gemma":[0.99904084,0.00024441158,0.00008163421,0.0004619108,0.0000737508,0.000097446136],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00167269,0.00018311905,0.0005094888,0.0005552655,0.0002170568,0.000206511,0.0004806199,0.000099346566,0.0000126433115],"category_scores_gemma":[0.00031413976,0.00013636582,0.00010069782,0.0017113631,0.00006094567,0.00025493948,0.0005014508,0.00020597146,0.0000118013995],"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":[0.000031629726,0.00005601679,0.55301666,0.000009421311,0.00006508533,0.000013224123,0.0066515445,0.43339345,0.00007256758,0.00015637513,0.000005461674,0.0065285596],"study_design_scores_gemma":[0.00021922258,0.0000084829035,0.5682315,0.000035464185,0.00002028448,2.431253e-7,0.00034609452,0.42413455,0.00018694851,0.006016599,0.0006415818,0.0001590287],"about_ca_topic_score_codex":0.0027622392,"about_ca_topic_score_gemma":0.013091287,"teacher_disagreement_score":0.30608523,"about_ca_system_score_codex":0.00013939835,"about_ca_system_score_gemma":0.000100109886,"threshold_uncertainty_score":0.73052454},"labels":[],"label_agreement":null},{"id":"W4411061749","doi":"10.3390/risks13060110","title":"Stock Returns’ Co-Movement: A Spatial Model with Convex Combination of Connectivity Matrices","year":2025,"lang":"en","type":"article","venue":"Risks","topic":"Complex Systems and Time Series Analysis","field":"Economics, Econometrics and Finance","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 Hearst","funders":"Al-Imam Muhammad Ibn Saud Islamic University","keywords":"Stock (firearms); Movement (music); Regular polygon; Econometrics; Financial economics; Economics; Mathematics; Geography; Geometry; Physics","score_opus":0.04237779194758526,"score_gpt":0.26636888644181506,"score_spread":0.2239910944942298,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411061749","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.83147085,0.0010381355,0.10679233,0.0004599609,0.00012187382,0.0003954559,0.0002874732,0.000037355487,0.05939657],"genre_scores_gemma":[0.9975779,0.00004868951,0.00021299534,0.0000614208,0.000015765294,0.000018942343,0.000017983699,0.000008711872,0.0020375778],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991106,0.0000142998815,0.00043471146,0.00025698933,0.00004543329,0.00013797492],"domain_scores_gemma":[0.9991729,0.00004441509,0.0004141116,0.00027424743,0.00006581987,0.000028463455],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002779007,0.000109230336,0.00044689677,0.00024339068,0.00008272633,0.000037225636,0.00012707838,0.000059303686,0.00025111198],"category_scores_gemma":[0.000025236188,0.000110532215,0.00009308813,0.00031114215,0.000044209857,0.00011199548,0.00003600215,0.00007766569,0.000022193806],"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":[0.00013822409,0.00028472662,0.3101873,0.00021695484,0.00048542765,0.0000015070366,0.00045750206,0.004293731,0.00006153438,0.68015456,0.0014562142,0.002262331],"study_design_scores_gemma":[0.002292068,0.00027401495,0.12731579,0.00007669214,0.000076092416,7.0523254e-7,0.00023471891,0.78427887,0.0006708563,0.07792134,0.00648462,0.0003742096],"about_ca_topic_score_codex":0.0071756705,"about_ca_topic_score_gemma":0.000722016,"teacher_disagreement_score":0.7799852,"about_ca_system_score_codex":0.0000629778,"about_ca_system_score_gemma":0.000026003261,"threshold_uncertainty_score":0.9994356},"labels":[],"label_agreement":null},{"id":"W4411614506","doi":"10.3390/risks13070119","title":"Copula Modeling of COVID-19 Excess Mortality","year":2025,"lang":"en","type":"article","venue":"Risks","topic":"COVID-19 epidemiological studies","field":"Mathematics","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":"University of St. Thomas","keywords":"Coronavirus disease 2019 (COVID-19); Copula (linguistics); 2019-20 coronavirus outbreak; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Econometrics; Excess mortality; Mathematics; Virology; Medicine; Mortality rate; Internal medicine; Infectious disease (medical specialty)","score_opus":0.6494676109673875,"score_gpt":0.5705657263117371,"score_spread":0.0789018846556504,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411614506","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.65293705,0.00068732514,0.33747548,0.0021441572,0.00014042057,0.00031858863,0.000029659865,0.00017404486,0.0060932906],"genre_scores_gemma":[0.9955482,0.00012399691,0.0027250675,0.0013390318,0.000028440101,0.00002877356,0.000002283349,0.0000066376724,0.00019760444],"study_design_codex":"observational","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9986757,0.00017410942,0.0005146209,0.00026329487,0.00016354572,0.00020871776],"domain_scores_gemma":[0.99700785,0.002298324,0.0001439902,0.00040060887,0.00007136419,0.00007784788],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.001218149,0.00013195605,0.0004982454,0.000051301708,0.000118048054,0.0000069465314,0.0002361425,0.00010731905,0.000075074866],"category_scores_gemma":[0.0138711305,0.00010265519,0.00012997964,0.00021385249,0.00009471893,0.00002936997,0.00023638828,0.00014721807,0.0000063705697],"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":[0.00009470645,0.00046817758,0.4861727,0.0031724044,0.00055346417,0.000018997676,0.0010602772,0.041782834,0.00014048142,0.43746182,0.02694357,0.0021305582],"study_design_scores_gemma":[0.00036875432,0.000019637748,0.0075035766,0.00005996883,0.00011791908,3.083984e-7,0.00020423546,0.06358318,0.00008299891,0.92528176,0.0026291297,0.00014849784],"about_ca_topic_score_codex":0.0029805014,"about_ca_topic_score_gemma":0.00029694458,"teacher_disagreement_score":0.48781997,"about_ca_system_score_codex":0.00012735209,"about_ca_system_score_gemma":0.000095112715,"threshold_uncertainty_score":0.9944354},"labels":[],"label_agreement":null},{"id":"W4412480754","doi":"10.3390/risks13070137","title":"A Novel Stochastic Copula Model for the Texas Energy Market","year":2025,"lang":"en","type":"article","venue":"Risks","topic":"Market Dynamics and Volatility","field":"Economics, Econometrics and Finance","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Institut de Valorisation des Données; Natural Sciences and Engineering Research Council of Canada; Mitacs; University of Calgary","keywords":"Copula (linguistics); Econometrics; Economics; Financial economics; Computer science","score_opus":0.05940699300402052,"score_gpt":0.2722531362454461,"score_spread":0.2128461432414256,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412480754","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":[0.0021697183,0.0009191453,0.9666247,0.00079799024,0.000319751,0.00021277057,0.00047029584,0.000019514115,0.028466163],"genre_scores_gemma":[0.9759445,0.000068819245,0.0013971634,0.00050586753,0.000041081476,0.000076958895,0.00001109146,0.0000131408015,0.021941384],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991514,0.0000052608243,0.00033065068,0.0002810887,0.000019530686,0.00021203535],"domain_scores_gemma":[0.99914515,0.00028358403,0.00012199591,0.0003882124,0.000028589377,0.00003244164],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00057431485,0.00011009552,0.00022114898,0.0000850366,0.00013937689,0.000055103414,0.00025130462,0.00008169212,0.00013839599],"category_scores_gemma":[0.0001889632,0.00009840058,0.00012597814,0.00013637912,0.00003866311,0.000043729862,0.00007020117,0.0000859335,0.000005504416],"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":[0.00011812761,0.00012259846,0.010768934,0.00004405228,0.00013044094,1.4097193e-7,0.00005202925,0.010373371,0.0000025200516,0.954532,0.015597425,0.008258341],"study_design_scores_gemma":[0.00034769502,0.000007553638,0.009569523,0.000006141011,0.00000997197,2.3667123e-7,0.0000046100427,0.85263854,4.6662402e-7,0.11977618,0.017546572,0.00009250061],"about_ca_topic_score_codex":0.0009426586,"about_ca_topic_score_gemma":0.00034493746,"teacher_disagreement_score":0.9737748,"about_ca_system_score_codex":0.000058253943,"about_ca_system_score_gemma":0.000030564566,"threshold_uncertainty_score":0.40126592},"labels":[],"label_agreement":null},{"id":"W4412526315","doi":"10.3390/risks13070140","title":"Enhanced Calibration of Spread Option Simulation Pricing","year":2025,"lang":"en","type":"article","venue":"Risks","topic":"Simulation Techniques and Applications","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":"McMaster University; Systems, Applications & Products in Data Processing (Canada)","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Calibration; Econometrics; Valuation of options; Computer science; Business; Economics; Mathematics; Statistics","score_opus":0.16386589131565232,"score_gpt":0.49043051958547745,"score_spread":0.32656462826982513,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412526315","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":[0.19819023,0.000018317496,0.7960654,0.00018822147,0.000054937656,0.00020175737,0.0000034721525,0.000055457796,0.0052221986],"genre_scores_gemma":[0.99580747,0.000005358046,0.0031032232,0.00006988503,0.00002231702,0.000017008522,0.000006940265,0.000003195373,0.0009646128],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988633,0.00005611797,0.00047040478,0.00020354659,0.0003363114,0.00007034945],"domain_scores_gemma":[0.9984745,0.0006895363,0.00020910644,0.0003471613,0.00026061523,0.000019129851],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005658663,0.000056126883,0.000121055804,0.00022162008,0.0000849388,0.000050729002,0.00018120957,0.00006497748,0.00009680537],"category_scores_gemma":[0.00063142186,0.00004695687,0.00005360309,0.0008354423,0.000026983893,0.00020575502,0.000037192905,0.00004888574,0.000016108002],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000028567634,0.0000726182,0.0051898514,0.0000065938607,0.000008734036,7.116928e-8,0.00030190818,0.46170068,0.04194016,0.07970519,0.00070420303,0.4103414],"study_design_scores_gemma":[0.00014582882,0.00001633315,0.021756165,0.000020147689,0.000008160101,4.6931593e-8,0.00008561305,0.7304273,0.16479242,0.08066075,0.0020293342,0.000057853762],"about_ca_topic_score_codex":0.000047694957,"about_ca_topic_score_gemma":0.0000061200303,"teacher_disagreement_score":0.79761726,"about_ca_system_score_codex":0.000024095914,"about_ca_system_score_gemma":0.00003144451,"threshold_uncertainty_score":0.19148456},"labels":[],"label_agreement":null},{"id":"W4414134010","doi":"10.3390/risks13090172","title":"Explainable Machine Learning Framework for Predicting Auto Loan Defaults","year":2025,"lang":"en","type":"article","venue":"Risks","topic":"Financial Distress and Bankruptcy Prediction","field":"Business, Management and Accounting","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":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Feature selection; Resampling; Default; Loan; Boosting (machine learning); Classifier (UML); Gradient boosting; Random forest; Selection (genetic algorithm)","score_opus":0.02389485357856856,"score_gpt":0.2769639987196327,"score_spread":0.2530691451410641,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414134010","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":[0.42660984,0.0013254205,0.49633574,0.0024907866,0.0034221564,0.0012565206,0.00004067835,0.0014953556,0.0670235],"genre_scores_gemma":[0.9957952,0.000021410411,0.0011339707,0.0005042383,0.0010303127,0.00009794146,0.00009441295,0.00002208361,0.001300437],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.9990488,0.0000069138905,0.00022856881,0.00027505943,0.00012669804,0.00031395705],"domain_scores_gemma":[0.9994981,0.00008960034,0.00014445792,0.00015232844,0.00010724262,0.000008245369],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027496496,0.00014711417,0.00017050952,0.0001594422,0.00057891157,0.00024081867,0.00015647677,0.00012986927,0.000068246074],"category_scores_gemma":[0.00072198396,0.00014267873,0.00009338286,0.00037658296,0.000022795137,0.00046036957,0.00011449136,0.0002533249,0.0000436965],"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":[0.00029050608,0.00017702923,0.43979436,0.00115784,0.00008197525,0.000009821663,0.00012349378,0.0029796232,0.00014909517,0.40369835,0.013859433,0.13767846],"study_design_scores_gemma":[0.0010668746,0.00003465071,0.105826445,0.00060283643,0.00016004032,5.5113657e-7,0.00038446634,0.27205285,0.00014306429,0.06969785,0.54967594,0.0003544333],"about_ca_topic_score_codex":0.0013902821,"about_ca_topic_score_gemma":0.00009572746,"teacher_disagreement_score":0.5691854,"about_ca_system_score_codex":0.000036312143,"about_ca_system_score_gemma":0.000017869026,"threshold_uncertainty_score":0.581827},"labels":[],"label_agreement":null},{"id":"W4414781712","doi":"10.3390/risks13100192","title":"The Italian Actuarial Climate Index: A National Implementation Within the Emerging European Framework","year":2025,"lang":"en","type":"article","venue":"Risks","topic":"demographic modeling and climate adaptation","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":"Composite index; Climate change; Index (typography); Vulnerability (computing); Climate risk; Risk assessment; Risk management; Robustness (evolution); Climate resilience","score_opus":0.14113659025576236,"score_gpt":0.47703827528974446,"score_spread":0.3359016850339821,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414781712","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.723323,0.0002965982,0.19883753,0.020321991,0.0044464907,0.0006390048,0.0000584726,0.00017094976,0.051905982],"genre_scores_gemma":[0.99829423,0.000052764713,0.00048310953,0.0007337478,0.00015835655,0.000017782713,0.000009816915,0.000008236034,0.00024196581],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9971832,0.00052180246,0.00062242383,0.00028853398,0.0011595014,0.00022454551],"domain_scores_gemma":[0.9974108,0.0015694478,0.00029850155,0.00037289227,0.0003152292,0.00003313395],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.008399833,0.00010553939,0.000095831405,0.00014515822,0.0013368803,0.0007432024,0.00065691915,0.00004414609,0.00007830679],"category_scores_gemma":[0.0016749958,0.000054491968,0.00010103127,0.00086385297,0.00009043253,0.00017306481,0.00013022983,0.0002450637,0.00010406127],"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":[0.00017680465,0.000053707357,0.04205515,0.000005655338,0.00013232554,0.000003010955,0.0070331176,0.028735837,0.00007974672,0.46382308,0.024389505,0.43351206],"study_design_scores_gemma":[0.00074053317,0.000044110242,0.17033347,0.000057230274,0.00005279001,0.0000031141747,0.03320371,0.15215331,0.00005363671,0.6062253,0.036885053,0.00024770535],"about_ca_topic_score_codex":0.00008195353,"about_ca_topic_score_gemma":0.00042267545,"teacher_disagreement_score":0.43326437,"about_ca_system_score_codex":0.000027638509,"about_ca_system_score_gemma":0.00008368453,"threshold_uncertainty_score":0.9999632},"labels":[],"label_agreement":null},{"id":"W4415127930","doi":"10.3390/risks13100198","title":"Application of Standard Machine Learning Models for Medicare Fraud Detection with Imbalanced Data","year":2025,"lang":"en","type":"article","venue":"Risks","topic":"Imbalanced Data Classification Techniques","field":"Computer Science","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":"Columbia College","funders":"","keywords":"Interpretability; Random forest; AdaBoost; Decision tree; Feature selection; Resampling; Preprocessor; Feature (linguistics); Ensemble learning","score_opus":0.04827192005258184,"score_gpt":0.32589928921471273,"score_spread":0.2776273691621309,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415127930","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":[0.00030452543,0.000121866455,0.99801224,0.00028618582,0.000040254323,0.00042752744,0.00015550746,0.0003060489,0.0003458097],"genre_scores_gemma":[0.80568963,0.00004988403,0.19381459,0.00004585806,0.000012535166,0.0001200213,0.00022402192,0.0000064306887,0.00003702423],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99902964,0.000037442504,0.00020720951,0.00039588904,0.00020584653,0.00012397178],"domain_scores_gemma":[0.9983293,0.00009284671,0.0001848058,0.0011818092,0.00018532366,0.000025932746],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040313962,0.00008675153,0.00015209953,0.000105713625,0.00009940589,0.00003407883,0.0010356519,0.00006252272,9.0048854e-7],"category_scores_gemma":[0.00008791656,0.000074955235,0.000016940561,0.00037318192,0.000042074902,0.00045145358,0.00023060612,0.00013081526,7.595351e-7],"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":[0.00013117553,0.000045486817,0.0016466734,0.00014029627,0.000046952493,3.003461e-7,0.00017907952,0.0017473417,0.015300153,0.042523377,0.00085480744,0.93738437],"study_design_scores_gemma":[0.0003760008,0.00009648426,0.00061206525,0.00003739777,0.000012765517,8.278001e-7,0.000019104904,0.92147344,0.057697777,0.007931235,0.011657059,0.00008582502],"about_ca_topic_score_codex":0.00009179469,"about_ca_topic_score_gemma":0.000049848495,"teacher_disagreement_score":0.93729854,"about_ca_system_score_codex":0.000048498285,"about_ca_system_score_gemma":0.00007706361,"threshold_uncertainty_score":0.30565858},"labels":[],"label_agreement":null},{"id":"W4416916868","doi":"10.3390/risks13120233","title":"Optimal Investment Considerations for a Single Cohort Life Insurance Portfolio","year":2025,"lang":"en","type":"article","venue":"Risks","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social 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":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Life annuity; Volatility (finance); Portfolio; Sharpe ratio; Stochastic volatility; Life insurance; Stock (firearms); Investment strategy; Present value","score_opus":0.06762674807421931,"score_gpt":0.35546046077406823,"score_spread":0.2878337126998489,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416916868","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6141729,0.0009020183,0.003105703,0.003354493,0.0015163556,0.002782743,0.00010610949,0.00030418206,0.3737555],"genre_scores_gemma":[0.9895791,0.00021422683,0.004244247,0.0034816633,0.00017682566,0.0004459899,0.000012226657,0.000013083934,0.0018326374],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.9983107,0.00014971758,0.00037606436,0.00037376815,0.0003605337,0.00042922355],"domain_scores_gemma":[0.99894303,0.00024566986,0.00013432818,0.00032597006,0.0002127033,0.00013831544],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00090717373,0.00015543874,0.00024987728,0.00019057543,0.00093573565,0.00018846062,0.00020049054,0.00010789629,0.000124631],"category_scores_gemma":[0.0006697628,0.00016824782,0.00015922567,0.00045399496,0.00036340015,0.00018832534,0.000055839333,0.000113659604,0.00002357795],"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":[0.000025514302,0.00036386013,0.45457318,0.00004538754,0.00029276687,0.000010018335,0.0016739145,0.0008082193,0.000048391972,0.46535343,0.07558331,0.0012219958],"study_design_scores_gemma":[0.0011499183,0.00009452869,0.62195873,0.00006974536,0.00019904917,7.85027e-7,0.0020658157,0.00027577544,0.00029589122,0.044100236,0.32929853,0.0004909825],"about_ca_topic_score_codex":0.0023381303,"about_ca_topic_score_gemma":0.0017706507,"teacher_disagreement_score":0.42125317,"about_ca_system_score_codex":0.00013249856,"about_ca_system_score_gemma":0.0003554735,"threshold_uncertainty_score":0.71970165},"labels":[],"label_agreement":null},{"id":"W4417331488","doi":"10.3390/risks13120252","title":"From Stochastic Orders to Volatility Surfaces: Revisiting the One-X Property","year":2025,"lang":"en","type":"article","venue":"Risks","topic":"Markov Chains and Monte Carlo Methods","field":"Mathematics","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":"Counterexample; Unimodality; Property (philosophy); Conjecture; Stochastic volatility; Volatility (finance); Stochastic process","score_opus":0.16328044363885794,"score_gpt":0.4108044017176865,"score_spread":0.24752395807882857,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417331488","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.66007715,0.00028241464,0.31990427,0.004741602,0.00034541416,0.0010062341,0.000036306632,0.00014115524,0.013465463],"genre_scores_gemma":[0.9428402,0.0000065990785,0.05184546,0.00063348864,0.00025267765,0.00005560617,0.0000037970212,0.000022880142,0.004339283],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99871397,0.0002932827,0.00029398175,0.00028976935,0.00018052656,0.00022845143],"domain_scores_gemma":[0.997783,0.0013333412,0.00007806354,0.0006339929,0.00010892834,0.0000626836],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012927469,0.00014551479,0.0002765703,0.00003565739,0.00017878896,0.000060462196,0.00027513228,0.000072442315,0.000057447523],"category_scores_gemma":[0.002954842,0.00007995629,0.00008407632,0.0002406967,0.000037976642,0.000036862708,0.00015832152,0.00022294589,8.8081384e-7],"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":[0.00045355328,0.00031775763,0.005726375,0.0010603126,0.0008035889,0.000012094837,0.017076066,0.0012518398,0.005646843,0.02771253,0.04972522,0.89021385],"study_design_scores_gemma":[0.004816152,0.00035183737,0.014690817,0.0064484696,0.0019588915,0.0000048142256,0.020077467,0.3364052,0.007469321,0.29451188,0.31012297,0.0031421862],"about_ca_topic_score_codex":0.0017032152,"about_ca_topic_score_gemma":0.00021992659,"teacher_disagreement_score":0.8870716,"about_ca_system_score_codex":0.00005758646,"about_ca_system_score_gemma":0.000060862614,"threshold_uncertainty_score":0.35374352},"labels":[],"label_agreement":null}]}