{"meta":{"page":1,"per_page":50,"max_per_page":100,"total":9,"total_is_capped":false,"direct_labels_cover":0,"predictions_cover":9,"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","author_layer_release":"2026-06-26"},"query_hash":"1fa538f3186d","filters":{"venue":"CIRJE F-Series"}},"results":[{"id":"W3021707751","doi":"","title":"Prediction in Multivariate Mixed Linear Models","year":2002,"lang":"en","type":"article","venue":"CIRJE F-Series","topic":"Advanced Statistical Methods and Models","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto","funders":"","keywords":"Mathematics; Estimator; Covariance; Statistics; Shrinkage estimator; Minimum-variance unbiased estimator; Multivariate statistics; Linear model; Linear regression; Estimation of covariance matrices; Bayesian multivariate linear regression; Covariance matrix; Mean squared error; Bias of an estimator","authors":[{"name":"Tatsuka Kubokawa","is_ca":false},{"name":"Muni S. Srivastava","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.2177594754420285,"gpt":0.3810548909846849,"spread":0.1632954155426564,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01995753,0.001831291,0.002641035,0.001467362,0.0006993252,0.00243339,0.003065106,0.002571037,0.002865],"category_scores_gemma":[0.06020194,0.001120604,0.00142271,0.002493112,0.002378284,0.003251296,0.002393426,0.003283717,0.0008433758],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001359506,"about_ca_system_score_gemma":0.001268704,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004293493,"about_ca_topic_score_gemma":0.002820256,"domain_scores_codex":[0.9875003,0.009242552,0.0003065009,0.00152596,0.001056553,0.0003681917],"domain_scores_gemma":[0.9595191,0.03341961,0.003309662,0.001835703,0.001621157,0.0002947706],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001920484,0.00007855828,0.006135535,0.0004838666,0.0004024972,0.0002858971,0.0002465901,0.5245208,0.0005380894,0.3674151,0.003433264,0.09626776],"study_design_scores_gemma":[0.00001791678,0.0000469345,0.0004935168,0.00004108288,0.00004959485,0.00003879436,0.00001800162,0.8378001,0.0002283132,0.1600603,0.001183482,0.0000221631],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006486607,0.001154869,0.9907681,0.0004692758,0.00008499376,0.00004418304,0.0001033491,0.0001399103,0.0007487083],"genre_scores_gemma":[0.5752352,0.006149044,0.403088,0.0006142489,0.001033063,0.0009676051,0.001241607,0.0002114884,0.01145971],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01995753,"threshold_uncertainty_score":0.1055468,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2267776323","doi":"","title":"Estimation of the Precision Matrix of a Singular Wishart Distribution and its Application in High Dimensional Data","year":2005,"lang":"en","type":"article","venue":"CIRJE F-Series","topic":"Random Matrices and Applications","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto","funders":"","keywords":"Wishart distribution; Identity matrix; Mathematics; Estimator; Scatter matrix; Statistics; Applied mathematics; Matrix (chemical analysis); Bayes' theorem; Eigenvalues and eigenvectors; Estimation of covariance matrices; Multivariate statistics; Bayesian probability; Physics","authors":[{"name":"Tatsuya Kubokawa","is_ca":false},{"name":"Muni S. Srivastava","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02386916596612523,"gpt":0.3060573563742562,"spread":0.282188190408131,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00988335,0.0007975021,0.001135323,0.002365106,0.0007532369,0.001747055,0.001184078,0.001486184,0.0009529269],"category_scores_gemma":[0.04692106,0.0006393309,0.0006424155,0.002162176,0.002545696,0.002646193,0.001883509,0.001961459,0.0004707751],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007779081,"about_ca_system_score_gemma":0.0009379855,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001190896,"about_ca_topic_score_gemma":0.000989469,"domain_scores_codex":[0.9971573,0.001272659,0.0001488335,0.0005153297,0.0008127658,0.00009311063],"domain_scores_gemma":[0.9751766,0.018144,0.001931948,0.002600265,0.001876409,0.0002707201],"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":[0.0002129542,0.00006977622,0.009462609,0.0003415659,0.0001787532,0.0004674622,0.0004946667,0.3194418,0.01021119,0.4118,0.001937114,0.2453824],"study_design_scores_gemma":[0.0000142329,0.00006905318,0.002788369,0.00006017993,0.00003354306,0.0004285314,0.00006999661,0.7627436,0.006434139,0.2250342,0.002241877,0.00008233486],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01171405,0.000545856,0.9868095,0.0001502693,0.00002255066,0.00001154741,0.00003359878,0.00010326,0.000609312],"genre_scores_gemma":[0.494535,0.001948149,0.5003579,0.0001436703,0.0002002791,0.0000960528,0.0002768053,0.0001537765,0.002288337],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00988335,"threshold_uncertainty_score":0.05226874,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2268468413","doi":"","title":"Tests for Multivariate Analysis of Variance in High Dimension Under Non-Normality","year":2011,"lang":"en","type":"article","venue":"CIRJE F-Series","topic":"Advanced Statistical Methods and Models","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto","funders":"","keywords":"Multivariate analysis of variance; Mathematics; Dimension (graph theory); Normality; Multivariate normal distribution; Null (SQL); Invariant (physics); Multivariate statistics; Statistics; Covariance; Null hypothesis; Covariance matrix; Null distribution; Pure mathematics; Statistical hypothesis testing; Test statistic; Computer science","authors":[{"name":"Muni S. Srivastava","is_ca":true},{"name":"Tatsuya Kubokawa","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1620083013010485,"gpt":0.4156167831212349,"spread":0.2536084818201865,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0431675,0.001783671,0.004020307,0.00391665,0.001435739,0.003925338,0.003953279,0.003013038,0.00388182],"category_scores_gemma":[0.2602169,0.0005872703,0.002381246,0.005051508,0.01066451,0.008147154,0.004319601,0.004867055,0.0006902003],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00110251,"about_ca_system_score_gemma":0.001685672,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004735114,"about_ca_topic_score_gemma":0.0001902547,"domain_scores_codex":[0.9259481,0.04746981,0.003901888,0.009902804,0.01136666,0.001410796],"domain_scores_gemma":[0.547189,0.3989411,0.02239475,0.02432071,0.005683479,0.001470932],"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":[0.002045436,0.0005462,0.08952332,0.001453739,0.004389707,0.002635843,0.001943526,0.08212288,0.005947677,0.5730606,0.002814435,0.2335166],"study_design_scores_gemma":[0.0002527491,0.002015935,0.03692339,0.0002534628,0.0004340566,0.001782793,0.0008396719,0.3422466,0.004860022,0.6061703,0.003962914,0.0002580713],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.07634457,0.000606016,0.9195093,0.0005629672,0.000190613,0.0001741098,0.0003770744,0.0003506345,0.001884672],"genre_scores_gemma":[0.8606008,0.000496426,0.1352699,0.000351386,0.0006108357,0.0009479074,0.000910064,0.0001157089,0.0006970085],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0431675,"threshold_uncertainty_score":0.2282943,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3121308753","doi":"","title":"Observational Equivalence between the Malmquist Index and the Solow Residual for the G-7 Countries","year":2005,"lang":"en","type":"article","venue":"CIRJE F-Series","topic":"Diverse Scientific and Engineering Research","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"","funders":"","keywords":"Solow residual; Residual; Econometrics; Index (typography); Malmquist index; Economics; Equivalence (formal languages); Mathematics; Total factor productivity; Statistics; Productivity; Growth accounting; Macroeconomics; Computer science; Discrete mathematics","authors":[{"name":"Jeong-Joon Lee","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.04080141940046116,"gpt":0.2544753984000813,"spread":0.2136739789996202,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005567937,0.0002588686,0.0006853748,0.002550544,0.0006746813,0.001917053,0.0005311615,0.0004701279,0.002812292],"category_scores_gemma":[0.0456778,0.00008915435,0.000486311,0.002847542,0.00195383,0.001707686,0.001208135,0.0008215893,0.0004490853],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009088784,"about_ca_system_score_gemma":0.001104144,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00468481,"about_ca_topic_score_gemma":0.002348527,"domain_scores_codex":[0.997057,0.001057012,0.0002624446,0.0005925176,0.000718707,0.0003123432],"domain_scores_gemma":[0.9644378,0.0184441,0.009184194,0.004767783,0.002563509,0.0006026894],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0009080567,0.0001030964,0.6778294,0.0001886749,0.0003959972,0.0004779171,0.002920211,0.01125345,0.001505159,0.2162371,0.003919049,0.08426179],"study_design_scores_gemma":[0.0001080698,0.0004149906,0.7521108,0.0001419355,0.0001234389,0.0003988661,0.003421532,0.01908976,0.002697848,0.2022982,0.01909712,0.00009747043],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9599485,0.0007591308,0.01628903,0.0007232645,0.00006881935,0.00003884031,0.001207047,0.00008924505,0.02087626],"genre_scores_gemma":[0.9974754,0.0001361548,0.001191289,0.0000391822,0.00004096434,0.00001696495,0.0007313675,0.00001720454,0.0003513367],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005567937,"threshold_uncertainty_score":0.02944642,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2232708164","doi":"","title":"Asymptotic Expansion and Estimation of EPMC for Linear Classification Rules in High Dimension","year":2011,"lang":"en","type":"article","venue":"CIRJE F-Series","topic":"Advanced Statistical Methods and Models","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto","funders":"","keywords":"Mathematics; Linear discriminant analysis; Estimator; Covariance matrix; Covariance; Dimension (graph theory); Applied mathematics; Scatter matrix; Multivariate normal distribution; Estimation of covariance matrices; Matrix (chemical analysis); Inverse; Bias of an estimator; Statistics; Multivariate statistics; Minimum-variance unbiased estimator; Combinatorics","authors":[{"name":"Tatsuya Kubokawa","is_ca":false},{"name":"Masashi Hyodo","is_ca":false},{"name":"Muni S. Srivastava","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1582716036607758,"gpt":0.3855056850579192,"spread":0.2272340813971434,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01064785,0.0007178762,0.001763024,0.001880633,0.0005531397,0.001496781,0.001675785,0.001380478,0.001387882],"category_scores_gemma":[0.05836691,0.0006861263,0.0009701166,0.001013636,0.002302938,0.002318279,0.001605,0.002705865,0.0004702288],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001298501,"about_ca_system_score_gemma":0.00125418,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002785499,"about_ca_topic_score_gemma":0.002372526,"domain_scores_codex":[0.9972098,0.00121607,0.0001465883,0.0004120795,0.0008236892,0.0001916775],"domain_scores_gemma":[0.9568746,0.03652497,0.001955464,0.001970174,0.002366762,0.0003079035],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001365736,0.0001266108,0.00596523,0.0002482378,0.0001594747,0.0002880738,0.0003320984,0.7958644,0.003158818,0.09110109,0.001169604,0.1014499],"study_design_scores_gemma":[0.00000346334,0.00001137729,0.0004189887,0.00001195003,0.000006068279,0.00003743637,0.000009225089,0.9878432,0.0003759841,0.01114924,0.0001261365,0.000007053963],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0252656,0.0003930784,0.973318,0.0001841418,0.00001933975,0.00002806483,0.00003214226,0.0001882903,0.0005714791],"genre_scores_gemma":[0.7130725,0.001122953,0.2799487,0.0002458768,0.0002201314,0.0004092618,0.0004765416,0.0001696829,0.00433424],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01064785,"threshold_uncertainty_score":0.05631191,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2234657795","doi":"","title":"Estimating the Covariance Matrix: A New Approach","year":2002,"lang":"en","type":"article","venue":"CIRJE F-Series","topic":"Advanced Statistical Methods and Models","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto","funders":"","keywords":"Invertible matrix; Mathematics; Estimator; Estimation of covariance matrices; Multivariate normal distribution; Minimax; Covariance matrix; Minimax estimator; Applied mathematics; Minimum-variance unbiased estimator; Covariance; Equivariant map; Scatter matrix; Matrix (chemical analysis); Statistics; Multivariate statistics; Mathematical optimization; Pure mathematics","authors":[{"name":"Tatsuya Kubokawa","is_ca":false},{"name":"Muni S. Srivastava","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1887256862051294,"gpt":0.4059936824820707,"spread":0.2172679962769414,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003889596,0.00108868,0.001852804,0.002453734,0.0006930121,0.00262139,0.002447945,0.001583063,0.002816951],"category_scores_gemma":[0.01643272,0.0007355206,0.001561024,0.001999317,0.00149993,0.004160048,0.003321448,0.003312533,0.001044276],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009561823,"about_ca_system_score_gemma":0.002173862,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003298151,"about_ca_topic_score_gemma":0.002826098,"domain_scores_codex":[0.9958615,0.001257682,0.0002212744,0.001109095,0.001431648,0.0001188736],"domain_scores_gemma":[0.995461,0.002285899,0.0003564798,0.0008262737,0.0009431124,0.000127245],"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":[0.00004938153,0.00007468885,0.001565147,0.0002531765,0.0003881858,0.0002964161,0.0002591882,0.1167155,0.005955822,0.6249213,0.005178482,0.2443427],"study_design_scores_gemma":[0.00002509405,0.00005762857,0.0006675103,0.00008285941,0.00009445962,0.0003433015,0.00004500204,0.5213453,0.00185465,0.4540137,0.02137531,0.00009507626],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0005024286,0.00023054,0.998472,0.0001328302,0.00006095125,0.000008150389,0.00002725417,0.00004580161,0.0005199881],"genre_scores_gemma":[0.04734603,0.002132312,0.9432161,0.0005115679,0.001023823,0.0001479744,0.000306443,0.0002260083,0.005089683],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003889596,"threshold_uncertainty_score":0.02057034,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2222472204","doi":"","title":"Comparison of Discrimination Methods for High Dimensional Data","year":2005,"lang":"en","type":"article","venue":"CIRJE F-Series","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto","funders":"","keywords":"Sample mean and sample covariance; Dimension (graph theory); Bayes' theorem; Covariance matrix; Matrix (chemical analysis); Data Matrix; Mathematics; Inverse; Sample (material); Statistics; Estimation of covariance matrices; Covariance; Computer science; Pattern recognition (psychology); Algorithm; Bayesian probability; Artificial intelligence; Combinatorics; Physics; Biology; Chromatography","authors":[{"name":"Muni S. Srivastava","is_ca":true},{"name":"Tatsuya Kubokawa","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.08454628698913609,"gpt":0.4324722981831362,"spread":0.3479260111940001,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01767159,0.0009164516,0.001098942,0.004093633,0.0005226767,0.001554309,0.00119261,0.001459894,0.001632032],"category_scores_gemma":[0.04890618,0.0002296545,0.000909675,0.001940132,0.0009367561,0.001770786,0.001599904,0.001508593,0.0006708513],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009156617,"about_ca_system_score_gemma":0.0008724282,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006788627,"about_ca_topic_score_gemma":0.0006291698,"domain_scores_codex":[0.9919045,0.004030844,0.0005065371,0.0007323102,0.002600299,0.0002253936],"domain_scores_gemma":[0.9382809,0.05437227,0.0009969898,0.002602364,0.003232466,0.0005150966],"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":[0.002026956,0.000437588,0.008426486,0.0005955489,0.0005765436,0.0001258459,0.0002280136,0.09505744,0.006569541,0.02786903,0.002887329,0.8551996],"study_design_scores_gemma":[0.0002426377,0.0006036537,0.009123788,0.0001085818,0.00009158567,0.000400795,0.0001711483,0.9265661,0.007633516,0.05100268,0.003934285,0.0001212546],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.09391725,0.004859595,0.8957141,0.000737506,0.0003717478,0.0001602319,0.0002572256,0.0009611583,0.003021189],"genre_scores_gemma":[0.5112526,0.00188033,0.4833189,0.0003470197,0.0002019382,0.0003279484,0.0008762998,0.0001401291,0.001654856],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01767159,"threshold_uncertainty_score":0.0934574,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2249412289","doi":"","title":"Estimating Interregional Utility Differentials","year":2007,"lang":"en","type":"article","venue":"CIRJE F-Series","topic":"Regional Economics and Spatial Analysis","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"","funders":"","keywords":"Stylized fact; Economics; Econometrics; Per capita; Per capita income; Macroeconomics; Population; Demography","authors":[{"name":"Kentaro Nakajima","is_ca":false},{"name":"Takatoshi Tabuchi","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.04416865952982407,"gpt":0.2362108370285159,"spread":0.1920421774986918,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001873384,0.0004179327,0.0004603921,0.002021163,0.0001901207,0.0009939867,0.0008024727,0.0004169797,0.0007508426],"category_scores_gemma":[0.009621472,0.0002447877,0.0004378429,0.003060644,0.0004092753,0.001246154,0.0008607834,0.0005466489,0.000163381],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001666143,"about_ca_system_score_gemma":0.0005256211,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02390205,"about_ca_topic_score_gemma":0.01776388,"domain_scores_codex":[0.9990543,0.0004741756,0.00004811715,0.0002343859,0.0001141402,0.00007493702],"domain_scores_gemma":[0.9975848,0.0009326673,0.0005888012,0.00044246,0.0003880103,0.00006322519],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001191313,0.00008556308,0.5020621,0.0000934691,0.0002935618,0.0001956793,0.0008152154,0.3993313,0.001671553,0.03843265,0.0007136913,0.05618609],"study_design_scores_gemma":[0.00001370867,0.00005327301,0.1690813,0.0000360017,0.00006986936,0.0001248984,0.0008264249,0.7960544,0.002666297,0.02784421,0.003183457,0.00004629583],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.873729,0.0002547654,0.1228149,0.00008685679,0.000004701747,0.00002500354,0.0009477677,0.00008669856,0.002050425],"genre_scores_gemma":[0.9863824,0.0000444276,0.01294849,0.000005835775,0.000001395501,0.000008737424,0.0004248505,0.000006234049,0.0001777055],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02390205,"threshold_uncertainty_score":0.04752588,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2240015078","doi":"","title":"Akaike Information Criterion for Selecting Components of the Mean Vector in High Dimensional Data with Fewer Observations","year":2007,"lang":"en","type":"article","venue":"CIRJE F-Series","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto","funders":"","keywords":"Akaike information criterion; Generality; Bayesian information criterion; Mathematics; Model selection; Statistics; Multivariate statistics; Context (archaeology); Selection (genetic algorithm); Information Criteria; Sample (material); Computer science; Artificial intelligence; Geography","authors":[{"name":"Muni S. Srivastava","is_ca":true},{"name":"Tatsuya Kubokawa","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1629597470990432,"gpt":0.3578357687649559,"spread":0.1948760216659126,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02183337,0.001386637,0.00279062,0.00460293,0.00133647,0.002397813,0.002345135,0.003239318,0.001760182],"category_scores_gemma":[0.09315868,0.0008063363,0.001568478,0.004829976,0.003038029,0.0031263,0.002320886,0.003104748,0.0008655112],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001359844,"about_ca_system_score_gemma":0.003966529,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003649595,"about_ca_topic_score_gemma":0.003588155,"domain_scores_codex":[0.9826853,0.01095772,0.00125795,0.001428166,0.003382413,0.0002884358],"domain_scores_gemma":[0.9402856,0.04951739,0.002588117,0.00315684,0.004029978,0.000422156],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0008285607,0.0003367267,0.02110435,0.001799283,0.001414332,0.001008993,0.0008580851,0.4354044,0.006509625,0.2088878,0.007414138,0.3144336],"study_design_scores_gemma":[0.00008466252,0.0002569657,0.007829687,0.000240393,0.0001397018,0.0003071103,0.0001504516,0.8498417,0.002883323,0.134054,0.004052952,0.0001590802],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.009550812,0.000784533,0.9883682,0.0001922288,0.00004487929,0.00007037069,0.0001248876,0.0001603503,0.0007037714],"genre_scores_gemma":[0.2411207,0.001353378,0.7535599,0.0002986495,0.0002020261,0.0009551646,0.001034763,0.0001395219,0.001335935],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02183337,"threshold_uncertainty_score":0.1154673,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null}]}