{"meta":{"query_hash":"4e239b0ee2b9","filters":{"venue":"Statistica Sinica"},"cohort_total":48,"direct_labels_cover":0,"predictions_cover":48,"exported":48,"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/4e239b0ee2b9","api":"https://metacan.xera.ac/api/v1/cohort?venue=Statistica+Sinica"},"results":[{"id":"W1968800433","doi":"10.5705/ss.2014.048","title":"Two-sample behrens-fisher problem for high-dimensional data","year":2014,"lang":"en","type":"article","venue":"Statistica Sinica","topic":"Stochastic processes and statistical mechanics","field":"Mathematics","cited_by":46,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Sample (material); Computer science; Statistics; Mathematics; Physics","score_opus":0.10159118278408773,"score_gpt":0.3780009574811228,"score_spread":0.27640977469703504,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1968800433","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021739634,0.0014720963,0.97229195,0.0019830153,0.00018436431,0.00021271304,0.0004404132,0.00020451807,0.0014713013],"genre_scores_gemma":[0.517352,0.0021023662,0.46887586,0.0012981894,0.0009750958,0.00162721,0.0016379519,0.00016377388,0.0059675975],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9699753,0.019681945,0.0015851058,0.004856594,0.0031867153,0.00071436365],"domain_scores_gemma":[0.8160571,0.164462,0.006550845,0.008549211,0.003260073,0.0011208265],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.05200711,0.0015559543,0.0042598727,0.0032950116,0.0017737335,0.0036592605,0.0030327518,0.0048365537,0.0061247866],"category_scores_gemma":[0.15162386,0.0013565484,0.0022414632,0.0038674488,0.010464128,0.0060512177,0.0046992684,0.0054098857,0.0007298847],"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":[0.00064387335,0.00014279608,0.012570539,0.0011794381,0.000866221,0.0020850617,0.0008696068,0.09966526,0.0014470865,0.7253199,0.0077680293,0.14744212],"study_design_scores_gemma":[0.0000767243,0.00014404491,0.0027343875,0.00008030829,0.00006618919,0.00044964996,0.0001236866,0.30775172,0.0007223125,0.6846922,0.00307365,0.00008502557],"about_ca_topic_score_codex":0.0030555918,"about_ca_topic_score_gemma":0.0018612124,"teacher_disagreement_score":0.05200711,"about_ca_system_score_codex":0.002242377,"about_ca_system_score_gemma":0.0026451051,"threshold_uncertainty_score":0.2750432},"labels":[],"label_agreement":null},{"id":"W2000225109","doi":"10.5705/ss.2011.038a","title":"On the Grenander estimator at zero","year":2011,"lang":"en","type":"article","venue":"Statistica Sinica","topic":"Functional Equations Stability Results","field":"Mathematics","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"National Institute of Allergy and Infectious Diseases","keywords":"Zero (linguistics); Monotone polygon; Estimator; Limit (mathematics); Infinity; Mathematics; Applied mathematics; Regular polygon; Mathematical optimization; Computer science; Mathematical analysis; Statistics","score_opus":0.21048706925434896,"score_gpt":0.3369452347161485,"score_spread":0.12645816546179955,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2000225109","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04309384,0.0012929533,0.93923235,0.0016248826,0.00013535937,0.00006250123,0.00010643402,0.00020648305,0.014245097],"genre_scores_gemma":[0.7047709,0.003904818,0.26465455,0.0025573447,0.0009649022,0.0006269506,0.0004937609,0.0007249695,0.021301812],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9958941,0.0024793837,0.00013087383,0.0005549371,0.00068071793,0.0002600365],"domain_scores_gemma":[0.9499062,0.04013446,0.0026598494,0.0021018856,0.003840021,0.0013576437],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01893556,0.001038085,0.0014340797,0.004008934,0.0011372599,0.0021467796,0.0031186517,0.0023681475,0.005668483],"category_scores_gemma":[0.06023043,0.00062534545,0.001693613,0.0012855558,0.006292337,0.0066266772,0.0041340915,0.004838471,0.0010698441],"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":[0.000028406716,0.000024862227,0.0008645979,0.00008918292,0.00002523887,0.00010322458,0.00022287089,0.011709321,0.0018879297,0.97794294,0.0007685783,0.0063329274],"study_design_scores_gemma":[0.000024920877,0.00009523895,0.0008031562,0.00015465,0.00003654782,0.00028464716,0.000079509424,0.27921352,0.0020920702,0.71408826,0.0030678064,0.00005965986],"about_ca_topic_score_codex":0.001890809,"about_ca_topic_score_gemma":0.0010400693,"teacher_disagreement_score":0.01893556,"about_ca_system_score_codex":0.0020735848,"about_ca_system_score_gemma":0.0012971961,"threshold_uncertainty_score":0.100142},"labels":[],"label_agreement":null},{"id":"W2007401066","doi":"10.5705/ss.2011.197","title":"Semiparametric accelerated failure time model for length-biased data with application to dementia study","year":2013,"lang":"en","type":"article","venue":"Statistica Sinica","topic":"Statistical Methods and Inference","field":"Mathematics","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":"National Cancer Institute","keywords":"Censoring (clinical trials); Estimator; Statistics; Econometrics; Estimating equations; Survival analysis; Computer science; Accelerated failure time model; Maximum likelihood; Population; Dementia; Mathematics; Medicine; Internal medicine; Disease","score_opus":0.17697939485765202,"score_gpt":0.4207345544160272,"score_spread":0.24375515955837518,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2007401066","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.022545414,0.0006690713,0.9754131,0.0004894766,0.000059868293,0.000112587026,0.00016935634,0.0001222995,0.00041890197],"genre_scores_gemma":[0.6660733,0.0022971462,0.32231453,0.0005132917,0.00035386434,0.0016949584,0.0010092326,0.000134448,0.0056091207],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9914545,0.0064169727,0.0003395082,0.000662629,0.0007742443,0.00035211942],"domain_scores_gemma":[0.9270513,0.06017278,0.0049572093,0.0036872101,0.0033983078,0.000733277],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.028358988,0.0011762634,0.0025782296,0.0023171976,0.0006424491,0.0018472872,0.0037055025,0.0018403683,0.0031084453],"category_scores_gemma":[0.07518317,0.00070160785,0.0025173894,0.0019769901,0.001958119,0.0019465393,0.0024637817,0.0028171383,0.00042421115],"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":[0.00036544356,0.00017541467,0.0266927,0.00063962286,0.0007869266,0.0011912787,0.0012147236,0.49220133,0.0019881797,0.4034486,0.0032662554,0.06802965],"study_design_scores_gemma":[0.000086161715,0.00015938353,0.0029153207,0.000066119144,0.00014352161,0.00027074508,0.00008159946,0.90361845,0.00027554244,0.09076952,0.0015640403,0.0000496491],"about_ca_topic_score_codex":0.007764863,"about_ca_topic_score_gemma":0.0038929787,"teacher_disagreement_score":0.028358988,"about_ca_system_score_codex":0.0013607129,"about_ca_system_score_gemma":0.0024737609,"threshold_uncertainty_score":0.1499784},"labels":[],"label_agreement":null},{"id":"W2088206359","doi":"10.5705/ss.2011.230","title":"Minimum description length principle for linear mixed effects models","year":2013,"lang":"en","type":"article","venue":"Statistica Sinica","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mathematics; Applied mathematics; Generalized linear mixed model; Computer science; Mathematical optimization","score_opus":0.10795537373540492,"score_gpt":0.39003594190725416,"score_spread":0.28208056817184923,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2088206359","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0006714825,0.0003796403,0.99792814,0.00029776662,0.00001985123,0.000048090238,0.000177006,0.000060221533,0.00041769474],"genre_scores_gemma":[0.04997733,0.0021324542,0.9404924,0.0008643304,0.00047517705,0.0020232452,0.001488458,0.00024382601,0.0023028036],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9798212,0.01432442,0.0010096015,0.0016392804,0.002837179,0.00036828624],"domain_scores_gemma":[0.90215725,0.08948645,0.0024788403,0.003205101,0.0022053602,0.0004670562],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.031008739,0.0018765659,0.002493795,0.0034385729,0.0010963733,0.0028521884,0.0048923185,0.0030778765,0.004763498],"category_scores_gemma":[0.0837349,0.0015662131,0.0034229592,0.0033493713,0.00354799,0.0043700826,0.00416073,0.005720209,0.0013262383],"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":[0.00011287274,0.000072822004,0.0011066885,0.00090694806,0.00034639635,0.00024651203,0.0003276979,0.13737741,0.0010958782,0.749979,0.0040787044,0.10434907],"study_design_scores_gemma":[0.00003823608,0.00006795931,0.00034904093,0.00012382964,0.00004451889,0.00009078761,0.00002457282,0.40026292,0.0004798947,0.5940471,0.004428272,0.000042777196],"about_ca_topic_score_codex":0.0030423123,"about_ca_topic_score_gemma":0.0025900565,"teacher_disagreement_score":0.031008739,"about_ca_system_score_codex":0.00258636,"about_ca_system_score_gemma":0.004014604,"threshold_uncertainty_score":0.16399181},"labels":[],"label_agreement":null},{"id":"W2120911483","doi":"10.5705/ss.2012.187","title":"Multiple-Inflation Poisson Model with L1 Regularization","year":2012,"lang":"en","type":"article","venue":"Statistica Sinica","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Poisson distribution; Regularization (linguistics); Inflation (cosmology); Mathematics; Econometrics; Applied mathematics; Computer science; Statistics; Artificial intelligence; Physics","score_opus":0.07571112703351526,"score_gpt":0.37693916781252407,"score_spread":0.3012280407790088,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2120911483","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0055384403,0.00018434144,0.9915177,0.0008047128,0.000057054924,0.0000675674,0.00029019403,0.00018969997,0.0013502531],"genre_scores_gemma":[0.35853046,0.0010692655,0.60963196,0.001283629,0.0007721878,0.0017085918,0.0018345712,0.0004105525,0.024758764],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9939422,0.003915845,0.00021717454,0.0007753303,0.000793878,0.00035549855],"domain_scores_gemma":[0.9846126,0.011159424,0.0014232357,0.0013071897,0.0011707023,0.00032677964],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.016168537,0.0011252773,0.0024911133,0.0018769295,0.0010224875,0.0023755515,0.0066699963,0.0031914571,0.005342876],"category_scores_gemma":[0.030613013,0.0009884759,0.002480537,0.0025331383,0.002223827,0.0034126977,0.0028924746,0.00473625,0.0016506589],"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":[0.00011251402,0.00007786294,0.0027951857,0.00017422093,0.000119047785,0.00037028003,0.00032114543,0.22874129,0.0006449075,0.7319935,0.0062069315,0.02844327],"study_design_scores_gemma":[0.000028239378,0.000026437972,0.00037052357,0.000019603836,0.000023401692,0.000098494645,0.000027607899,0.8552714,0.00017585667,0.1414043,0.0025217356,0.000032350188],"about_ca_topic_score_codex":0.008205591,"about_ca_topic_score_gemma":0.0047712126,"teacher_disagreement_score":0.016168537,"about_ca_system_score_codex":0.0021209044,"about_ca_system_score_gemma":0.0022282687,"threshold_uncertainty_score":0.085508406},"labels":[],"label_agreement":null},{"id":"W2121472144","doi":"10.5705/ss.2011.275","title":"Penalized minimum average variance estimation","year":2012,"lang":"en","type":"article","venue":"Statistica Sinica","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Estimation; Statistics; Variance (accounting); Mathematics; Minimum-variance unbiased estimator; Computer science; Econometrics; Mean squared error; Economics","score_opus":0.10005025851724722,"score_gpt":0.41404335083984695,"score_spread":0.31399309232259975,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2121472144","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00078791927,0.0001329863,0.9987953,0.000049740618,0.000020192701,0.000012309448,0.000014420273,0.000074003416,0.0001130715],"genre_scores_gemma":[0.11674871,0.00054424343,0.8797839,0.0002455368,0.00026381473,0.00032391728,0.0003625094,0.0001617201,0.0015656374],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9958929,0.002553858,0.00017711427,0.00057599985,0.00064543233,0.00015458513],"domain_scores_gemma":[0.994325,0.0039634295,0.00043732102,0.00065892184,0.00052487734,0.0000905469],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005285812,0.0012745721,0.0018865454,0.0014473255,0.00044770385,0.0011674021,0.002007327,0.0014519487,0.001857989],"category_scores_gemma":[0.017490389,0.0006625948,0.0015806186,0.0016716988,0.0010115501,0.0014827457,0.0016016528,0.002552585,0.00074896705],"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":[0.00025801934,0.00019523474,0.0030559122,0.0005638645,0.00070665014,0.00021283126,0.0001297073,0.46025276,0.010697742,0.15046427,0.008080029,0.36538306],"study_design_scores_gemma":[0.00001787124,0.000053889693,0.00033055828,0.000019538877,0.000029658206,0.00005804021,0.000004491298,0.96654075,0.0012375619,0.03004874,0.0016412678,0.0000177142],"about_ca_topic_score_codex":0.0010898852,"about_ca_topic_score_gemma":0.0011806047,"teacher_disagreement_score":0.005285812,"about_ca_system_score_codex":0.0004681096,"about_ca_system_score_gemma":0.0011183065,"threshold_uncertainty_score":0.0279544},"labels":[],"label_agreement":null},{"id":"W2125501900","doi":"10.5705/ss.2010.062","title":"Semiparametric mixture of binomial regression with a degenerate component","year":2011,"lang":"en","type":"article","venue":"Statistica Sinica","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Component (thermodynamics); Semiparametric regression; Binomial regression; Econometrics; Mathematics; Statistics; Binomial (polynomial); Regression; Negative binomial distribution; Regression analysis; Poisson distribution; Physics","score_opus":0.03833413702423937,"score_gpt":0.26995462710201973,"score_spread":0.23162049007778035,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2125501900","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.042356312,0.00023666675,0.95625955,0.00022077508,0.000024503699,0.000072148105,0.0001380016,0.00019311075,0.00049888436],"genre_scores_gemma":[0.7461827,0.0004537053,0.24737354,0.00023511663,0.00014190743,0.00047254754,0.000932212,0.00013468895,0.004073636],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9938753,0.0042193155,0.00021554499,0.00087355817,0.00054452155,0.00027174441],"domain_scores_gemma":[0.96747744,0.026882483,0.0021481304,0.0019338863,0.0012149746,0.00034298838],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.015700577,0.0011045928,0.0027714309,0.0020074828,0.00069040596,0.0018940539,0.0032086314,0.0020755827,0.0028073739],"category_scores_gemma":[0.041475028,0.0014163501,0.0021732096,0.002165253,0.002579985,0.0033539536,0.0033177007,0.00258332,0.0007310584],"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":[0.00051953585,0.00019155849,0.015248322,0.00034586655,0.00045106554,0.0004096853,0.00048879825,0.6977922,0.0017995943,0.22335425,0.0017966909,0.05760246],"study_design_scores_gemma":[0.000022287784,0.000021872673,0.001122241,0.000021684004,0.000028657463,0.00007238535,0.000021292683,0.9676021,0.00027122474,0.03042952,0.00036251434,0.000024214027],"about_ca_topic_score_codex":0.003970163,"about_ca_topic_score_gemma":0.0031210051,"teacher_disagreement_score":0.015700577,"about_ca_system_score_codex":0.0011594794,"about_ca_system_score_gemma":0.00071444496,"threshold_uncertainty_score":0.08303356},"labels":[],"label_agreement":null},{"id":"W2135248502","doi":"10.5705/ss.2009.191","title":"Sufficient dimension reduction in regression with missing predictors","year":2011,"lang":"en","type":"article","venue":"Statistica Sinica","topic":"Statistical Methods and Inference","field":"Mathematics","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":"Toronto Metropolitan University","funders":"","keywords":"Sufficient dimension reduction; Sliced inverse regression; Dimensionality reduction; Dimension (graph theory); Mathematics; Regression; Statistics; Reduction (mathematics); Missing data; Regression analysis; Computer science; Artificial intelligence; Combinatorics; Geometry","score_opus":0.12685894194943254,"score_gpt":0.37173227244801976,"score_spread":0.24487333049858723,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2135248502","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0068834038,0.0004725596,0.9915469,0.0004047992,0.0000271035,0.000034018725,0.00011573742,0.000116319134,0.0003991812],"genre_scores_gemma":[0.33532867,0.0019211228,0.6575816,0.0005766678,0.0003713853,0.0013142064,0.0011551536,0.00023459597,0.0015165335],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9727859,0.023042714,0.00085619884,0.0011052787,0.0018055226,0.0004044015],"domain_scores_gemma":[0.88732517,0.093143724,0.004339548,0.010268717,0.0041337237,0.0007891622],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.032549884,0.0009864259,0.0024468794,0.0023760395,0.0010755637,0.0018210375,0.0018572146,0.0014920549,0.0017961236],"category_scores_gemma":[0.11017579,0.0010245153,0.00199112,0.0022422331,0.0029776434,0.0026435633,0.0035978463,0.0029567205,0.0004574646],"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":[0.00034788414,0.00014567269,0.005833055,0.0008026353,0.00045181124,0.00041716368,0.0006697877,0.24240269,0.0016977212,0.6373378,0.006626126,0.1032676],"study_design_scores_gemma":[0.00006303992,0.000074721254,0.0009338563,0.0001323522,0.00004211661,0.0000954093,0.000056557958,0.58312035,0.00069277064,0.4123084,0.0024297303,0.000050788236],"about_ca_topic_score_codex":0.0010053709,"about_ca_topic_score_gemma":0.0009155898,"teacher_disagreement_score":0.032549884,"about_ca_system_score_codex":0.00093625695,"about_ca_system_score_gemma":0.002578861,"threshold_uncertainty_score":0.17214233},"labels":[],"label_agreement":null},{"id":"W2159217576","doi":"10.5705/ss.2011.024a","title":"On variance estimation under auxiliary value imputation in sample surveys","year":2011,"lang":"en","type":"article","venue":"Statistica Sinica","topic":"Survey Sampling and Estimation Techniques","field":"Mathematics","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Statistics; Imputation (statistics); Variance (accounting); Estimation; Value (mathematics); Econometrics; Sample (material); Mathematics; Missing data; Economics; Accounting","score_opus":0.15025223259561843,"score_gpt":0.3878844082013983,"score_spread":0.23763217560577987,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2159217576","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0032262506,0.00046899877,0.99544466,0.00023775328,0.000027717437,0.00003649721,0.000027037408,0.00005383444,0.00047722858],"genre_scores_gemma":[0.26200825,0.0028427304,0.72958887,0.00065685005,0.000618663,0.0011318508,0.0004840369,0.00021976419,0.0024489642],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.92937505,0.06291409,0.0010701432,0.0024107518,0.003373805,0.000856148],"domain_scores_gemma":[0.70271415,0.27298492,0.0069587175,0.011210976,0.005582003,0.0005492677],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.08115849,0.0013939795,0.003036973,0.0036177782,0.0009521613,0.0026588002,0.0039521363,0.002966681,0.0014113649],"category_scores_gemma":[0.2429376,0.0012324073,0.0022952317,0.0051125768,0.0053016003,0.0044846153,0.004845189,0.0034529855,0.00043613982],"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":[0.00016022233,0.00006852575,0.006042727,0.0005166016,0.00044923296,0.00020712332,0.00052339776,0.15129736,0.0006094368,0.7722707,0.0017479825,0.06610679],"study_design_scores_gemma":[0.00007023363,0.00011812326,0.001055827,0.00016853638,0.00008904404,0.00012611192,0.00007063034,0.51655054,0.0007826406,0.47828338,0.002637855,0.0000470303],"about_ca_topic_score_codex":0.002225819,"about_ca_topic_score_gemma":0.0012973481,"teacher_disagreement_score":0.08115849,"about_ca_system_score_codex":0.0017949663,"about_ca_system_score_gemma":0.0021039085,"threshold_uncertainty_score":0.42921227},"labels":[],"label_agreement":null},{"id":"W2313674309","doi":"10.5705/ss.2013.322t","title":"Robust sampling designs for a possibly misspecified stochastic process","year":2014,"lang":"en","type":"article","venue":"Statistica Sinica","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Sampling (signal processing); Process (computing); Econometrics; Statistics; Mathematics","score_opus":0.5440202437885654,"score_gpt":0.5280115648360327,"score_spread":0.016008678952532662,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2313674309","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00944019,0.0001388795,0.98993903,0.000053770735,0.000014031242,0.00009832276,0.000024529965,0.000047380203,0.00024387994],"genre_scores_gemma":[0.25823894,0.0003180183,0.7395147,0.00008609121,0.000053466872,0.001029438,0.00013896122,0.000036889942,0.0005834795],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9807974,0.0145764435,0.00059969776,0.0018289312,0.00188829,0.00030928463],"domain_scores_gemma":[0.95447385,0.034873165,0.0055787964,0.0030306699,0.0017929123,0.0002506646],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02758706,0.0011471792,0.0016568168,0.001432963,0.0004105655,0.0010717456,0.0017299948,0.0020313333,0.0015907557],"category_scores_gemma":[0.057207033,0.0007104564,0.0013218316,0.00085686153,0.0021990573,0.0011791324,0.0012455864,0.0013617008,0.00031532356],"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":[0.0007995369,0.00023219659,0.0016733564,0.00048843067,0.00042661166,0.00013111766,0.00028264686,0.7118527,0.0085039595,0.19452573,0.00034542824,0.080738276],"study_design_scores_gemma":[0.0002549007,0.001091923,0.0007581811,0.00007737213,0.000110764515,0.00005558839,0.00002997505,0.898898,0.004849061,0.092127696,0.001693479,0.000052991243],"about_ca_topic_score_codex":0.0006331348,"about_ca_topic_score_gemma":0.0005159311,"teacher_disagreement_score":0.02758706,"about_ca_system_score_codex":0.0010386795,"about_ca_system_score_gemma":0.0013018635,"threshold_uncertainty_score":0.14589602},"labels":[],"label_agreement":null},{"id":"W2340170030","doi":"","title":"TRANSFORMED PARTIAL LEAST SQUARES FOR MULTIVARIATE DATA","year":2007,"lang":"en","type":"article","venue":"Statistica Sinica","topic":"Advanced Statistical Methods and Models","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":"Toronto Metropolitan University","funders":"","keywords":"Covariate; Mathematics; Multivariate statistics; Collinearity; Estimator; Nonparametric regression; Transformation (genetics); Curse of dimensionality; Sufficient dimension reduction; Mathematical optimization; Statistics; Regression","score_opus":0.4059033074726503,"score_gpt":0.5512652903696592,"score_spread":0.1453619828970089,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2340170030","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0005285592,0.00020724337,0.9986089,0.00008110212,0.00003060591,0.000027396762,0.000105601524,0.00027169965,0.00013893268],"genre_scores_gemma":[0.029009435,0.00080693845,0.966559,0.00010616482,0.00011460494,0.00064274215,0.00095303095,0.00039006688,0.0014180401],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9901186,0.0068440186,0.00036459748,0.0011562109,0.0013512673,0.00016526393],"domain_scores_gemma":[0.98379207,0.011676263,0.0010728545,0.0020078802,0.0012984647,0.00015245508],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0082104,0.0018597629,0.0021215677,0.0020041799,0.0007223086,0.001352452,0.0024730172,0.0013750893,0.0053349044],"category_scores_gemma":[0.0357928,0.0010530254,0.002313172,0.0056119226,0.0016469159,0.0018646448,0.0024837116,0.0041891863,0.0028934237],"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":[0.00015699389,0.000105032006,0.002224988,0.0011033093,0.0006418002,0.000360771,0.00041060153,0.41707796,0.004109592,0.15409248,0.011592184,0.4081243],"study_design_scores_gemma":[0.000029709348,0.00006387021,0.0007258921,0.00005591084,0.000031045733,0.00012089959,0.000049462793,0.8850268,0.0008742358,0.10185567,0.01112149,0.00004490875],"about_ca_topic_score_codex":0.00475855,"about_ca_topic_score_gemma":0.0047369148,"teacher_disagreement_score":0.0082104,"about_ca_system_score_codex":0.00118658,"about_ca_system_score_gemma":0.0029251806,"threshold_uncertainty_score":0.04342127},"labels":[],"label_agreement":null},{"id":"W2548316006","doi":"","title":"ON DIMENSION REDUCTION IN REGRESSIONS WITH MULTIVARIATE RESPONSES","year":2010,"lang":"en","type":"article","venue":"Statistica Sinica","topic":"Advanced Statistical Methods and Models","field":"Mathematics","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":"Toronto Metropolitan University","funders":"","keywords":"Sliced inverse regression; Sufficient dimension reduction; Mathematics; Dimensionality reduction; Multivariate statistics; Regression; Statistics; Dimension (graph theory); Kernel (algebra); Regression analysis; Bayesian multivariate linear regression; Computer science; Artificial intelligence; Combinatorics","score_opus":0.10729982546909402,"score_gpt":0.4670370551983176,"score_spread":0.3597372297292236,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2548316006","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004972386,0.0006856038,0.9933509,0.00019869885,0.000041356685,0.000022000764,0.00004835336,0.00007814124,0.00060266006],"genre_scores_gemma":[0.20957088,0.0052013276,0.77710307,0.0005870075,0.0009035354,0.0005956045,0.0007062251,0.0003294018,0.005002874],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9929797,0.005072647,0.00020870699,0.00067724974,0.0008578124,0.00020388086],"domain_scores_gemma":[0.9835095,0.012588528,0.0010339391,0.0016306861,0.0010497791,0.0001875247],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0095189465,0.0016145617,0.0015549479,0.0017196588,0.0006590708,0.0010818342,0.0010295694,0.00092111545,0.0014868517],"category_scores_gemma":[0.027125534,0.0005083353,0.0017191136,0.0019872908,0.0032879906,0.0020641172,0.0025270323,0.0027656436,0.00076236285],"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":[0.00022276577,0.00013227493,0.0022683686,0.00053507165,0.0003129895,0.0001940083,0.00051380816,0.25575805,0.0076923263,0.49407792,0.004179805,0.23411259],"study_design_scores_gemma":[0.00003236849,0.00014282165,0.0016233503,0.00009556932,0.00006187838,0.00010849704,0.000061567465,0.6508381,0.004267272,0.3359113,0.006767396,0.00008989816],"about_ca_topic_score_codex":0.0017841639,"about_ca_topic_score_gemma":0.0012161783,"teacher_disagreement_score":0.0095189465,"about_ca_system_score_codex":0.0007000797,"about_ca_system_score_gemma":0.0010368392,"threshold_uncertainty_score":0.050341606},"labels":[],"label_agreement":null},{"id":"W2591963023","doi":"10.5705/ss.202015.0199","title":"Composite $T^2$ test for high-dimensional data","year":2017,"lang":"en","type":"article","venue":"Statistica Sinica","topic":"Simulation Techniques and Applications","field":"Decision Sciences","cited_by":8,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Composite number; Test (biology); Computer science; Geology; Algorithm","score_opus":0.34192179901794323,"score_gpt":0.5277443372598961,"score_spread":0.18582253824195288,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2591963023","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06533656,0.0005714244,0.9267094,0.00095424696,0.0005692205,0.00052778836,0.0014026836,0.00079340325,0.0031352472],"genre_scores_gemma":[0.53506005,0.00033153268,0.4531038,0.0010208059,0.00048266153,0.0032557542,0.0027765932,0.00040812048,0.0035606562],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.93710786,0.04541091,0.0021810953,0.00793261,0.0061370167,0.0012305267],"domain_scores_gemma":[0.6254792,0.3463371,0.0043811286,0.017282728,0.0046247626,0.0018951173],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.061340056,0.0014932028,0.0054913154,0.0036066042,0.0019972564,0.0030605055,0.004084474,0.0040540975,0.021272596],"category_scores_gemma":[0.21512794,0.0007576481,0.0042615314,0.0035127467,0.0039585326,0.0034282107,0.0026926205,0.0056362725,0.0020140766],"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":[0.015243577,0.0021757584,0.10741726,0.0029356845,0.0084521435,0.0032635443,0.0014640781,0.08108928,0.009014772,0.2898109,0.032136925,0.44699606],"study_design_scores_gemma":[0.001259957,0.0046991426,0.04036473,0.00034899975,0.0011715797,0.0024093308,0.00070950855,0.6001854,0.0072291642,0.3262325,0.015092236,0.00029739516],"about_ca_topic_score_codex":0.0013077323,"about_ca_topic_score_gemma":0.0011904422,"teacher_disagreement_score":0.061340056,"about_ca_system_score_codex":0.0011299584,"about_ca_system_score_gemma":0.0034590731,"threshold_uncertainty_score":0.32440108},"labels":[],"label_agreement":null},{"id":"W2592603888","doi":"","title":"Statistica Sinica: Introduction to special issue","year":2011,"lang":"en","type":"article","venue":"Statistica Sinica","topic":"Census and Population Estimation","field":"Mathematics","cited_by":12,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science","score_opus":0.08899211119962684,"score_gpt":0.3615312301941618,"score_spread":0.2725391189945349,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2592603888","genre_codex":"editorial","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0009831905,0.14031924,0.08556813,0.14037499,0.55690783,0.00022797855,0.0058361487,0.0025804008,0.067202106],"genre_scores_gemma":[0.012413987,0.121699564,0.03069391,0.038932517,0.61698985,0.001134246,0.0073094657,0.003661469,0.167165],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9928871,0.002957597,0.0010110437,0.0009043016,0.0019683484,0.0002716519],"domain_scores_gemma":[0.96808964,0.01504431,0.0018647151,0.0033035926,0.010762225,0.0009355636],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009212917,0.0022616822,0.0026695926,0.0077862074,0.0010323765,0.0037915402,0.0014723191,0.003179115,0.04728174],"category_scores_gemma":[0.05065939,0.0011210323,0.0015565887,0.008224846,0.002064794,0.0043494487,0.0026820938,0.007841141,0.04820321],"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":[0.000026763288,0.000021699423,0.00033008453,0.00031661225,0.000020761237,0.000046539764,0.00003066276,0.00017866566,0.00006982191,0.008991636,0.9408944,0.049072433],"study_design_scores_gemma":[0.000013270908,0.000027613009,0.0015013405,0.00030380758,0.000028702636,0.00017049555,0.000032095468,0.0005554738,0.00010511953,0.0199237,0.9773162,0.000022196711],"about_ca_topic_score_codex":0.00279534,"about_ca_topic_score_gemma":0.0034023442,"teacher_disagreement_score":0.04728174,"about_ca_system_score_codex":0.0016634451,"about_ca_system_score_gemma":0.0040379376,"threshold_uncertainty_score":0.1581732},"labels":[],"label_agreement":null},{"id":"W2592938669","doi":"10.5705/ss.202015.0019","title":"A general construction for space-filling Latin hypercubes","year":2015,"lang":"en","type":"article","venue":"Statistica Sinica","topic":"Interconnection Networks and Systems","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Latin hypercube sampling; Hypercube; Space (punctuation); Latin Americans; Computer science; Mathematics; Political science; Parallel computing; Statistics; Law","score_opus":0.06060965390519691,"score_gpt":0.2946521162037869,"score_spread":0.23404246229859,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2592938669","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0026580493,0.00008504435,0.994589,0.00003541749,0.000032569642,0.000055046283,0.000047120688,0.00023314366,0.0022646992],"genre_scores_gemma":[0.06338928,0.0002472383,0.9330531,0.000087078144,0.000048240796,0.00062291144,0.00014922532,0.00015975423,0.0022431326],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982967,0.00081367814,0.000106434236,0.0002737805,0.00041668702,0.000092893286],"domain_scores_gemma":[0.9986883,0.00046646656,0.00016551575,0.00032001417,0.0002989558,0.00006076744],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016992133,0.00079234235,0.0007431825,0.0010035373,0.0006352467,0.0010054364,0.0010092335,0.0006199568,0.005003709],"category_scores_gemma":[0.0041138697,0.00061856717,0.0009822198,0.001100979,0.0013601481,0.00128858,0.0016516973,0.0011755538,0.0014951954],"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":[0.00023355604,0.00008095923,0.00075923465,0.0006053914,0.0000699817,0.00025815083,0.00037069406,0.14583191,0.07763479,0.5893883,0.0034635,0.18130349],"study_design_scores_gemma":[0.00018559028,0.00064253074,0.00052927644,0.00016920353,0.000062475054,0.0007500797,0.00015739753,0.48699266,0.07063047,0.35375047,0.08597613,0.0001537309],"about_ca_topic_score_codex":0.0002883567,"about_ca_topic_score_gemma":0.00031549134,"teacher_disagreement_score":0.005003709,"about_ca_system_score_codex":0.00047496904,"about_ca_system_score_gemma":0.0008633285,"threshold_uncertainty_score":0.01673913},"labels":[],"label_agreement":null},{"id":"W2605428180","doi":"10.5705/ss.202016.0222","title":"Gradient-induced Model-free Variable Selection with Composite Quantile Regression","year":2017,"lang":"en","type":"article","venue":"Statistica Sinica","topic":"Statistical Methods and 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":"Toronto Metropolitan University","funders":"National Natural Science Foundation of China","keywords":"Quantile regression; Statistics; Feature selection; Econometrics; Composite number; Variable (mathematics); Model selection; Selection (genetic algorithm); Regression analysis; Regression; Quantile; Mathematics; Computer science; Artificial intelligence; Algorithm; Mathematical analysis","score_opus":0.1205521500059368,"score_gpt":0.402373790250657,"score_spread":0.28182164024472023,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2605428180","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0021824404,0.000090773334,0.9972716,0.00007522668,0.000013791098,0.000024252655,0.00002046267,0.00013562308,0.00018588922],"genre_scores_gemma":[0.22758245,0.000496326,0.7681273,0.0002424415,0.00018472281,0.0003968738,0.00041987584,0.00023605305,0.0023139282],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9947789,0.0039517786,0.00009954562,0.00047229708,0.00056737865,0.00013028072],"domain_scores_gemma":[0.9887995,0.008886575,0.00053711474,0.00089420966,0.00070077233,0.00018178165],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008552151,0.0010401317,0.0018965796,0.0015125984,0.00061674346,0.001164052,0.0021738466,0.0011468278,0.0022990361],"category_scores_gemma":[0.025925767,0.0006682313,0.0013214679,0.0020504869,0.0015976758,0.001407888,0.0024108377,0.0024420551,0.0006549556],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000354236,0.00014730605,0.0028074253,0.00025801169,0.0002848131,0.00021466757,0.00018434587,0.64677256,0.0018203143,0.18427141,0.0037029895,0.15918183],"study_design_scores_gemma":[0.000042212738,0.00003366681,0.00026390687,0.000011689669,0.000015836466,0.000029765532,0.0000063014445,0.9476959,0.00047053487,0.05052704,0.00088982785,0.000013271242],"about_ca_topic_score_codex":0.002722888,"about_ca_topic_score_gemma":0.0021975832,"teacher_disagreement_score":0.008552151,"about_ca_system_score_codex":0.0008568791,"about_ca_system_score_gemma":0.0019394305,"threshold_uncertainty_score":0.0452286},"labels":[],"label_agreement":null},{"id":"W2612728037","doi":"","title":"MAXIMUM LIKELIHOOD INFERENCE IN ROBUST LINEAR MIXED-EFFECTS MODELS USING MULTIVARIATE t DISTRIBUTIONS","year":2007,"lang":"en","type":"article","venue":"Statistica Sinica","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":42,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta; University of Waterloo","funders":"","keywords":"Akaike information criterion; Outlier; Restricted maximum likelihood; Estimator; Random effects model; Multivariate statistics; Generalized linear mixed model; Mathematics; Mixed model; Maximum likelihood; Statistics; Inference; Expectation–maximization algorithm; Bayesian information criterion; Degrees of freedom (physics and chemistry); Maximum likelihood sequence estimation; Linear model; M-estimator; Computer science; Artificial intelligence","score_opus":0.11992058314677775,"score_gpt":0.41746524140847996,"score_spread":0.2975446582617022,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2612728037","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0007776585,0.00018628797,0.99868387,0.00006436031,0.000008636867,0.00002159397,0.000023995715,0.00009859658,0.00013488256],"genre_scores_gemma":[0.059387833,0.00088912103,0.9378976,0.00012605224,0.00010512491,0.000521286,0.00025345362,0.00019175907,0.00062780705],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9772703,0.019425921,0.00059620454,0.0013456129,0.0011309077,0.00023105361],"domain_scores_gemma":[0.9072016,0.08681141,0.0028238792,0.0018473889,0.0010796594,0.0002360235],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.030817565,0.0023474556,0.0036440836,0.0036274027,0.0009651653,0.0026089177,0.0038945598,0.0028452494,0.0029984957],"category_scores_gemma":[0.12784044,0.0016178872,0.003735115,0.0042482587,0.0030264966,0.0035026944,0.0033300682,0.003629699,0.00094829104],"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":[0.00022686203,0.000097406926,0.0013502139,0.00071667193,0.00071729964,0.0003311698,0.00036255317,0.5915732,0.00076691713,0.27986193,0.0019069742,0.12208882],"study_design_scores_gemma":[0.00005829127,0.000044627275,0.00021520893,0.000071207556,0.0000523601,0.00005850669,0.000031704476,0.730365,0.0004165883,0.2675628,0.0010895437,0.00003412935],"about_ca_topic_score_codex":0.0041863727,"about_ca_topic_score_gemma":0.0037611427,"teacher_disagreement_score":0.030817565,"about_ca_system_score_codex":0.0015797038,"about_ca_system_score_gemma":0.0025148548,"threshold_uncertainty_score":0.16298085},"labels":[],"label_agreement":null},{"id":"W2732028845","doi":"","title":"MODIFIED LIKELIHOOD RATIO TEST FOR HOMOGENEITY IN A TWO-SAMPLE PROBLEM","year":2009,"lang":"en","type":"article","venue":"Statistica Sinica","topic":"Bayesian Methods and Mixture Models","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":"University of British Columbia; York University","funders":"","keywords":"Homogeneity (statistics); Likelihood-ratio test; Statistics; Statistic; Mathematics; Null hypothesis; Limiting; Test statistic; Null distribution; Applied mathematics; Statistical hypothesis testing; Asymptotic distribution; Score test; Computer science; Estimator","score_opus":0.027275126479994912,"score_gpt":0.33271468729070847,"score_spread":0.30543956081071355,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2732028845","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.043885354,0.0004939202,0.9525218,0.00092580816,0.00008649812,0.0003720566,0.0002165279,0.00029838222,0.001199563],"genre_scores_gemma":[0.5783154,0.0003302369,0.4165197,0.0007321921,0.00045837354,0.0016183307,0.0006126189,0.00012267337,0.0012905775],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.92033845,0.0641666,0.0022922887,0.006800415,0.0054101655,0.0009920212],"domain_scores_gemma":[0.6774513,0.2998219,0.008377993,0.010077463,0.003122027,0.0011493008],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.06511718,0.0012828875,0.0046308762,0.0037809608,0.0009137936,0.0026870908,0.004584545,0.0053312494,0.0049613034],"category_scores_gemma":[0.2672355,0.00085178186,0.0023013963,0.0026052175,0.005443634,0.0043637305,0.00301423,0.003263273,0.00080152927],"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":[0.004490457,0.0009761602,0.061027188,0.0020319119,0.0051912596,0.0071761776,0.0021890542,0.1557583,0.0118456725,0.4056769,0.0053200535,0.33831692],"study_design_scores_gemma":[0.0008637337,0.0024340327,0.014014381,0.00018482839,0.00047250406,0.0031763443,0.00037412244,0.59120816,0.005068598,0.37717393,0.004767119,0.00026221893],"about_ca_topic_score_codex":0.00073532265,"about_ca_topic_score_gemma":0.00029313855,"teacher_disagreement_score":0.06511718,"about_ca_system_score_codex":0.0009812139,"about_ca_system_score_gemma":0.0016561451,"threshold_uncertainty_score":0.34437668},"labels":[],"label_agreement":null},{"id":"W2951067337","doi":"10.5705/ss.2011.261","title":"Jackknife Empirical Likelihood Test for Equality of Two High Dimensional Means","year":2013,"lang":"en","type":"article","venue":"Statistica Sinica","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":33,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Jackknife resampling; Mathematics; Covariance; Dimension (graph theory); Statistics; Sample size determination; Empirical likelihood; Econometrics; Multivariate statistics; Resampling; Sample (material); Test (biology); Confidence interval; Estimator; Combinatorics","score_opus":0.14928474064638073,"score_gpt":0.4503231951425118,"score_spread":0.3010384544961311,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2951067337","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.053003352,0.00038998763,0.9420114,0.00039076962,0.00010457487,0.00016972978,0.00024776452,0.0003400089,0.0033424043],"genre_scores_gemma":[0.67266405,0.0003587748,0.32157597,0.00057862053,0.0002022175,0.0010621258,0.0009830083,0.00018117604,0.0023940946],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9804749,0.0115329465,0.0010454776,0.0032555494,0.002901688,0.00078940205],"domain_scores_gemma":[0.89674056,0.08027969,0.0068508196,0.009098267,0.0050477595,0.0019829448],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02248327,0.0008618716,0.0031957033,0.0025631727,0.0018023015,0.0021917254,0.0029175,0.0030003279,0.006141383],"category_scores_gemma":[0.18606882,0.00059625093,0.0014277908,0.002567857,0.0054016532,0.005189031,0.0044528586,0.0035953,0.0013670523],"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":[0.002312072,0.0005289626,0.10436007,0.00095922337,0.0014838419,0.0023375647,0.002158152,0.053309076,0.0064877165,0.5574705,0.009008342,0.25958443],"study_design_scores_gemma":[0.0004047772,0.001045121,0.032936852,0.00040722368,0.00022729939,0.0027534235,0.0013652422,0.34795022,0.007774113,0.5896151,0.015190846,0.00032976313],"about_ca_topic_score_codex":0.0013018696,"about_ca_topic_score_gemma":0.0008080412,"teacher_disagreement_score":0.02248327,"about_ca_system_score_codex":0.0007211692,"about_ca_system_score_gemma":0.0021169954,"threshold_uncertainty_score":0.11890435},"labels":[],"label_agreement":null},{"id":"W3037492211","doi":"10.5705/ss.202018.0499","title":"FUNCTIONAL ADDITIVE QUANTILE REGRESSION","year":2019,"lang":"en","type":"article","venue":"Statistica Sinica","topic":"Statistical Methods and 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":"Toronto Metropolitan University","funders":"National Natural Science Foundation of China","keywords":"Quantile regression; Econometrics; Regression; Quantile; Statistics; Regression analysis; Computer science; Mathematics","score_opus":0.10477043512057574,"score_gpt":0.4024661169651799,"score_spread":0.29769568184460415,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3037492211","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0077534206,0.00042828382,0.98994815,0.0002447781,0.00004196543,0.000021809241,0.0000852714,0.00012329387,0.001352963],"genre_scores_gemma":[0.71627665,0.0020384775,0.26969874,0.0005665908,0.000359218,0.00022415566,0.0004884681,0.00018132161,0.010166376],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9957604,0.0027155569,0.0000950509,0.0006033144,0.0005839506,0.00024174289],"domain_scores_gemma":[0.9946241,0.0033226411,0.00061689527,0.00057575037,0.00071319233,0.00014735856],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0057720477,0.0011981861,0.0012899148,0.0012974073,0.0004654005,0.0015226129,0.0028079732,0.0014356251,0.0036654146],"category_scores_gemma":[0.0132701155,0.0004409981,0.0013823741,0.0023087743,0.0015360701,0.0014726508,0.0018044105,0.002118131,0.0006524663],"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":[0.00009548669,0.000107286316,0.0054695886,0.0002669665,0.0002586038,0.00025579886,0.00015013186,0.58155715,0.0022384147,0.33323768,0.0024057366,0.073957205],"study_design_scores_gemma":[0.000010663342,0.000058995905,0.0010570592,0.000019569767,0.0000447894,0.00007325778,0.000022285249,0.9520156,0.00051837735,0.04395679,0.0022005357,0.000022056474],"about_ca_topic_score_codex":0.0044524237,"about_ca_topic_score_gemma":0.0024415036,"teacher_disagreement_score":0.0057720477,"about_ca_system_score_codex":0.0010551462,"about_ca_system_score_gemma":0.0010952693,"threshold_uncertainty_score":0.030525923},"labels":[],"label_agreement":null},{"id":"W3123861198","doi":"10.5705/ss.202017.0034","title":"OPTIMAL MODEL AVERAGING OF VARYING COEFFICIENT MODELS","year":2017,"lang":"en","type":"preprint","venue":"Statistica Sinica","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Social Sciences and Humanities Research Council of Canada; Natural Sciences and Engineering Research Council of Canada; National Science Foundation","keywords":"Estimator; Categorical variable; Monte Carlo method; Applied mathematics; Sample (material); Simple (philosophy); Mathematics; Set (abstract data type); Computer science; Code (set theory); Mathematical optimization; Statistics","score_opus":0.2461490589473006,"score_gpt":0.4381490800827414,"score_spread":0.1920000211354408,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3123861198","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008750381,0.0002757876,0.9900907,0.00015811193,0.000025356288,0.000016456028,0.000079947094,0.00014426657,0.00045902966],"genre_scores_gemma":[0.55193514,0.0013924104,0.44096693,0.00038802106,0.00033280166,0.00031661763,0.0010624961,0.0003374706,0.003268121],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9934175,0.0039117653,0.00030079362,0.0014168859,0.0006255127,0.00032753756],"domain_scores_gemma":[0.98391104,0.011506584,0.0015152369,0.002069346,0.0007115195,0.0002862327],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01115771,0.0012993525,0.002173849,0.0013514889,0.00057116867,0.0016567775,0.0031003938,0.0014510315,0.0021283997],"category_scores_gemma":[0.042399965,0.0008076437,0.0021658929,0.0017498644,0.0015007606,0.0026946664,0.0022568915,0.0020508724,0.00052797666],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012969345,0.000059340964,0.0034172228,0.00023855353,0.000519667,0.00025331124,0.0002041454,0.6612203,0.0020706134,0.24649715,0.0021270858,0.08326292],"study_design_scores_gemma":[0.000011678993,0.00005337137,0.0004892024,0.000021773294,0.00006262847,0.00006023788,0.000019954894,0.8407001,0.0008190758,0.15636027,0.0013732938,0.000028479337],"about_ca_topic_score_codex":0.0049389224,"about_ca_topic_score_gemma":0.003967021,"teacher_disagreement_score":0.01115771,"about_ca_system_score_codex":0.0010250014,"about_ca_system_score_gemma":0.0013951302,"threshold_uncertainty_score":0.0590083},"labels":[],"label_agreement":null},{"id":"W3163011639","doi":"10.5705/ss.202021.0112","title":"Shape Constrained Kernel PDF and PMF Estimation","year":2022,"lang":"en","type":"article","venue":"Statistica Sinica","topic":"Image Retrieval and Classification Techniques","field":"Computer 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":"McMaster University","funders":"National Science Foundation","keywords":"Pointwise; Convexity; Mathematics; Kernel density estimation; Kernel (algebra); Boundary (topology); Monotonic function; Probability density function; Logarithm; Function (biology); Upper and lower bounds; Convex function; Range (aeronautics); Applied mathematics; Mathematical optimization; Probability mass function; Logarithmically convex function; Regular polygon; Combinatorics; Convex optimization; Convex combination; Estimator; Mathematical analysis; Statistics; Geometry","score_opus":0.01613423607724034,"score_gpt":0.2731335359089429,"score_spread":0.25699929983170255,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3163011639","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0017233796,0.00016311472,0.99720985,0.00011198518,0.000022656355,0.000017155166,0.0000514751,0.00012363741,0.0005767484],"genre_scores_gemma":[0.37674913,0.0015434434,0.6123639,0.0003505595,0.00038587555,0.00022099825,0.00062198186,0.00031286507,0.007451205],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99824107,0.00059504854,0.00008286685,0.00046065205,0.00045410643,0.00016632502],"domain_scores_gemma":[0.99358153,0.00379314,0.0005944842,0.001102836,0.00077492703,0.00015311055],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0032122917,0.0013527566,0.0015523938,0.0019137884,0.0005476484,0.0024246213,0.0025464164,0.0033229634,0.0034111917],"category_scores_gemma":[0.02156159,0.0007563243,0.0015800889,0.0022853012,0.0016286721,0.0036317518,0.0021025215,0.002309116,0.0015360372],"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":[0.00008035753,0.000063538704,0.0016692323,0.0002408372,0.000097501725,0.00029375014,0.000096843396,0.71069384,0.0030635563,0.16101094,0.0029699192,0.119719625],"study_design_scores_gemma":[0.000004586813,0.000016872504,0.00026773123,0.000015754418,0.0000110404235,0.00009282612,0.000009395137,0.9603388,0.0008233242,0.036616046,0.0017831983,0.000020502594],"about_ca_topic_score_codex":0.0043248213,"about_ca_topic_score_gemma":0.0020230955,"teacher_disagreement_score":0.0043248213,"about_ca_system_score_codex":0.0010222076,"about_ca_system_score_gemma":0.0015380379,"threshold_uncertainty_score":0.016988397},"labels":[],"label_agreement":null},{"id":"W3173462899","doi":"10.5705/ss.202019.0243","title":"FULL-SEMIPARAMETRIC-LIKELIHOOD-BASED INFERENCE FOR NON-IGNORABLE MISSING DATA","year":2020,"lang":"en","type":"article","venue":"Statistica Sinica","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Inference; Missing data; Semiparametric model; Econometrics; Computer science; Semiparametric regression; Statistics; Mathematics; Artificial intelligence; Nonparametric statistics","score_opus":0.21381248393687877,"score_gpt":0.4448338917614719,"score_spread":0.23102140782459316,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3173462899","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0014514902,0.00025226775,0.9979074,0.000107220745,0.000008381415,0.000012286571,0.00004191659,0.000057231857,0.00016186853],"genre_scores_gemma":[0.26455715,0.0018754856,0.7299717,0.0003594965,0.00019360823,0.00037005945,0.00076799444,0.00019749835,0.001706915],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9919552,0.006347958,0.00029898138,0.00053855154,0.0007190579,0.00014028752],"domain_scores_gemma":[0.9692797,0.026124705,0.0012414684,0.0022835708,0.0008296453,0.0002409013],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.016946945,0.0010115549,0.0022710552,0.001921741,0.00049327716,0.0014981013,0.0033717363,0.0014664724,0.0027588212],"category_scores_gemma":[0.05321547,0.00097600115,0.0021223652,0.0020157027,0.002031743,0.0028137553,0.003444933,0.0028414691,0.00066870236],"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":[0.00021616624,0.00015053728,0.0040761735,0.0012894348,0.0006905277,0.00045624276,0.00040419347,0.38235247,0.002212196,0.43827266,0.0026860011,0.16719332],"study_design_scores_gemma":[0.00003262871,0.000031882657,0.00043799749,0.0000693575,0.00004393274,0.00015878498,0.00003070151,0.7086712,0.00069115445,0.28825182,0.001553841,0.000026621534],"about_ca_topic_score_codex":0.0012534139,"about_ca_topic_score_gemma":0.0015588744,"teacher_disagreement_score":0.016946945,"about_ca_system_score_codex":0.00080089335,"about_ca_system_score_gemma":0.0021616933,"threshold_uncertainty_score":0.08962506},"labels":[],"label_agreement":null},{"id":"W3208102029","doi":"10.5705/ss.202022.0276","title":"Unbiased Statistical Estimation and Valid Confidence Intervals Under Differential Privacy","year":2024,"lang":"en","type":"article","venue":"Statistica Sinica","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; University of Waterloo","keywords":"Statistics; Confidence interval; Differential privacy; Estimation; Unbiased Estimation; Computer science; Mathematics; Estimator; Engineering","score_opus":0.05127638862177194,"score_gpt":0.3524253205251359,"score_spread":0.301148931903364,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3208102029","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002294336,0.000110878456,0.99658585,0.000113056936,0.000014494988,0.000022879078,0.00004857999,0.00019674578,0.0006131667],"genre_scores_gemma":[0.3435741,0.00062340783,0.6519504,0.0005713022,0.00024791338,0.0005592745,0.0005387277,0.00045775145,0.0014771157],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.96500266,0.01799944,0.0018923648,0.004287892,0.009304385,0.0015133086],"domain_scores_gemma":[0.7853481,0.15453535,0.010799081,0.037382785,0.010542178,0.0013924709],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.029748585,0.0016693877,0.0024760398,0.004022528,0.0013528785,0.004852844,0.0037931115,0.0031073904,0.0027829807],"category_scores_gemma":[0.2573657,0.0014082533,0.0019901528,0.003542214,0.005417629,0.008339768,0.0075556743,0.0061427276,0.0013139416],"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":[0.00055829436,0.00011180016,0.0037687768,0.0003229741,0.00023917026,0.0006038746,0.0007441281,0.16087261,0.0069554616,0.6702278,0.0027759415,0.15281917],"study_design_scores_gemma":[0.000069885566,0.00012724419,0.00052618916,0.00012375061,0.000054070937,0.0005177948,0.00006576985,0.4515555,0.012936871,0.5305264,0.0034227867,0.000073737385],"about_ca_topic_score_codex":0.0005983003,"about_ca_topic_score_gemma":0.00033575416,"teacher_disagreement_score":0.029748585,"about_ca_system_score_codex":0.0016343996,"about_ca_system_score_gemma":0.002294258,"threshold_uncertainty_score":0.15732747},"labels":[],"label_agreement":null},{"id":"W4200152913","doi":"10.5705/ss.202021.0051","title":"Sieve Estimation of a Class of Partially Linear Transformation Models With Interval-Censored Competing Risks Data","year":2021,"lang":"en","type":"article","venue":"Statistica Sinica","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"National Institute on Drug Abuse; National Institute of Mental Health; National Cancer Institute; National Institutes of Health; National Institute of Allergy and Infectious Diseases; Natural Sciences and Engineering Research Council of Canada; National Institute of Dental and Craniofacial Research; U.S. President’s Emergency Plan for AIDS Relief; United States Agency for International Development","keywords":"Estimator; Mathematics; Applied mathematics; Parametric statistics; Nonparametric statistics; Sieve (category theory); Asymptotic distribution; Consistency (knowledge bases); Mathematical optimization; Statistics; Discrete mathematics","score_opus":0.2722866766512904,"score_gpt":0.4446018574114688,"score_spread":0.17231518076017838,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200152913","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021964598,0.00009142421,0.97729254,0.0001210136,0.0000075427556,0.0000285814,0.00006206328,0.000077101155,0.00035506886],"genre_scores_gemma":[0.58915204,0.0006678516,0.40448964,0.00021249687,0.000071427094,0.0005312673,0.00094993034,0.000105633604,0.0038197588],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9962059,0.0024146063,0.00012087381,0.00055936916,0.0005340822,0.00016518404],"domain_scores_gemma":[0.9837426,0.0127512235,0.0012616935,0.0014300194,0.0005110136,0.00030335717],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0077385,0.00095178135,0.0020760333,0.0015452773,0.0004544569,0.0017663126,0.002498815,0.00154202,0.0018936996],"category_scores_gemma":[0.027624143,0.0007774579,0.0020894164,0.0013431433,0.00209819,0.0029243643,0.0027873286,0.002828079,0.00033691362],"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":[0.00014927851,0.00011707377,0.00446149,0.00013109589,0.00022273575,0.00024193004,0.00025928524,0.6697468,0.0015271096,0.26435694,0.0006060414,0.058180198],"study_design_scores_gemma":[0.000014274818,0.000037040183,0.00042480882,0.000013747217,0.0000112430735,0.00005241063,0.000019257805,0.9350365,0.0002714042,0.063643925,0.00046007053,0.000015336685],"about_ca_topic_score_codex":0.0017681741,"about_ca_topic_score_gemma":0.0014563113,"teacher_disagreement_score":0.0077385,"about_ca_system_score_codex":0.00063373183,"about_ca_system_score_gemma":0.0013729197,"threshold_uncertainty_score":0.040925622},"labels":[],"label_agreement":null},{"id":"W4200472720","doi":"10.5705/ss.202021.0024","title":"On Construction of Nonregular Two-Level Factorial Designs With Maximum Generalized Resolutions","year":2021,"lang":"en","type":"article","venue":"Statistica Sinica","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mathematics; Fractional factorial design; Factorial; Statistics; Factorial experiment; Mathematical analysis","score_opus":0.29358897282061996,"score_gpt":0.47063372037859424,"score_spread":0.17704474755797428,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200472720","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0051186774,0.00015424185,0.9937448,0.000042714426,0.00002075421,0.0000892336,0.000051950024,0.000079359575,0.00069821713],"genre_scores_gemma":[0.07262109,0.00031813324,0.9247495,0.00017699877,0.00006186398,0.0008572253,0.00020570296,0.00008800438,0.00092145725],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.96428406,0.028657485,0.0010837296,0.0025602006,0.0028946374,0.0005199295],"domain_scores_gemma":[0.94836026,0.041394,0.0030364213,0.0048640426,0.0018140068,0.00053123984],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.022195825,0.0012462,0.0020158142,0.0020321975,0.00055782316,0.0011843374,0.001799819,0.0010391858,0.004923443],"category_scores_gemma":[0.05136493,0.0008805203,0.0027526477,0.0017506033,0.0024641403,0.0018416449,0.0025079383,0.0026030988,0.0008619255],"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":[0.0008573205,0.00021366705,0.0016733528,0.0012993675,0.0002496092,0.00020881342,0.0005166344,0.059190247,0.016336113,0.6712276,0.0015589637,0.24666844],"study_design_scores_gemma":[0.00039954795,0.002020638,0.0022154232,0.0003723749,0.00016506443,0.00041653728,0.00011392362,0.28555483,0.015756404,0.6794354,0.013411248,0.00013856619],"about_ca_topic_score_codex":0.00021325385,"about_ca_topic_score_gemma":0.00024979253,"teacher_disagreement_score":0.022195825,"about_ca_system_score_codex":0.0007459124,"about_ca_system_score_gemma":0.0016131495,"threshold_uncertainty_score":0.117384195},"labels":[],"label_agreement":null},{"id":"W4318827822","doi":"10.5705/ss.202022.0142","title":"Outlier Detection via a Minimum Ridge Covariance Determinant Estimator","year":2023,"lang":"en","type":"article","venue":"Statistica Sinica","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","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 Science Foundation of Anhui Province; Natural Sciences and Engineering Research Council of Canada","keywords":"Covariance; Estimator; Ridge; Outlier; Minimum-variance unbiased estimator; Mathematics; Estimation of covariance matrices; Statistics; Anomaly detection; Pattern recognition (psychology); Computer science; Artificial intelligence; Geology","score_opus":0.01671460335240198,"score_gpt":0.28799321868621336,"score_spread":0.27127861533381137,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4318827822","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008249782,0.000062924104,0.99123526,0.000039940085,0.000009005304,0.000013797634,0.000016435803,0.00019451632,0.0001783626],"genre_scores_gemma":[0.34472454,0.00019779326,0.65349275,0.00009513123,0.00009047961,0.000087868306,0.00023047054,0.00016621935,0.0009147495],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9970489,0.0010406106,0.00016624398,0.00055382535,0.001029488,0.00016088459],"domain_scores_gemma":[0.9935363,0.0032227354,0.0008246407,0.00089368294,0.0013665325,0.00015599602],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028378023,0.0005508347,0.0013569217,0.0015519719,0.00044274886,0.0011166157,0.0015017284,0.001123037,0.00078809954],"category_scores_gemma":[0.01505108,0.00041006538,0.0008534127,0.0012239313,0.0010469935,0.0015044945,0.0014982739,0.001540529,0.0006249981],"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":[0.0005508621,0.00030117293,0.018030828,0.0004496104,0.00042119774,0.0006791259,0.0003634319,0.28586105,0.082887776,0.08784901,0.003648761,0.5189572],"study_design_scores_gemma":[0.000021424961,0.0000880605,0.0019723787,0.0000140738985,0.000017770015,0.0002890252,0.00002219547,0.97491395,0.0094331615,0.012188084,0.0010028771,0.000037062036],"about_ca_topic_score_codex":0.00064684445,"about_ca_topic_score_gemma":0.0005669119,"teacher_disagreement_score":0.0028378023,"about_ca_system_score_codex":0.0003282214,"about_ca_system_score_gemma":0.0008675853,"threshold_uncertainty_score":0.015007913},"labels":[],"label_agreement":null},{"id":"W4328050300","doi":"10.5705/ss.202020.0318","title":"Hypothesis Test on a Mixture Forward-Incubation-Time Epidemic Model With Application to COVID-19 Outbreak","year":2023,"lang":"en","type":"article","venue":"Statistica Sinica","topic":"COVID-19 epidemiological studies","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":"University of Waterloo","funders":"Higher Education Discipline Innovation Project; East China Normal University; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Incubation period; Statistics; Coronavirus disease 2019 (COVID-19); Outbreak; Incubation; Mathematics; Identifiability; Time point; Likelihood-ratio test; Mixture model; Econometrics; Disease; Medicine; Biology; Virology; Infectious disease (medical specialty)","score_opus":0.1769253859928497,"score_gpt":0.42358909097427644,"score_spread":0.24666370498142673,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4328050300","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12128497,0.00055156933,0.87443155,0.0009997002,0.000091922426,0.00023017795,0.00035908623,0.00027972838,0.001771303],"genre_scores_gemma":[0.87568647,0.0007432481,0.114856265,0.0003685392,0.00021333441,0.0008609216,0.0008871281,0.00013106201,0.006252959],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9911447,0.0057614148,0.00033991906,0.0014540086,0.00062578614,0.00067412993],"domain_scores_gemma":[0.8582144,0.13090889,0.004462559,0.0017652381,0.0033026987,0.0013462608],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.027709791,0.0016540264,0.0037589832,0.0018339246,0.00081886747,0.0020059387,0.0037616987,0.0031833793,0.0053688725],"category_scores_gemma":[0.08112796,0.0010925278,0.0030572976,0.0012451872,0.0032929513,0.0037708608,0.0035207493,0.003928109,0.00038566723],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011122071,0.00028448086,0.018479755,0.0003794871,0.00059617264,0.0016188701,0.00080869894,0.7833289,0.0021866981,0.1625379,0.0015278468,0.02713896],"study_design_scores_gemma":[0.000063225896,0.00010728402,0.0010491429,0.000016867934,0.000059504317,0.000065464425,0.000071111,0.982346,0.00023765439,0.01573767,0.00021188518,0.00003414443],"about_ca_topic_score_codex":0.0071731047,"about_ca_topic_score_gemma":0.0025847526,"teacher_disagreement_score":0.027709791,"about_ca_system_score_codex":0.0014406972,"about_ca_system_score_gemma":0.0019127495,"threshold_uncertainty_score":0.14654517},"labels":[],"label_agreement":null},{"id":"W4366980285","doi":"10.5705/ss.202022.0328","title":"Necessary and Sufficient Conditions for Multiple Objective Optimal Regression Designs","year":2023,"lang":"en","type":"article","venue":"Statistica Sinica","topic":"Advanced Statistical Methods and Models","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":"University of Victoria; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Regression; Regression analysis; Mathematics; Statistics; Mathematical optimization; Econometrics","score_opus":0.23348590791983168,"score_gpt":0.4910828888198904,"score_spread":0.25759698090005867,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4366980285","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0076397513,0.00033714026,0.98482174,0.0006534728,0.0000477014,0.00047835748,0.0004751287,0.00017630972,0.0053704423],"genre_scores_gemma":[0.24208982,0.0013447972,0.7434365,0.0011384115,0.00034168764,0.0074957423,0.0011670053,0.00044803045,0.002538068],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9539714,0.026067182,0.0036127262,0.006063021,0.008027431,0.0022582628],"domain_scores_gemma":[0.6423179,0.3112455,0.021221144,0.008742977,0.01415851,0.0023139908],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.05834437,0.0032058735,0.004502193,0.0035100887,0.0011807521,0.0024779413,0.0025663332,0.0036742524,0.014836805],"category_scores_gemma":[0.20030394,0.0026567779,0.0027801543,0.0020198931,0.0067652063,0.005557926,0.0038074956,0.0056974837,0.0025662002],"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":[0.0006531951,0.0004934235,0.0041009835,0.0013433686,0.00033420708,0.00040758384,0.0005215769,0.13651218,0.006634146,0.8045959,0.003934735,0.040468846],"study_design_scores_gemma":[0.0007219717,0.001001255,0.0028107348,0.0005903533,0.00015816733,0.00040223042,0.0001972547,0.24430938,0.007968847,0.7335151,0.008204467,0.00012023444],"about_ca_topic_score_codex":0.0008067983,"about_ca_topic_score_gemma":0.0008025691,"teacher_disagreement_score":0.05834437,"about_ca_system_score_codex":0.0023848827,"about_ca_system_score_gemma":0.0077381954,"threshold_uncertainty_score":0.3085583},"labels":[],"label_agreement":null},{"id":"W4376627396","doi":"10.5705/ss.202022.0394","title":"Directional Tests in Gaussian Graphical Models","year":2023,"lang":"en","type":"article","venue":"Statistica Sinica","topic":"Soil Geostatistics and Mapping","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":"University of Toronto","funders":"","keywords":"Graphical model; Gaussian; Computer science; Mathematics; Artificial intelligence; Physics","score_opus":0.026744643644048976,"score_gpt":0.2845423781525168,"score_spread":0.2577977345084678,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4376627396","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017402917,0.0002105247,0.9804026,0.00020423443,0.00004104682,0.000090628906,0.00022546104,0.00030964936,0.0011129382],"genre_scores_gemma":[0.5896527,0.00056128995,0.40443352,0.0005009261,0.00023329152,0.0008572112,0.0013580829,0.00031239435,0.0020904592],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9700252,0.023579342,0.00083951274,0.002850903,0.0020638532,0.0006411474],"domain_scores_gemma":[0.821066,0.15632066,0.0063567003,0.010791092,0.0041677067,0.0012979037],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.032826055,0.0010882999,0.0018088889,0.0037904924,0.0009035243,0.0020108474,0.0028864725,0.0020952078,0.004918159],"category_scores_gemma":[0.15973939,0.00064243504,0.0021274914,0.0034589204,0.0047526616,0.0037343004,0.003608908,0.0031288704,0.0007790649],"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":[0.00051003345,0.000109048706,0.017771017,0.00030749963,0.000499475,0.0005240871,0.000317056,0.23766787,0.0018345969,0.61202246,0.0032658067,0.1251711],"study_design_scores_gemma":[0.00007380663,0.00026067742,0.0024785935,0.000047704656,0.000056901055,0.00017057566,0.000082578816,0.65300137,0.0009546037,0.34074232,0.0020785106,0.000052287196],"about_ca_topic_score_codex":0.0017979573,"about_ca_topic_score_gemma":0.0014390264,"teacher_disagreement_score":0.032826055,"about_ca_system_score_codex":0.0011412008,"about_ca_system_score_gemma":0.0017519013,"threshold_uncertainty_score":0.17360282},"labels":[],"label_agreement":null},{"id":"W4378699434","doi":"10.5705/ss.202021.0405","title":"Parametric Modal Regression with Autocorrelated Error Process","year":2023,"lang":"en","type":"article","venue":"Statistica Sinica","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Autocorrelation; Modal; Parametric statistics; Statistics; Computer science; Semiparametric regression; Regression; Process (computing); Regression analysis; Econometrics; Mathematics","score_opus":0.014969583882176882,"score_gpt":0.2804239529979285,"score_spread":0.2654543691157516,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4378699434","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0053246454,0.000025494017,0.99429214,0.000026842388,0.000004370397,0.000010032621,0.000012971865,0.00009469765,0.00020888064],"genre_scores_gemma":[0.45450646,0.00019942036,0.54151016,0.000094624855,0.00006115916,0.00020003722,0.0001999675,0.00010711955,0.003121063],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99852175,0.0007254782,0.00005637457,0.0002616804,0.000354128,0.00008061541],"domain_scores_gemma":[0.9947537,0.00346698,0.0007265979,0.00050942093,0.00048094307,0.00006237964],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002974918,0.00067856937,0.0007970823,0.00084236055,0.00030506987,0.000684291,0.0015416354,0.0008544311,0.0020181802],"category_scores_gemma":[0.013521638,0.00035650388,0.0008011841,0.0007178047,0.00078617147,0.0010932591,0.0014957637,0.0012733787,0.00043951496],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013830423,0.00009802394,0.0045660753,0.00017162377,0.000118863536,0.00020105076,0.00016097442,0.6845304,0.009868551,0.13199796,0.0010180661,0.16713016],"study_design_scores_gemma":[0.0000028610486,0.000019787942,0.00037769967,0.0000046164214,0.000004408391,0.000023431518,0.000005388,0.9916106,0.00078768743,0.006889146,0.000265227,0.000009177016],"about_ca_topic_score_codex":0.002044708,"about_ca_topic_score_gemma":0.0022293823,"teacher_disagreement_score":0.002974918,"about_ca_system_score_codex":0.00046094737,"about_ca_system_score_gemma":0.00079062395,"threshold_uncertainty_score":0.015733004},"labels":[],"label_agreement":null},{"id":"W4386229360","doi":"10.5705/ss.202022.0282","title":"Differentially Private Regularized Stochastic Convex Optimization with Heavy-Tailed Data","year":2023,"lang":"en","type":"article","venue":"Statistica Sinica","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University; University of Alberta","funders":"","keywords":"Regular polygon; Mathematics; Convex optimization; Econometrics; Computer science; Mathematical optimization","score_opus":0.13856239532222742,"score_gpt":0.39415158567922504,"score_spread":0.2555891903569976,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386229360","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0050506825,0.0001910069,0.99339867,0.0003957547,0.000021838183,0.000044375356,0.000062526495,0.00014607931,0.0006890231],"genre_scores_gemma":[0.47777766,0.00093571236,0.51507497,0.0009038755,0.00023760262,0.0004813039,0.00045955388,0.0002317944,0.003897456],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9923045,0.0041498854,0.0003551527,0.0012900912,0.0014714403,0.00042894558],"domain_scores_gemma":[0.970317,0.020213818,0.00213312,0.0052994075,0.0014903455,0.00054630655],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01285148,0.0014501793,0.0022654794,0.00090732053,0.00069898105,0.0028658705,0.0032261317,0.0022702503,0.0024680605],"category_scores_gemma":[0.050716612,0.00096528395,0.0014987426,0.0013500538,0.0032149085,0.0046392027,0.004757125,0.0056568556,0.0007229704],"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":[0.00040340703,0.00015031296,0.0022618114,0.0002772966,0.00019204302,0.00018004952,0.00019651509,0.6377917,0.0024892562,0.27429852,0.0036723535,0.07808676],"study_design_scores_gemma":[0.000024878145,0.00006201249,0.00021077786,0.000029611028,0.000012580858,0.000079283585,0.000017062695,0.91629153,0.0012103475,0.08111028,0.00093315827,0.00001849896],"about_ca_topic_score_codex":0.0010739558,"about_ca_topic_score_gemma":0.0012417104,"teacher_disagreement_score":0.01285148,"about_ca_system_score_codex":0.002062294,"about_ca_system_score_gemma":0.0028136712,"threshold_uncertainty_score":0.067965925},"labels":[],"label_agreement":null},{"id":"W4386313332","doi":"10.5705/ss.202022.0016","title":"Asymptotic Behaviour of the Modified Likelihood Root","year":2023,"lang":"en","type":"article","venue":"Statistica Sinica","topic":"Scientific Research and Discoveries","field":"Physics and Astronomy","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; University of Toronto","keywords":"Root (linguistics); Mathematics; Econometrics; Applied mathematics; Statistics; Maximum likelihood; Philosophy; Linguistics","score_opus":0.021749939701250942,"score_gpt":0.30717275665214516,"score_spread":0.2854228169508942,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386313332","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.068087325,0.001298986,0.9148549,0.0019999633,0.00010536944,0.0000390545,0.00016506457,0.000848811,0.012600602],"genre_scores_gemma":[0.8793978,0.0012429598,0.10585865,0.00051974197,0.00039447536,0.00015372214,0.0003937971,0.0007622941,0.011276559],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99751496,0.0012267284,0.0000892226,0.0003897605,0.000619776,0.00015957915],"domain_scores_gemma":[0.95562184,0.03334986,0.0033914286,0.003769424,0.0030073712,0.0008600431],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0065344255,0.0007811524,0.0008008777,0.0018282396,0.00047456555,0.0016141236,0.0024147467,0.001158489,0.006296289],"category_scores_gemma":[0.085286565,0.00053645176,0.0008751066,0.001138462,0.003332655,0.0048328233,0.0021416927,0.002377643,0.0015212934],"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":[0.0001257149,0.000031845295,0.0060534026,0.00018691635,0.000065988505,0.0007037204,0.000492044,0.09189507,0.0053976467,0.8649799,0.0029403758,0.02712724],"study_design_scores_gemma":[0.00002324481,0.000051579176,0.004254138,0.0000694312,0.000025289626,0.00091526296,0.0000925538,0.6099386,0.0018733647,0.37944958,0.003223442,0.00008345335],"about_ca_topic_score_codex":0.0018518814,"about_ca_topic_score_gemma":0.0011255356,"teacher_disagreement_score":0.0065344255,"about_ca_system_score_codex":0.0019030934,"about_ca_system_score_gemma":0.00094796764,"threshold_uncertainty_score":0.03455776},"labels":[],"label_agreement":null},{"id":"W4388442861","doi":"10.5705/ss.202022.0340","title":"Estimating Boltzmann Averages for Protein Structural Quantities Using Sequential Monte Carlo","year":2023,"lang":"en","type":"article","venue":"Statistica Sinica","topic":"Machine Learning in Materials Science","field":"Materials 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":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Monte Carlo method; Statistical physics; Boltzmann constant; Monte Carlo molecular modeling; Mathematics; Applied mathematics; Computer science; Markov chain Monte Carlo; Statistics; Physics; Thermodynamics","score_opus":0.051992006488622185,"score_gpt":0.3635998737589393,"score_spread":0.31160786727031714,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388442861","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.028292352,0.0001772622,0.97044206,0.0000921011,0.00002513444,0.00005680855,0.000043451953,0.00038293423,0.0004878355],"genre_scores_gemma":[0.54702455,0.0004424305,0.44938293,0.0001623837,0.00011890004,0.00043554878,0.0005559794,0.00023403979,0.0016432613],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989471,0.00046640125,0.000054630964,0.0001955291,0.00025694707,0.00007933294],"domain_scores_gemma":[0.9931503,0.005207704,0.00043690976,0.0005316378,0.00047007654,0.00020350267],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0035511167,0.00082348345,0.0011255575,0.0012397498,0.00072191365,0.0011216732,0.001886417,0.0010726858,0.0015633389],"category_scores_gemma":[0.01180572,0.00085347256,0.0010272836,0.00092807185,0.0014128819,0.0016462355,0.0010504824,0.0014562337,0.0003948646],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009583468,0.00006153298,0.0028180815,0.00007203388,0.000087756016,0.000066775654,0.00006727127,0.9454293,0.0017990917,0.023690116,0.00046507057,0.025347065],"study_design_scores_gemma":[0.000006841981,0.000009193529,0.000103590486,0.0000030815736,0.0000031084658,0.000007752093,0.0000032632192,0.9922965,0.00028274418,0.007137366,0.0001424607,0.0000039908923],"about_ca_topic_score_codex":0.008874949,"about_ca_topic_score_gemma":0.0105011,"teacher_disagreement_score":0.008874949,"about_ca_system_score_codex":0.001188798,"about_ca_system_score_gemma":0.0018416002,"threshold_uncertainty_score":0.018780291},"labels":[],"label_agreement":null},{"id":"W4390053810","doi":"10.5705/ss.202023.0165","title":"Minimum Aberration Factorial Designs Under A Mixed Parametrization","year":2023,"lang":"en","type":"article","venue":"Statistica Sinica","topic":"Advanced optical system design","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada; Simon Fraser University","keywords":"Parametrization (atmospheric modeling); Fractional factorial design; Factorial experiment; Mathematics; Factorial; Computer science; Mathematical optimization; Statistics; Mathematical analysis; Physics; Optics","score_opus":0.05295927351967635,"score_gpt":0.29048137054264234,"score_spread":0.23752209702296598,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390053810","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0060389005,0.000074387,0.99274904,0.000054596734,0.000013507996,0.00013716432,0.00007062096,0.00017114924,0.00069064344],"genre_scores_gemma":[0.07228078,0.000099963225,0.9245133,0.00008504685,0.000039417424,0.0015243102,0.00017111847,0.00009333392,0.0011928099],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9761253,0.01649059,0.0011974475,0.002528223,0.0030961328,0.00056231936],"domain_scores_gemma":[0.9746625,0.01679798,0.0019471599,0.004096793,0.002158319,0.00033716578],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.015123262,0.0018260108,0.0015822389,0.0013480597,0.00067064475,0.001712692,0.0016301094,0.001374411,0.0052536307],"category_scores_gemma":[0.043357927,0.000898054,0.0017512019,0.0016917525,0.0018500754,0.002685102,0.0020358406,0.0024620863,0.0013074265],"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":[0.002266581,0.00030562756,0.0030368613,0.00076685526,0.00026315486,0.00017041642,0.0007491421,0.11043172,0.051476277,0.4982921,0.0023869288,0.32985428],"study_design_scores_gemma":[0.00058955816,0.0042527593,0.005835174,0.00018899482,0.0002449367,0.000368219,0.00015295148,0.54109657,0.03968321,0.37932175,0.028043514,0.00022234548],"about_ca_topic_score_codex":0.00036156265,"about_ca_topic_score_gemma":0.0004152768,"teacher_disagreement_score":0.015123262,"about_ca_system_score_codex":0.0012474034,"about_ca_system_score_gemma":0.0015530178,"threshold_uncertainty_score":0.07998043},"labels":[],"label_agreement":null},{"id":"W4391588840","doi":"10.5705/ss.202023.0091","title":"Functional Adaptive Double-Sparsity Estimator for Functional Linear Regression Model with Multiple Functional Covariates","year":2024,"lang":"en","type":"article","venue":"Statistica Sinica","topic":"Advanced Statistical Methods and Models","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":"Simon Fraser University","funders":"Innovation and Technology Commission; City University of Hong Kong","keywords":"Covariate; Estimator; Linear regression; Linear model; Econometrics; Statistics; Regression; Computer science; Mathematics","score_opus":0.2590965294916933,"score_gpt":0.42410207102220265,"score_spread":0.16500554153050934,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391588840","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0040816707,0.00013382874,0.99534225,0.00010791134,0.00001904394,0.000017253715,0.000041021776,0.00005922676,0.00019774797],"genre_scores_gemma":[0.48901296,0.00117689,0.504265,0.00043058026,0.00028425182,0.00048728543,0.0008388984,0.0001153211,0.0033887904],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.99805427,0.0010902612,0.00009327832,0.00034438036,0.00030885547,0.000108946544],"domain_scores_gemma":[0.9938745,0.0043210154,0.00056015345,0.0005150041,0.00058047933,0.0001488872],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0053463588,0.001038127,0.0015309039,0.00065119873,0.00029356562,0.00067991903,0.0014496386,0.0013403919,0.001803496],"category_scores_gemma":[0.013276661,0.0004660897,0.0010785774,0.0007296859,0.0011748032,0.00195584,0.0017701295,0.0021978102,0.00042655415],"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":[0.0004580111,0.00015968985,0.006871464,0.0006472906,0.00029760253,0.00033203443,0.00030351966,0.6448936,0.008629332,0.16610187,0.0041233487,0.16718224],"study_design_scores_gemma":[0.000019080891,0.00005984098,0.000282288,0.000015464262,0.000014006805,0.00006377644,0.000014988836,0.9855401,0.0005759037,0.012716815,0.0006862056,0.000011495035],"about_ca_topic_score_codex":0.0014473944,"about_ca_topic_score_gemma":0.0012272864,"teacher_disagreement_score":0.0053463588,"about_ca_system_score_codex":0.00040656337,"about_ca_system_score_gemma":0.0010621495,"threshold_uncertainty_score":0.028274596},"labels":[],"label_agreement":null},{"id":"W4391837384","doi":"10.5705/ss.202023.0100","title":"Estimation and Variable Selection under the Function-on-scalar Linear Model with Covariate Measurement Error","year":2024,"lang":"en","type":"article","venue":"Statistica Sinica","topic":"Advanced Statistical Methods and Models","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; Canada Research Chairs","keywords":"Covariate; Statistics; Mathematics; Variable (mathematics); Feature selection; Errors-in-variables models; Estimation; Applied mathematics; Observational error; Scalar (mathematics); Model selection; Selection (genetic algorithm); Econometrics; Computer science; Artificial intelligence; Mathematical analysis; Economics","score_opus":0.19361584910761756,"score_gpt":0.41828816707578453,"score_spread":0.22467231796816697,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391837384","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005746621,0.00025388537,0.9930849,0.0004516579,0.000030171192,0.00003387389,0.000083462066,0.00009761064,0.00021789737],"genre_scores_gemma":[0.33675298,0.002715931,0.6477983,0.0011135524,0.0007182939,0.001188613,0.002000739,0.0003401128,0.0073715155],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9913157,0.0067140665,0.00023364722,0.0009352487,0.0005198046,0.000281408],"domain_scores_gemma":[0.9796839,0.016185066,0.0013181852,0.0014623189,0.0010604045,0.00029009386],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.017970165,0.001743705,0.0026527971,0.0013314855,0.0005876225,0.0015332913,0.0023653228,0.002060299,0.003065404],"category_scores_gemma":[0.039466146,0.0008137984,0.001805231,0.0020870178,0.002873088,0.0026722357,0.0034331703,0.0030571378,0.0009865472],"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":[0.00041421223,0.00015931048,0.0054804576,0.00061540306,0.0004104755,0.00045870067,0.00036637587,0.4756349,0.0021438,0.37530237,0.0061509116,0.13286313],"study_design_scores_gemma":[0.000065290835,0.000096242795,0.00062078156,0.0000422537,0.00003440093,0.00007649522,0.000022900142,0.88839674,0.0005699138,0.108570985,0.0014730436,0.000030906776],"about_ca_topic_score_codex":0.0024841323,"about_ca_topic_score_gemma":0.0018384045,"teacher_disagreement_score":0.017970165,"about_ca_system_score_codex":0.0008759813,"about_ca_system_score_gemma":0.0023845173,"threshold_uncertainty_score":0.09503645},"labels":[],"label_agreement":null},{"id":"W4401781551","doi":"10.5705/ss.202024.0028","title":"Functional Linear Models with Latent Factors","year":2024,"lang":"en","type":"article","venue":"Statistica Sinica","topic":"Bayesian Modeling and Causal Inference","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Humanities and Social Science Fund of Ministry of Education of China; Shanghai University of Finance and Economics; Canada Research Chairs; Ministry of Education of the People's Republic of China; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Computer science; Econometrics; Mathematics","score_opus":0.0668611045816903,"score_gpt":0.28263975365849725,"score_spread":0.21577864907680694,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401781551","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0031492915,0.00067266054,0.99423933,0.00062540895,0.00005591331,0.000019218718,0.00022244906,0.0001597145,0.0008559634],"genre_scores_gemma":[0.35749495,0.0038038064,0.6187752,0.0007579052,0.00078656536,0.0010366505,0.0023976525,0.00060789206,0.01433943],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.98674345,0.010440939,0.00033781314,0.0014389948,0.00066878326,0.0003700067],"domain_scores_gemma":[0.95159644,0.042148277,0.00189809,0.0025443907,0.0014557882,0.00035709664],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.014946785,0.002484352,0.0023484407,0.0025342444,0.00091138546,0.0037148355,0.0039481623,0.0036107718,0.009102185],"category_scores_gemma":[0.050435808,0.0020863554,0.002812486,0.0031884206,0.0034817397,0.0053269374,0.0023894305,0.004764023,0.0021148287],"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":[0.000116563635,0.00006508057,0.0012970384,0.00020870756,0.000251,0.000118903925,0.00025044236,0.11098497,0.000225871,0.8537761,0.0025672105,0.030138157],"study_design_scores_gemma":[0.00004177354,0.000021904714,0.00032479435,0.000056257333,0.00007356776,0.000070969516,0.000031798158,0.3928952,0.0001140017,0.60378003,0.0025568139,0.00003294625],"about_ca_topic_score_codex":0.008649986,"about_ca_topic_score_gemma":0.008649569,"teacher_disagreement_score":0.014946785,"about_ca_system_score_codex":0.0017950363,"about_ca_system_score_gemma":0.0023962578,"threshold_uncertainty_score":0.07904714},"labels":[],"label_agreement":null},{"id":"W4404456042","doi":"10.5705/ss.202024.0213","title":"Inference for Delay Differential Equations Using Manifold-Constrained Gaussian Processes","year":2024,"lang":"en","type":"article","venue":"Statistica Sinica","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Inference; Manifold (fluid mechanics); Applied mathematics; Gaussian; Mathematics; Differential equation; Computer science; Mathematical optimization; Mathematical analysis; Artificial intelligence; Physics","score_opus":0.03967192229583435,"score_gpt":0.3327578115398602,"score_spread":0.2930858892440259,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404456042","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008925237,0.00021991503,0.99027026,0.00020865464,0.00002430926,0.000012045357,0.00005727353,0.00007388231,0.0002084832],"genre_scores_gemma":[0.6161423,0.00218827,0.3739675,0.00032583598,0.00034800798,0.00022129972,0.00092545734,0.00028405315,0.005597237],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99818796,0.00072709966,0.00011843038,0.0005160821,0.00029874424,0.00015163173],"domain_scores_gemma":[0.974969,0.021344235,0.0013741452,0.000777208,0.0011596546,0.00037577053],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005914828,0.0013143822,0.00253491,0.002240025,0.0009120618,0.0024076228,0.002679748,0.0024375953,0.0021025003],"category_scores_gemma":[0.035085507,0.0018633512,0.0019567292,0.0019095162,0.0035400516,0.00476999,0.0024784708,0.003575388,0.0003292255],"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":[0.00010762794,0.000051304836,0.0014474755,0.000107463726,0.00016139219,0.00007441681,0.00013024508,0.7681246,0.0008061195,0.20676044,0.00085475773,0.021374194],"study_design_scores_gemma":[0.000011495812,0.000006934829,0.00014190229,0.000007954885,0.000010256112,0.00001007137,0.0000074700447,0.91778797,0.00015618857,0.08164278,0.00020356303,0.000013368508],"about_ca_topic_score_codex":0.017139718,"about_ca_topic_score_gemma":0.012129085,"teacher_disagreement_score":0.017139718,"about_ca_system_score_codex":0.0025277764,"about_ca_system_score_gemma":0.003404314,"threshold_uncertainty_score":0.03407991},"labels":[],"label_agreement":null},{"id":"W4406604070","doi":"10.5705/ss.202023.0415","title":"Functional Linear Operator Quantile Regression for Sparse Longitudinal Data","year":2025,"lang":"en","type":"article","venue":"Statistica Sinica","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Alberta Machine Intelligence Institute; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China; Canadian Institute for Advanced Research","keywords":"Quantile regression; Quantile; Regression; Linear regression; Longitudinal data; Statistics; Mathematics; Computer science; Econometrics; Data mining","score_opus":0.3547275428208971,"score_gpt":0.4923425000952632,"score_spread":0.13761495727436612,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406604070","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00388797,0.00044293443,0.99469745,0.000437532,0.000037398033,0.000019195053,0.000115702926,0.00014177084,0.0002199168],"genre_scores_gemma":[0.3895065,0.0034350296,0.5942432,0.00095243973,0.0008341909,0.00087647437,0.002054812,0.0006171821,0.007480114],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99187684,0.0060823555,0.00028374587,0.0007946266,0.0006737464,0.00028859766],"domain_scores_gemma":[0.93801486,0.05275307,0.0025731572,0.0040141298,0.0020903198,0.0005544015],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.022080353,0.0008799394,0.001776964,0.0015776197,0.00050836534,0.0013832577,0.0021080822,0.0015544246,0.00435338],"category_scores_gemma":[0.08039948,0.00067373866,0.0012849853,0.002317933,0.0026828374,0.0026087142,0.002642805,0.0031725848,0.00062663655],"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":[0.0002602278,0.00011523034,0.005521233,0.0005891458,0.00038923268,0.00036164594,0.00041784882,0.16216768,0.0017922886,0.7000549,0.007708921,0.12062162],"study_design_scores_gemma":[0.000043030712,0.000048586116,0.0011418088,0.000042732016,0.000045077228,0.00011817269,0.00004334555,0.65113586,0.00031173398,0.34460688,0.002442121,0.000020621332],"about_ca_topic_score_codex":0.004615203,"about_ca_topic_score_gemma":0.00296687,"teacher_disagreement_score":0.022080353,"about_ca_system_score_codex":0.0010152956,"about_ca_system_score_gemma":0.002485144,"threshold_uncertainty_score":0.11677343},"labels":[],"label_agreement":null},{"id":"W4407095074","doi":"10.5705/ss.202024.0029","title":"Grouped Orthogonal Arrays And Their Construction Methods","year":2025,"lang":"en","type":"article","venue":"Statistica Sinica","topic":"Antenna Design and Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"National Key Research and Development Program of China; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Computer science","score_opus":0.008922315270341161,"score_gpt":0.2741168986670125,"score_spread":0.26519458339667135,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407095074","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0005108077,0.00008023133,0.9986419,0.000019747977,0.000030055891,0.00007050809,0.000036096044,0.00013855105,0.00047213925],"genre_scores_gemma":[0.012834722,0.00026166503,0.98407054,0.00008234678,0.00006387019,0.0012381513,0.00016953824,0.0001564152,0.001122835],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9859437,0.009745638,0.00049784675,0.0013186029,0.0021350237,0.00035914977],"domain_scores_gemma":[0.98629725,0.008504213,0.0012430333,0.0020736307,0.0016522637,0.00022964615],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.011329965,0.0016794901,0.001364217,0.002296373,0.00080083375,0.0012071874,0.0016605224,0.0012107083,0.009254805],"category_scores_gemma":[0.027092632,0.0009062492,0.0019708683,0.0031404197,0.0019765547,0.0016541627,0.002423391,0.0020396556,0.0040220674],"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":[0.00041155575,0.00020653145,0.001282331,0.00096834253,0.00019260278,0.00011320762,0.00053264195,0.07908895,0.0097611705,0.3306103,0.007100302,0.56973207],"study_design_scores_gemma":[0.00035360135,0.0014766249,0.0014414892,0.00041482635,0.00019248103,0.00046914487,0.00026099844,0.38015857,0.015973588,0.51711005,0.08193263,0.00021602356],"about_ca_topic_score_codex":0.00052917044,"about_ca_topic_score_gemma":0.0005161621,"teacher_disagreement_score":0.011329965,"about_ca_system_score_codex":0.0007086152,"about_ca_system_score_gemma":0.001522692,"threshold_uncertainty_score":0.059919298},"labels":[],"label_agreement":null},{"id":"W4407609314","doi":"10.5705/ss.202022.0206","title":"Asymmetric Estimation for Varying-Coefficient Additive Model with Functional Response in Reproducing Kernel Hilbert Space","year":2025,"lang":"en","type":"article","venue":"Statistica Sinica","topic":"Numerical methods in inverse problems","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"York University; Alberta Machine Intelligence Institute; Natural Sciences and Engineering Research Council of Canada; Canadian Institute for Advanced Research","keywords":"Reproducing kernel Hilbert space; Mathematics; Kernel (algebra); Hilbert space; Applied mathematics; Space (punctuation); Computer science; Mathematical analysis; Pure mathematics","score_opus":0.06567305580183261,"score_gpt":0.3759863705466956,"score_spread":0.310313314744863,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407609314","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006968721,0.00010876095,0.9924355,0.00015456956,0.000008496828,0.000016081853,0.000030795978,0.000051945495,0.00022510574],"genre_scores_gemma":[0.6296841,0.0009669953,0.3629442,0.00036909466,0.0001348114,0.00044837088,0.00041135921,0.0001703451,0.004870698],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99522,0.0036209074,0.0001212793,0.00045276366,0.0004254378,0.00015958292],"domain_scores_gemma":[0.98807716,0.009593422,0.00084573356,0.0007384686,0.0005802493,0.00016499398],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010764071,0.0008630307,0.0013033311,0.0006413403,0.00027780017,0.00095873786,0.0014798167,0.0010719304,0.0021024444],"category_scores_gemma":[0.024472313,0.00045991622,0.0010047776,0.00072414015,0.0014043468,0.0014665918,0.0017677391,0.0022743195,0.00042081546],"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":[0.00016398168,0.00008192562,0.003359964,0.00026755861,0.00013554763,0.00018762334,0.00020293304,0.67414355,0.0027745925,0.25348237,0.001328151,0.06387186],"study_design_scores_gemma":[0.000009942981,0.000036033165,0.00039711795,0.000011169976,0.000010455792,0.000030931784,0.000012188485,0.96900845,0.00042308864,0.02956861,0.00047793332,0.000014173397],"about_ca_topic_score_codex":0.002151861,"about_ca_topic_score_gemma":0.001699723,"teacher_disagreement_score":0.010764071,"about_ca_system_score_codex":0.0008847336,"about_ca_system_score_gemma":0.0012741156,"threshold_uncertainty_score":0.05692655},"labels":[],"label_agreement":null},{"id":"W4408595536","doi":"10.5705/ss.202023.0202","title":"Addressing Label Noise in Causation Classification via Kernel Embeddings","year":2025,"lang":"en","type":"article","venue":"Statistica Sinica","topic":"Artificial Intelligence in Law","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Kernel (algebra); Noise (video); Causation; Computer science; Pattern recognition (psychology); Artificial intelligence; Mathematics; Machine learning; Natural language processing; Pure mathematics; Epistemology; Philosophy","score_opus":0.16765569629565666,"score_gpt":0.4664966625380305,"score_spread":0.2988409662423739,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408595536","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.026452543,0.00049256225,0.97104317,0.0010411448,0.00006195206,0.00006909453,0.00009061289,0.00018678505,0.00056206115],"genre_scores_gemma":[0.6453138,0.0005900582,0.35070994,0.0005234436,0.0002601558,0.00036966693,0.0005214765,0.000114537346,0.0015969679],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9880514,0.0069209365,0.0007894055,0.0020476803,0.0017362658,0.00045425157],"domain_scores_gemma":[0.8903726,0.086945795,0.0066826926,0.010336138,0.004963956,0.0006987887],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.024837375,0.0010705526,0.00211183,0.0026252312,0.0016933131,0.0032691672,0.0030716443,0.0037741065,0.0019823932],"category_scores_gemma":[0.13018018,0.00071585027,0.0013107237,0.0025201784,0.004252666,0.008224242,0.0050047226,0.0046762973,0.00040703223],"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":[0.00042987478,0.00043082406,0.021337744,0.00072538777,0.00037531048,0.0003107824,0.0017649792,0.21209507,0.0023305325,0.4148234,0.005427414,0.33994865],"study_design_scores_gemma":[0.00003517034,0.00006238287,0.0012758686,0.00009200189,0.000041968236,0.000091136564,0.0001386442,0.64350486,0.0009820715,0.3522608,0.0014790095,0.00003611208],"about_ca_topic_score_codex":0.0022310482,"about_ca_topic_score_gemma":0.002001823,"teacher_disagreement_score":0.024837375,"about_ca_system_score_codex":0.0020578406,"about_ca_system_score_gemma":0.0021805896,"threshold_uncertainty_score":0.13135415},"labels":[],"label_agreement":null},{"id":"W4409419699","doi":"10.5705/ss.202024.0204","title":"Identification and Efficient Estimation in Regression Analysis with Response Missing Not At Random","year":2025,"lang":"en","type":"article","venue":"Statistica Sinica","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; National Institutes of Health; National Science Foundation","keywords":"Identification (biology); Computer science; Estimation; Missing data; Regression; Regression analysis; Statistics; Econometrics; Mathematics; Economics","score_opus":0.0057639393814984145,"score_gpt":0.266048712784652,"score_spread":0.26028477340315354,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409419699","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0016763083,0.00020269718,0.9976369,0.00012830009,0.000009144266,0.000018573215,0.000023762934,0.000093502626,0.00021076367],"genre_scores_gemma":[0.11721856,0.0011236013,0.8789002,0.00028658676,0.00012340081,0.0004572866,0.000380832,0.00018635561,0.0013232065],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9838434,0.012621375,0.0005665572,0.0015727326,0.001114097,0.0002818657],"domain_scores_gemma":[0.94805706,0.04374513,0.0026000093,0.004323178,0.0010582191,0.00021646688],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.024870647,0.0015562789,0.0028543116,0.0023452558,0.00076222926,0.0018263368,0.0027825837,0.0025084228,0.0024369878],"category_scores_gemma":[0.08542453,0.0014910011,0.0023873264,0.003427351,0.0026598473,0.003016347,0.0032827707,0.0038016166,0.0012216379],"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":[0.00023210609,0.00020633462,0.0055962484,0.0009985939,0.000612661,0.00057335285,0.00063862174,0.31330755,0.0040574907,0.3909195,0.0029268186,0.27993077],"study_design_scores_gemma":[0.00004587484,0.00009349484,0.0013237336,0.00013583312,0.000060231105,0.0003220124,0.00010910107,0.73836696,0.0025799815,0.25239035,0.0045184707,0.000053917236],"about_ca_topic_score_codex":0.0014839415,"about_ca_topic_score_gemma":0.0017516079,"teacher_disagreement_score":0.024870647,"about_ca_system_score_codex":0.0008452631,"about_ca_system_score_gemma":0.0026041253,"threshold_uncertainty_score":0.1315301},"labels":[],"label_agreement":null},{"id":"W4410933705","doi":"10.5705/ss.202025.0108","title":"Inference for Non-stationary Time Series Quantile Regression with Inequality Constraints","year":2025,"lang":"en","type":"article","venue":"Statistica Sinica","topic":"Statistical Methods and Inference","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":"Lunenfeld-Tanenbaum Research Institute","funders":"","keywords":"Inference; Quantile regression; Econometrics; Series (stratigraphy); Quantile; Inequality; Computer science; Time series; Regression; Statistics; Mathematics; Artificial intelligence","score_opus":0.07614427457556376,"score_gpt":0.4326824053843521,"score_spread":0.3565381308087883,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410933705","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007964316,0.00013394096,0.9911157,0.00025046075,0.000014474952,0.000018167471,0.00006189621,0.000047259557,0.00039386365],"genre_scores_gemma":[0.63144636,0.0012068292,0.36302966,0.00035469347,0.0002977861,0.00039298207,0.0006638112,0.00017520665,0.0024326437],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9906156,0.00681894,0.00033707832,0.0010343504,0.0008671848,0.0003269241],"domain_scores_gemma":[0.94006574,0.05160814,0.004218662,0.0022371314,0.0015365129,0.00033375036],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.017030766,0.0013796575,0.001951107,0.001641314,0.00071445515,0.0019480907,0.002736843,0.0015446696,0.0032061099],"category_scores_gemma":[0.09742136,0.0006891766,0.0013737555,0.0022908244,0.0036224066,0.0033151691,0.002562044,0.0032755113,0.00038389186],"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":[0.00008997161,0.000053282576,0.0048638773,0.00016240438,0.00020004276,0.0003767896,0.00014955088,0.49362203,0.0007889489,0.46469203,0.0010118342,0.033989146],"study_design_scores_gemma":[0.00001744595,0.000030253223,0.00061008724,0.00001974914,0.00001714144,0.000035829347,0.0000236692,0.8233411,0.0005038145,0.17491433,0.00046985777,0.000016770055],"about_ca_topic_score_codex":0.0052144183,"about_ca_topic_score_gemma":0.0026888652,"teacher_disagreement_score":0.017030766,"about_ca_system_score_codex":0.0014348726,"about_ca_system_score_gemma":0.0018056364,"threshold_uncertainty_score":0.09006834},"labels":[],"label_agreement":null},{"id":"W4412769435","doi":"10.5705/ss.202024.0339","title":"Nonparametric Spatial Modeling towards the Mode","year":2025,"lang":"en","type":"article","venue":"Statistica Sinica","topic":"demographic modeling and climate adaptation","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Social Sciences and Humanities Research Council of Canada; Natural Sciences and Engineering Research Council of Canada; National Science Foundation","keywords":"Nonparametric statistics; Mode (computer interface); Computer science; Econometrics; Artificial intelligence; Statistics; Mathematics; Human–computer interaction","score_opus":0.15885666239947396,"score_gpt":0.4400197098582641,"score_spread":0.28116304745879017,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412769435","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004319689,0.00011458744,0.9945615,0.00020562459,0.000015499045,0.00001484743,0.0000806841,0.00006775906,0.0006196831],"genre_scores_gemma":[0.5062034,0.0014736423,0.4820044,0.00056339137,0.0003427824,0.0006193937,0.0005884768,0.00023338197,0.007971071],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99697006,0.0019120802,0.000086997235,0.0004811987,0.0004183439,0.00013129604],"domain_scores_gemma":[0.98834527,0.008661249,0.00081193907,0.0011719267,0.0008430066,0.0001666771],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008741566,0.0008993393,0.0011231626,0.0013968699,0.0004983054,0.0015508345,0.0028399483,0.0012100517,0.0029153826],"category_scores_gemma":[0.026084078,0.0007597296,0.0013691025,0.0016861574,0.002025528,0.0028910541,0.0026646913,0.0029130257,0.00050863577],"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":[0.000046107987,0.00003329136,0.0025007066,0.00010266663,0.00007858274,0.00009475336,0.00023226767,0.38417542,0.0007998763,0.57949376,0.001548133,0.03089438],"study_design_scores_gemma":[0.0000050512167,0.000012955727,0.00032400503,0.000016099584,0.000009955426,0.000023488334,0.000019237083,0.8488108,0.00015078328,0.14933214,0.0012851871,0.000010263868],"about_ca_topic_score_codex":0.0044021877,"about_ca_topic_score_gemma":0.0030938485,"teacher_disagreement_score":0.008741566,"about_ca_system_score_codex":0.0011020189,"about_ca_system_score_gemma":0.0012350825,"threshold_uncertainty_score":0.046230435},"labels":[],"label_agreement":null},{"id":"W4415778819","doi":"10.5705/ss.202024.0351","title":"Adaptive Estimation for High-Dimensional Quantile Regression with Misspecification and Nonresponse","year":2025,"lang":"","type":"article","venue":"Statistica Sinica","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"China Postdoctoral Science Foundation; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Quantile regression; Estimation; Quantile; Regression; Regression analysis; Cross-sectional regression","score_opus":0.07563978319834366,"score_gpt":0.4006755080721672,"score_spread":0.3250357248738235,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415778819","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0064484673,0.00019519817,0.9926923,0.00021623042,0.000034608838,0.000036682,0.00006396034,0.00016264794,0.00014996623],"genre_scores_gemma":[0.3903967,0.000960066,0.5987312,0.0006094484,0.00047108266,0.0010832613,0.0012304059,0.00039213223,0.0061256737],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9689369,0.024079459,0.0011499586,0.0034180065,0.001606212,0.00080942136],"domain_scores_gemma":[0.86350757,0.112356074,0.0049906843,0.015536591,0.0029530304,0.0006561726],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.045658756,0.0013747168,0.0035012101,0.0014947882,0.0008923656,0.0020408933,0.006895275,0.0029762054,0.0047165602],"category_scores_gemma":[0.15046716,0.002303606,0.0027280762,0.0026558354,0.003056984,0.0037558828,0.0042616427,0.005317007,0.000988103],"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":[0.00081520545,0.00039348777,0.016497634,0.0007655338,0.001480399,0.0005600974,0.00080438866,0.48492858,0.0029653744,0.31455007,0.0052587516,0.1709805],"study_design_scores_gemma":[0.000081984974,0.00006060825,0.002125391,0.000040745126,0.000073644296,0.00010792221,0.000046311307,0.89935106,0.0006066579,0.09627545,0.0011883739,0.000041845244],"about_ca_topic_score_codex":0.0052781035,"about_ca_topic_score_gemma":0.004196234,"teacher_disagreement_score":0.045658756,"about_ca_system_score_codex":0.0016908371,"about_ca_system_score_gemma":0.0022688177,"threshold_uncertainty_score":0.24146944},"labels":[],"label_agreement":null},{"id":"W7083836982","doi":"10.5705/ss.202024.0346","title":"Gaussian Variational Approximation with Composite Likelihood for Crossed Random Effect Models","year":2025,"lang":"en","type":"article","venue":"Statistica Sinica","topic":"Genetic Mapping and Diversity in Plants and Animals","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Government of Jiangsu Province","keywords":"Gaussian; Composite number; Gaussian random field; Maximum likelihood; Gaussian process; Quasi-maximum likelihood","score_opus":0.008017529223782358,"score_gpt":0.2539910298729487,"score_spread":0.24597350064916634,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7083836982","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0013750637,0.00009516226,0.9978886,0.00008462098,0.000012933212,0.000016649057,0.000047354264,0.00011379941,0.0003658277],"genre_scores_gemma":[0.1276521,0.000646417,0.86344683,0.00039807468,0.00013646664,0.0005432139,0.0006526859,0.0007534172,0.005770704],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99495476,0.0034890554,0.00017177999,0.00060522056,0.000568724,0.00021049412],"domain_scores_gemma":[0.98596174,0.011933589,0.00043629383,0.0008280653,0.0005919455,0.00024835908],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.013591967,0.0013094466,0.0016782027,0.0017285297,0.0005936558,0.0019369777,0.004621292,0.0024950006,0.005399177],"category_scores_gemma":[0.036071505,0.0012441925,0.0025463826,0.0017749512,0.002529074,0.0030710716,0.0029598624,0.004164543,0.0013506159],"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":[0.00011582171,0.000051527197,0.001724004,0.00018295074,0.00023935662,0.00024546575,0.00027430992,0.40496948,0.0014700316,0.5536663,0.0021879426,0.0348728],"study_design_scores_gemma":[0.000015564348,0.0000142320805,0.00017621562,0.000015437732,0.000017738654,0.000052963085,0.000014971614,0.87272763,0.00025743298,0.1251479,0.0015392596,0.000020781072],"about_ca_topic_score_codex":0.009706143,"about_ca_topic_score_gemma":0.008309997,"teacher_disagreement_score":0.013591967,"about_ca_system_score_codex":0.0019754807,"about_ca_system_score_gemma":0.0028107746,"threshold_uncertainty_score":0.07188201},"labels":[],"label_agreement":null}]}