{"meta":{"query_hash":"eb1fac968d1c","filters":{"venue":"Monte Carlo Methods and Applications"},"cohort_total":9,"direct_labels_cover":0,"predictions_cover":9,"exported":9,"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/eb1fac968d1c","api":"https://metacan.xera.ac/api/v1/cohort?venue=Monte+Carlo+Methods+and+Applications"},"results":[{"id":"W1439202096","doi":"10.1515/mcma-2013-0019","title":"Rare event simulation for diffusion processes via two-stage importance sampling","year":2014,"lang":"en","type":"article","venue":"Monte Carlo Methods and Applications","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":"Western University; Wilfrid Laurier University","funders":"Natural Sciences and Engineering Research Council of Canada; Fonds Québécois de la Recherche sur la Nature et les Technologies","keywords":"Estimator; Brownian motion; Rare events; Importance sampling; Mathematics; Measure (data warehouse); Entropy (arrow of time); Applied mathematics; Event (particle physics); Sampling (signal processing); Large deviations theory; Statistical physics; Path (computing); Computer science; Mathematical optimization; Monte Carlo method; Statistics; Data mining; Physics","score_opus":0.17639376914693541,"score_gpt":0.5054004319798635,"score_spread":0.3290066628329281,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1439202096","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":[0.050532658,0.000552063,0.94717664,0.00036200177,0.000045055935,0.0010523361,0.000044368633,0.00004915193,0.00018570531],"genre_scores_gemma":[0.67146933,0.000033382494,0.3271405,0.00014381444,0.0001193529,0.00064750324,0.000008257507,0.000011147202,0.00042669536],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99813914,0.00021414492,0.0005671059,0.00059396337,0.00029429916,0.00019132884],"domain_scores_gemma":[0.9947348,0.0038286825,0.00027567463,0.00057921757,0.00046674113,0.00011485449],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003920282,0.00013687501,0.00026168555,0.000078130855,0.0005025416,0.0001386865,0.00029885911,0.000073625866,0.000011778773],"category_scores_gemma":[0.002091191,0.00009953311,0.00009286916,0.00040718904,0.000078738376,0.00021087342,0.00008929472,0.00008507138,0.000002075191],"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":[0.000043943823,0.000092237824,0.0041178335,0.00007861734,0.0000080441205,5.1404175e-8,0.0004888746,0.12439217,0.0037710194,0.013897816,0.00003880966,0.85307056],"study_design_scores_gemma":[0.00047514902,0.00003503861,0.0012695821,0.0000118524695,0.000021058839,7.985376e-7,0.00015192319,0.6554542,0.0006707941,0.23460539,0.10713404,0.00017018082],"about_ca_topic_score_codex":0.000026372612,"about_ca_topic_score_gemma":0.00009094111,"teacher_disagreement_score":0.8529004,"about_ca_system_score_codex":0.000023197184,"about_ca_system_score_gemma":0.000035299585,"threshold_uncertainty_score":0.40588424},"labels":[],"label_agreement":null},{"id":"W2111112077","doi":"10.1515/mcma.2010.010","title":"Exact simulation of Bessel diffusions","year":2010,"lang":"en","type":"preprint","venue":"Monte Carlo Methods and Applications","topic":"Stochastic processes and financial applications","field":"Economics, Econometrics and Finance","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Bessel process; Bessel function; Mathematics; Applied mathematics; Hitting time; Markov chain; Mathematical analysis; Statistics; Orthogonal polynomials","score_opus":0.04791509176371623,"score_gpt":0.3383240000867005,"score_spread":0.29040890832298427,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2111112077","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":[0.0052156877,0.0044476073,0.9846664,0.00034632251,0.00018043177,0.0009770786,0.0010449466,0.000056295394,0.0030651968],"genre_scores_gemma":[0.73061115,0.0005045684,0.2662141,0.0000828436,0.00033322952,0.0018952935,0.000076555385,0.000052390526,0.00022986296],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99815285,0.000012118434,0.00087156997,0.0007067813,0.000044235243,0.00021245229],"domain_scores_gemma":[0.9977252,0.0003221284,0.0008221177,0.00085382076,0.00015343449,0.00012331623],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00054761907,0.00025903078,0.00066807837,0.00021010003,0.00022101599,0.00005964235,0.00036289563,0.0004506959,0.000019473462],"category_scores_gemma":[0.00019624298,0.00029764243,0.00018189997,0.00028982194,0.0001643903,0.000057744594,0.00040842505,0.000586774,0.00000823232],"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":[0.000005388978,0.00018140975,0.00031563235,0.00021561401,0.00004836262,1.237925e-7,0.0003060062,0.0032141458,0.0005476596,0.9426998,0.000028477998,0.052437365],"study_design_scores_gemma":[0.00023435011,0.000023372062,0.0042285346,0.00004175207,0.00006529008,0.0000013764078,0.00006202128,0.08838811,0.00015224708,0.8472976,0.059050847,0.00045448262],"about_ca_topic_score_codex":0.00046034495,"about_ca_topic_score_gemma":0.00003172196,"teacher_disagreement_score":0.7253955,"about_ca_system_score_codex":0.000030382553,"about_ca_system_score_gemma":0.000047471935,"threshold_uncertainty_score":0.99994755},"labels":[],"label_agreement":null},{"id":"W2209871569","doi":"10.1515/mcma-2015-0105","title":"Simulating from the Heston model: A gamma approximation scheme","year":2015,"lang":"en","type":"article","venue":"Monte Carlo Methods and Applications","topic":"Stochastic processes and financial applications","field":"Economics, Econometrics and Finance","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University; Université de Montréal; HEC Montréal","funders":"","keywords":"Heston model; Stochastic volatility; Applied mathematics; Mathematics; Representation (politics); Scheme (mathematics); Convolution (computer science); Volatility (finance); Mathematical optimization; Computer science; SABR volatility model; Econometrics; Mathematical analysis","score_opus":0.10329936177941684,"score_gpt":0.32931993421836736,"score_spread":0.22602057243895052,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2209871569","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":[0.0047214646,0.0034755177,0.986719,0.0013714533,0.00003565361,0.0005124338,0.00015888122,0.000060945455,0.0029446145],"genre_scores_gemma":[0.34769335,0.0000472656,0.6501632,0.00043568615,0.00028462042,0.0011700644,0.000028307853,0.000026930587,0.00015057379],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9989806,0.000011421377,0.0004060286,0.00038975637,0.000039440718,0.00017274893],"domain_scores_gemma":[0.998936,0.00021527124,0.00023715507,0.00042322685,0.00008069359,0.000107666],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006550195,0.00013058005,0.00023104285,0.000040175113,0.00026787797,0.00008844159,0.00021902367,0.00008223757,0.0000036945262],"category_scores_gemma":[0.00021581662,0.00011782174,0.000056202818,0.00029341236,0.00007753598,0.00012348323,0.00008342742,0.00013698211,0.000022827788],"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":[0.000004284037,0.00003306806,0.0005327733,0.0000062575878,0.000013576879,5.008257e-8,0.00066240394,0.001773748,0.000085650485,0.9671889,0.00012677755,0.029572533],"study_design_scores_gemma":[0.00018218608,0.000008512529,0.0004425683,0.000004483962,0.000009203116,7.3125324e-7,0.00019622892,0.4410134,0.000027692624,0.53533465,0.02265337,0.00012698348],"about_ca_topic_score_codex":0.0005183752,"about_ca_topic_score_gemma":0.000018369223,"teacher_disagreement_score":0.43923965,"about_ca_system_score_codex":0.000043516215,"about_ca_system_score_gemma":0.000028096678,"threshold_uncertainty_score":0.48046312},"labels":[],"label_agreement":null},{"id":"W2804758477","doi":"10.1515/mcma-2018-0010","title":"Bayesian estimation of ordinary differential equation models when the likelihood has multiple local modes","year":2018,"lang":"en","type":"article","venue":"Monte Carlo Methods and Applications","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Ode; Ordinary differential equation; Likelihood function; Applied mathematics; Mathematical optimization; Function (biology); Mathematics; Bayesian probability; Estimation theory; Computer science; Differential equation; Algorithm; Statistics; Mathematical analysis","score_opus":0.13375221748237848,"score_gpt":0.39930478170336353,"score_spread":0.26555256422098505,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2804758477","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":[0.0059413863,0.00008572962,0.9921582,0.00034449002,0.000035562065,0.0006206359,0.000051702948,0.00004528833,0.0007170286],"genre_scores_gemma":[0.48367298,0.0000063903735,0.5159911,0.0000220397,0.0000599008,0.00020891336,0.0000032071516,0.000011102274,0.00002435733],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985927,0.00036420903,0.00041245195,0.0002746955,0.00016853775,0.00018740747],"domain_scores_gemma":[0.9969652,0.0021238502,0.00018126241,0.00043469222,0.00020981245,0.00008513195],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007610876,0.0001558957,0.00027015762,0.00004884629,0.00032682793,0.000056207453,0.00018357408,0.00009171626,0.00003248202],"category_scores_gemma":[0.00050639134,0.00010918417,0.000063862164,0.00014663397,0.0004744193,0.000095621996,0.00009321309,0.00012608577,0.0000011277713],"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":[0.000020390382,0.00009035,0.00002241736,0.000054721622,0.000024679839,7.9969254e-8,0.0007364611,0.00036179862,0.0021645206,0.3139236,0.00014698683,0.682454],"study_design_scores_gemma":[0.00012230984,0.000048652197,0.000107102416,0.000015214055,0.000055852797,0.00000121126,0.00015594764,0.5274525,0.0018099169,0.47003627,0.0001252618,0.00006976454],"about_ca_topic_score_codex":0.00022132203,"about_ca_topic_score_gemma":0.000029999846,"teacher_disagreement_score":0.68238425,"about_ca_system_score_codex":0.000021640288,"about_ca_system_score_gemma":0.000034507233,"threshold_uncertainty_score":0.4452401},"labels":[],"label_agreement":null},{"id":"W2970759111","doi":"10.1515/mcma-2019-2045","title":"Quasi-Monte Carlo method for solving Fredholm equations","year":2019,"lang":"en","type":"article","venue":"Monte Carlo Methods and Applications","topic":"Mathematical Approximation and Integration","field":"Mathematics","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":"Atomic Energy (Canada)","funders":"","keywords":"Monte Carlo method; Fredholm integral equation; Hybrid Monte Carlo; Applied mathematics; Statistical physics; Dynamic Monte Carlo method; Monte Carlo molecular modeling; Monte Carlo method in statistical physics; Fredholm theory; Mathematics; Computer science; Integral equation; Markov chain Monte Carlo; Physics; Mathematical analysis; Statistics","score_opus":0.07318058783061929,"score_gpt":0.43883995062900416,"score_spread":0.36565936279838485,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2970759111","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":[0.0028945399,0.00019469493,0.9875889,0.0010549508,0.0000834682,0.0025441267,0.00006816246,0.00017372363,0.005397422],"genre_scores_gemma":[0.0132867815,0.000021064105,0.9786371,0.00024531723,0.00014044327,0.0025442608,0.000014023014,0.000051607683,0.0050594127],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99817044,0.00019512835,0.00064257724,0.0004902923,0.00020488628,0.00029665857],"domain_scores_gemma":[0.9951533,0.0034887874,0.00026496372,0.0006310452,0.00031205761,0.00014982936],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019363705,0.0002545938,0.00047761726,0.00012084873,0.00029599058,0.00013009345,0.00021704021,0.00016191264,0.00011751992],"category_scores_gemma":[0.0010273404,0.00021751918,0.00019770069,0.00027815305,0.000050174473,0.00019956885,0.00006574209,0.00019466938,0.000018723302],"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":[0.000008289521,0.00018567176,0.00001670274,0.0002473257,0.000046806363,5.938113e-8,0.0007759716,0.000022370124,0.0036827791,0.92857504,0.00088141987,0.06555755],"study_design_scores_gemma":[0.0006577094,0.0001228687,0.00002230712,0.00005951811,0.00015728259,0.000005551382,0.0013063694,0.43630564,0.0024748521,0.51670396,0.04183456,0.0003494005],"about_ca_topic_score_codex":0.000063561376,"about_ca_topic_score_gemma":0.000043850407,"teacher_disagreement_score":0.43628326,"about_ca_system_score_codex":0.000058117188,"about_ca_system_score_gemma":0.00005057455,"threshold_uncertainty_score":0.8870174},"labels":[],"label_agreement":null},{"id":"W3044317697","doi":"10.1515/mcma-2020-2067","title":"QMC integration errors and quasi-asymptotics","year":2020,"lang":"en","type":"article","venue":"Monte Carlo Methods and Applications","topic":"Mathematical Approximation and Integration","field":"Mathematics","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":"Atomic Energy (Canada)","funders":"","keywords":"Applied mathematics; Statistical physics; Mathematics; Physics","score_opus":0.1042611699164301,"score_gpt":0.4104468060276037,"score_spread":0.3061856361111736,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3044317697","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":[0.0106968805,0.00015043312,0.9806956,0.003993794,0.000016214508,0.0006153523,0.000012665562,0.0001274802,0.003691562],"genre_scores_gemma":[0.07650435,0.000089276065,0.92193097,0.0007199232,0.0001058058,0.00029257438,0.000008203643,0.000024257071,0.00032463754],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9989936,0.00012583964,0.00035759417,0.00027857418,0.00012051147,0.0001238908],"domain_scores_gemma":[0.9989883,0.00042155676,0.000120515004,0.0001924109,0.000102411155,0.00017481786],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041325227,0.00015495169,0.00024940606,0.000038402803,0.00014864905,0.000077919816,0.00008508258,0.000088185356,0.00003852903],"category_scores_gemma":[0.00057341636,0.00012425416,0.000048481637,0.00019618246,0.00008424504,0.000117880016,0.00004735327,0.0001592156,0.00000518716],"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":[0.000006256704,0.00005961244,0.000019767627,0.000110803,0.000013565113,1.2929515e-7,0.0015860633,7.557334e-7,0.0018426151,0.88530606,0.0006418111,0.110412546],"study_design_scores_gemma":[0.0005180434,0.00021865532,0.00013614351,0.000055899975,0.00014846276,0.0000126166515,0.0036834106,0.21626474,0.0063431878,0.7364325,0.035759263,0.0004271115],"about_ca_topic_score_codex":0.000007763499,"about_ca_topic_score_gemma":0.0000088940915,"teacher_disagreement_score":0.21626398,"about_ca_system_score_codex":0.000014570153,"about_ca_system_score_gemma":0.000013914818,"threshold_uncertainty_score":0.5066938},"labels":[],"label_agreement":null},{"id":"W3125876763","doi":"10.1515/mcma-2020-2079","title":"On the dependence structure and quality of scrambled (<i>t</i>, <i>m</i>, <i>s</i>)-nets","year":2021,"lang":"en","type":"article","venue":"Monte Carlo Methods and Applications","topic":"Mathematical Approximation and Integration","field":"Mathematics","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Austrian Science Fund","keywords":"Combinatorics; Physics; Mathematics","score_opus":0.10087156607183571,"score_gpt":0.42095491618540953,"score_spread":0.3200833501135738,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3125876763","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":[0.2989303,0.000562547,0.68880665,0.0030505473,0.000030283269,0.00085799635,0.00008643757,0.000055495144,0.007619768],"genre_scores_gemma":[0.3735463,0.00010816182,0.6245024,0.000913036,0.00003158902,0.00021177434,0.0000059220315,0.000017538448,0.00066324306],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99880755,0.00033122298,0.00036625811,0.00022934363,0.00016695398,0.000098683784],"domain_scores_gemma":[0.99708396,0.0021079185,0.00016650272,0.00040312318,0.00017957571,0.000058895526],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00079535495,0.00011776615,0.00024876196,0.000020802923,0.00015812632,0.000044700755,0.00009466332,0.000070908165,0.00007444004],"category_scores_gemma":[0.0010005756,0.0000751427,0.00005001329,0.00019535307,0.00010584676,0.00004739543,0.000054284177,0.00015406254,5.643861e-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":[0.0000042702263,0.000046562676,0.000026345828,0.00010072248,0.000014167592,8.650209e-8,0.00020790161,8.183536e-7,0.02398108,0.9664673,0.00023850177,0.008912201],"study_design_scores_gemma":[0.00016995992,0.000016394382,0.00025264572,0.000036603557,0.00003124675,0.000005921187,0.00042698067,0.0012236936,0.07591449,0.9198112,0.0020050243,0.00010586816],"about_ca_topic_score_codex":0.000014964179,"about_ca_topic_score_gemma":0.000032105618,"teacher_disagreement_score":0.074616015,"about_ca_system_score_codex":0.000008322946,"about_ca_system_score_gemma":0.000023003257,"threshold_uncertainty_score":0.30642304},"labels":[],"label_agreement":null},{"id":"W4212872317","doi":"10.1515/mcma-2022-2106","title":"Moment matching adaptive importance sampling with skew-student proposals","year":2022,"lang":"en","type":"article","venue":"Monte Carlo Methods and Applications","topic":"Statistical Distribution Estimation and Applications","field":"Mathematics","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":"Simon Fraser University","funders":"","keywords":"Skew; Matching (statistics); Sampling (signal processing); Moment (physics); Rejection sampling; Class (philosophy); Computer science; Importance sampling; Applied mathematics; Adaptive sampling; Mathematics; Statistics; Artificial intelligence; Monte Carlo method; Physics; Markov chain Monte Carlo","score_opus":0.10492411160656036,"score_gpt":0.4550021656866213,"score_spread":0.35007805408006093,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4212872317","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":[0.009549294,0.00014655874,0.98656356,0.00074548216,0.000014161778,0.0011739522,0.00038260795,0.00012705519,0.0012973424],"genre_scores_gemma":[0.20183583,0.000012744296,0.791526,0.00020111293,0.000036155296,0.00599457,0.000039410195,0.000025856554,0.00032829258],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9986328,0.00014927167,0.00035585873,0.00038189546,0.00027271343,0.00020749212],"domain_scores_gemma":[0.99871427,0.00050914043,0.00018422837,0.00035924744,0.00010324808,0.0001298408],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005908017,0.00016078395,0.00022793796,0.000048605056,0.0008595519,0.000054452874,0.00017244763,0.000026505784,0.00013192772],"category_scores_gemma":[0.000034231052,0.00014233953,0.00004447623,0.00033062158,0.00008715741,0.000052510266,0.0001415009,0.00022062051,0.0000024059962],"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":[0.000009789825,0.00017959246,0.00016228431,0.000020074916,0.00003516849,7.679802e-7,0.00040180472,0.00026243896,0.0005072717,0.9785805,0.00027949957,0.019560803],"study_design_scores_gemma":[0.0011100696,0.00023212038,0.00723734,0.000036091813,0.0002974029,0.000074127085,0.0084984135,0.012099699,0.00054926134,0.8746724,0.09434504,0.00084802264],"about_ca_topic_score_codex":0.00002376851,"about_ca_topic_score_gemma":0.000007962719,"teacher_disagreement_score":0.19503751,"about_ca_system_score_codex":0.0001268682,"about_ca_system_score_gemma":0.000044049586,"threshold_uncertainty_score":0.6611065},"labels":[],"label_agreement":null},{"id":"W4297995547","doi":"10.1515/mcma-2022-2123","title":"Estimation of entropy and extropy based on right censored data: A Bayesian non-parametric approach","year":2022,"lang":"en","type":"article","venue":"Monte Carlo Methods and Applications","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Estimator; Parametric statistics; Bayesian probability; Mathematics; Entropy (arrow of time); Principle of maximum entropy; Econometrics; Statistics; Applied mathematics; Computer science; Physics","score_opus":0.02316970924384907,"score_gpt":0.32584309438981424,"score_spread":0.30267338514596515,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4297995547","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":[0.00039901023,0.00045921933,0.99714315,0.0003894752,0.000045461948,0.00067283335,0.00008538568,0.00004912069,0.0007563681],"genre_scores_gemma":[0.13619538,0.000027835333,0.8630457,0.00018402761,0.00002688722,0.00041809838,0.000028092842,0.000012501221,0.00006147739],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99795806,0.0005440152,0.000310224,0.000733337,0.00025018302,0.00020415419],"domain_scores_gemma":[0.99787974,0.0004311727,0.00018644855,0.0013281627,0.000045490375,0.00012898451],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015065401,0.0001731017,0.00030655166,0.0002625287,0.00037055727,0.000083716564,0.0007274987,0.00005205688,0.000004742327],"category_scores_gemma":[0.00005855167,0.00015687643,0.00004977797,0.00091831456,0.00008134376,0.00018069777,0.00044532388,0.00022161784,1.6228964e-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":[0.00001672275,0.00023509361,0.00004436282,0.00005350778,0.000021384823,0.0000013841044,0.000193317,0.005719633,0.0012690099,0.19024712,0.00027443958,0.80192405],"study_design_scores_gemma":[0.00036815362,0.00009032336,0.00027286916,0.000004826786,0.000033522407,0.000010677417,0.000024731775,0.98247176,0.00067345536,0.011463892,0.0044252467,0.00016052941],"about_ca_topic_score_codex":0.000043654272,"about_ca_topic_score_gemma":4.5631174e-7,"teacher_disagreement_score":0.97675216,"about_ca_system_score_codex":0.000031658867,"about_ca_system_score_gemma":0.00005124559,"threshold_uncertainty_score":0.6397235},"labels":[],"label_agreement":null}]}