{"meta":{"page":1,"per_page":50,"max_per_page":100,"total":6,"total_is_capped":false,"direct_labels_cover":0,"predictions_cover":6,"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":"47b2030b8de7","filters":{"venue":"Communications for Statistical Applications and Methods"}},"results":[{"id":"W2559900549","doi":"10.5351/csam.2016.23.6.445","title":"Nonparametric Bayesian methods: a gentle introduction and overview","year":2016,"lang":"en","type":"article","venue":"Communications for Statistical Applications and Methods","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"","funders":"University of Toronto; Ohio State University; National Science Foundation","keywords":"Dirichlet process; Nonparametric statistics; Consistency (knowledge bases); Bayesian probability; Dirichlet distribution; Computer science; Econometrics; Bayesian statistics; Mathematics; Data mining; Machine learning; Bayesian inference; Artificial intelligence","retraction":null,"screen_n_in":null,"score":{"opus":0.05321361381197987,"gpt":0.4345534778964141,"spread":0.3813398640844342,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002455859,0.0001750383,0.0002998466,0.0001787803,0.0004582127,0.0001242261,0.0008164724,0.00009492586,0.00001483041],"category_scores_gemma":[0.0006729685,0.0001285126,0.0000512562,0.0005789985,0.0003451582,0.0002402006,0.0005656271,0.0001247864,0.000004095178],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003705555,"about_ca_system_score_gemma":0.00004809598,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008901972,"about_ca_topic_score_gemma":0.000002901006,"domain_scores_codex":[0.9978123,0.0008776881,0.0004003858,0.0005667846,0.00008429417,0.0002585302],"domain_scores_gemma":[0.9925898,0.004904069,0.0001302218,0.001983718,0.0001732465,0.0002189332],"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":[0.000001494446,0.00003022618,0.000003862975,0.00001653817,0.00001005781,1.931045e-8,0.00002539794,2.580365e-8,0.0009496643,0.4951688,0.0002378067,0.5035561],"study_design_scores_gemma":[0.0002410931,0.00004794327,0.0002480551,0.00001006198,0.0000445543,0.00001841375,0.000007438168,0.01498181,0.0004838014,0.6030129,0.3807386,0.0001654052],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.000001727561,0.005688987,0.9819351,0.01077919,0.00006192482,0.0008363246,0.00006518809,0.00009771984,0.0005338346],"genre_scores_gemma":[0.0004339009,0.004342899,0.9931832,0.000232276,0.00007267491,0.001462871,0.0000157976,0.00001722459,0.0002391802],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.5033907,"threshold_uncertainty_score":0.5240591,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2403206287","doi":"10.5351/csam.2015.22.4.321","title":"Geodesic Clustering for Covariance Matrices","year":2015,"lang":"en","type":"article","venue":"Communications for Statistical Applications and Methods","topic":"Morphological variations and asymmetry","field":"Mathematics","cited_by":8,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada; Ministry of Education, Science and Technology; National Research Foundation of Korea; National Research Foundation","keywords":"Cluster analysis; Mathematics; Positive-definite matrix; Geodesic; Riemannian manifold; Correlation clustering; CURE data clustering algorithm; Covariance; Pattern recognition (psychology); Artificial intelligence; Computer science; Pure mathematics; Eigenvalues and eigenvectors; Mathematical analysis; Statistics; Physics","retraction":null,"screen_n_in":null,"score":{"opus":0.2505522578633579,"gpt":0.4996673149255536,"spread":0.2491150570621956,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001608695,0.0001318586,0.0002583003,0.00006706812,0.0004356557,0.00007262727,0.0004546862,0.00008801671,0.00001211751],"category_scores_gemma":[0.001626943,0.0001174996,0.00005196894,0.0002341956,0.0001846431,0.00006801194,0.0002079446,0.0001052905,0.000004139297],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003816477,"about_ca_system_score_gemma":0.0000543414,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001200399,"about_ca_topic_score_gemma":0.000009117882,"domain_scores_codex":[0.9988793,0.0001640187,0.0004033177,0.0002667863,0.00007458746,0.0002120299],"domain_scores_gemma":[0.9911914,0.007239767,0.0001440254,0.0009605953,0.0002860042,0.0001782371],"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":[0.00001329848,0.00011944,0.00001194414,0.00006413159,0.00001927958,2.602413e-8,0.00003843774,0.000004198908,0.00008746618,0.8879496,0.003467817,0.1082243],"study_design_scores_gemma":[0.0004439861,0.0000604784,0.00006059886,0.000007993759,0.00008714914,0.000004627455,0.0001227446,0.04136567,0.00003376871,0.6294124,0.3282682,0.0001324171],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00001655445,0.0006727885,0.9942423,0.001236357,0.00003685424,0.001595241,0.000493231,0.00008887132,0.001617798],"genre_scores_gemma":[0.003602187,0.0001289665,0.9903472,0.0001264467,0.0000555989,0.005247227,0.00017752,0.00002338545,0.0002914599],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.3248004,"threshold_uncertainty_score":0.4791494,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4318617680","doi":"10.29220/csam.2023.30.1.001","title":"A case study of competing risk analysis in the presence of missing data","year":2023,"lang":"en","type":"article","venue":"Communications for Statistical Applications and Methods","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Sunnybrook Hospital; University of Toronto; Institute for Clinical Evaluative Sciences","funders":"Ontario Ministry of Health and Long-Term Care; Cancer Care Ontario; Heart and Stroke Foundation of Canada","keywords":"Missing data; Econometrics; Statistics; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.4231507496666088,"gpt":0.5811249774642331,"spread":0.1579742277976243,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006488536,0.00009295986,0.0003940118,0.000189276,0.0002877662,0.00002306337,0.001001708,0.00003545147,0.000006448337],"category_scores_gemma":[0.008298055,0.00006962907,0.00003285338,0.001706716,0.0003390452,0.00003387108,0.0005513957,0.000170483,2.48822e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000006022685,"about_ca_system_score_gemma":0.00002826235,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005283431,"about_ca_topic_score_gemma":0.0003209897,"domain_scores_codex":[0.99698,0.001798664,0.0006987239,0.0002569511,0.000126622,0.000139071],"domain_scores_gemma":[0.9351931,0.06179994,0.0002792927,0.002533328,0.0001519201,0.00004237754],"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":[0.000007356732,0.0006423426,0.002572183,0.0001194708,0.0001418612,0.000002807508,0.003178667,0.000009976351,0.00004559436,0.5607057,0.00004918827,0.4325249],"study_design_scores_gemma":[0.0003703002,0.0001166011,0.00779281,0.00001932299,0.001271439,0.00002621829,0.02100908,0.4027117,0.00001203131,0.5658783,0.0006723478,0.0001198915],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01344877,0.00009316144,0.9840007,0.0001193343,0.000005353522,0.001096467,0.001052628,0.00001756935,0.0001660468],"genre_scores_gemma":[0.3357321,0.00005605032,0.6637486,0.000003818386,0.000003447805,0.0003911847,0.00005692144,0.00000573002,0.000002125147],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.432405,"threshold_uncertainty_score":0.9934146,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4410967593","doi":"10.29220/csam.2025.32.3.259","title":"A comparison of propensity score-based causal estimators for analyzing partially missing confounder","year":2025,"lang":"en","type":"article","venue":"Communications for Statistical Applications and Methods","topic":"Advanced Causal Inference Techniques","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 Calgary; McGill University; Montreal Neurological Institute and Hospital","funders":"","keywords":"Propensity score matching; Missing data; Estimator; Statistics; Confounding; Econometrics; Mathematics; Causal inference","retraction":null,"screen_n_in":null,"score":{"opus":0.4389368396050024,"gpt":0.6008971631796389,"spread":0.1619603235746365,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001016012,0.0001415728,0.0004629027,0.0001121549,0.0004483221,0.00004188114,0.0003771706,0.00008546586,0.000004567849],"category_scores_gemma":[0.00261848,0.0001360452,0.00005886103,0.0002641791,0.0004588654,0.00005244527,0.0001387721,0.0001344747,1.673376e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004960983,"about_ca_system_score_gemma":0.0001795443,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001097764,"about_ca_topic_score_gemma":0.0000298206,"domain_scores_codex":[0.9987114,0.0001815321,0.0006294617,0.0002356397,0.0000632447,0.0001787007],"domain_scores_gemma":[0.9850073,0.01318264,0.0002488889,0.001027764,0.000462065,0.0000712933],"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":[0.00002415455,0.0002083652,0.001165213,0.0002876035,0.00003362123,9.415957e-9,0.00003821887,0.000005763599,0.002131896,0.886987,0.0002010017,0.1089171],"study_design_scores_gemma":[0.0003300995,0.00007390363,0.0007359015,0.00009399879,0.0002552941,3.767822e-7,0.00005003718,0.0696815,0.01156119,0.9065776,0.01049411,0.0001460061],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0001623564,0.0002155457,0.9962034,0.0005848514,0.00001264085,0.002155647,0.0001799139,0.0001078289,0.0003778427],"genre_scores_gemma":[0.1207026,0.00001405819,0.8765277,0.00005139075,0.000005238772,0.002543638,0.0001108473,0.00001549025,0.00002897498],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1205403,"threshold_uncertainty_score":0.5547763,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W869473736","doi":"10.5351/ckss.2009.16.1.041","title":"Seasonal Adjustment on Chain-Linking","year":2009,"lang":"en","type":"article","venue":"Communications for Statistical Applications and Methods","topic":"Regional Economic 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":"Seasonality; Chain (unit); Econometrics; Seasonal adjustment; Quarter (Canadian coin); Volume (thermodynamics); Economics; Statistics; Environmental science; Mathematics; Geography; Variable (mathematics)","retraction":null,"screen_n_in":null,"score":{"opus":0.08355609058846013,"gpt":0.3737072205541622,"spread":0.2901511299657021,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000609476,0.0001139312,0.0002864574,0.0001071172,0.0003609859,0.00004424915,0.0003454903,0.00005915774,0.00005482721],"category_scores_gemma":[0.0000862173,0.0001222084,0.00008148282,0.000142572,0.0001350212,0.00004760441,0.00005662389,0.0001119057,0.00005642471],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005529316,"about_ca_system_score_gemma":0.00001453507,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001825315,"about_ca_topic_score_gemma":0.000003850846,"domain_scores_codex":[0.9990246,0.00003877433,0.0004366225,0.0003125442,0.00002044181,0.0001669743],"domain_scores_gemma":[0.9981727,0.0007541621,0.0001624669,0.0007557962,0.00004183414,0.0001130563],"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":[0.000005574871,0.00009502516,0.00007396321,0.000004632575,0.00002067501,1.608939e-8,0.00002576484,0.00001411607,0.000006732262,0.7308782,0.0002215334,0.2686538],"study_design_scores_gemma":[0.0002032622,0.00007296616,0.005213915,0.000004998415,0.00001817132,0.000001055767,0.00002179133,0.05480108,0.00000608983,0.6074147,0.3321111,0.0001308034],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00008899719,0.002266375,0.9760553,0.005808245,0.00002563402,0.0004416805,0.0003678493,0.00002909153,0.01491686],"genre_scores_gemma":[0.1231216,0.001310406,0.8727099,0.0012329,0.00007897302,0.000678038,0.0003255636,0.00001199195,0.0005306971],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.3318896,"threshold_uncertainty_score":0.4983516,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3016269762","doi":"10.29220/csam.2020.27.2.189","title":"Bayesian inference for an ordered multiple linear regression with skew normal errors","year":2020,"lang":"en","type":"article","venue":"Communications for Statistical Applications and Methods","topic":"Statistical Distribution Estimation and Applications","field":"Mathematics","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":"Deviance information criterion; Bayesian linear regression; Skew normal distribution; Bayes factor; Mathematics; Prior probability; Skewness; Bayesian probability; Statistics; Bayesian inference; Skew; Markov chain Monte Carlo; Econometrics; Computer science","retraction":null,"screen_n_in":null,"score":{"opus":0.2105209950038546,"gpt":0.5138118535951399,"spread":0.3032908585912853,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004453526,0.0002317724,0.0003461533,0.00005012172,0.000883513,0.0000704232,0.0005459996,0.0001453921,0.0000471684],"category_scores_gemma":[0.002707371,0.000195619,0.00005263941,0.0003622022,0.000453188,0.0001375545,0.0001341291,0.0002570067,0.000004148241],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002741979,"about_ca_system_score_gemma":0.0001039803,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001075495,"about_ca_topic_score_gemma":0.00003503331,"domain_scores_codex":[0.9983904,0.0001966584,0.0005467967,0.0004563328,0.0001314747,0.0002782905],"domain_scores_gemma":[0.990253,0.007592893,0.000210421,0.001030906,0.0004679348,0.0004448107],"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":[0.00007332615,0.0002630871,0.00004205256,0.0001380802,0.00002265791,3.62554e-8,0.0001652605,0.00001097579,0.0003389062,0.9132495,0.000805875,0.08489027],"study_design_scores_gemma":[0.001086639,0.0003198786,0.0002581495,0.00002797671,0.0001546488,0.000003302536,0.0002553278,0.6065797,0.0002566523,0.2919225,0.09882284,0.0003124616],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00003744844,0.00005099883,0.9889632,0.003969974,0.000007466237,0.003388214,0.003049167,0.0001907998,0.0003426989],"genre_scores_gemma":[0.07047209,0.00003579451,0.9171885,0.000367797,0.00004143526,0.009784463,0.002028322,0.00003973054,0.00004193168],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.621327,"threshold_uncertainty_score":0.797711,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null}]}