{"meta":{"page":1,"per_page":50,"max_per_page":100,"total":26,"total_is_capped":false,"direct_labels_cover":0,"predictions_cover":26,"direct_label_status":"direct model label, unvalidated","prediction_status":"machine_predicted_unvalidated (Codex and Gemma teacher distillation)","score_status":"score_only:v0-immature-baseline (scores rank; they never assert a category)","snapshot":{"source":"OpenAlex, pinned release, all 482 partitions","release":"2026-06-24","frame_built":"2026-07-12"},"query_hash":"141638cbfe58","filters":{"venue":"Survey methodology"}},"results":[{"id":"W2133470535","doi":"","title":"A multivariate technique for multiply imputing missing values using a sequence of regression models","year":2001,"lang":"en","type":"article","venue":"Survey methodology","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":1995,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"","keywords":"Missing data; Imputation (statistics); Statistics; Mathematics; Logistic regression; Multivariate statistics; Regression analysis; Regression; Computer science","retraction":null,"screen_n_in":null,"score":{"opus":0.6952655713588801,"gpt":0.5554953450934195,"spread":0.1397702262654607,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.01731374,0.0002320629,0.0007861007,0.0001498129,0.0001465292,0.00001498883,0.000243806,0.0002638219,0.00001902199],"category_scores_gemma":[0.05322669,0.0001880308,0.0001169717,0.0002996124,0.0001884152,0.0000966208,0.0001111639,0.0002104684,3.008097e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005136448,"about_ca_system_score_gemma":0.0001134847,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00109249,"about_ca_topic_score_gemma":0.00002295808,"domain_scores_codex":[0.9910111,0.007218135,0.0007354728,0.0004342387,0.0001561006,0.0004449659],"domain_scores_gemma":[0.9546812,0.04396953,0.000493488,0.0003654598,0.0004039466,0.00008640467],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0005854881,0.0001104266,0.001702416,0.0003494326,0.00006160983,0.00001554044,0.0008114954,0.00009653245,0.8243917,0.08490388,0.00001904194,0.08695244],"study_design_scores_gemma":[0.0003547673,0.00009055543,0.000764198,0.0002566096,0.00003887838,0.00005139301,0.0000473184,0.1433366,0.03906141,0.8158058,0.000003571666,0.0001889286],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01733645,0.00004547577,0.9815258,0.0000261799,0.0001765981,0.0006199842,0.0001160219,0.00005793044,0.00009557391],"genre_scores_gemma":[0.05859389,0.000006914779,0.9412245,0.00002791279,0.00004685922,0.00003688822,0.000008265373,0.00003867888,0.00001616592],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.7853303,"threshold_uncertainty_score":0.9547484,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2993287308","doi":"","title":"Multi-objective optimisation for optimum allocation in multivariate stratified sampling","year":2008,"lang":"en","type":"article","venue":"Survey methodology","topic":"Survey Sampling and Estimation Techniques","field":"Mathematics","cited_by":33,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"","keywords":"Stratified sampling; Multivariate statistics; Statistics; Sampling (signal processing); Mathematics; Computer science","retraction":null,"screen_n_in":null,"score":{"opus":0.6825131120236855,"gpt":0.5037334109630854,"spread":0.1787797010606,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.01482643,0.0002081149,0.0004818099,0.0002781261,0.000162774,0.00001457812,0.0001701209,0.0002779681,0.00001917022],"category_scores_gemma":[0.03186441,0.0002136872,0.00007927205,0.0003042045,0.00008716888,0.0001410596,0.00003224974,0.0002106098,0.000004987093],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001095123,"about_ca_system_score_gemma":0.0001162634,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002218652,"about_ca_topic_score_gemma":0.001305849,"domain_scores_codex":[0.9945441,0.003906262,0.0006463013,0.0004412328,0.00012781,0.0003343262],"domain_scores_gemma":[0.9772353,0.0217331,0.0002765864,0.0003134114,0.0003888634,0.0000527616],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.01167889,0.006318099,0.2578678,0.00163076,0.001328742,0.00004159192,0.08525735,0.0973061,0.3555956,0.06803167,0.003711846,0.1112315],"study_design_scores_gemma":[0.003391148,0.0003101794,0.7806591,0.00007418545,0.00003735372,0.00003424034,0.0004660024,0.08433104,0.03517011,0.09467565,0.00005309381,0.0007979132],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2215482,0.00001779344,0.7772363,0.00003990226,0.0002221245,0.0006591398,0.00004278956,0.0001919226,0.0000418493],"genre_scores_gemma":[0.2940463,0.00001009644,0.7053984,0.00002912504,0.00003220807,0.0001932597,0.0001733695,0.00003008917,0.00008722288],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.5227913,"threshold_uncertainty_score":0.9762906,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2553278379","doi":"","title":"A comparison of variance estimators for poststratification to estimated control totals","year":2010,"lang":"en","type":"article","venue":"Survey methodology","topic":"Control Systems and Identification","field":"Engineering","cited_by":29,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"","keywords":"Estimator; Variance (accounting); Statistics; Mathematics; Econometrics; Economics","retraction":null,"screen_n_in":null,"score":{"opus":0.2086994047101145,"gpt":0.4336219901237659,"spread":0.2249225854136514,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004047683,0.0001112558,0.0004461491,0.0001019479,0.00003497088,0.00001842379,0.0001438399,0.0001528418,0.00002108615],"category_scores_gemma":[0.003763283,0.0001151611,0.0000477615,0.0001799435,0.00002681577,0.00005413972,0.000006273895,0.0001135349,0.00001562965],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001382847,"about_ca_system_score_gemma":0.00002236944,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003778152,"about_ca_topic_score_gemma":0.001060037,"domain_scores_codex":[0.9985756,0.0004748897,0.0005111425,0.0001860349,0.00007209131,0.0001802905],"domain_scores_gemma":[0.9967804,0.002474579,0.0001169781,0.0003061902,0.0002564656,0.00006538707],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0001027767,0.00002658394,0.02080812,0.00006123013,0.00004914222,7.940731e-8,0.0001920785,0.01016497,0.9564564,0.002008483,0.0007316918,0.009398445],"study_design_scores_gemma":[0.0005915104,0.00008102084,0.7430015,0.00001073583,0.00002908298,0.000002856296,0.00001939137,0.213503,0.04162481,0.0004129227,0.0005591443,0.0001640122],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3755687,0.0000473074,0.622439,0.00004936027,0.00118863,0.0004831761,0.0001073812,0.00007279182,0.00004364287],"genre_scores_gemma":[0.9088526,4.445477e-7,0.09082989,0.000009521004,0.00004880842,0.0001354678,0.00007062205,0.00001995455,0.00003262827],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9148316,"threshold_uncertainty_score":0.4696133,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2184159343","doi":"","title":"Confidence interval estimation of small area parameters shrinking both means and variances","year":2012,"lang":"en","type":"article","venue":"Survey methodology","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":21,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"","keywords":"Estimator; Small area estimation; Statistics; Confidence interval; Mathematics; Mean squared error; Likelihood function; Maximum likelihood; Efficiency; Sampling (signal processing); Estimation; Applied mathematics; Computer science","retraction":null,"screen_n_in":null,"score":{"opus":0.4756757551178532,"gpt":0.4510408120011815,"spread":0.02463494311667175,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.01270962,0.0001443409,0.0005243004,0.00007360885,0.00004206269,0.00001493136,0.0001460646,0.0001179963,0.00008195007],"category_scores_gemma":[0.03454403,0.0001218609,0.00003986536,0.0001286333,0.0002631512,0.00009202254,0.00007603449,0.000157003,0.000002023782],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001356391,"about_ca_system_score_gemma":0.00002546702,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005220682,"about_ca_topic_score_gemma":0.0001388979,"domain_scores_codex":[0.9932109,0.005726732,0.0004391962,0.0002090509,0.0001117606,0.0003023713],"domain_scores_gemma":[0.9580892,0.04127865,0.0002470569,0.0002147088,0.00007808511,0.00009234507],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000114373,0.00006507312,0.03351904,0.0002026605,0.00007178885,0.000001841233,0.001489016,0.000008875775,0.0006661206,0.8663871,0.00003562814,0.09743846],"study_design_scores_gemma":[0.0001728803,0.0001126061,0.1350591,0.00007394025,0.00005533268,0.00002345434,0.0001097977,0.003783366,0.002061591,0.8583874,0.00000737635,0.0001531642],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.08368256,0.0001038669,0.9153627,0.00002786608,0.0003380303,0.0001209849,0.00003205623,0.00002414372,0.0003077551],"genre_scores_gemma":[0.2919642,0.00001075956,0.7079452,0.0000356901,0.00001477223,0.000007730523,0.000003639827,0.000009189609,0.000008775644],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.2082816,"threshold_uncertainty_score":0.9735884,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2618479281","doi":"","title":"An Approximate Design Effect for Unequal Weighting When Measurements May Correlate with Selection Probabilities","year":2000,"lang":"en","type":"article","venue":"Survey methodology","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":20,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"","keywords":"Selection (genetic algorithm); Weighting; Statistics; Mathematics; Computer science; Artificial intelligence; Physics","retraction":null,"screen_n_in":null,"score":{"opus":0.6404714675082755,"gpt":0.5156032129815941,"spread":0.1248682545266815,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","metaepi_narrow","insufficient_payload"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.1411556,0.0004257499,0.001034203,0.0003052128,0.0003886597,0.0002275762,0.0008078509,0.0002856157,0.0009832893],"category_scores_gemma":[0.01224405,0.0002939175,0.0001372736,0.0007900334,0.0003064001,0.0005859789,0.00004463154,0.0002772199,0.00008585351],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001367479,"about_ca_system_score_gemma":0.0001518569,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004897322,"about_ca_topic_score_gemma":0.00015359,"domain_scores_codex":[0.9249417,0.07100408,0.0009532811,0.001282311,0.001045394,0.000773208],"domain_scores_gemma":[0.9584668,0.03970522,0.0003578095,0.0006851662,0.0005804582,0.0002045364],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.04114296,0.0006727774,0.1027924,0.0001016969,0.0004922327,0.00001007151,0.009266234,0.06138249,0.2164698,0.001054291,0.001193588,0.5654215],"study_design_scores_gemma":[0.008704267,0.0297036,0.07245251,0.00009944568,0.0002845109,0.0001874378,0.001189755,0.1512279,0.6366047,0.09508958,0.001821526,0.002634716],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2503586,0.0000780136,0.7465387,0.00002427819,0.0003769712,0.001554311,0.00002113939,0.0001291338,0.0009188364],"genre_scores_gemma":[0.1621968,0.00000154553,0.8358517,0.00007189196,0.00007144724,0.0003505485,0.00002260347,0.0000510908,0.001382394],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.5627868,"threshold_uncertainty_score":0.9999513,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2552298465","doi":"","title":"Fence method for nonparametric small area estimation","year":2010,"lang":"en","type":"article","venue":"Survey methodology","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":19,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"","keywords":"Fence (mathematics); Nonparametric statistics; Small area estimation; Estimation; Mathematics; Computer science; Function (biology); Statistics; Type (biology); Econometrics; Algorithm; Engineering; Combinatorics","retraction":null,"screen_n_in":null,"score":{"opus":0.5702197929104776,"gpt":0.5181411432265587,"spread":0.05207864968391895,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.02855262,0.0002272241,0.00065179,0.0002118553,0.0001167206,0.00003425356,0.0003644032,0.0003188087,0.0003070059],"category_scores_gemma":[0.3047045,0.0001949066,0.0001083393,0.000534943,0.0001345432,0.00004391118,0.00007775646,0.0004561199,0.00001965094],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001844044,"about_ca_system_score_gemma":0.00008945656,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003989233,"about_ca_topic_score_gemma":0.0006033473,"domain_scores_codex":[0.9929388,0.005378719,0.0005601384,0.0005064274,0.0001498254,0.0004661043],"domain_scores_gemma":[0.8418543,0.156889,0.000240006,0.0004974598,0.0003861455,0.0001330859],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001874211,0.0001168923,0.002212757,0.0001233851,0.00005054219,0.000002427545,0.0001850155,0.00001867604,0.01251277,0.5414286,0.0006608452,0.4425007],"study_design_scores_gemma":[0.0003794345,0.0001855708,0.03149377,0.000007784338,0.0000536368,0.00002296065,0.00001403899,0.04643579,0.005298354,0.9153105,0.0005352149,0.0002629164],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03291788,0.00001347355,0.9644359,0.000111892,0.001226511,0.0004854557,0.0001444368,0.00009291607,0.0005715727],"genre_scores_gemma":[0.007738334,0.000002552349,0.9916192,0.0001635185,0.00007750175,0.0001341033,0.00003095109,0.00003541242,0.0001983709],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4422377,"threshold_uncertainty_score":0.9895824,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2943998842","doi":"","title":"Sample survey theory and methods: Past, present, and future directions","year":2017,"lang":"en","type":"article","venue":"Survey methodology","topic":"Survey Methodology and Nonresponse","field":"Social Sciences","cited_by":15,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"","keywords":"Computer science; Estimator; Survey data collection; Imputation (statistics); Survey sampling; Weighting; Sample (material); Statistics; Inference; Sampling (signal processing); Software; Data mining; Survey methodology; Data collection; Sampling design; Resampling; Sample size determination; Consistency (knowledge bases); Econometrics; Missing data; Mathematics; Algorithm; Machine learning; Artificial intelligence","retraction":null,"screen_n_in":null,"score":{"opus":0.4722577731315136,"gpt":0.5515094383971101,"spread":0.07925166526559646,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","sts"],"consensus_categories":["metaresearch","sts"],"category_scores_codex":[0.480717,0.0002695537,0.0007264594,0.0001876005,0.003209424,0.0001744185,0.0005808262,0.0005912596,0.0003173874],"category_scores_gemma":[0.2878395,0.0002428703,0.00008034556,0.0002323024,0.002739002,0.0002979539,0.000360203,0.0004746389,0.000006632217],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002719569,"about_ca_system_score_gemma":0.0001834666,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.05203691,"about_ca_topic_score_gemma":0.04237982,"domain_scores_codex":[0.4471824,0.5508302,0.0003665212,0.0007571624,0.0001850626,0.0006786636],"domain_scores_gemma":[0.6784006,0.320276,0.0002538229,0.0006208229,0.0002149814,0.0002337261],"domain_codex":"methods","domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.005253012,0.00004965047,0.663637,0.00001108754,0.0001933904,0.000006037239,0.00675446,1.816824e-7,0.0001622567,0.01701685,0.00119081,0.3057252],"study_design_scores_gemma":[0.0002788792,0.0000496101,0.8119867,0.000002722174,0.00002958409,0.000006274667,0.0009189647,0.000002159732,0.00007961519,0.02319298,0.1632344,0.0002181378],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.5920004,0.009177697,0.3755795,0.007315317,0.00904288,0.0009312549,0.000773315,0.00027108,0.004908604],"genre_scores_gemma":[0.06433979,0.01039863,0.9093444,0.0009766059,0.007289652,0.0001717395,0.0001980891,0.0001418786,0.007139228],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5337649,"threshold_uncertainty_score":0.999975,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2189001323","doi":"","title":"Combining Synthetic Data with Subsampling to Create Public Use Microdata Files for Large Scale Surveys","year":2012,"lang":"en","type":"article","venue":"Survey methodology","topic":"demographic modeling and climate adaptation","field":"Decision Sciences","cited_by":11,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"","keywords":"Microdata (statistics); Confidentiality; Computer science; Scale (ratio); Sample (material); Imputation (statistics); Statistics; Sample size determination; Statistical inference; Data file; Data science; Data mining; Econometrics; Database; Missing data; Mathematics; Census; Computer security; Geography; Machine learning","retraction":null,"screen_n_in":null,"score":{"opus":0.7970682118326551,"gpt":0.5130019730252127,"spread":0.2840662388074424,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.1820991,0.0002556976,0.0006893667,0.0005373491,0.0003836248,0.0004434412,0.001749245,0.0001670499,0.0001671856],"category_scores_gemma":[0.08109479,0.0001892139,0.00007614737,0.001270708,0.0001116306,0.001279593,0.0006384425,0.0001881617,0.0000941752],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001711896,"about_ca_system_score_gemma":0.00008961422,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001153342,"about_ca_topic_score_gemma":0.009099188,"domain_scores_codex":[0.9771793,0.0187289,0.0009402788,0.001269312,0.0007121524,0.00117003],"domain_scores_gemma":[0.9328368,0.06292264,0.0003559359,0.002564587,0.0009451943,0.0003748915],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003902788,0.0002495412,0.9633021,0.0000157522,0.0001055551,0.000001385905,0.001383909,0.0002240023,0.001194625,0.001192513,0.004748253,0.02719204],"study_design_scores_gemma":[0.00103982,0.000228498,0.9603989,0.0000353572,0.00007824395,0.00003608966,0.002112197,0.01556676,0.0003250998,0.002274395,0.01728116,0.0006235259],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4025036,0.000096107,0.5941917,0.0002124057,0.0004854666,0.0002441272,0.002172223,0.00004873278,0.0000456856],"genre_scores_gemma":[0.7030889,0.00001315734,0.294649,0.000232201,0.00006096344,0.00003335913,0.001670923,0.00003665149,0.0002148022],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.3005854,"threshold_uncertainty_score":0.9266455,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2520722247","doi":"","title":"Modeling interviewer effects in panel surveys: - an application","year":2000,"lang":"en","type":"article","venue":"Survey methodology","topic":"Social Capital and Networks","field":"Social Sciences","cited_by":11,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"","keywords":"Interview; Statistics; Econometrics; Computer science; Mathematics; Sociology","retraction":null,"screen_n_in":null,"score":{"opus":0.3534255851812836,"gpt":0.4454731828123156,"spread":0.09204759763103199,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.03402236,0.0001072447,0.0002973919,0.00006364431,0.000167272,0.00002251328,0.0002816205,0.0002636407,0.0002842452],"category_scores_gemma":[0.001584351,0.0001090803,0.00005016554,0.0004787947,0.0001446335,0.0001609472,0.00002359567,0.0002146216,0.0000814311],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006221983,"about_ca_system_score_gemma":0.00005837836,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.1040551,"about_ca_topic_score_gemma":0.1895519,"domain_scores_codex":[0.9591468,0.03973102,0.0002503654,0.0003263056,0.0001518957,0.0003936358],"domain_scores_gemma":[0.9963159,0.00328188,0.00003716354,0.0001847319,0.00007755477,0.0001027873],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00011606,0.0001386953,0.04428323,0.00001483596,0.00001672932,0.000004650806,0.02917706,0.002658089,0.0001048775,0.003419869,0.00005991993,0.920006],"study_design_scores_gemma":[0.0006553601,0.0001675173,0.9095144,0.00002433287,0.00001506238,0.000001225626,0.002786074,0.02283153,0.00003441029,0.06181586,0.001627493,0.0005267353],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9131666,0.0004254438,0.08285708,0.000102359,0.0004173523,0.0002931374,0.000004070474,0.00007257586,0.002661421],"genre_scores_gemma":[0.9962117,0.0002139511,0.002627084,0.0002314036,0.0002782185,0.00005723874,0.00005058838,0.00001438786,0.000315405],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9194793,"threshold_uncertainty_score":0.9946772,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2189585271","doi":"","title":"Conservative variance estimation for sampling designs with zero pairwise inclusion probabilities","year":2012,"lang":"en","type":"article","venue":"Survey methodology","topic":"Survey Sampling and Estimation Techniques","field":"Mathematics","cited_by":10,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"","keywords":"Estimator; Pairwise comparison; Statistics; Mathematics; Variance (accounting); Standard error; Zero (linguistics); Population variance; Sampling (signal processing); Bias of an estimator; Econometrics; Minimum-variance unbiased estimator; Computer science","retraction":null,"screen_n_in":null,"score":{"opus":0.6908408875368,"gpt":0.4942956986946006,"spread":0.1965451888421993,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.02888399,0.0002342566,0.0004999022,0.0001212338,0.0004151572,0.00002160029,0.0001793662,0.00019887,0.00002661223],"category_scores_gemma":[0.03830421,0.0001919331,0.00005906705,0.0002515528,0.0001717792,0.0002689601,0.0001432013,0.0001595013,0.000004162281],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000904491,"about_ca_system_score_gemma":0.0001074182,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004154249,"about_ca_topic_score_gemma":0.0001650031,"domain_scores_codex":[0.9935906,0.005002656,0.0004661088,0.000297544,0.0001817179,0.0004613754],"domain_scores_gemma":[0.9486688,0.05008023,0.0003198416,0.0003561182,0.0004830523,0.00009200418],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.007405387,0.001732677,0.2098206,0.003021883,0.0007497926,0.000002508983,0.05325132,0.003655257,0.005578458,0.6061436,0.009609051,0.09902951],"study_design_scores_gemma":[0.0007101329,0.0004421051,0.04236456,0.0001294028,0.00007319721,0.00002177411,0.0001767369,0.004448678,0.01010988,0.9406498,0.0003855693,0.000488147],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1234975,0.00006596881,0.8748333,0.0001108995,0.0001992108,0.0008279035,0.00007334686,0.0003208691,0.00007101246],"genre_scores_gemma":[0.2541598,0.000002521943,0.7451828,0.00009450139,0.00004153211,0.0002796046,0.0001045665,0.00003697945,0.0000977141],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.3345062,"threshold_uncertainty_score":0.9999683,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2403892912","doi":"","title":"Dealing with non-ignorable nonresponse in survey sampling: A latent modeling approach","year":2015,"lang":"en","type":"article","venue":"Survey methodology","topic":"Survey Methodology and Nonresponse","field":"Social Sciences","cited_by":8,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"","keywords":"Covariate; Latent variable; Estimator; Latent variable model; Statistics; Econometrics; Computer science; Variable (mathematics); Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.8087179749783673,"gpt":0.522277081248507,"spread":0.2864408937298603,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","metaepi_narrow"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.5002098,0.0003913028,0.001067325,0.0005821724,0.000403085,0.00006496799,0.00069726,0.0006675962,0.00005545769],"category_scores_gemma":[0.1610014,0.0003500816,0.00009450388,0.001604404,0.0005815981,0.0002482396,0.0001507552,0.0007804485,0.00003387921],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002268954,"about_ca_system_score_gemma":0.001547723,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.1185334,"about_ca_topic_score_gemma":0.1592863,"domain_scores_codex":[0.6622247,0.3335488,0.0008926571,0.001158108,0.000669774,0.001505987],"domain_scores_gemma":[0.9035406,0.0944768,0.000227161,0.000561869,0.0007162404,0.000477402],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.06748531,0.0003096708,0.8282226,0.00001757667,0.0001368091,0.00006483673,0.01893101,0.08200499,0.0002003177,0.0009600067,0.0001078971,0.001558981],"study_design_scores_gemma":[0.002649579,0.0003682242,0.9760863,0.00003257141,0.00003467438,0.00002306813,0.004443735,0.01215669,0.0001128039,0.002760641,0.0004453143,0.0008864441],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5812443,0.0002148282,0.4161455,0.00009401891,0.0004712975,0.000353379,0.0000299967,0.00007108886,0.001375624],"genre_scores_gemma":[0.7810003,0.00004650517,0.217907,0.0001913366,0.0001018348,0.00006154383,0.0001105737,0.00005595424,0.0005249995],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3392084,"threshold_uncertainty_score":0.9998951,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1638540087","doi":"","title":"An Optimal Calibration Distance Leading to the Optimal Regresion Estimator","year":2005,"lang":"en","type":"article","venue":"Survey methodology","topic":"Survey Sampling and Estimation Techniques","field":"Mathematics","cited_by":8,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"Statistics Canada","funders":"","keywords":"Estimator; Statistics; Mathematics; Calibration; Population; Optimal design; Best linear unbiased prediction; Population mean; Sampling (signal processing); Mean squared error; Computer science; Artificial intelligence; Medicine","retraction":null,"screen_n_in":null,"score":{"opus":0.4191802732753334,"gpt":0.491450805124762,"spread":0.07227053184942867,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.01771473,0.0002227852,0.0003951756,0.000128887,0.0003071994,0.00007902084,0.0004564445,0.0001817981,0.0001156489],"category_scores_gemma":[0.01315093,0.0001671698,0.00006509746,0.0003438224,0.00008798575,0.0002708258,0.00007385889,0.0002580443,0.0000578276],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007303368,"about_ca_system_score_gemma":0.00005744939,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001776648,"about_ca_topic_score_gemma":0.0005922868,"domain_scores_codex":[0.9923487,0.006076774,0.0005039181,0.0004415197,0.0002390749,0.0003899855],"domain_scores_gemma":[0.9900575,0.008728982,0.0001864716,0.0007124611,0.0001751587,0.0001394117],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.005026256,0.001236546,0.03195752,0.0002871029,0.0003059875,0.00003041506,0.01664988,0.2621719,0.03100287,0.1166079,0.1595193,0.3752043],"study_design_scores_gemma":[0.001646038,0.001944367,0.09091369,0.0002852721,0.0001855872,0.0002197807,0.001056797,0.5962316,0.2304062,0.04052959,0.03363901,0.002942079],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1789546,0.00004657427,0.8190399,0.0009213587,0.0002147821,0.0002790364,0.00004102091,0.0003746299,0.0001281256],"genre_scores_gemma":[0.2818546,0.000003756234,0.7173678,0.000222344,0.0001737603,0.00005129886,0.00006346089,0.00003705708,0.0002259152],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.3722622,"threshold_uncertainty_score":0.9951617,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2554863279","doi":"","title":"Observed best prediction via nested-error regression with potentially misspecified mean and variance","year":2015,"lang":"en","type":"article","venue":"Survey methodology","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":7,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"","keywords":"Estimator; Small area estimation; Statistics; Mean squared error; Best linear unbiased prediction; Variance (accounting); Mean squared prediction error; Mathematics; Computer science; Linear regression; Regression; Econometrics; Machine learning","retraction":null,"screen_n_in":null,"score":{"opus":0.6677220592976109,"gpt":0.4530988934963726,"spread":0.2146231658012383,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.008995382,0.0002288022,0.0005563684,0.00006934922,0.00008803562,0.00002833689,0.0001730062,0.0002400047,0.00005909669],"category_scores_gemma":[0.0182022,0.0001622882,0.00002853095,0.0002393364,0.0002070875,0.00008553432,0.0000877921,0.0002877442,0.000007510693],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003019003,"about_ca_system_score_gemma":0.00009085215,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004358881,"about_ca_topic_score_gemma":0.0005068254,"domain_scores_codex":[0.989343,0.009209893,0.0004181237,0.0004671316,0.0002578828,0.0003039984],"domain_scores_gemma":[0.9893093,0.009379169,0.0002301691,0.000415421,0.0004086334,0.0002573175],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.01835709,0.00212701,0.1705639,0.001215816,0.001118427,0.0005804964,0.01330955,0.0002155939,0.05633018,0.3119271,0.01092433,0.4133305],"study_design_scores_gemma":[0.003883353,0.002714211,0.3432477,0.0002784492,0.0002958052,0.000315784,0.000970434,0.01000698,0.002359222,0.634145,0.0009142943,0.0008688581],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1385518,0.0001389148,0.8598936,0.0001035608,0.0004896322,0.0002266531,0.00004577337,0.00007230747,0.0004777627],"genre_scores_gemma":[0.05529381,0.00001772873,0.9440192,0.0000680559,0.0001021906,0.00001961889,0.00002836277,0.00003477458,0.0004162898],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4124616,"threshold_uncertainty_score":0.9900679,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2136733697","doi":"","title":"Estimating agreement coefficients from sample survey data","year":2012,"lang":"en","type":"article","venue":"Survey methodology","topic":"Reliability and Agreement in Measurement","field":"Decision Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"","keywords":"Jackknife resampling; Statistics; Concordance; Mathematics; Cohen's kappa; Standard error; Correlation coefficient; Variance (accounting); Sampling (signal processing); Sample (material); Concordance correlation coefficient; Linearization; Computer science; Medicine; Nonlinear system; Accounting","retraction":null,"screen_n_in":null,"score":{"opus":0.8726461020763974,"gpt":0.5551506380714631,"spread":0.3174954640049342,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","insufficient_payload"],"consensus_categories":["metaresearch","insufficient_payload"],"category_scores_codex":[0.2762679,0.0002991375,0.0007991191,0.0001885164,0.0003019797,0.000169002,0.003276782,0.0001864755,0.003435806],"category_scores_gemma":[0.313595,0.0002243194,0.00009042594,0.000956719,0.0002294131,0.0005006731,0.001646553,0.0003146736,0.001199535],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008274973,"about_ca_system_score_gemma":0.0001082893,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02778998,"about_ca_topic_score_gemma":0.008170281,"domain_scores_codex":[0.938705,0.05395361,0.001854113,0.001529703,0.002826548,0.00113108],"domain_scores_gemma":[0.8328434,0.1601443,0.0007821087,0.004868311,0.0009122931,0.0004496143],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001066463,0.0002511707,0.9368598,0.00000294907,0.00005658237,5.663479e-7,0.0004150082,0.0006549975,0.000272407,0.00003978461,0.01116959,0.05017048],"study_design_scores_gemma":[0.0003664162,0.00005511587,0.9806669,0.000008128782,0.00002015894,9.480149e-7,0.0001664747,0.006334972,0.0001991232,0.004805822,0.007109928,0.0002660559],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3556263,0.0002874872,0.6363247,0.0001099742,0.004616592,0.0002694585,0.002457933,0.00003000024,0.000277561],"genre_scores_gemma":[0.6076887,0.00000569372,0.3894898,0.0003987242,0.0003331569,0.00001112812,0.001900157,0.00001701324,0.0001556291],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.2520625,"threshold_uncertainty_score":0.9995781,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2612643943","doi":"","title":"Some contributions to jackknifing two-phase sampling estimators","year":2010,"lang":"en","type":"article","venue":"Survey methodology","topic":"Survey Sampling and Estimation Techniques","field":"Mathematics","cited_by":4,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"","keywords":"Estimator; Statistics; Mathematics; Sampling (signal processing); Econometrics; Computer science","retraction":null,"screen_n_in":null,"score":{"opus":0.4680701557935121,"gpt":0.5642361375453836,"spread":0.09616598175187152,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.02572515,0.0002611017,0.0006176886,0.0003299364,0.0002863167,0.00005849167,0.0003602263,0.0002621946,0.0001564683],"category_scores_gemma":[0.1126643,0.0002570474,0.0001124379,0.0004414488,0.0001260277,0.0001361381,0.0001287586,0.0006190824,0.0001210782],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000500245,"about_ca_system_score_gemma":0.0001249087,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001025433,"about_ca_topic_score_gemma":0.0007695263,"domain_scores_codex":[0.9949763,0.003128804,0.0006529075,0.0004701948,0.0002001463,0.0005716886],"domain_scores_gemma":[0.9706113,0.02771346,0.0002076032,0.0006876293,0.0005150047,0.0002650045],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0005485725,0.0007492684,0.01330845,0.0001109446,0.0002273157,0.00001977783,0.001484853,0.00026587,0.2624787,0.6648191,0.009480405,0.04650676],"study_design_scores_gemma":[0.00168764,0.0002305693,0.01501312,0.00004447047,0.00006610947,0.00007874656,0.00006129283,0.0009950942,0.07592414,0.9025037,0.00260994,0.0007851259],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.3878962,0.00001545979,0.6098402,0.0002253261,0.0009712826,0.0002509817,0.0002409244,0.0004823824,0.00007725915],"genre_scores_gemma":[0.2453654,0.000002042299,0.753849,0.000223332,0.0002626296,0.00007245092,0.0001076963,0.00004520011,0.00007229175],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.2376847,"threshold_uncertainty_score":0.9999882,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2148687539","doi":"","title":"Does the first impression count? : Examining the effect of the welcome screen design on the response rate","year":2013,"lang":"en","type":"article","venue":"Survey methodology","topic":"Psychology of Social Influence","field":"Social Sciences","cited_by":3,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"","keywords":"Impression; Psychology; Statistics; Mathematics; Computer science; World Wide Web","retraction":null,"screen_n_in":null,"score":{"opus":0.3050377151179383,"gpt":0.4355875978737875,"spread":0.1305498827558493,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","sts"],"consensus_categories":["metaresearch","sts"],"category_scores_codex":[0.160953,0.0001903539,0.0003382867,0.00003763105,0.002241243,0.00006461678,0.002047672,0.0002753453,0.0002690876],"category_scores_gemma":[0.06308688,0.00005720221,0.0001098052,0.0005345432,0.00362895,0.000105078,0.0002209976,0.0006162769,0.0000726359],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005590343,"about_ca_system_score_gemma":0.0001197643,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01766816,"about_ca_topic_score_gemma":0.003351338,"domain_scores_codex":[0.80878,0.1898004,0.0003049324,0.0003011407,0.0003530376,0.0004604654],"domain_scores_gemma":[0.7742524,0.2244932,0.000331375,0.0007465198,0.0001357944,0.00004067063],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.01840572,0.0001341113,0.6746953,0.0000356282,0.0007640341,0.000009082642,0.1272325,0.0006348598,0.06056893,0.008813795,0.0730785,0.03562756],"study_design_scores_gemma":[0.0002031774,0.0002315747,0.9877124,0.0000260028,0.00002094066,7.524295e-7,0.001564384,0.00001199765,0.003087436,0.002959649,0.004085199,0.00009649026],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9579477,0.00009716289,0.0008375422,0.03665166,0.001895932,0.001408681,0.00001442635,0.00003688262,0.00110999],"genre_scores_gemma":[0.9958656,0.00003466351,0.0002854548,0.002747781,0.0001772226,0.0001644892,5.902101e-7,0.00001686105,0.0007072678],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3130171,"threshold_uncertainty_score":0.9990826,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3148502279","doi":"","title":"A grouping genetic algorithm for joint stratification and sample allocation designs","year":2019,"lang":"en","type":"article","venue":"Survey methodology","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"","keywords":"Mathematical optimization; Cartesian product; Selection (genetic algorithm); Genetic algorithm; Algorithm; Population stratification; Population; Sample size determination; Multivariate statistics; Mathematics; Computer science; Statistics; Artificial intelligence; Combinatorics","retraction":null,"screen_n_in":null,"score":{"opus":0.6926526353938932,"gpt":0.5259590208262171,"spread":0.1666936145676761,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.04159457,0.0001720482,0.000487076,0.0002508388,0.0001128436,0.0001134147,0.00031264,0.0001625785,0.0002110253],"category_scores_gemma":[0.02659719,0.0001482143,0.00007848648,0.0004514012,0.0001192332,0.0001985565,0.00008854775,0.0001057761,0.00006774822],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004638586,"about_ca_system_score_gemma":0.00007926171,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005772166,"about_ca_topic_score_gemma":0.00005873676,"domain_scores_codex":[0.9857646,0.01192288,0.0007624073,0.0008227359,0.0004064172,0.0003209584],"domain_scores_gemma":[0.9548246,0.04381722,0.0003128772,0.0005586856,0.0003843993,0.0001021872],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00009803681,0.00003836393,0.006962689,0.000008204789,0.00002485241,3.859919e-7,0.0004910755,0.0001950939,0.2586027,0.002480636,0.0001350082,0.730963],"study_design_scores_gemma":[0.0009891202,0.0008692655,0.6207905,0.000008959852,0.00002266672,0.00002671483,0.0009522733,0.1624662,0.06421185,0.1485315,0.0006713975,0.0004595217],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.07280751,0.0002309038,0.925258,0.00008504971,0.0006100814,0.0008307349,0.00005404353,0.00002959489,0.00009411509],"genre_scores_gemma":[0.04963154,0.00001106107,0.949787,0.0001436942,0.00004889657,0.00008476043,0.00003594809,0.00002112486,0.0002359787],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.7305034,"threshold_uncertainty_score":0.9868801,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2143245567","doi":"","title":"Adjustment of unemployment estimates based on small area estimation in Korea","year":2003,"lang":"en","type":"article","venue":"Survey methodology","topic":"Spatial and Panel Data Analysis","field":"Economics, Econometrics and Finance","cited_by":2,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"","keywords":"Small area estimation; Statistics; Jackknife resampling; Estimator; Econometrics; Estimation; Unemployment; Mean squared error; Metropolitan area; Population; Efficiency; Mathematics; Geography; Economics; Demography; Economic growth","retraction":null,"screen_n_in":null,"score":{"opus":0.3280569702114287,"gpt":0.332284060759851,"spread":0.004227090548422352,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005900272,0.0001331656,0.0005690483,0.0004092635,0.00002104502,0.000006896238,0.0001344323,0.000103156,0.0005494704],"category_scores_gemma":[0.00434179,0.0001386639,0.00007769171,0.0004018672,0.00004446768,0.00003356538,0.00001636306,0.00008930773,0.0000637676],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000600551,"about_ca_system_score_gemma":0.00002319984,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00720665,"about_ca_topic_score_gemma":0.002074045,"domain_scores_codex":[0.9980401,0.0007692017,0.000617237,0.000335721,0.00003021204,0.0002075206],"domain_scores_gemma":[0.9968894,0.002401123,0.0002950031,0.0003408503,0.00003047908,0.00004319556],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001214241,0.0002695146,0.8946408,0.00003201012,0.00006464485,0.000002916362,0.0001287865,0.06953047,0.00005010983,0.0314933,0.00006675215,0.003599277],"study_design_scores_gemma":[0.0007080305,0.0002165355,0.8575425,0.00001513404,0.00001485695,8.281948e-7,0.00001484041,0.115398,0.001396479,0.02411754,0.0003703504,0.0002048838],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4263999,0.0005212056,0.5688485,0.0001247781,0.0004443091,0.0002348533,0.0005081372,0.00001912799,0.002899254],"genre_scores_gemma":[0.8806387,0.00003701821,0.1186488,0.0001820621,0.000006775595,0.00002961568,0.0003950066,0.00001387401,0.00004817108],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.4542388,"threshold_uncertainty_score":0.9994044,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2803428860","doi":"","title":"Application of Markov Latent Class Analysis to the CPS","year":2001,"lang":"en","type":"article","venue":"Survey methodology","topic":"Technology and Data Analysis","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"","keywords":"Latent class model; Markov chain; Class (philosophy); Computer science; Mathematics; Statistics; Artificial intelligence","retraction":null,"screen_n_in":null,"score":{"opus":0.1307583275160842,"gpt":0.3678946917757851,"spread":0.2371363642597009,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00852092,0.00008906293,0.0003385918,0.0004151154,0.00007314757,0.00001198898,0.001415812,0.0001269429,0.00002643701],"category_scores_gemma":[0.001130856,0.00006291822,0.000122667,0.004109829,0.00008037533,0.00006910332,0.000370765,0.0001263048,0.00005418416],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001469893,"about_ca_system_score_gemma":0.00001959874,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001915142,"about_ca_topic_score_gemma":0.005280335,"domain_scores_codex":[0.9959746,0.003014674,0.000284773,0.0003908197,0.0001282083,0.0002068999],"domain_scores_gemma":[0.996216,0.002052171,0.000141693,0.001411891,0.0001346006,0.00004365126],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0000758083,0.00008153494,0.6460485,0.000003243117,0.001233335,0.000005523599,0.0002544709,0.002622546,0.001934389,0.04273742,0.001477358,0.3035258],"study_design_scores_gemma":[0.00006956689,0.00003975662,0.966,3.938756e-7,0.0001634431,0.000005904727,0.00001325099,0.0227028,0.0008012456,0.002698733,0.007411856,0.00009301915],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03659802,0.00006933522,0.959652,0.00330112,0.00008489634,0.00009174985,0.00001875408,0.00006352548,0.0001206153],"genre_scores_gemma":[0.7722266,0.00002652599,0.2270292,0.000498637,0.00001457537,0.00002680634,0.0000477039,0.000003496444,0.0001264774],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7356286,"threshold_uncertainty_score":0.2953198,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W20740463","doi":"","title":"Contact and cooperation in the Belgian fertility and family survey","year":2004,"lang":"en","type":"article","venue":"Survey methodology","topic":"demographic modeling and climate adaptation","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"Vlaamse regering; Fonds Wetenschappelijk Onderzoek","keywords":"Fertility; Demographic economics; Geography; Demography; Sociology; Economics; Population","retraction":null,"screen_n_in":null,"score":{"opus":0.6166600698174581,"gpt":0.4877275647754358,"spread":0.1289325050420223,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.09329619,0.0001069781,0.0003000432,0.0001682756,0.0001425764,0.0001284626,0.000228121,0.000116521,0.00001004824],"category_scores_gemma":[0.0232849,0.00006662874,0.00002344397,0.0006756863,0.0001500666,0.0001482495,0.00004574436,0.0001734794,0.000007556303],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001080678,"about_ca_system_score_gemma":0.00005512547,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.01680875,"about_ca_topic_score_gemma":0.06845113,"domain_scores_codex":[0.977358,0.02101287,0.0005553604,0.0004944081,0.0003621729,0.0002172131],"domain_scores_gemma":[0.9806264,0.0186369,0.0001059586,0.0003244926,0.000256393,0.00004986364],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003791871,0.00004912472,0.963692,0.000002944167,0.000009412654,0.000003682226,0.002417155,0.0003792629,0.001115188,0.001882485,0.00005276868,0.03001675],"study_design_scores_gemma":[0.0004422055,0.00008693066,0.9773788,0.000002678991,0.000003203098,0.000004520469,0.0006210139,0.000692073,0.00001830329,0.02064249,0.00003493811,0.00007284469],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9133794,0.0003538847,0.0853711,0.0003908323,0.0001646951,0.0001466219,0.00003543133,0.00001010709,0.0001479522],"genre_scores_gemma":[0.9958267,0.00006435641,0.003518388,0.0005191528,0.000006313866,0.000007138296,0.00003636682,0.000004650569,0.00001694848],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08244731,"threshold_uncertainty_score":0.9897384,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3048692611","doi":"","title":"An assessment of accuracy improvement by adaptive survey design : [Une évaluation de l’amélioration de l’exactitude au moyen d’un plan de sondage adaptatif]","year":2019,"lang":"fr","type":"article","venue":"Survey methodology","topic":"Census and Population Estimation","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"","keywords":"Computer science","retraction":null,"screen_n_in":null,"score":{"opus":0.4336167277882685,"gpt":0.4782844676439482,"spread":0.04466773985567962,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","metaepi_narrow"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.0711055,0.0004341656,0.0008988967,0.0002246681,0.0001889579,0.00006024484,0.0003417651,0.0006692222,0.0005279229],"category_scores_gemma":[0.009548865,0.0004869015,0.0001205946,0.0005085749,0.0001218949,0.0004895787,0.00006816075,0.000509869,0.00002001912],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001299936,"about_ca_system_score_gemma":0.001945672,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.06428429,"about_ca_topic_score_gemma":0.01316822,"domain_scores_codex":[0.9463636,0.05005946,0.001399017,0.00067219,0.0005831802,0.0009225902],"domain_scores_gemma":[0.9718388,0.02506964,0.001451756,0.0006740989,0.0007247307,0.0002410007],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002257475,0.001872262,0.4749681,0.0005899101,0.0007319284,0.000009736225,0.01799623,0.1381544,0.1970846,0.01409374,0.001414386,0.1508272],"study_design_scores_gemma":[0.0009555154,0.00102411,0.6334735,0.00004908647,0.0001526626,0.00001167492,0.0003882887,0.3434464,0.009105771,0.01104394,0.00004113797,0.0003079974],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4549632,0.00008815323,0.5429592,0.0001501448,0.0004336335,0.0008647259,0.0004431737,0.00002832812,0.00006942321],"genre_scores_gemma":[0.5495072,0.00005949864,0.4487942,0.00007200793,0.00006262582,0.00004446303,0.001203543,0.00004890958,0.0002076299],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.205292,"threshold_uncertainty_score":0.9997582,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2573621420","doi":"","title":"A short note on quantile and expectile estimation in unequal probability samples","year":2016,"lang":"en","type":"article","venue":"Survey methodology","topic":"Advanced Statistical Process Monitoring","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"","keywords":"Quantile; Estimator; Mathematics; Quantile regression; Statistics; Econometrics; Generalization","retraction":null,"screen_n_in":null,"score":{"opus":0.6814941744892106,"gpt":0.5625075198466303,"spread":0.1189866546425803,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.02193238,0.0001398563,0.0004355568,0.0002049816,0.0000713211,0.00003118838,0.0002618236,0.0001140468,0.00006116289],"category_scores_gemma":[0.2231894,0.00008647359,0.00002660874,0.0004355986,0.0002673745,0.0002203197,0.0001181703,0.00013973,0.00004014394],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007342274,"about_ca_system_score_gemma":0.00004484262,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002548441,"about_ca_topic_score_gemma":0.001319617,"domain_scores_codex":[0.9913634,0.006474609,0.0006392887,0.0007424284,0.0004685459,0.0003117178],"domain_scores_gemma":[0.9029201,0.09629017,0.0001104256,0.0003979091,0.0001922863,0.00008914858],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0005527586,0.00005107555,0.2177766,0.000006862901,0.000003543304,0.000005455206,0.0004003558,0.0006365833,0.002357143,0.00670201,0.00003753016,0.7714701],"study_design_scores_gemma":[0.000178398,0.00009974116,0.69749,0.00001197514,0.000001371781,0.00000170905,0.00005818762,0.0009538662,0.001688282,0.2993432,0.00006988418,0.0001034161],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.40076,0.00001892705,0.5986572,0.00009399857,0.0002393222,0.0001111289,0.00004413533,0.00002005137,0.00005524106],"genre_scores_gemma":[0.7329708,0.000003053894,0.2669273,0.00001604077,0.00001947618,0.00002183946,0.000002290982,0.000007440435,0.0000317358],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7713667,"threshold_uncertainty_score":0.783354,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2274680738","doi":"","title":"Small Area Estimation for Adjusting Subarea Unemployment and Its Application","year":2003,"lang":"en","type":"article","venue":"Survey methodology","topic":"Regional Economic and Spatial Analysis","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"","keywords":"Estimation; Small area estimation; Unemployment; Statistics; Computer science; Mathematics; Economics; Economic growth","retraction":null,"screen_n_in":null,"score":{"opus":0.4238741577399343,"gpt":0.3289356158029341,"spread":0.09493854193700024,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00521027,0.0001122518,0.0004116913,0.0001364032,0.0000945496,0.00001644228,0.00008285242,0.0001067299,0.00004996277],"category_scores_gemma":[0.00268897,0.0001286457,0.00007037402,0.0001232836,0.00002889409,0.00006022841,0.00001774202,0.00005655946,0.00003937309],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000481318,"about_ca_system_score_gemma":0.00001576024,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007865758,"about_ca_topic_score_gemma":0.000555873,"domain_scores_codex":[0.9986587,0.000271921,0.0004726499,0.0003921495,0.00001137623,0.0001931678],"domain_scores_gemma":[0.9978802,0.001548406,0.000307772,0.0001509468,0.00005428381,0.00005841243],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00007770346,0.00005713863,0.2162903,0.00006393213,0.0001570473,3.759133e-7,0.0002554238,0.00540775,0.0001473381,0.7626879,0.0001046215,0.0147505],"study_design_scores_gemma":[0.001348675,0.0001782786,0.2394761,0.000008277905,0.00005578543,0.00001248057,0.00008501743,0.3868044,0.0008619716,0.3541282,0.01637722,0.0006637012],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3077316,0.0007861182,0.6902084,0.0001931158,0.0001141689,0.0002304571,0.00007948288,0.0000143738,0.0006423784],"genre_scores_gemma":[0.9431205,0.000108848,0.05588032,0.0001731039,0.0000290251,0.0001345505,0.0001516868,0.00001768746,0.0003842331],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.635389,"threshold_uncertainty_score":0.5246019,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3041227018","doi":"","title":"“Optimal” calibration weights under unit nonresponse in survey sampling","year":2019,"lang":"en","type":"article","venue":"Survey methodology","topic":"Survey Sampling and Estimation Techniques","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"","keywords":"Statistics; Estimator; Calibration; Sampling (signal processing); Sample (material); Variance (accounting); Population; Survey sampling; Econometrics; Non-response bias; Mathematics; Computer science; Demography; Physics; Economics","retraction":null,"screen_n_in":null,"score":{"opus":0.6246140539733327,"gpt":0.4963449919555593,"spread":0.1282690620177734,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","metaepi_narrow"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.05163281,0.0002738963,0.0006945209,0.0004705333,0.00007267875,0.00004379086,0.0003128319,0.0003860501,0.0004003736],"category_scores_gemma":[0.02317187,0.0002623035,0.00007852363,0.0007110535,0.00007503028,0.0001808554,0.00009730448,0.0004024202,0.00009147714],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007611829,"about_ca_system_score_gemma":0.0001558342,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006372406,"about_ca_topic_score_gemma":0.008610399,"domain_scores_codex":[0.969604,0.0283122,0.0007878898,0.0005516409,0.0002494843,0.0004948271],"domain_scores_gemma":[0.9324145,0.06639218,0.000247175,0.0006218445,0.0002411807,0.00008308396],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00206513,0.0002549383,0.9745179,0.0001077901,0.00009171698,0.000005885116,0.0009265218,0.003226171,0.003179071,0.01341622,0.0006580393,0.001550586],"study_design_scores_gemma":[0.000530959,0.0001052338,0.9351648,0.00003985982,0.000008472342,0.000009781493,0.00007832022,0.003209205,0.002861825,0.05753006,0.0001118431,0.0003496242],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5688576,0.00002675556,0.4301544,0.00004085719,0.0003053961,0.0002509675,0.00005108533,0.0001612406,0.0001516626],"genre_scores_gemma":[0.5342919,0.00001258195,0.4643499,0.0001347264,0.00003145093,0.00002894699,0.0003466789,0.00006032348,0.0007435362],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04411384,"threshold_uncertainty_score":0.9999829,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2963777145","doi":"","title":"Multiple Imputation of Missing Values in Household Data with Structural Zeros","year":2017,"lang":"en","type":"article","venue":"Survey methodology","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"","keywords":"Categorical variable; Imputation (statistics); Multivariate statistics; Missing data; Statistics; Econometrics; Gibbs sampling; Multivariate normal distribution; Population; Mathematics; Latent variable; Computer science; Demography; Bayesian probability","retraction":null,"screen_n_in":null,"score":{"opus":0.6315137670957544,"gpt":0.5150164246912262,"spread":0.1164973424045281,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.00852286,0.0001300265,0.000517503,0.00006816249,0.0001128255,0.00003814481,0.0006090778,0.0001035916,0.00002033446],"category_scores_gemma":[0.06458776,0.00009786656,0.00001821636,0.00006834964,0.0003000929,0.0001608008,0.0002189877,0.0001714407,5.428428e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001332915,"about_ca_system_score_gemma":0.00006329553,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002553836,"about_ca_topic_score_gemma":0.001289169,"domain_scores_codex":[0.9947771,0.004139083,0.000382297,0.000335642,0.000139238,0.0002265856],"domain_scores_gemma":[0.9759409,0.02245287,0.000394199,0.001074445,0.00009068505,0.00004689089],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0006564083,0.0000663485,0.672151,0.0002516089,0.00007719824,0.00004054726,0.0006958908,0.00001192139,0.005055412,0.03660261,0.0001121282,0.284279],"study_design_scores_gemma":[0.0003914572,0.00005379687,0.5879843,0.00003218968,0.000014598,0.000008902123,0.00002643118,0.00313893,0.001642487,0.406617,0.000001411437,0.00008849205],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2844692,0.00001304186,0.7149218,0.00003350753,0.000121873,0.0001099987,0.0002604607,0.00001356807,0.00005664375],"genre_scores_gemma":[0.3977431,0.000001357147,0.6021873,0.000007728268,0.00001682393,0.000001788353,0.00002563121,0.00001178992,0.0000044497],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.3700144,"threshold_uncertainty_score":0.9432916,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2733262089","doi":"","title":"Mixed Methods in Value Research: An Analysis of the Validity of the Russian Version of the Schwartz Value Survey (SVS) Using Cognitive Interviewing, Multidimensional Scaling (MDS), and Confirmatory Factor Analysis (CFA)","year":2013,"lang":"en","type":"article","venue":"Survey methodology","topic":"Customer Service Quality and Loyalty","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"","keywords":"Confirmatory factor analysis; Psychology; Multidimensional scaling; Cognitive interview; Value (mathematics); Cognition; Statistics; Structural equation modeling; Mathematics; Psychiatry","retraction":null,"screen_n_in":null,"score":{"opus":0.5546124260343751,"gpt":0.480816874291878,"spread":0.07379555174249708,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.06302185,0.0002548037,0.001200271,0.001065521,0.0003455657,0.00004590077,0.000812267,0.0002482891,0.0002409344],"category_scores_gemma":[0.01124698,0.0001519993,0.0005241354,0.006722298,0.0008531819,0.0003968119,0.001265916,0.0005805828,0.00000176724],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005209115,"about_ca_system_score_gemma":0.0001129943,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.1749324,"about_ca_topic_score_gemma":0.04210218,"domain_scores_codex":[0.9427273,0.05466593,0.001032074,0.0005153075,0.00066524,0.0003941114],"domain_scores_gemma":[0.981253,0.01567714,0.00113048,0.0008879944,0.001016524,0.0000348314],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001509424,0.0001496056,0.9876066,0.0001740199,0.001250791,2.306844e-7,0.001044605,0.002410261,0.005379738,0.0008213526,0.000005744631,0.001006133],"study_design_scores_gemma":[0.0003882104,0.00001305461,0.9257538,0.00009476508,0.001340671,3.090913e-7,0.001596609,0.0659535,0.004313058,0.0003794097,0.00001886071,0.0001477116],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9898897,0.00009948833,0.008684767,0.0001660296,0.0004586062,0.0004740026,0.0001797005,0.000008947028,0.00003880098],"genre_scores_gemma":[0.9948004,0.000006352032,0.004855065,0.0002067346,0.00003349605,0.000007048204,0.00006334693,0.00001894158,0.000008624319],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1328302,"threshold_uncertainty_score":0.9970817,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null}]}